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Rice production is threatened by climate change and the productivity of rainfed rice is increasingly challenged. A better understanding of the future trends of rice production associated with climate change is important for improving food security. Rice production under irrigated and rainfed conditions was simulated using the rice crop model ORYZA2000. Simulated rice yield representing crop and environment interaction was used to evaluate the drought impact of climate change on rainfed rice in South Asia. If rainfed rice system was applied in all current rice cultivating areas in South Asia, drought stress could result to yield losses of more than 80 in 22 %, but crop failure was lower than 40 in 73 % of the areas under mild and severe SRES A1B and A2. The spatial patterns of drought stress on rainfed rice were similar under both A1B and A2, and the yield loss and crop failure decreased slightly in the far future (2045 to 2074) in areas where drought risk was high in the near future (2015 to 2044), but the impacts would gradually increase over initially low-impact areas. Both A1B and A2 would shift the best sowing season of rainfed rice to be earlier or later by up to 90 days in 30 years. Appropriate adjustment of sowing season is a major adaptation strategy for rainfed rice production in South Asia to benefit from climate change. In this case, rainfed rice yield could potentially increase by about 10 % in most areas of South Asia associated with 10 to 50 % lower inter-annual variation and slightly higher risk for crop failure. [ABSTRACT FROM AUTHOR]

Predicting rice ( Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [ CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [ CO2] and temperature. [ABSTRACT FROM AUTHOR]

Highlights: [•] Rice introgression breeding for drought tolerance was confirmed as an effective approach. [•] Identified two key strategies of Green Super Rice (GSR) to cope with drought stress for high yield. [•] GSR cultivars could have a yield advantage of 31–36% across environments against drought check. [•] Easy biomass measurements can be used to study the mechanism of stress tolerance of rice cultivar. [Copyright &y& Elsevier]

Highlights: [•] First crop modeling framework of ORYZA2000 for the selection of drought resistant rice. [•] Multiple environment trials are consistently expanded to large environments by modeling. [•] Increase in the number of environment enhances genotypic effect and heritability. [•] Different genotypes can be selected to match breeding targets with modeling outputs. [•] ORYZA2000 is effective and highly repeatable tool to aid rice breeding. [ABSTRACT FROM AUTHOR]

Multi-Environment Trials (MET) are conventionally used to evaluate varietal performance prior to national yield trials, but the accuracy of MET is constrained by the number of test environments. A modeling approach was innovated to evaluate varietal performance in a large number of environments using the rice model ORYZA (v3). Modeled yields representing genotype by environment interactions were used to classify the target population of environments (TPE) and analyze varietal yield and yield stability. Eight Green Super Rice (GSR) and three check varieties were evaluated across 3796 environments and 14 seasons in Southern Asia. Based on drought stress imposed on rainfed rice, environments were classified into nine TPEs. Relative to the check varieties, all GSR varieties performed well except GSR-IR1-5-S14-S2-Y2, with GSR-IR1-1-Y4-Y1, and GSR-IR1-8-S6-S3-Y2 consistently performing better in all TPEs. Varietal evaluation using ORYZA (v3) significantly corresponded to the evaluation based on actual MET data within specific sites, but not with considerably larger environments. ORYZA-based evaluation demonstrated the advantage of GSR varieties in diverse environments. This study substantiated that the modeling approach could be an effective, reliable, and advanced approach to complement MET in the assessment of varietal performance on spatial and temporal scales whenever quality soil and weather information are accessible. With available local weather and soil information, this approach can also be adopted to other rice producing domains or other crops using appropriate crop models., Competing Interests: The authors have declared that no competing interests exist.