Abstract
Rice is the staple food for about half of the world’s population. Although global production has more than doubled in the last 40 years, food security problems still persist and need to be managed based on early and reliable forecasting activities. This is especially true since the frequency of extreme weather events is forecasted to increase by the intergovernmental panel on climate change (IPCC). The most advanced crop yield forecasting systems are based on simulation models. However, examples of operational systems implementing models which are suitable for reproducing the peculiarities of paddy rice, especially on small scales, are missing. The rice model WARM is used within the crop yield forecasting system of the European Commission. In this article we evaluated the WARM model for the simulation of rice growth under flooded and unflooded conditions in China and Italy. The WARM model simulates crop growth and development, floodwater effect on the vertical thermal profile, blast disease, cold-shock induced spikelet sterility during the pre-flowering period and hydrological peculiarities of paddy soils. We identified the most relevant model parameters through sensitivity analyses carried out using the Sobol’ method and then calibrated using the simplex algorithm. Data from 11 published experiments, covering 13 locations and 10 years, were used. Two groups of rice varieties were identified for each country. Our results show that the model was able to reproduce rice growth in both countries. Specifically, the average relative root mean square error calculated on aboveground biomass curves was 21.9% for the calibration and 23.6% for validation. The parameters of the linear regression equation between measured and simulated values were always satisfactory. Indeed, intercept and slope were always close to their optima and R2 was always higher than 0.79. For some of the combinations of country and simulated variable, the indices of agreement calculated for the validation datasets were better then the corresponding ones computed at the end of the calibration, indirectly proving the robustness of the modeling approach. WARM’s robustness and accuracy, combined with the low requirements in terms of inputs and the implementation of modules for reproducing biophysical processes strongly influencing the year-to-year yield variation, make the model suitable for forecasting rice yields on regional, national and international scales.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Acutis M., Confalonieri R. (2006) Optimization algorithms for calibrating cropping systems simulation models. A case study with simplex-derived methods integrated in the WARM simulation environment, Ital. J. Agrometeorol. 3, 26–34.
Bannayan M., Crout N.M.J. (1999) A stochastic modelling approach for real-time forecasting of winter wheat yield, Field Crop. Res. 62, 85–95.
Bechini L., Bocchi S., Maggiore T., Confalonieri R. (2006) Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.), Environ. Model. Softw. 21, 1042–1054.
Belder P., Bouman B.A.M., Cabangong R., Guoan L., Quilang E.J.P., Yuanhua L., Spiertz J.H.J., Tuong T.P. (2004) Effects of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia, Agr. Water Manage. 65, 193–210.
Bezuidenhout C.N., Singels A. (2007) Operational forecasting of South African sugarcane production: Part 1 — System description, Agr. Syst. 92, 23–38.
Boschetti M., Bocchi S., Stroppiana D., Brivio P.A. (2006) Estimation of parameters describing morpho-physiological features of Mediterranean rice group for crop modelling purposes, Ital. J. Agrometeorol. 3, 17–25.
Bouman B.A.M., Xiaoguang Y., Huaqi W., Zhimin W., Junfang Z., Bin C. (2006) Performance of aerobic rice varieties under irrigated conditions in North China, Field Crop. Res. 97, 53–65.
Campbell C.S., Heilman J.L., McInnes K.J., Wilson L.T., Medley J.C., Wu G., Cobos D.R. (2001) Seasonal variation in radiation use efficiency of irrigated rice, Agric. Forest Meteorol. 110, 45–54.
Casanova D., Epema G.F., Goudriaan J. (1998) Monitoring rice reflectance at field level for estimating biomass and LAI, Field Crop. Res. 55, 83–92.
Confalonieri R., Bocchi S. (2005) Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy, Eur. J. Agron. 23, 315–326.
Confalonieri R., Mariani L., Bocchi S. (2005) Analysis and modelling of water and near water temperatures in flooded rice (Oryza sativa L.), Ecol. Model. 183, 269–280.
Confalonieri R., Acutis M., Bellocchi G., Cerrani I., Tarantola S., Donatelli M., Genovese G. (2006a) Exploratory sensitivity analysis of CropSyst, WARM and WOFOST: a case-study with rice biomass simulations, Ital. J. Agrometeorol. 3, 17–25.
Confalonieri R., Gusberti G., Bocchi S., Acutis M. (2006b) The CropSyst model to simulate the N balance of rice for alternative management, Agron. Sustain. Dev. 26, 241–249.
Denison R.F., Loomis R.S. (1989) An Integrative Physiological Model of Alfalfa Growth and Development, Univ. Calif. Div. Agric. Natural Resources Publ. 1926, 73 p.
Dingkuhn M., Jones M.P., Johnson D.E., Sow A. (1998) Growth and yield potential of Oryza sativa and O. glaberrima upland rice cultivars and their interspecific progenies, Field Crop. Res. 57, 57–69.
Dingkuhn M., Johnson D.E., Sow A., Audebert A.Y. (1999) Relationships between upland rice canopy characteristics and weed competitiveness, Field Crop. Res. 61, 79–95.
Duan Q., Sorooshian S., Gupta V.K. (1992) Effective and efficient global optimization for conceptual rainfall models, Water Resour. Res. 28, 1015–1031.
Feng L., Bouman B.A.M., Tuong T.P., Cabangong R.J., Li Y., Lu G., Feng Y. (2007) Exploring options to grow rice using less water in northern China using a modeling approach. I. Field experiments and model evaluation, Agr. Water Manage. 88, 1–13.
Genovese G., Vignolles C., Nègre T., Passera G. (2001) A methodology for a combined use of normalized difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting A case study on Spain, Agronomie 21, 91–111.
IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, in: Pachauri R.K., Reisinger A. (Eds.), IPCC, Geneva, Switzerland, 104 p.
Jing Q., Bouman B.A.M., Hengsdijik H., Van Keulen H., Cao W. (2007) Exploring options to combine high yields with high nitrogen use efficiencies in irrigated rice in China, Eur. J. Agron. 26, 166–177.
Kiniry J.R., McCauley G., Xie Yun, Arnold J.G. (2001) Rice parameters describing crop performance of four U.S. cultivars, Agron. J. 93, 1354–1361.
Kropff M.J., van Laar H.H., Matthews R.B. (1994) ORYZA1: An ecophysiological model for irrigated rice production SARP Research Proceedings, International Rice Research Institute: Los Banos, Philippines, 110 p.
Mall R.K., Aggarwal P.K. (2002) Climate change and rice yields in diverse agro-environments of India. I. Evaluation of impact assessment models, Climatic Change 52, 315–330.
Monteith J.L. (1996) The quest for balance in crop modeling, Agron. J. 88, 695–697.
Olesen J.E., Carter T.R., Diaz-Ambrona C.H., Fronzek S., Heidmann T., Hickler T., Holt T., Quemada M., Ruiz-Ramos M., Rubaek G.H., Sau F., Smith B., Sykes M.T. (2007) Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models, Climatic Change 81, 123–143.
Rodriguez D., Van Oijen M., Schapendonk A.H.M.C. (1999) LINGRA-CC: a sink-source model to simulate the impact of climate change and management on grassland productivity, New Phytol. 144, 359–368.
Satterthwaite F.E. (1946) An approximate distribution of estimates of variance components, Biometrics Bull. 2, 110–114.
Sié M., Dingkuhn M., Wopereis M.C.S., Miezan K.M. (1998) Rice crop duration and leaf appearance rate in a variable thermal environment. I. Development of an empirically based model, Field Crop. Res. 57, 1–13.
Singh U., Ritchie J.T., Godwin D.C. (1993) A user’s guide to CERES Rice — v2.10. International Fertilizer Development Center: Muscle Shoals, AL, USA.
Sobol’ I.M. (1993) Sensitivity estimates for nonlinear mathematical models, Math. Model. Computat. Exp. 1, 407–414.
Solh M. (2003) Keynote address: Issues and challenges in rice technological development for sustainable food security, in: Sustainable rice production for food security; Proceedings of the 20th Session of the International Rice Commission. Rome, Italy, FAO.
Stöckle C.O., Williams J.R., Rosenberg C.A., Jones C.A. (1992) A method for estimating direct and climatic effects of rising atmospheric carbon dioxide on crops growth and yield, Agr. Syst. 38, 225–238.
Van Diepen C.A., Rappoldt C., Wolf J., Van Keulen H. (1988) Crop growth simulation model WOFOST. Documentation version 4.1. Centre for world food studies, Wageningen, p. 299.
Van Ittersum M.K., Leffelaar P.A., Van Keulen H., Kropff M.J., Bastiaans L., Goudriaan J. (2003) On approaches and applications of the Wageningen crop models, Eur. J. Agron. 18, 201–234.
Van Keulen H., Wolf J. (1986) Modelling of agricultural production: weather soils and crops Simulation Monographs. Pudoc, Wageningen, The Netherlands, p. 479.
Welch B.L. (1947) The generalization of “student’s” problem when several different population variances are involved, Biometrika 34, 28–35.
Williams J.R., Jones C.A., Dyke P.T. (1984) A modeling approach to determining the relationship between erosion and soil productivity, T. ASAE 27, 129–144.
Wit A.J.W., Boogaard H.L., Van Diepen C.A. (2005) Spatial resolution of precipitation and radiation: The effect on regional crop yield forecasts, Agr. Forest Meteorol. 135, 156–168.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Confalonieri, R., Rosenmund, A.S. & Baruth, B. An improved model to simulate rice yield. Agron. Sustain. Dev. 29, 463–474 (2009). https://doi.org/10.1051/agro/2009005
Accepted:
Issue Date:
DOI: https://doi.org/10.1051/agro/2009005