Comparison of Three Modelling Approaches to Simulate Regional Crop Yield: A Case Study of Winter Wheat Yield in Western Germany

Authors
A. Soltani 1
M. Bakker 2
A. Veldkamp 3
J. Stoorvogel 1
1 Soil Geography and Landscape Group, Wageningen University, P. O. 47, 6700 AA Wageningen, The Netherlands.
2 Land Use Planning Group, Wageningen University, P. O. 47, 6700 AA Wageningen, The Netherlands.
3 Geo-Information Science and Earth Observation Faculty (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
Abstract
The need for more comparisons among models is widely recognized. This study aimed to compare three different modelling approaches for their capability to simulate and predict trends and patterns of winter wheat yield in Western Germany. The three modelling approaches included an empirical model, a process-based model (LINTUL2), and a metamodel derived from the process-based model. The models outcomes were aggregated to general climate zones level of Western Germany to allow for a comparison with agricultural census data for validation purposes. The spatial patterns and temporal trends of winter wheat yield seemed to be better represented by the empirical model (R2= 70%, RMSE= 0.48 t ha-1 yr-1, and CV-RMSE= 8%) than by the LINTUL2 model (R2= 65%, RMSE= 0.67 t ha-1 yr-1, and CV-RMSE=11%) and the metamodel (R2= 57%, RMSE= 0.77 t ha-1 yr-1, and CV-RMSE=13%). All models demonstrated a similar order of magnitude of yield prediction and associated uncertainties. The suitability of the three models is context dependent. Empirical modelling is most suitable to analyze and project past and current crop-yield patterns, while crop growth simulation models are more suited for future projections with climate scenarios. The derived metamodels are fast reliable alternatives for areas with well calibrated crop growth simulation models. A model comparison helps to reveal shortcomings and strengths of the models. In our case, a performance comparison between the three modelling approaches indicated that, for simulating winter wheat growth in Western Germany, higher sensitivity to soil depth and lower sensitivity to drought in the LINTUL2 model would probably lead to better predictions.

Keywords

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1.       Adam, M., Belhouchette, H., Corbeels, M., Ewert, F., Perrin, A., Casellas, E., Celette, F. and Wery, J. 2012. Protocol to Support Model Selection and Evaluation in a Modular Crop Modelling Framework: An Application for Simulating Crop Response to Nitrogen Supply. Comput. Electron. Agr., 86: 43–54.
2.       Allen, R. G., Dirk, R. and Smith, M. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome, Italy.
3.       Andersen, E., Elbersen, B., Hazeu, G., Van Diepen, C. A., Baruth, B., Verhoog, A. D., Terluin, I. J., Borkowski, N. and Janssen, S. J. C. 2010. The Environmental Component, the Farming Systems Component and the Socio-economic Component of the Final Version of the SEAMLESS Database, D4.3.5-D4.4.5-D4.5.4. SEAMLESS Integrated Project, EU 6th Framework Programme, Contract No. 010036-2, PP. 401–401. www.SEAMLESS-IP.org.
4.       Angulo, C., Lock, R., Enders, A., Rötter, R., Fronzek, S., Carter, T. and Ewert, F. 2012. Implication of Crop Model Calibration Strategies for Assessing Regional Impacts of Climate Change in Europe. Agric. Forest Meteorol., 170: 32-46.
5.       Apipattanavis, S., Bert, F., Podestá, G. and Rajagopalan, B. 2010. Linking Weather Generators and Crop Models for Assessment of Climate Forecast Outcomes. Agric. Forest Meteorol., 150: 166-74.
6.       Audsley, E., Pearn, K. R., Harrison, P. A. and Berry, P. M. 2008. The Impact of Future Socio-economic and Climate Changes on Agricultural Land Use and the Wider Environment in East Anglia and North West England Using a Metamodel System. Clim. Change, 90: 57-88.
7.       Bakker, M. M., Govers, G., Ewert, F., Rounsevell, M. and Jones, R. 2005. Variability in Regional Wheat Yields as a Function of Climate, Soil and Economic Variables: Assessing the Risk of Confounding. Agric. Ecosyst. Environ., 110: 195–209.
8.       Bakker, M. M., Hatna, E., Mucher, S. and Kuhlman, T. 2011. Changing Environmental Characteristics of European Cropland. Agr. Sys., 104: 522 – 532.
9.       Bakker, M. and Veldkamp, A. 2012. Changing Relationships between Land Use and Environmental Characteristics and Their Consequences for Spatially Explicit Land-use Change Prediction. J. Land Use Sci., 7: 407-24.
10.    Barton, R. R. 1998. Simulation Metamodels. In: “Proceedings of the Winter Simulation Conference”, (Eds.): Medeiros, D. J., Watson, E. F., Carson, J. S. and Manivannan, M. S.. IEEE, PP. 167–74.
11.    Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F. X. and Huard, F. 2010. Why Are Wheat Yields Stagnating in Europe? A Comprehensive Data Analysis for France. Field Crops Res., 119: 201–12.
12.    Challinor, A. J., Ewert, F., Arnold, S., Simelton, E. and Fraser, E. 2009. Crops and Climate Change: Progress, Trends, and Challenges in Simulating Impacts and Informing Adaptation. J. Exp. Bot., 60: 2775–89.
13.    DDC IPCC. 2010. Data Distribution Centre of the Intergovernmental Panel on Climate Change. URL http://www.ipcc-data.org/.
14.    Donatelli, M., Russell, G., Rizzoli, A. E., Acutis, M., Adam, M., Athanasiadis, I. N., Balderacchi, M., Bechini, L., Belhouchette, H., Bellocchi, G., Bergez, J. E., Botta, M., Braudeau, E., Bregaglio, S., Carlini, L., Casellas, E., Celette, F., Ceotto, E., Charron Moirez, M. H., Confalonieri, R., Corbeels, M., Criscuolo, L., Cruz, P., Guardo, A., Ditto, D., Dupraz, C., Duru, M., Fiorani, D., Gentile, A., Ewert, F., Gary, C., Habyarimana, E., Jouany, C., Kansou, K., Knapen, R., Filippi, G. L., Leffelaar, P. A., Manici, L., Martin, G., Martin, P., Meuter, E., Mugueta, N., Mulia, R., Noordwijk, M., Oomen, R., Rosenmund, A., Rossi, V., Salinari, F., Serrano, A., Sorce, A., Vincent, G., Theau, J. P., Thérond, O., Trevisan, M., Trevisiol, P., Evert, F. K., Wallach, D., Wery, J., Zerourou, A., 2010. A Component-based Framework for Simulating Agricultural Production and Externalities. In: “Environmental and Agricultural Modeling”, (Eds.): Brouwer, F. M. and Ittersum, M. K.. Springer, Dordrecht, The Netherlands, PP. 63–108.
15.    Easterling, W. E., Mearns, L. O., Hays, C. J. and Marx, D. 2001. Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios. Part II. Accounting for Adaptation and CO2 Direct Effects. Clim. Change, 51: 173–97.
16.    Ewert, F. And Pleijel, H. 1999. Phenological Development, Leaf Emergence, Tillering and Leaf Area Ondex, and Duration of Spring Wheat across Europe in Response to CO2 and Ozone. Eur. J. Agron., 10: 171–184.
17.    Ewert, F., van Oijen, M. and Porter, J.R. 1999. Simulation of Growth and Development Processes of Spring Wheat in Response to CO2 and Ozone for Different Sites and Years in Europe Using Mechanistic Crop Simulation Models. Eur. J. Agron., 10: 231–47.
18.    Ewert, F., Rodriguez, D., Jamieson, P., Semenov, M. A., Mitchell, R. A. C., Goudriaan, J., Porter, J. R., Kimball, B. A., Pinter, P. J., Manderscheid, R., Weigel, H. J., Fangmeier, A., Fereres, E. And Villalobos, F. 2002. Effects of Elevated CO2 and Drought on Wheat: Testing Crop Simulation Models for Different Experimental and Climatic Conditions. Agr. Ecosyst. Enrivon., 93: 249–66.
19.    Ewert, F. 2004. Modelling Changes in Global Regionalized Food Production Systems under Changing Climate and Consequences for Food Security and Environment: Development of An Approach for Improved Crop Modelling within IMAGE. Plant Production Systems Group, Department of Plant Sciences, National Institute for Public Health and Environment (RIVM), Wageningen University and Netherlands Environmental Assessment Agency (MNP), 55 PP.
20.    Ewert, F., Rounsevell, M. D.A., Reginster, I., Metzger, M. J. and Leemans, R. 2005. Future Scenarios of European Agricultural Land Use. I. Estimating Changes in Crop Productivity. Agr. Ecosyst. Enrivon., 107: 101–16.
21.    Graybosch, R. A. and Peterson, C. J. 2010. Genetic Improvement in Winter Wheat Yields in the Great Plains of North America, 1959-2008. Crop Sci., 50: 1882-90.
22.    Hansen, J. W. and Jones, J. W. 2000. Scaling-up Crop Models for Climate Variability Spplication. Agr. Sys., 65: 43-72.
23.    Harrison, P. A. and Butterfield, R. E. 1996. Effects of Climate Change on Europe-wide Winter Wheat and Sunflower Productivity. Clim. Res., 7: 225–41.
24.    Harrison, P.A., Porter, J. R. And Downing, T. E. 2000. Scaling-up the AFRCWHEAT2 Model to Assess Phenological Development for Wheat in Europe. Agric. Forest Meteorol.,1001: 167–186.
25.    Hazeu, G.W., Elbersen, B., Andersen, E., Baruth, B., Van Diepen, K. andMetzger, M. J. 2010. A Biophysical Typology for a Spatially-explicit Agri-environmental Modeling Framework. In: “Environmental and Agricultural Modelling: Integrated Approaches for Policy Impact Assessment”, (Eds.): Brouwer, F. and Van Ittersum, M. K. Springer Academic Publishing, 159-187.
26.    IPCC, 2001. Climate Change 2001: The Scientific Basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
27.    Janssen, S., Andersen, E., Athanasiadis, I. N. and Van Ittersum, M. K. 2009. A Database for Integrated Assessment of European Agricultural Systems. Environ. Sci. Policy., 12: 573–87.
28.    JRC. 1998. Estimation of the Phenological Calendar, Kc-curve and TemperatureS for cereals, Sugar Beet, Potato, Sunflower and Rapeseed across Pan Europe, Turkey and the Maghreb Countries by Means of Transfer Procedures. In: “Agrometeorological Transfer Procedures”, (Eds.): Willekens, A., van Orshoven, J. and Feyen, J.. European Commission (JRC-SAI), PP. 1–3.
29.    Kersebaum, K. Ch., Nendel, C., Mirschel, W., Wenkel, K. O., Manderscheid, R, and Weigel, H. J. 2009. Simulated Effects of Climate Change and Rising CO2 on Crop Production and Management in Different Regions of Germany. IOP Conf. Ser.: Earth Environ. Sci. 6. IOP publishing Ltd. Copenhagen, Denmark.
30.    Kleijnen, J. P. C. and Sargent, R. G. 2000. A Methodology for Fitting and Validating Metamodels in Simulation. Eur. J. Oper. Res., 120: 14–29.
31.    Kok, K. and Veldkamp, A. 2001. Evaluating the Impact of Spatial Scales on Land Use Pattern Analysis in Central America. Agric. Ecosyst. Environ., 85: 205–21.
32.    Kok, K. and Veldkamp, A. 2011. Scale and Governance: Conceptual Considerations and Practical Implications. Ecol. Soc., 16: 23.
33.    Landau, S., Mitchell, R. A. C., Barnett, V., Colls, J. J., Craigon, J. and Payne, R. W. 2000. A Parsimonious, Multiple-regression Model of Wheat Yield Response to Environment. Agric. Forest Meteorol., 101: 151–166.
34.    Lobell, D. B. and Burke, M. B. 2010. On the Use of Statistical Models to Predict Crop Yield Responses to Climate Change. Agric. Forest Meteorol., 150: 1443–52.
35.    Mendicino, G. and Versace, P. 2003. Space-time Analysis of Water Deficit. Eur. Water., 3(4): 3-8.
36.    Metzger, M. J., Bunce, R.G. H., Jongman, R. H. G., Mücher, C. A. And Watkins, J. W. 2005. A Climatic Stratification of the Environment of Europe. Global Ecol. Biogeogr., 14(6): 549–563.
37.    Reimer, J. J. and Li, M. 2009. Yield Variability and Agricultural Trade. Agric. Res. Econ. Rev., 38: 258–70.
38.    Rotter, R. P., Palosuo, T., Kersebaum, K. C., Angulo, C., Bindi, M., Ewert, F., Ferrise, R., Hlavinka, P., Moriondo, M., Nendel, C., Olesen, J. E., Patil, R. H., Ruget, F., Takac, J. and Trnka, M. 2012. Simulation of Spring Barley Yield in Different Climatic Zons of Northern and Central Europe. A Comparison of Nine Crop Models. Field Crop. Res., 133: 23–36.
39.    PESERA. 1999. The Pan European Soil Erosion Risk Assessment Project. Contact No: QLK5-1999-01323, http://www.pesera.jrc.it).
40.    Qian, B., De Jong, R., Yang, J., Wang, H. And Gameda, S. 2011. Comparing Simulated Crop Yields with Observed and Synthetic Weather Data. Agric. Forest Meteorol., 151: 1781-91.
41.    Saarikko, R. A., 2000. Applying a Site Based Crop Model to Estimate Regional Yields under Current and Changed Climates. Ecol. Modell., 131: 191–206.
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Journal of Agricultural Science and Technology
Volume 18, Issue 1
January and February 2016
Pages 191-206