ELSEVIER
Ecological Modelling 81 ‘.1995) 197-212
Simulation of crop production, water and nitrogen balances in two German agro-ecosystems using the DAISY model H. Svendsen *, S. Hansen, H.E. Jensen Royal
Veterinary
and Agricultural
Unicersify,
Department of Agricultural Copenhagen Denmark
Sciences,
Thorcaldwnwej
40, 1871
Frederikstwg
C,
Received 1September 1993;accepted 11May1994
Abstract The Danish model DAISY was used to simulate water and nitrogen dynamics and biomass production in a sandy and in a loamy soil located in Southern and Eastern Lower Saxony, Germany. The soil part of the model has a one-dimensional vertical structure. The soil profile is divided into layers on the basis of physical and chemical soil characteristics. The simulated results were compared to experimental data including water content, water tension. mineral nitrogen content, and temperature in the soil, crop canopy development, crop biomass production, and nitrogenaccumulationin the abovegroundpart of the crop in winter wheat and sugar beet. The ability of the model to simulate the main elements in the behavior of the agro-ecosystem was demonstrated.
Keywords: Nitrogen; Water dynamics
1. Introduction
nitrogen dynamics in agro-ecosystems various management strategies.
In order to decrease losses of nitrogen to the aquatic environment and at the sametime develop strategiesfor economicallyand environ-
mentallysustainableagriculturalcrop production the comprehensiveDanish simulation model DAISY was developedwithin the frameworkof the Danish NPO ResearchProgramme(DyhrNielsenet al., 1991)and as part of the EC EnvironmentalResearchProgramme(Thomassonet al., 1991).The modelis designedto enablesimulation of crop production, water dynamicsand
subject to
2. The DAISYsimulationmodel Tne DAISY simulationmodel is describedin detail elsewhere(Hansen et al., 1990, MUa, 1991b).The modelcomprisesa numberof main modules,includingonesfor water dynamics,soil temperature,nitrogendynamics,crop production, and management. 2.1. Wafer dynamics module
l
Corresponding author.
0304-3800/95/$09.50
The hydrological processes considered in the model include snow accumulation and melting,
0 1995 Elseviet Science B.V. All rights reserved
SSDI 0304-3800(94)00171-5
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H. Srlendsen et al. /Ecological
interception of precipitation by the crop canopy, evaporation from crop and soil surfaces, infiltration, water movement to plant roots, transpiration, and verticaf movement of water in the soil profile, In the model snow melting is influenced by incident radiation, and soil and air temperatxes. Interception is limited by the precipitation or by the interception capacity of the crop canopy. The soil water dynamics is modelled by the classical Richards equation (Richard, 1931) in the pressure potential formulation. Modelling of the water uptake by plant roots is based on a root-soil contact resistance concept and a quasi-steady state approximation for radial flow to the root surfaces.The potential evapotranspiration constitutes the upper limit for the considered evaporation and transpiration processes.In the present study the potential evapotranspiration (PE) was calculated by using a modified Makkink equation (Makkink, 1957; Hansen, 1984). A Beer’s law
Modelling 81 (1995) 197-212
type equation is adopted in order to distribute PE between crop and soil (PEC and PES, respectively). In the case that transpiration occurs at the potential rate (PT) the distribution of water uptake by the root system is governed by an internal potential in the plant (located at the transition between root and shoot). If the transpiration occurs at a smaller rate than the PT, transport of water from the bulk soil to the root surfaces is assumed to determine the water uptake. In this case it is assumed that a common potential equai to the pressure potential at the wilting point exists along the root surfaces. 2.2. Soil temperaturemodule The soil temperature is modelled by solving an extended heat flow equation taking heat transfer by conduction and convection into account and including the effect of frost and thaw processes.
F-------“’ I c I r-l
Fig. 1. Pools of organic matterand related partitioning microbial biomass; SOM: soil organic matter.
coefficients
AoM2
for flow between pools. AOM:
i : t
iI
added organic matter; SMB: soil
H. Suet&en
et al. / Ecological ModeUtg
The freezing process induces water flow in the soil as ice formation is assumed to take place in the large soil pores extracting water from small pores resulting in water flow towards the freezing zone (Miller, 1980). 2.3. Ni&rogen module The transformation and transport processes considered in the model include net mineralization of nitrogen, nitrification, denitrification, nitrogen movement to plant roots, and nitrogen leaching from the root zone. It appears that before mineralization can take pIace the soil organic matter has to be degraded and enter into the soil solution. These processes may be considered as the steps determining the turnover rate of soil organic matter (Nielsen et al., 1988). The net mineralization is thus governed by the turnover of organic matter in the soil, which is conceptually divided into three main pools, viz. dead native soil organic matter (SOM), soil microbial biomass (SMB), and added organic matter (AOM). Each main pool of organic matter is subdivided into two subpools, each one being characterized by a particular carbon-nitrogen ratio and by a particular turnover rate (Fig. 1). Thus SOM is subdivided into two subpools designated SON,, and SOM,, respectively. The rates of decomposition of SOM, and SOM, are simulated by first-or&r reaction kinetics. The subpool SOM, is assumed to consist of the more chemically stabilized organic matter, i.e. compounds with a chemical structure that implies resistance to biological attack. Furthermore, part of the organic matter in SOM, as well as in SOMz is assumed to be physically stabilized, i.e. protected against biologicai attack by adsorption to soil colloids or entrapment within soil aggregates (Jenkinson and Rayner, 1977; van Veen et al., 1984,1985). The SMB is subdivided into two subpools designated SMB, and SMB,, respectively, in order to obtain a relatively stable part as well as a more dynamic part of the microbial biomass (van Veen et al., 1985).The subpool SMB, is considered to be the more stable part while subpool SMB, is considered to be the more dynamic part of the
81 I1 995) 197-212
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soil microbial biomass. Simulation of soil microbial biomass turnover is based on growth efficien,y, maintenance respiration, and death rate coefficients. The AOM originates from applied organic fertilizers or from crop residues left in the field after harvest. The AOM is allocated to the subpools AOM,, AOM, and SOM,. The subpool AOM, is assumed to consist of mainly cell wall material while AOM, is assumed to consist of mainly water-extractable cell material. The organic matter allocated to the soil subpool SOM, is assumed to consist of mainly lignin and other resistant compounds (van Veen and Paul, 1981).The subpool AOM, is substrate for both SMB, and SMB, and decomposes slowiy, while AO&, which is easydecomposable,is substrate for SMB, only. The rates of decomposition of AOM, and AOM, are simulated by first-order reaction kinetics. The abiotic factors assumed to influence carbon turnover are soil temperature and water status, and additionally in the case of the subpools SOM,, SOM,, SMB,, clay content. It is assumed that the effect of the various abiotic factors is multiplicative. The abiotic function adopted for adjustment of decomposition rate coefficients were derived from various sources in literature (Miller and Johnson, 1964; Stanford et al., 1973; Stanford and Epstein, 1974 Urensen, 1975;Anderson, 1979; Cambell et al., 1981;van Veen and Paul, 1981; Addiscott, 1983; Orchard and Cook, 1983; Stott et al., 1984). Referring to Fig, 1 a carbon balance for each pool of organic matter can be established. As each pool of organic matter is characterized by a fiied [C/N]-ratio, an overall organic nitrogen balance can be established resulting in an equation for net mineralization. If net mineralization is negative, i.e. net immobilization occurs, it is assumed that ammonium is utilized in preference to nitrate by the microbial biomass. It is assumed that nitrification may be expressed by Michaelis-Menten type reaction kinetics. The considered abiotic factors influencing the nitrification are soil temperature and soi water status. The overall effect is assumed to be muftiplicative. The abiotic functions adopted for
200
H. Srendsen et al. /Ecological
adjustment of nitrification rate were derived from various sourcesin literature (Miller and Johnson, 1964;Reichman et al., 1966;Sabey, 1969;Flowers and O’Callaghan, 1983).As the 0, concentration in soil solution is usually correlated with temperature and soil water content, the effect of 0, concentration on the nitrification rates is impiicitly included in the combined effect of soil temperature and soil water content. Denitrification is modeiled by defining a potential denitrification rate, i.e. the denitrification rate under completely anaerobic conditions at the actual soil temperature. The potential denitrification rate 6,’ is assumedto be proportional to the CO, evolution rate (Lind, 1980). Under partly anaerobic conditions &’ is reduced according to the oxygen status of the soil expressedby a function of soil water content adapted from Rolston et al. (1984). The actual denitrification rate is either determined as the reduced ,$ or as the rate at which nitrate in soil is available for denitrification. The nitrogen uptake model is based on the concept of a potential nitrogen demand simulated by the crop model, and the availability of nitrogen in the soil for plant uptake, i.e. the rate at which nitrogen can be made available at the root surfaces.The transport of nitrogen from the bulk soil to the root surfaces is based on a number of a.ssumptions.Each root exploits an averageeffective volume of soil which is assumed to be a cylinder around each root. The radius of this cylinder is assumed to correspond to the average half distance between the roots. It is assumed that nitrogen is transferred to the root surface by convection as well as by diffusion and that the concentration-distance profile develops in time in a stepwise manner, and at each timestep it approximates to a steady state profile (Baldwin et al., 1973).in the present model it is assumed that nitrogen uptake equals the nitrogen flux towards the root surface. If the uptake is limited by the availability of nitrogen in the soil, then the root is assumed to act as a zero sink. In the case of ample nitrogen supply the total nitrogen uptake 1sdetermined by the potential nitrogen demand. Then total uptake is distributed over the entire root zone by assuming a common value of nitro-
Modelling 81 (1995) 197-212
gen concentration at the root surface to exist along the root surfaces of the entire root system. The calculations are performed for both ammonium and nitrate. It is assumedthat ammonium is taken up by plant roots in preference to nitrate. Simulation of the vertical movement of nitrogen is modelled by solving the convection-dispersion equation. The equation is solved for ammonium as weli as for nitrate. In the case of ammonium the relation between adsorbed and dissolved ammonium is described by an adsorption-desorption isotherm derived from van Schouwenburg and Schuffelen (1963), whiIe in the case of nitrate, adsorption is considered insignificant. The source term included in the convection-dispersion equation integrates transformation and transfer processes,viz. net mineralization, nitrification, and plant uptake in the case of ammonium and nitrification, denitrification, and plant uptake in the case of nitrate. 2.4. Crop module In this model a crop is considered to consist of two or three parts, viz. shoot and root, and OCCBsionally storage organ. The shoot is characterized by dry matter content and leaf area index, while the root system is characterized by dry matter content, rooting depth, and root length density. The phenological part of the crop model is based on the thermal unit concept which implies that crop development from emergence to harvest can be described in terms of temperature sums. Plant emergence and photosynthetically active crop area index Lai at the early stage of crop canopy development are simulated solely as functions of temperature sum while L,i at later stagesof crop canopy development is simulated as a function of both temperature sum and accumulated amounts of shoot dry matter: saiwt
CT, ESA,
where W, is the accumulated amount of shoot dry
H. Scendserr
et ul. /Ecological
Modelling
matter, X:T, is the temperature sum calculated from emergence, and Sai, a), A,, and A: are empirical constants (Table 1). Simulation of crop dry matter production is based on caWation of daily grosscanopy photosynthesis,partitioning of assimilates between crop components, and respiration of each component, respectively. Maximum daily gross canopy photosynthesis,
81 (I 995)
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F is calculated according to Hansen et al. (18&0,1991b).The model is based on the assumptions that grcessleaf photosynthesis can be described by a single light response curve (Goudriaan and van Laar, 1978) and that the light distribution within the crop canopy can be described by Beer’s law. FB,, may be reduced due to water and nitrogen deficiency, FgJ and FR3, respectively. It is assumed that under conditions
Table 1 Parameters used in the crop growth model Parameter
Unit
Value Barley
Development of photosynthetically active canopy area, Eq. 1 m2 kg-‘* 20 a, L 3.0 A2 C day0 450 AL C daya 1450 ilocation of assimilatesto roots, Eq. 3 Y0 0.60 Y’ 0.15 A’ C day0 800 Allocation of assimilatesto storage organs, Eq. 3 Y0 Y’ A’ C day Respiration and net production of root Y kg DM/kg CH ,O 0.54 ‘ivt kg CH ?O,‘kg DM 0.065 rm * kg CH,O/kg DMQ 0.015 Am C day 1200 Respiration and net production of shoot Y kg Dhijkg CH ,O 0.72 *m c kg CH,O/kg DM 0.015 b kg CH ,O/kg DMo 0.010 rtll AIn Cday 1200 Respiration and net production of storage organs ’ kg DM/kg CH ,Oo Y Crop nitrogen parameters - uptake limiting concentrations, Eq. 5 ,,&I C day0 200 pu.2 C day 900 Root p*I g N/b DM 18 p.2 g N/kg Dbf 10 Shoot p.1 B N/kg DM 50 p.2 g N/kg DM 12 Storage organs p.1 = pL2 g N/kg DM hi
a Used before the transition time A, is reached. ’ Used after the transition time A, is reached. ’ Maintenance respiration of storage organs is neglected.
Wheat
Sugar beet
14 1.8 450 loo0
11 1.0 1600 1500
0.60 0.10 700
0.60 0.20 900 0.00 0.60 900
0.54 0.065 0.015 800
0.54 0.065
0.72 0.015 0.008 800
0.83 0.030
0.85 100 1100
0 300
18 10
I5 15
50 I2
40 40 8
H. Svendsen
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et al. /Ecological
of water deficiency the ratio F&F,,, corresponds to the ratio between the actual and potential crop evapotranspiration. Fg,z may be reduced fu;ther due to nitrogen deficiency, Fg,3. Fg.3
=
F&2
NC-N,” Na c -NP
where NC, NC“and IV,” is the amount of nitrogen in the crop at the actual nitrogen supply, at extremely Iow nitrogen supply, and at just ample nitrogen supply, respectively. The assimilate partitioning between crop components is considered to be a function of temper-
Modelling
81 (1995)
197-212
ature sum, The allocation of assimilates to the roots (yr) is calculated as:
YP
CT,rO
YP + (Yr* - ygm,/II:
0 < CT, < n:
A: 2 CT,
YC*
(3) where the parameters can be obtained from Table 2. The allocation of assimilates to storage organs is calculated in a similar way, The respiration is assumed to include growth respiration as well as a temperature-dependent
Tabie 2 Parameters and initial values used in the soil nitrogen model Parameter Carbon
turnover
Unit
Value
day-’ day’ ’ day” day-’ day-r day-’ day- t day- ’
2.7.
model
Decomposition rate coefficient a of SOM, Decomposition rate coefficient a of SOM, Decomposition rate coefficient a of AOM, (plant material) Decomposition rate coefficient a of AOM, (plant material) Death rate coefficient a of SMB, Death rate coefficient a of SMB, Maintenance coefficient a of SMB, Maintenance coefficient a of SMB, Substrate utilization efficiency of SMBt Substrate utilization efficiency of SMB, Partitioning constant fsoMz + SMBI
Partitioning ~SMB~ -*SMBI . . . constantfsMsl + SMBZ, Parttttonmg constant fAoM1 + sMBl hitid CQ&Jn COfltenl Fraction of soil C allocated to SOM 1 Fraction of soil C allocated to SOM 2 Fraction of soil C allocated to SMBt Fraction of soil C allocated to SMBr Amoun: of C allocated to AOM, (rcot material) Amount of C allocated to AOM, (root material) Nel nitrogen mineralization (ammonification) C/N ra+ in pool SOM, C/N ratio in pal SOM z C/N ratio in pool SMB, C/N ratio in pool SMB, C/N ratio in pool AOM, Niirifcatiott Maximum nitrification rate coefficient a Half-saturation constant (Michaelis-Menten)
lo--’ 1.4 ’ 10-4 1.0*10-2 1.0 * 10-r 1.0 * 10-3 t.O~lo-z 1.0 ’ 10-2 1.0 ’ 10-a 0.60 0.60 0.90 0.60 0.50 0.692 0.300 0.005
t ha-’ t ha”
o.ou3 0.50 0 10 10 6
10 100 kgN mm3 day-’ kgNmm3
5 * 1o-3 5 * 10-2
g Gas-N/g CO,-C
0.1
cm
8
Denittification
Empirical constant, rc; Nitrogen movement in soil Longitudinal dispenivity
’ At standard conditions, i.e. 10°C optimum water content, and no clay content.
H. Svendsen
et al. /Ecological
maintenance respiration (McCree, 1970).The assimilate conversion efficiency, Y,, depends on the type of dry matter produced. The maintenance respiration coefficient, rm, is temperature dependent (Qlo = 2) and maintenance respiration is proportional to the accumulated dry matter in the considered crop component. Root penetration is assumed to take place if a daily net root growth AW, occurs, if the soil temperature is above 4*C, and if the actual rooting depth d, is less than the maximum rooting depth allowed in the particular soil considered. Daily root penetration Ad, is calculated as a linear function of soil temperature (Jacobsen, 1976). The total root length is assumed to be proportional to ihe root weight. The distribution of root length density L in the soil profile is described in accordance with Gerwitz and Page (1974) assuming fhat the root density at the rooting depth d, is 0.1 cm cmm3. The daily potential nitrogen demand AN,P is calculated as: AN,p =Np-NC (4) where N,â&#x20AC;? is the potential amount of nitrogen in the crop. The nitrogen concentrations corresponding to Ncp, N,â&#x20AC;&#x153;, and Np, respectively, are assumed to be functions of temperature sum, and the general form of the functions is:
Mode!ling
81 (1995)
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At harvest a predescribed fraction of the various parts of the crop are removed from the field. Aboveground parts of a crop left at the soil surface are assumed to enter the soil fully or partly when soil tillage is performed. Seed bed preparation is simulated by mixing the top soil layers to a depth corresponding to a specified seed bed preparation depth. Other soil tillage operations (ploughing, rotavation, stubble cultivation1 are simulated by specifying the soil tillage depth, incorporation depth and incorporation fraction for plant residues. Irrigation can be effected at a predescribed date or simulated by the model. Two methods of fertilization can be applied viz. surface broadcasting and banding. Inorganic fertilizers are characterized by the amounts of nitrogen applied as nitrate and ammonium, respectively. Liquid ammonia and urea are considered as ammonium fertilizers. Organic fertilizers are characterized by dry matter content, carbon and nitrogen concentrations in dry matter and fraction of nitrogen present as ammonium, respectively. The organic part of the fertilizer is characterized in terms of pools of organic matter with particular properties. 3, Experimental datasets The experimental data used in connection with the present simulation study is described in detail by McVoy et al. (1995). The data were obtained from field experiments carried out in two catchments, Krummbach and Eisenbach, located in Southern and Eastern Lower Saxony, Germany.
where 1 refers to the crop component (shoot, root, or storage organs). Parameters can be obtained from Table 1.
4. Initialization model
2.5, The managementmodule
4.1. Water dynamics mu&de
The management module permits simulation of crop production, water dynamics and nitrogen dynamics in various agro-ecosystemssubject to various system management strategies. The management module includes crop planting, soil tillage, irrigation, fertilization, and crop harvest.
Default parameter values were used in the parameterization of the DAISY model (Hansen et al., 1990,1991a,1991b)except for the required soil hydraulic properties, which are based on the information in the dataset. It was assumed both in the case of the Intensive Loam Site and in the
and pafametcrimti0n
of the
204
H. Srendsen et al. / Ecologiral
Modelling 81 (1995) 197412
case of the Intensive Sand Site that the soil profile could be represented by three â&#x20AC;&#x153;horizonsâ&#x20AC;? each characterized by its own retention and conductivity curve (Figs. 2 and 3). Based on retention data provided the retention curves were established by use of a cubic spline interpolation method.
The
relative
hydraulic
conductivity
was
calculated utilizing the information found in the retention curve by use of the method proposed by Kunze et al. (1968). The simulations were initialized on JuIy 1, I987 and on January I, 1989 for the Intensive Loam Site and Intensive Sand Site, respectively, by use of data from the data set. Data from the first winter were used to calibrate the calculated hydraulic conductivity function. The adopted functions are shown in Figs. 2 and 3 for the Intensive Loam Site and Intensive Sand Site, respectively. 4.2. Soil temperaturemodule Soil thermal properties were calculated according to de Vries (1963) and the initialization
Fig. 3. Retention curves and corresponding hydraulic conductivity for the three horizons at the Intensive Sand Site. a, b: O-27.5 cm, c, d: 27.5-45 cm, and e, f: > 45 cm.
6 l
4
. .
3
a
of the model was based on data obtained from the dataset.
.
2
.
1
4.3. Nitrogetamodule
0m 5 .
4 +:
Parameterization and initialization are partly based on data obtained from the dataset and partly based on default values. Initialization of the soil mineral nitrogen is based on data obtained from the dataset. Important parameter values are given in Table 2 together with the initialization of the organic pools considered by the model.
C
l
1 0 lli 5 4
a
3 2
â&#x20AC;&#x2122; rkk
.
v.
4.4. Crop module I
0.0 0.1 0.2 0.3 0.4 0.6 0.0 0.1 0.2 0.3 0;s 0:s Soil Watef Content Fig. 2. Retention curves and corresponding hydraulic conductivity for the three horizons at the Intensive Loam Site. a, b: O-32.5 cm, c, d: 32.5-83.5 cm, and e, f: > 83.5 cm.
Winter wheat and spring barley parameters are obtained from Hansen et al. (1991b) and Hansen et al. (1991a), respectively. Sugar-beet parameters are obtained by adjustment of fodder-beet parameters obtained from Hansen et al. (1990). Important crop parameters are shown
H. Smmdsen et al. /Ecological
in Table 1. It is noted that for the same species different cultivars may require different parameters, but in the present study winter wheat and spring barley parameters have not been adjusted. In general the following parameter values were adopted for roots: radius rr = 0.1 mm; specific root weight of 100km root per kg root DM; and a root penetration rate of 0.25 and 0.20 cm per day per 0 C above 4” C. Harvest index for the cereal crops was obtained from the data set.
Modeiling 81 (1995) 197-212
20s
Table 3 Annual water balance in mm Loam Site
89 Yo 91
Sand
89
Site
I’ 4% 621 479 610 613 535 613 535
Yo 91 90 91
1
Ea 486 503 464 551 531 482 476 483
0 0 0 157 140 69 0 69
4, 24 % 31 149 204 130 128 121
AS, - 14 +22 -16 +67 + I8 -8 +9 0
P = Precipiiation. I = Irrigation. E, - Evapoktanspiration. q,
4.5. Management module Available management data was obtained from the data set and additional parameter values were obtained from Hansen et al. (1990). The DAISY model does not contain any module for the simulation of volatilization of ammonia. As a considerable fraction of tbe applied nitrogen fertilizer is given as urea, ammonia volatilization can be expected, Based on Rachhpal-Singh and Nye (1988) it was assumed that 10% of the applied urea was lost by volatilization. 5. Simulation
results
The detailed presentation of the simulations concentrates on two examples, viz. the winter wheat in 1989at the Intensive Loam Site, and the sugar beet in 1990 grown at the Intensive Sand Site. The main results for other years are shown for both sites. 5.1. Intensiw loam site Annual water and nitrogen balancesare shown in Tables 3 and 4, respectively. It is noted that the year 1990 was characterized by a fairly large precipitation compared to the previous and the following year, resulting in a fairly large percolation and a correspondingly relative large nitrogen leaching, but the actual size of the leaching itself is small. The nitrogen concentration in the ieaching water was estimated to be within 8-10 mg/l for all three years. Furthermore, it is noted that the total gaseousloss of nitrogen from the system (volatilization plus denitrification) is simulated to
= Percchtion.
AS,
= Chmge
in
soil water storage.
be of the same magnitude as the estimated at-
spheric deposition. Comparing the simulated net mineralization in 1989, 1990,and 1991it is ncted that the net mineralization of 1989 is much smaller than that of 1990 or 1991. This is due to incorporation of a large amounts of wheat straw in 1988 and 1989 (Table 5) and a subsequent immobilization of mineral nitrogen. The relatively large mineralization in 1990 is partly due to the August 1989application of farmyard manure containing 115 kg organic N ha-’ and the incorporation of plant residuals left after the sugar-beet crop in 1990. The mineralization in 1991 was positively affected by 1990 incorporation of large amounts of sugar-beet residues, and negatively affected by the 1991 incorporation of large amounts of wheat straw after harvest (Table 5). Table
4
Annual mineral nitrogen baIance in kg N ha-’ N, N, N,,, Nd + NV Nu N, AN, Loam Site 89 90 91 Sum Sand Site 89 90 91 90
247 132 232 611 159 194
11 15 18 13 132 9
130 194
11
88
18
35 235 45 15
78
15
95 19
16
13 15
61 92
13 19
91 130 13 54 13 Sum (lrr. 90) 483 43 ?34 48 Sum (Not Irr.1 483 43 224 48
Nh
2542 245 9 256 3 755 14 204 27 236 34 176 29 211 15
-1 + 14 +s4 +67 +S +15 -14 +56
174 172 179 525 133 151
177
-46
126
53
125
143
616 90 +6 409 592 95 +15 402
N,,, = Net mineralization. Nd = Denitrification. N,, = Plant up take. AN, = Change in storage. N, = Atmospheric deposition. NV= Ammonia volatilization. NI = Leaching. Nh = Removed at harvest.
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H. Suendsen
et al. /Ecological
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81 (IW)
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Table S Annual dry stems and/or
Loam
Sum Sand
matter harvest leaves (W,)
89 90 91
Site
Site
10.4 20.8
9.9 2.7
2.1 1.1
9.2
9.7 22.3
I.7 4.9
4.9 3.2 6.1
1.2 1.4 1.1
2.4
0.9
37.1
6.1 14.2
1.1 3.7
33.4
13.4
3.2
89 90 91
30.4 12.4 19.1 5.6
89
15.4
90
5.6
Sum (Irr. 90) Sum (Not hr.)
in straw,
(Y,,) and plant residuals and in roots (IV,). t ha-â&#x20AC;&#x2122;
Comparison of the amount of organic nitrogen allocated to the field (N,-N,) and the net mineralization during the three years (Table 4) shows that the system is in balance regarding organic nitrogen. Yields and crop residues in terms of dry matter are shown in Table 5. Simulated and observed soil water dynamics are shown in Figs. 4 and 5 in terms of volumetric soil water content and soil water pressure potential (expressed as tension) respectively. Discrep-
J F M A-M Fig. 5. Soil water pressure potential for the Intensive barn Site 1989. Depths are a: IU cm, b: 20 cm, and c: 40 cm. Solid line is simulated pressure potentials, stippled line is experimental vahes.
ancies between simulated and observed values are most clearly identified in the more sensitive soil water pressure potential. It is noted that discrepancies especially develop in May, when transpiration or plant water uptake becomes one of the dominant parts of the water balance. It is also noted that discrepancies especially develop at the 10 cm and 20 cm depths, while the simulation at 40 cm is very satisfactory. Figs. 4 and 5 indicate that the total volume of water removed from the soil is underestimated in May and that the underestimation takes place in the upper soil layers. Most of the water removed in May from the upper soil layers is likely to be due to extraction by plant roots and subsequent transpiration
as the crop canopyhasclosedat that time (Fig. 7). This fact indicates that the potential evapotranspiration is underestimated in May. The large
rain eventin the beginningof Julyis not reflected
0.0
]
i i i i i JFMAMJJASONâ&#x20AC;&#x2122;D
i
i
i
i
;
:
t
Fig. 4. Precipitation and volumetric soil water content at the Intensive Loam Site 1989 with winter wheat. a: precipitation; b, c, d: volumetric soil water content, b: O-30 cm, c: 30-60 cm, and d: 60-90 cm. Solid line is simulated liquid water plus ice content. shadowed area is ice content.
in the observations but is clearly reflected in the simulation (Fig. 4). The increase in soil water contentis first observed in mid-July and then the increase is larger than the simulations, hence the discrepancies observed in July-August are difficult to interpret. Simulated and observed soil temperatures are shown in Fig. 6. We conclude that the simulation of soil temperature in the present example is satisfactory for the present purpose, viz. simulating the temperature effect on soil biological processes.
H. Suendsen et al. /Ecological
Modellhg
81 (1995) 197412
207
b
Fig. 8. Nitrogen content of shoot of winter lntensive Loam Site 1989 and corresponding content W90 cm).
tiJFMAMJJASOND
Fig. 6. Air and soil temperatures for a winter wheat crop at the Intensive Loam Site 1989. a: air temperature; b, c, d: solid line is simulated soil temperature, shadowed area is the discrepancy between simulated and experimental values.
Depths are b: 5 cm, c: 20 cm, and d: 50 cm.
Fig. 7 shows the simulated and observedvalues of the temporal development of leaf area index (MI) and accumulated shoot dry matter. The DAISY model does not simulate I&, but it simulates photosynthetically active canopy area (PACM) and total canopy area in terms of area indexes.In young cereal pIants the simulated
PACAl is a good approximationto LAI, but as stemsbecomemore importantthe approximation becomesless accurate.In Fig. 7 the simulated valuesof PACAI are used as an approximation for LAI until the end of April, when the canopy can be consideredto be closed.It is especially
,
wheat at the soil nitrogen
important that the simulationof the development
of the earlycanopyis accurate.Whenthe canopy is closedthe model becomesinsensitiveto the actual value of PACAL We concludethat the simulationof crop developmentand crop growth in this particular caseis very satisfactory.The simulationindicatedthat the crop sufferedfrom only a very mild water stress. The nitrogen accumulation in the shoot part of
the crop is shownin Fig. &. Comparisonbetween simulated and observed values shows a satisfac-
tory agreementexceptfor June,when large discrepancies are observed.The simulation uverestimates the uptake, but this overestimationis not reflectedin the storageof mineral nitrogen in the root zone (Fig. Sb). The observations indicate nitrogen immobilization in Juneand a very rapid
mineralizationin early July. This is difficult to explain. The overall result of the comparison betweensimulatedand observed values is fairly good,althoughthe observationsindicatethat the simulatedimmobilization,after the simulatedinitial mineralizationfollowingthe harvest,is crverestimated. 5.2. Intensive sand site Annual water andnitrogenbalancesareshown in Tables 3 and 4, respectively.The measuredsoil water data made us question whether the stated
JFMAMJJAS Fig, 7. Dty matter production and LA1 development wheat at the Intensive Loam Site 1989.
of winter
irrigation in 1990was correct (see further discussion below). Therefore, simulations were performed assuming the stated irrigation as well as no irrigation in 1990. Of course this has a pro-
208
H. Swndsen ef al. / EcologicalModelling 81 (19951 197-212
found influenceon the water balancein 1990,but the influenceon the water balancein 1991is limited (Table 3). Compared to the Intensive Loam Site the percolation at the Intensive Sand Site is fairty large, resulting in much larger nitrogen leaching. The basic assumption about whether the site was irrigated or not in 1990 also has a profound influence on the nitrogen balance for 1990and 1991(Table 4). The irrigation enhances the nitrogen uptake by the crop in 1990 due to better crop growth (TabIe 51, but it also con-
tributes to the leaching.Comparingthe leaching basedon the two assumptions, it appearsthat for all three years the difference is much smaller than the differencesobtainedfor eachindividual year.The nitrogenconcentrationin leachingwater wasestimatedto be within 17-22mg/l when irrigation was assumed and 11-43 mg/l when no
irrigationwasassumedin 1990.The highvalueof 43 mg/l wasobtainedin 1991becausean appreciable amount of nitrogen was left in the soil after the non-irrigated sugar-beet crop in 1990. Simulated and observed soil water dynamics are shown in Figs. 9 and 10 in terms of volumetric soil water content and soil water pressure potential (expressedas tension), respectively. It is noted that the irrigation applied in July-August is not reflectedin the measurements of soil water content (Fig. 91, neither is it reflected in the mea-
suredsoil water tension(Fig. 10).This indicates someuncertaintyregardingthe amountof actual irrigation. Hence the simulation was repeated assumingno irrigationin 1990.The resultsof this simulationare also shownin Figs.9 and 10. In the upper soil layers the observedsoil water contentsdiffer from the simulationsregardlessof which of the two assumptionsit is basedon, but in most casesthe observedvaluesare within the rangedefinedby the two simulations.In general, the simulation of the soil water dynamics is less satisfactory at the Intensive Sand Site than at the Intensive Loam Site. This may be attributed to uncertainty ,regarding the irrigation, or to a less satisfactory description of the soil hydraulic properties, or to the description of the soil water
uptakeby the roots. Simulatedand observedsoil temperature in the casewhere irrigationwasassumedare shown
b ,
JFMAMJJASOND
Fig. 9. Precipitation and volumetric soil water content at the Intensive Sand Site 1990 with irrigated sugar beet. a: solid line
is precipitation, stippled line irrigation. b, c, d: solid line is simulated contentof liquid waterplus ice with irrigation, shadowed area is ice content, stippled water content assuming no irrigation. 30-60 em, and d: 60-90 cm.
line Depths:
is simulated soil b: O-30 cm, c:
in Fig. 11. Compared to the simulation for the
IntensiveLoam Site, this simulationis lesssatisfactory, especiallyin spring and early summer. The less satisfactory simufations coincide with the
time when the soil was not fully coveredby the crop. When the crop canopy is open, radiation may play an important role for the surfacetem-
M
J
J
A
M
J
J
A
Fig. 10. Soil waterpressure potentialfor theIntensiveSand Site 1990. Depths area,d: 10 cm,b,e: 20 cm,andc,f: 40cm. Solid line is simulated pressure potential? stippled line is experimental values. a, b, and c is with irrigation, d, e, and f is without irrigation.
H. Svendsen et al. /Ecological
Modeling
81 (1995) 197-212
209
$lyJ+j JFMAMJJASOND
JFMAâ&#x20AC;&#x2122;MJJASOID
Fig. 11. Air and soil temperatures for the Intensive 1990 with irrigated sugar beet. a: air temperature; temperatures at b: 5 cm, c: 20 cm, and d: 50 cm.
Sand Site b. c. d: soil Solid line is
simulated soil temperature, shadowed area is the discrepancy between simulated and experimental values.
perature of the soil, i.e. in this case the surface conditions used in soil temperature model were not fully adequate. Eventhough the simulation of soil temperature is not fully satisfactory in this case, we conclude that this simulation is satisfactory for the present purpose, viz. simulating the
temperatureeffect on soil biologicalprocesses. In the case where irrigation was assumed, Fig. 12 showsthe simulated and observed values of the temporal development of leaf area index (LAD and accumulated shoot and heet dry matter. The DAISY
model
does not simulate
LAI,
but it simulates photosynthetically active canopy area (PACAI). For beet the simulated PACAI is presumed to be a good approximation to LAI.
Fig. 13. Nitrogen content of top and tuber of inigalcd sugar beet at the Intensive Sand Site t990 and corresponding soil nitrogen content (O-60 cmI+ In a: solid line is tuber DM. stippled line is top DM. In b: solid line is simulated with irrigation. stippled line is without irriga!ion.
Fig. 12 shows some scatter in the observed LAI, nevertheless the simulation of LA1 is considered satisfactory. The simulation of the dry matter production agrees fairly well with the observations, except in early July. Corresponding to Fig. 12 the nitrogen accumulation in the shoot and beet part of the crop is shown in Fig. 13a and the storage of mineral nitrogen in the root zone is shown in Fig. 13b. It is noted that large discrepancies exist in early July. In this case the simulation seems to overestimate nitrogen uptake by the crop, and it also overestimates the amountof nitrogenleft in the root zone storage. The discrepancies are much smaller at the next observations in mid-August. The discrepancybetween observed and simulated nitrogen in the root zone storage found in midJune may be explained in part by the fact that the ammonia volatilization was assumed to occur immediately at the time of fertilization. The increase in soil mineral nitrogen observed in Cktober-November is due to mineralization of the incorporated shoot part of the beet crop. Assumptions regarding whether or not the site was irrigated
MJJASO Fig.
12.
irrigated
Dry
matter
production
and
LAf
development
sugar beef at the Intensive Sand Site 1990.
of
in 1990havea strong influenceon
the distribution of nitrate in the soil profile. Fig. 14 shows the temporal variation in observed and simulated soilnitrate content.It is notedthat the smallest discrepancies were obtained when the
210
observed and simulated values. The lack of response to irrigation observed at Intensive Sand
Sitein 1990maybe explainedby unevendistribution of irrigation water. Taking spatial variability
into account,the accuracyby whichthe soilwater
25. oLy..-:y ;: JFYAHJJASOND
.
?uayeppq
Fig. 14. Soil content of N-NO, at the Intensive Sand Site 1990. a: O-30 cm, b: 30-60 cm, and c: 60-90 cm. Solid line is with irrigation, stippled line is without irrigation.
basic assumption was that the site was irrigated in accordancewith the management data. 6. Discussion and conclusion In order to describe the behavior of an agroecosystem precisely, management information, especially regarding crop rotation, irrigation and fertilizer application, are necessary. In normal agricultural practice, uneven distribution of irrigation water and fertifizers may introduce a considerable uncertainty in the description of the behavior of the agro-ecosystem. An accurate description of soil hydraulic properties is necessaryin order to obtain an accurate simulation of the soil water dynamics. Due to spatial variability in soil properties,a simulation
of soilwater dynamicsusingRichardsequationin deterministicmodels requires representative pa-
dynamics is simulated is considered satisfactory. The simulation of soil temperature was considered satisfactory for the present purpose but comparison with observationsrevealed discrepancies when the soil was not fully covered by the crop. This points to an inadequate description of the upper boundary condition, i.e. the surface temperature. The simulation of crop growth requires a number of crop-specific parameters. For a selected crop many of these parameters may vary among different cultivars. Hence some adjustment of
crop parameterscan be expectedif the parameters were not originallyassessed for the present cuftivar. In this study, parameters had to be adjusted for the potato crop and the sugar-beet crop. The original potato parameters were assessedfor early potatoes, and the beet parameters were assessedfor fodder beets. It was shown that the crop model was able to describe the main elements of the behavior of the crops. The soil nitrogen content is the result of a number of processes. For many of these processes no data is available for evaluating the simulations. I-Iencethe simulation of the nitrogen dynamics contains a number of degrees of freedom. In the present study only the distribution of organic matter between SOM, and SOM, (Fig. 1) was used in the calibration of the model. For the rest, the default parameters were used. The simulation showed that the two agro-ecosystemscould
be consideredas systemsin equilibrium,i.e. no increaseor declinein the soil organicnitrogen wassimulated.
rameters, hence some kind of calibration of the
modelis required.In the presentstudy the calibration included the assessment of a single matching factor used to scale the hydraulic conductivity function. This procedure does not ensure a correct form of the conductivity function. The fact that the DAISY model considers only Buckingham-Darcy flow in the soil matrix may atso explain some of the discrepancies between
Acknowledgements
This paper is basedon researchfinancedin part by the Danish Environmental Research Programme 1992-1993. The experimental data set provided by the Technical University of Braunschweig is gratefully acknowledged.
H. SL?endsen
et al. / Ecological
MorleNng 81 (I 995) 197-212
211
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