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Wallach, D., Mearns, L. O., Ruane, A. C., Rötter, R. P., & Asseng, S. (2016). Lessons from climate modeling on the design and use of ensembles for crop modeling. Clim. Change, 139(3-4), 551–564.
Abstract: Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.
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Perego, A., Sanna, M., Giussani, A., Chiodini, M. E., Fumagalli, M., Pilu, S. R., et al. (2014). Designing a high-yielding maize ideotype for a changing climate in Lombardy plain northern Italy. Science of the Total Environment, 499, 497–509.
Abstract: • ARMOSA model simulated a maize ideotype with drought adaptation under climate change. • The ideotype needs less water for higher yield compared to current hybrids. • Higher production involves more crop residues that enhance soil C sequestration. • Soil organic C may generally decrease and N leaching will increase in sandy soil. The expected climate change will affect the maize yields in view of air temperature increase and scarce water availability. The application of biophysical models offers the chance to design a drought-resistant ideotype and to assist plant breeders and agronomists in the assessment of its suitability in future scenarios. The aim of the present work was to perform a model-based estimation of the yields of two hybrids, current vs ideotype, under future climate scenarios (2030–2060 and 2070–2100) in Lombardy (northern Italy), testing two options of irrigation (small amount at fixed dates vs optimal water supply), nitrogen (N) fertilization (300 vs 400 kg N ha− 1), and crop cycle durations (current vs extended). For the designing of the ideotype we set several parameters of the ARMOSA process-based crop model: the root elongation rate and maximum depth, stomatal resistance, four stage-specific crop coefficients for the actual transpiration estimation, and drought tolerance factor. The work findings indicated that the current hybrid ensures good production only with high irrigation amount (245–565 mm y− 1). With respect to the current hybrid, the ideotype will require less irrigation water (− 13%, p < 0.01) and it resulted in significantly higher yield under water stress condition (+ 15%, p < 0.01) and optimal water supply (+ 2%, p < 0.05). The elongated cycle has a positive effect on yield under any combination of options. Moreover, higher yields projected for the ideotype implicate more crop residues to be incorporated into the soil, which are positively correlated with the SOC sequestration and negatively with N leaching. The crop N uptake is expected to be adequate in view of higher rate of soil mineralization; the N fertilization rate of 400 kg N ha− 1 will involve significant increasing of grain yield, and it is expected to involve a higher rate of SOC sequestration.
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Toscano, P., Ranieri, R., Matese, A., Vaccari, F. P., Gioli, B., Zaldei, A., et al. (2012). Durum wheat modeling: The Delphi system, 11 years of observations in Italy. European Journal of Agronomy, 43, 108–118.
Abstract: ► Delphi system, based on AFRCWHEAT2 model, for durum wheat forecast. ► AFRCWHEAT2 model was calibrated and validated for three years. ► A scenario approach was applied to simulation of durum wheat yield. ► Operational mode for eleven years in rainfed and water limiting conditions. ► Accurate forecast as an useful planning tool. Crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. The aim of this work was to test the predictive capacity of the Delphi system, calibrated and determined for each pedoclimatic factor affecting durum wheat during phenological development. at regional scale. We present an innovative system capable of predicting spatial yield variation and temporal yield fluctuation in long-term analysis, that are the main purposes of regional crop simulation study. The Delphi system was applied to simulate growth and yield of durum wheat in the major Italian supply basins (Basilicata, Capitanata, Marche, Tuscany). The model was validated and evaluated for three years (1995-1997) at 11 experimental fields and then used in operational mode for eleven years (1999-2009), showing an excellent/good accuracy in predicting grain yield even before maturity for a wide range of growing conditions in the Mediterranean climate, governed by different annual weather patterns. The results were evaluated on the basis of regression and normalized root mean squared error with known crop yield statistics at regional level. (c) 2012 Elsevier B.V. All rights reserved.
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