Efficiency Evaluation of The Agricultural Sector in Senegal Based on Suitable Combinations of Inputs

International Journal of Modern Research in Engineering & Management (IJMREM) ||Volume|| 2 ||Issue|| 3 ||Pages|| 23-33 || March 2019 || ISSN: 2581-4540

Efficiency Evaluation of The Agricultural Sector in Senegal Based on Suitable Combinations of Inputs, CCR, And BBC in DEA 1,

Souleymane Diba, 2, Kuntano Mr. Kuntano 1

College of Economics and Management Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China 1 College of Economics and Management Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

-----------------------------------------------------ABSTRACT----------------------------------------------------With regards to the global competitive environment that becomes more and more intense due to local and foreign competitions resulting from international trade and foreign direct investments, the emphasis has been put on the implementation of efficient production system in the Senegalese agricultural sector in order to gain high earnings and adapt in the global competitive market by means of achieving an efficient production and a high-quality product. The purpose of this paper is to analyze the efficiency of the agricultural sector in Senegal based on suitable combinations of inputs in order to enhance performance of the sector in Senegal. We have adopted an approach based on a data envelopment analysis (DEA) to obtain reliable and accurate results through purposeful combinations of inputs and output. We employed an input orientation approach when inserting labor and capital as our two inputs and earnings as output. The results highlight that measures can be taken through the interpretations of the efficiency scores and demonstrate that changes in inputs through an increase in labor input and a decrease in capital input constitute a suitable input combination that diminishes cost input costs while offering the best output.

KEYWORDS: Senegalese agricultural sector, Data Envelopment Analysis, input congestion ----------------------------------------------------------------------------------------------------------------------------- ---------Date of Submission: Date, 26 March 2019 Date of Accepted: 27. March 2019 ----------------------------------------------------------------------------------------------------------------------------- ----------

I.

INTRODUCTION

At the time of the independence, the African economy inherited an agricultural setting characterized by a traditional-modern dichotomy. The traditional agricultural setting in one that had been carried on from the precolonial times, survived the colonial economies and largely covered production related to basic household and agricultural needs at a traditional handicraft level by artisans-blacksmith, porters, carpenters, carvers and weavers.

This traditional industrialization was purely based on human and animal power and used mainly local resources in addition to metal scraps and wastes materials. On the other dichotomy, was the modern agricultural setting which involved imported technology, machinery, equipment materials and production system. However, Senegal is also with these features.

In Senegal, the importance of agriculture as an engine of economic growth and development cannot be overemphasized. With only about 9% of the land irrigated, the country keeps relying on rain-fed agriculture, which represents about 62% of its workforce. Agriculture in Senegal is dominated by peanuts (45 percent of cultivated lands and 60 percent of total agricultural exports in 2005) and cotton (22 percent of cultivated lands)— both being important sources of foreign exchange income—as well as millet, rice, corn, sugarcane, and livestock [1] at low skill levels, facilitates denser links across the services and agricultural sectors between rural and urban economies and between consumers, intermediates and capital goods industries. Cultivation and supply of groundnut was of archaic nature throughout most rural Senegal before being recently modernized to a closely monopoly sector through the establishment of SONACOS (a manufacturing edible oil company) which supervises sizeable part of the production, purchase and processing of groundnuts. Similarly, cotton production followed the same process through its integration with SODEFITEX (a local textile company). In addition, prices of agricultural commodities also known as primary goods, for exports are significantly volatile and extremely susceptible to long-term deterioration than those of manufactured goods, making them particularly strategic in highly commodity-dependent developing countries. Furthermore, agriculture is a critical tool in employment generation, poverty eradication, and regional development policies despite efficiency and quality production problems met in this sector.

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Efficiency Evaluation of The Agricultural Sector in Senegal Based…

II.

LITERATURE REVIEW

There is an extensive and rich literature on the objective of increasing the quantity and quality of inputs in agriculture in developing countries. According to Bravo Ureta and Pinheiro [2], it is possible to improve output value despite the given a low quality of inputs by boosting the overall economic efficiency of farmers. The concept of efficiency is critical in developing country agriculture. Given the level and quality of inputs available, how well farmers are able to utilize these inputs is an important determinant of the quantity of output they are able to produce.

Recent measurement of farmer efficiency has been based on the seminal paper by Farrell [3], who decomposed economic efficiency into its technical and allocative components. Technical efficiency refers to the ability of a producing unit to obtain maximum (optimal) output from a given amount of inputs. Formally, the level of technical efficiency is measured by the distance of farm production from the optimal production frontier. A firm that sits on the production frontier is said to be technically efficient [4]. Allocative (or price) efficiency refers to the ability of the firm to choose its inputs in a cost-minimizing manner [5]-[6]. For allocative efficiency to hold, farmers must equalize their marginal returns with true factor market prices. Thus, technical inefficiency is related to deviations from the frontier isoquant, while allocative inefficiency reflects deviations from the minimum cost input ratios [2].

In addition to technical and allocative efficiency, Farrell [3] also defined the concept of overall efficiency (renamed economic efficiency by later literature). Economic efficiency refers to “the capacity of a firm to produce a predetermined quantity of output at minimum cost for a given level of technology” [3] and is derived by multiplying the technical and allocative components of efficiency [2]. All three measures are bounded between zero and one [5] . Parametric and non-parametric methods are often utilized to measure economic efficiency. The most common specifications are the stochastic frontier models, which have been extensively specified in Nigeria for a wide variety of crops [7]-[8]-[9]-[10]. Parametric methods assume that the functional form of the production function is known while non-parametric methods do away with the restrictive functional form assumptions, instead relying on the data to specify the production frontier. Data envelope analysis models are the most commonly used forms of non-parametric models [7]. Using either methodology (parametric or non-parametric), it is possible to estimate technical efficiency and allocative efficiency for each observation in the dataset. Most studies report mean levels of technical and allocative efficiency for the sample under observation. Studies that have applied both methodologies report no substantive differences in estimates of efficiency [7].

The desire to acquire more reliable results and solutions for better ways to improve efficiency of tourism sector incites some researchers to adopt a widely known method known as the Data Envelopment Analysis (DEA) Khan et al. [11] proposed an adjustment of agricultural investment referring to the law of decreasing marginal benefit by comparing data of agricultural public input and the marginal benefit linked to agricultural subsidies based on DEA approach. Few years later, Hou [12] evaluate the proper scale of fiscal expenditure under the scope of policy optimization and target selection of China’s agricultural investment. They were supported by Li [13] who argued that, with limited financial support in agriculture, its efficiency can be improved by lowering operating expenses of agriculture. Finally, He. Z and Wang. Q [14] estimated the benefits of fiscal expenditure by measuring the effects of agricultural policies and agricultural infrastructure on agricultural output.

The primary goal for using DEA was directed towards the performance evaluation of a firm on a scope of production economics. Previously, this methodology was proposed by Charnes et al. [15] to measure the performance of different DMUs—a set of some decision-making units. Regarding this, some researchers considered the DEA methodology as a mathematical programming model applied to observed data that presents a new way of obtaining empirical estimates of extremal relationships such as the production functions and/or efficiency production possibility surfaces that can be seen as the cornerstones of modern economics. Many different methods have been adopted over the last 50 years to estimate frontiers. Among them, the most popular methods are the Data envelopment analysis (DEA) and stochastic frontier analysis (SFA), which respectively employ mathematical programming and econometrics methods. DEA also incorporates previous efficiency evaluation methods such as Farell [3] ’s measure of technical efficiency. Some authors stated that both Farell’s measure of technical efficiency and any non-zero input or output slacks should be combined to offer an accurate and reliable indication of technical efficiency of a firm in DEA analysis. Ali and Seiford [16], Charnes et al.[17] , Coelli et al.

[18] , Cooper et al. [19] and Zhu [20] are outstanding references to address the basic aspects of DEA models, DEA notation, formulation and geometric interpretation (input and output orientations). These models part the DMUs into two different sets: on one side we have the firms that lie on the frontier of the envelopment surface, and are considered efficient units, on the other side those who are inefficient because they do not lie on the frontier [21].

A large number of authors such as Afriat [22], Fare, Logan and Grosskopf [23] and Banker, Charnes and Cooper (BCC) [24] proposed the idea of adjusting the Constant return to scale (CRS) DEA to account for variable return to scale (VRS) perspectives. This model is often referred as Banker, Charnes and Cooper (BBC) model which is seen as a complement of the old Charnes, Cooper and Rhodes [15] model as part of DEA. It exist three basic

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Efficiency Evaluation of The Agricultural Sector in Senegal Based‌ conventional DEA models: constant returns to scale (CRS), variable returns to scale (VRS), Scale Efficiency (SE) and additive models.

III. METHODOLOGY Efficiency evaluation using DEA’s CRS, VRS and SE: The objective is to evaluate the efficiency of the agricultural sector in Senegal between 2000 and 2016. For this purpose, DEA will be employed to measure the efficiency of the decision-making units throughout the input-output combination. Here, efficiency values are ranked between interval of 0 and 1 where 0 represents an absolute level of inefficient and 1 a total efficiency level.

The DEA formulation under output orientation for is mth given as follows (Vaidya, 2014): For output-oriented DEA, đ?’‹

đ?’Žđ?’‚đ?’™ đ?œźđ?’Ž = ∑ đ?‘˝đ?’‹đ?’Ž đ?’€đ?’‹đ?’Ž đ?’‹=đ?&#x;?

Subject to, đ?’‹

∑ đ?‘źđ?’Šđ?’Ž đ?‘żđ?’Šđ?’Ž = đ?&#x;? đ?’‹=đ?&#x;? đ?’‹

đ?’‹

∑ đ?‘˝đ?’‹đ?’Ž đ?’€đ?’‹đ?’? − ∑ đ?‘źđ?’Šđ?’Ž đ?‘żđ?’Šđ?’? ≤ đ?&#x;Ž đ?’‹=đ?&#x;?

đ?‘˝đ?’‹đ?’Ž , đ?‘źđ?’Šđ?’Ž ≼ đ?&#x;Ž

đ?’‹=đ?&#x;?

∀

đ?’Š = đ?&#x;?, đ?&#x;?, ‌ , đ?‘° đ?’‚đ?’?đ?’… đ?’‹ = đ?&#x;?, đ?&#x;?, ‌ , đ?‘ą

Where, đ?œźđ?’Ž is the efficiency of c. đ?’€đ?’‹đ?’Ž is the jth output of mth DMU. đ?‘˝đ?’‹đ?’Ž is the weight of jth output. đ?‘żđ?’Šđ?’Ž is the ith input of the mth DMU. đ?‘źđ?’Šđ?’Ž is the weight of the ith input. đ?’€đ?’‹đ?’? and đ?‘żđ?’Šđ?’? are the jth output and ith input of the nth DMU. In order to compute the efficiency of each year within 2000 to 2016, the production technology set is defined as follows: T = (đ?‘Ľ1 , đ?‘Ľ2 , đ?‘Ľ3 , đ?‘Ś1 , đ?‘Ś2 ) âˆś (đ?‘Ľ1 , đ?‘Ľ2 , đ?‘Ľ3 )đ?‘?đ?‘Žđ?‘› đ?‘?đ?‘&#x;đ?‘œđ?‘‘đ?‘˘đ?‘?đ?‘’ (đ?‘Ś1 , đ?‘Ś2 ) Each year will be considered as a DMU and the parameters utilized in this paper for efficiency analysis are, the labour or number of employees (đ?‘Ľ1 ), the capital invested in the agriculture in Senegal (đ?‘Ľ2 ) and the profits (earned by farmers and Senegalese government) (đ?‘Ś1 ).The inputs and outputs of the DEA system are labelled in details in table 1 for the years between 2000 and 2016. In this table, capital and profits earned by farmers and government are expressed in millions of Francs CFA, while labour is expressed in thousands of people. In addition, Capital here, includes money and value of all assets such as lands, resources etc. as well as equipment and technology.

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Efficiency Evaluation of The Agricultural Sector in Senegal Based‌ Table 1: Input and output values between 2000 and 2016 Input 1 Input 2 Output Year Labor (in thousand) Capital (in thousand) Profit (in millions) 2000 389.00 19.86 856.02 2001 412.30 21.16 866.85 2002 423.50 17.08 956.04 2003 417.30 18.10 1082.94 2004 570.00 12.61 1273.20 2005 600.50 13.45 1230.72 2006 641.10 15.91 1410.66 2007 715.30 23.72 1728.16 2008 736.00 25.97 2109.57 2009 745.00 18.24 2291.08 2010 756.00 14.40 2533.27 2011 743.00 17.50 2899.16 2012 684.00 25.08 3520.74 2013 691.00 25.45 4949.93 2014 673.00 29.35 4604.00 2015 634.00 23.05 4722.29 2016 595.00 25.02 4760.28 Source: Direction de l’Analyse, de la Prevision, et des Statistiques Agricoles (DAPSA), 2018 Efficiency evaluation using input slack variables and congestion Definitions : The objective is figure out the DMUs efficiency under VRS using the proposed model by Banker, Charnes and Cooper (BCC) with output orientation. It measures đ??ˇđ?‘€đ?‘ˆ0 as follows:

Referring to the model above, efficiency definition is as follows Definition 1 (Efficiency): đ??ˇđ?‘€đ?‘ˆ0 is efficient when in optimal solution (i)

(i) ∅0∗ = 1

(ii) All slack variables are equal to zero Output maximization in this model as well as in other model, is obtained by using at least input amount of estimating DMU. However, a much better output is often obtained through few changes in some input elements. Here, the side of input changes will be emphasized by determining what input should be decreased and which one should be increased by showing some models. The results of solving this model are analysed and interpreted based on the gathered data on agriculture sector in Senegal and the congestion value in inputs will also be presented.

Definition 2 (Technical inefficiency): đ??ˇđ?‘€đ?‘ˆ0 is inefficiency in situations when it is possible to increase some inputs or outputs without deteriorating other inputs or outputs.

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Efficiency Evaluation of The Agricultural Sector in Senegal Based‌ Definition 3 (Congestion): đ??ˇđ?‘€đ?‘ˆ0 contains input congestion in case reductions in one or more inputs influences increase in one or more outputs without worsening any other inputs or outputs, or inversely if augmentations in one or more inputs is found to have any effect on the reductions in one or more outputs without boosting any other inputs or outputs.

Definition 4 (Technical efficiency): đ??ˇđ?‘€đ?‘ˆ0 is efficient if and only if it is impossible to improve some inputs or outputs without worsening the value of other inputs or outputs.

Proposed models : For purposes, of implementing profound idea when taking into account đ?‘– đ?‘Ąâ„Ž input of estimated DMU (j= 0), we get (a) Increasing đ?‘– đ?‘Ąâ„Ž input (b) Decreasing đ?‘– đ?‘Ąâ„Ž input That is to say, just one single of the following inequalities must be active for i th input:

Even though, only one the inequalities in model can be made active by involving zero and one element, we prevent it from happening because of the problems encountered when solving integer programming. In this regards, we propose the following approach: To begin, we introduce free variable đ?‘ đ?‘– linked to đ?‘– đ?‘Ąâ„Ž input:

Then decomposing it into two non-negative variables, we obtain:

Due to the fact that corresponding columns of đ?‘†đ?‘–1 − and đ?‘†đ?‘–2 + are linear dependent, employing simplex method, the highest among these two variables is positive in optimal solution.

Moreover, only a single one of those two inequalities will practically be active. So one of these three following situations may happen:

(I) (II) (III)

If đ?‘†đ?‘–1 −∗ is positive, đ?‘– đ?‘Ąâ„Ž input will be reduced as much as đ?‘†đ?‘–1 −∗ amount.

If đ?‘†đ?‘–2 +∗ is positive, đ?‘– đ?‘Ąâ„Ž input will be raised as much as đ?‘†đ?‘–2 +∗ amount.

If đ?‘†đ?‘–1 −∗ = đ?‘†đ?‘–1 +∗ =0 , đ?‘– đ?‘Ąâ„Ž input will not be subject of any changes.

Below, we present an objective function so that đ?‘†đ?‘–1 −∗ and đ?‘†đ?‘–2 +∗ scores are obtained in maximum and minimum values in sequence. In other words, highest possible value is reduced or at least useful value is added to it.

Since our objective is get the highest ouput level, we intoduce the following model:

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Efficiency Evaluation of The Agricultural Sector in Senegal Based‌

(I) Here, đ??ˇđ?‘€đ?‘ˆ0 is efficient under model (I) in case these following conditions are satisfied: (ii)

∅∗0 = 1

(ii) Optimal amount of all input slacks is equal to zero We then include an additive model linked to the above model:

(II) Here, đ??ˇđ?‘€đ?‘ˆ0 is efficient under model (II) in the maximum value of the objective function is zero.

Input congestion : A part from Cooper’s approach, both model (I) and model (II) can be exploited to determine input congestion. To achieve this, we employ model (I) to follow Cooper’s approach and solve the model below, developed by Cong:

We finally define congestion value through the following equation:

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Efficiency Evaluation of The Agricultural Sector in Senegal Based…

DEAP software : In our estimation of techinical efficiencies for CRS, VRS, SE in one hand, and our evaluation of input and output slacks as well as input changes when applying DEA model which is related to our proposed model (I) and model (II), we proceed by computing these values using DEAP software. The reason behind this, is that DEAP software provides accurate and reliable values for the above variables. Results for each of these parameters will be presented in table form.

IV. EMPIRICAL EVIDENCE Efficiency evaluation analysis using DEA’s CRS, VRS and SE: To start, Table 2 below named ―The efficiency performance of the agricultural sector in each year, represents the constant returns to scale technical efficient (crste), the variable returns to scale technical efficiency (vrste) and the scale efficiency (crste/vrste) which are successively calculated of these years starting from 2000 to 2016 under the scope of output orientation. As it is shown, the agricultural sector in Senegal records a full technical efficiency value of 1 only in years 2015 and 2016 under a constant return to scale (CRS) assumption. These two years of optimal efficiency level are known as peers of the inefficient years. While the agricultural sector was totally efficient in the years 2000, 2002, 2003, 2004, 2010, 2013, 2015 and 2016. It is noteworthy to bring into attention that the agricultural sector is both efficient the years 2015 and 2016 when CRS and VRS are assumed. Except these two years, the technical efficiency was always higher in VRS situations rather than in CRS. This points out the easiness to reach full efficiency under VRS compared to CRS. This is due to the fact that, CRS should only be assumed when the agricultural sector was operating at full scale. In case we violate this rule, there will be a creation of the scale efficiency (SE) which will take place. To avoid the formation of the scale efficiency in situations where the agricultural sector failed to operate at optimal, scale we focus on the technical efficiency scores given by the VRS. In other words, the VRS provides us a reliable and accurate technical efficiency commonly known as “pure technical efficiency” following these given years in which the agricultural sector were performing in Senegal. Therefore, the higher efficiency values in CRS compared to VRS for each year was resulting from these scale efficiencies that confound (reduce) the CRS technical efficiencies for each given year except 2015 and 2016 where efficiencies of both CRS and VRS reached the maximum value of 1. For these two years, a graphical representation of efficiency of all decisions making units (years) would show that both of these two years are positioned at the intersection point of the CRS linear line and the VRS curve (non-linear) which depicts the non-parametric piece wise surface or frontier.

Table 2: Efficiency Performance of agricultural sector in each year ear

crste

vrste

scale

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean

0.275 0.263 0.288 0.324 0.493 0.447 0.433 0.356 0.396 0.613 0.859 0.809 0.688 0.956 0.956 1.000 1.000 0.591

1.000 0.668 1.000 1.000 1.000 0.747 0.552 0.361 0.426 0.654 1.000 0.874 0.716 1.000 0.937 1.000 1.000 0.820

0.275 0.393 0.288 0.324 0.493 0.598 0.785 0.985 0.930 0.938 0.859 0.925 0.961 0.956 0.913 1.000 1.000 0.743

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scale nature IRS IRS IRS IRS IRS IRS IRS DRS DRS IRS IRS IRS IRS IRS DRS DRS DRS

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Efficiency Evaluation of The Agricultural Sector in Senegal Based‌ Moreover, the lowest level of technical efficiency was recorded in 2010 under VRS with a score of 0.361. This is justified by an over-exploitation of labour (715.30 thousand) which unfortunately did not produce the expected output (Profits) but instead resulted in creating an unexpected and lower output (Profits) of 1728.16 million of Francs CFA as we observe in table 1 which exhibits input and output values of the agricultural sector in Senegal between 2000 and 2016. Table 1’s data were obtained from the Senegalese agricultural statistics bureau (DAPSA) in 2018.For this year, output (Profits earned by famers and the Senegalese government) should be increased by 63.9% by re-allocating our two inputs which �1 and �2 which are labour and capital. Seeing that our two inputs must remain constant and that output value (profits) must be increased, there must be an internal re-allocation of in input x1 which is labour. That is to say, due to our defined objective of output orientation, each job task must be suitably designed (to fit working environment) and assigned to the employee who is better qualified to get it done. In addition to this, capital should be efficiently invested in fertile lands and equipment that boost efficiency in the production of primary commodities and as a result favour high earnings. Another way to increase output earnings is through a flexible combination of inputs which involves the reduction of one input and the augmentation of another input seeing that only two inputs are incorporated. Table 3 and its interpretations bring more light to this combination of inputs. (See section 4.2 below) Finally, the worst scale efficiency (SE) was recorded in 2000 with a value of 0.275. In this year there is also and increasing return to scale (IRS). The low value of the scale efficiency in this year is the resulting from the low CRS technical efficiency (TE) of 0.275 while its VRS efficiency was fully efficient with score of 1. This can be explained by the fact that the agricultural sector in 2000 was not operating at full scale and was benchmarked against years when it was operating at considerably higher scale than in 2000. In this regard, the TECRS in 2000 was confounded by its scale efficiency (SE) and the TEVRS in the same year, was devoid of this SE of 0.275.

Therefore, the nature of the scale efficiency (as to whether increasing or decreasing) can be comprehended by comparing the non-increasing return to scale (NIRS) TE score and the VRS TE score. The inequality of these two scores gives this situation of increasing return to scale (IRS) in 2000.

In this perspective, it is also noteworthy to point out that, for some other years (year 2016 for example) there is a decreasing return to scale (DRS) because in each of these years NIRS TE and VRS TE are unequal. So for this year as well as it is for other years with IRS, technical efficiency can increase quickly and considerably while for years with DRS the increase in technical efficiency is low due to marginal return to scale (MRS) Efficiency evaluation using input slack variables and congestion : Table 3 exhibits the computational results for (I) and (II) models and BCC models based on the input slacks in BBC situations, the changes of input and output, the output slacks for model (II) and the of the agricultural sector in Senegal for the 17 years starting from 2000 to 2016. As we can see in the obtained results in this table, input changes remain the same for both model (I) and model (II). These results are pointed out in columns 3 and 6 for these two models. In addition, output slacks are both equal to zero for both model (I) and BBC. Finally, computational results are shown in columns 2 and 6, columns 3 and 7, and the last 3 columns respectively for model (I), model (II) and BBC. The results demonstrate that outputs for most years can be increased many times as much as the previous outputs we obtained by means of an input combination through input changes in the agricultural sector. In this regard, this confirm that technical efficiency of the agricultural sector can be improved by reducing input 1 (labour) by the amount of its slack decrement đ?‘†11 −∗ whose value is 24.3 and input 2 (Capital) must be increased by the amount of its slack incrementđ?‘†22 +∗ which is 1.73. By doing so, the agricultural sector for the 2004 can reach full technical efficiency level.

Similarly, the efficiency in the year 2014 under VRS accounts for 0.937 which shows an inefficiency status. In order to boost output for this year, the input 1(labour) must be increased its slack increment (đ?‘†12 +∗ ) value of 18 and input 2 (capital) must be decreased by the score of its slack decrement đ?‘†21 −∗ which is 3.9. The same thing can be done for all the years when the agricultural sector presents inefficient input amounts or input congestion (See table 4 below). These confirm the hypothesis given in the above section 3.2.2 of the methodology which provides the proposed models.

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Efficiency Evaluation of The Agricultural Sector in Senegal Based‌ Table 3: Computational results for models (I) and (II) and BCC model (Input slacks)

Moreover, in Table 4, the reason behind the efficient years of the agricultural sector in Senegal that we pointed out in our efficiency analysis in section 4.2, are shown. Here, according to the efficiency definition 1 of section 3.2.1, a DMU is efficient if it satisfies two conditions which are (i) ∅0 ∗ = 1 and (ii) Optimal amount of all input slacks is equal to zero. As we can see in the last three columns of this table, in years 2003, 2004, 2005, 2006, 2010, 2013, 2015 and 2016 where TEVRS is at full score of 1, both the input slacks for input x1(labour) and input x2 (capital) are equal to zero.

Finally, table 4 presents the results for using model (I) and Cong model on the input data values of the table 1 above. In this scope, the amounts of technical inefficiencies for all Decision-making units (years here) have a value of zero, so the amounts of input congestion are the same than the amount of technical inefficiencies.

Just for the years between 2007 and 2011, techinical inefficiencies are recorded which as a consequence, create the same respective values for input congestion in these years in terms of input x1 which represents labor. However, for other years in this column technical inefficiencies are nul which make labor input congestion equal to zero for these years.

As for the capital congestion, only years 2008 and 2012 had techinical inefficiencies and therefore input congestion of same amounts respectively. In this perspective, if we refer to the difinition of the congestion in section 3.2.1 which states that đ??ˇđ?‘€đ?‘ˆ0 contains input congestion in case reductions in one or more inputs influences increase in one or more outputs without worsening any other inputs or outputs, or inversely if augmentations in one or more inputs is found to have any effect on the reductions in one or more outputs without boosting any other inputs or outputs, we can say that combination of inputs (reducing one input and increasing the other) can only be employed for years that present input congestion values, which means for years 2007, 2008, 2009, 2010, 2011 and 2014.

Table 4: Results of input congestion in agricultural sector in Senegal Year 2000 2001 2002 2003 2004 2005 2006

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Labor 0 0 0 0 0 0 0

Capital congestion 0 0 0 0 0 0 0

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24.3 45 54 65 52 0 0 0 0 0

0 0.52 0 0 0 0 0 3.9 0 0

In reality, both of the input re-allocations (while keeping each input value unchanged) and combination of inputs (reducing one input and increasing the other input) can be done based on human resource management (HRM).

HRM methods could be used to train and motivate employees as to make input �1 (labour) more productive (when being used with capital �2 ) in producing higher output level (profits). Training and team development can help to improve technical skills of labour so that it can be more flexible in doing different job tasks at lower time. This can help to save costs and make productivity of agricultural primary commodities more efficient. Therefore, HRM’s job design could also be exploited based on employees’ attitudes and competencies so that the suitable job task will be given to the suitable and qualified employee to perform. In addition to this, HRM motivation methods should also be employed. These motivation methods include promoting future career development for employees, performance management system rewards, involving employees in decision-making and compensate employees’ contribution. Other methods such as financial accounting or project management could provide assistance in the re-allocation, increase or reduction of capital used in the agricultural sector in Senegal. Input reallocation and combination of inputs (which is based on input congestion) must be used to make these possible by concretizing them through active actions.

V.

CONCLUSION

After nearly 35 years of reforms, it is refutable to see success in Senegal’s impetus for dynamic and efficient agriculture productivity that mimics those of the East Asian nations and similar emerging economies. That is to say that presently, Senegal has failed in its goal of molding an competitive agricultural sector that could both supply sufficient and quality primary commodities to its population in one hand, and support manufacturing industries (for finished goods in terms of foods and textile) by creating a direct link (a vertical integration) between agricultural sector and manufacturing sector. The results provided in this paper through the exploitation of the data envelopment analysis (DEA) by evaluating the efficiency of this agricultural sector between 2000 and 2016, confirm this failure in boosting efficiency of production and of agricultural profits. Despite the sizeable status of the informal sector (that has been growing over time), DEA analysis and interpretations of results provides remedies to avoid inefficient agricultural production through an output orientation by re-allocating inputs such as labor and capital or by using a combination of inputs depending on input congestion. For practical use of these DEA solutions, it has been shown that human resource management methods for training and motivating labor, must be employed, linked and harmonized with other fields of study such as financial management and project management. However, it is demonstrated that reducing labor and increasing capital in agricultural sector often create severe tensions in rural areas between authorities and farmers. Moreover, DEA could be an important tool to evaluate future efficiency results for the agriculture in Senegal. These DEA efficiency measures could be analyzed and interpreted for the benefit of avoiding future problems related to low efficiency and low quality in the production of primary commodities. In this regard, future researches may employ forecasting tools to provide predicted data in this sector (seeing that we have input and output data for 17 years, 2000-2016), that be studied for purpose of giving warnings and suggestions to authorities involved in this sector, for future and fruitful agricultural policies through a proactive approach.

ACKNOWLEGEMENT The authors want to thank the farmers in the agricultural sector in Senegal for their contribution to our research and for providing us the necessary information regarding their job, the Statistic Bureau of Senegal for the data collection and the anonymous reviewers for their constructive comments and suggestions.

REFERENCES [1]

Agence Nationale de la Statistique et de la Demographie (ANDS), (2018), Bulletin mensuelle des statistiques d’Aout 2018, Senegal

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