Volume 4 - 2014

ISSN 0719 - 2452

VOLUME 4 - 2014 DOI: 10.5027/jnrd.v4i0.01 - DOI: 10.5027/jnrd.v4i0.12

INTEGRATED WATER RESOURCES MANAGEMENT LAND USE DYNAMICS AND BIODIVERSITY ENERGY EFFICIENCY AND RENEWABLE RESOURCES REGIONAL MANAGEMENT AND SUSTAINABLE LIVELIHOOD OF THE POOR

Volume IV

Contents

Journal of Natural Resources and Development 2014; 04: 1- 88

A new novel index for evaluating model performance

1

Authors: Md Hossain Ali, I. Abustan DOI: 10.5027/jnrd.v4i0.01 Assessment of encroachment of urban streams in Ghana: a case study of Wa Municipality

10

Authors: Raymond Aabeyir, Michael S. Aduah DOI: 10.5027/jnrd.v4i0.02 A critical review on the national energy efficiency action plan of Egypt

18

Authors: Hatem Elrefaei, Marwa A. Khalifa DOI: 10.5027/jnrd.v4i0.03 Conservation through community: An attempt to untangle a tangled word [Commentary]

25

Authors: Deeraj Koul DOI: 10.5027/jnrd.v4i0.04 Artificial Neural Networks to predict decreasing saturated hydraulic conductivity in soils irrigated with saline-sodic water

27

Authors: Younes Daw Ezlit, Ahmed Ibrahim Ekhmaj, MukhtarMahmud Elaalem DOI: 10.5027/jnrd.v4i0.05 Fundamentals of agricultural sustainability or the quest for the Golden Fleece

34

Author: Marc Janssens, Hartmut Gaese, Norbert Keutgen, Rodrigo Ortega, Juan Carlos Torrico, Juergen Pohlan DOI: 10.5027/jnrd.v4i0.06 Factors influencing adoption of farm management practices in three agrobiodiversity hotspots in India: an analysis using the Count Data Model

46

Author: Prabhakaran T. Raghu, Varghese Manaloor, V. Arivudai Nambi DOI: 10.5027/jnrd.v4i0.07 Using QUAL2K Model and river pollution index for water quality management in Mahmoudia Canal, Egypt

54

Authors: Ehab A. Elsayed DOI: 10.5027/jnrd.v4i0.08 Malaysia water services reform: legislative issues [Commentary]

64

Authors: Nabsiah Abdul Wahid, Zainal Ariffin Ahmad, Rozita Arshad DOI: 10.5027/jnrd.v4i0.09 Monitoring of ground water quality and heavy metals in soil during large scale bioremediation of petroleum hydrocarbon contaminated waste in India: case studies

65

Author: Ajoy Kumar Mandal, Atanu Jana, Abhijit Datta, Priyangshu M. Sarma, Banwari Lal, Jayati Datta DOI: 10.5027/jnrd.v4i0.10 Analyzing the biophysical inputs and outputs embodied in global commodity chains - the case of Israeli meat consumption

75

Author: Shira Dickler, Meidad Kissinger DOI: 10.5027/jnrd.v4i0.11 Effectiveness of microinsurance during and after disaster Authors: Arshad Ali, Asad Mahmood DOI: 10.5027/jnrd.v4i0.12

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Journal of Natural Resources and Development 2014; 04: 1-9

DOI number: 10.5027/jnrd.v4i0.01

1

JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT A new novel index for evaluating model performance.

M. H. Ali*, I. Abustan School of Civil Engineering, University Sains Malaysia * Corresponding author : mha_bina@yahoo.com, Bangladesh Institute of Nuclear Agriculture

Article history Received 27.01.2013 Accepted 11.06.2013 Published 27.01.2014

Keywords Model evaluation Statistical indicators Model performance

hossain.ali.bina@gmail.com. Permanent address: Agricultural Engineering Division,

Abstract A vast array of scientific literature is concerned with simulation models. The aim of models is to predict the unknown situation as close to as real one. To do this, models are validated and examined for their performance under known condition. In this paper, commonly used model performance evaluation indices are overviewed and examined under different situations. Difference based, efficiency based (Nash and Sutcliffe coefficient, model efficiency of Loague and Green, Legates and McCabe’s index) and composite indices (such as index of agreement, d, and dr) were found ambiguous, inconsistent and not logical in many cases. A new index, Percent Mean Relative Absolute Error (PMRAE), is proposed which is found unambiguous, logical, straight-forward, and interpretable; thus can be used to evaluate model performance. The model evaluation performance ratings based on PMRAE are also suggested.

Introduction A range of simulation models and decision support systems have been developed and are being used for several decades in different fields. Simulation models have been successfully used to provide simulations of crop growth and development (Geerts et al. 2009, Stockle et al. 2003), hydrologic variables (Suleiman 2008), water and solute transport (Crescimanno and Garofalo 2005,Dust et al. 2000), solar radiation (Rivington et al. 2005), environmental impacts (Stockle et al. 1992) and many other areas. One important aspect in the model development process is the model evaluation. Model outputs are compared /examined with observed (or known) data gathered under respective conditions, both by quantitative and graphical methods.

Various statistical and efficiency-based indices/indicators and test statistics have been suggested and used by diffident model developers and users to judge the model performance. Among these,

recommendation by Nash and Sutcliffe (1970), Fox (1981), Willmott (1982, 1985), and Loague and Green (1991) are prominent.

Among statistical indices, some of them quantify the departure of the model output from observed or experimental measurements, while others focus on correlation between model predictions and measurements. In essence, Fox (1981) recommended that the following four types of difference measures should be calculated and reported: mean error, mean absolute error, variance of the distribution of difference, and root mean square error (or its square - the mean square error).These difference-based statistics quantify the departure of the model outputs from the measurements.

Indicators for specific fields are also suggested. Bellocchi et al.

(2002) proposed a fuzzy expert system to calculate a composite indicator for performance evaluation of solar radiation. They used

DOI number: 10.5027/jnrd.v4i0.01

correlation coefficient (r), relative root mean square error (RRMS), model efficiency (EF), and t-Student probability to make aggregated form. Confalonieri et al. (2010) proposed a fuzzy-based, indicator for evaluation of soil water content simulation. Jacovides and Kontoyiannis (1995) proposed mean bias error (MBE) and root mean square error (RMSE) in combination with the t-statistic as statistical indicators for the evaluation and comparison of evapotranspiration computing models.

Among the difference and/or statistical measures, mean error (ME), root mean square error (RMSE), relative error (RE), and correlation coefficient (r) are widely used in different fields –crop growth and yield (Geerts et al. 2009), irrigation scheduling (Liu et al. 1998), hydrological (Shen et al. 2009), environmental (Wagener and Kollat 2007), solar radiation (Rivington et al. 2005), pollution simulation model (Yang et al. 2007), etc. Model efficiency (EF) is used in almost every field of simulation. The above indices are used for both single model evaluation and comparison of multiple models (Prasher et al.

1996). Martorana and Bellocchi (1999) identified the mean squared error of prediction as the fundamental statistical index on which other widely used squared differences are based. While Willmott and Matsuura (2006) noted that RMSE is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (average error).

Yang et al. (2000) evaluated different statistical methods to evaluate crop-nitrogen simulation model, N_ABLE. They suggested that two sets statistics can be used: (a) mean of error (ME), root mean square error (RMSE), forecasting efficiency, and paired t-statistic; (b) ME, mean absolute error, forecasting coefficient, and F-ratio of lack of fit over experimental error. They noted that either set can give the same conclusions which could not be quantitatively detected by graphical method. The use of test statistics (e.g. F, t-test, etc.) to judge the error variance between observed and simulated outputs have the possibility of producing type-I or type-II error.

Willmott (1981) demonstrated that the correlation coefficient, r (Pearson’s product-moment correlation coefficient) can be misleading measure of accuracy – ‘r’ between very dissimilar model-predicted variable and observed one can easily approach 1.0. Willmott (1982) discussed other drawbacks of ‘r’ and ‘R2’, and proposed an “index of agreement (d)”. He noted that the index ‘d’ is intended to be a descriptive measure, and it is both a relative and bounded measure which can be widely applied for cross-comparisons between models.

Willmott et al. (2011) suggested a refined index (dr) considering the problem of d.

Among the efficiency-based indices (EF) suggested for model performance evaluation, widely used ones are Nash and Sutcliffe coefficient (Nash and Sutcliffe 1970) and model efficiency of Loague and Green (1991). Many researchers (Addiscott and Whitmore 1987, Martorana and Bellocchi 1999, Rivington et al. 2005, Moriasi et al.

2007) noted that a model may be judged suitable according to one statistic but it may be deficient according to another statistic.

Alexandrov et al. (2011) emphasized the need of standardized evaluation tool.

The purpose of this paper is to examine all of the above indices, and suggest a logical, stable, unambiguous and straight-forward index for model performance evaluation.

Journal of Natural Resources and Development 2014; 04: 1-9

2

Materials and methods Definition of commonly used statistical measures and indices for model performance evaluation Before going to analyze the indices, it would be useful to define them along with their perspectives. So, they are described below.

For synchronization of all the indices, observed or measured value is designated by Oi, and predicted or simulated value is designated by Pi, although the original proposed symbol may be different in some cases.

Difference based Statistical indicators (i) Mean bias or Mean error (ME)(Fox 1981): N

ME = 1 Σ (P i - O i) N i=l

(1)

Where, N is the number of observations. (ii) Mean Absolute error (MAE) (Fox 1981): i=N

MAE = 1 Σ |P i - O i | N i=1

(2)

(iii) Root mean square error (RMSE): N

1 Σ (P √N

RMSE =

i=1

i

- O i)2

(3)

The RMSE quantifies the dispersion between simulated and measured data. Ideally, the value of ME, MAE, and RMSE should be zero.

iv) Relative error (RE) or relative root mean square error (RRMSE) (Loague and Green 1991, Bellocchi et al. 2002): RE =

RMSE x 100 O

(4)

Where, O is the mean of observed values. The RE may vary from 0 to positive infinity. The smaller the RE is, the better the model performance. Sometimes it is expressed as percentage form.

v) Scaled Root-mean-Square-Error (SRMSE) (Dust et al. 2000): SRMSE = 1 O

N

1 Σ (P √N i=1

i

- O i)2

In essence, the RE and SRMSE are the same.

(5)

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Efficiency based indicators

Where, m1 =

(i) Nash and Sutcliffe Coefficient of efficiency (ENS) (Nash and Sutcliffe 1970): N

ENS = 1 -

Σ (O i - P i)2 i=1

Σ (O i - O )2 N

Σ

i=1

Proposed new index Percent mean absolute relative error (PMARE) It is the ‘mean absolute relative error’, expressed in percentage. n

PMARE(%) = 100 n i=1

Σ

N

Σ (O i - P i)2

i=1

(7)

(Oi - O)2

An ideal value of ELG is unity. Its upper limit is 1, and lower value can negative infinity.

The Nash-Sutcliffe coefficient of efficiency (ENS) and the model efficiency of Loague and Green (ELG) are the same. So, only one will be discussed in the later section. (iii) Legates and McCabe’s index (ELM) (Legates and McCabe 1999) Legates and McCabe’s index (ELM) is written as: N

Σ Abs(Pi - Oi) i=1

ELM = 1 -

and c = 2

i=1

(ii) Model efficiency of Loague and Green (ELG) (Loague and Green 1991): i=1

i=1

m2 =Σ Abs(Oi - Oi)

(6)

N

Nash-Sutcliffe coefficient of efficiency (ENS) varies between - ∞ and 1.0, and ENS=1 is the optimum value. The ENS≤0.0 indicates unsatisfactory performance, and 0<ENS<1 is considered as the acceptable range.

ELG =

N

Σ Abs(Pi - Oi)

N

Σ (Oi - O)2 i=1

N

3

(8)

N

Σ Abs(Oi - O) i=1

Other composite indicators

Abs(Oi - Pi)

(11)

Oi

Where, ‘Abs’ indicates absolute value (of the difference between observed and simulated value). Theoretically, the value of PMARE ranges from 0% to ∞ (positive infinity). The interpretation and characterization of the index are discussed later.

Data for comparison of indices To test the statistics and indices, both the field observed data and simulated random data were used.

Simulation comparison with field observed data Field data are originated from wheat experiment, where diverse irrigation treatments were applied representing different strategies of deficit irrigation. Simulation was performed using AquaCrop model of FAO (Steduto et al. 2009). Before simulation, calibration of the model was performed using one year data. The model AquaCrop produces inferior simulations at extreme dry condition (herein referred as ‘odd simulation’– sometimes referred in the literature as ‘outliers’), which is a common problem in many models. Observed and simulated outputs are summarized in Table 1, which are used to explore the behavior of the indices.

(i) Index of Agreement (d) (Willmott 1982): Table 1. Observed and simulated yield of wheat grain & total biomass

N

d =1-

Σ (O i - P i)2 i=1 N

Σ [O’i + P’i]2 i=1

, 0 ≤ d ≤1

(9)

Data year

where O’i = |Oi - P| , P’i = |Pi - P| , Oi is the observed value, Pi is the simulated value and P is the simulated mean. (ii) Refined index of Willmott et al. (2011) The refined index of Willmott et al. (2011) (dr) can be written as: dr = 1 - m1 c*m2 = c*m2 - 1 m1

, when m1≤ c*m2 when m1> c*m2

1st

(10)

Treatment/ Sl.no.

Grain yield (t/ha)

Total biomass yield (t/ ha)

Observed

Simulated

Observed

1

2.071

1.293

7.06

Simulated 6.614

2

3.978

3.956

11.649

11.384

3

3.721

3.956

10.351

11.383

4

3.872

3.779

10.643

10.962

5

3.859

3.734

11.197

10.887

6

3.846

3.586

10.946

10.649

7

3.739

3.191

10.276

9.741

8

3.618

3.734

10.227

10.886

9

4.017

4.015

11.85

11.473

10

3.281

1.707

9.588

7.384

Journal of Natural Resources and Development 2014; 04: 1-9

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4

Table 1. Observed and simulated yield of wheat grain & total biomass (continuation) Data year

2nd

Total biomass yield (t/ ha)

Grain yield (t/ha)

Treatment/ Sl.no.

Observed

Simulated

Observed

Simulated

1

1.574

0

4.246

0

2

3.404

3.802

10.50

11.151

3

3.144

3.798

11.223

11.142

4

3.169

3.688

10.366

10.854

5

3.168

3.613

10.145

10.694

6

3.395

3.271

10.265

10.05

7

3.141

2.901

8.991

9.281

8

2.994

2.567

9.24

9.004

9

3.48

3.802

11.61

11.151

10

2.779

1.519

9.045

7.167

Simulation comparison with Random data To show the behavior of the indices under different patterns of data series, values of O and P were created (generated) using a random data generator. More specifically, 3 sets of random data of size n=20 were generated separately for O and P using a random number generator (RANDOM.ORG, 2012) [Table 2, Fig.1]. The randomness comes from atmospheric noise.

Calculation of the indices The indices were calculated using Microsoft spreadsheet following the equations mentioned earlier.

Table 2. Data sets (Random numbers) generated using ‘Radom Number Generator’ Set-1

Set-2

Set-3

Sl no.

Observed

Simulated

Observed

Simulated

Observed

Simulated

1

33

46

11

24

17

14

2

50

23

12

11

41

19

3

36

35

44

43

7

35

4

33

45

36

8

41

4

5

25

21

20

20

27

18

6

7

22

17

16

36

24

7

2

23

33

50

29

26

8

42

43

30

8

36

7

9

17

18

38

26

21

5

10

18

42

17

42

10

32

11

45

12

21

16

42

38

12

23

39

8

12

39

27

13

21

7

40

46

28

32

14

42

44

20

15

30

6

15

47

13

18

12

43

47

16

7

7

32

24

46

41

17

11

34

16

1

10

33

18

20

15

18

5

42

13

19

29

23

32

31

5

46

20

12

22

21

10

46

17

Figure 1. Pattern of observed versus simulated random data sets

Results and Discussions Grain yield of wheat The statistical parameters and indices under different conditions (“with” and “without” odd simulated values) are presented in Table 3.

The data points (with odd values) are graphically illustrated in Fig.2 along with 1:1 line.

5

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For the simulation year-1, while the ‘odd simulated’ values are omitted from the calculation, the difference-based statistical indicators – mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and relative error (RE) decreased compared to those with ‘odd simulated values’; which is logical. The efficiency based indicators – ENS or ELG, ELM, index of agreement d, and new index of agreement dr decreased; but they should be increased. Similar behaviors are also observed for the year-2.

For the combined data, the statistical indicators followed the logical behavior. Here, the ENS and ELM followed the logical trend – higher values for ‘without odd data’ (i.e. with good simulated data). But the d followed the reverse behavior – decreased with good simulated values. The PMARE always followed the logical behavior, and no ambiguous result.

From the different data sets, it is revealed that the difference-based statistical indicators gave consistent and logical measures. The behavior of ENS and ELM is inconsistent, and reverse in two cases. The behavior of d is reverse in all the studied cases. Similar behavior is also noticed by ‘r’.

The behavior of ENS and ELM may be due to their inherent formulation/ structure. From the equation of ENS and ELM, it is revealed that they are more dependent on observation range (Oi and O) than the difference between the observed and predicted values. Thus, the ENS and ELM are more sensitive to observed range/fluctuation. Hence the output is not consistent and reliable. Similar behavior is also noticed by d. In the studies cases, the outputs are consistently reverse to the logical direction. For dr, the behavior is inconsistent for 2 cases – 1st& 2nd year data.

Case of total biomass yield

Figure 2. Pattern of observed versus simulated grain yield of wheat

The observed and simulated total biomasses are illustrated in Fig. 3 along with 1:1 line. The statistical parameters and efficiency indices are presented in Table 4. The r value shows reverse behavior (opposite

Table 3. Statistical and efficiency indicators for evaluating simulation performance of wheat grain yield under different conditions 1st year

2nd year

Combined data

Statistical indicator

With odd simulations

Without odd simulations (excluding no. 1 & 10)

With odd simulation

Without odd simulation (excluding no. 1 & 10)

With odd simulations

Without odd simulations

Mean Bias (t/ha)

-0.305

-0.087

-0.129

0.193

-0.217

0.053

Mean absolute bias (MAE) (t/ha)

0.375

0.175

0.596

0.391

0.486

0.283

RMSE (t/ha)

0.595

0.240

0.740

0.420

0.672

0.342

RE (%)

16.54

6.268

24.47

12.98

20.28

9.68

Pearson’s moment correlation coefficient (r)

0.887

0.440

0.930

0.581

0.867

0.572

ENS or ELG (%)

-18.44

-269.59

-101.14

-612.66

-22.40

-7.93

ELM

-0.0152

-0.685

-0.726

-1.753

-0.081

0.0473

Index of agreement (d)

0.951

0.534

0.809

0.611

0.838

0.774

New index of agreement, dr

0.492

0.158

0.14

-0.273

0.459

0.524

PMARE (%)

12.27

4.65

24.3

12.22

18.3

8.43

Journal of Natural Resources and Development 2014; 04: 1-9

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to logical, MAE & PMARE) for 2nd year & combined data - higher value for “with odd simulation’.

6

Behavior with Random data The values of statistical and efficiency based indices for 3 random data sets, with ‘all data’ (herein referred as ‘with odd simulation’) and ‘without extreme values’ (2 extremes) (herein referred as ‘without odd simulation’) are summarized in Table 5. Here, the ENS and dr do not follow the logical behavior of difference-based measures MAE and RMSE (and also PMARE) for the 1st & 3rd data sets. The ELM shows reverse trend for 3rd data set. The results indicate that the r, ENS, ELM, and dr show ambiguous performance rating under different conditions.

Discussion Now-a-days, the modeling and the use of models are becoming the major thrust in all branches of science. It is increasingly important that discussion of model evaluation procedure to be expanded in order that logical, consistent and generally accepted indicator(s) is identified. The indicators should appropriately quantify objective of model evaluation – that is, should direct towards the answer of model usability. It is logical demand that an ‘ideal indicator’ for model performance evaluation should: (i) Have straight-forward physical meaning and interpretation (ii) Indicate the strength (accuracy) or pit-fall (weakness) of the prediction capability, so that decision can be made regarding usefulness of the model (iii) Have consistent value/trend with the logical direction, and no ambiguous performance rating

Figure 3. Pattern of observed versus simulated biomass in different years

Graphical method gives the overall and real picture, while the different indices give quantitative measures. The diagnosis that can be made from the graph, must be supported by the quantitative measures.

The indices should also be consistent in their results. Otherwise, the

Table 4. Statistical and efficiency based indicators for evaluating simulation performance of total biomass yield under different conditions 1st year data Statistical indicator

2nd year data

With odd simulations

Without odd simulations (excluding no. 1 & 10)

Combined data

With odd simulation

Without odd simulation (excluding no. 1 & 10)

With odd simulations

Without odd simulations

Mean Bias (t/ha)

-0.242

-0.024

-0.514

0.123

-0.378

0.045

Mean absolute bias, (MAE) (t/ha)

0.644

0.471

0.909

0.371

0.777

0.424

RMSE (t/ha)

0.857

0.525

1.514

0.413

1.230

0.476

RE (%)

8.26

5.02

15.83

4.02

12.34

4.58

Pearson’s moment correlation coefficient (r)

0.87

0.93

0.972

0.885

0.943

0.917

ENS or ELG (%)

55.55

84.30

40.11

75.04

47.92

82.03

ELM

0.266

0.464

0.324

0.413

0.333

0.449

Index of agreement, d

0.910

0.963

0.915

0.934

0.918

0.956

New index of agreement, dr

0.633

0.732

0.662

0.707

0.667

0.724

PMARE (%)

6.49

4.65

14.96

3.61

10.72

4.16

7

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Table 5. Statistical and efficiency based indicators for evaluating simulation performance based on random data 1st data set

2nd data set

Statistical indicator

All data (With odd simulations)

Without odd simulation (excluding 2 extremes)

Mean Bias (ME)

0.70

Mean absolute bias (MAE)

3rd data set

All data

Without odd simulation (excluding 2 extremes)

All data

Without odd simulations

-1.67

-3.20

-5.66

-5.60

-10.05

0.644

0.471

0.909

0.371

0.777

0.424

13.1

12.11

9.70

8.66

17.80

15.94

RMSE

16.87

16.20

12.68

11.60

21.38

19.26

RE (%)

64.90

57.52

52.40

45.78

71.76

59.38

Pearson’s moment correlation coefficient (r)

0.408

0.45

0.271

0.50

-0.546

-0.358

ENS or ELG (%)

-41.22

-50.14

-53.64

-31.34

-155.39

-187.91

ELM

-0.07

-0.05

-0.06

0.05

-0.56

-0.638

New index of agreement, dr

0.467

0.446

0.469

0.526

0.221

0.181

PMARE (%)

108.96

51.12

45.48

35.79

117.61

62.89

particular quantitative index is not suitable for model comparison, and should be abundant from model performance measure.

Legates and McCabe (1999) suggested that correlation and correlationbased measures (e.g. the coefficient of determination, R2) should not be used to assess the goodness-of-fit of hydrologic or hydro-climatic model, as these measures were found over-sensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observation. Willmott (1981) found ambiguous behavior of correlation coefficient,‘r’. The present study also showed ambiguous behavior of ‘r’.

Within the domain of efficiency based indicators, McCuen et al. (2006) showed that the outliers can significantly influence sample values of the Nash–Sutcliffe efficiency index (ENS). In the present study, ENS and ELG also showed ambiguous result due to the presence or absence of externalities (extreme values).

Willmott et al. (2011) proposed a new index, dr, and they compared the dr with ‘mean absolute error (MAE)’ of the data sets, which varies logically with MAE. But this should be compared with mean absolute relative error, because MAE can vary with different data pattern/set, while the ‘mean absolute relative error’ value may be the same (i.e. no change in relative pattern). In the present study, the dr index does not follow the logical trend within a particular data set, as in Table 2 (combined analysis); and also ambiguous among different sets (1st year and combined data) – with PMARE value. Similar inconsistencies are also observed for random data sets (Table 4, 1st & 3rd data sets – with PMARE).

As the behavior of EF, d, dr and r are not consistent and logical for all cases (ambiguous, conflicting performance rating); they should be avoided from model performance measure.

The ‘mean absolute error’ (MAE)and ‘mean bias error’ (ME) have been suggested by Willmott and Matsuura (2006). But the MAE or ME does not tell about the level or degree of error, and the MBE can ‘neutralize the amount of error’ if the error occurs on both positive and negative directions. The ‘mean absolute relative error’, when expressed as percentage, that is ‘percent mean absolute relative error’ (PMARE)

(eqn.11), overcomes the above deficiencies. It has merit over ‘mean absolute error’ that it directly indicates the strength or weakness of the simulation; and thus helps to decide accept or reject the model.

Theoretically, the value of PMARE can range from 0% to ∞ (positive infinity). As it is a measure of error (but relative – with respect to observed, which is logical than any other measure), the optimum value is 0.0, indicating no error (that is perfect simulation). Low magnitudes indicate less error (i.e. better model simulation) and the higher values indicate higher error (i.e. less perfect simulation). The 0<PMARE<100 can be considered as the practical/acceptable range.

Performance rating based on any indicator may depend on the model type, field of application (i.e. sensitivity of the work/project where the model output will be used), availability of real-world data, etc. In general, for the PMARE value, the following ratings may be used as a guide (Table 6): Table 6. Suggested performance rating for model evaluation based on new index, PMARE PMARE value (%)

Model rating

0-5 5-10 10 - 15 15 - 20 20 - 25 >25

Excellent Very good Good Fair Moderate Unsatisfactory

The above threshold/maximum limit for rating a model is determined/ suggested after examining various data sets, by sequentially omitting the data having higher difference (in percent) between observed and simulated values, and determining PMARE. Based on the required precision, the user can choose lower PMARE value. On the other hand, where no other means/data are available, the user can use a model having even a higher PMARE value (say, 25%) to get a forecast.

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DOI number: 10.5027/jnrd.v4i0.01

The PMARE has distinct advantages over the other indicators: (i) It is simple to calculate (ii) Has direct physical meaning (iii) Indicates directly the accuracy or pit-fall of the simulation, and thus helps to decide about the acceptability (or usefulness) of the model (iv) No ambiguous result (v) Follow the logical direction (vi) Relative measure, thus applicable to any field of observation, regardless of units (scales of measurements) and range of values

Confalonieri, R., S. Bregaglio, S. Bocchi, M. Acutis. 2010. An integrated procedure to evaluate hydrological models. Hydrol. Proces. 24(19): 2762-2770 Crescimanno G., Garofalo P., 2005. Application and evaluation of the SWAP model for simulating water and solute transport in a cracking clay soil. Soil Sci. Soc. Am. J. 69, 1943-1954.

Dust M., Baran N., Errera G., Huston J.L., Mouvet C., Schafet H., Vereecken H., Walker A., 2000. Simulation of water and solute transport in field soils with the LEACHP model.

Agric. Water Manage. 44, 225-245.

Fox D.G., 1981. Judging air quality model performance. Bull. Am. Meteorol. Soc. 62, 599609.

Geerts S.,

Summary and Conclusion

8

Raes D., Garcia M., Miranda R., Cusicanqui J. A., Taboada C., Mendoza J.,

Huanca R., Mamani A., Condori O., Mamani J., Morales B., Osco V., Steduto P., 2009.

Simulating Yield Response of Quinoa to Water Availability with AquaCrop. Agron.

J. 101(3), 499-508.

Previous studies have produced comparable information for model evaluation indices (for selected models or in general). But no comprehensive standardization (or concrete suggestion) is available including recently developed indices. The purpose of this investigation is to review and evaluate available indices for model performance evaluation and explore a logical, interpretable, and unambiguous index for general use in model evaluation. The r, R2, and RMSE have been regarded as non-logical, ambiguous and misinterpretable from previous studies (and have been suggested to abundant from the array of performance testing indicators) and also from this study.

The present investigation demonstrates that the index of agreement (d) between very dissimilar model-predicted variable and observed data can approach to one (1.0), but can have lower value for nearly similar data sets. The ambiguous and inconsistent behavior of dr are also observed, thus cannot be regarded as a reliable indicator. The investigation also demonstrates that the efficiency based indicators such as ENS and ELG, are not consistent with logical trend (and shows reverse trend in some cases), and also with widely accepted difference-based measures (e.g. MAE, RMSE).

The PMARE (which is based on similar principle of MAE, but relative to observed data) shows consistent, robust, descriptive (clear interpretative), and logical behavior, and thus can be used as an ideal indicator for model evaluation under diverse output conditions.

The performance rating based on PMARE is also suggested. From investigation of various data sets (diverse in nature), it can be concluded that the index is measuring error with both accuracy and precision.

Jacovides C.P., Kontoyiannis H., 1995. Statistical procedures for the evaluation of evapotranspiration models. Agric. Water Manage. 27, 365-371.

Legates D.R, McCabe G.J. Jr. 1999. Evaluating the use of “goodness of fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research 35(1): 233–241.

Liu Y., Teixeira J.L., Zhang H.J., Pereira L.S., 1998. Model validation and crop coefficients for irrigation scheduling in the North China plain. Agric. Water Manage. 36, 233-246.

Loague K., Green R. E., 1991. Statistical and graphical methods for evaluating solute transport models: Overview and application. J. Contam. Hydrol. 7, 51 – 73.

Martorana F., Bellocchi G., 1999. A review of methodologies to evaluate agroecosystem simulation models. Ital. J. Agron. 3(1), 19-39.

McCuen, R.H., Z. Knight, A.G. Cutter. 2006 . Evaluation of the Nash–Sutcliffe Efficiency Index. J. Hydrologic Engrg. 11: 597-602.

Moriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harmel R.D., Veith T.L., 2007.

Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE. 50(3), 885-900.

Nash J. E., Sutcliffe J. V., 1970. River flow forecasting through conceptual models. 1. A discussion of principles. J. Hydrol. 10, 282- 290.

Prasher S.O., Madani A., Clemente R.S., Geng G.Q., Bhardwaj A., 1996. Evaluation of two water table management models for Atlantic Canada. Agric. Water Manage. 32, 4969.

RANDOM.ORG. 2012. Random Integer Generator. www.random.org/integers, Accessed on 20th Feb. 2012.

Rivington M., Bellocchi G., Matthews K.B., Buchan K., 2005. Evaluation of three model estimations of solar radiation at 24 UK stations. Agril. and Forest Meteorl. 132, 228243.

Shen Z.Y., Gong Y.W., Li Y.H., Hong Q., Xu L., Liu R.M., 2009. A comparison of WEPP and SWAT for modeling soil erosion of the Zhangjiachong watershed in the three Gorges reservoir area. Agric. Water Manage. 96, 1435-1442.

References

Steduto P., Hsiao T.C. , Raes D., Fereres E., 2009. AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101(3), 101- 426.

Addiscott T. M., Whitmore A. P.,1987. Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring. J. of Agril. Sci., Cambridge, 109, 141 – 157.

Alexandrov, G.A.,

D. Ames, G. Bellocchi, B. Michael, N. Crout, M. Erechtchoukova,

A. Hildebrandt, F. Hoffman, C. Jackisch, P. Khaiter, G. Mannina, T. Matsunaga, S.T. Purucker, M. Rivington, L. Samaniego. 2011. Technical assessment and evaluation of environmental models and software : Letter to the Editor. Environ. Modell. and Soft.

26 (3): 328-336.

Bellocchi G., Acuit M., Fila G., Donatelli M., 2002. An indicator of solar radiation model performance based on a fuzzy expert system. Agron. J. 94, 1222-1233.

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Stockle C.O., Donatelli M., Nelson R., 2003. CropSyst – a cropping systems simulation model. Eur.J.Agron. 18, 289-307.

Suleiman A.A., 2008. Modeling daily soil water dynamics during vertical drainage using the incoming flow concept. Catena 73, 312-320.

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2007.

Numerical and visual evaluation of hydrological and

environmental models using the Monte Carlo analysis toolbox. Environ. Modelling & Softwares, 22, 1021-1033.

Willmott C. J., 1981. On the validation of models. Phys. Geogr. 2, 184-194.

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Willmott C. J., 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63, 1309 â&#x20AC;&#x201C; 1313.

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Robeson, S. M.

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and Matsuura, K. (2011). A refined index of model

performance. Int. J. Climatol. DOI: 10.1002/joc.2419

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10

JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Assessment of encroachment of urban streams in Ghana: a case study of Wa Municipality.

Raymond Aabeyir a* and Michael S. Aduah b Department of Environment and Resource Studies University for Development Studies, Wa Campus, P.O. Box 520 Wa Ghana

a

Department of Geomatics Engineering University of Mines and Technology, P.O. Box 237, Tarkwa Ghana

b

* Corresponding author : raypacka2012@gmail.com

Article history Receivedâ&#x20AC;&#x192; 24.06.2013 Acceptedâ&#x20AC;&#x192; 28.08.2013 Published 24.02.2014

Keywords Stream Buffer zone Urban area Encroachment Population growth

Abstract This paper assessed encroachment of streams due to physical development in Wa Urban Area of the Upper West Region of Ghana. The assessment was informed by the recognition that the roles played by streams in flood control are undermined by physical developments. This affects sustainable urban development and renders the urban area vulnerable to floods. The assessment was based on the 300m buffer zone standard set by the Ghana Water Resources Commission as a protective zone for such streams in the country. It is mandatory to offset all physical development from this zone but that is not the situation on the ground. For the purpose of this study each buffer zone was divided into sub-buffer zones of 100m in order to appreciate how far development has moved into the prohibited buffer zone. The streams and physical structures were mapped with a Trimble GPS receiver while land owners and tenants were purposively selected and interviewed. The buffer zone and sub-buffer zones were defined using GIS and overlaid with map of the physical structures. The categories of structures found in the buffer zones were residential (93.4 %), commercial (5.1%), public (1.3%) and agriculture (0.2%). The results of the study indicates that more than 50 % of physical structures mapped are located in the inner buffer and the land acquisition process for development of these structures amongst others in Wa is mostly initiated by developers.

Introduction Streams in urban environments are important features that add aesthetic and recreational appeal to the urban environment. They also play a critical role in flood control by serving as reservoirs for excess surface water during heavy downpour. The state of urban streams remains an important indicator of sustainable urban development (Furumai et al. 2009, Hai & Yamaguchi 2006) as it is regarded that deliberate occupation of these streams is a sign of unmanaged urbanisation (Drakakis - Smith 2000, WMO 2008 and Karley 2009).

In developing countries, streams suffer deliberate encroachment (Darteh et al. 2010 and Ahmed & Dinye 2012) despite the fact that they have become an integral part of urban infrastructural network.

Though many factors account for the encroachment, demographicbased urbanisation is a major driving force (Gould 1998, Songsore 2003). This makes residential and commercial land uses in urban areas so lucrative that other important land uses are constrained (Darteh et al. 2010). In such situations urbanisation triggers physical

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well in the area. Generally, the Municipality has two marked seasons namely, the wet and dry seasons. The mean annual rainfall varies between 840 mm and 1400 mm. Most of the rainfall occur between June and September and is generally low and unreliable both in its timing and duration. The long dry season, dwindling and the erratic rainfall pattern encourages physical development in natural water ways.

Wa is the biggest and most urbanized settlement in the region.

Observations of the Wa Township show that there are manifestations of haphazard development, isolated congestion of settlements and associated issues of environmental degradation and pollution of water ways. Housing development is increasing at a fast rate (Ahmed and Dinye 2011) particularly along the Wa-Kumasi highway where the University for Development Studies (UDS) permanent Campus is located, the Wa-Kpongu road where the Wa Polytechnic is situated, and the Wa-Dorimon road where the temporal UDS Campus is located. The two institutions are major development growth poles in the urban area and have contributed significantly to the increasing population and physical development in the study area.

2°35'0"W

2°30'0"W

2°25'0"W

2°20'0"W

10°10'0"N

10°10'0"N

Ü !Sagu

!Nyagili

!Charia

!Cheringu

!Yibile

!Zingu

!

Dobile ! !Mangu !

!Dinaso

Wa

! .

!Chorkor !Konta

!Sombo

!Tabiase

!Juonga

Ext.

10°5'0"N

!Kperisi !Konjiahi

10°5'0"N

development without regard for buffer zones of streams especially where suitable land for physical development is scarce (UN 2009).

The occupation of buffer zones of streams is a major cause of human induced floods in the urban areas in Africa (UN 2011). This poses a serious challenge to the attainment of the Millennium Development Goal 7 which is aimed at ensuring environmental sustainability by 2015.

Expansion of existing physical infrastructure to accommodate the growing urban population is threatening the existence of urban streams and the important roles they play. They have become endangered resources in urban areas as the global urban population is estimated at 4.98 billion by 2030 (Cohen 2004, International Federation of Surveyors (FIG) 2010). In Ghana, the Water Resources Commission has set out a 300 m buffer zone on each side of a stream which legally prohibits physical development in this zone (Water resources commission 2008). The Town and Country Planning Department (TCPD) and the building Inspectorate Unit of the District, Municipal or Metropolitan Assemblies are on the ground to enforce it. However, uncontrolled physical development in relation to buffer zones of urban streams is characteristics of Wa Urban area, a fast growing city in terms of population and infrastructure.

The issue of uncontrolled urbanisation in the Wa Urban Area was studied by Ahmed and Dinye (2011) and Boamah et al. (2012) with a focus on the challenges of enforcing development controls. This was done without a spatial appreciation of the magnitude of the problem of uncontrolled physical development especially in high-risk areas such as the immediate surroundings of streams which is relevant for understanding and solving the problem.

This study assessed the state of the streams in Wa urban area in relation to physical development. The locations of the streams and physical structures mapped with a Trimble Global Position System (GPS) receiver and analysed using GIS while land owners and tenants were purposively selected and interviewed for the assessment.

11

10°0'0"N

!Bamahu

Legend

!Sing

!

! .UDS Permanent ! Campus Konfabiala

!Nakori

!Kpongu

!Chansa

!

! . !Siiro

Town Major Tertiary Institution Road Stream

9°55'0"N

9°55'0"N

!Dandafuri

Upper East Upper West

!Dukpong !Dobile !Mangu

Brong Ahafo

!Wa

Volta

9°50'0"N

Northern

9°50'0"N

Ashanti Eastern Western

4

2

Central

0

4

Greater Accra

!Konta Ext.

!Chorkor

8

12

9°45'0"N

The Study was conducted in the Wa Urban area which constitutes the business hub of the Wa Municipality. The Municipality shares administrative boundaries with the Nadowli District to the North, the Wa East District to the East and South, and the Wa West District to the West and South (Figure 1). It lies within latitudes 2º40’N and 2º15’N, and longitudes 9º55’ and 10º20’W and is located in the Savannah zone, which is gently undulating with an average height between 160 m and 300 m above mean sea level. The topography is no barrier to physical development. The Wa municipal is within the Noumbiel sub-basin of the Volta Basin. The low lying areas have given rise to two main drainage systems, Bilibor and its tributaries to the North and the Sing-Bakpong and its tributaries to the South. The streams under consideration in this article are tributaries of the Bilibor. The streams are seasonal and dry up during the long dry season while they experience floods annually in the rainy season depending on the rainfall amounts (Wa Municipal Assembly 2011).

The vegetation is Guinea Savannah grassland type. Common trees found are shea trees, Parkia biglobosa (Dawadawa), Ceiba pentandra (Kapok) and Adansonia digitata (Baobab). Anacardium occidentale (Cashew) and Mangifera indica (mango) are exotic species growing

9°45'0"N

Study area

10°0'0"N

!Danko

! .Wa Polytechnic

16 Kilometers

2°35'0"W

2°30'0"W

2°25'0"W

2°20'0"W

Figure 1. Map of Wa Municipality (Landsat 08th February 2011, band432)

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DOI number: 10.5027/jnrd.v4i0.02

Methods and materials

related disasters in the urban area, issuance of permits, availability of development layouts and the level of compliance by physical developers, and enforcement of building regulations.

Mapping Physical Development Sections of the streams were purposively selected based on proximity to the Central Business District (CBD). The streams that were close to the CBD were selected for the study because this is where the demand for land for physical development is high and competitive.

The geographic coordinates of all buildings within and close to the selected streams were mapped with the Trimble GPS receiver in March 2012. The centre of each stream was picked as a line which also serves as basis for the definition of the buffer zone. The mapping was done starting from the centre of the water way and moving outward. This was to ensure that all the structures within the core area (first 100 m sub-buffer zone) of the streams were mapped. The concentration of the mapping of the structures was on the core of the water ways because it is the high-risk area of the buffer zone. It is assumed that the decision to build in the core area of the buffer zone meant that the outer area is already occupied because of the high risks associated with the core area. A total number of one thousand two hundred and seventy-four (1,274) structures were captured as points and two dams as polygons.

Sampling The study area was stratified into three sections based on the sections of streams mapped earlier: Dobile-Mangu-Chorkor, Kumbiehi, and Kambali-Kunta. Purposive sampling was used to select landlords and tenants for interview because of the legal implications associated with building in the buffer zone. The targeted respondents were landlords and tenants who were willing to respond to the land issues without fear. This was to ensure that respondents were sincere as much as possible in providing the information sought. Hence, 68 people were interviewed, 38 tenants and 30 landlords (Table 1).

Table 1. Distribution of Samples according to the Various Strata Zones

12

Questionnaires administered Landlords

Tenants

Dobile-Mangu-Chorkor

14

18

Kumbiehi

8

10

Kambali-Kunta

8

10

Total

30

38

SOURCE: Field Survey, 2012.

Interviews The land lords and the tenants were interviewed based on a questionnaire. The interviews were centred on the initiators of the acquisition process, the motivation for the acquisition and the challenges of developing or living in the buffer zones of the streams. The Town and Country Planning Office and the National Disaster Management Organization (NADMO) of the Wa Municipal Assembly were engaged in separate discussions centred on flood

Data Processing and Analysis The GPS data were downloaded using GPS Pathfinder 4.00 software as shapefiles. The shape files were assigned WGS1984 coordinate system and projected to Universe Transverse Mercator (UTM) Zone 30N coordinates system in order to carry out the buffer analysis but the output maps were displayed in global units so that the locations are easily understood within the global context. The steams were overlaid on a February 8, 2011 Landsat Image (which was already in UTM Zone 30N coordinates system) to confirm that the lines depicting the water ways actually define the centre of the streams before the buffer analysis. The image captured these streams very well. For the purpose of this study, the overlay did not show any significant deviations between the lines and the streams as captured in the image.

Spatial data (points, lines and areas) were plotted to generate maps which clearly depicted the spatial relationship between these features within the study area. The 300m buffer zone of each stream was generated using GIS with the centre of the stream as reference. The buffer was divided into three sub-buffer zones of 100m each. The physical structures were then overlaid on the buffer zones and classified into public, residential, commercial and others.

The responses from the landlords and tenants were processed and analysed to generate charts and tables that facilitate appreciation of state of the streams under study.

Results Spatial distribution of physical development The spatial distribution showed that physical development has spread into the core area (100m from the centre of the stream) of the buffer zones in the selected streams (Table 2, Figures 2, 3 and 4). There are also patches of clustering of physical development in all the selected streams. Out of the one thousand two hundred and seventy-four (1274) structures mapped in the buffer zone, 50.3% were in the core area, 49.70% in the rest of the 200 m outer zone of the 300m buffer (Table 2). The buffer zones were flooded with just 60 mm rainfall (Wa Meteorological Office) on 8th May 2012 (Figure 2).

Table 2. Buffer zones and houses Sub-buffer zone

Number

%

First 100m

642

50.3

Second 100m

555

43.7

Third 100m

77

6.0

Total

1274

100.0

The categories of physical structures sited in the buffer zone were residential, commercial, public and agriculture. The residential structures were compound, detached and semi-detached homes

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which were privately owned (Table 3 and Figure 5). The public structures were churches, mosques, schools and markets. The commercial buildings were stores, hostels, guest houses, washing bays and a filling station whilst agricultural consisted of basically fenced gardens. The fence consisted of mud which can impede free flow of water. The residential structures constituted 93.4% of all the structures mapped in the buffer zones (Tables 3 and 4). Commercial and public constituted 5.1% and 1.3% respectively. The rest were fenced gardens.

Table 3. Classification of buildings within the buffer zone

13

Table 4. Statistics of buildings within buffer zone for suburbs Sub-buffer zone

Residential

Commercial

Public

Fenced Gardens

Mangu-Chakor

First 100m

154

11

0

0

Second 100m

184

4

3

1

Third 100m

27

0

0

0

Total

365

15

3

1

Kambali First 100m

211

21

4

1

Second 100m

162

13

4

0

0

0

8

1

Type

Number

%

Third 100m

36

1

Total

409

35

Residential

1190

93.4

Commercial

65

5.1

Public

16

1.3

First 100m

233

6

1

0

0.2

Second 100m

171

8

4

1

100

Third 100m

12

1

0

0

Total

416

15

5

1

Fenced Gardens Total

3 1274

Kumbiehi

Figure 2. Map showing buffer zones in Dobile â&#x20AC;&#x201C; Mangu â&#x20AC;&#x201C; Chorkor (Pictures were taken on 9th May 2012)

DOI number: 10.5027/jnrd.v4i0.02

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Figure 3. Map showing buffer zones in Kambale â&#x20AC;&#x201C; Kunta

Figure 4. Map showing buffer zones in Kumbiehe (Pictures were taken on 28th August 2012)

14

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15

Educational Status of Respondents

Motivation for physical development buffer zone of urban streams

The educational status of the respondents shows that majority of the tenants attained tertiary education while majority of the landlords did not have any formal education. Out of 38 tenants interviewed, 60.5% had tertiary education while 18.4% attained secondary. The rest had either basic education or no formal education (Figure 2). The fact that majority of the tenants are literate and are in a better position to understand the implications of living in a waterway yet accept to live in such places explains the acute accommodation problem in the urban area. In the case of the landlords, out of 30 respondents interviewed, 46.7% had no formal education while 23.3% and 13.3% had tertiary and secondary education respectively. The rest had basic education. Thus the distribution of the educational level of the landlords indicates that a significant number (26.6%) are in a better position to know the consequences of developing infrastructure in waterways. This shows that there are other factors that influence the tenants and the landlords to live in the waterways.

Despite the unsuitable nature of the buffer zone for physical development particularly for residential purposes, people are motivated to either build or rent houses/rooms there. What is then the motivation? The motivations for physical development in the study area were: availability of land near streams, affordable land, wetland for gardening, and proximity to Central Business District (CBD), access to water and affordable accommodation. However, the main motivations for physical development were availability of land near streams and affordable land. Majority of the landlords indicated that development in the buffer zone was not deliberate but it was due to their inability to access land outside the buffer zone. This is also evident from the fact that public places such as the market being sited in the buffer zone by the Wa Municipal Assembly, a government institution, which is supposed to enforce the regulation against building in the 300 m buffer zone for natural waterways. Besides, developers have easy access to these lands at low cost and enjoy the opportunity of high water table for the construction of wells. These results indicate that social services and facilities were not evenly distributed. Hence, compelling people to move closer to CBD where they can benefit or enjoy such facilities.

Close observation by the research team reveals that population explosion could be one of the influencing factors for the rapid development of physical structures on water ways. This is supported by the data gathered from the TCPD (2009) of Wa Municipal Assembly.

The continuous erection of illegal buildings and other structures on water ways include; weak enforcement of existing building, planning and environmental laws, unnecessary delays in the issuance of building permit, uncontrolled allocation and demarcation of land (TCPD, 2009).

Figure 5. Educational Status of Landlords and Tenants

Table 6. Reasons for acquiring land in waterways Reasons for acquiring land/Renting a house

Initiators of Land acquisition process Physical development in waterways are prone to challenges such as floods and insanitary conditions (Figure 2 and Table 7), it is therefore important to know how the land acquisition process starts.

The findings (Table 5) revealed that, 86.7% of the land acquisition processes were initiated by landlords, (the landlords request the land from the land owners) while 13.3% were initiated by the landowners, (the land owner advertises the land to prospective developers).

Figure 2 supports this revelation as it shows that parts of the buffer zone have been demarcated into plots (see Survey Pillar in Plate 4 of Figure 2). This means that either land outside the buffer zone is scarce or it is cheap to acquire land in natural waterways. Hence, these lands were acquired for various purposes without considering the dangers associated.

Table 5. Initiator of Land Acquisition Initiator of Land Acquisition

Response

% 86.7

Landlord

26

Land Owner

4

13.3

Total

30

100.0

Landlords

Tenants

Number

%

Number

Availability of land near water ways

7

23.3

-

Low cost of land

7

23.3

-

A availability of wetlands for dry season Gardening

5

16.7

-

Proximity to CBD

6

20.0

12

31.6

Access to Water

5

16.7

10

26.3

Cheap accommodation

-

-

16

42.1

30

100.0

38

100.0

Total

%

Challenges in the rainy season According to the survey, 20% of landlords and 42% of tenants believed that flooding is the consequence of physical development.

Also, mosquito infestation is predominant according to responses of both landlords and tenants. From the survey and the field visits it can be concluded that the main consequence of physical development on waterways in the study area is flooding. Erosion and deposition

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of waste materials in the environment by flood waters are the least in terms of responses as indicated in Table 7. These responses show that the study area is a flood risk area in the event of heavy rainfall.

Therefore, Government, NADMO and other stakeholders should put in place measures to protect these people who are at the risk of losing their properties, causing environmental degradation, outbreak of diseases, loss of lives and pollution of water bodies.

Table 7. Challenges during the Rainy Season Challenges Faced

Landlords

Tenants

Responses

%

Responses

%

Flooding

18

60.0

25

65.8

Mosquito

10

33.3

9

23.7

Erosion

2

6.7

2

5.3

Littering Total

-

-

2

5.2

30

100.0

38

100.0

Discussion The distribution of physical structures in the buffer zones clearly shows that the core zones of the buffer zones of the selected streams have been encroached by physical development. This contradicts the norm that streams should be free of physical development to allow for effective flood control since the most effective measure to control floods is to empty the streams of uses that are in direct conflict with their naturally and environmentally accepted purpose (Havlick, 1974). In the Wa case, construction of physical structures in streams have created an enabling environment for floods even with the least amount of rainfall. Floods are inevitable in the area and the current state of the streams has serious implications for lives and property since 93.0% of the structures mapped in the buffer zones are residential. It also points out that urbanisation in terms of physical development remains uncontrolled in the Wa Municipality and will lead to the disappearance of the natural water ways if this trend of development is not checked The occupation of the core zone of the buffer zones in the urban area raises many issues such as lack of enforcement of building regulations, high rate of population growth, scarcity of suitable land for residential purposes close to the CBD, inadequate planning schemes, difficulty in accessing essential services, and high cost of rents (Boamah et al.

2012; Ahmed and Dinye, 2011). The increasing population can no longer be accommodated by the current residential facilities making accommodation an urgent issue in the urban area. This creates market and opportunities for land lords to increase rents which also encourage tenants to build their own houses instead of continuing to rent. Thus the demand for land for residential purposes increases.

Lack of potable water in the periphery of the urban area, lack of access roads and high financial burden of extending electricity to the outskirts of the urban area repels people away from the periphery of the town and compels them to develop any available space including streams (Darteh et al. 2010). It points out to the fact that the urban area is experiencing an infill growth pattern and the patches of unoccupied core zone are not free areas. This is exerting a lot of

16

pressure on the natural ways in the urban area. The fact that the land acquisition process in the study sites within the Wa Municipality is initiated by the land lords shows that there is market for the land owners to sell unsuitable land. It also reveals a serious challenge faced by the institutions (Water Resources Commission, the Town and Country Planning and the Wa Municipal Assembly) mandated by law to ensure proper development controls, enforce building regulations and protect natural water ways. The occupation of the buffer zones creates insanitary living conditions for the occupants and others which are silently looked on by both the residents and Wa Municipal authorities. This is simply because the land lords who have accepted to acquire lands in these areas, and the tenants who have accepted to rent such houses have quietly entered into a “Covenant of No Complain” with nature and municipal authorities since physical development in buffer zone is illegal. By this, they have accepted not to complain about the difficulties they face such as floods and insanitary conditions (Figure 2).

Though majority of the land lords did not have any formal education, they are aware of the consequences of developing within the immediate environment of streams (Boamah et al. 2012, Ahmed and Dinye 2011). Plate 5 of Figure 4 clearly supports this as it shows heaps of gravel that are meant to fill the water way before development. So the issue at hand therefore goes beyond ignorance as suggested by Ahmend and Dinye (2011). The fact that the occupation of streams is motivated by land availability explains that prospective land owners see water ways as cheap lands and not as protected areas. The encroachment will continue if this is the perception of the general public.

Conclusions The paper revealed that the buffer zones in the selected streams in the Wa Urban area are seriously encroached by physical development.

The streams by their nature are disappearing thus giving way to settlements. About 93% of the encroachments are private residential facilities. Floods are inevitable in these areas and will have serious repercussions on human lives, property and the development of the urban area in general.

References Ahmed, A. and Dinye, R. D. (2011). Urbanisation and the Challenges of Development Controls in Ghana: a Case Study of Wa Township, Journal of Sustainable Development in Africa, Volume 13, No.7, 2011.

Ahmed, A. and Dinye, R. D. (2012). Impact of land use activities on Subin and Aboabo Rivers in Kumasi Metropolis, International Journal of Water Resources and Environmental Engineering Vol. 4(7), pp. 241-251, July 2012 Boamah N. A., Gyimah C., Nelson J. K. B. (2012). Challenges to the enforcement of development controls in the Wa municipality, Habitat International 36 (2012) 136 – 142 Cohen, B. (2004). Urban Growth in Developing Countries: A Review of Current Trends and a Caution Regarding Existing Forecasts. World Development. Vol. 32, No. 1, 23–51 Darteh B., Adank M., Manu K. S. (2010). Integrated Urban Water Management in Accra: Institutional Arrangements and Map. SWITCH Report of Institutional Mapping in Accra, Ghana

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Drakakis-Smith, D. W. (2000). The Dimension of Urban Growth in the Third World Cities, 2nd ed. New York: Routledge Publication Fururmai, H., F. Kazama, H. Nagaoka, and Jun Nakajima (2009) Collaborative development of water environment quality index in Japan, in Innovations in Collaborative Urban Regeneration (M. Horita and H. Koizumi, eds.),Springer, Tokyo Gould, W.T.S., (1998), “African Mortality and the New ‘Urban Penalty’”, Health and Place, Vol. 4, No. 2, pp. 171-181.

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Songsore, J. (2003), Towards a Better Understanding of Urban Change: Urbanization, National Development and Inequality in Ghana, (Accra: Ghana Universities Press).

Town and Country Planning Department (TCPD). (2011). Protecting Waterways from Encroachment. Wa, Upper West Region United Nations (UN) (2011). Population Distribution, Urbanization, Internal Migration and Development: An International Perspective. ESA/P/WP/223 United Nations, New York, NY 10017, USA

Hai, P. M. & Yamaguchi, Y. (2006). Monitoring land cover change of Hanoi City Center

United Nations. 2009. Spatial Planning: Key Instrument for Development and Effective

under Impacts of Urbanization by Using Remote Sensing. International Symposium

Governance with Special Reference to Countries in Transition. Economic Commission

on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2006 Havlick, S. W. (1974). The Urban Organism: The City’s Natural Resources from an Environmental Perspective. Macmillan Publishing Co. Inc., New York International Federation of Surveyors (FIG) (2010). Rapid Urbanization and Mega Cities: The Need for Spatial Information Management. Research study by FIG Commission 3, Copenhagen, Denmark Karley, N. K. (2009). Flooding and Physical Planning in Urban Areas in West Africa:Situational Analysis of Accra, Ghana. Theoritical and Emperical Research in Urban Management, Number 4 (13) 25 – 41

for Europe, Geneva, Switzerland Wa Municipal Assembly. (2011). Performance Review of 2009-2011 MTDP. Wa: Wa Municipal Assembly.

Water Resources Commission (2008). Final draft Buffer Zone Policy for Managing River Basins in Ghana.

World Meteorological Organisation (WMO) (2008). Urban Flood Risk Management – A Tool for Integrated Flood Management Version 1.0. Associated Programme on Flood Management (APFM) Technical Document No. 11, a Floor Management Tool Series.

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT A critical review on the National energy efficiency action plan of Egypt Hatem Elrefaei a, Marwa A. Khalifa b* a

Associate Prof., Department of Engineering Physics and Mathematics, Faculty of Engineering, Ain Sahms University.

b

Associate Prof., Department of Urban Planning & Design, Faculty of Engineering, Ain Sahms University.

* Corresponding author : marwa_khalifa@eng.asu.edu.eg

Article history Receivedâ&#x20AC;&#x192; 07.09.2013 Acceptedâ&#x20AC;&#x192; 21.01.2014 Published 17.03.2014

Keywords Energy Efficiency Renewable Energies Energy Policy Egypt

Abstract Egypt, as with other developing countries, faces a major energy security problem, which strongly impacts all national plans for economic development. A sound energy strategy is crucially needed, and should be based on two pillars: first, boosting the production of clean energy from various renewable and non-renewable sources, and second, managing and rationalizing energy demand, with related reforms. Some steps were taken by previous Egyptian governments regarding these two pillars. In February 2008, the Ministry of Electricity and Energy of Egypt put a target of 20% of electricity to come from renewable energy resources by 2020. In July 2012, the Ministerial Cabinet approved both the Egyptian Solar Plan targeting 3500 MW of solar energy by 2027, and the National Energy Efficiency Action Plan (NEEAP) to reduce energy consumption 5% during the period from 2012-2015 compared to the average consumption of the previous 5 years. We believe that these plans will not bring their expected fruits unless they are well orchestrated with other sectoral development plans in areas such as agriculture, transport, housing and services, amongst others. This paper aims to investigate the Egyptian NEEAP and assess whether the adopted national energy efficiency plan and the associated policies on all other development sectors adopted by the government have sound implications. We aim to find out whether the development policies with a focus on energy policy are set in an integrated or fragmented way.

1. Introduction Energy is a prime source of livelihood for many nations and is a cause of affluence for others. The world is facing twin energy-related threats: (i) not having adequate and secure supplies of energy at affordable prices and (ii) environmental harm caused by consuming too much of it. Consequently, it leads to a continuous increase in CO2 emissions, which represents a little more than three quarters of the net greenhouse radiative forcing by human-made emissions (Mann,

Alley & Pugh, 2013). As Prindle, Eldridge, Eckhardt, & Frederick (2007) argued, energy efficiency and renewable energy are the twin pillars of sustainable energy policy. In many countries energy efficiency is also seen to have a national security benefit because it can be used to reduce the level of energy imports from foreign countries and may slow down the rate at which domestic energy resources are depleted (Prindle, Eldridge, Eckhardt & Frederick, 2007). According to

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the International Energy Agency (IEA), improved energy efficiency in buildings, industrial processes and transport could reduce the world’s energy needs in 2050 by one third, and help control global emissions of greenhouse gases ( International Energy Agency, 2006).

Egypt is a society under change. Where the main drivers in the shorter term appear to be socio-political and internal, the country will also be affected in the long term by global and climate change. This poses a series of threats to the livelihoods of people caused by limited access to natural resources in relation to the population size and economic growth. Specifically, Egypt faces a major energy security problem, which strongly impacts all national plans for economic development.

The country’s oil production peaked in 1993, and it became a net importer in 2010 (Mushalik, 2013). Due to population and economic growth this gap is forecasted to open further. On the other hand, national gas production initially grew steeply but has stagnated for the past three years. As it is mainly feeding the ever-growing demand for electricity, Egypt has also become a net importer of natural gas (BP, 2013).

A number of serious initiatives to reduce energy consumption have been initiated by concerned Ministries and organizations. Amongst them is the National Energy Efficiency Action Plan (NEEAP), aimed to reduce energy consumption by 5% during the period from 20122015 compared to the average consumption of the previous 5 years (Ministry of Electricity and Energy, 2012) .

This paper principally studies the Egyptian NEEAP and assesses whether there are sound implications for the adopted national energy efficiency plan and the associated policies in all other development sectors adopted by the government. We aim to find out whether the development policies with a focus on energy policy are set in an integrated or fragmented way.

The structure of this paper is as follows; section (2) explains the status quo of energy production and consumption in Egypt. Section (3) illustrates the Egyptian National Energy Efficiency Action Plan (NEEAP) in terms of its main targets and programs. Section (4) includes a critique to the current Egyptian NEEAP and suggestions for future development, and finally, section (5) includes the concluding remarks.

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during the exploration phase (EGAS, 2012). For more than a decade now, Egypt has been purchasing part of the oil owned by these exploration companies, until it became insufficient and now Egypt is importing oil from other foreign countries (Mushalik, 2013).

Figure 1. Evolution of national oil production and consumption from 1965 till 2011 (BP, 2013).

In terms of oil reserves, Figure 2 shows a stable reserve of around 4 billion barrels for the last 3 decades indicating no more large oil fields are expected to be discovered in the future and Egypt most probably will not be an oil rich country like its neighbours (Libya, Sudan, and Saudi Arabia) (BP, 2013). With an increasing population, oil consumption is expected to increase, and with decreasing oil production and flat reserves, Egypt will become more dependent on oil imports and be more susceptible to the high prices of oil. This means more burdens on an already exhausted national budget.

2. Energy challenges in Egypt 2.1 Challenges of Conventional Primary Energy Production Analyzing the situation of national fossil fuel supply in Egypt reveals the difficult situation facing any Egyptian government in attempting to satisfy its needs using national resources alone. Figure 1 illustrates the historical development of national oil production and consumption in Egypt, showing that Egypt’s oil production peaked in 1993 and has decreased since then. Despite the slight increase in production from 2006 until 2009, the overall historical pattern is similar to more than 60 other oil producing countries in the world (BP, 2013). It also reveals that by 2010 Egypt became a net oil importer from outside its national land. It is important to mention that the national production shown in the figure is not totally owned by the Egyptian government, but rather a great deal of it (30% to 50%) is owned by oil exploration companies in return for investments made

Figure 2. Evolution of oil reserve from 1980 till 2011 (BP, 2013).

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Looking at natural gas numbers, the major fuel for power plants in Egypt, it can be seen that the situation is less dramatic, since national production is still greater than national consumption, as shown in Figure 3 (BP, 2013). It also shows that while consumption is monotonically increasing, production came to a halt in 2009. If both production and consumption keep their patterns, national consumption will surpass national production in just 4 years.

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In terms of natural gas reserves, their rate of increase has been decreasing since the beginning of the millennium, and the reserve value almost came to a peak in 2010 as shown in Figure 4 (BP, 2013).

Currently, the Egyptian government is opening areas for international exploration companies in the deep water of the Mediterranean Sea where there are great hopes of finding large natural gas reserves similar to those of some neighbouring countries.

2.2 Electricity Generation in Egypt Analyzing the electricity sector indicates that the installed capacity from thermal power plants constitutes the majority of the total installed capacity in Egypt, followed by hydro-electric power and a very minor contribution from wind power and only one solar thermal power plant, as shown in Figure 5 (NREA, 2013). Additionally, the dependence on thermal power plants has increased over the last decade, as shown in Figure 6. Comparing the figures in Figures 5 & 6 corresponding to the total installed capacity of 2002/2003 and 2011/2012, respectively, indicates that the contribution of thermal power plants to total installed capacity increased from 84.2% to 88% at the expense of hydro-electric power which decreased from 15.4% to 9.6% (NREA, 2013).

Figure 3. Evolution of natural gas production and consumption from 1970 till 2011 (BP, 2013).

It is worth mentioning here that according to the Egyptian Electricity Holding Company statistics for 2011 about 78.5% (19.4 M toe of a total 24.7 M toe of fossil fuel used in all production companies) of fossil fuel supplied to power plants is in the form of natural gas (Egyptian Electricity Holding Company, 2013). Despite the fact that gas production is greater than consumption, since 2010 power plants have experienced production disruption especially during summer times because of frequent pressure drops in natural gas pipes. Contrary to Egyptâ&#x20AC;&#x2122;s experience of a stable and continuous supply of electricity for decades, in the last 3 years Egyptians have experienced frequent electricity outages as the Ministry of Electricity and Energy mitigate the effect of disruption in natural gas supply by implementing rolling black-outs.

Figure 4. Evolution of natural gas reserve from 1980 till 2011 (BP, 2013).

Figure 5. Total installed capacity 2002/2003 (17,833 MW) (NREA, 2013).

Figure 6. Total installed capacity 2011/2012 (29,076 MW) (NREA, 2013).

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2.3 Electricity consumption in Egypt

USA

35 30

1000$/capita

According to Electricity Holding Company in Egypt, half of the electricity generated on low and medium voltage networks is consumed in the residential sector (50.7%), followed by the industrial sector (19.3%) and then the governmental sector (16.4%) as shown in Figure 7 (Egyptian Electricity Holding Company, 2013) . In addition, the residential sector represents the fastest growing sector in electricity consumption over the last 5 years. This is principally due to the expansion of residential compounds and new communities in addition to the widespread use of domestic appliances, especially household air conditioners due to hot weather during summer months.

40

Germany Israel Italy

25

15

5 0

Sweden

France Japan Koria

Spain

20

10

21

Turkey Iran, Brazil

Malaysia

Russia

South Africa

India Egypt 0

2

4

6

8

10

12

14

16

MWh/capita

Figure 8. GDP per capita versus electrical energy consumption per capita for selected countries (OECD/IEA, 2013).

Figure 7. Electricity consumption by sector at low and medium voltage network for 2011/2012 (Egyptian Electricity Holding Company, 2013).

This non-productive consumption behavior results in a high burden on Egypt’s economy where high levels of investment have to be made year after year in order to satisfy the growing consumption in the residential sector without a comparable growth in the industrial sector.

3. The National Energy Efficiency Action Plan (NEEAP): Targets and programs Electrical Energy consumption has increased tremendously in recent years without a proportional increase in the national GDP. Electricity intensity defined as the ratio of electricity consumption to the national GDP is a standard measure for how efficiently energy is used in a country. Figure 8 shows the GDP per capita versus electrical energy consumption per capita for selected countries, including Egypt.

This shows that most European countries and emerging economies (Brazil, Turkey, Malaysia, and South Africa) are doing much better in terms of energy intensity than Egypt (OECD/IEA, 2013). For example, Turkey (which has almost the same population as Egypt) has more than double the GDP per capita of Egypt while consuming only 33% more electricity per capita than Egypt. This shows a better utilization of resources pushing a growing economy.

Realizing the severity of primary energy availability on the one hand, and high non-production consumption of electricity on the other, the Egyptian government has developed the National Energy Efficiency Action Plan (NEEAP) for the period 2012-2015. The NEEAP, which was approved by the Cabinet on 11/7/2012, includes targets and measures aiming to improve energy efficiency of end-users in some sectors of consumption and will be implemented in cooperation with the Ministry of Electricity and Energy and other ministries such as the Ministry of Industry and Foreign Trade, the Ministry of Housing, the Ministry of Local Development and the Ministry of Tourism (Egyptian Electricity Holding Company, 2013).

Overall, the Egyptian NEEAP has set a target to achieve cumulative energy savings of 5% of the average of the last five years of consumption (i.e. for the period 2008-2012). This is achieved through 4 different types of procedure: • Procedures in main sectors • Procedures in complementary sectors • Procedures across different sectors • Evaluation of the progress in energy efficiency policies In the following subsections, a description of each of these procedures will be illustrated.

3.1 Energy efficiency procedures in main sectors Main sectors include 3 main areas: the residential sector, public utilities and governmental buildings, and the tourism sector.

3.1.1 Residential Sector: efficient lighting and high efficiency household appliances In 2010/2011, consumption in the residential sector reached 41% of total consumption. Out of this amount, 30% is consumed for lighting and 70% for the use of electrical appliances, especially air conditioners in summer (Abdin, 2009). The NEEAP aims to implement 3 procedures in the residential sector: efficient lamps, efficient appliances, and the increased use of solar water heaters.

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It is worth mentioning that Compact Fluorescent Lamps (CFL) are not new in Egypt, electricity distribution companies have sold 12 million lamps at half price with an 18-month guarantee period, and another 3 million lamps are being sold with the same conditions. The NEEAP targets selling 12 million more lamps during the aforementioned period (RCREEE, 2013). Regarding energy standards and labels, they have been developed for 5 household appliances (refrigerators, automatic washing machines, air conditioners, electrical water heaters, CFL and electronic ballasts). Ministerial decrees have been issued to enforce these standards, testing laboratories have been set up under the New and Renewable Energy Authority (NREA), and a guide book has been prepared to assist consumers in their selection.

3.1.2 Public Utilities and Governmental Buildings Given that street lighting consumes 4.9% of total energy consumption in Egypt, a program has been developed to replace the 400, and 250 watt sodium, mercury vapor, and incandescent lamps with high efficiency 250, and 150 watt sodium lamps or 85, and 120 watt CFLs without affecting lighting conditions as required by national and international norms. The NEEAP target is to replace 1 million lamps, and 340,000 lamps have been replaced to date (Egyptian Electricity Holding Company, 2013).

Governmental buildings consume about 5% of total consumption.

A study has been conducted to investigate the opportunities of reducing electricity consumption in this sector leading to the following recommendations (Egyptian Electricity Holding Company, 2013): 1. An energy efficiency code for governmental buildings has been developed and a ministerial decree has been issued for its enforcement.

2. A resolution has been adopted by the Supreme Council of Energy for improving energy efficiency of governmental buildings.

3. Electricity distribution companies implemented energy efficiency projects in buildings belonging to the power sector or within the geographical area of the electricity distribution companies.

4. The Energy Efficiency Improvement project, funded by GEF and UNDP, executed by the Ministry of Electricity and Energy has implemented energy efficiency pilot projects in some governmental buildings (Ministry of Irrigation and Water Resources) and achieved 17% savings through replacing lighting systems with more efficient ones.

5. Capacity-building and training sessions have been provided to employees of governmental buildings in the field of energy efficiency auditing and efficient lighting.

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the three main sectors: residential, public utilities and governmental buildings sector, and tourism, as well as the expected energy savings (GWh) for each sector in the period from 2012 to 2015.

Table 1. NEEAP procedures in the residential sector.

Procedure

Expected energy saved (GWh) (2012-2015)

The use of high efficient lamps in the residential sector (distributing 12 million lamps)

3330

The use of efficient appliances (second stage of standards and labels program)

663

Develop and implement a financing mechanism with a bank or several banks to facilitate solar water heater ownership

67

3.2 Procedures in complementary sectors In this section, the Ministry of Electricity and Energy lists a set of renewable energy projects, 4 wind projects and 3 solar projects, in addition to 3 combined cycle power plants. It also includes a renovation of Aswan and the high dam turbines, and increasing the efficiency of 3 existing thermal power plants.

Many awareness and media campaigns are also listed to increase knowledge on energy saving among the general public. The ministry also targets reducing energy loss in the power network and increasing the use of smart meters in the residential sector.

3.3 Procedures across different sectors This item of the NEEAP focuses on evaluating the existing potential of energy saving in the industrial, commercial and public sectors. It also provides the necessary knowledge and skills for workers and managers in these sectors to apply energy efficiency measures in their entities. Thus, the procedures range from energy auditing to capacity building, and extend to building certified labs for efficient lighting and reviewing existing legislation to promote energy efficiency (Ministry of Electricity and Energy, 2012) 3.4 Assess the evolution of energy efficiency policies

As for public utilities, a program is currently underway for drinking water plants and sanitary stations to improve the power factor for stations with a power factor less than 0.9.

Here the NEEAP focuses on procedures that improve the structural and legislative setting in the government to boost energy efficiency in Egypt. This includes proposing a new law or decree to address energy efficiency, establishing a renewable energy fund to finance new renewable energy projects, setting a standard and color code for efficient appliances, and creating legislation to reduce or completely ban the use of inefficient lighting systems (Ministry of Electricity and Energy, 2012) .

3.1.3 Tourism Sector The targeted energy saving in the tourism sector focuses on increasing the use of solar water heaters in hotels located in Red Sea and Sinai governorates. Table 1 indicates the adopted procedures in

It is worth mentioning that except for the projects listed in the first paragraph of 3.2, the remaining procedures in 3.2, 3.3, and 3.4 have no quantitative evaluation of the expected amount of energy savings over the period of the NEEAP.

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4. Egyptian NEEAP: Critique and Future Development This section presents the authors’ critique of the current Egyptian NEEAP as well as suggestions for further improvements, as follows: 1. The fragmentation of the energy sector in Egypt between 2 ministries, the Ministry of Electricity and Energy and the Ministry of Petroleum, means that no one is actually overseeing the entire energy chain and planning for energy savings. This is a major structural deficiency in the Egyptian energy sector that needs a drastic decision by the ruling powers in order to merge the two ministries into one Ministry of Energy. The existence of such a single entity that looks after the whole energy chain is a must in order to be able to formulate a good master strategic plan for energy efficiency, for both primary and final energy. Unfortunately the disturbance in the Egyptian political system since the January 2011 revolution has been an obstacle to such a decision being taken by any governing power during this period.

2. The presented NEEAP is more a plan to improve electrical efficiency than energy efficiency. It introduces procedures to improve the efficiency of final energy (electricity) use and not primary energy use. This strengthens the notion that the Ministry of Electricity and Energy in Egypt is working solely as a Ministry of Electricity and not as a Ministry of Energy.

3. Though Egypt has become a net importer of fossil fuel, the NEEAP does not include any procedures for fossil fuel savings. This comes as no surprise since, as we mentioned in the first point, no one is looking after the whole energy chain in Egypt. It is worth mentioning that, lately, the government has started a smart card program for car fueling. This program was a reaction to fuel smuggling that led to repeated shortages of liquid fuel during 2012/2013, and not as a coordinated plan for energy saving. Fuel smuggling comes as a natural result of heavy fuel subsidies in Egypt while neighboring countries sell liquid fuel almost at international prices (Ministry of Petroleum, 2013).

4. The NEEAP does not address the root cause behind the overconsumption of electricity in Egypt from the residential sector which is the heavy subsidy on electricity. While the average cost of electricity for residential consumption is 33.1 pst/kWh (4.8 cent$/ kWh), the average selling price is 13.9 pst/kWh (2 cent$/kWh).

With residential consumption around 6000 GWh, the total subsidy for this sector alone reaches 10.6 B EGP (1.54 B $), which is far above all other sectors (EgyptEra, 2013). It is worth mentioning that the 6 brackets of electricity prices for the residential sector are 5, 11, 16, 24, 39, and 48 pst/kWh (0.72, 1.59, 2.32, 3.47, 5.64 cent$/ kWh). So, it can be noticed that up to the 4th bracket with total consumption up to 650 kWh/month, the electricity price is well below the cost of generation. This heavy subsidy in the electricity sector leads to market distortion and thus over-consumption in the residential sector.

The cost of subsidies can be summarized according to Fattouh and El-Katiri (2012) in three major areas as follows:

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a) Economic cost: energy subsidies encourage over-usage of energy, leading to high energy consumption growth rates and less interest in energy efficiency. b) Social cost: while energy subsidies play an important role in the well-being of the poor, richer households are likely to benefit from it as well. Additionally, a burdened national budget by fuel subsidies results in weak services in the ‘pro-poor’ sectors such as health and education. c) Environmental cost: as mentioned in the economic cost, energy subsidies increase irrational energy use which consequently leads to potential adverse environmental harm. Fuel subsidies also make renewable and clean energy technologies less competitive if not equally subsidized, and increase the burden on the national budget.

5. The amount of targeted saving is very minute considering the low energy intensity of Egypt and the huge amount of electricity consumed in non-production activities. The target saving of 5% of the average consumption of the period 2012-2015 will definitely be less than 5% by 2015, the final year of the program. Referring to Figure 8, if Egypt had the same electricity intensity as Brazil and Turkey while still maintaining the same GDP per capita as it has now, Egypt would consume 25% and 40% less electricity, respectively. This reveals that the saving potential in Egypt is much more than the targeted 5%.

6. As a result of the previous points, the NEEAP has no energy saving component in the transport and agriculture sectors and these two sectors are heavily dependent on fossil fuel rather than electricity.

7. Given the significant contribution residential buildings can make to reducing energy consumption, the focus on energy efficiency in this sector cannot be confined only to the use of efficient lamps, efficient appliances, and increased use of solar water heaters as illustrated in the Egyptian NEEAP. But rather, using less energy in buildings can be achieved through 3 wider approaches (WBCSD, 2008): a) A holistic design approach begins with master planning, takes the whole life cycle into account and embraces integrated building design processes. b) More appropriate financial mechanisms can support growing interest in high-performance buildings. For example, financial incentives can play a key role in helping energy-efficient buildings make business sense. New tax breaks and emerging markets for renewable energy should assist companies overcome internal financial obstacles and are expected to promote further investment in energy-efficient buildings. c) Drastic behavioral change of users.

8. Though a considerable ratio of energy consumption in the residential sector comes from the widespread use of air conditioners, there is no component in the NEEAP addressing the issue of the “building envelope”, which is critical to energy efficient design. Principally, the integration of factors like shade, orientation, daylight, ventilation and appropriate materials is urgently needed.

The energy-efficiency planning of the building envelope means

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guaranteeing that necessary interior climate conditions can be maintained throughout the year with low energy requirements. A building envelope optimized for energy aspects has a maximized passive capacity and hence represents the foundation for valuable energy concepts in the future. Possible measures that can be taken into consideration during the planning and design of buildings are as follows (Hegger, Fuchs, Stark, & Zeumer, 2008): For sites: a) Longitudinal facades of buildings with Northern and Southern orientation b) Evergreen trees on the north side to protect from winter winds For buildings: a) Masonry materials that change temperature slowly b) Courtyard with fountain or pool for cooling effect c) Overhangs or thick walls to protect windows from sun d) Windows placed for good cross ventilation e) Light exterior colors to reflect sun’s heat 9. The NEEAP includes activities regarding renewable energy projects, however: a) These projects are not energy efficiency projects, but rather electricity generating projects. b) Saving electricity from existing over-consumption behavior is environmentally and economically better than generating electricity from expensive renewable energy resources, especially in a country such as Egypt where electricity is heavily subsidized.

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and innovations. It begins at the community planning level to gain efficiencies on a larger scale than can be achieved in individual buildings and to integrate other energy uses, such as transport.

Finally, raising society’s awareness of the necessity to implement energy efficiency measures and the crucial need of behavioral change in users is one of the main factors needed to promote the measures addressed in the NEEAP and to foster the achievements of its targets.

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http://www.bp.com/

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from:http://egyptera.org/Downloads/reports/%D8%AA%D9%82%D

8%B1%D9%8A%D8%B1%20%D8%AA%D9%83%D9%84%D9%81%D8%A9%20 % D 8 % A 3 % D 9 % 8 6 % D 8 % A A % D 8 % A 7 % D 8 % A C % 2 0 %D9%88%D9%86%D9%82%D9%84%20%D9%88%D8%AA%D9%88%D8%B2%D9%8A%D8%B9.pdf Egyptian Electricity Holding Company (2013). Annual Report 2011/2012. Arab: Republic of Egypt, Ministry of Electricity and Energy. http://www.egelec.com/mysite1/ annual%20report/annual%20report.htm Egyptian Natural Gas Holding Company “EGAS” (2012). International Bid Round, Main Contract Terms and Conditions http://www.egas.com.eg/BidRound2012/Terms_ Conditions_2012.pdf Fattouh, B and El-Katiri, L. (2012). Energy Subsidies in the Arab World: United Nations

5. Concluding remarks Though the Egyptian NEEAP, issued in 2012, can be considered an initial step in the right direction to improving energy use in Egypt, more could have been planned and targeted by this document, not only in terms of quantitative measures but also of the structure of the energy sector as a whole. The fragmentation of how the energy sector is managed in Egypt between 2 ministries leads to conflict and a lack of a single responsible entity in charge of overseeing the entire energy chain. The issue of high electricity subsidies, particularly in the residential sector, is perceived as one of the major causes of over-consumption in this sector. The focus of the NEEAP is principally on electrical energy reduction and not energy efficiency in its wider perspective; it has no energy saving component in the transport and agriculture sectors despite their importance as two sectors that are heavily dependent on fossil fuels rather than electricity. Given the fact that approximately half of the electricity generated on low and medium voltage networks is consumed in the residential sector, attention should be placed on more innovative techniques to reduce energy consumption in this sector in particular. The NEEAP aims to implement 3 procedures in the residential sector to reduce electrical energy consumption: efficient lamps, efficient appliances, and the increased use of solar water heaters; however, these are perceived as insufficient. A holistic approach beginning with master planning, taking the whole life-cycle into account and embracing integrated building design processes is indispensable. This approach is essential to maximizing the potential of individual technologies

Development Programme, Regional Bureau for Arab States Arab Human Development - Report Research Paper Series.

Hegger, M., Fuchs, M., Stark, T., & Zeumer, M. (2008). Energy Manual: Sustainable Architecture: Birkhauser; Basel. Boston.Berlin.

International Energy Agency (2006). World Energy Outlook 2006: OECD/IEA. Mann, M., Alley, R., & Pugh, E. (2013). METEO 469: From Meteorology to Mitigation: Understanding Global Warming: The Pennsylvania State University. Retrieved from https://www.e-education.psu.edu/meteo469/node/181.

Ministry of Electricity and Energy (2012). Egypt’s National Energy Efficiency Action Plan (NEEAP): Ministry of Electricity and Energy, Arabic version.

Ministry of Petroleum (2013). Smart Card Program for Car Fueling. Retrieved from https://www.esp.gov.eg/ Mushalik, M. (2013). 2/3 of Egypt’s oil is gone 20 years after its peak. Crude Oil Peak, Retrieved from http://crudeoilpeak.info/23-of-egypt%E2%80%99s-oil-is-gone-20years-after-its-peak NREA (2013). Annual Report 2011/2012.New Renewable Energy Authority Ministry of Electricity and Energy. http://www.nrea.gov.eg OECD/IEA (2013). International Energy Agency statistics http://www.iea.org/statistics/ Prindle, W., Eldridge, M., Eckhardt, M., & Frederick, A. (2007). The Twin Pillars of Sustainable Energy: Synergies between Energy Efficiency and Renewable Energy Technology and Policy: American Council for an Energy-Efficient Economy (ACEEE), Report Number E074. http://www.aceee.org/research-report/e074 Regional Center for Renewable Energy and Energy Efficiency “RCREEE” (2013). Egyptian NEEAP Follow-up [PowerPoint slides]. Retrieved from https://www.slideshare.net/ secret/H12nxtKtKEMsyI WBCSD. (2008). Energy Efficiency in Buildings, Business Realities and opportunities: Facts & Trends: World Business Council for Sustainable Development.

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Commentary

Conservation through community: An attempt to untangle a tangled word.

Deeraj Koul Masters in Ecology and Environment from the Sikkim Manipal University of Medical and Technological Sciences, India.

K.dheraj@gmail.com

Conservation is not something that can be thrust upon a community, and neither can it be labelled as a measure to take away a people’s rights and nor can it be used as a force to make others adhere to rules and laws from outside; by outside I mean you and me and your or my Government.

I am not writing to highlight that conservation should not happen with the community but I do believe that older community bonds are broken or they have started to brake and the ways implied earlier no longer hold in the field. Most of us are still hanging on to older approaches to community conservation but the younger generation in a community has moved beyond, now it is rare to see a community come together for the sake of collective benefits and also when conservation measures take longer to deliver the fruits and the sustained release of impacts over a period of time it makes people forget the changes made to the immediate environment, except for a few visible ones. This too is forgotten with time until the cycle of destruction repeats itself.

Previously, the community used to look at water, forests and other natural resources as common resources and they used to protect them, but the present generation does not understand common resources. In their terms it has to be yours or mine or else it is the Government’s. There is no fourth dimension, i.e. the common resource which used to be a main dimension of conservation. The vanishing concept of the common resource has brought about the biggest destruction ever.

New ways of conservation have to be found as older traditional ways of conservation may still work in some places but they will be not adhered to by the majority. I am not saying this because community led work isn’t good enough, but because the community system was previously woven into social fabric and there was mutual dependence. However, the social fabric has changed a lot over recent years and so has traditional community bonding. The west lost it a long time ago but other regions are catching up very quickly; community bonds are getting weaker and weaker and other ways of social bonding are taking over and we all are still struggling to solve problems through our narrow prism of community. We need to broaden it. I don’t mean that community shouldn’t be involved, it is the only way, but we need to understand the psyche of the community and try new ways to involve it.

I hope I have not confused you and that you will continue reading.

For everyone the mantra of conservation is through community and almost all conservation activities revolve around one particular community.

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However, I feel there is a problem with the way it is projected and the percentage success rate is very low. In other words, in a world were two brothers do not have same level of thinking and the same approach to a single issue, how can we expect a community to work together on a single focused agenda and hold on to it for a long period at a time when people have started to weigh everything on a scale of personal gain.

Also, since conservation is a time-consuming process it is quite likely that the efforts of a community and its initial enthusiasm will fade over time. On this point a lot of people will try to pounce on me and try to prove me wrong with data and by showcasing successful community level projects. I do not deny that there are lots of success stories but are we getting the percentage of results that should have been achieved by now with so many NGOs, aid agencies and government efforts coming together? What is the success rate of these projects? An officer from the forest department once told me that in the last 30 years in India so many plantations have been made through community that no space would be available for plantations if all the work had born results. He may have exaggerated or he may be referring to poor implementation but there might still be some truth to his word. Also, I do expect the same kind of results around the world with some variations, since so much money has been spent through reforestation, community forestry. There seems to be a huge difference in conservation through community available on paper records and the reality on the ground, meaning that no matter how much money is spent, conservation has a low success rate and we are still persisting with ideas that have a very low percentage of success.

In a community project a lot of management committees are formed, a forest committee, a watershed committee, a management committee; people participate and committees are formed but most of the committees become defunct over a period of time or they remain but are inactive.

What are the reasons behind this? Maybe there is very little personal stake or low interest due to the high probability of getting bad reputation in the eyes of people if a community member tries to ensure that the system is adhered to. As the needs of people are unlimited and resources are limited a conflict of interest is bound to arise. I agree that other factors such as local politics etc. are also responsible, and I also agree that we need to persist in conservation through community as any other option will be suicidal.

Some will try to argue that for any project the work to be undertaken is decided by the community and even the priority of work is decided by the community, at each step the community is involved from initiation to completion. But how many of us see sustained community support and adherence from initiation to completion and beyond. As we know from the human tendency most times we humans get involved keenly only when we see direct benefits, and not through indirect benefits or co-benefits. Thus, in order to make everyone get involved each person has to see some kind of a personal stake in the project, otherwise only a few will persist and others will not.

We as conservationists think that it is the duty of a community to protect itself. Conservation will not be successful because we think that the community should protect itself. Even within a community there are different views and they will come together but will only disintegrate with time.

To see how well a project will work I believe that we first need to analyse how much the members of the community are dependent on each other, how much the community is woven into the social fabric and its bonds, how much the community is not affected by my political factionalism. And if the community lacks in the above then we have to change the way conservation is conducted.

Community approaches like collective thinking but direct individual benefits need to be incorporated more into the programmes so that better results can be achieved, by direct benefit I donâ&#x20AC;&#x2122;t mean monetary benefit but giving every individual a sense of belief that whatever conservation effort he has done at his level is his own, it is his creation and he is the master of its destiny.

I also trust that we are witnessing new social bondingâ&#x20AC;&#x2122;s which are independent of regional background, politics, colour, rich and poor differentiation, taking shape and blooming as social media groups, they have a power to evolve a new thinking, bring change and develop a new form of common resource concept which can take conservation of globalised community to a new platform.

These are not the only ways but surely other ways of uniting a community need to be found in order to generate a truly participatory approach.

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Artificial Neural Networks to predict decreasing saturated hydraulic conductivity in soils irrigated with saline-sodic water.

Younes Daw Ezlita, Ahmed Ibrahim Ekhmaja and Mukhtar Mahmud Elaalema a

Department of Soil and Water Science, Faculty of Agriculture, University of Tripoli, Libya

Article history Receivedâ&#x20AC;&#x192; 09.01.2014 Acceptedâ&#x20AC;&#x192; 08.04.2014 Published 08.05.2014

Keywords Sodicity Salinity Soil Hydraulic Conductivity Artificial Neural Networks

Abstract Multilayer Artificial Neural Networks (ANNs) with the backpropagation algorithm were used to estimate the decrease in relative saturated conductivity due to an increase in sodicity and salinity.

Data from the literature on the relative saturated hydraulic conductivity measured using water having levels of sodicity and salinity in different types of semiarid soils were used. The clay content of these soils is predominantly montmorillonite. The input data consisted of clay percentage, cation exchange capacity, electrolyte concentration, and estimated soil exchangeable sodium percentage at equilibrium stage with the solution applied. The data was divided into three groups randomly to meet the three phases required for developing the ANNs model (i. e. training, evaluation, and testing).The activation function selected was the TANSIG layer in the middle, while the exit function was the PURELIN layer. The comparisons between the experimental and predicted data on relative saturated hydraulic conductivity during training and testing phases showed good agreement. This was evident from the statistical indicators used for the evaluation process. For the training phase, the values of mean absolute error (MAE), root mean square error (RMSE), the correlation coefficient (r) and the determination coefficient (R2) were 0.08, 0.13, 0.91, and 0.83, respectively. The performance of the ANNs model was evaluated against a part of the data selected randomly form the whole set of data collected (i. e. data not used during the model testing phase). The resultant values for MAE and RMSE, r and R2 were 0.12, 0.16, 0.82 and 0.68, respectively. It should be noted that many factors were not considered, such as soil pH, type of clay, and organic matter, due to the limitations of the data available. Using these factors as input in ANNs might improve model predictions. However, the results suggested that the ANNs model performs well in soils with very low levels of organic matter.

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1. Introduction The shortage of fresh water for irrigation has led to overuse of water with high levels of salinity and sodicity. Salinity is the increase of concentration of soluble salts in the water solution, while sodicity is the relative concentration of sodium (Na+) compared with divalent cations, mainly calcium (Ca2+) and magnesium (Mg2+) in water or soil solution (Ezlit et al. 2010). Using water with high levels of salinity and sodicity may initiate soil structure stability problems in irrigated areas. The increase of salinity in the root zone limits the growth and development of crops and reduces yield. Rising sodicity induces structural instability in soils containing a significant clay percentage.

Degradation of soil structure results in negative changes in soil hydraulic properties, reduces aeration and soil logging, and adversely affects nutrient balance in the root zone. Sodicity is also common in soils irrigated with water containing considerable bicarbonate concentrations. This is because bicarbonate anions raise soil pH and can result in precipitation of divalent cations and an increase in the relative sodium concentration (Ezlit et al. 2011).

Sodicity is usually evaluated in terms of the sodium adsorption ratio (SAR) in irrigation water. The exchangeable sodium percentage (ESP) was employed to determine the level of sodicity in soils. ESP is closely related to SAR and in the literature it is used interchangeably to express the level of sodicity. In addition, the magnitudes of deflocculation are expressed in terms of decreases in saturated hydraulic conductivity (Ksat), and occasionally in the change in infiltration rates (Simunek and Suarez 1997). Coupling the percentage of the hydraulic conductivity reduction to the real measured values of ESP of the soil or SAR of the applied water allows quantification of the sodicity effect. This approach has been intensively applied since first introduced by Quirk and Schofield (1955). They measured the decrease in Ksat as a result of water sodicity and salinity using soil columns equilibrated with Mixed-Salt solutions at given SAR values and different electrolyte concentrations.

Quirk and Schofield (1955) introduced two indicators to evaluate the degree of adverse sodicity effect in relation to the total salinity concentration and the SAR of the water applied. These indicators are the threshold electrolyte concentration (TEC) and turbidity concentration (TC). TEC was defined as the salt concentration at which the soil permeability starts decreasing to a certain sodicity level. The value of TEC is very important in setting an irrigation management program using highly saline sodic water. TC was defined as the salt concentration at which clay fractions appear in the percolate. TC indicates that the soil structure is highly affected and should not be reached in practice. It should be noted that some level of Ksat reduction occurs due to pore clogging as a natural process of water movement conveying fine particles. Fine particles may plug some of the fine effective pores. In addition, soil slaking occurs the first time water is added. For a given soil, Quirk and Schofield (1955) proposed a critical decrease in Ksat of 10 to 15% of the optimal Ksat value.

However, McNeal et al. (1966) recommended using a 25% reduction as critical of TEC for some American soils tested using backed columns. In addition, Cook et al. (2006) adopted a 20% reduction in Ksat as a critical value to determine TEC; this was later adopted by Ezlit (2009). Ezlit (2009) used an improved experimental design based on the Quirk and Schofield (1955) method to produce the TEC for a number of Australian soils (i.e. semiarid soils). The results showed

that the TEC varies from one soil to another. Bennett and Raine (2012) used the same technique to demonstrate the need for producing a TEC curve for a single soil to better manage irrigation using highly saline sodic water.

Various researchers have developed general soil stability guidelines based on the TEC concept for different soils in relation to the total salinity concentration and SAR of the water applied (e.g. Quirk and Schofield 1955, Rengasamy et al. 1984, and Ayers and Westcot 1985).

These guidelines are useful for general demonstration of the effect of sodicity. However, these guidelines were derived for specific soils, and may not be suitable for other soil types (Ezlit 2009). The variation in the TEC for different soils is significant due to many interrelated and dynamic factors (Rengasamy et al. 1984). The variation in TEC is mainly caused by the differences in the clay mineral types and content, as well as soil texture (McNeal and Coleman 1966, Frenkel et al. 1978). Setting good irrigation management using saline sodic water requires identification of the level of soil structural stability under applications of saline sodic water.

Modeling soil structure instability due to sodicity was first introduced

by McNeal (1968). The McNeal (1968) method is based on a semiempirical model that relates the montmorillonite swelling approach from Norrish (1954) (i.e. swelling factor) to the change in relative reduction in saturated hydraulic conductivity as relative RKsat. In addition, Lagerwerff et al. (1969) proposed a different semi-empirical model that relates RKsat calculated using the Kozeny-Carmen equation (Carman 1937, 1948) to clay swelling calculated based on the diffuse double layer theory. Furthermore, Yaron and Thomas (1968) provided a simple empirical equation to predict RKsat from the average ESP of the soil. Jayawardane (1979) proposed the equivalent salt solutions method to predict RKsat due to rising sodicity. Jayawardane (1979) defined the equivalent salt solutions as solutions with combinations of sodium adsorption ratio (SAR) and solute concentration (Co) that produce the same extent of clay swelling in a given soil. In addition, Ezlit (2009) and Ezlit et al. (2013) provided a modification of the McNeal model with guidelines to predict the model parameters.

Research on these models showed more or less appropriateness (e.g.

Russo and Bresler 1977, and Mustafa and Hamid 1977). Nevertheless, in all aforementioned models, obtaining the model parameters for a specific soil is required. The process of parameter determination is complicated, time consuming, and tedious experiments are needed.

The situation of the unsaturated conditions is more complex.

Different models have also been proposed to quantify the decrease in unsaturated hydraulic conductivity to be applied under field conditions. Examples of those approaches are Simunek et al. (1996) and Russo and Bresler (1977). Simunek et al. (1996) used a reduction function based on the McNeal (1968) model and a soil pH effect function. The Simunek (1996) approach assumes that the effect of sodicity in soils under saturated conditions is similar to that in unsaturated conditions.

Despite considerable studies, there is no generic approach to predicting the TEC for different soils based on readily available soil data. However, this model can be developed by utilizing the new techniques in computer technology. The best available technique is artificial neural networks (ANNs).

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ANNs are able to accurately approximate complicated non-linear input/output relationships. The ANNs methodology has been used in applications where the characteristics of the processes are difficult to describe using simple physical equations. There are a number of studies (e.g. Elizondo et al. 1994, Schultz and Wieland 1997, Ekhmaj et al. 2007) in which some environmental phenomena are described by mathematical models based on an ANNs approach. For solute transport problems, ANNs applications have been used successfully to predict the transport parameters and solute distribution in groundwater (Morshed and Kaluarachchi 1998, Almasri and Kaluarachchi 2005). Nevertheless, no study has been conducted to use the input– output mapping of ANNs to predict the effect of sodicity seen in the decrease in saturated hydraulic conductivity associated with applications of saline-sodic water. The current study aims to develop an ANNs model to simulate the decrease in saturated hydraulic conductivity associated with applications of saline-sodic water using RKsat data obtained from laboratory experiments.

2. Material and Methods 2.1 Data collection TEC experiments for 10 semi-arid soil groups from different sources were used in this study. The soils selected have very low organic matter levels and represent semi-arid soils from the US and Australia.

Table 1 provides a summary of the main soil input data used to develop the ANNs model. In all data sourced, the experimental design used is similar to that adopted by Quirk and Schofield (1955), where different NaCl-CaCl2 solutions having different SAR and solute concentrations (C0) were applied to soil columns. The RKsat values were calculated by dividing the measured Ksat using a NaCl-CaCl2 solution by the Ksat measured for normal water (low SAR and higher C0). The measured RKsat in solution having a value of SAR and C0 for a given soil were treated as an individual case.

Individual cases of RKsat data (the values ranged from 0.1 to 1) corresponding to Electrolyte concentration (C0) (ranged from 2.5 to 640 mmolc/Litre), exchangeable sodium percentage (ESP) (ranged Table 1: Main properties of the soils

from zero to 100%), cation exchange capacity (CEC) (between 94.45 and 433.2 mmolc/Kgsoil), clay content (%) (from 5.7 to 53.4) were organized in Excel spreadsheets as cases. The data collected represents the quality of the water applied and the soil properties.

The ESP values were estimated from the measured sodium adsorption ratio (SAR) for applied water using the USSL Staff (1954) SAR-ESP relationship. Both solute concentration (C0) and ESP as a function of SAR determine the degree of the sodicity effect on the soil’s stability due to the water applied. The CEC is one of the main soil properties.

CEC is a relative function of the type of clay and organic matter. Since the data selected represents soils having less than 1% organic matter, most of the CEC values come from clay content. Thus, CEC is expected to improve the predictions generated by the model. Clay content reflects the percentage of soil that can affect the conductivity if entirely dispersed. However, not all clay types can result in soil deflocculation, the effect of clay can be better refined by using the percentage of dispersed clay such as montmorillonite. However, not all the data available in the literature has such values, though the data selected for this study is generally from soils predominant in montmorillonite clay which makes total clay percentage a useful variable for the model.

2.2 Standardization of the data: The data collected were processed to meet of the ANN model. Data were arranged as (i = 1, 2,…, n), and transformed according expression as suggested by Vamsidhar

the requirement numbered cases to the following et al. (2010):

(1) Where: minA and maxA are the minimum and maximum values of an attribute such as C0, ESP, clay %, CEC and RKsat. Min-max normalization maps a value, v, of the A attribute to v’ in the range [new minA; new maxA].

In this study, all data values fall between 0 and 1, which is required by the model’s algorithm. New minA was set at 0.0 and new maxA equal to 1.0. used to develop the ANNs Model

Soil name

Clay

CEC

Number of cases

(As reported)

%

(mmolc/kg)

(RKsat, ESP, C0, clay %, CEC)

1

Alluvial

36.9

433.2

30

2

Red Brown

40.6

245.7

12

3

Imperial soils group a

5.7

94.6

20

4

Imperial soils group b

16.2

153.8

20

5

Imperial soils group c

48.5

336

20

6

Gray Vertisols

53.4

258.4

25

7

Sodosols

12.9

99

24

8

Brown Vertisols

47.1

270

24

9

Soil 9

37

252.2

33

10

Soil 8

38

275.4

30

Soil No.

29

Source Jayawardane (1977) McNeal (1968), McNeal et al. (1968)

Ezlit (2009) Leigh (2010)

30

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The data were divided into three groups. Group A were used for the model training and comprises 50% of the data collected (119 cases). Group B was employed for the internal process of the ANNs model. Group C was used for the internal model validations and comprises 25% of the data collected (59 cases). The testing phase used the third group which comprises 25% of the data set (60).

(2)

(3)

2.3 ANNs Model Matlab program (Version 7.0) of Neural Networks Toolbox (Graphical User Interface) was used to develop the ANN model for this study. The activation function selected was the “TANSIG” layer in the middle, while the exit function was the layer. The model used is demonstrated in figure 1.

(4) Where Oi and Pi are the estimated and experimental values of RKsat, So is the sample standard deviation of the observed data, Sp is the sample standard deviation of the predicted data and n is number of pairs of observations. All data were processed and loaded into the neural modeling application Matlab program (Version 7.0) Neural Networks Toolbox (Graphical User Interface).

3. Results and Discussion 3.1 Development of the ANN model

Figure 1. The architecture of (4 – 4 – 1) backpropagation neural networks used in this study 2.3 Performance Evaluation Criteria In order to evaluate number of statistical are the correlation estimated RKsat (r), absolute error (MAE)

the developed neural network model, a parameters were used. These indicators coefficient between experimental and determination coefficient (R2), mean and root mean square error (RMSE).

Despite the many models proposed to quantify the decrease of saturated hydraulic conductivity, none can be used without determining the model parameters for a given soil. The perfect relationship with limited factors is not available in the literature.

That is because of the complexity of the sodicity effect within the soil. Therefore, there are no standard rules to building the networks structure; the optimum networks was identified using a trial and error process. The best results were obtained using a multilayer networks including the backpropagation algorithm. The input layer consists of 4 neurons for CEC, C0, ESP, and clay content. The output layer has only one neuron to provide the predicted RKsat.

The hidden layer was started with a small number of neurons and increased progressively until the optimum structure was reached. Too few neurons could lead to underfitting and difficulties in mapping

Figure 2. Plots of experimental (solid line with filled circles) versus estimated values (dashed with unfilled circles) of RKsat for the training phase.

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the process, while having too many neurons leads to overfitting and increased training time. The optimum model structure was accomplished through trial and error to determine the number of hidden layers and the number of neurons in each layer. It was found that the optimum networks structure to simulate the decrease in RKsat for the networks of four neurons in the input layer needs one hidden layer with 4 neurons to provide one neuron in the output layer.

Therefore, the optimum structure was 4-4-1 as illustrated in Figure 1.

Figure 3. The correlation between the predicted and the experimental RKsat during the training phase.

The ANNs model predictions compared with the real data selected during the training phase are illustrated in Figure 2. It can be noted that the predicted values of RKsat are in good agreement with the experimental values. The good performance of the model is evident from the values of MEA and RMSE which are 0.08 and 0.13, respectively.

The correlation between the predicted and the experimental RKsat data during the training phase is shown in Figure 3. The correlation coefficient obtained was 0.91, and the R2 is about 0.83. From 119 values of RKsat plotted, few points fall away from the regression line.

This shows that the model was able to estimate RKsat values in most

31

cases during the training phase. However, there are some points where the model tends to overestimate RKsat compared with the experimental data. This may be due to the random errors which are inherent to the experiments. However, the statistic indices confirm that the model of RKsat was appropriate during the training phase.

The plots of experimental versus estimated values of RKsat for the data selected in the testing phase are illustrated in Figure 4. Generally, the performance of the ANN model was good. The model was able to describe the change in RKsat, as is evident from the values of RMSE and MAE in the testing phase, 0.16 and 0.12, respectively. The performance of the model may also be considered acceptable based on the value of the correlation coefficient, which is 0.82. The value of R2 was 0.68.

For the testing phase, statistical indices indicate that the model was to some extent able to predict the decrease due to sodicity. In addition, the parameters chosen to describe soil characteristics were significant in the model (Figure 5). However, the model tends to overestimate RKsat compared with the experimental data. That is probably due to differences associated with random errors which are inherent to the experiments. The data sourced from the literature obtained under different conditions and standards regarding soil columns and solute preparations, which may lead to errors. The amount and the timing of water application were also different for the different experiments, which may result in uncertainty regarding the final RKsat.

Such variations are expected in view of the absence of a standard methodology to obtain the TEC curves for a given soil. The variation can also be attributed to the differences in the ratio of dominant clay type (i.e. montmorillonite) in the clay percentage. For example, montmorillonite clay has less thickness and dispersion occurs due to the nature of clay swelling. A slight difference in the montmorillonite ratio could affect RKsat, however, other clay types may be less sensitive to the increase in sodicity levels in ambient solutions, and may also have no role in the dispersion process. The change in soil pH is one of the factors that may cause variation and induce unexpected results as it may alter CEC and significantly change ESP. However, the magnitude of this change is complicated and may differ with the chemical complex and the orientation of the clay particles. Thus, there is a need to set a standard method to estimate RKsat for different soils, which may improve the predictions generated by the model.

Figure 4. Plots of experimental (solid line with filled circles) versus estimated values (dashed with unfilled circles) of RKsat for the testing phase.

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concentration analysis. Hobart, Australia.

Carman, P.H., 1937. Fluid flow through granular beds. Transactions Institute of Chemistry and Engineering. 15, 150-166.

Carman, P.H., 1948. Some physical aspects of water flow in porous media. Faraday Society Discussions. 3, 72-77.

Cook, F.J., Jayawardane, N.S., Rassam, D.W., Christen, E.W., Hornbuckle, J.W., Stirzaker, R.J., Bristow, K.L. and Biswas, K.T., 2006. The state of measuring, diagnosing, amelioration and managing solute effects in irrigated systems. Futures, C.f.I. (ed), Australia.

Ekhmaj, A.I., Abdulaziz, A.M. and Almdny, A.M., 2007. Artificial neural networks approach to estimate wetting pattern under point source trickle irrigation. African Crop Science Society, El-Minia, Egypt.

Figure 5. The correlation between the predicted and experimental RKsat during the testing phase

4. Conclusions and Recommendations Despite the complexity of soil sodicity mechanisms, which hinders the task of generating a generic model to describe the change in conductivity, the ANNs model developed herein describes the decrease in saturated hydraulic conductivity with appropriate accuracy. The performance of ANNs can be attributed to their structural and functional characteristics, such as nonlinear model capability. For the purpose of soil management under irrigation using sodic and saline water, the ANNs model developed here provided enhanced information on the soil’s structural instability compared with traditional indictors. However, many factors were not considered in this study, such as soil pH, type of clay and organic matter. This is because of the limitation in the available data.

Therefore, there is a need to further examine this model taking other factors into consideration. However, the results suggest that the ANN model performs well in soils with low percentage of organic matter (< %1). In addition, the model can be coupled with chemical, water and solute movement to enhance the modeling process.

It is recommended that the research continue in this area toward enhancement and improvement the ANNs model for improved results.

5. References

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Ezlit, Y.D., Bennett, J.M., Raine, S.R. and Smith, R.J., 2013. Modification of the McNeal Clay Swelling Model Improves Prediction of Saturated Hydraulic Conductivity as a Function of Applied Water Quality. Soil Sci. Soc. Am. J. 77(6), 2149-2156.

Ezlit, Y.D., Smith, R.J. and Raine, s.r., 2010. A review of salinity and sodicity in irrigation.

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Ezlit, Y.D., Smith, R.J. and Raine, S.R., 2011. Management options to use highly salinesodic water for irrigation. In “Novelty, C.a.S. (ed), p. 6pp, Chonburi, Thailand.

Frenkel, H., Goertzen, J.O. and Rhoades, J.D., 1978. Effects of Clay Type and Content, Exchangeable Sodium Percentage, and Electrolyte Concentration on Clay Dispersion and Soil Hydraulic Conductivity1. Soil Sci. Soc. Am. J. 42(1), 32-39.

Jayawardane, N.S., 1977. The effect of salt composition of groundwaters on the rate of salinisation of soils from a watertable. University of Tasmania, Australia.

Jayawardane, N.S., 1979. An equivalent salt solutions method for predicting hydraulic conductivities of soils for different salt solutions. Soil Research. 17(3), 423-428.

Lagerwerff, J.V., Nakayama, F.S. and Frere, M.H., 1969. Hydraulic Conductivity Related to Porosity and Swelling of Soil1. Soil Sci. Soc. Am. J. 33(1), 3-11.

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McNeal, B.L., Layfield, D.A., Norvell, W.A. and Rhoades, J.D., 1968. Factors Influencing Hydraulic Conductivity of Soils in the Presence of Mixed-Salt Solutions1. Soil Sci. Soc. Am. J. 32(2), 187-190.

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Fundamentals of agricultural sustainability or the quest for the Golden Fleece Marc Janssens a*, Hartmut Gaese b, Norbert Keutgen c, Rodrigo Ortega d, Juan Carlos Torrico b, Juergen Pohlan e University of Bonn, Germany.

ITT - Cologne University of Applied Sciences, Germany. c University of Technology and Life Sciences in Bydgoszcz, Poland d Universidad Técnica Federico Santa María, Chile e Freelance consultant in tropical agriculture * Corresponding author: marc.janssens@uni-bonn.de a

b

Article history Received Accepted Published

16.04.2013 18.03.2014 05.06.2014

Keywords Sustainability Resilience Adaptability Energy efficiency Input efficiency

Abstract In this paper, different aspects of development sustainability will be highlighted by stressing the fact that even the smartest drivers are necessarily characterized by the continuous uncertainty we all must live with. Different development drivers will be illustrated in the field of agriculture, nature and environment, all attempting to weigh the contradicting, even conflicting parameters of life and decay. Agricultural sustainability drivers will encompass human, cultural, social and political aspects together with components of metabolism, genetics, energy, environment and farm management.

It will be concluded that each sustainability approach should be precisely documented using exact parameters and not unproven social or emotional attributes. Quantitative cost to benefit ratios will be proposed as sustainability indicators. In short, sustainability is an ideal state in the area of conflict between environmental change, evolution of life and thermodynamic laws. It cannot be defined as a stable state, but as a state of relative stability during a certain but limited period of time.

Sustainability strongly depends on a reliable energy resource that, in thermodynamic terms, enables the preservation of order in an open (eco-) system at the expense of the order of the environment.

1. Semantic definitions and anthropomorphic derivatives of sustainability In recent years, the term sustainability has become a mythological definition of endlessly revolving processes where all components are deemed renewable as if it were a “perpetuum mobile” i.e. the quest for the unattainable Golden Fleece. Sustainability is generously

used in a large spectrum of events, without understanding its actual meaning. This lack of definition allows an overuse of sustainability qualifications, which nobody understands, believes, nor refutes. It adorns a majority of research and development projects as proof of

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unrivalled quality, which is swiftly considered as a vital compliance to the ultimate development prerequisite for which all zealous stakeholders and administrators are likely to agree, or better said, likely to find enough security for their personal insecurity.

Resilience is the companion concept that acknowledges the invariant model status of virgin nature. In fact, this is a gimmick. Most geological, geomorphological and climatic changes are unique and irreversible, and hence, they will prevent any resilience process to recover the original status, albeit well-meant. A case in point is the very recent geomorphological processes (quaternary) that have changed the face of the African continent in an irreversible way: the Ubangi-Shari [Oubangui-Chari in French] disruption inducing the reduction of the Lake Chad watershed basin and contributing to the expansion of the great Sahara desert and, concomitantly the reduction of the original vegetation. “In the 1960s, a plan was proposed to divert waters from the Ubangi to the Chari River which empties into Lake Chad. According to the plan, the water from the Ubangi would revitalize that lake and provide livelihood in fishing and enhanced agriculture to tens of millions of central Africans and Sahelians” (Wikipedia 2013a). And yet, we still observe some remnants of this earlier lush period at the edge of the Sahara desert in the form of “paleovalleys” which can come alive all of sudden for a few months and then disappear. North and South of this enormous space we still encounter occasional lotifagous people eating the same nymphea spp. as their ancestors used to (Figure 1).

“The submerged leaves, the starchy, horizontal creeping rhizomes, and the protein-rich seeds of the larger species have been used as food by humans throughout history” (Encyclopedia Brittanica 2012).

Figure 1: Nymphea or Nympheoid aquatic plants are still collected along the Komadugu river between Niger and Nigeria (Source: Marc Janssens) The term sustainability was coined by the Brundtland report of the “World Commission on Environment and Development” of the United Nations: “sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs” United Nations General Assembly (1987).

Thereafter, sustainability was defined as a principle for resource

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management in Agenda 21 (Rio de Janeiro 1992). “Sustainable Development” underpinned the eight Millennium Development Goals (MDG) which have strongly influenced development cooperation since 2000. At the 2005 World Summit on Social Development it was noted that it requires the reconciliation of present needs with the needs of the environmental, social equity and economic demands - the “three pillars” of sustainability (or the 3 Es) (UN 2005). These ideas were adopted by the European Report on Development 2012 (European Union 2012) and by the final document of UN-conference 2012 (Rio+20). All participating countries agreed to formulate common goals for “Sustainable Development”. The idea is to work out rules and guidelines for Sustainable Development Goals (SDG) with targets and indicators, which shall be implemented by 2015 and shall have validity for all Industrialized and Developing Countries and not only Developing countries as with the MDG. This means validity for all sectors in the economy (Wagner and Wellmer 2009).

Some derivatives of “sustainability” We all dream of an eternal (sustainable) and happy life, whatever religion, philosophy or humanist school we belong to. At the core of most religions or schools of wisdom, the message is to free oneself from all temptations or evil actions diverting us from being good and charitable with our family and fellow citizens. If so, eternal (sustainable) happiness will be bestowed upon you. The medieval scholastic preacher, Meister Eckhart used to say: “When I preach, I usually speak of detachment and say that a man should be empty of self and all things; and secondly, that he should be reconstructed in the simple good that God is; and thirdly, that he should consider the great aristocracy, which God has set up in the soul, such that by means of it man may wonderfully attain to God; and fourthly, of the purity of the divine nature”. If the soul shall see with the right eye into eternity, then the left eye must close itself and refrain from working, and be as though it were dead (Wikipedia 2013b) .

In social and psychological sciences the question remains, how to educate children so that they achieve a successful, i.e. eternal (sustainable) and happy, life until death? How can they be rendered to believe in good actions, in working hard, in behaving under freely accepted moral rules? It is understood that a harmonious person will be respectful of the achievements of other persons and will perform much better because s/he concentrates her/his energy for the best instead of losing energy through entropy by either self-destructive or idle, if not evil, behaviour. The same is true at the community level, where a positive environment will concentrate energy for the overall wellbeing of a family, a community or a country at minimal energy cost. And last but not least, how can a family’s or people’s traditions and values be transferred to the next generation?.

Sustainability in a general sense is the capacity to support, maintain or endure. The attractiveness behind the ideal of sustainability in public and scientific discussion is difficult to explain, but may in some way be related to the all too human experience that everything is coming to an end, whether it be holidays, human life, life on Earth in general or even the Universe – although, with respect to the latter, hope still remains. Realizing the impossibility of maintaining a status quo

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until the end of days, it is not surprising that, from a more practical point of view, sustainability is considered achieved when a system is stable for a limited period of time – the reference here frequently being human generations: “We should hand over the Earth to our children the way we received it from our parents”. In this context, it becomes unimportant that once upon a time on the place where we grow our wine, an ocean existed as indicated by fossil shells. It is regarded sustainable, when a grandson is still able to cultivate good-quality wine on the vineyard that a grandfather designed.

In natural sciences there are opposing views depending on the particular science. Geologists and astronomers will easily accept the fact that most natural phenomena are unique and time-specific, when considering large time scales. Nobody would dare argue that dinosaurs, mammoths or the Jurassic period are likely to revive. Resilience is not a subject of consideration. On the contrary, bio-sciences are tending towards equating sustainability as a naturally recurrent phenomenon insofar as no human hand will disturb its resilient return to a socalled pristine, virgin status called “eco-climax” or even “repository”.

When dealing with agricultural sustainability it is clear that we should drop all possible anthropomorphic wishful thinking.

“Sustainable agriculture is the act of farming using principles of ecology, the study of relationships between organisms and their environment” (Gordon McClymont, 2002 in: Wikipedia 2013c).

Gordon McClymont also defined sustainable agriculture as “an integrated system of plant and animal production practices having a site-specific application that will last over the long term:

- Satisfy human food and fibre needs; - Enhance environmental quality and the natural resources on which the agricultural economy depends; - Make the most efficient use of non-renewable resources and onfarm resources and integrate, where appropriate, natural biological cycles and controls; - Sustain the economic viability of farm operations; - Enhance the quality of life for farmers and society as a whole”.

Raviv (2010) attempted to define sustainability in the field of organic horticulture. He underlined the difficulty of quantifying sustainability and pointed to the usefulness of the recently developed energy analysis for measuring both environmental services and material from production services.

It is feared that the world’s population is about to exceed the carrying capacity determined by present agricultural potential. This means that current agricultural technology does not permit further demographic growth worldwide. Also with respect to economic growth, in 1972, the Club of Rome (1972) advocated zero-growth.

“It predicted that economic growth could not continue indefinitely because of the limited availability of natural resources, particularly oil”.

The present article deals with the fundamental and economic drivers of sustainability. It attempts to discard anthropomorphic interpretations or the abuse of terms like ‘sustainability’ or ‘resilience’ in agricultural development by suggesting indicators of “dynamic

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sustainability” implying buffering, adaptive and energy/resource saving strategies (Annex 1). An upcoming article (Janssens et al.

2014) will attempt to outline the integration of complex agricultural and environmental drivers of sustainability across generations.

2. Sustainability and metabolism If we critically consider the aspects of life on Earth in the context of sustainability, isn’t it a fact that the only stable (sustainable) aspect of life is change? And, more importantly, what would evolution be without change (to the environmental conditions)?.

The ancestral biosynthesis: Considering the development of life on Earth, there is evidence that the Wood–Ljungdahl pathway is the phylogenetically oldest pathway for assimilation of CO2. It already existed a billion years before the first formation of oxygen (Ragsdale, 2004). Nowadays, it is still used by some strictly anaerobic bacteria and archaea. This pathway enables the use of elemental hydrogen (H2) as an electron donor and CO2 as an electron acceptor as well as a building block for biosynthesis. Moreover, it combines CO2 assimilation into acetyl-CoA with the production of ATP via an energized cell membrane (Poehlein et al., 2012). Hence, during the very early stage of development of life on Earth, resources in the Earth’s primeval ocean were consumed by chemosynthesis, for instance those available at hydrothermal vents (black smokers).

Comparable ecosystems still exist today and have therefore been sustainable for more than 4 billion years. With the development of photosynthesis, a new resource of energy was made accessible for life, sunlight, allowing the use of an almost inexhaustible energy source.

The Great Oxygenation Event through Photosynthesis: The production of oxygen by photosynthesis resulted in the Great Oxygenation Event (GOE) around 2.4 billion years ago that wiped out a huge portion of the Earth’s anaerobic inhabitants at that time. The production of oxygen, which is toxic to anaerobic organisms, was responsible for what was likely the largest extinction event in Earth’s history and possibly also for the following ice age (snowball Earth). Hence, the cause of the GOE is a good example that sustainable strategies may end in disaster for ecosystems. Still, it was undoubtedly in this period of time when the evolution of the antioxidative system, protecting cells from reactive oxygen species and of oxidative signalling, was boosted.

Sustainability and longevity: During the first period of their life all organisms grow, hence anabolic processes dominate catabolic ones. Later, organisms reach some kind of steady state, where anabolic and catabolic processes are balanced, at the end, at least in some organisms, catabolic processes dominate – the organisms are dying. Nevertheless, other organisms such as bacteria, Hydra sp. and Turritopsis nutricula are potentially biologically immortal, although they are also susceptible to predation or disease. The longevity of these species depends either on the ability to balance anabolic and catabolic processes long-term (and to survive stressful situations) or to undergo a kind of rejuvenation process.

Disturbance of sustainable climax vegetation by evolution: In

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ecosystems, growth comparable to that of individuals can be identified, resulting in a so-called climax vegetation. Climax vegetations, such as forests, tundras, savannahs, grassland etc., are vegetations that establish themselves on a given site for given climatic conditions in the absence of major disturbance over a long time. They represent the quasi-equilibrium state of a given local ecosystem, where the biomass remains almost constant. The sum of anabolic and catabolic processes is balanced as in an adult individuum. These climax vegetations are considered sustainable, as long as there are no major disturbances in the environment. However, even if local conditions remain stable, we shall not forget about the impact of evolution, for instance the evolution of a species called Homo sapiens some 200,000 years ago in Africa that is today actively changing ecosystems all over the planet.

maintains order by creating disorder (Demirel & Sandler, 2002).

Sustainability is a catabolic-anabolic tandem: In this final sense, sustainability is the successful coupling of catabolic and anabolic processes. Even adding fertilizer and pesticides to an agro-ecosystem may be seen as an anabolic process that is necessary to maintain order. As long as the input by human activity ensures the survival of the agro-ecosystem, it may even be considered sustainable (using the term sustainability in the general sense mentioned above). Human activity, however, is determined by economic aspects and, hence, a certain agro-ecosystem will be maintained as long as it is profitable. If not profitable, the “environmental” conditions have changed and another, new climax state (vegetation) will be established by the farmer or will establish itself.

Sustainability and resilience in agro-ecosystems: Since the main focus of this article is on man-made agro-ecosystems, we may consider here a typical monoculture, viz. the cultivation of a single crop or plant species over a wide area and for several consecutive years.

Generally, this practice is considered not to be sustainable, as it leads to a faster spread of diseases and soil degradation. Instead, crop rotation and diversification are accepted as agricultural measures to increase sustainability, allowing agro-ecosystems to respond to a perturbation or disturbance by resisting damage and recovering quickly (resilience). In this way the concepts of sustainability and resilience are closely linked. An agro-ecosystem is only sustainable if it resists damage and endures for a certain period of time.

In conclusion, sustainability strongly depends on “environmental” conditions. If the “environmental” conditions remain almost stable, spontaneous catabolic processes are easier (more stable) coupled with non-spontaneous anabolic processes. Yet, these anabolic processes rely on the input of energy. Several energy resources may be considered. However, here only two representative examples are discussed: the fossil energy consumed by recent human activities and the “natural” light energy emitted by the sun. The fossil energy used by man to maintain e.g. agro-ecosystems (viz. creating order) results a.o. in the increase of atmospheric CO2 concentrations (viz. creating disorder) with the consequence of global climate change and its impact on global ecosystems. Similarly, the development of photosynthesis to exploit the sunlight using H2O as electron donor resulted in the release of O2, leading to the GOE. Obviously, making new energy resources available may result in a serious disturbance of life, but life on Earth may be able to adapt. If the new energy resource, as in the case of sunlight, is – in human dimensions – eternally available, a new equilibrium may evolve, leading to sustainable ecosystems. If, however, as in case of fossil energy resources, their availability is limited, life (or in the case of man: lifestyle) relying on these resources may not reach a sustainable state in the long term.

How to manage agro-climax in a sustainable way? This latter aspect raises the question of how far man in general is able to achieve a kind of sustainable management? If we understand sustainability as the capacity to support or maintain a status quo, almost any human activity at the beginning can be seen as unsustainable, since it is a characteristic of human activity to adapt the environment to our own needs. However, this newly transformed environment (ecosystem) may reach a new climax state (for instance an agroclimax state). In some cases sustainability may be reached, in others not – at least in the long term. For instance, productivity of ecosystems is highest long before the climax state is reached, which is why in agro-ecosystems early succession-types instead of climax states dominate (Janssens et al., 2008). This type of cultivation practice does not in principle exclude sustainable management, but usually results in the exploitation of resources (e.g. soil nutrients), which then have to be added as fertilisers. Consequently, agroecosystems cannot be seen as closed systems. Nevertheless, natural ecosystems are also not closed, since energy (sunlight) and water (e.g. rain, river) enters these ecosystems from outside. In addition, ecosystems must obey the second law of thermodynamics, which states that in any closed system, the amount of entropy tends to increase. As a consequence, ecosystems exchange matter and energy with their surroundings. As a matter of fact, living systems (cells, organisms, and even ecosystems) are not in a state of equilibrium, but instead are dissipative systems that maintain their state of high complexity by causing a larger increase in the entropy of their environments (Stockar and Liu, 1999). Life achieves this by coupling the spontaneous processes of catabolism to the non-spontaneous processes of anabolism. In thermodynamic terms, metabolism

In summary, sustainability is an ideal state in the area of conflict between environmental change, evolution of life and thermodynamic laws. It cannot be defined as a stable state, but as a state of relative stability during a certain but limited period of time. Sustainability strongly depends on a reliable energy resource that, in thermodynamic terms, allows order to be maintained in an open (eco-) system at the expense of the order of the environment.

3. Genetic background of agriculture and sustainability In farming systems we are dealing with agro-diversity. This implies the use of a broad genetic basis at the landscape level. This diversity encompasses a large array of different species and within species a mixture or varieties/cultivars/clones. Eventually, we should encourage some heterozygous (say hybrid) status to the cultivars. Even with so called inbreeding species, hybrid combinations often prove to have superior field performances. In fruit cropping and forestry, quick progress can be achieved by cloning. As a result, there is a tendency to use the most rewarding clones on a large scale. If we want to averse

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risk in the long run, we should mix a minimum number of different clones to ensure enough genetic diversity and hence, homeostasis.

Genetic material should then be adapted to the target area. If the target area is characterized by low soil fertility and/or difficult climatic conditions we have to breed according to low-input ideotypes (Janssens et al. 1989). If the environment is fertile and we want to achieve high yield we will breed along a high-input model. The critical issue is that this agro-climax environment is not constant but moving all the time, not so much because of climate change, but particularly because of market demand and regulations as well as advances in mechanisation and automation (even robotisation). Nowadays, many dairy farms are introducing automatic milking by robots. Milking frequency and concentrate feeding is steered by computers on an individual basis by the same token. Similarly, large vineyards and fruit orchards are increasingly turning to mechanical harvesting in order to reduce labour costs. This step is rendered possible by breeding animals or plants which will be easily treated mechanically. It all means that changing agro-climax conditions require quick adaptation of the genetic material and hence, of the pursued ideotype models.

In Australia, wheat scientists are selecting new genotypes with resistance against newly created rust races i.e. laboratory constructs, such as to be ready with adequate wheat cultivars at the outbreak of a new rust race. This strategy is unfortunately not extended to all modern ideotypes. If we look at the substantial agro-climax changes in Europe during the last 50 years, farmers have had to change from high fertilisation and pesticide levels into severely controlled demand driven supply schemes. This has been successful, particularly for nitrogen and for some of the more toxic pesticides.

In the Americas zero tillage became another spectacular success, in fact even more important than the earlier green revolution in India. This major breakthrough was rendered possible by the end of the patent protection for glyphosate-based herbicides and by the development of adapted tillage machinery in Brazil, and finally by the development of Round-up-Ready varieties (RR) for major field crops (soybean, maize, wheat, rapeseed, etc.). Interestingly, the genetic engineering originated from Europe (University of Ghent, Belgium) but was applied elsewhere because of GMO restrictions, the major reasons being that some of the RR lines might intercross with wild relatives and that the high levels of herbicides may in the end induce weed populations to become extremely aggressive.

On the other hand, experience shows that breeding for genotypes combining both wide environmental (geographic) and biotic adaptation is too much of the good. Recently, manioc breeders purposely selected against the mosaic virus and discovered later that the mosaic resistant lines tended to be susceptible to either the brown virus or bacterial wilt or both. In cotton breeding against boll worm a major breakthrough was achieved with the help of genetic engineering on the basis of Bacillus thuringensis, leading to spectacular results both in the Americas and in Asia, until the resistance of the newly developed BT lines regressed. Similar breakdowns are reported for BT maize lines either against Busseola fusca (stem borer) in South Africa or Spodoptera frugiperda (fall armyworm) in Puerto Rico.

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One could conclude that plant breeders should on the one hand be ready to quickly adapt their ideotypes to changing agro-climax conditions, and, on the other hand, refrain from unrealistic goals like adaptation to a too wide target area and/or an unrealistic combination of all possible desirable traits (Janssens 1987).

Should we prefer perennial to annual crops in the future? If we want to strive towards more agricultural sustainability, there will be no other choice than moving massively from annual crops towards perennial crops, wherever possible (Janssens and Subramaniam 2000). Indeed, for most agricultural base products one could choose between annual and perennial crops (Annex 3). Perennial crops are not only input efficient but they also offer better eco-capacity in terms of eco-volume, micro-climate, litter fall and hence, soil fertility. Even a tree monocrop is notably better than an annual monocrop. The large majority of tree monocrops are associated with an herbaceous cover crop, preferably a leguminous crop. In addition, tree crop rotation is easy to implement. There will be a major challenge in this conversion process in that new mechanisation techniques, adapted to tree crops, need to be developed both for harvesting and pruning (op.cit.).

4. Growth efficiency is closely related to the efficiency of spatial colonisation by plants Natural eco-systems maximize eco-volume per unit of available energy. Under agricultural systems, the desired produce will be maximized at the expense of eco-volume, as can be seen with sugar cane in Mexico (Annex 2). The different ways chosen by plants under different agro-ecological conditions are there to ensure species survival and perpetuation. Under difficult situations plants will try to develop highly specialized reserve organs with highly concentrated energy storage means. When considering eco-volume as a major characteristic of each crop morphotype or each vegetation type rather than biomass, it follows that energy and input flows should be divided by eco-volume. How large can an eco-volume be developed per unit of water or per unit of solar input, per each season of the year? Eventually, the crop morphotypes/vegetation types with largest RUE (rain use efficiency), WUE (water use efficiency) or NUE (nutrient use efficiency) on an eco-volume basis will take the lead in a particular environment (Figure 1). Plant growth is in fact the development of a maximum eco-volume with a minimum of energy. In turn, this eco-volume will increase with improved use of rain and nutrients. Hence, ecovolume will eventually lead towards better environmental efficiency.

Therefore, the best adapted eco-system will follow the principle of minimum energy and develop the greatest eco-volume (in space), which in turn will produce the largest biomass per unit of surface in agreement with the maximum power theory of Odum (1995).

Maximum Empower Principle This optimizing principle is one of the most daring aspects of energy analysis. Having its roots in work by Lotka (1922), the Maximum Empower Principle claims that all self-organizing systems tend to maximize their rate of energy use or empower (Odum, 1995).

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That is, “ecosystems, earth systems, astronomical systems, and possibly all systems are organized in hierarchies, because this design maximizes useful energy processing”. Thus, this principle can determine which species or ecosystems or any system will survive.

Efficiency of precipitations (RUE) in the Oueme basin, Benin (2004) 3500

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Figure 1. Rain use efficiency and eco-volume/rain rate in the Oueme basin, Benin

5. Input efficiency, sustainability and bio-economics Sustainability: The term “sustainability” and its meaning were always seen as a principle for production in forest and agricultural sciences as a long-term aspect since the implementation and foundation of the first agricultural faculties and universities in the 19th century as a reaction to severe degradation processes in the rural areas of European countries. Sustainability is seen as a principle of farm management – especially in typical European peasant farming systems, where the peasants aim to maintain the fertility of the soil for the coming generations (von Dietze, 1967). Within this historic perspective, the Brundtland-Report (1987) re-assesses the intergenerational justice aspect, while defining sustainable development as the “ability to satisfy present needs without curtailing the ability of future generations to satisfy theirs” (cited in EU-Report on Development, 2012). This is nothing more than the long-term aspect of production intensity without overusing natural resources. Such overuse leads to scarcity of natural resources. This scenario was first brought into discussion by the “Meadow-Report” (Club of Rome), the catastrophic predictions of which could fortunately not be verified.

Misuse of sustainability: Publications by prestigious institutions are full of mainstream opinions considering sustainable use or overuse and deterioration of natural resources and their costs for society. In the European Report on Development (2012) one can find a tabular presentation of “the costs of business as usual for the future: some illustrative examples”. The table is divided into three parts, where examples from international publications are cited for

39

environmental costs, economic costs and social costs, all examples support the favoured tendency of the publication in question without considering publications with contrary findings, e.g.: 1. Environmental costs: “we live in the anthropocene epoch, an environment of which there is no historical experience” (op. cit.). Who would compare historical epochs without considering the circumstances and particular conditions of each epoch?

This “golden principle of historical research” seems to have the character of a tautology.

2. Economic costs: “Failure to act on climate change will reduce world GDP by 20%” (op.cit.); this unilateral estimation has no scientific calculation.

3. Social costs: “Agriculture is currently not intensified in Africa, but applying the technology behind the Green Revolution will not sustainably produce food for 9 billion people” (op.cit.); the first statement is simply wrong – for the second statement one can find many contrary findings in the scientific literature (e.g.

FAO - 2012).

Many publications on sustainability or “resource use” overlook the three generally accepted strategies of achieving environmental, economic and social sustainability as stated in the “Agenda 21” of the UN-Earth summit in Rio de Janeiro (UNCED 1992): - Efficiency strategy is the most important strategy and refers to innovations and technology that improve efficiency or productivity, or more generally – the input/output relation in using resources for a certain purpose e.g. if a new rice variety has higher yields (ceteris paribus), breeding has improved productivity of land use.

- Consistency strategy refers to possible resource saving effects of closing cycles in using resources – if it is economically, socially and environmentally feasible. Re-use of water is a very good example and well-studied, e.g. by FAO (2010).

- Sufficiency strategy refers to the behaviour of people in overusing resources, very often because of the fact that the consumer prices are lower than the social prices (a worldwide problem with water for irrigation where very often the prices are subsidized). Behaviour in resource use can be changed by means of education, higher prices or legal measures.

Inclusive societies against inequity driven sustainability: In the EU Report mentioned above, a further definition is brought into discussion (European Union 20121987): …“we define inclusive and sustainable growth broadly as a type of growth that is consistent with the natural cycles that allow ecosystems to replenish resources, absorb waste, and maintain adequate conditions for life; and that at the same time offers all people an equal opportunity to participate in and enjoy the benefits of increased wealth”. As it refers to sustainability of ecosystem services and “eternal” duration of wellbeing it is only a re-wording of the three dimensions of development as expressed in Agenda 21, bearing in mind that economic growth is the basis of development. EU introduced “inclusiveness” as a notion referring to the participation of current generations in sharing global wealth. “In addition, more equitable or inclusive societies tend to perform better economically and politically than unequal ones”.

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40

The report mentions, in addition to the publications of UNDP and the World Bank, that inequality is something like “wasted potential” and could give rise to conflict and violence in extreme forms. However, if we follow historical experience and the different opinions of serious scientists (von Weizsäcker, 2000) it seems likely that a certain level of inequity is necessary for economic growth.

such as savannahs, grassland, etc. (see above), these represent a quasi-equilibrium or “steady” state (Walter 1993). Indeed, these climax vegetation types must be considered sustainable, in spite of periodical and inherent fire disturbances. It seems plausible that land use systems which do not disturb the quasiequilibrium state could be seen as sustainable land use systems.

Intergenerational justice: When we speak about the productivity of ecosystems using the notion of “ecosystem services” we address the potential of natural resources in a anthropocentric view postulating a long-term “steady state” (Walter 1990); and this postulate includes the aspect of “intergenerational justice”. We come back to the definition of sustainable development claimed in the famous “Brundtland – Report” (Brundtland-Commission 1987), which defines sustainable development as the ability to satisfy present needs without curtailing the ability of future generations to satisfy theirs (see above). Hence, intergenerational justice must take into account both present and future generations.

Recent intensification efforts in animal husbandry: Gaese (2006) verifies the ecosystem-friendly extensive production system in animal husbandry where the comparative advantages of production costs are considerable compared to European meat production systems.

On the contrary, feedlots are intensive production systems. They represent a new tendency in meat producing countries with grassland like Argentina, Brazil and Uruguay. Feedlots require concentrated food production, high energy supply, high transportation costs, high concentrated and overused resources like water and land with high environmental impacts. The land use change from extensive to intensive animal husbandry systems is environmentally highly problematic, as the sum of anabolic and catabolic processes is out of balance with such high intensity. However, we can now observe strong development in sylvo-pastoral cattle ranches in the tropics.

These offer advantages both from an environmental and from an economic viewpoint. Indeed, beef fattening will allow highly desirable cash-flow three years after the establishment of the forest plantation.

Dynamic equilibrium and entropy: The two basic laws of thermodynamics and entropy give us the “highway striping” for resource management following a concept of “long-lasting-view” (the fashionable word is “sustainability”) assuming that the state of dynamic equilibrium should have a minimum of entropy production.

The material balance principle (equation) is: A= B+C+D; where B+C+D represents the discharge flows to the environment, and where the ecosystem energy requirements (A) are nearer to the balance (or “equilibrium flow”). Therefore, if we have sufficient energy, we can resort to all kinds of resource use (for food production, bioenergy etc.). This clarifies the importance of energy policy (Gaese 2012).

Regulating global carrying capacity through energy input: The density of the world’s population and its energy consumption are far higher today than during the Neolithic period. Mohr (2000) calls it the “Neolithic Green Revolution”. Ecosystem services were reduced in that time, but simultaneously the ability of humans to dominate negative externalities through technology and management rose (Gaese, 2012 and Mohr, 2000). Increased “carrying capacity” enabled the world population to increase from app. 5 M humans (8000 B.C.) to 100 M (4000 B.C.) and 200 M humans around year 0. The industrial revolution caused a similar boost to population growth. Today more than 7 billion humans need to be supplied with resources. The sustainability question is: Are we able to maintain the high artificial carrying capacity and for how long in the future? Ecosystems today are very far from “minimum entropy production and require a permanent energy input to be maintained. These systems are far from the “selfregulation” of the original systems. How can we maintain the balance?

Steady state or equilibrium in flow: The notion of “steady state” was used by the internationally renowned botanist Heinrich Walter (former professor of botany at the University of Stuttgart-Hohenheim) – who founded the “ecology of plant communities” (see Walter 1990) in the nineteen-eighties. In a larger sense steady state has something to do with the “equilibrium in flow” as a dynamic flow which was theoretically analysed and postulated as a principle of material flow by Aristotle.

As pointed out with reference to climax vegetation in eco-systems

Precision farming and zero-tillage: In crop production systems there is a general tendency for higher intensity production processes, and catabolic processes are compensated by technical progress (innovations). The highest standard is achieved with socalled “precision farming” (preferably in combination with zerotillage), a technology where catabolic processes are controlled and minimized by the system, following the strategy of increasing resource use efficiency (OECD, 2010). Branscheid (2012) mentions indicators for sustainability in meat production and consumption.

Increased input efficiency through technology advances and resource scarcity: The aforementioned strategies for decreasing resource abuse and stress are undoubtedly the most important ways to reduce resource consumption and to come nearer to an ecological “equilibrium in flow”, or sustainability. A further important aspect is the interaction between economic growth and resource use: it is very probable that higher economic growth leads to less overuse of resources: The interaction between economic growth and less overuse of resources is rebus sic stantibus very probable (technology changes induced by higher prices for other production factors e.g. labour): 1. High prices of resources (land, water, energy etc.) will be a signal for investors to invest in technologies with lower resource requirements; this should also encourage politicians to manage the “Ordnungspolitik” (guarantee for the functioning of free markets).

2. Higher investments in research and development (R&D) will generate technology to substitute resource consumption.

3. This is also important for poor countries, since “the demand for environmental goods and services has a high elasticity in relation to demand”. This means there is a positive relation between economic growth and environmental protection.

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41

Sustainability and financial short thinking

(2010), developed a formula for estimating social time preference:

In a publication dealing with the recent instability of the monetary system, the Club of Rome (Lietaerd et al., 2013) mentioned five characteristics which trigger behaviour that is directly incompatible with sustainability

S = P+UxG; Where: S = social rate of time preference P = pure rate of time preference, the rate at which utility is discounted U = rate at which marginal utility declines as consumption increases G = expected growth in consumption per head

1. Amplification of boom and bust cycles: Banks are said to amplify the business cycle towards boom or bust.

2. Short-term thinking, manifested by discounting cash flows in investment feasibility studies.

3. Compulsory growth: exponential growth is said to be unsustainable in a finite world.

4. Concentration of wealth: positive interest rates are said to generate inequalities, which will impoverish middle classes worldwide.

5. Devaluation of social capital: money tends to promote selfish and non-collaborative behaviour (“money is not value neutral”).

While all these aspects recognize only a critical and negative view of sustainability, one of these five “characteristics” shall be analysed in a little more detail: the problem of “short-term thinking” which seems to be normal in everyday life. Short-term thinking is seemingly typical for decisions of small farmers (or about 70% of all farmers worldwide according to FAO). The reason for this behaviour is very simple: small farmers are not able to accumulate capital for investments – they have to concentrate on how to survive today and tomorrow. Shortterm thinking will discount future values and intergenerational justice.

Consumption rate of interest and social rate of time preference: The aforementioned Brundtland-Report (1987) gives a definition of sustainability focussing on the intergenerational long-term aspect meaning the “ability to satisfy present needs without curtailing the ability of future generations to satisfy theirs”. Using the opportunity costs of capital in resource-protecting projects, where very often inflows are registered in the far future, whereas investments (outflow) are realized at the beginning of the implementation phase, the project would never be feasible based on calculating the “Net Present Value”. The high opportunity costs of capital (high interest rates) would discount the future values of inflow (e.g. rehabilitation of a watershed). It is very clear that we have to favour a low interest rate to make such projects and activities possible, as they would not be considered if we were not able to think in long-term scenarios.

Known as “long-term thinkers” and “sustainability-fans”, the less developed an economy and the scarcer the production factor capital, the higher the interest rate (or cost of capital). Entrepreneurs normally think in shorter periods as it is beneficial for social wellbeing. From a scientific perspective we should always use the “accounting rate of interest”, representing the opportunity costs of production factor capital. Nevertheless, as this would not allow important projects like reforestation or rehabilitation of landscape units (or e.g. rehabilitation of ecosystem services), in projects where the so-called “social time” preference is low, the principle of accounting rate of interest cannot be applied. Therefore we have to focus on a “consumption rate of interest”. Consumption rate of interest requires a normative decision; we have to estimate the “social benefit” and the “social time preference” of that activity (project). OECD (1995), cited in FAO Wealth of Waste

For developed countries (relatively low opportunity cost of capital), OECD recommends the parameters P= 2%; U = 1.5%; G = 2% giving a value for S of 2.03%. In a poor developing country with good growth prospects it is plausible to substitute values of P = 5% and G = 3%, giving S = 5.045%. For poor countries with poor or negative growth prospects, the higher value for P would be wholly or partly offset by low or negative values of G. Even though estimating social time preference may be appropriate for poor developing countries with low economic growth rate, considering their future development and the wellbeing of future generations, this will always be a normative act using the “Net Present Value” as an indicator for economic development. If the development process depends on the activity of private investors and entrepreneurs the “decision makers” have to take into account that they are basing their decisions on future time-periods that are shorter than is often economically recommendable. In the long-run, economic sustainability of the whole economy depends on sustainable management of enterprise; time preference calculations as above cannot be sustainable financially. Short-term thinking is important as an engine for accumulation of capital, which is a long-term flow per se. In a wider sense it may be seen as sustainable to a point.

In other words: if society needs the rehabilitation of a watershed – a project which would not be considered in a political decision process (because of the discounted future values and therefore low or no financial return on the investment) – the investment has to be decided without proven sustainability. Potentially, this may result in an intergenerational threat. Consumption rate of interest (or social time preference) is a normative factor and means abnegation (abstinence) of possible inflow for the benefit of future inflows – or future generations.

Another aspect is that in contradiction to the intergenerational argument in terms of financial flows and accumulation of capital, all heritage traded to the next generation are values accumulated sometimes over generations. This fact may be considered if moral arguments are used in the discussion on intergenerational obligation.

Politicians frequently argue that “we solve our problems today and the future generations have to solve their problems (even if we create them). Today, we may not have the technique(s) to solve certain problems, but tomorrow they may be available”. What is the consequence of such argumentation for sustainable approaches?

Today, an investment into a sustainable agricultural practice may, unfortunately, not be advisable for economic reasons. Tomorrow, it will be realised even at higher costs. However, how can such a strategy be successful? In order to understand this apparent contradiction, we must remember a basic aspect of money: Money per se has no value.

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You cannot eat it and it won’t warm you up. It is just a means to buy something. However, what do we buy first of all, when not considering a positive cash flow in trade? What we need, especially right now.

This restriction here and now, as already mentioned, is the basis for the decisions of small farmers, but also of other decision-makers that are unable or unwilling to accumulate capital for investments. Or, in other words, when considering that 70% of all farmers worldwide according to FAO (must) follow this practice, then an investment in the future must be regarded as a kind of luxury. This creates an interesting question: Is our discussion of sustainability a luxury discussion or an absolute necessity? The discussion above clearly indicates the economic constraints with respect to the realisation of any project, but it does not in any way question the necessity of a certain measure. In addition, we should not forget that a certain measure to maintain/achieve sustainability seems today the best option, but is it the best option tomorrow, when the problem must be solved (meaning that the problem is so pressing that it cannot be left for the next generation)? Here, we have to keep in mind, as mentioned above, the view of geologists and astronomers on sustainability that most phenomena are time specific. In this respect it must be concluded that sustainability is not only in conflict with economic constraints but also with the need of innovative approaches at a given time. This does not explicitly exclude the possibility that realising a sustainable approach per se may represent an innovative measure.

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Annexes Annex 1: List of selected drivers conducive to Agricultural sustainability

Sustainability driver

Upgrading

Balance*

Degrading Autodestruction

Remarks

1.Psychology

Education, Character training

>

1.Philosophy

Dialectic thinking

>

Self-centered mainstream thinking

Closed integrist schools of thought

1.Religion

Soul liberation

>

Dogmatic fear

Cf. Meister Eckhart

1.Politics

Teleonomic Multiform structures

>

Uniform blocks (dictatorship etc.)

Efficient in the short run but not sustainable

1.Organic-social entities

Integration

>

Sum of components

Integrism = amplification of one component

1.Arts

Harmonious music/painting/ architecture

>

Fancy fashion, mainstream mimetism

2.Metabolism

Anabolism

>

Katabolism

2.Biochemical

H2

>

O2

H2O = survival buffer H2 = major anti-oxidative driver

2.Photosynthesis

(CH2)x

>

CO2

PAR made more efficient in hot environment. Heat loss through entropy somewhat alleviated

2.Proteins

(NH)

>

NOx

2.Hormonal regulation

Homeostasis

>

Unbalanced

See doping, drugs, unilateral use of phytohormones

6.Genetics

Outcrossing

>

Inbreeding

Hybrid vigour degeneration of pharaoh civilization, “cousinage”

10. Which crops?

Perennial crops

>

Annual crops

Saving resources through tree crops and higher eco-volume

Schizophrenia

Human brain mobilizes ¼ of metabolic energy

44

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Annex 1: List of selected drivers conducive to Agricultural sustainability (continuation) 3. Spatial efficiency

Veco/Vbio

>>>

100

11. Input efficiency

Compounded if not catalytic effect

>>>

Energy and value of input

12.Economic return

Economic profit of cropped land

>

Loss of eco-system services

Hence, marginal crops should be abandoned

4. Agro-climax

Veco/Vbio

<<<

100

Maximum (power & Vbio)/m² and Minimum Veco

5.Farming

Concentration of energy and resources

>

Dilution of energy and resources

Modern precision systems more efficient at diluting e.g. drip irrigation under plastic tunnels

5.Farm residues

Added value through transformation

>

Wasted farm residues

Green chemistry and processing

Minimum energy for spatial maximum

7.Energy

Output (O)

>

Input (I)

Non-renewable input (cf. Energy analysis by Odum)

7.Carbon

Carbon sequestration

>

CO2 loss i.e. energy loss

Rate of return within landscapes to be improved

9.Nitrogen

Internal N supply

>

External N input

N fertilisers account for > 1/2 the energy inputs of most crops.

8.Water

Green water

>

Blue water + red (fossil) water

Rate of return w/n watersheds to be improved

8. Reversibility and resilience

Reversibility and resilience

>

Agro-ecosystem drift

See Chile and Rodrigo

9.Agrosphere

Vitality of organic matter

>

Environmental “load” of abiotic inputs

Dilution/neutralizing capacity of agrosphere

9. Best agricult. practices

Green light

>

(Red & yellow) lights

Red light system

10. Sustainable, intensive crop husbandry

Nutrient uptake

>

Nutrient losses

Humification

>

Mineralization

IPN, permanent diagnosis, input efficiency

* Balanced flow + contingency surplus

Annex 2: Estimates of eco-volume and bio-volume of sugar cane in Mexico, Chiapas, Huixtla (average of 6 years) Yield (fresh matter in t/ha/year)

Biometrical characteristics of sugar cane stand Basal Area (BA)

Eco-volume Veco Bio-volume (m³/ha) = (m3/ha) BA * d

Wesenberg (w)

Cane

Cane tops

Total

Eco-height (d)

Green cane

125

18.7

143.7

2.46

131

24600

322.3

76.3

Burn 1x

96

14.4

110.4

2.09

97.5

20900

203.8

102.6

1

Burn 2x

89

13.3

102.3

1.97

72.3

19700

142.4

138.3

0.7

MJ/m3

MJ/m3

m²/ha

Dry matter yield (t/ha/year)

(Veco/Vbio)

1.3

Energy content Yield (GJ/ha)

Cane

Cane tops

Total

MJ/kg dry matter

Output

Loss

eco-volume

bio-volume

41.7

6.3

48

18

864

0

35.1

2681

Burn 1x

32

4.8

36.8

18

662.4

201.6

31.7

3250

Burn 2x

29.7

4.5

34.2

18

615.6

248.4

31.2

4323

Green cane

Ci = 100/w (%)

Agricultural concen- Bio-industrial concentration tration

Maximum power law

Concentration path Site

Atmosphere

Active ingredient

CO2 350 ppm

Energy status

0 MJ/m3

Eco-volume

Bio-volume (CH2)n

> 30 MJ/m3

> 2500 MJ/m3

Saccharose

Bio-ethanol

C12H22O1

C2H5OH

17MJ/kg

30 MJ/kg

Source: Estimated after Toledo Toldedo, E. et al. 2006

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45

Annex 3: Comparing annual to perennial crops for major agricultural categories and functions (Janssens et al. 2000) Agricultural

Subcategory

Annual crops

Permanent crops

Starch

Cereals, Roots & Tubers

Plantain, Bread tree, Jackfruit, Treculia africana, Sago palm, Ensete banana, Chestnut, etc.

Sugar

Sugar beet, Sugar cane*, Sweet corn, sweet sorghum etc.

Borassus flabellifer, Sugar maple, Sugar palm, Kitul palm, Nypa palm, Date palm, Chilean sugar palm, etc.

Protein

Pulses, (Pigeonpea*)

Avocado, Baobab Leguminous tree crops: Tamarind, Caroub tree, Bean tree, Parkia, Quamachil (?)

Lipid

Peanut, Soyabean, Sunflower, Sesame, Cotton, Rape, Flax, Safflower, etc.

Oil palm, Coconut palm, Babassu palm, Karite, Avocado, African pear, Olive tree, Castor oil, Aleurites, Pejibaye, Balanites, Argan tree

category

Carbohydrates

Leaf

Salad, spinach

Baobab, Moringa, Sesban, Mulberry

Other

Bulbs, Fruit vegetables etc.

Palm heart (Palmetto etc.)

Nut

Peanut, Bambara nut

Cashew nut, Brazil nut, Pistachio, Hazel nut, Almond, Macadamia nut, Walnut, etc.

Other

Fruit vegetables (as above)

Annonaceae, Sapindaceae, Sapotaceae, Rosaceae, Passifloraceae, Rutaceae, Moraceae, etc.

Fuel wood

Limited

Numerous

Grazing

Numerous

Numerous. Underexploited

Fibre

Cotton, Flax, Hemp, Ramie, Jute, Kenaf

Kapok, Musa textilis, Agave, Bombax ceiba, Gossypium arboreum, Raphia palm

Vege-tables

Fruit

Rubber

Guayule

Rubber tree, Guttaperche, Euphorbia tirucalli, Ficus elastica

Insecticidal use

Tobacco, Pyrethrum*,

Neem, Derris, Aglaia, Mammey apple, etc.

Spices/ Flavours

Ginger, Chili*, Turmeric, Vanilla*, Fennel, Hops*

Black pepper, Clove, Cinnamon, Nutmeg, Curry leaf tree

Essentialoils/ Perfumes

Mint, Chamomile, Lavender*, Rosemary

Eucalyptus, Rose, Artabotrys, Jasmine, Cajaput tree

Dyes

Indigo, Safflower, Woad

Annatto, Campeachy wood, Henna

*Perennial crops grown facultatively as annual crops

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46

JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Factors influencing adoption of farm management practices in three agrobiodiversity hotspots in India: an analysis using the count data model Prabhakaran T. Raghu a* , Varghese Manaloor b , V. Arivudai Nambi a a b

*

M.S.Swaminathan Research Foundation, Biodiversity Department, Chennai, India University of Alberta, Augustana Campus, Department of Social Sciences, Camrose, Canada Corresponding author: tr.prabha@mssrf.res.in

Article history Receivedâ&#x20AC;&#x192; 01.10.2013 Acceptedâ&#x20AC;&#x192; 14.04.2014 Published 10.07.2014

Keywords Count data model Negative binomial regression Farm management practices Agrobiodiversity hotspots India

Abstract Sustainable agricultural practices require, among other factors, adoption of improved nutrient management techniques, pest mitigation technology and soil conservation measures. Such improved management practices can be tools for enhancing crop productivity. Data on microlevel farm management practices from developing countries is either scarce or unavailable, despite the importance of their policy implications with regard to resource allocation. The present study investigates adoption of some farm management practices and factors influencing the adoption behavior of farm households in three agrobiodiversity hotspots in India: Kundra block in the Koraput district of Odisha, Meenangadi panchayat in the Wayanad district of Kerala and Kolli Hills in the Namakkal district of Tamil Nadu. Information on farm management practices was collected from November 2011 to February 2012 from 3845 households, of which the data from 2726 farm households was used for analysis.

The three most popular farm management practices adopted by farmers include: application of chemical fertilizers, farm yard manure and green manure for managing nutrients; application of chemical pesticides, inter-cropping and mixed cropping for mitigating pests; and contour bunds, grass bunds and trenches for soil conservation. A Negative Binomial count data regression model was used to estimate factors influencing decision-making by farmers on farm management practices.

The regression results indicate that farmers who received information from agricultural extension are statistically significant and positively related to the adoption of farm management practices. Another key finding shows the negative relationship between cultivation of local varieties and adoption of farm management practices.

DOI number: 10.5027/jnrd.v4i0.07

Introduction Crop productivity in the developing world faces several constraints.

One of the major crop productivity constraints is the unavailability of crop nutrients in the appropriate amount and form (Hussain et al. 2006). The roles of both macro and micronutrients are crucial in crop nutrition and thus important for achieving higher yields (Arif et al. 2006). However, most soils are deficient in these nutrients (Jahiruddin et al. 1995) and need to be supplemented through proper crop nutrients. Crop loss due to pests is another serious problem that limits or reduces production. The control of pests using chemical methods is predominant, but traditional pest control practices continue, especially in remote areas (Pathak 2002; Sharma et al. 2002). Cultivable land located in mild and steep slopes and shallow soils, risk soil erosion and yield loss at times of high rainfall. In India, large government programs have devoted substantial resources to promote soil conservation, but the results have been disappointing, as adoption and maintenance of introduced conservation technologies has been limited (Kerr and Sanghi 1992). Sustainable agricultural practices require among other factors, adoption of improved nutrient management, pest mitigation and soil conservation measures. Such improved management practices can be tools for enhancing crop productivity.

Farm management practices can be influenced by the crops and varieties cultivated and access to agricultural extension.

Indian agriculture is predominantly driven by small holders, with about 83 percent of farmers cultivating an area of 2 hectares or less (Directorate of Economics and Statistics, India, 2011). Crop production and management decisions, especially among small farmers, depend to some extent on extension workers. More than 90 percent of the world’s extension personnel are located in developing countries (Umali and Schwartz 1994), where indeed the majority of the world’s farmers are located. The goals of extension include the transfer of knowledge from researchers to farmers, advising farmers on their decision-making and educating farmers to make better decisions, enabling farmers to clarify their own goals and possibilities, and stimulating desirable agricultural developments (Van den Ban and Hawkins 1996). The adoption of technology by farmers is inevitably affected by several factors (Feder et al. 1986).

Adoption can be influenced by educating farmers about improved varieties, cropping techniques, optimal input use, prices and market conditions, efficient methods of production management, storage and nutrition. Anderson and Feder (2003) mentioned that the low literacy rates among small and marginal farmers implies that they are not able to take advantage of information available in electronic mass media like written materials, radio, television, internet, which could potentially be used as intervention to motivate farmers to adopt new technologies and production practices.

Several scholars have studied technology adoption in agriculture and the factors influencing adoption behavior among farming households (Abdulai and Huffman 2005; Akinola and Owombo 2012; Deressa et al. 2009; Howley et al. 2012; Mariano et al. 2012).

Literature on technology adoption shows that binary logit and

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47

probit models have been extensively used to analyze technology and best practice adoption by farm households. Ramierez and Shultz (2000) used a poisson count model to analyze adoption of integrated pest management in selected Central American countries. In addition to studies related to a single technology adoption, there are several studies that look at multiple technology adoption (Chaves and Riley 2001; Cooper 2003; Isgin et al. 2008).

Sharma et al. (2011) used parametric and non-parametric models to examine the intensity of technology adoption and integrated pest management strategies employed by farmers in the UK. Given this background, the present study aims to capture the actual adoption of farm management practices and estimate factors influencing the adoption behavior of farm households using negative binomial count data regression. The study was carried out as part of the research project, “Alleviating Poverty and Malnutrition in Agrobiodiversity Hotspots (APM)”, implemented jointly by the M.S.Swaminathan Research Foundation (MSSRF), Chennai, India and the University of Alberta (U of A), Edmonton, Canada. The project is being implemented in three agrobiodiversity hotspots: Kundra block in the Koraput region, Wayanad district in the Malabar region and Kolli Hills block in the Kaveri region. The present study was carried out to address one of the primary objectives of the APM project, related to increasing farm productivity through sustainable farm management practices: nutrient management, pest mitigation and soil conservation and enhancement. Such knowledge can possibly be used to formulate specific policies and target specific groups of producers to promote adoption of sustainable agricultural practices.

In the following section, we describe the methodological framework including study area, data collection, the negative binomial model and descriptive figures of variables used in the regression. Section 3 discusses the results and major findings regarding the general characteristics of farm households, actual adoption of farm management practices, technology count of adoption and estimation of factors influencing adoption behavior. Concluding remarks are provided in the final section.

Methodology Study area India is one of twelve mega-diverse countries in the world and is considered as a major center of domestication of crop plants. Farming communities from time immemorial have grown and developed a rich cornucopia of crop plants through selection and adaptation.

It is reported that at least 166 crop plants and about 320 species of wild relatives of cultivated plants originated in India (Nayar et al.

2009a). In 2007, the Protection of Plant Varieties & Farmers’ Rights Authority (PPV&FRA) of the Government of India (GoI) constituted a task force to characterize, demarcate and list the agrobiodiversity hotspots in India. The task force identified 22 hotspots across India, based on listing the species of botanical and agricultural importance, endemic and endangered species and socio-cultural aspects of the

DOI number: 10.5027/jnrd.v4i0.07

areas (Nayar et al. 2009b). The current research is being implemented in three of the agrobiodiversity hotspots identified by the task force – the Kundra block in the Koraput region, Wayanad district in the Malabar region and Kolli Hills block in the Kaveri region.

Koraput is a center of biodiversity for many food crops and forest species. It is considered as the secondary center of origin of Asian cultivated rice Oryza sativa L (Mishra et al. 2012). The district covers an area of about 8379 km2 (Arunachalam et al. 2008). The mean elevation is 2900 feet above sea level. It is also well known for its rich human cultural diversity. Sixty-two tribal communities constituting 54.45% of its population live in the district (Mohanti et al. 2006). For generations, they have played a major role in identifying, conserving, improving and utilizing local plant genetic resources as well as in sustaining them. Their tireless efforts have conserved and improved the quality of many food crops. Besides rice, a variety of millets, pulses, oilseeds and vegetables (Mishra and Chaudhury 2012) have also been conserved. Even today they possess a high level of traditional knowledge regarding the various fields that governs their livelihood. Low literacy rates and poor financial condition of farmers limit improvements in crop productivity (Mishra and Taraputia 2013).

Wayanad district, situated in the Western Ghats in the north-eastern part of Kerala, India, is considered one of the world’s most important biodiversity hotspots. It is spread over an area of 2136 km2, where 37% of the land area is covered by forests and 55% is cultivated (Kumar et al. 2003). Wayanad is a plateau with an altitude varying from 700 to 2100 m above sea level. The difference in altitude of each locality within the district leads to variations in climatic conditions.

The small hills have many plantations such as tea, coffee, pepper and cardamom, while the valleys see a predominance of paddy fields (Siljal et al. 2008). Tribal population represents 17% of the total population of the district, and is the largest tribal population in the state of Kerala (Josephat 1997). The district is characterized by high ethnic diversity, with five dominant tribal groups – Kurichiya, Kuruma, Paniya, Adiya and Kattunaikka - and seven minor communities (Kumar et al. 2003).

Kolli Hills is a mountainous area with a temperate climate located on the eastern border of the Namakkal district in Tamil Nadu. Forests occupy 44 per cent of the total area of 28,293 ha, while agricultural activities take place on 52 per cent of the total area, leaving 4 per cent for other activities (Kumaran 2004). Agricultural land-use in the Kolli Hills can be classified into three types: (i) spring-fed valley lands, mainly under paddy, (ii) rain-fed lands, allocated for millets and cassava, and (iii) land on the valley fringes, under pineapple, coffee, pepper and other crops (Gruere et al. 2009; Kumaran 2004). The Kolli Hills region is characterized by significant in-situ crop genetic diversity of minor millets (Jayakumar et al. 2002; King et al. 2008). More than 95 per cent of the inhabitants are tribal people belonging to the Malayali tribal community (MSSRF 2002).

Data collection The present study area was selected mainly because of its low socioeconomic level with low human indices, contrasted by its rich genetic

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48

diversity. The major livelihood of the communities in the study area is agriculture. The project is being implemented with the objective of enhancing the livelihood of the communities based on genetic resources and agricultural development. The data for this study was collected using a structured questionnaire. The questionnaire was pretested and modified before the actual initiation of the survey process.

The actual survey was conducted during November 2011 to February 2012, and the information collected pertains to the reference year of 2010-2011. The enumerators involved in the data collection were familiar with the local, social and cultural norms, and were trained using mock-interviews, and were consistently monitored. The collected data were periodically examined in order to identify and correct errors.

The primary data collection was carried out by employing the census method, which covered the entire households from three study areas; resulting in 3845 households: 2004 households in 32 villages of Kundra block, 1000 households in 31 villages of Meenangadi panchayat and 841 households in 31 villages of Kolli Hills. The results presented in this study are restricted to those households engaged in crop production: 1307 farm households in Kundra block, 675 farm households in Meenangadi panchayat and 744 farm households in Kolli Hills; making a total sample of 2726 farm households. Data pertaining to general household socio-economic information such as age, gender, education, primary occupation, information on seeded area, cropping pattern, input use, adoption of farm management practices including nutrient management, pest mitigation and soil conservation, information on livestock, status of savings and credit, and access to information were elicited.

The negative binomial model Following Greene (2008), the negative binomial model is employed as a functional form that relaxes the equidispersion restriction of the Poisson model. A useful way to motivate the model is through the introduction of latent heterogeneity in the conditional mean of the Poisson model. Thus: E[yἱ/Xἱ,εἱ]=exp (α+Xἱ’β+εἱ)=hἱλἱ,

(2.1)

where hἱ= exp (εἱ) is assumed to have a one parameter gamma distribution, G(θ,θ) with mean 1 and variance 1 / θ = κ; (2.2) After integrating hἱ out of the joint distribution, we obtain the marginal negative binomial (NB) distribution, ,

(2.3)

The latent heterogeneity induces overdispersion while preserving the conditional mean;

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(2.4) (2.5)

E[yἱ/Xἱ]= λἱ,

Var[yἱ/Xἱ]= λἱ[1+(1/θ)λἱ]=λἱ [1+kλἱ] Where k = Var[hἱ]

Maximum likelihood estimation of the parameters of the NB model (α,β,θ) is straightforward, as documented in Greene (2007), for example. Inference proceeds along similar lines.

Inference regarding the specification, specifically the presence of overdispersion, is the subject of a lengthy literature, as documented in Cameron and Trivedi (1990, 1998, 2005) and Hilbe (2007).

Variables used in the regression The dependent variable (yἱ) used in the regression analysis is a count model. The dependent variable used in the study is a count of technologies adopted by each farm household. A maximum of five technologies each in nutrient management and pest mitigation, and six technologies in soil conservation was adopted by surveyed households Table 1. Independent variables (Xἱ) used to explain adoption behavior of farmers fall under three categories: characteristics of household head, such as gender, age, primary occupation, farm related variables notably farm size, access to agricultural extension and variety cultivated and location factors.

Table 1: Description of variables used in the regression Mean

Std. dev.

Description of variable

Nutrient_management (Model 1)

1.9

0.92

values from 0 to 5

Pest_mitigation (Model 2)

0.79

0.66

values from 0 to 5

Soil_conservation (Model 3)

0.97

0.87

values from 0 to 6

Variable name Dependent Variable

Independent Variables

Results and discussion General characteristics of farm households This section provides the general characteristics of the farm households Table 2. The average household size in all three study locations is approximately 4.5. The majority of the households are male headed households: 94 percent in Kundra, 85 percent in Meenangadi and 93 percent in the Kolli Hills. The average age of household heads across the study area ranges from 43 to 52 years. The number of years of education of the household head is highest in Meenangadi with 3.4 years and lowest in Kundra with 1.7 years. Crop production is the primary occupation of the majority of households: 87 percent in Kundra, 86 percent in Meenangadi and 91 percent in Kolli Hills. The remaining households used in the analysis are also engaged in crop production, but the primary occupation is non-farm work, such as salary, business or non-agricultural wage income. The average farm size is 1.12 hectares in Kundra, 0.67 hectares in Meenangadi and 0.88 hectares in Kolli Hills. The major crops cultivated in Kundra are Paddy [Kharif (rainy), Rabi (winter) and summer], small millets, maize, sugarcane, niger, green gram, black gram and horse gram. In Meenangadi, paddy (kharif and summer), banana, tapioca, coffee, areca nut, coconut, elephant foot yam, green gram and ginger are cultivated. The major crops cultivated in Kolli Hills are paddy (kharif and summer), small millets, tapioca, banana, coffee and pepper, pineapple and green gram. About 99.2 percent of households in Kolli Hills, 42.1 percent in Kundra and 20.3 percent in Meenangadi comprise of Scheduled Tribes.

Table 2: General characteristics of farm households

Sample size (number) Average household size (number)

1. Household head characteristics

Male headed household (%)

Gender_household head

0.91

0.28

1=male, 0=female

Age_household head

45.26

13.15

in years

Primary occupation_household head

0.73

0.45

1=farming, 0=others

Average age of household head (years) Average education household head (years) Farming as primary occupation of household head (%)

2. Farm Characteristics

49

Kundra

Meenangadi

Kolli Hills

1307

675

744

4.6 (1.9)

4.4 (1.5)

4.5 (1.8)

93.9

84.9

92.7

42.7 (12.5)

52.4 (12.4)

43.3 (12.7)

1.7 (1.0)

3.4 (1.6)

2.4 (1.7)

86.7

85.6

91.3

1.12 (1.66)

0.67 (1.77)

0.88 (0.85)

Farm_size

0.94

1.53

in hectare

Agriculture_extension

0.15

0.36

1=yes, 0=no

Social category of household (%)

Local_variety

0.24

0.42

1=yes, 0=no

General/ Forward Caste

8.6

34.4

0

Backward Caste

24.6

41.9

0.7

0

0.7

0.1

Scheduled Caste

25.6

2.7

0

Scheduled Tribe

41.2

20.3

99.2

3. Location dummies Dummy_Kundra

0.48

0.5

1=yes, 0=no

Dummy_Meenangadi

0.25

0.43

1=yes, 0=no

Dummy_Kolli Hills

0.27

0.45

1=yes, 0=no

Farm size (hectare)

Most Backward Caste

Note: Figures in the parenthesis is standard deviation

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Adoption of farm management practices

to the number of farm management technologies adopted by each farm household for each nutrient management, pest mitigation and soil conservation component Table 3. The technology count of each farmer is used as a dependent variable in the negative binomial regression analysis to estimate the factors influencing adoption of farm management technologies by farm households.

The survey results show that 90 percent of farm households adopt at least one of the nutrient management technologies, 69 and 74 percent of households also adopt at least one of the pest mitigation and soil conservation technologies, respectively. A maximum of five nutrient management and pest mitigation technologies, and six soil conservation technologies were adopted by some farm households. The majority of farm households adopt two technologies for nutrient management (56.2%) and one technology each for pest mitigation (63.2%) and soil conservation (59.2%).

Nutrient management: The most adopted nutrient management technologies in the study area is application of chemical fertilizer by 86.7 percent of farm households, followed by the application of farmyard manure (73.4% of households) and the application of green manure (21.9% of households). Approximately 10.2 percent of households do not adopt any nutrient management technologies. The other nutrient management technologies practiced by households in the study area are inter-cropping systems (4.4%), application of organic manure (0.9%), composting/ vermi-composting (0.7%), crop rotation with legumes (0.6%), application of bio-fertilizer (0.5%) and other measures (0.5%).

Pest mitigation: Majority of households (62.9%) apply chemical pesticides for mitigating pests and diseases. Approximately one-third of the households do not adopt any pest mitigation technology. The next most adopted anticipatory pest mitigation technology is intercropping and mixed cropping with 4.6 and 4.2 percent of households, respectively. Other pest mitigation technologies adopted by households are agro-forestry/hedgerows (3.9%), application of natural pesticides (0.7%), physical traps (0.4%), mulching (0.4%), trap crops (0.2%), pheromone traps (0.1%) and other measures (1.7%).

Estimation of Negative Binomial regression for technology adoption The nature of the dependent variable used in the regression analysis corresponds to a count model. In this case, negative binomial regression was used since it was less likely that the unconditional mean of the dependent variable would be equal to its variance. Negative Binomial regression was used to estimate the factors influencing the adoption behavior of farm management practices, specifically nutrient management, pest mitigation and soil conservation measures Table 4. Three independent count data regression models were run, using different sets of independent variables to estimate the adoption behavior of the farm households.

For instance, gender of the household head is not included in model 1 and 2, while age of the household head is not included in model 3. In the case of location dummies, (n-1) location is used in all the models.

Soil conservation: Around one-third of the households adopt contour bunds as a soil conservation measure. Twenty six percent of farmers do not adopt any soil conservation technology.

The next most adopted soil conservation technology is grass bunds (22.2 % of households), followed by trenches (9.3% of households). Other soil conservation technologies adopted by the households include mulching (8.7%), terracing (7.5%), hedge rows (5.8%), agro-forestry (4.2%), strip cropping systems (0.8%), application of green manure (0.1%) and other measures (7.0%).

The regression results indicate that for every unit increase in male headed households, the expected adoption count of soil conservation technologies will decrease by 0.15. The age of the head of the household is statistically significant and positively associated with nutrient management and pest mitigation. When farmers with farming as their primary occupation increases by a unit, the

Technology count of adoption The section above provided the actual data on the adoption of farm management technologies in the study area, and the present section explains the technology adoption counts. Technology count refers

Table 3: Technology adoption frequency distribution Nutrient management

Technology counts

Frequency

Percentage

0

277

1

375

2

50

Pest mitigation Frequency

Percentage

10.16

832

13.76

1723

1532

56.2

3

440

4

97

5

Soil conservation Frequency

Percentage

30.52

710

26.05

63.21

1615

59.24

94

3.45

250

9.17

16.14

67

2.46

86

3.15

3.56

9

0.33

42

1.54

5

0.18

1

0.04

22

0.81

6

0

0

0

0

1

0.04

Total

2726

100

2726

100

2726

100

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DOI number: 10.5027/jnrd.v4i0.07

expected count of technology adoption increases by 0.18 for nutrient management, 0.27 for pest mitigation and 0.37 for soil conservation measures. Since effective agriculture requires a substantial amount of managerial time, technology adoption may be constrained when the farmer works off-farm, because it competes with on-farm managerial time. The impact of adoption and on-farm work as full time is expected to have a positive relationship. This hypothesis is consistent with research reported elsewhere (Kara et al. 2008).

Farm size is positively associated and statistically significant among all three management practices. In explaining adoption decisions, farm size is considered as one of the most consistent variables to exhibit statistical significance. Several theoretical and empirical

51

examples in the literature on technology adoption highlight the importance of farm size (Harper et al. 1990; Pitt and Sumodiningrat 1991; Smale and Heisey 1993). In general, farm size is hypothesized to have a positive impact on adoption decisions (Polson and Spencer 1991; Norris and Batie 1987). As farmers receiving information from agricultural extension increases by one unit, the expected count of technology adoption increases by 0.29 for nutrient management, 0.43 for pest mitigation and 0.60 for soil conservation. Another key finding is the negative relationship between cultivation of local varieties and farm management practices. For every one unit increase in cultivation of local varieties, the results for technology adoption count decrease by 0.45 for nutrient management, 0.35 for pest mitigation and 0.29 for soil conservation.

Table 4: Estimation of negative binomial regression for the technology adoption Nutrient management

Pest mitigation

(Model 1)

Variables Gender_household head Age_ household head Primary occupation_ household head Farm_size Agri_extension

Soil conservation

(Model 2)

(Model 3)

Coefficient

Std. Error

Coefficient

Std. Error

Coefficient

Std. Error

-

-

-

-

-0.1457**

0.067

0.0023**

0.001

0.0043**

0.002

-

-

0.1795***

0.041

0.2662***

0.063

0.3724***

0.058

0.0178**

0.008

0.006

0.012

0.0230**

0.011

0.2940***

0.039

0.4333***

0.058

0.6031***

0.051

Local_variety

-0.4467***

0.042

-0.3477***

0.063

-0.2890***

0.057

Dummy_Kundra

-0.2571***

0.045

0.6717***

0.061

-

-

-

-

0.6929***

0.082

0.4863***

0.061

-0.2208***

0.049

-

-

0.4414***

0.046

0.5982**

0.074

-1.1807***

0.114

-0.5249***

0.085

Dummy_Meenangadi Dummy_Kolli Hills Constant Number of observations LR Chi (7) 2

Prob> Chi Pseudo R

2

2

2726

2726

2726

339.38

307.23

425.92

0

0

0

0.04

0.05

0.06

***represent 1% significance level, **represent 5% significance level and *represent 10% significance level

Conclusion The key findings from the farm household survey on factors influencing adoption of farm management practices are summarized in this section. Only 15 percent of the surveyed households have access to agriculture extension. Farmers who received information from agricultural extension are highly influenced to adopt nutrient management techniques, improved pest mitigation technologies and soil conservation practices. Our results therefore reinforce the importance of expanding agricultural extension, particularly for small and marginal farmers. Considering the fact that new technologies are being introduced rapidly and knowledge transfer in agriculture is generally on the wane, agricultural extension is likely to become an important source of knowledge and information for the younger

generation of farmers. Incidentally, small farmers in various countries have indicated a willingness to pay for extension services that meet their needs (Gautam 2000; Holloway and Ehui 2001). Farmers from the surveyed households use chemical fertilizer (87%) and farm yard manure (73%), while the majority of households use a combination of both. This has important implications both for productivity and long term sustainability. A small section of the farmersâ&#x20AC;&#x2122; surveyed (9%) use inter and mixed cropping practices for dealing with pests and diseases.

Promotion of such non-chemical management practices is likely to help farmers and the environment in the long run. The results also indicate that a section of farmers (26%) are yet to adopt soil conservation measures. It is understood that soil conservation measures are critical for sustainable natural resource management in the long run, and hence it would be appropriate to intensify action in this direction.

DOI number: 10.5027/jnrd.v4i0.07

Acknowledgement This study was carried out as part of the project, “Alleviating Poverty and Malnutrition in the Agrobiodiversity Hotspots (APM)” implemented jointly by the M.S.Swaminathan Research Foundation (MSSRF), Chennai, India and the University of Alberta (U of A), Edmonton, Canada. This work was carried out with the aid of a grant from the International Development Research Centre (IDRC), Ottawa, Canada and with financial support from the Government of Canada, provided through Foreign Affairs, Trade and Development Canada (DFATD). The authors are also grateful to Dr.Brent Swallow, Dr. Sandeep Mohapatra and Dr. Henry An of the U of A and Mr. B.Chandra Guptha, MSSRF for providing critical comments during the estimation stage and on an earlier version of the manuscript. The authors are obliged to the enumerators and social scientists from the project sites, specifically Mr. N.N.Kalaiselvan, Mr. R.K.Mahana, Mr. G.Venkatesan and Mr. R.Arunraj who were involved in the survey. We are also extremely grateful for the critical comments of two anonymous reviewers, which helped us to reshape and significantly improve the text of the manuscript.

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Feder G., Just R.E., Zilberman D., 1986. Adoption of Agricultural Innovations in Developing Countries: A Survey. Economic Development and Cultural Change, 35(1), 255-98.

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Journal of Natural Resources and Development 2014; 04: 54 - 63

DOI number: 10.5027/jnrd.v4i0.08

54

JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Using QUAL2K Model and river pollution index for water quality management in Mahmoudia Canal, Egypt Ehab A. Elsayed Drainage Research Institute, National Water Research Center, El-Qanater El-Khairiya, Egypt.

Corresponding author: ehab7878@hotmail.com ; ehab78new@yahoo.com Article history

Abstract

Receivedâ&#x20AC;&#x192; 18.02.2014 Acceptedâ&#x20AC;&#x192; 11.06.2014 Published 07.08.2014

The Mahmoudia Canal is the main source of municipal and industrial water supply for Alexandria (the second largest city in Egypt) and many other towns and villages. In recent years, considerable water quality degradation has been observed in the Mahmoudia Canal. This problem has attracted increasing attention from both the public and the Egyptian government. As a result, this study aims at assessing the current seasonal variations in water quality in the Mahmoudia Canal and simulating various water quality management scenarios for the canal. The present research involves the application of the water quality model, QUAL2K, to predict water quality along the Mahmoudia Canal on a seasonal basis for the considered scenarios. Based on the QUAL2K simulations, the River Pollution Index (RPI) was used to appraise the conditions of water pollution at the intakes of the twelve water treatment plants (WTPs) located along Mahmoudia Canal.

Keywords

The results showed that the QUAL2K model is successfully applied to simulate the water quantity and quality parameters of the Mahmoudia Canal in different seasons. For the current status of the canal, it was found that the highest pollution level occurred in autumn in which effluent water quality at all WTPs along the Mahmoudia Canal was classified as moderately polluted. In the other seasons, effluent water quality was categorized as moderately polluted at most WTPs in the Beheira governorate and negligibly polluted at all WTPs in the Alexandria governorate. Moreover, it was concluded that controlling the Rahawy drain discharge or treating its pollution loads before mixing with the Rosetta Branch may solve water quality problems of the Mahmoudia Canal and allow re-running of the Edko re-use pump station in summer, winter, and spring. However in autumn, additional measures will be required to mitigate pollution levels in the canal.

Canals Water reclamation Simulation Drinking water treatment plants Egypt

DOI number: 10.5027/jnrd.v4i0.08

Introduction Throughout the last fifty years the population of Egypt has grown more than threefold while, the availability of renewable water resources has remained the same. Consequently, the annual per capita share of renewable water resources (mainly provided by the Nile) has dramatically reduced from more than 2500 m3 in 1950 to less than 900 m3 in 2000, which is below 1000 m3 cap-1 yr-1 “the international standard of water scarcity limit”. It is further projected to fall to about 500 m3 cap-1 yr-1 by the year 2050 (Ahmed Wagdy, 2008).

Therefore, the Egyptian government has endorsed several policies to confront prevailing water scarcity. Conservation of water, mainly by recycling agricultural drainage water in irrigation, has become the core of these policies. This drainage water is one of the most valuable water resources in Egypt created by intensive and large irrigation/drainage systems. Drainage water reuse is also requires relatively less infrastructure to be constructed and is a cheaper option. However, drainage water reuse practices have been threatened by deteriorating drainage water quality due to municipal and industrial wastewater pollution. Thus, environmental impacts are very important in the implementation of drainage reuse projects (Abdel-Azim and Allam, 2005).

The Rosetta Branch is one of two main branches of the Nile delta.

Five agricultural drains (EL-Rahawy, Sabal, El Tahrir, Zaweit ElBahr, and Tala drains) mix their water into the Rosetta Branch.

Unfortunately, the branch receives more than 3 M m3 day-1 of untreated or partially treated domestic and industrial waste. The Rahawy drain (which receives considerable wastewater from the Greater Cairo area) is the most important pollution source affecting Rosetta Branch. It was found that by controlling the Rahawy drain discharge, the water quality of the Rosetta Branch will be improved significantly and will comply with water quality standards (El Bouraie et al., 2011, El Gammal and El Shazely, 2008, and Donia, 2005).

The Mahmoudia Canal, the focus of the present study, takes fresh water from the Rosetta Branch at km 194.200. The Mahmoudia Canal has an important role in the economy, development and prosperity of the people in the western Nile Delta. In addition to providing irrigation water, the Mahmoudia Canal is a navigable canal and is the main source for the municipal and industrial water supply for Alexandria and many other towns and villages.

In Alexandria (the second largest city in Egypt), the Mahmoudia Canal provides water supply companies with about 2.5 to 3 M m3day-1, which varies in different seasons. In summer, due to large number of tourists, water demands increase and thus drinking water production increases also (Hamdard, 2010 and El-Gamal et al., 2009).

Examination of detailed data records held by the Drainage Research Institute (DRI) for the Mahmoudia Canal show that water quality parameters generally comply with the legal requirements, including salinity, and industrial and agrochemical contamination standards. Nevertheless, sewage pollution indicators were the only parameters in this canal that were consistently in excess. This may result in major negative impacts on water quality upstream of the

Journal of Natural Resources and Development 2014; 04: 54 - 63

55

intakes of drinking water plants causing serious effects on human health and the surrounding environment in general (NRMED, 2005).

Natural self-purification of the Mahmoudia Canal water is calculated and observed in two cases (Abukila, 2012). The first is a normal case, in which no drainage water is discharged directly into the Mahmoudia Canal downstream of its intake. The second is a simulated case, in which a simulated Edko irrigation pump station lifts drainage water from the Zarqun drain into the canal. It was evident that the Mahmoudia Canal receives pollutants from point and non-point sources (i.e. the Edko irrigation pump station and thrown garbage and wastewater input from local towns and villages located along the canal). However, the majority of the water quality problems of the Mahmoudia Canal are due to receiving low grade water from the Rosetta Branch. Furthermore, the canal needs 10.83 km to get rid of the influence of pollutants from the Edko irrigation pump station discharge. As a result, most of the water treatment plants (fed by the Mahmoudia Canal) in the Beheira Governorate will be affected by the Edko irrigation pump station discharge. However, not all the water treatment plants in Alexandria Governorate will be affected by the Edko irrigation pump station discharge.

In recent decades, many water quality models have been developed to assist in river water quality management. The QUAL2K model is one of the most famous tools for water quality simulations due to its flexibility, ease of use, and free availability. Numerous typical applications of the model have been developed and utilized on various river systems in many countries (Rashed and El-Sayed, 2014, Hanfeng et al., 2013; Ruibin Zhang et al. 2012; Vasudevan et al., 2011, and Kalburgi et al., 2010).

In order to facilitate the evaluation of water quality parameters, Horton (1965) proposed the first water quality index (WQI). House (1989) stated that the use of a Water Quality Index (WQI) allows ‘good’ and ‘bad’ water quality to be quantified by reducing a large quantity of data on a range of physical-chemical and biological variables to a single number in a simple, objective and reproducible manner.

The River Pollution Index (RPI) is an integrated indicator, which is employed by the Environmental Protection Administration of Taiwan (EPA) to explore and monitor trends for both planning and day-to-day management of surface water quality. The Taiwan EPA has used RPI to assess the conditions of surface water pollution over the past two decades (Wang et al., 2013; Chen et al., 2012, and Liou et al., 2003).

As stated above, many research studies have been conducted to assess the water quality of the Rosetta Branch and the Mahmoudia Canal (Abukila, 2012, El Bouraie et al., 2011, Hamdard, 2010, ElGamal et al., 2009, El Gammal and El Shazely, 2008, and NRMED, 2005). Although most of the previous studies mention that the water uses and discharges of these canals vary significantly in different seasons of the year; almost no research has focused on evaluating or maintaining water quality on a seasonal basis.

Consequently, this research was initiated with the objectives of assessing the current seasonal water quality of the Mahmoudia Canal and simulating various water quality management scenarios

DOI number: 10.5027/jnrd.v4i0.08

for the canal. In this investigation, the QUAL2K model was applied to simulate the seasonal variations in water quality in the Mahmoudia Canal for the considered scenarios. Based on the QUAL2K simulations, the River Pollution Index (RPI) was used to appraise the conditions of water pollution at the intakes of the twelve water treatment plants located along the Mahmoudia Canal.

Journal of Natural Resources and Development 2014; 04: 54 - 63

monthly amounts of water discharged into the Mahmoudia Canal from the El-Atf pump station and the El- Khandak El-Sharki Canal.

Table 1: Drinking water treatment plants fed from the Mahmoudia Canal Governorate

Study area description The Mahmoudia Canal is located near the northern edge of the west Nile delta in the Behaira governorate Figure 1. It has been exploited to support agriculture, fisheries, industry, hydroelectric power, and recreation in the western delta region.

Moreover, the major drinking water treatment plants (WTPs) in the Alexandria and Behaira governorates receive fresh water from the Mahmoudia Canal Table 1. The canal runs for a distance of 77.170 km from the Rosetta branch of the Nile down to the Mediterranean Sea at Alexandria. It serves a total command area of about 130,200 hectares through 70 branch (distribution) canals.

56

Behaira

Alexandria

Water treatment plant

Production (m3 day-1)

Al-Gadih

25000

Ficha

25000

Monchat Nassar

25000

Abou Hommos

100000

Plant intake location

km 4.500 Mahmoudia Canal

Com Alkuenatur

250000

Kafr El-Dawar

100000

Mahmoudia Canal

Al-Sayouif

970000

Al-Nozha

240000 630000

Al-Manshia

420000

Bab Sharki

50000

Forn El-Garia

200000

km 5.500 km 16.500 km 27.500

Kenawia Branch Canal â&#x20AC;&#x201C; km 28.410 at Mahmoudia Canal

Al-Mamoura

Chain (km)

Mahmoudia Canal Pipeline from Al-Sayouif WTP Mahmoudia Canal Drinking Water Canal â&#x20AC;&#x201C; km 54.650 at Mahmoudia Canal

km 7.100 km 42.000 km 61.300 km 66.000 km 61.300 km 15.250 km 15.500 km 15.450

Table 2: The monthly amounts of water discharged into the Mahmoudia Canal from the El-Atf pump station and the El- Khandak El-Sharki Canal Month

Figure 1: Mahmoudia Canal The Mahmoudia Canal receives water from three different sources.

The main source (that lifts about 80 percent of the total annual supply to the canal) is the El- Atf pumping station on the Rosetta branch. The canal is also fed from two subsidiary sources, namely the Edku pumping station, which lifts drainage water from Zarkon drain into the canal at km 8.850, and excess flow from the ElKhandak El-Sharki Canal at km 15.270. However, the mixing of the drainage water of the Etay El-Barud pump station into the ElKhandak El-Sharki Canal lowers its water quality. It should also be noted that the Edko Irrigation Pump Station was stopped from June 2009 due to the observed water quality problems in the Mahmoudia Canal, especially upstream of the intakes of drinking water plants (Abukila, 2012 and NRMED, 2005). Table 2 displays the

Discharge (Mll m3 month-1) El-Atf pump station

El- Khandak El-Sharki Canal

Jul, 2010

371.786

46.5

Aug, 2010

346.819

46.5

Sep, 2010

288.056

45

Oct, 2010

240.332

46.5

Nov, 2010

197.236

45

Dec, 2010

147.97

46.5

Jan, 2011

79.902

46.5

Feb, 2011

131.224

42

Mar, 2011

139.648

46.5

Apr, 2011

228.372

45

May, 2011

313.024

46.5

Jun, 2011

328.546

45

Total

2812.915

547.5

Materials and methods In order to achieve the study objectives, a methodology was designed to proceed with the investigation phases.

DOI number: 10.5027/jnrd.v4i0.08

Water Sampling and Discharge Data Under the umbrella of the Egyptian Ministry of Water Recourses and Irrigation (MWRI), the Drainage Research Institute (DRI) and Irrigation Sector (IS) measure the monthly discharges (Q) for all studied canals and drains.

Water samples were collected monthly during the year 2010/2011 (from July; 2010 to June, 2011) from six locations, four sites along the Mahmoudia Canal, one along the El- Khandak El-Sharki canal, and one along the Zarkon drain, Figure 2. The collected water samples were analyzed at the Central Laboratories for Environmental Quality Monitoring (CLEQM) of the National Water Research Center (NWRC) for water quality parameters including: Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD5), Total Suspended Solids (TSS), and Ammonia Nitrogen (NH4-N).

The water quality and quantity monitoring work was conducted once a month and the average values of the four seasons (summer and autumn of 2010, winter and spring of 2011) are presented in Table 3.

Journal of Natural Resources and Development 2014; 04: 54 - 63

57

such as geographic and hydraulic characteristics were collected from the Integrated Irrigation Improvement and Management Project (IIIMP), MWRI. Finally, the required seasonal meteorological data were obtained from the Alexandria International weather station, as it is the nearest weather station to the study area (http://www.wunderground. com/weather-forecast/EG/Alexandria_International.html?MR=1).

The accuracy of the model predictions was measured using statistics based on Mean Multiplicative Error (MME). The MME is employed as the error metric, owing to its significant advantages: scaling, proper sensitivity, lack of bias in determining critical deficit, and invariance with coefficient choice (Moog and Jirca, 1998). MME was calculated as follows:

Where N is the number of measurements, KP is the predicted value of the QUAL2K Model, and KM is the measured value.

In this research, the QUAL2K model was applied to simulate four water quality management scenarios on the Mahmoudia Canal. For each scenario, QUAL2K investigated water quantity and quality status along the canal during the 4 seasons (Summer, Autumn, Winter, and Spring) for 6 parameters (Q, EC, DO, BOD5, TSS, and NH4-N). The considered scenarios are as follows: (1) The first scenario: Simulating the current status of the canal in which the Edko irrigation pump station stops lifting drainage water from the Zarkon drain into the Mahmoudia Canal. The main purpose of this case is to validate the QUAL2K model and also to assess the current seasonal water quality along the canal.

(2) The second scenario: Re-running the Edko irrigation pump station, with its design capacity (5 m3 s-1), in order to cover irrigation needs along the canal and preserve/maximize the benefit of the great investment spent on the infrastructure of this vital pump station.

Figure 2: Schematic diagram of the measured water sample Numerical Modeling and Simulations The QUAL2K model can simulate up to 16 water quality parameters in dendritic streams that are well mixed laterally and vertically. A complete discussion of the model theory is described in the QUAL2K documentation and userâ&#x20AC;&#x2122;s manual (Chapra and Pelletier, 2008).

In the current research, the Mahmoudia Canal is a dendritic canal and the transport is dominated by longitudinal changes. Thus, QUAL2K was used in this investigation as an appropriate model of water quality simulation. The total length of the Mahmoudia Canal was modeled and then subdivided into 13 different reaches in accordance with the geometry of the canal. The input data for the QUAL2K model were based on the monitoring data of the year 2010/2011. Additional data,

(3) The third scenario: Controlling the Rahawy drain discharge or treating its pollution loads in order to improve the water quality of the Rosetta Branch; hence the water quality at the head of the Mahmoudia Canal will comply with required standards (Donia, 2005 and El Gammal and El Shazely, 2008).

In this scenario, the Edko Irrigation Pump Station is stopped.

(4) The fourth scenario: Controlling the Rahawy drain discharge and re-running the Edko irrigation pump station simultaneously.

River Pollution Index (RPI) The River Pollution Index (RPI), which is employed by the Environmental Protection Administration of Taiwan (EPA), was chosen for the present study due to its reliability in evaluating surface water pollution and ease of application. The RPI is calculated using concentration levels of four parameters: DO, BOD5, TSS, and NH3-N,

Journal of Natural Resources and Development 2014; 04: 54 - 63

DOI number: 10.5027/jnrd.v4i0.08

58

Table 3: Average seasonal discharges and water quality parameters at the monitoring locations Location

Water quantity and quality parameters Site Code

Season

Q

EC

DO

m s

umhos

mg L

Summer

134.66

463

2.87

11

8

700

Autumn

93.32

678

2.67

19

21

700

Winter

46.18

705

4.47

7

16

700

Spring

87.58

702

2.57

14

19

700

Summer

124

369

5.87

10

16

1144

Autumn

90

412

5.9

17

19

900

Winter

53

387

5.97

19

17

500

Spring

87

380

5.67

18

12

500

Summer

67

451

8.3

5

6

1400

Autumn

50

678

8.13

15

19

2400

Winter

29

733

7.77

12

17

800

Spring

45

675

8.13

11

17

900

Summer

38

409

8.13

4

6

1300

Autumn

30

659

8.1

10

21

2400

Winter

17

705

7.73

5

17

600

Spring

26

638

8.13

6

19

600

Summer

17.75

369

5.9

7

14

2373

Autumn

17.55

409

5.87

12

20

1200

Winter

17.36

388

4.13

14

19

200

Spring

17.75

376

5.7

12

17

200

Summer

-

985

2.25

11

25

1100

Autumn

-

1142

1.97

24

40

6000

Winter

-

1143

2.47

24

43

1500

Spring

-

1444

2.07

19

40

1200

3

Mahmoudia Canal Headwater

Mahmoudia Canal Downstream of the junction with El-Khandak Canal

Mahmoudia Canal Upstream of the Kafr Dawwar drinking water intake

Mahmoudia Canal Upstream of the Alexandria drinking water intake

Khandak Canal Upstream of the junction with Mahmoudia Canal

Outfall of Zarkon drain upstream of Edko Irrigation Pump Station

WI11

WI07

WI08

WI09

WI06

WE03

each of which is ultimately converted into one of four index scores (1, 3, 6, and 10). Notably, RPI refers to the arithmetic average of these index scores and the RPI value ranges from 1 to 10 (Chen et al., 2012).

According to the river pollution index listed in Table 4, the four pollution classifications are: unpolluted, negligibly polluted, moderately polluted, and severely polluted. In the present research, laboratory test results expressed Nitrogen content in water samples as NH4-N. Accordingly and in order to calculate RPI, the values of NH3-N were estimated using the molar mass approach as follows: NH3-N = (17.031/18.039)Ă&#x2014;NH4-N = 0.944Ă&#x2014;NH4-N. Based on the QUAL2K simulations, the River Pollution Index (RPI) was used to assess the conditions of water pollution at the intakes of the twelve water treatment plants located along the Mahmoudia Canal.

For each of the four studied scenarios, RPI was calculated at each WTP intake in the four seasons (summer, autumn, winter, and spring).

Results and discussion QUAL2K calibration and validation QUAL2K was calibrated and compared with monitoring data taken

-1

-1

BOD5

TSS

mg L

mg L

-1

NH4-N -1

ug L-1

along the Mahmoudia Canal in the four seasons (summer and autumn of 2010, winter and spring of 2011). It was then validated further.

Figure 3 displays EC, DO, BOD5, NH4, TSS and Q profiles along the Mahmoudia Canal for the first scenario (the current status of the canal).

Table 5 shows the values of Mean Multiplicative Error (MME) of the QUAL2K simulations for the water quality and quantity parameters in different seasons. MME results showed that on average the model predictions were in error by a factor ranged between 0.94 and 1.09. It was thus evident that the simulated values of the model were in close agreement with the measured values. This means that QUAL2K can be effectively applied to simulate the water quantity and quality parameters of the Mahmoudia Canal in different seasons.

From Figure 3 it was observed that EC concentrations along the Mahmoudia Canal were less than 750 umhos in all seasons. This may allow planting all field crops, fruits, vegetables, and forage crops cultivated in the study area throughout the year without any lowering of productivity (NWRC, 2007). These results were in agreement with previous results obtained by NRMED (2005).

Along the canal, the concentrations of DO were greater than 6 mg L-1, which complies with the standards of Egyptian law 48/1982. The DO level indicates that the Mahmoudia Canal is a healthy aquatic

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59

Table 4: Definition of River Pollution Index (RPI), Chen et al., 2012 Parameters

Ranks Un Polluted (UP) Negligibly Polluted (NP)

Severely Polluted (SP)

DO (mg L-1)

DO ≥ 6.5

6.5 >DO ≥ 4.6

4.5 ≥ DO ≥ 2.0

DO < 2.0

-1

BOD5 (mg L )

BOD5 ≤ 3.0

3.0<BOD5 ≤ 4.9

5.0 ≤ BOD5 ≤ 15.0

BOD5 > 15.0

TSS (mg L )

TSS ≤ 20.0

20.0<TSS ≤ 49.9

50.0 ≤ TSS ≤ 100

TSS >100

NH3-N (ug L )

NH3 ≤ 500

500<NH3 ≤ 990

1000 ≤ NH3 ≤ 3000

NH3 >3000

Index Scores

1

3

6

10

RPI

RPI ≤ 2.0

2.0< RPI ≤ 3.0

3.1≤ RPI ≤ 6.0

RPI > 6.0

-1

-1

ecosystem. It is also clear that DO concentrations, in all seasons, were less than 6 mg L-1 at the head of the canal as a result of receiving low quality water from the Rosetta Branch. However, DO levels increased gradually in the downstream direction of the Mahmoudia Canal owing to natural self-purification of the canal water.

In all seasons except summer, BOD5 and NH4 values along the canal exceeded 6 mg L-1 and 500 ug L-1, respectively, which violates Egyptian law 48/1982. However, in summer, BOD5 levels were less than 6 mg L-1 in the last 38 km of the canal. The minimum values of BOD5 and NH4 occurred in summer and winter, respectively; while, the maximum values occurred in autumn. In addition, it was seen that TSS concentrations along the canal met the standards of Egyptian law 48/1982 in all seasons.

Table 5: Mean Multiplicative Error (MME) of QUAL2K simulations for the Mahmoudia Canal EC

DO

BOD5

NH4

TSS

Q

Summer

1.1

0.94

0.92

0.93

1.01

1

Autumn

1.07

0.95

0.97

0.96

0.96

1

Winter

1.04

0.99

0.82

1

0.95

0.98

Spring

1.17

0.97

1.06

1.08

1.1

1.01

Average

1.09

0.96

0.94

0.99

1

1

Parameter

Mean Multiplicative Error (MME)

Moderately Polluted (MP)

QUAL2K Simulation Results Confident with the calibration and validation process results, QUAL2K was implemented to simulate the seasonal variation of the considered parameters along the Mahmoudia Canal for the proposed operating scenarios. Table 6 shows the model output data at km 16.500 on the canal, the abstraction point of the Monchat Nassar WTP. This plant is the first WTP located downstream of the mixing points of the Edku pumping station and the outflow of the El- Khandak El-Sharki canal.

For the 4 studied scenarios, it was found that EC, DO, and TSS concentrations were within the acceptable limits of Egyptian law 48/1982 in the 4 seasons. This confirmed that for all considered scenarios, the Mahmoudia Canal was a healthy aquatic ecosystem and its water quality generally complied with the legal requirements of agricultural practices throughout the year. On the other hand, for scenario 1, BOD5 and NH4 values were higher than

the allowable limits of Egyptian law 48/1982 in all seasons. This indicated the existence of organic loads in the Mahmoudia Canal.

Similar to the results of scenario 1, it was evident that for scenario 2, BOD5 and NH4 values in all seasons violated Law 48/1982. Furthermore, it was clear that values of all water quality parameters in scenario 2 were worst than those of scenario 1 in all 4 seasons, especially in winter. This means that for the current situation of the Mahmoudia Canal, re-running the Edko irrigation pump station (with its design capacity) will cause further deterioration in the canal’s water quality.

It should be noted that for scenarios 3 and 4, BOD5 and NH4 concentrations were improved significantly; but the concentrations of scenario 4 were higher than those of scenario 3. For both scenarios, BOD5 values were below the acceptable limit of law 48/1982 in summer; while, BOD5 slightly exceeded the limit in the other seasons.

Moreover, NH4 levels met the standards of Egyptian law 48/1982 in winter and spring; but in autumn and summer, NH4 violated the law.

River Pollution Index Calculations For scenario 1, RPI was calculated at all WTPs along the Mahmoudia Canal in the 4 seasons, Figure 4. According to the figure, the highest pollution level occurred in autumn in which all WTPs along the Mahmoudia Canal were classified as moderately polluted (MP). In summer, water quality was categorized as MP at all WTPs in the Beheira governorate and negligibly polluted (NP) at all WTPs in the Alexandria governorate. This may be due to the natural self-purification of the Mahmoudia Canal water.

In winter, water quality was classified as NP for all WTPs except for the Monchat Nassar WTP which was classified as MP. In spring, water quality was categorized as MP and NP for WTPs located upstream and downstream of the mixing point of the El- Khandak El-Sharki canal, respectively. This indicated that the concentration of contaminates in the water of the El- Khandak El-Sharki canal, resulting from the mixing of the drainage water of the Etay El-Barud pump station, was higher in winter, causing deteriorated water quality in the Mahmoudia Canal. On the other hand, the pollutants in the water of the El- Khandak El-Sharki canal were less than the Mahmoudia Canal, resulting in improved water quality in spring.

Figure 5 shows RPI values at WTPs along the Mahmoudia Canal for scenario 2. It is clear that pollution levels in scenario

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2 were like those in scenario 1 in summer, autumn, and spring, whereas in winter, it is evident that re-running the Edko irrigation pump station led to an increase in pollution level at some WTPs from negligibly polluted to moderately polluted.

Based on Figure 6, it can be seen that the pollution level along the Mahmoudia Canal was reduced significantly in scenario 3. The water quality at all WTPs along the canal was classified as unpolluted (UP) or negligibly polluted (NP) in summer, winter, and spring. But in autumn, pollution level at most WTPs located downstream of the mixing point of the El- Khandak El-Sharki canal was categorized as

60

moderately polluted. This may be due to the existence of non-point pollution sources in addition to the high concentration of pollutants in the water of the El- Khandak El-Sharki canal in autumn.

As shown in Figure 7, the RPI values for scenario 4 were estimated at all WTPs along the Mahmoudia Canal. It was found that RPI classification in scenario 4 was similar to that in scenario 3 in summer, winter, and spring. But in autumn, water quality at all WTPs located downstream of the Edko irrigation pump station was classified as moderately polluted.

Table 6: QUAL2K output data at the abstraction point of the Monchat Nassar Water Treatment Plant Site Code

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Water quantity and quality parameters Season

EC (umhos)

(mg L-1)DO

≤ 750 *

≥6*

Summer

449

6.66

Autumn

617

6.66

Winter

602

6.47

BOD5 (mg L-1) ≤6*

TSS (mg L-1) NH4-N (ug L-1) ≤ 500 *

≤ 500 *

7. 1

8.5

1080

15.1

19.2

1348

9.1

16.3

581

Spring

637

6.65

12.3

17.4

656

Summer

467

6.64

7.1

9

1084

Autumn

641

6.6

15.4

19.9

1570

Winter

645

6.18

10.2

18

660

Spring

677

6.55

12.6

18.3

688

Summer

449

6.86

4.4

8.5

886

Autumn

475

7.09

6.4

19.2

1155

Winter

464

6.6

8.4

16.3

424

Spring

475

7.13

6.8

17.4

466

Summer

467

6.83

4.5

9

897

Autumn

506

7.02

7.1

19.9

1386

Winter

517

6.31

9.5

18

498

Spring 523 7.01 7.3 18.3 (*) Egyptian law 48/1982 (**) Shaded cells are the values that exceed allowable Egyptian law limits

490

Conclusion and recommendations •

The QUAL2K model is successfully applied to simulate the water quantity and quality parameters of the Mahmoudia Canal in different seasons.

As a general result, all considered water quality parameters improved gradually in the downstream direction of the Mahmoudia Canal in most seasons. This confirmed the effective natural self-purification of the canal water.

•

Similar to the results of previous research, it was found that the majority of water quality problems on the Mahmoudia Canal were due to receiving low grade water from the Rosetta Branch.

However, it was concluded that the concentration of pollutants in the water of the El- Khandak El-Sharki canal was higher in winter and autumn, causing greater deterioration in the water quality of the Mahmoudia Canal.

For all studied water quality management scenarios, DO, TSS and EC concentrations indicated that the Mahmoudia Canal was a healthy aquatic ecosystem and its water quality generally complied with the legal requirements of agricultural practices throughout the year. However, exceeding the thresholds for BOD5 and NH4 in some seasons limits the other uses of the canal water, especially domestic uses.

•

From River Pollution Index (RPI) calculations for the current status of the Mahmoudia Canal, it was evident that the highest

From the above research, the following can be concluded: •

•

DOI number: 10.5027/jnrd.v4i0.08

pollution level in the canal occurred in autumn in which water quality at all WTPs along the canal were classified as moderately polluted. In the other seasons, water quality was categorized as moderately polluted at most WTPs in the Beheira governorate and negligibly polluted at all WTPs in the Alexandria governorate. â&#x20AC;˘

â&#x20AC;˘

For the current situation of the Mahmoudia Canal, re-running the Edko irrigation pump station with its design capacity (5 m3 s-1) may not be acceptable from an environmental viewpoint because it will cause further deterioration in the canal water quality, especially in winter.

The results show that the pollution level along the Mahmoudia Canal was reduced significantly in scenarios 3 and 4. The water

Journal of Natural Resources and Development 2014; 04: 54 - 63

61

quality at all WTPs along the canal was classified as unpolluted (UP) or negligibly polluted (NP) in summer, winter, and spring.

However, in autumn, pollution levels at most WTPs were categorized as moderately polluted.

As a result, controlling the Rahawy drain discharge or treating its pollution loads before mixing with the Rosetta Branch is highly recommended in order to achieve optimum water quality management for the Mahmoudia canal in summer, winter, and spring. However, in autumn, additional measures will be required to mitigate pollution levels in the canal (i.e. reducing non-point pollution sources and decreasing contaminant concentrations in the El- Khandak El-Sharki canal).

Figure 3: Verification of water quantity and quality parameters along the Mahmoudia Canal a)EC b) DO c) BOD5 d) NH4 e) TSS f) Q

DOI number: 10.5027/jnrd.v4i0.08

Journal of Natural Resources and Development 2014; 04: 54 - 63

Figure 4: River Pollution Index at drinking water treatment plants along the Mahmoudia Canal for scenario 1

Figure 5: River Pollution Index at drinking water treatment plants along the Mahmoudia Canal for scenario 2

Figure 6: River Pollution Index at drinking water treatment plants along the Mahmoudia Canal for scenario 3

62

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63

Figure 7: River Pollution Index at drinking water treatment plants along the Mahmoudia Canal for scenario 4

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104-110, (doi: http://dx.doi.org/10.1061/(ASCE)0733-9372(1998)124:2(104) ).

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NWRC, National Water Research Center, “The operational drainage water reuse

1st Technical Meeting of Muslim Water Researchers Cooperation (MUWAREC), Malaysia, December 2008.

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Rashed, A. and El-Sayed, E. A. (2014). “Simulating Agricultural Drainage Water Reuse

Chapra, S.C.; Pelletier, and G.J.; Tao, H., “QUAL2K: A Modeling Framework for Simulating

Using QUAL2K Model: Case Study of the Ismailia Canal Catchment Area,

River and Stream Water Quality”, Version 2.11: Documentation and Users

Egypt”, Journal of Irrigation and Drainage Engineering, 10.1061/(ASCE)IR.1943-

Manual; Department of Civil and Environmental Engineering, Tufts University: Medford, OR, USA, 2008.

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Hanfeng Y. E., Shuhai G.U.O., Fengmei L.I., and Gang L.I., “Water Quality Evaluation In Tidal River Reaches Of Liaohe River Estuary, China Using A Revised QUAL2K Model”, Chinese Geographical Science, Vol. 23, No. 3, pp. 301–311, 2013.

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Commentary

Malaysia water services reform: Legislative issues Nabsiah Abdul Wahid a*, Zainal Ariffin Ahmad b, Rozita Arshad c Graduate School of Business, Universiti Sains Malaysia, Malaysia.

College of Graduate Studies, Universiti Tenaga Nasional, Malaysia. drzaaba2001@yahoo.com c College of Law, Government and International Studies, Universiti Utara Malaysia, Malaysia. roz@uum.edu.my a

b

*Corresponding author: nabsiah@hotmail.com The latest attempt by the Malaysian government to restructure its water sector has managed to promulgate two important acts, the Suruhanjaya Perkhidmatan Air Negara (SPAN) Act (Act 654) and the Water Services Industry Act (WSIA/Act 655); these also complicate the governing of water services and water resources in the country as they affect the sovereignty of a state’s land and water issues. In Malaysia’s federated system of governance, water resources are placed fully within the purview of each State’s government, as stated in the Waters Act 1920 (Revised 1989), while water services are straddled across the purview of both the State and Federal government (Water Supply Enactment 1955). Any reforms will remain problematic unless further analysis is carried out on the available legislation that directly impacts said reform, particularly the Waters Act and Water Supply Enactment. For example, when the Waters Act stipulates “the entire property in and control of all rivers in any State is vested solely in the Ruler of that State”, it is clear that the Federal Government has no authority whatsoever over water resources of any states. The Water Supply Enactment 1955 (adopted by several States) further empowers the state’s water supply authorities to supply water to domestic and commercial consumers. Other legislation that has been enacted to govern land and water issues in the country include the Geological Act 1974 on groundwater abstraction and the Environmental Quality Act 1974 (incorporating all amendments up to 1st January 2006) on some aspects of the environmental impact of groundwater abstraction. While these legislations seemed to provide adequate coverage on the governance of groundwater abstraction; treatment, distribution and wastewater management, which form the water supply value chain in the country, are not covered.

Similarly, the Sewerage Services Act 1993 covers only wastewater governance issues rather than the whole value chain or process.

The fact that upon independence in 1957 the Malaysian constitution accorded separate jurisdiction for the state and federal authorities on land and water issues has given rise to various points of contention when dealing with water policy reform, particularly the role, power and ownership of water resources between the state and the federal governments. In conclusion, the problems observed in Malaysia’s water services industry reform are mainly with regard to legislation. In-depth analysis of how the SPAN Act and WSIA impact available legislation and how these legislations can create an integrated water resource management system that works on both Federal and State levels are crucial. It is thus fundamental for legal regimes for water resources to support the legal regimes for water services.

Only then, will the Federal government be able to take appropriate steps in restructuring the country’s water governance in its entirety.

Acknowledgement The authors acknowledge the research grant provided by the Ministry of Education Malaysia under the Long Term Research Grant Scheme (LRGS) 203/PKT/6726002 and those who have took part and provided us with information for this study. The authors also thank the panel of reviewers who provided us with constructive comments in the preparation of this commentary.

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Monitoring ground water quality and heavy metals in soil during large-scale bioremediation of petroleum hydrocarbon contaminated waste in India: Case studies Ajoy Kumar Mandal a*, Atanu Jana b, Abhijit Datta b, Priyangshu M. Sarma a, Banwari Lal a , Jayati Datta b a

The Energy and Resources Institute (TERI), Habitat Place, Lodhi Road, New Delhi, India.

b

Bengal Engineering and Science University, Sibpur, PO: Botanic Garden, Dist: Howrah, West Bengal, India.

* Corresponding author: akmandal@teri.res.in ; ajoy_mandal@hotmail.com Article history Received 17.05.2013 Accepted 03.09.2014 Published 02.10.2014

Keywords Bioremediation Oily waste Microbial consortium Total Petroleum Hydrocarbon (TPH) Heavy metals Oil and grease

Abstract Bioremediation using microbes has been well accepted as an environmentally friendly and economical treatment method for disposal of hazardous petroleum hydrocarbon contaminated waste (oily waste) and this type of bioremediation has been successfully conducted in laboratory and on a pilot scale in various countries, including India. Presently there are no federal regulatory guidelines available in India for carrying out field-scale bioremediation of oily waste using microbes. The results of the present study describe the analysis of ground water quality as well as selected heavy metals in oily waste in some of the large-scale field case studies on bioremediation of oily waste (solid waste) carried out at various oil installations in India. The results show that there was no contribution of oil and grease and selected heavy metals to the ground water in the nearby area due to adoption of this bioremediation process. The results further reveal that there were no changes in pH and EC of the groundwater due to bioremediation. In almost all cases the selected heavy metals in residual oily waste were within the permissible limits as per Schedule – II of Hazardous Waste Management, Handling and Transboundary Movement Act, Amendment 2008, (HWM Act 2008), by the Ministry of Environment and Forests (MoEF), Government of India (GoI).

DOI number: 10.5027/jnrd.v4i0.10

Introduction Disposal of petroleum hydrocarbon contaminated waste in an improper manner may cause serious environmental problems as its components are highly toxic to the environment [1]. Petroleum hydrocarbon contaminated solid waste generated by the oil and gas industries mainly in the form of tank bottom and effluent treatment plant (ETP) oily sludge, and oil contaminated soil, are commonly termed “oily waste”. Oily wastes are identified as hazardous in India and in OECD (Organization for Economic Co-operation and Development) countries as well as by the US EPA (United States Environment Protection Agency) [2] and [3].

Conventional methods like land filling, incineration, air sparging, etc. have been applied for many years for oily waste remediation.

The common drawback is that they are not a permanent solution for environmental pollution and they are sometimes not cost effective [4]-[7]. Bioremediation has emerged as one of the most promising treatment options for oil decontamination in terms of affordability, ecological approachability and its effectiveness in treating the contamination [8]-[10]. Microbes have been widely used in the bioremediation process where the toxic molecules are broken down to simpler nontoxic compounds like carbon dioxide, water and dead biomass through different metabolic pathways. All types of microbes, such as bacteria, archaebacteria, yeast, algae and fungi, have been widely used and studied in association with bioremediation. Of these bacteria, Bacillus, Pseudomonas, Achromobacter, Alcaligenes, Arthrobacter, Acinetobacter, Corynebacterium, Flavobacterium, Micrococcus, Mycobacterium, Norcardia, Vibrio, Rhodococcus, Sphingomonas, Burkholderia, etc. are used for the treatment of contaminated sites containing a wide variety of pollutants. Yeast species such as Candida, Clavispora, Debaryomyces, Leucosporidium, Pichia, Rhodosporidium, Rhodotorula, Sporidiobolus, Sporobolomyces, Stephanoascus, Trichosporon and Yarrowia are used in bioremediation process and show biodegrading properties. Algal species such as Aphanocapsa sp., Oscillatoria salina, Plectonema terebrans and Synechococcus sp. have been successfully used in bioremediation of oil spills in different parts of the world. Fungi such as the white rot fungus Phanaerochaete chrysosporium and Polyporus sp. show the ability to degrade an extremely diverse range of persistent or toxic environmental pollutants such as petroleum hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), explosives, polychlorinated biphenyls (PCBs), and organochlorine pesticides [11]. Researchers at the author’s institute alone have isolated more than 100 different species of bacteria for biodegradation of oily waste [5].

Although extensive research has been conducted on oil bioremediation, recent studies have concentrated on either evaluating the feasibility of the bioremediation process or testing specific products and methods [12]. Only limited numbers of pilot and field trials with small quantities of oily waste, possibly providing the most convincing demonstrations of this technology, have been carried out [13], [14], [7], [15].

In recent years large-scale bioremediation of >150,000 tonnes of

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66

dissimilar types of oily waste has been successfully carried out in batches at assorted oil installations in India using an indigenouslydeveloped microbial consortium which was able to biodegrade all the fractions of TPH of the oily waste to environmentally friendly end-products [16]. Bioremediation can be carried out in situ and ex situ in the field. Hence it is thought that there may be leaching of contaminated oil and some heavy metals present in the oil to local groundwater near the bioremediation site. In addition, for disposal of the bioremediated material to the environment, it is felt necessary to determine the concentration of selected heavy metals present in it, and match the same with the Schedule – II of HWM Act 2008, GoI. Presently there are no federal regulatory guidelines available in India for carrying out bioremediation of oily waste using microbes in the field. Hence, the MoEF, GoI, also insists that groundwater quality is monitored for the presence of oil and grease and selected heavy metals as well as the heavy metal concentration in the residual oily waste before and after bioremediation in the field.

The quality of water is of vital concern for mankind as it plays an important role in sustaining life on earth and is directly related to human welfare. A report by the Central Pollution Control Board (CPCB), GoI, states that ground water quality varies from place to place, with the depth of the watertable, and from season to season, and that it is primarily governed by the extent and composition of dissolved solids present in it [17]. A perturbation in the ecosystem comprising of water, air, oil and sediments as well as plant and animal life may be caused by the presence of metal ions and organic compounds beyond their natural levels [18]. One of the most visible tragedies caused by water pollution is Minamata disease caused due to Hg poisoning [19]. The contamination of ground water by heavy metals and pesticides has also assumed great significance during recent years due to their toxicity and accumulative behaviour [17]. The main threats to human health are associated with exposure to lead, cadmium, mercury and arsenic. These metals have been studied and their effects on human health regularly reviewed by the World Health Organization (WHO) [20]. Arsenic concentrations in groundwater in Bengal, Southeast Asia, and elsewhere constitute a major hazard to the health of people using these waters for drinking, cooking, or irrigation purposes [21]. Another major concern is groundwater pollution due to leaching of pollutants from surface sources like agricultural fields and waste dumps, which leads to chronic health hazards [22]. In addition to drinking purposes, the major volume of groundwater is utilized for irrigation, cooling and general operation in industry, and domestic sanitation. Hence, analysis of groundwater quality not only ascertains its physiological or domestic acceptability but also its technological usage. Assessment of quality of soil and groundwater will, therefore, help in ensuring the effectiveness of the bioremediation process from an environmental impact point of view.

Keeping these in view, the present study was undertaken to monitor groundwater quality and selected heavy metals in soil during largescale field case studies on bioremediation of oily waste carried out at selected oil installations in India.

DOI number: 10.5027/jnrd.v4i0.10

Methodology Outline of Bioremediation Process Large scale bioremediation was carried out both in situ at the contaminated site itself and ex situ, where a HDPE (high density polyethylene) lined secured bioremediation site was prepared inside the installation’s premises. The oily waste was excavated and transported to the secured site using an excavator, dumper and trailer. The required quantity of indigenous oil degrading microbial consortium was produced in bulk in a bioreactor and transported to the bioremediation site to be mixed with the oily waste at intervals.

The tilling of the oily waste was carried out at regular intervals to ensure proper aeration of the microbes. In order to maintain moisture content, site was watered as required. The process was continued until completion, where the total petroleum hydrocarbon (TPH) content was ~ 10 g/kg waste [5], [16], [23], [24].

Selection of Bioremediation Sites A large-scale bioremediation field study of a total of 88,438 tonnes of oily waste was carried out in 127 batches at oil installations located in a variety of climatic zones spread all over India: Indian Oil Corporation Limited (IOCL) refineries, Chennai Petroleum Corporation Limited (CPCL), Mangalore Refinery and Petrochemicals Limited (MRPL), Oil and Natural Gas Corporation Limited (ONGC), Oil India Limited (OIL), BG Exploration and Production India Limited (BGEPIL) and Cairn Energy Pty India Limited (CEIL) (Table 1). The type of contamination included acidic oily sludge (at Digboi refinery, IOCL) and non-acidic waste oily sludge (at other IOCL refineries, CPCL, MRPL), ETP / Tank bottom oily sludge and oil contaminated soil (at ONGC), synthetic oil-based mud (SOBM) waste (at BGEPIL) and oil contaminated drill cuttings (at CEIL).

Selection of Microbial Consortium Over the past few years, indigenous microbial strains had been isolated by the authors’ institute, from fifteen oil-contaminated sites located in different geo-climatic regions in India. The efficacy of the strains was evaluated for biodegradation of TPH component of oily waste and based on the functional diversity of the isolated strains, the best degraders for the major components of the TPH fractions were selected to form a consortium whose details have been reported in earlier studies by the institute [25-30], [14], [31]-[33], [6] and [34][37]. This consortium has been reported previously for application of biodegradation studies carried out either in the laboratory or in the field [5], [38]-[41], [16], [23], [24], and [42]. In all the field studies the application rate of the microbial consortium was in the range of 1.04 – 9.50 kg per tonne of waste depending upon various treatment conditions (Table 2).

Sampling Residual oily waste samples from the bioremediation sites were collected at day zero, i.e. before application of the microbes to the waste, and at regular intervals until completion for monitoring

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67

the performance of the process and the selected heavy metals.

Groundwater samples from bore wells near the bioremediation sites were collected at day zero and after completion of the bioremediation process. The sampling was carried out as per the detailed method described in Mandal et al., [24].

Analysis of Residual Oily Waste and Groundwater Samples 1) Analysis of residual oily waste: The residual oily waste samples were analysed for TPH, pH and selected heavy metals. TPH and pH was analysed as per the method described in Mandal et.al, [24].

Selected heavy metals, Zinc (Zn), Manganese (Mn), Copper (Cu), Nickel (Ni), Lead (Pb), Cobalt (Co), Arsenic (As), Cadmium (Cd), Chromium (Cr) (total) and Selenium (Se), were analysed in composite samples of the residual oily waste. The sample was digested in nitric acid as per the USEPA 3050 B method. The digested extract was diluted to the required concentration and used for determination of the selected heavy metals by the following methods depending upon the availability of resources: a) Using an Atomic Absorption Spectrophotometer (AAS) (AAS – TJA, SOLAAR M Series, Unicam, USA), where metals like Se, and As were analysed using an AAS equipped with a hydride generation system or cold vapour technique. b) By the Stripping Voltammetric method using a VoltammetryAmperometry (VA) trace analyzer (757 VA Computrace) by Metrohm 663 VA Stand (Swiss made) combined with AUTOLAB 30 Potentiostat–Galvanostat by standard addition procedure using a hanging mercury drop (drop size 0.20 mm2) electrode (HMDE) as the working electrode, Ag/AgCl (3 mol/L KCl) as the reference electrode and a large area glassy carbon as the counter electrode in an inert atmosphere by purging XL grade N2.

2) Analysis of Groundwater Samples The groundwater samples were analysed for pH, Electrical conductivity (EC), Oil and grease and selected heavy metals (Zn, Mn, Cu, Ni, Pb, Co, As, Cd, Cr(total) and Se) and selected anions: Fluoride (F-), Chloride (Cl-), Bromide (Br-), Nitrate (NO3-), Sulphate (SO4-2) and Phosphate (PO4-3).

The pH was measured directly, after filtration of the suspended solids if appropriate, using a standard pH meter (Orion Expandable Ion Analyzer, model: EA–940) which was calibrated using standard buffer solution before taking the reading. The EC was measured using a standard conductivity meter (Control Dynamics Conductivity Meter, model:APX 185) which was calibrated using standard potassium chloride (KCl) solution before determining EC. Oil and grease was determined as per the standard method IS 3025 (P 39): 1991. Selected heavy metals were analysed using the stripping Voltammetric method as well as using AAS as described above. The selected anions present in the groundwater samples were analysed by Ion Chromatography

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using an Ion Chromatograph (IC) with a conductivity detector (Metrohm IC 734). The analysis was carried out as per the standard operating manual of IC 734 supplied by Metrohm. The IC instrument was calibrated each time before performing the experiments [43][45].

68

In both analyses, the monitoring parameters were selected considering the characteristics of crude oil used by the respective oil installations and also the quantum of environmental impact of the respective parameters as studied in the literature.

Table 1: Details of oily waste undertaken for large scale bioremediation at oil installations in India.

Particulars of oil installations

Table 1

Geo-climatic conditions of the oil installation (Location/ climate/ temperature/ annual rainfall)

Quantity Type of oily waste undertaken for bioreme(tonnes) of oily diation waste

No. of batches

CPCL, Chennai

13002’N & 80010’E/ tropical coastal climate - hot & humid weather / Oily sludge from tank cleaning and Effluent ~160C – 450C / ~ 1,400 mm treatment plant (ETP).

4,100

4

IOCL, Barauni

25025’N & 86008’E in Ganges plain/ continental monsoon climate/ ~ 7 Oily sludge from tank cleaning and Effluent 0 C – 400C/ ~1384 mm treatment plant (ETP).

5,250

3

IOCL, Digboi

27023’N & 95038’E/temperate, tropical monsoon & high humidity/ ~ Oily sludge generated from dewaxing process 160C to 280C / ~2483 mm Acidic oily sludge

9,258

12

IOCL, Gujarat

22018’N & 73011’E / semi-arid & tropical savanna climate / ~ 120C - Oily sludge from tank cleaning and Effluent 400C / ~ 930 mm. treatment plant (ETP).

11,500

7

IOCL, Haldia

22002’N & 88004’E / typical moderate climate / ~ 7 0C - 39 0C /~ 1703 mm Oily sludge from tank cleaning and Effluent (heavy rain in monsoon) treatment plant (ETP).

10,500

6

IOCL, Mathura

27017’N & 77025’E / essentially dry climate/ ~140C - 450C / ~593 mm

Oily sludge from tank cleaning and Effluent treatment plant (ETP).

2,850

3

IOCL, Panipat

29023’N & 76058’E / sub-tropical & semi-arid climate/ ~ 80C - 40 0C / Oily sludge from tank cleaning and Effluent ~ 680 mm. treatment plant (ETP).

3,333

7

MRPL, Mangalore

12052’N & 74053’E/ coastal & fully terrain zone with tropical monsoon / Oily sludge from tank cleaning and Effluent 27 – 340C / 42418 mm. treatment plant (ETP).

2,150

2

21042’N & 72058’E/ extreme & tropical savanna climate/ ~ 23 0C – 40 0C Oily sludge from tank cleaning and Effluent /~800-1200 mm treatment plant (ETP).

3,063

7

10056’N & 79050’E/ coastal region with tropical maritime climate/ ~16 Oily sludge from tank cleaning and Effluent 0 C – 35 0C / ~1,260 mm treatment plant (ETP), emulsified oily sludge.

966

3

23° 35’ N & 72° 23’ E/ semi-arid & extreme dry or semi dry / ~ 150C - 50 Oil contaminated land, soil & water; effluent & 0 C max. / ~ 625 to 875 mm sludge pits, heavy viscous asphaltic oily waste.

16,938

42

26055’N & 94044’E/ humid subtropical monsoon / ~ 10 0C - 40 0C max./ Oily sludge from tank cleaning and Effluent ~ 2485 mm treatment plant (ETP)/ oil contaminated site

9,739

13

27o30’N & 94o22’E/temperate climatic zone with tropical monsoon/ ~ 9 Accidental oil spill on land due to fire accident 0 C - 310C/ ~2528 mm and waste oily sludge pits.

5,805

10

21°46’N & 72°09’E / semi-arid & fairly humid coastal climate/ ~12°C - Synthetic oil based mud waste generated during 40°C /~ 550 mm drilling operations.

2,185

3

Heavy oil contaminated drill cutting waste generated during drilling operations.

641

3

Mumbai Oil Spill 2010, 18 °54´N & 72°53´E / Tropical wet and dry climate / ~ 18 0C – 30 0C / ~ Furnace oil contaminated sand due to ship Mumbai 2,422 mm wreckage in sea.

160

2

88,438

127

ONGC Ankleshwar Asset ONGC

Cauvery Asset ONGC Mehsana Asset ONGC Assam Asset OIL Assam BGEPIL, Bhavanagar CEIL, Barmer, Rajasthan

Total / Range

25045’ N & 71023’ E / desert climatic zone / ~ 0 0C – 51 0C / ~277 mm.

DOI number: 10.5027/jnrd.v4i0.10

Results and discussion Analysis of residual oily waste: The study of the bioremediation of 88,438 tonnes of oily waste was carried out in 127 batches at different oil installations in India.

The initial TPH content range at all the installations was 57.50 662.70 g/kg waste, which was reduced to 0.50 - 57.10 g/kg after bioremediation. In most cases, the TPH content in the remediated soil was < 10 g/kg. The average time for bioremediation in each batch was 2 to 12 months. In a small number of cases, the bioremediation study took > 21 months depending on the initial oil content (ONGC Ankleshwar and Assam), type of waste (ONGC Cauvery and Mehsana, IOCL Digboi) and the geo-climatic condition of the site (MRPL, CPCL, ONGC Cauvery and Assam) (Table 2). The biodegradation rate varied from 0.07 to 1.93 Kg TPH/day/m2 and in almost all the studies the initial TPH was biodegraded by more than 90%. Figure 1 shows one oil contaminated site before and after bioremediation using the microbial consortium.

Figure 1

Figure 1: Oil contaminated site at IOCL Mathura refinery, IndiaBefore (Left) and After (Right) bioremediation using indigenouslydeveloped microbial consortium.

Table 2 describes the pH details of the residual oily waste samples of the respective oil installations before and after bioremediation.

Throughout the bioremediation process, the pH of all the samples was within the safe range of 6.5 to 8.5, except for the acidic oily sludge at the Digboi refinery, where the initial pH of the oily sludge was < 2 which was increased to 5.5 after bioremediation.

The details of the concentrations of the selected heavy metals in the residual oily waste samples before and after bioremediation are depicted in Table 3a & Table 3b. Figure 2 describes the Voltammetric diagram as a typical example for analysis of heavy metals using the Stripping Voltametry method. It can be observed that there was no considerable change in the concentration of selected heavy metals in the residual oily waste before and after bioremediation. However, the concentration of selected heavy metals varied among the oil installations, which was due to the type of crude oil processed by the respective installations. For example, the concentration range of Zn was 2 - 12 mg/kg in oily sludge from IOCL refineries, whereas the same was 130 – 150 mg/kg in acidic oily sludge from Digboi, 538 – 542 mg/kg in oily sludge from MRPL, 1 – 6 mg/kg in oily sludge from ONGC, 91 – 98 mg/kg in oily sludge from OIL, ~15 mg/kg in SOBM waste from BGEPIL, ~46 mg/kg in drill cuttings from CEIL and ~70 mg/kg in oily waste from the Mumbai oil spill. It was also observed

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69

that the concentration of all the selected heavy metals in the oily sludge at IOCL refineries was <<10 mg/kg and in ONGC sludge it was << 6 mg/kg, whereas there was diversity in other installations, OIL, MRPL, CEIL, BGEPIL and Mumbai. For example, in the case of the MRPL refinery, most of the selected heavy metals (except arsenic, cadmium and selenium) were at higher concentrations even more than 500 mg/kg. In almost all the studies the selected heavy metals in the residual oily waste was within the permissible limit as per Schedule – II of HWM Act 2008, GoI. No change in the heavy metal concentration before and after the bioremediation process (Table 3a & Table 3b) indicated that the microbes used for biodegradation of the oily waste were not able to biodegrade the heavy metals present in the oily waste. From the analysis of the microbial population [5], [46], and [47], it can be observed that the respective concentration of the heavy metals present in the oily waste did not affect the survival of the microbes, i.e. the microbes could tolerate the heavy metals concentration levels (even the highest concentration of >500 mg/kg) present in the respective soils. While correlating the biodegradation rate (Table 2) with the heavy metal concentration present in the waste (Table 3a & Table 3b), it was observed that at 7 installations out of 16, the concentration of a few of the heavy metals was on the higher side i.e. >12 to <1388 mg/kg. However, the biodegradation rate in those installations varied from 0.07 to 1.93 Kg TPH /day/m2, whereas, at the remaining 9 installations, the heavy metals concentration was <<12 mg/kg and the biodegradation rate varied from 0.19 to 0.43 Kg TPH /day/m2. Hence, the impact of only heavy metal concentration on the biodegradation performance of the microbes could not be concluded, as the bioremediation performance also depends upon other factors such as climatic condition, frequency of tilling, composition of waste, quantity of microbes applied, initial TPH content, etc.. A separate manuscript, in this regard, entitled “Factors Affecting Large Scale Bioremediation of Petroleum Hydrocarbon Contaminated Waste in Indian climate” is under publication by the author.

Analysis of ground water samples: The detailed groundwater characteristics near the bioremediation sites are described in Table 4a and Table 4b. Figure 3 describes the ion chromatograph as a sample of analysis for groundwater using Ion chromatography. It is observed that the pH in the groundwater samples before and after bioremediation was from 7.5 to 8.5, indicating no considerable change in pH after the bioremediation process. Similarly, there was no considerable change in EC after the bioremediation process. However, the EC range varied from 0.175 to 45.3 mS/cm depending on the geographical location of installations. In all groundwater samples the oil and grease was ‘nil’ indicating that no leaching of oil contamination to the underground water occurred during the bioremediation process. There was no considerable change in the concentrations of the selected heavy metals and anions before and after bioremediation. The results suggest that the bioremediation process of solid oily waste carried out in a secured HDPE lined bioremediation pit does not have any impact on groundwater quality. However, the selected heavy metals in the groundwater samples from the oil installations varied

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DOI number: 10.5027/jnrd.v4i0.10

considerably depending on geographical location. For example, the concentration range of zinc was 0.031 – 7.368 mg/l, manganese 0.02 - 0.950 mg/l, copper <0.001 – 0.414 mg/l, etc. It was also noted that in all the groundwater samples, the selenium content was < 0.001

Oil refineries

70

mg/l. In groundwater samples for the cases studies, the heavy metals and anion concentrations were within the permissible limits as per WHO & the Bureau of Indian Standards (BSI) and the Environment Protection Agency - Liquid Industrial Effluent (EPA -LIE).

Table 2: TPH and pH of oily waste in the present case studies before and after bioremediation.

Table 2 TPH (g/kg oily waste) pH* of waste Before biore- After biorememediation diation

CPCL, Chennai

129.50 - 437.10

IOCL, Barauni IOCL,Digboi

% Biodegradation (w / w)

8.80 - 14.30

93.20 - 97.80

162.00 - 212.20

3.70 - 50.70

170.40 - 560.00

8.70 - 49.00

IOCL, Gujarat

132.00 - 270.00

IOCL, Haldia IOCL, Mathura

Time for bio- Biodegradation remediation rate (Kg TPH / (months) day/m2 area)

Microbes Before biore- After biore- applied (Kg/ mediation mediation tonne waste)

3 – 13

0.21 ± 0.07

6.8

7.2

1.35 ± 0.31

70.18 - 98.14

5 - 5.5

0.43 ± 0.17

7.77

7.42

1.16 ± 0.11

86.93 - 97.27

2.5 – 15

0.86 ± 0.66

1.53

5.51

1.32 ± 0.15

3.90 - 34.50

82.54 - 98.13

2 – 12

0.41 ± 0.36

7.12

7.52

1.46 ± 0.27

193.00 - 269.00

5.60 - 12.50

94.47 - 97.44

6 – 10

0.19 ± 0.05

7.68

7.81

1.54 ± 0.15

152.50 - 223.10

3.50 - 8.50

96.19 - 97.70

4 – 12

0.37 ± 0.20

7.65

7.35

1.14 ± 0.02

IOCL, Panipat

206.50 - 238.00

2.60 - 8.00

96.51 - 98.86

3 – 10

0.38 ± 0.09

7.93

7.42

1.04 ± 0.08

MRPL, Mangalore

83.50 - 198.60

8.40 - 9.10

89.94 - 95.12

21 – 24

0.07 ± 0.03

8.02

7.95

3.71 ± 1.03

ONGC, Ankleshwar Asset

424.80 - 662.70

6.70 - 12.80

97.75 - 98.60

5 – 15

0.50 ± 0.21

7.83

7.76

4.36 ± 2.18

ONGC, Cauvery Asset

161.00 - 515.00

5.30 - 6.80

96.71 - 98.91

14 - 21.5

0.34 ± 0.31

7.71

7.73

9.50 ± 3.87

ONGC, Mehsana Asset

69.20 - 475.40

5.80 - 15.00

90.98 - 97.78

4.5 – 33

0.22 ± 0.15

8.12

8.02

2.57 ± 1.44

ONGC, Assam Asset

109.60 - 641.90

2.10 - 57.10

91.09 - 98.49

2 – 19

1.10 ± 0.84

7.73

7.62

1.98 ± 0.82

OIL, Assam

351.00 - 601.70

7.70 - 9.80

97.57 - 98.53

6 - 13.5

0.64 ± 0.51

7.81

7.75

1.35 ± 0.33

BGEPIL, Bhavanagar

57.50 - 106.70

2.60 - 6.90

90.09 - 95.83

4 - 7.4

0.10 ± 0.03

7.77

8.01

3.42 ± 0.53

CEIL, Barmer

98.10 - 188.10

8.20 - 10.90

90.03 - 95.64

3–4

0.61 ± 0.30

8.04

7.84

2.99 ± 0.57

Mumbai Oil Spill

60.00 - 381.00

0.50 – 3.60

90.55 - 99.17

2–5

1.93 ± 0.64

8.4

8.28

6.33 ± 0.47

* pH of 20% (w/w) solution of oily waste sample in distilled water

Figure 2: Typical Anodic Stripping Voltammogram and corresponding current-concentration plot for analysis of heavy metals.

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Figure 3

Figure 3: The chromatograms for the Anion analysis from bore well water using Ion Chromatography.

Conclusion From the present study 88,438 tonnes of oily waste was successfully

71

bioremediated using an indigenously developed microbial consortium in 127 batches at different oil installations in India. The overall results show that the initial TPH content of 57.50 - 662.70 g/kg oily waste was biodegraded to 0.50 - 57.10 g/kg waste. The average time for bioremediation in each batch was 2 to 12 months depending upon the initial oil content and the climatic conditions at the contaminated site. The rate of biodegradation of the oily waste was 0.07 â&#x20AC;&#x201C; 1.93 Kg TPH/day/m2 area. There was no considerable change in the concentration of the selected heavy metals in the oily waste before and after bioremediation. This indicates that the existing heavy metal concentration of the oily waste does not have any negative impact on the bioremediation process and also the microbes used for bioremediation of the oily waste do not biodegrade the heavy metals. The bioremediation process restricts leaching of oil to the groundwater and hence it has no impact on groundwater quality with respect to contaminating heavy metals and anion concentrations.

There was diversity in the concentration level of the selected heavy metals in the residual oily waste due to the types of crude processed by the respective oil installations. Diversity was also observed in groundwater quality depending on the geographical locations of the oil installations.

Table 3a: Heavy metal concentration in residual oily waste before and after bioremediation study.

Table 3a

Concentration of heavy metal (mg/kg waste) in oily waste before and after bioremediation study (typical) at Pe r m i s s i b l e Heavy metals Limit (mg/kg CPCL, Chennai IOCL, Barauni IOCL, Digboi IOCL, Gujarat IOCL, Haldia IOCL, Mathura IOCL, Panipat MRPL, Mangalore waste) * Before After Before After Before After Before After Before After Before After Before After Before After Zinc (Zn)

20000

4.21

4.11

2.81

2.44

150.96 131.28

4.61

4.35

2.81

2.28

12.1

10.4

5.47

5.22

541.471

538.66

Manganese (Mn)

5000

5.1

5.01

0.35

0.24

233.79 277.63

1.28

1.21

2.5

2.36

0.12

0.11

0.61

0.58

316.897

312.69

Copper (Cu)

5000

4.68

4.48

0.21

0.33

49.51

36.34

0.32

0.29

1.21

1.01

0.43

0.32

66.575

64.57

Nickel (Ni)

5000

5.89

5.79

0.44

0.21

2.93

2.02

0.55

0.49

4.4

3.89

0.21

0.18

6.31

5.99

138.891

138.11

Lead (Pb)

5000

2.25

2.12

0.33

0.43

1.031

1.092

0.6

0.42

0.33

0.56

0.41

0.35

2.93

2.85

15.257

14.35

Cobalt (Co)

5000

0.88

0.86

0.365

0.361

0.22

0.32

0.33

0.31

0.54

0.51

29.729

28.11

Arsenic (As)

50

1.43

1.41

0.577

0.542

0.07

0.05

0.15

0.1

0.36

0.32

< 0.001

< 0.001

Cadmium (Cd)

50

0.05

0.04

0.005

0.004

0.17

0.11

<0.001 <0.001 < 0.001

< 0.001

Chromium (Cr) (Total)

5000

5.11

5.05

0.64

0.59

0.098

0.086

0.69

0.58

2.27

3.21

0.89

0.77

1.29

1.25

146.539

141.28

Selenium (Se)

50

2.26

2.11

0.22

0.21

0.097

0.077

0.31

0.22

0.21

0.16

0.54

0.49

0.41

0.38

< 0.001

< 0.001

< 0.001 < 0.001 0.52

0.44

< 0.001 < 0.001

<0.001 <0.001 0.25

0.19

<0.001 <0.001 <0.001 <0.001

<0.001 <0.001

* As per Schedule â&#x20AC;&#x201C; II of Hazardous Waste Management, Handling and Transboundary Movement Act (Amendment 2008), by Government of India.

72

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DOI number: 10.5027/jnrd.v4i0.10

Table 3b: Heavy metal concentration in residual oily waste before and after bioremediation study.

Table 3b

Concentration of heavy metal (mg/kg waste) in oily waste before and after bioremediation study (typical) at Heavy metals Permissible Limit (mg/ ONGC Ankleshwar ONGC, Cauvery ONGC, Mehsana kg waste) *

ONGC, Assam

OIL, Assam

BGEPIL, Bhavnagar

CEIL, Barmer

Mumbai Oil Spill

Before

After

Before

After

Before

After

Before

After

Before

After

Before

After

Before

After

Before

After

46.13

70.85

68.78

Zinc (Zn)

20000

6.82

5.96

4.23

3.67

2.6

2.4

1.2

1.16

98.56

91.16

15.13

14.59

45.8

Manganese (Mn)

5000

<0.001

<0.001

0.76

0.71

2.7

2.4

27.4

26.89

33.87

30.65

12.14

11.47

163.86

Copper (Cu)

5000

1.13

0.96

3.22

2.87

2.15

1.4

1.2

1.18

10.26

8.79

7.89

7.56

7.12

7.46

67.57

62.19

Nickel (Ni)

5000

0.85

0.74

0.49

0.08

1.1

0.75

2.83

2.78

2.05

1.68

14.21

13.88

12.46

11.98

41.55

38.22

Lead (Pb)

5000

1.46

1.17

0.06

0.05

0.04

0.06

5.63

5.39

2.11

2.01

Cobalt (Co)

5000

1.23

1.08

<0.001

<0.001

0.2

0.18

0.11

0.09

Arsenic (As)

50

1.07

0.89

4.09

4.38

0.1

0.2

1.82

1.77

2.11

Cadmium (Cd)

50

<0.001

<0.001

<0.001

<0.001

0.03

0.02

0.05

0.04

Chromium (Cr) (Total)

5000

1.43

1.21

1.51

<0.001

1.3

1.2

6.85

Selenium (Se)

50

1.26

0.97

<0.001

<0.001

<0.001

<0.001

0.06

175.42 1388.2

1374.31

3.48

2.78

7.82

6.65

7.32

7.11

0.008

0.007

0.81

0.76

0.06

0.05

1.98

0.059

0.044

0.65

0.53

0.08

0.06

0.11

0.09

<0.001

<0.001

0.98

0.87

1.12

1.03

5.99

4.34

3.69

0.012

0.011

16.99

16.99

277.13

267.54

0.05

0.05

0.03

0.012

0.011

0.04

0.03

0.31

0.26

<0.001 <0.001

* As per Schedule – II of Hazardous Waste Management, Handling and Transboundary Movement Act (Amendment 2008), by Government of India.

Table 4a: Ground water characteristics near bioremediation sites before and after bioremediation study.

Table 4a Permissible limits

Ground water quality near bioremediation site before and after bioremediation study (typical) at

as per

Particulars

CPCL, Chennai IOCL, Barauni BSI / EPA WHO * (LIE) ** Before After Before After

Selected Heavy Metals Zn Mn

5 ppm

(mg/L) (mg/L) (mg/L) (mg/L) 1 ppm

IOCL, Digboi Before

After

IOCL, Gujarat Before

After

IOCL, Haldia Before

After

IOCL, Mathura Before

After

(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)

IOCL, Panipat Before

After

MRPL, Mangalore Before

(mg/L) (mg/L) (mg/L)

After (mg/L)

0.042

0.031

0.071

0.069

3.026

2.986

0.06

0.05

0.08

0.06

1.32

0.95

0.05

0.04

0.09

0.08

0.1 ppm 1.5 ppm 0.541

0.432

0.055

0.049

0.182

0.181

0.04

0.02

0.04

0.03

0.25

0.23

0.04

0.03

0.06

0.06

0.02

Cu

1 ppm

1 ppm

0.414

0.297

0.012

0.005

<0.001

<0.001

0.02

0.01

0.01

0.002

0.002

0.01

0.01

0.03

0.02

Ni

5 ppb

1 ppm

0.001

0.001

0.002

0.001

<0.001

<0.001

0.001

0.001

0.001

<0.001 0.001

0.001

<0.001

<0.001

0.003

0.003

Pb

5 ppb

0.5 ppm 0.005

0.004

0.003

0.003

<0.001

<0.001

0.002

0.002

0.001

0.001

0.003

0.003

0.001

<0.001

0.002

0.001

Co

5 ppb

0.002

0.002

0.001

<0.001

<0.001

0.001

0.001

0.003

0.001

0.001

0.001

<0.001

<0.001

0.001

0.001

As

5 ppb

0.5 ppm <0.001 <0.001

0.002

0.002

<0.001

<0.001

0.002

0.002

0.002

0.003

0.002

0.002

0.003

0.002

<0.001

<0.001

Cd

1 ppb

0.01 pm <0.001 <0.001

0.001

<0.001

<0.001

<0.001

0.006

0.004

<0.001 <0.001 0.001

0.001

<0.001

<0.001

<0.001

<0.001

0.005

0.004

0.001

0.002

Cr (Total)

5 ppb

Se

0.5 ppb

---

1 ppm

0.002

0.002

0.002

0.002

0.001

<0.001

<0.001

0.5 ppm 0.001

0.001

<0.001

<0.001

<0.001

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

<0.001 0.002

0.002

0.001

0.002

0.002

<0.001

<0.001

<0.001

<0.001

Physico-chemical properties: pH

---

6 – 10

7.71

7.62

7.64

7.23

6.98

7.21

7.35

7.33

7.81

7.73

7.22

7.13

7.6

7.5

6.89

7.04

EC (mS/cm)

---

---

2.18

2.16

2.97

3.21

0.259

0.205

2.15

2.13

3.99

3.81

3.35

3.2

2.71

2.7

0.177

0.175

Oil / Grease

---

10 ppm

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Journal of Natural Resources and Development 2014; 04: 65 - 74

DOI number: 10.5027/jnrd.v4i0.10

73

Table 4b: Ground water characteristics near bioremediation sites before and after bioremediation study.

Table 4b

Permissible limits as per Particulars BSI / EPA (LIE) WHO * **

Selected Heavy Metals

Ground water quality near bioremediation site before and after bioremediation study (typical) at ONGC Ankleshwar

ONGC, Cauvery ONGC, Mehsana ONGC, Assam

Before

Before

After

(mg/L) (mg/L) (mg/L)

After

Before

After

Before

After

OIL, Assam Before

After

BGEPIL, Bhavnagar Before

After

CEIL, Barmer Before

After

Mumbai Oil Spill Before

(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)

After (mg/L)

Zn

5 ppm

1 ppm

0.081

0.076

2.312

2.131

7.368

7.286

2.044

1.79

1.27

1.23

0.058

0.05

0.049

0.044

0.088

0.079

Mn

0.1 ppm

1.5 ppm

0.033

0.03

0.076

0.069

0.336

0.302

0.651

0.579

0.83

0.95

0.046

0.037

0.036

0.031

0.059

0.052

Cu

1 ppm

1 ppm

0.03

0.026

0.008

0.006

0.032

0.01

0.011

0.01

0.037

0.029

Ni

5 ppb

1 ppm

<0.001 <0.001

0.002

0.001

0.002

0.002

Pb

5 ppb

0.5 ppm

<0.001

0.003

0.002

Co

5 ppb

---

<0.001 <0.001

0.002

0.001

As

5 ppb

0.5 ppm

0.002

0.001

<0.001

<0.001

0.001

0.001

Cd

1 ppb

0.01 ppm 0.008

0.006

<0.001

Cr (Total)

5 ppb

0.005

Se

0.5 ppb

<0.001 <0.001 <0.001

0.026

0.197

<0.001 <0.001 <0.001 0.186

<0.001 <0.001

<0.001

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001

0.001

0.002

<0.001 <0.001 <0.001

<0.001

<0.001 <0.001

0.001

0.001

<0.001 <0.001

0.003

0.002

<0.001

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001

0.007

0.004

<0.001

<0.001

<0.001 <0.001

0.004

0.003

0.5 ppm <0.001 <0.001 <0.001

<0.001

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

0.007

0.002

0.031

0.047

<0.001

1 ppm

0.002

<0.001

<0.001 <0.001

0.025

0.021

0.044 0.071

0.05

<0.001 <0.001 <0.001 0.068

<0.001 <0.001 <0.001

0.001

0.003

0.002

<0.001 <0.001 <0.001 0.002

0.001

0.003

<0.001 0.002 <0.001

Physico-chemical properties: pH

---

6 – 10

7.48

7.35

7.26

7.15

7.62

7.35

7.39

7.61

8.01

7.92

7.69

7.54

7.7

7.6

7.19

7.21

EC (mS/cm)

---

---

1.89

1.65

45.3

43.7

2.12

1.89

2.29

2.16

2.65

2.47

2.18

2.09

2.59

2.56

2.37

2.32

Oil / Grease

---

10 ppm

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

Nil

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Journal of Natural Resources and Development 2014; 04: 75 - 83

DOI number: 10.5027/jnrd.v4i0.11

75

JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Analyzing the biophysical inputs and outputs embodied in global commodity chains - the case of Israeli meat consumption Shira Dickler a* , Meidad Kissinger a a

Ben-Gurion University of the Negev, Israel

*Correponding author: dickler@post.bgu.ac.il

Article history Received 31.10.2013 Accepted 03.09.2014 Published 06.11.2014

Keywords Meat Consumption Sustainable Food Systems Interregional Sustainability Carbon Footprint Land Footprint

Abstract The prevailing global livestock industry relies heavily on natural capital and is responsible for high emissions of greenhouse gases (GHG). In recent years, nations have begun to take more of an active role in measuring their resource inputs and GHG outputs for various products. However, up until now, most nations have been recording data for production, focusing on processes within their geographical boundaries. Some recent studies have suggested the need to also embrace a consumption-based approach. It follows that in an increasingly globalized interconnected world, to be able to generate a sustainable food policy, a full systems approach should be embraced. The case of Israeli meat consumption presents an interesting opportunity for analysis, as the country does not have sufficient resources or the climatic conditions needed to produce enough food to support its population.

Therefore, Israel, like a growing number of other countries that are dependent on external resources, relies on imports to meet demand, displacing the environmental impact of meat consumption to other countries. This research utilizes a multi-regional consumption perspective, aiming to measure the carbon and land footprints demanded by Israeli cattle and chicken meat consumption, following both domestic production and imports of inputs and products. The results of this research show that the “virtual land” required for producing meat for consumption in Israel is equivalent to 62% of the geographical area of the country. Moreover, almost 80% of meat consumption is provided by locally produced chicken products but the ecological impact of this source is inconsequential compared to the beef supply chain; beef imports comprise only 13% of meat consumption in Israel but are responsible for 71% of the carbon footprint and 83% of the land footprint. The sources of Israel’s meat supply are currently excluded from environmental impact assessments of Israeli processes. However, they constitute a significant fraction of the system’s natural capital usage, so they must be included in a comprehensive assessment of Israel’s consumption habits. Only then can policy be created for a sustainable food system, and inter-regional sustainability be achieved.

DOI number: 10.5027/jnrd.v4i0.11

Introduction In recent decades, the international trade of food commodities has become a central means of supplying the needs and wants of billions of consumers all over the world [4] [42]. Food supply chains have grown longer and more intricate, involving stages taking place in multiple regions [40]. In a globalized world with a rapidly increasing population, poor management of natural resources may lead to soil erosion, water shortages, climate change, and pollution, threatening food security in both the developed and developing worlds [11] [54] [60] [62] [67].

For countries with limited bio-capacity where domestic supply is dependent on the global system, national food system sustainability is also reliant on other regions; low yields or ecological damage will not only affect the country of production, but might also dramatically affect countries with roles farther down the commodity chain [40].

Furthermore, as the virtual distance between the source of production and the consumer grows, direct environmental ramifications caused by the production of a commodity become more difficult for the consumer to perceive [40]. Although various academic research studies have explored the biophysical inputs and outputs demanded by food production or processes within a single country (e.g., [5] [19] [24] [72]), a growing number of studies advocate for taking a fullsystem or consumption-based approach, accounting for activities taking place throughout the entire life cycle (e.g., [10] [13] [14] [15] [16] [35] [39] [40] [48] [51] [56] [57] [58] [59]).

The geographic attributes of the state of Israel, in particular, place heavy limitations on agricultural yields, making the country poorly suited for feeding its rapidly increasing population. Consequently, Israel is dependent on imports from many other countries to support domestic food supply, especially for inputs to the meat system.

This is especially relevant as many studies have found that the global meat system contributes significantly to global natural capital use and greenhouse gas emissions (GHG) (e.g., [23] [46] [47] [50]). To date, very little is known about the global environmental dimension of the Israeli meat system. This study asks what the breadth is of two major biophysical components of the meat system – land resources and emissions of greenhouse gases.

It analyzes the state of Israel’s meat land and carbon footprints from each foreign and domestic source of supply and explores how local consumption may influence international food security and interregional sustainability.

Background Global Meat Commodity Chains The global food system has undergone drastic changes in the past several decades, due in part to the availability of fossil energy, the development and increased use of artificial inputs such as fertilizers and pesticides, and transformations in shipping technologies [4] [18] [28] [42] [53]. At the same time, free-trade agreements and the phasing out of food reserves, along with national food policies

Journal of Natural Resources and Development 2014; 04: 75 - 83

76

have fostered the increasing interconnectivity and dependency of a country on the global system [3] [29]. All of these transformations have contributed to shaping the current globalized food industry, one where the typical commodity chain traverses multiple continents before reaching the consumer, and consumers have access to the highest variety of products than ever before in human history [1] [12] [41]. Coupled with this expansion, meat has emerged as a primary commodity in the typical diet, where developed countries may fill 70% of their protein consumption with animal-based products, sometimes reaching over 300 grams of meat per person daily [44] [53] [64]). Since 1980, developing countries have nearly doubled their meat intake per capita as a result of growing incomes, urbanization, and shifts in food preferences, and world meat exports have increased exponentially (and are projected to continue climbing) to fill demand [20] [23] [64] [70].

The last four decades have shown an increased adoption of industrialized livestock rearing practices to accommodate growing consumption habits, including higher inputs of fossil fuels, expansion of built structures, and industrialized feed production (replacing conventional pasture-based systems) [2] [44]. Yet these systemic changes hold substantial implications for the environment and the availability of natural resources. The report Livestock’s Long Shadow, sponsored by the United Nations Food and Agricultural Organization (FAO), estimates the livestock sector as causing 18% of anthropogenic GHG emissions, using 30% of global land resources and 8% of global available water [66]. The FAO recently published a follow up to this report, updating the greenhouse gas burden of the livestock sector as 14.5% of human-induced emissions [28]. Both reports cite the production stages with the greatest impact on natural resources, including animal digestion (CH4 emissions), decomposition of fertilizers and animal waste (CH4 and N2O emissions), burning of fossil fuels to create fertilizers used in feed production (CO2 emissions), land-use changes for producing feed or grazing (land resources), and land degradation [66]. A growing awareness of the issues involved in meat production has generated different directions to improve productivity in the last few decades, such as increasing the feed conversion efficiency of the animal and an increased prevalence of mixed and landless production systems [2].

Conventional vs. Emerging Approaches to Biophysical Resource Accounting The conventional approaches of tools to measure the environmental impact of a product, process, or nation include factors attributed only to production, accounting for environmental burdens that take place within the country’s borders. These studies also typically measure biophysical inputs and outputs only for a single unit of analysis (i.e. one kilogram of meat), and do not present the total burden for the entire production system (a macro-scale approach). Recently, researchers acknowledge the need to consider a consumption perspective, agreeing that the ecological impact of a product also lies with the individual consumer, or the consuming nation. A growing number of studies advocate the use of a multi-regional consumption approach to measure an individual or a nation’s impact on the environment (e.g. [10] [13] [14] [15] [16] [35] [39] [40] [48] [51] [56] [57] [58] [59]).

DOI number: 10.5027/jnrd.v4i0.11

A small but increasing number of footprint analyses are now taking the consumption approach in analyzing the biophysical resource impacts of a particular country’s meat consumption [24] [36] [71] [72].

The carbon and land footprint tools are especially useful for analyzing global commodity chains. These indicators are defined by a consumption-based perspective, serving to track anthropogenic impacts on the environment in the form of greenhouse gas emissions and land resources. This method may be used to measure the impact of individuals, products, processes, sectors, as well as cities, nations, or the world [25].

The carbon footprint is used for calculating greenhouse gas emissions (GHG), formulating a total number comprised of the different types of GHG’s that make significant contributions to radiative forcing, namely, carbon dioxide, nitrous oxide, methane, and several fluorinated gases [65]. The land footprint calculates the real land (in hectares) used in each country to sustain a product or process, taking a place-oriented approach as described in [38].

The Israeli Meat System To date, very little research has used this method to explore Israel’s overall food system, and none have studied the national meat system. This is especially relevant as per capita consumption of all meat products in Israel has seen a profound increase in the last two decades, with beef and chicken serving as the two most highly consumed meats, followed by pig meat, mutton, and goat meat [20] [49]. As of 2009, Israel is considered the world’s 13th highest per capita consumer of overall meat products, growing from 30.3 kg/ person in 1961 to 107.3 kg/person in 2011, an increase of over 250% [20]. Other types of meat represent smaller shares of the overall meat consumption in Israel including (as of 2010) pig meat (2.5 kg/person) and mutton/goat (1.84 kg/person) [20].

Israel’s food system is heavily reliant on foreign imports of products and supplemental resources, and while data may show that Israel is self-sufficient in certain products, it most likely does not incorporate the imported materials and energy used to create them (e.g. imported livestock feed). The Israeli beef and chicken supply-chains drastically differ in scale and magnitude; the beef sector is heavily reliant on a considerable number of foreign sources for meat, livestock, and feed while the chicken sector is mainly encompassed within Israeli borders, dependent primarily on external sources of feed. Filling in the gaps from production to consumption along the two distinctive lifecycles, from animal husbandry to slaughter and shipping, would show the true global warming impact and land resources required for Israeli meat consumption, identifying barriers and solutions for achieving food security, interregional sustainability, and building a sustainable food system.

Methods Our study takes a multi-regional consumption perspective to account for beef and chicken consumption in Israel, documenting domestic sources of production, as well as the import of beef, calves,

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77

and feed. Data sources include national and international databases, interviews with key local officials, analysis of policy documents, and peer-reviewed journal articles. The results represent activities taking place in 2010, following the most recent data available.

The four primary categories considered include: (1) beef import, (2) calf import, (3) domestic beef production, and (4) domestic chicken production. Category 1 includes boneless beef and beef cuts that are imported from several countries, mainly Latin America, Europe, and China. Category 2 follows calves exported to Israel from Australia and Eastern Europe when they are between two and five months old, then fattened in Israeli feedlots until reaching slaughter weight. The cows produced and consumed entirely in Israel (category 3) include pastured cows and calves, culled dairy cows, and calves born in the dairy sector that are fattened in feedlots. Finally, category 4 follows the local broiler system that is almost entirely sourced by domestic poultry production, with negligible import/export quantities of chicken meat or products.

Our analysis encompasses the following stages: a) Calculation of overall meat consumption from each source of supply; b) Measurement of the main sources of greenhouse gas emissions and land resources involved in production from each source; and c) Quantification of GHG emissions related to overseas transport to Israel.

Carbon and Land Footprints This research estimates the carbon footprint along the full commodity chain of consuming one ton of cattle or chicken meat in Israel, accounting for all burdens resulting from the production and transport of feed, on-farm operations (animal husbandry, fertilizer application, machinery), slaughter, and overseas transport. Results are presented in CO2 equivalent, using the factors from the IPCC 4th Assessment Report of 1 kg CO2/kg CO2, 298 kg N2O/kg CO2, and 25 kg CH4/kg CO2, [65]. The land resources considered include pastureland and cropland for cattle, and for chickens, cropland and land required for chicken coops (“coop-land”), calculating the actual area of land needed per unit of meat or feed for consumption in Israel. Overseas transportation to Israel was calculated from the nearest port of the source country to Israel using the most direct route. Table 1 and Table 3 present the factors and data sources used for each region of analysis.

To accommodate for the multi-functionality of the cow’s carcass, we assume that 87% of the value is in the beef carcass, and that the remaining value is in the slaughter fats, offal, and hide (as cited in [9] and [43]). We allocate this percentage to the final results for data related to the beef supply chain, for sources that have not already included this allocation.

Research Limitations Due to the scope of this study and lack of data availability, certain components of the Israeli meat system are not accounted for in this study. The two sources of meat considered make up the majority of national meat consumption, therefore, other sources of meat

Journal of Natural Resources and Development 2014; 04: 75 - 83

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consumption such as turkey, pig, sheep, goat, and other poultry products are not considered. These products are recommended for inclusion in future research on biophysical inputs and outputs of the Israeli meat system.

When specific figures were not available or were unreliable for this study, they were either not considered or assumptions are made. Some omitted components include implications of land-use change for pasture and cropland production, CO2 emissions and land resources within calf-exporting countries, and transportation occurring within the country post-production, such as from the farmgate to the port. Additionally, factors estimated for Brazil are used as a proxy for the other Latin American countries considered.

The approach of this research encompasses the impacts directly related to the production of meat for Israeli consumption, by measuring the effect for the actual slaughtered animal. To this degree, emissions and land and water resources related to land-use change and the supporting cow-calf herd are not included. Based on studies that have included the land-use change component, the inclusion of this data may result in an increase between 50 to 100 percent in the footprint [43]. Similarly, the inclusion of the biophysical inputs and

78

outputs related to the cow-calf herd required to support the cows slaughtered each year would result in a significant increase in each footprint. Footprints are also not considered for culled dairy cows or the production of agricultural resides used in feed, as these would ideally be accounted for in footprint studies of those respective sectors.

Finally, this studyâ&#x20AC;&#x2122;s boundaries extend to the production of one ton of beef or chicken, and do not include any subsequent stages in the lifecycle. Further research would be needed to estimate the full carbon and land footprints of consumption from cradle to grave, such as processing into meat products, transportation to vendor and consumer, storage, food preparation, and final waste disposal.

Given that the data used is the most up-to-date and accessible from is the information available, the limitations presented here should not impair the impact of the research. However, we do acknowledge that this study is the first step in evaluating the biophysical impact of Israeli meat consumption and encourage continued refining of the data in the future to present the most accurate and reliable picture of the system.

Table 1: Data factors for beef import Enteric Fermentation

Country

Manure Ma- Feed Production/ Manure Ma- Feed Producnagement Farm Operations nagement tion

Slaughter

Pasture Land

(kg CO2/ ton*km)g

(kg beef/ hectare)

Cropland (hectares)

(kg CH4/ head)a

(kg CH4/ head)a

(kg CO2/ kg carcass weight)

(kg N2O / head)a

(kg N2O/ kg carcass weight)

Argentina

56

1

0.3b

11.91

N/A

0.2

13,505

0.016

46h

N/A

Brazil

56

1

0.3b

13.76

N/A

0.2

9,304

0.016

49h

N/A

China

188

1

4.12c

7.95

1.43 c

0.2

12,966

0.016

481

8,340

France

57

7

d

3.99

7.9

i

1.48

0.2

2,948

0.016

248

4,860i

Netherlands

57

6

2.65d

4.5

0.86i

0.2

6,219

0.016

1,189i

90i

Panama

56

1

0.3b

12.51

N/A

0.2

11,838

0.016

46h

N/A

Paraguay

56

1

0.3b

21.29

N/A

0.2

12,047

0.016

54h

N/A

Poland

58

6

4.98

5.8

i

1.32

0.2

7,599

0.016

359

k

6,240i

UK

57

6

4.04d

8.98

2.26i

0.2

5,569

0.016

129i

2,000i

Uruguay

57

1

0.3b

14.15

N/A

0.2

13,505

0.016

49h

N/A

Rest of the World

57c

3c

4.12c

6.8

1.43c

0.2

N/A

N/A

481 c

10,530 c

d

(kg CO2/ kg carcass weight)e

Shipping Distance (km)f

Shipping factor

i

a. [20]; b. [7]; c. Calculated average; d. [43]; e. [63]; f. [55]; g. [17]; h. Calculated data using [6]; i. Calculated data using [43]

Table 2: Data factors for calf import Region

Australia Eastern Europe

Enteric Fermentation

Manure Management

(kg CH4/head)a

(kg CH4/head)a

53.5b

1.4b

52a

1.8a

Shipping factor

Slaughter

(kg CO2/ton*km)d

(kg CO2/head)e

17,050

0.013

69

2,113

0.7

69

Shipping Distance (km)c

a. Calculated average using factors of Australia and Israel from [20]; b. Calculated average using factors of E. Europe and Israel from [20]; c. [55] ; d. [17]; e. Calculated data using [32] and [33]

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79

Table 3: Data factors for domestic production Region

Enteric Fermentation (kg CH4/head)a

Manure Management (kg CH4/head)a

Manure Management (N2O) Kg CO2e/kg boneless beef

N/A

0.02

0.55

0.75c

N/A

2.2 x 10-5

47

1

1.88

69b

2.5

N/A

Israel (Chicken) Israel (Cattle)

Slaughter Pasture Land (kg CO2/head) (Cow-calf/hectare)d

Coop Land (hectares/head)e

a. [20]; b. Calculated data using [32] and [33]; c. Calculated data using [32] and [34], 2013; d. [61]; e. [30]

Table 4: Data factors for feed import (calculated data using [38]) North America

Data Factor/Region % of Grain Supply Nitrogen (Kg N2O/ton) Fertilizer (Kg CO2/ton) Machinery (Kg CO2/ton) Shipping (Kg CO2/ton) Land (hectares/ton)

Black Sea Region

Europe

Israel

ROW

Wheat

15

60

11

14

N/A

Maize

63

N/A

N/A

5

32

Wheat

154

101

149

262

N/A

Maize

104

N/A

N/A

104

104

Wheat

63

79

45

76

N/A

Maize

32

N/A

N/A

33

32

Wheat

54

59

32

48

N/A

Maize

14

N/A

N/A

6

14

Wheat

57

28

24

N/A

N/A

Maize

57

N/A

N/A

N/A

57

Wheat

0.46

0.4

0.17

0.45

N/A

Maize

0.11

N/A

N/A

0.06

0.11

Results Figure 1 presents the distribution of the sources of Israeli meat consumption across the world. This map highlights that the main source of supply is chicken meat, providing 78% of total meat consumption. Imports of beef and calves provide 13 and 4% of consumption, respectively, and domestic sources of beef production contribute 5% (Figure 1).

Table 4 presents the four main processes contributing to cattle and chicken meat consumption in Israel, and the quantities of consumption from each category in 2010. Appendix 1 details the specific countries contributing to each category and their overall burden.

Table 5: Israeli meat consumption, by category* Process

Consumption quantity (kg)

Consumption per capita (kg)

% of Total Meat Consumption

Beef Import

71,150,000

9.6

13

Calf Import

21,200,000

2.9

4

Domestic Beef Production

28,000,000

3.8

5

Domestic Chicken Production

435,000,000

58.6

78

Total

555,350,000

74.9

100

* This table represents local data collected from [34] and [68].

Carbon Footprint Figure 1: Sources of Israeli Meat Consumption, by Region and Percentage Contribution

The global warming potential for average Israeli beef consumption in

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2010 measures at 2,273,000 tons CO2e overall and 22 tons CO2e per ton of beef. Results for chicken meat are 511,000 tons CO2e overall and 1.2 tons CO2e per ton of meat. Factors influencing the size of the beef footprint include: the quantities of meat consumed from each region of production, the type of cattle, feed, and energy sources used in each supplying region, and the distance of shipping between the source country and Israel. Relevant components in the chicken meat

80

footprint are the quantity of consumption, quantity of feed, energy sources, and shipping of feed from overseas. The following figures break down the overall footprint into these considerations, showing the carbon footprint of each meat product by stage of production and shipping, with the share of the total GWP for each source of meat supply (Figure 2) and GWP per kg by source (Figure 3).

Figure 2: Carbon Footprint, by Stage, Source of Supply, and Share of Burden (kg COÂŹ2e) The figures show that the majority of the GHG emissions are attributed to beef import (over 2,000,000 tons), through production taking place outside the country, and the most significant process in this category is enteric fermentation. The calf import and domestic beef production categories together account for about 11% of emissions (~345,000 tons).

Compared to the beef system, chicken production holds the highest burden in the categories of shipping, feed production, and slaughter.

However, as shown in Figure 2, these results are primarily attributed to the high magnitude of local poultry production (220 million chickens slaughtered/year), as this source holds the lowest emissions per kg compared to the other categories considered.

Figure 3: Carbon Footprint per Unit by Source of Supply (kg CO2e/ kg meat)

Land Footprint The global land footprint for average Israeli beef consumption in 2010 measures 1,150,000 hectares overall and 9.5 hectares per ton of beef, where 96% of the system is pasture-based and the remainder is based on crop-land. Chicken meat consumption requires 200,000 hectares of land, and 0.5 hectares per ton of meat. The key factors impacting the size of the footprint include: the quantities of meat consumed from each region of production (presented in Table 1 above) and the type and quantity of feed used in each supplying region.

Figure 4 and Figure 5 break down the overall footprint by source, land-type, and percentage contribution to the footprint. As mentioned above, land sources within calf exporting regions are not considered due to data uncertainty.

Similar to GHG emissions, the greatest burden on the land footprint falls outside of Israelâ&#x20AC;&#x2122;s borders through beef imports, both in total land resources and in hectares per unit. Figure 4 shows that domestic chicken production requires more hectares than domestic beef production, and uses the greatest amount of cropland compared to the other categories. As the land impact per kg of chicken production presents the lowest results (Figure 5), this high burden of cropland is primarily due to the significant levels of chicken meat consumption.

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81

Figure 4: Land Footprint, by Source of Supply, Land Type, and Percentage of Footprint

Figure 5: Land Footprint per Unit (hectares/kg), by Source of Supply

Discussion and conclusions According to 2009 data, Israel is the world’s 13th highest per capita consumer of meat products, 18th highest per capita consumer of beef (90,000 tons), and the 5th highest consumer of chicken (nearly 400,000 tons) [20]. Israeli meat consumption, both overall and per capita, has grown significantly in the past several years and has continued to rise since the year analyzed in this research. This study measures the population’s burden on the meat system at 410 kg CO2e emissions per capita, and land area at 0.2 hectares per capita.

While few studies take the same approach as the research presented here, studies focusing on countries with more self-contained production systems present similar results. Research on beef systems includes Peters et al. [52] (Australia beef production- 22,000 kg CO2e/ton), Leip et al. [43] (Netherlands beef production- 16,400 kg CO2e/ton; United Kingdom beef production-7.9 hectares/ton), and Cederberg et al. [7] (Brazil beef production- 40,000 kg CO2e/ ton; 25 hectares/ton). Select studies on chicken meat systems include both production and consumption perspectives, such as Mogensen et al. [46] (Denmark chicken production-2,600 kg CO2/ ton; 0.5 hectares/ton*), Meier et al. [45] (Germany chicken production and packaging- 0.891 hectares/ton*), Williams et al. [73] (United Kingdom chicken production- 0.6 hectares/ton), Williams et al. [74] (Brazil chicken production- 0.4 hectares/ton), Fiala [23] (USA chicken *

Converted from m2/kg

consumption-1,100 kg CO2/ton), and Leip et al. [43] (Ireland chicken consumption-1,600 kg CO2/ton).

The results of this research indicate that although chicken makes up 78% of meat consumption in Israel, it makes a low contribution to the overall carbon and land footprints attributed to Israeli meat consumption. Furthermore, beef imports comprise only 13% of meat consumption in Israel, but are responsible for 71% of the carbon footprint and 83% of the land footprint, with the majority of this impact caused by imports from the pasture-based production system in Latin America. While cattle and poultry meat production within Israel requires almost half of the total agricultural land in the country, the virtual land footprint of consumption exceeds the total area of Israeli agricultural land by over 150%, the equivalent of more than 60% of the geographical area of the country. This indicates that small increases in beef imports to fill growing consumption habits would likely elevate the already significant carbon and land footprints, a great deal more than a large increase in local chicken production. Furthermore, this study finds that the difference between the local and foreign production systems is significant, whereby if the quantities of the imported meat and local production were switched, the carbon and land footprints would decrease by up to 30 and 50 percent, respectively.

The factors contributing to changes in the system’s biophysical

82

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resource requirements undergo fluctuations between years, such as the composition of the energy mix used in Israel, more meat being imported from Latin America, or shifts in modes of international transport. Depending on the type of change, the footprints may experience a significant increase or decrease. While certain shifts, such as sources of supply, are driven by purchasing decisions by producers, a consumer’s choice of what type of meat to consume can have a significant impact. If Israeli consumers reduced intake of beef by 50% and replaced this quantity with chicken, the carbon and land footprints would decrease by 40% and 41%, respectively.

As illustrated by this research, the trans-boundary implications of Israeli meat consumption cannot be ignored, and will continue to grow in magnitude until they are addressed. Considering the consumption side of global food systems, such as the contribution of Israel to the global meat commodity chain, is a necessary exercise in learning to live within our ecological limits and is the first step to achieving a global sustainable food system.

Countries with limited biophysical resources, such as Israel, have little choice but to import large quantities of food products and source materials if they wish to maintain the population’s consumption levels. In light of rising meat consumption habits over the years, Israel has recognized that it is not effectual to expand domestic beef production to supply this demand, whether for reasons of economic considerations or physical limitations. While trade is typically an essential part of allowing countries access to unavailable food products and can be a cost-effective alternative to local manufacturing, the negative implications of Israel’s meat system supersede the positive. This study demonstrates that the source of supply for the imported product does matter, especially for meat products, and raises questions about food system sustainability across these supply chains. Israel is essentially importing the biocapacity from more resourceful regions, yet each source country from which Israel imports has diverse production practices with different effects on natural capital usage. When an exporting country uses cattle-rearing methods that cause resource exploitation, the lines of who is responsible become blurred.

Appendix 1: Makeup of Israeli meat consumption, by region

While this research focuses primarily on Israeli consumption patterns, it demonstrates the need to consider interregional sustainability, and contains implications for all countries that have roles at different points along global commodity chains. The analysis encourages Israel and other consumer countries to consider ecological processes occurring in export countries, and to consider their responsibility in the natural capital exploitation in the global industry. While a diversified disaggregated system of many sources of supply may protect Israel in case one region suffers from drought or low yields, the export countries are depleting their natural capital in order for other countries to benefit, threatening interregional sustainability.

Moreover, when a source country does experience ecological pressures, it may jeopardize the food systems of all regions that depend on it [40]. While the majority of meat consumption in Israel is not attributed to beef imports, it is not the only country to benefit from beef products produced in the sources evaluated in this study; the regions of supply considered in this research are among the greatest beef exporters in the world. An analysis of the greenhouse gas and land efficiency of the global beef import network would most likely reveal a system where the negative externalities exceed the economic benefits. However, there exists potential to turn these into positive environmental outcomes; under proper management of the commodity chain, the existing network can be used to source production from regions that are more environmentally efficient.

[1]

Appendix

Beef Import

Regions Considered (% of source)

Data Sources

Latin America (77%)

[6] [7] [20]

Europe (18%)

[7] [43]

Asia (2%)

[7]

Rest of the World (3%)

[7] [20]

Australia (45%)

[20] [26] [68] [69]

Europe (55%)

[20] [26] [68] [69]

Domestic Beef Production

Israel (100%)

[8] [21] [26] [32] [38] [61] [68] [69]

Domestic Chicken Production

Israel (100%)

[8] [22] [30] [33] [38]

Calf Import

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Effectiveness of microinsurance during and after a disaster Arshad Ali *, Asad Mahmood, Shahnila Gul National University of Science and Technology, Islamabad, Pakistan *Correponding author: aliarshad08@yahoo.com

Article history Received 21.04.2014 Accepted 08.09.2014 Published 04.12.2014

Keywords Disaster risk transfer Microinsurance models Community based microinsurance model

Abstract This research looks at the effectiveness of microinsurance services during and after a disaster and at disaster management as an effective tool for community betterment. A detailed review has been done on available research and case studies. Unfortunately, underdeveloped countries suffer due to a lack of finances during and after a disaster. Developed countries are usually not ready for any disaster at government and public levels. A disaster affected country will also be keen for financial help from donor agencies and other counties. Microinsurance would be very helpful during any disaster to overcome the financial needs at the community level. Microinsurance is a practice that can share the financial liability with the affected population during a disaster. There is no trend in Pakistan for community based microinsurance for certain reasons, although there are very good examples available for review in the region. These include microinsurance services based on community microinsurance models such as SEWA (Gujarat), Weather-Index-based insurance (Ethiopia) and Crop insurance against typhoons (Philippine). These have played a vital role in disaster risk transfer during and after disasters. This study will identify the implementation and outcome of microinsurance in Pakistan during a disaster and understand how much beneficial microinsurance would be for the betterment and recovery of affective community on an urgent basis.

Introduction Recently there has been an increase in micro-financing. Microfinancing tools like micro-credit have been involved in economic development. These products are offered by Non-Governmental Organizations (NGO’s), Micro-Financial Institutions (MFI’s) and other organizations. With the increase in demand for micro-credit, the demand for micro-insurance as a risk transferring tool has also increased. Risk financing and risk transferring tools when included within risk reduction activities, increase the capacity of the community

to bridge financial vulnerability gaps. Risk financing is a mechanism to reduce risk by ensuring funds are available whenever disasters occur. Funds are kept aside to be used whenever needed or taking loans from pre-arranged external financial facilities [8], [11], [20].

The International Association of Insurance Supervisors (IAIS) defines microinsurance as “protection of low income people against specific perils in exchange for regular premium payments proportionate

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to the likelihood and cost of the risk involved”. Microinsurance is a service whose main focus is on poor people, to manage their risks.

The target market for microinsurance is low and irregular income people, with low literacy levels, informal means of livelihood or who are self-employed. It is a tool used for risk reduction through propoor financing, rural development, social security and agriculture [16]. Microinsurance can further be categorized into profit-based and nonprofit-based microinsurance. The key difference between Profit-Based Microinsurance (Profit-Based MI) and Non-Profit Based Microinsurance (Non-Profit-Based MI) is that in later there is flexibility in premium payments, the client can pay in cash or in kind (wheat, maize, sugar etc.) [11].

One of the important aspects of microinsurance is its delivery to the client. Some microinsurance delivery models are as follows [1], [15]: •

Partner-Agent Model (PAM): In this model a public insurer works together with MFI’s or NGO’s. They develop a microinsurance product and the liability of absorbing the risk is on the insurer.

It is distributed through established networks to lower the cost and improve accessibility.

Figure 1: Community based microinsurance model

•

Community-Based Model (CBM): This is also called a mutual model. In this model the local people together in the form of a group and then with the understanding of MFI’s and NGO’s, develop and distribute a microinsurance product. They all have to absorb the risk mutually.

In the CB-MI model, the clients are the policy holders and manage the whole insurance program and interact with the insurance service providers. CB-MI has the advantage of being easily marketed [5]. The key features of CB-MI are;

•

Full Service Model (FSM): This is a commercial or public insurance model in which full services are provided. The risk has been absorbed by the insurers who provide the framework for development of the product and its distribution.

•

Provider Model (PM): In this model the services are provided directly to the customers. This requires a legal obligation to contract. This kind of insurance is usually in connection with a credit facility. e.g., to insure for risk of default.

CB-MI reduces the cost related to any risk. In CB-MI the chance of fraud is very low due to its cashless system. In this model the focus is on a marginalized group within the community e.g. women. The premium and risk range are developed with mutual consultation to reduce unnecessary costs. The products are correctly priced and so attract a large number of people [3], [19].

What is community based microinsurance?

Community based microinsurance schemes in different forms are vital sources of financing all around the world. One important factor for their success is that the members/clients are mostly given the opportunity to get involved in the administration and management of the schemes. This is shown in Figure 1, including the scheme procedures; the product design, marketing, and servicing revolves around the clients, who are members of the community.

• • • • • • •

The needs of the target group can be easily identified.

Trust of members is built through strong cooperative structure and close relationships.

The ability of self-auditing.

The marketing of the insurance is done through word-of-mouth.

The environment of trust and collective decision-making.

Most important is the feeling of ownership.

It attracts voluntary activities within the community.

Community-based insurance involves risk anticipation on a community level and is an investment in physical and social infrastructure, social structure and community participation in decision-making. For risk mitigation CBI helps in building markets for household assets, helps in building community credit unions and saving habits. CBI draws down community assets that helps in risk coping. Some of the successful non-profit microinsurance project for a single risk are BRAC Health Insurance (Bangladesh), Grameen Life Insurance (Bangladesh), and Mutuelles de Sante (West Africa).

For multiple risks, some of the most important micro-insurance organizations around the world are: Vimo-SEWA (India), Groupe de Rechercheetd’ Echanges technologiques (GRET) (Cambodia), IPTK (Bolivia) and SeguroBasico de Salud (Bolivia) [14].

The importance of CB-MI in disaster risk transfer can be described as follows: the Hyogo Framework for Action ([1], [7]) calls for building communities and nations that are resilient to disasters. In order to reduce human, economic, social and environmental losses, various useful policies and systems are put in place to reduce disaster risk.

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These policies should address some of the following important aspects of disaster risk reduction: • • • • • • •

Need for human, financial and other resources to be allocated for disaster risk reduction.

Integration of disaster risk reduction (DRR) into development.

Strong legal, institutional and operational basis.

Hazard, risk and vulnerability assessments in order to build a comprehensive early warning system.

Participation of policy makers, scientists and other relevant organizations in the planning processes.

Engaging media to raise awareness.

Preparedness for an effective response.

Priority Action Number Four of the HFA’s five priority actions is to reduce disaster losses by reducing the underlying risk factors. One of the important aspects of this priority action is to strengthen risk financing mechanisms. Risk transfer may not be a direct source of risk reduction but it can lessen the economic impact caused by any disaster. One of the main tools for risk transfer is insurance.

Microinsurance, which is protection focused on individuals living beneath the neediness line, is a viable result. It serves to set up a successful and transparent component soon after the disaster hits.

It is rougher, speedier and guarantees sufficient liquidity at the time of disaster, consequently maneuvering the trouble in general society [12]. It offers security against stocks, for example sickness, accidents, deaths, and crop and animal damages.

low income clients in developing countries the need for insurance is seen as unimportant. The lack of trust and the inability to understand the risk-pooling concept make them think that insurance is just for the rich and they do not need to be insured and should not spend their scarce resources on insurance.

In one study the main reasons why microinsurance is more beneficial than other risk management schemes has been evaluated. According to the author the advantage of microinsurance is that it targets the poorest communities in developing countries. The microinsurance schemes give an opportunity for at-risk families to direct their resources on improving their standard of living rather than spending all of their resources on risk management. Finally, microinsurance services enable the communities to avoid becoming dependent on costly loans [9]. It remarks that the provision of microinsurance policies and schemes should be easy to understand, accessible and simple.

Some successful microinsurance schemes from around the worlds include [17], [6]; •

SEWA (Gujarat): This is a multiple risk (Life, health and housing) microinsurance program; it works within Gujarat state (India) and it started working in 1992. The operations are through a bank that also runs the social insurance scheme. Their customers are self-employed women providing political and organizational support along with hospital charges and insurance for their husbands. This insurance scheme includes health, life and asset insurance. The insurance policies are against fire, flood and other natural disasters. They have three options for premium per annum, (a) $ 1.53, (b) $ 3.67, (c) $ 7.44 [17].

•

World Bank weather-Index based insurance (Ethiopia): This operates in Alabawere da (South Ehtiopía). It was established in 2005 and targets any voluntary customer. It works under a commercial bank and operates a full service model with insurance provided by the Ethiopian insurance corporation.

They cover rainfall below 20% moving average. For this underrainfall insurance, all the insurance holders have a uniform claim for damages in deficient rainfall. One of the biggest downfalls is that those who are associated with agriculture cannot claim superior incentives. The premium per annum is 6% of the basic loan amount of rainfall insurance [18].

•

Crop insurance against typhoons (Philippines): Crop insurance for rice farmers against typhoons. Farmers whose farms lie in the path of a typhoon can claim for payouts. The amount of the payout is dependent on some factors. Firstly the coverage, secondly on the distance from the typhoon path and thirdly on the wind speed. Premiums should be paid at 8 to 10% of the production cost. Micro Ensure is working as an agent and it has developed this product, while the Malayan Insurance Co is the underwriter. The data is assimilated from the Japan meteorological agency. Banks, cooperatives and MFI’s sell the policies [4].

Literature review To understand the level of importance microinsurance carries, Green, Rebekah, and Marla Petal pinpoint that even international developmental organizations like the World Bank have carried much research into microinsurance as a disaster risk transfer tool. A study reveals that the supply of microinsurance is very limited and it is required to link insurance companies or government programs with community-based insurance schemes, as the community-based organizations are widespread and take on different forms at a local level. The importance of the microinsurance is that it has the ability to break the “cycle of poverty” by securing the livelihood of farmers, businesses and households by providing them access to liquidity after their livelihood is disturbed by a disaster [13]. This insurance will help insurance product developers to invest in risk reduction projects to decrease their profit uncertainty; this will benefit the community at risk. A report has been written on the use of technology to increase the supply of microinsurance products to lowest income population. According to this report the use of low-cost methods based on the use of the internet, mobile phones and chip cards will increase the supply growth of microinsurance. Tata AIG in India is providing mobile-based microinsurance and is being very successful.

The premium is also deducted from a mobile credit facility. Microcare in Uganda is providing the facility of client registration and other online forms. This research further gives an insight to the cultural barriers to microinsurance service providers, showing that for most

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Drawbacks of CB-I model In the community-based model the emphasis is on the group interest rather than the individual interest. This increases interaction and so the information flow increases. This informal and frequent information flow can create privacy issues, as some people will not want to be exposed within the community, in case of serious physical or mental illness.

Another setback to such mutual schemes is that they have a smaller group size, creating uncertainty regarding claim expenses and greater exposure to risk.

The lack of technical and management skills has been one of the key problems for community-based microinsurance schemes.

Conclusion It has been accepted that the contribution of microinsurance reduces future disaster losses and financial vulnerability of the poor. The poor simply claim for damages and so the disaster risk is transferred.

Experience of disaster microinsurance is mixed with relation to its contribution to reducing long-term losses and also the vulnerability of the poor. Though insurers have reliably and quickly settled claims, there is very little information as to how these payments could have mitigated post-disaster financial conditions. To date, there is no clear proof concerning the connection between microinsurance and shifts to higher-risk/higher-yield activities. There are issues with the affordability of premiums, trust regarding the integrity and ability of MFI’s and non-acceptance of microinsurance by the poor. Despite all these issues microinsurance has played a vital role in disaster risk transfer and there is still room to attract more customers and distribute more microinsurance products.

Recommendations A first step should be to encourage residents of hazard-prone areas towards risk-reducing behavior. They should not always be looking to the government for compensation following a disaster. They should start keeping reserves; invest in preventative measures or insurance in order to bear the loss of recovery. Market research is very crucial for microinsurance service providers, to identify the needs of the target market and the associated risks. Governments need to support microinsurance to lessen the burden on the economy and increase access for the vulnerable sectors of the population by institutionalizing MFI’s and NGO’s that provide microinsurance products.

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