Modeling crop nitrogen requirements: a critical analysis

©1997 Elsevier Science B.V. All rights reserved Perspectives for Agronomy - Adopting Ecological Principles and Managing Resource Use M.K. van litersum and S.C. van de Geijn (Editors)

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Modeling crop nitrogen requirements: a critical analysis CO. Stockle^'*, P. Debaeke'' ^Biological Systems Engineering Department, Washington State University, Pullman, WA 99164-6120, ^INRA, Station d'Agronomie, BP 27, 31326 Castanet Tolosan cedex, France Accepted 2 June 1997

Abstract Four approaches to simulate N requirements of wheat were tested using data collected at the Auzeville experiment station of INRA, near Toulouse, France, from an experiment providing a wide range of soil N available for crop uptake. In these approaches, crop N requirements are expressed in terms of characteristic plant N concentration curves (maximum, critical, and minimum), representing expected concentration for a given crop N status throughout the growing season. Modeling approaches were evaluated for their ability to discriminate between N-limited and non-limited wheat plots as well as to properly represent the upper and lower limits of observed plant N concentrations. Best results were obtained using the growth dilution concept to represent the characteristic curves, while others based on temperature sums, growth stages, or fraction of the growth cycle were less satisfactory. Simulation of crop growth and N uptake based on N requirements estimated using the growth dilution concept resulted in a relationship between biomass at harvest and N uptake that correctly described an upper boundary for all observed data points. However, simulated and observed crop N uptake on a plot by plot basis resulted in low agreement. This was attributed to uncertainty in the measurement of initial soil N and crop N uptake, and the effect of other growth reducing factors (e.g. diseases) and possibly physical and/or chemical restrictions to field N uptake normally not accounted for by crop growth models. © 1997 Elsevier Science B.V. Keywords: Growth; Model; Simulation; Wheat

1. Introduction Simulation models are increasingly used for the assessment of crop productivity and the impact on the environment that may result from given combinations of weather, soil, crop characteristics, and water and N management. For this purpose, the proper simulation of crop N requirements is important, both in terms of amount and distribution throughout the growing season. Several approaches have been proposed to simulate * Corresponding author. Tel.: +1 509 3353564; fax: +1 509 3352722; e-mail: [email protected]

crop N requirements. We selected four that are representative and able to model wheat N requirements, corresponding to those included in the following crop growth models: AFRCWHEAT2 (Porter, 1993), Daisy (Hansen et al., 1991), EPIC (Williams et al., 1989), and CropSyst (Stockle and Nelson, 1996). In these approaches, crop N requirements are expressed in terms of characteristic plant N concentration curves, which represent the expected concentration for a given crop N status throughout the growing season. The most complete approaches define three such curves: a maximum (A^max). a critical (A^crit)» and a minimum (Nmin) plant N concentration. Plant growth is not limited by N if plant concentration

Reprintedfrom the European Journal of Agronomy 7 (1997) 161-169

218 is at or above A^crit» while A^^ax establishes the maximum crop N uptake. Below Ncnt, plant growth is reduced, stopping completely when plant N concentration reaches A^min- It should be noticed that these definitions do not consider the quality of the harvested crop. Plant N concentration is not constant but decreases with time, and so do the three characteristic curves. To describe this process, models express these curves as a function of crop growth stage (AFRCWHEAT2), the fraction of the growth cycle (EPIC), or thermal time (Daisy). On the other hand, research has shown that A^crit decreases with increasing shoot biomass according to an allometric equation (Salette and Lemaire, 1981; Greenwood et al., 1990), usually referred to as the growth dilution law. This concept has been tested with field data and shown able to discriminate between well-supplied and N-deficient crops (e.g. Justes et al, 1994; Plenet, 1995). Furthermore, single allometric equations for C3 and C4 crops have been

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proposed (Greenwood et al., 1990). Similar functional relationships with biomass may be used for A^max and A^min (Justes et al., 1994). An implementation based on this concept was introduced to the CropSyst model. It must be noted, however, that adequate simulation of crop N requirements may not guarantee the ability of models to simulate crop growth in response to soil available N and N management, which is the ultimate objective in the application of these models. In most models, N uptake depends on crop N requirements (as needed to maintain A^^ax) but also on the attainable N uptake as given by N concentration, moisture, and root distribution in the soil profile (Jones and Kiniry, 1986; Williams et al., 1989; Hansen et al, 1991; Porter, 1993; Stockle et al., 1994). The attainable N uptake may or may not satisfy crop N requirements. The objectives of this study were (1) to test four approaches to model crop N requirements using data collected for winter wheat at Auzeville, southern France, during the growing season of 1993, and (2)

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Fig. 1. Comparison of four models for estimating characteristic plant N concentration curves with data from N-limited and non-limited plots of winter wheat cultivar Soisson grown at Auzeville, France in 1992-1993. (Symbols: open square, N limited; closed square, N non-limited; Lines: dashed, A^max; solid, Ncnt; dotted, Nmm)-

219 compare observed and simulated biomass production and crop N uptake associated with crop N requirements, estimated using the best of the four approaches from objective 1, under a wide range of available nitrogen.

curves are defined. A generic exponential function of the fraction of the growing season (Fgs) is used to calculate A/'crit- The value of Fgs is determined as the ratio of the current temperature sum to the total temperature sum at maturity. For wheat, the function is A^crit = 100 (0.01 + 0.05 exp(-2.67 F^J). N^rnn is calculated as half of A^crit-

2. Approaches to model crop N requirements 2.4. CropSyst A description of four approaches to determine the characteristic concentration curves used to model crop N requirements throughout a growing season is given below. Some of these include a separate set of curves for shoots and roots. For this study, only shoot concentrations are of interest. 2,L AFRCWHEAT2 In this wheat model, A^max and A^min curves are defined. TV^ax is set at 4.5% from emergence to initiation of the terminal spikelet, from which point it falls to a value of 0.5% by the end of grain filling. A^min starts at 2.5%, increases slightly until the double ridge stage, and then falls to 0.25% by the end of grain filling. The details of the change of A^^ax a^^^ A^min as a function of developmental stage are given graphically by Porter (1993), and are reproduced here in Fig. 1 using the phenological scale proposed by Zadoks et al, (1974). 2.2, Daisy In this wheat model, A^max* A^crit» and A^min curves are defined. Characteristic N concentrations are given as a function of air temperature sum (base 0°C) from emergence. All curves have a constant concentration up to a temperature sum of 100°C - days, with values of 5%, 3%, and 2% for A^max* ^crit» and A^^in, respectively. After a temperature sum of 1100°C-days, these values are constant at values of 1.2%, 1.0%, and 0.7%, respectively. All concentrations decay exponentially for temperature sums between these two boundaries. These three curves are given graphically by Hansen et al. (1991), and reproduced in Fig. 1. 2.3. EPIC In this generic crop growth model, A^crit and A'min

TVmaxi A^crit* and A^min curvcs are defined in this generic crop growth model. Plant N concentration (N%), in percent, is assumed to be related with biomass accumulation (B) as follows: N% = a B " ^ where a and b are fitted parameters (Salette and Lemaire, 1981; Greenwood et al, 1990; Justes et al., 1994; Plenet, 1995). The value of A^^ax during early growth is required as input parameter. Then, the characteristic N concentration curves are given by: A^^ax = niin (A^max, an,ax B"^"^^), A^crit = m i u ( 0 . 7 A^„,ax, aent B ' ^ " ^ ' ) ,

and A^min = ™n (0.4 A^^ax* amin B ^^^), where B is biomass for an unstressed crop (t/ha), a^ax = A^max/ (2-«-^'), acrit = 0.7 iV,ax/(1.5-"''), and a„,in = 0.4 A^max/(0-5 ^^^). This implementation, based on data from the references given above, is a generalization that works well for both C3 and C4 species. For wheat, a value of 5.0% was used for A^^ax during early growth (see Fig. 1). The growth dilution law seems to work well up to flowering (Justes et al., 1994). After this point, the CropSyst implementation reduces linearly the three characteristic curves so as to meet specified ' in at (input parameters) values of A^max* A^crit* and A/m maturity.

3. Methods Experimental data collected for winter wheat during 1993 growing season at the INRA station in Auzeville, southwestern France, were used to compare the four modeling approaches (Debaeke et al., 1996). The soil was a deep silty-clay loam, with an organic matter content of 1.6% (0-30 cm). The cultivar Soissons was grown in 16 plots (8 preceding crops x2 input levels). Preceding crops were faba bean, maize, pea, rapeseed, sorghum, soybean, sunflower, and wheat. Input levels differed by sowing date (high-input sown on 30 November 1992 and low-input on 16 December

220 1992) and N fertilization. N fertilizer rate was calculated using the French balance sheet method (Remy, 1981), adapted for southern France, using yield goals of 5 (low-input, Nl) and 8 t/ha (high-input, N3). Briefly, this method calculates the N fertilizer dose as a function of soil availability and crop requirements for a yield objective, and includes a correction factor to increase the N dose when a limitation to N uptake by soil structure or soil moisture is expected. No supplementary irrigation was required in 1993. Each wheat plot was divided into two sections: (1) a central area (410 m^), receiving N fertilizer according to the yield objective (Nx treatment); (2) an unfertilized lateral area (60 m^), which was kept free of N fertilizer to assess the soil N contribution (NO treatment), yielding a total of 32 treatment combinations. Mineral N was determined before N fertilizer supply, in December 1992, from 5 soil cores per plot taken to a depth of 1.20 m in 0.2-m increments. Above-ground dry matter and N concentration were measured at tillering, stem elongation, shooting, anthesis and maturity. At each sampling date, the plants in five 0.25-m^ quadrats were collected for biomass and N determination. At maturity, dry matter and N concentration were measured separately for grain, culm and chaff using the Kjeldahl method. Growth stages were monitored regularly and calculations of thermal time were done from emergence. Data were analyzed to discriminate between N-limited and non-limited plots using the method described by Justes et al. (1994). As a clarification, this method of discrimination is not based on the growth dilution model for A^crit given above, and its use does not affect the predictive ability of this model. The A^crit curves calculated using the four modeling approaches introduced above were evaluated for their ability to separate N-limited and non-limited plots throughout the growing season. Also A^^ax and A^min curves for each approach were evaluated for their ability to define the upper and lower limits of observed plant N concentrations. To examine the biomass production and crop N uptake associated with crop N requirements, crop growth simulations using the best approach for estimating crop N requirements (objective 1) were performed for the 32 treatment combinations and compared to the experimental observations. Modeling of N uptake and associated crop growth is concep-

tually similar in most crop growth models, including the four introduced above. The implementation in the CropSyst model was used for this evaluation. Details of concepts and equations used to model crop growth as affected by water and nitrogen availability are given elsewhere (Stockle et al., 1994; Stockle and Nelson, 1996). Table 1 shows the crop input parameters used to implement simulations of winter wheat growth. These parameters are typical for the wheat cultivar Soissons grown in Auzeville. Phenology was adjusted as observed in the experimental plots. A few calibrated crop parameters (Table 1) were set based on plots not included in the evaluation reported herein.

4. Results and discussion 4.1. Evaluation offour approaches to model crop nitrogen requirements Fig. 1 compares the performance of the four approaches evaluated. The lower curve (Nmin) in the Table 1 Summary of crop parameters for CropSyst simulations Parameters Degree - days emergence (°C - days) Degree - days begin flowering (°C - days) Degree - days peak LAI (°C - days) Degree - days begin grain filling (°C - days) Degree - days maturity (°C - days) Base temperature (°C) Cutoff temperature (°C) Maximum root depth (m) Maximum LAI Specific leaf area (m^/kg) Stem/leaf partition coefficient Leaf duration (°C - days) Solar radiation extinction coefficient ET crop coefficient Maximum water uptake rate (mm/day) Critical canopy water potential (kPa) Wilting canopy water potential (kPa) Biomass-transpiration coefficient (Pa) Radiation-use efficiency (g/MJ) Maximum harvest index, HI

Obs Obs Obs Obs Obs Man Man Obs Obs Obs Obs Obs Man Cal Man Man Man Cal Man Obs

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Parameters were set as observed experimentally (Obs), extracted from the CropSyst manual (Man), or set by calibration (Cal).

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AFRCWHEAT2 model effectively includes the observed minimum concentrations, with a few data points lying below the curve. However, the upper limit curve (iVmax) has problems up to stage 35 (midshooting), and again at about stage 90 (maturity), with many data points (even for N-limited conditions) above the A^max curve. This will result in underprediction of the N requirement of wheat, thus affecting the simulation of crop growth in relation to available N. The performance of the approach in the EPIC model also presents problems. The A^^in curve appears adequate, but the A^crit curve has problems separating N-limited from non-limited data points, particularly before a fraction of the cycle of 0.4. Because crop N stress is simulated once plant N concentrations are below A^crit» this will lead to misrepresentation of stress during early growth (before completion of 40% of the growing cycle), a critical period of wheat growth and development. In addition, because a curve for N^ax is not specified, crop N requirements are underpredicted, which may lead to improper simulation of crop growth in relation to available N, and will misrepresent the potential for N extraction. The implementation in the Daisy model, which includes three characteristic curves, has the worst performance of all approaches tested. Throughout most of the growing season, described in terms of temperature sums, most data points from plots not limited by N and a significant amount of N-limited data points are above the iV^ax curve. Under these conditions, wheat N requirements will be severely underpredicted. The A/crit curve does not discriminate between N-limited and non-limited data points. The A^min curve, however, represents well the minimum concentrations observed. Given this performance, this approach should have significant limitations in predicting crop N requirements and crop response to N availability for these Auzeville data. The method based on growth dilution (CropSyst) is able to describe well the three characteristic curves that define crop N requirements and crop response to N availability. The A^cnt curve discriminated well between N-Iimited and non-limited data points. The A^max and Nmm curvcs effectively served as envelope curves to define the maximum and minimum limits for most observed data points with few exceptions. It seems that methods based on the growth dilution concept (Greenwood et al., 1990) should be preferred to

simulate crop N requirements. This approach has also the advantage of being easier to implement across different crop species and cultivars than the crop specific relations of some of the other models, and it is likely to be more transferable among growth environments. 4.2. Simulating nitrogen uptake and crop growth in relation to crop N requirements After establishing the good performance of the growth dilution concept for the simulation of crop N requirements, an analysis of the nitrogen uptake and crop growth associated with such requirements was performed. Fig. 2 shows a comparison of measured (symbols) and simulated (lines) evolution of biomass and plant N concentration for selected plots. Plot labels are composed of a plot identifier (first two characters) and a N treatment identifier (last two characters). As introduced above, NO and N3 correspond to no- and high-nitrogen treatments, respectively. For plots F8-N3 and F8-N0, following pea, there was an excellent agreement between observed and simulated values (good soil structure, large amount of initial N). The same was the case for F6-N3, after early-harvested sorghum. For plots F6-N0 and C5-N3, the agreement was good for plant N concentration, and also for biomass, except for the last (C5N3) or two last (F6-N0) observations. In the case of C5~N3, the plant N concentration was overpredicted for the measurement previous to the last, but the agreement between simulated and observed biomass was good. This resulted in a slight overprediction of simulated N uptake and a final simulated biomass larger than observed. A similar situation was found for plot F6-N0, somewhat enhanced by simultaneous overprediction of biomass and plant N concentration before the last observation. For plot CI-NO, although measured and simulated plant N concentrations agreed well, simulated crop N uptake was insufficient to support the observed biomass production. Fig. 3 shows the comparison between observed and simulated aboveground N uptake for all the treatment combinations. The linear regression between simulated and observed N uptake has an interception of zero and slope of 0.98, very close to the 1:1 Une of perfect agreement, but with a weak correlation

222 coefficient (r = 0.822). The agreement between simulated and observed N uptake is low for reliable model applications. One source of uncertainty is the varia-

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Observed N Uptake (kg/ha) Fig. 3. Comparison between observed and simulated N uptake for 32 N treatment combinations on winter wheat cultivar Soisson grown at Auzeville, France in 1992-1993.

29.1%. Plotting the 32 means versus all the samples resulted in a linear regression explaining 88.5% of the observed variability. Other sources of variability exist, as discussed below. Fig. 4 compares observed and simulated N uptake as a function of soil N availability. Available soil N was determined as the sum of the initial soil N to a depth of 120 cm, plus fertilization and mineralization during the growing season. Initial soil N fluctuated from 50 to 215 kg/ha, and fertilization from 0 to 200 kg/ha. Soil N mineralization was estimated as 40 kg/ha from simulations, a magnitude in agreement with the N balance sheet method introduced above. Thus, a wide range of available soil N is represented in the 32 treatment combinations included in this study, fluctuating from 90 to 370 kg/ha. For this wide range, the simulated N uptake is strongly linearly correlated with soil N availability (r = 0.998), as expected for a system where no constraints to uptake and growth other than available soil N are assumed (soil moisture and rooting depth were not limiting factors). For the measured N uptake, the correlation is weaker (r = 0.84), with large variability around the regression line. For example, for a soil N availability of about 325 kg/ha, observed N uptake fluctuated from 146 to 257 kg/ha.

Other agronomic factors may have affected crop growth in some of the experimental plots (e.g., rust infection under low-input management) reducing N uptake in relation to N available. Sampling size (5 cores/470-m^ plots) to determine initial soil N content, a quantity with significant spatial variability, may have been insufficient to obtain adequate measurements. The differences in uptake for similar N availability levels, however, appear high to be explained by this kind of reasoning only. It is possible that physical and/or chemical restrictions to N uptake in the field, other than those normally accounted for by crop growth models, may have also played a role. For example, soil structure could have been affected by wet soil conditions during harvest of some of the preceding crops as well as during sowing of the winter wheat. Fig. 5 shows observed and simulated biomass as a function of the corresponding observed or simulated aboveground N uptake. Simulated data points describe a N uptake/biomass production function that is an upper envelop for the observed data points, the latter showing large variability. This indicates that the characteristic plant N concentration curves were effective in defining crop N requirements and limitations to growth associated with plant N concentration. 300 250 -I o

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Data points falling below the simulated envelop must have resulted from growth reducing factors not accounted for by the model, which would also explain the differences between simulation and measurements shown in Fig. 2. No pattern of relationship between these data points and preceding crop or N input level was found.

5. Conclusions The use of the growth dilution concept provided a good base to estimate characteristic plant N concentration curves (maximum, critical, and minimum concentrations) throughout the growth cycle. Using the implementation of this concept in the CropSyst model, the simulated critical curve discriminated well between N-limited and non-limited experimental wheat plots growing under a wide range of soil N availability. The simulated maximum and minimum curves effectively served as envelope to define the upper and lower N concentration limits for most observed data. Other approaches, based on empirical relationships with the fraction of the growth cycle, growth stages, or temperature sums, were less satisfactory.

The simulated relationship between biomass production and N uptake correctly described an upper boundary for all observed data points. Points falling below the curve represented limitation to growth other than nitrogen. Despite adequate simulation of crop N requirements, simulated and observed crop N uptake on a plot by plot basis resulted in low agreement. Simulated crop N uptake was highly correlated with soil N availability, while the correlation for the observed data was weaker. Uncertainty in the measurement of initial soil N and crop N uptake, other growth reducing factors (e.g., diseases) not accounted for by the model, and possibly physical and/or chemical restrictions to field N uptake not normally included in crop growth models may have limited the ability to simulate crop growth in response to N availability.

References Debaeke, P., Aussenac, T., Fabre, J.L., Hilaire, A., Pujol, B. and Thuries, L. 1996. Grain nitrogen content of winter bread wheat {Triticum aestivum L.) as related to crop management and to the previous crop. Eur. J. Agron., 5: 273-286. Greenwood, D.J., Lemaire, G., Gosse, G., Cruz, P., Draycott, A. and Neeteson, J.J. 1990. Decline in percentage N of C3 and C4 crops with increasing plant mass. Ann. Bot., 66: 425-436. Hansen, S., Jensen, H.E., Nielsen, N.E. and Svendsen, H. 1991. Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model Daisy. Pert. Res., 27: 245-259. Jones, C.A. and Kiniry, J.R. 1986. CERES-Maize, a Simulation Model of Maize Growth and Development. Texas A and M University Press, Texas, TX, 193 pp. Justes, E., Mary, B., Meynard, J.M., Machet, J.M. and ThelierHuche, L. 1994. Determination of a critical nitrogen dilution curve for winter wheat crops. Ann. Bot., 74: 397-407. Plenet, D. 1995. Fonctionnement des cultures de mai's sous contrainte azotee. Doctoral Thesis, Academic de Nancy-Metz, Institut National Polytechnique de Lorraine, France, 247 pp. Porter, J.R. 1993. AFRCWHEAT2: a model of the growth and development of wheat incorporating responses to water and nitrogen. Eur. J. Agron., 2: 69-82. Remy, J.C. 1981. Etat actuel et perspectives de la mise en oeuvre des techniques de prevision de la fumure azotee. C. R. Acad. Agric. Fr., 67: 859-874. Salette, J. and Lemaire, G. 1981. Sur la variation de la teneur en azote des graminees fourrageres pendant leur croissance: formulation d'une loi de dilution. C. R. Acad. Sci. Paris Ser. m , 292: 875-878. Stockle, CO., Martin, S. and Campbell, G.S. 1994. CropSyst, a

225 cropping systems model: water/nitrogen budgets and crop yield. Agric. Syst., 46: 335-359. Stockle, C O . and Nelson, R. 1996. CropSyst User's Manual. Biological Systems Engineering Department, Washington State University, Pullman, WA, 186 pp. Williams, J.R., Jones, C.A., Kiniry, JR. and Spanel, D.A.

1989. The EPIC crop growth model. Trans. ASAE, 32: 497511. Zadoks, J.C., Chang, T.T. and Konzak, T.T. 1974. A decimal code for the growth stages of cereals. Weed Res., 14: 415421.