STICS crop model; Climate variability; LARS-WG; Yield prediction; Log-normal distribution; Central Limit Theorem; Convergence in Law Theorem
Abstract :
[en] The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield (Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach.
Allen R., Hanuschak G.A., Craig M.E. Forecasting crop acreages and yield in the face of and in spite of floods, crop yield forecasting methods. Proceedings of the Seminar Villefranche-sur-Mer 1994, 87-110.
Basso B., Ritchie J.T., Cammarano D., Sartori L. A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable field. Eur. J. Agron. 2011, 35(4):215-222.
Beaudoin N., Launay M., Sauboua E., Ponsardin G., Mary B. Evaluation of the soil crop model STICS over 8 years against the 'on farm' database of Bruyères catchment. Eur. J. Argon. 2008, 29:46-57.
Brisson N., Gary C., Justes E., Roche R., Mary B., Ripoche D., Zimmer D., Sierra J., Bertuzzi P., Burger P., Bussière F., Cabidoche Y.M., Cellier P., Debaeke P., Gaudillère J.P., Hénault C., Maraux F., Seguin B., Sinoquet H. An overview of the crop model STICS. Eur. J. Agron. 2003, 18(3-4):309-332.
Brisson, N., Launay, M., Mary, B., Beaudoin, N., 2009. Conceptual basis, formalisations and parameterization of the STICS crop model. Editions Quae. Collection Update Sciences and technologies.
Brisson N., Mary B., Ripoche D., Jeuffroy M.H., Ruget F., Nicoullaud B., Gate P., Devienne-Barret F., Antonioletti R., Durr C., Richard G., Beaudoin N., Recous S., Tayot X., Plenet D., Cellier P., Machet J.-M., Meynard J.M., Delécolle R. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomy 1998, 18(5-6):311-346.
Brisson N., Ruget F., Gate P., Lorgeau J., Nicoulaud B., Tayo X., Plenet D., Jeuffroy M.H., Bouthier A., Ripoche D., Mary B., Justes E. STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize. Agronomy 2002, 22:69-82.
Campbell G.S., Norman J.M. The description and measurement of plant canopy structure. Plant Canopies: Their Growth, Form and Function 1989, 1-20. Cambridge University Press. G. Russell, B. Marshall, P.G. Jarvis (Eds.).
Cantelaube P., Terres J.-M. Seasonal weather forecasts for crop yield modelling in Europe. Tellus A 2005, 57(3):476-487.
Challinor A.J., Slingo J.M., Wheeler T.R., Doblas-Reyes F.J. Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles. Tellus A 2005, 57(3):498-512.
Dagnelie P. Statistique théorique et appliquée. Tome 2. Inférence Statistique à Une et à Deux Dimensions 2011, 736. ISBN 978-2-8041-6336-5. De Boeck Editions.
Day R.H. Probability distributions of field crop yields. J. Farm Econ. 1965, 47(3):713-741.
de Moivre A. The Doctrine of Chances 1756, Millar, London. 3rd ed.
Doraiswamy P.C., Akhmedov B., Beard L., Stern A.J., Mueller R. Operational prediction of crop yields using MODIS data and products. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences Special Publications: Commission Working Group VIII WG VIII/10 2007, 1-5. European Commision DG JRC-Institute for the Protection and Security of the Citizen, Ispra, Italy. J. Chen, S.C. Saunders, K.D. Brosofske, T.R. Crow (Eds.).
Du X., Hennessy D., Yu C. Testing day's conjecture that more nitrogen decreases crop yield skewness. Am. J. Agric. Econ. 2012, 94(1):225-237.
Dumont B., Basso B., Leemans V., Bodson B., Destain J., Destain M. Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions. Precis. Agric. 2014, (In press). http://link.springer.com/article/10.1007/s11119-014-9380-7.
Dumont B., Basso B., Leemans V., Bodson B., Destain J.P., Destain M.F. Yield variability linked to climate uncertainty and nitrogen fertilisation. Precision Agriculture '13 2013, 427-434. Wageningen Academic Publishers. J. Stafford (Ed.).
Dumont B., Leemans V., Ferrandis S., Vancutsem F., Bodson B., Destain J., Destain M. Assessing the potential to predict wheat yields supplying the future by a daily mean climatic database. Precis. Agric. 2014, 15(3):255-272.
Dumont B., Leemans V., Mansouri M., Bodson B., Destain J., Destain M. Parameter optimisation of the STICS crop model, with an accelerated formal MCMC approach (DREAM algorithm). Environ. Modell. Softw. 2014, 52:121-135.
Ewert F., van Ittersum M.K., Heckelei T., Therond O., Bezlepkina I., Andersen E. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agric. Ecosys. Environ. 2011, 142(1-2):6-17.
Feller W. On the Kolmogorov-Smirnov limit theorems for empirical distributions. Ann. Math. Stat. 1948, 19(2):177-189.
Hennessy D.A. Crop yield skewness and the normal distribution. J. Agric. Econ. Res. 2009, 34(1):34-52.
Hennessy D.A. Crop yield skewness under law of the minimum technology. Am. J. Agric. Econ. 2009, 91(1):197-208.
Hennessy D.A. Modeling stochastic crop yield expectations with a limiting beta distribution. J. Agric. Econ. Res. 2011, 36(1):177-191.
Hewitt C.D. Ensembles-based predictions of climate changes and their impacts. Eos Trans. AGU 2004, 85(52).
Jamieson P.D., Semenov M.A., Brooking I.R., Francis G.S. Sirius: a mechanistic model of wheat response to environmental variation?. Eur. J. Agron. 1998, 8(3-4):161-179.
Just R.E., Weninger Q. Are crop yields normally distributed?. Am. J. Agric. Econ. 1999, 81(2):287-304.
Keller T.P., Wigton W.H. Composite predictions of yield for agricultural commodities. FCSM Conference Papers - Economic indicators 2003, 106-110.
Lawless C., Semenov M.A. Assessing lead-time for predicting wheat growth using a crop simulation model?. Agric. For. Meteorol. 2005, 135(1-4):302-313.
Mavromatis T., Hansen J.W. Interannual variability characteristics and simulated crop response of four stochastic weather generators. Agric. For. Meteorol. 2001, 109(4):283-296.
Mavromatis T., Jones P.D. Comparison of climate change scenario construction methodologies for impact assessment studies. Agric. For. Meteorol. 1998, 91(1-2):51-67.
Monteith J.L., Moss C.J. Climate and the efficiency of crop production in Britain [and Discussion]. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 1977, 281(980):277-294.
Nonhebel S. The effects of use of average instead of daily weather data in crop growth simulation models. Agric. Syst. 1994, 44(4):377-396.
Palmer T.N., Doblas-Reyes F.J., Hagedorn R., Weisheimer A. Probabilistic prediction of climate using multi-model ensembles: from basics to applications. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 2005, 360(1463):8.
Palosuo T., Kersebaum K.C., Angulo C., Hlavinka P., Moriondo M., Olesen J.E., Patil R.H., Ruget F., Rumbaur C., Takáč J., Trnka M., Bindi M., Çaldağ B., Ewert F., Ferrise R., Mirschel W., Şaylan L., Šiška B., Rötter R. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur. J. Agron. 2011, 35(3):103-114.
Penman H.L. Natural evaporation from open water, bare soil and grass. Proc. R. Soc. Lond. A 1948, 194:120-145.
Porter J.R., Semenov M.A. Climate variability and crop yields in Europe. Nature 1999, 400(6746):724.
Porter J.R., Semenov M.A. Crop responses to climatic variation. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2005, 360(1463):2021-2035.
Racsko P., Szeidl L., Semenov M. A serial approach to local stochastic weather models?. Ecol. Modell. 1991, 57(1-2):27-41.
Ramirez O.A., Misra S.K., Field J.E. Are crop yields normally distributed?. American Agricultural Economics Association. 2001 Annual Meeting 2001, 29pp.
Riha S.J., Wilks D.S., Simoens P. Impact of temperature and precipitation variability on crop model predictions. Clim. Change 1996, 32(3):293-311.
Semenov M., Porter J. Climatic variability and the modelling of crop yields?. Agric. For. Meteorol. 1995, 73(3-4):265-283.
Semenov M.A., Barrow E.M. Use of a stochastic weather generator in the development of climate change scenarios. Clim. Change 1997, 35(4):397-414.
Semenov, M.A., Barrow, E.M., 2002. LARS-WG-A stochastic weather generator for use in climate impact studies. User Manual, version 3.0, August 2002. Tech. rep., Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK.
Semenov M.A., Doblas-Reyes F.J. Utility of dynamical seasonal forecasts in predicting crop yield. Clim. Res. 2007, 34(1):71-81.
Semenov M.A., Jamieson P.D., Martre P. Deconvoluting nitrogen use efficiency in wheat: a simulation study. Eur. J. Agron 2007, 26(3):283-294.
Semenov M.A., Martre P., Jamieson P.D. Quantifying effects of simple wheat traits on yield in water-limited environments using a modelling approach?. Agric. For. Meteorol. 2009, 149(6-7):1095-1104.
Sinclair T.R., Seligman N.G. Crop modeling: from infancy to maturity. J. Agron. 1996, 88(5):698-704.
Singh U., Thornton P.K. Using Crop Models for Sustainability and Environmental Quality Assessment. Outlook Agric. 1992, 21(3):209-218.
Supit I., van Diepen C.A., de Wit A.J.W., Wolf J., Kabat P., Baruth B., Ludwig F. Assessing climate change effects on European crop yields using the Crop Growth Monitoring System and a weather generator. Agric. For. Meteorol. 2012, 164:96-111.
Thorp K.R., DeJonge K.C., Kaleita A.L., Batchelor W.D., Paz J.O. Methodology for the use of DSSAT models for precision agriculture decision support. Comput. Electron. Agric. 2008, 64(2):276-285.
Vrugt J.A., Braak C.J.F.T., Diks C.G.H., Robinson B.A., Hyman J.M., Higdon D. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. Int. J. Nonlinear Sci. Numer. Simul. 2009, 10(3):273-290.
