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Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles.

The Government of India has pursued policies supporting intensive use of irrigation and fertilizer for the development of agrarian economy. A first order assessment of the impacts of these development policies on mitigation, adaptation, resilience and sustainability has been made quantitatively with the help of specific performance indicators. The analysis is based on the hypothesis that productivity enhancement serves better to deal with climate change as it minimizes deforestation and therefore reduces intensification of greenhouse gas emissions. The assessment establishes that development policies have been highly successful in reducing potential greenhouse gas intensification and increasing the adaptation capacity in terms of food grain production. The performance of policies, particularly of water resources development, with respect to sustainability is, however, questionable. The relatively new initiative on micro irrigation fares well in respect of most performance indicators, calling for further policy support for its scaling out.

Weather based insurance is a resilience strategy adopted by farmers. It is intended to provide protection to the cultivator against declined rainfall, which is deemed to adversely affect the crop during its cultivation period. It is becoming popular nowadays in India due to high fluctuation in rainfall and other climate related parameters. The present paper provides a method to compute the initial premium for each crop based on the premium structure given by Agricultural Insurance Company of India Limited, New Delhi. For this, the duration in each stage of selected crop identified by Crop Production Guide(2005) jointly published by Tamil Nadu Agricultural University and Department of Agriculture, Government of Tamil Nadu and 30 years of rainfall data from Indian Meteorological Department (IMD) were used. The payout structure was derived for each stage of the selected crop in the respective district. The strike or upper threshold of the rainfall corresponds to the 30 year average accumulated rainfall of the district reference weather station while the exit or lower threshold is intended to equal the water requirement of the respective crop necessary to avoid complete crop failure. This way, the weather based crop insurance acts as a resilience mechanism for rainfall uncertainties., 和文のものは、英文の報告の内容を要約したものとなっている

Understanding and increasing yield potential of cereals is essential to meet the growing food demand in Asia. A crop growth simulation model -WTGROWS- was developed to quantify the climatically determined potential grain yields and yield gaps in wheat in tropics and sub-tropics. The model written in PCSMP simulates daily dry matter production as a function of solar radiation, maximum and minimum temperatures, and water and nitrogen stresses. Comparison of simulated and measured quantities indicated satisfactory performance of the model in reference to water and nitrogen uptake, dry matter growth and grain yield in potential as well as water- and N-limited environments. The wheat yield potential in India varied between 2.6 and 8.3 t ha -1depending upon the location. Economically optimal yields in irrigated environments were estimated for all locations based on current price ratios of N fertilizer and grain, native soil fertility, simulated crop response to N fertilizer and other costs related to transport, harvesting and market forces. Yield gaps were found to be small in irrigated regions of northwestern India but significantly large in eastern regions. Almost 35 - 50% of the gap could be ascribed to delayed sowing. Crop simulation with different amounts of nitrogen and irrigation showed significant interaction between water and N availability and climatic variability, particularly with low inputs. The effect of climate change was more pronounced in central India where yield potential is already low.The model was also used to explore the opportunities for growing wheat in irrigated and rainfed tropical southeastern Asia. The results indicated that potential yields exceed 3 t ha -1at all places and increased further with latitude and elevation. At sea level, between equator and 8°N latitude, potential grain yield was 3 t ha -1. It increased to 5 t ha -1at 21°N and 4.5 t ha -1at 10°S latitudes. Realization of the yield potential of the presently available varieties may be limited because of several agronomic constraints.A simulation framework has been developed to determine the relative importance of different plant traits in isolation or in combination for increasing yield potential. In this approach, hypothetical genotypes are 'created' by changing the specific crop parameters of a crop simulation model. The impact of simultaneous change in many traits is assessed by randomly combining different traits in the hypothetical genotype. The approach is illustrated with examples for rice in tropics. No trait individually or in combination provides more than 5% advantage in yield at the level of management typically practiced by breeders. In such environments, even though genotypes may possess traits for higher yield potential, they will not be able to express them. Another framework is presented for using crop simulation models and statistical analysis together to increase the efficiency of multi-environment genotype testing.