Search

• There is an optimal rainfall for cowpea beyond which yield declined. • Cowpea yield response to water deficit and excess depends on soil type. • Projected heavy rainfalls would lead to excessive soil water even in dryland. • Excessive soil water poses as much of a threat as drought to legume production. • The need for countermeasures for both soil water deficit and excess is underscored. The intensification of the hydrological cycle has increased heavy rainfall and drought events in a changing climate. However, compared to drought, the impacts of heavy rainfall on crop production are under-studied. Using field experimental data and a calibrated crop model CYGMA, we showed that excessive soil water associated with heavy rainfall events is having a detrimental effect on cowpea yields, even in the dry environments of West Africa where cowpea is an important, protein-rich cash crop. Cowpea yields are susceptible to heavy rainfall in areas with poorly drained soils, and to drought in soils that have a low water-retention capacity. The crop model captured of the main characteristics of the observed development, growth, and yield, as well as the characteristics of root-zone soil water contents and how they vary by soil type. The analysis of d4PDF factual and counterfactual climate model simulations revealed that heavy rainfall events associated with anthropogenic climate change have increased in recent decades, and that they are projected to increase in future. Further, changes in seasonal rainfall and the number of dry days would be largely absent from CMIP6 climate projections by mid-century. Reductions in cowpea yields due to excessive soil water is projected to become more frequent, and the potential damage in a 1-in-100 extremely wet year would be comparable to the damage currently experienced in droughts, irrespective of soil types. Simulations of the projected damage due to drought show that the situation will be similar to current levels, with drought remaining a major climate hazard. However, excessive soil water is projected to be a serious threat to food security in the region. Our findings indicate that, even in dry environments, cropping systems need to be implemented in order to reduce the susceptibility of soils to both drought and excessive soil water. [ABSTRACT FROM AUTHOR]

Abstract: A Bayesian approach, the Markov Chain Monte Carlo (MCMC) technique, was applied to a newly developed large-scale crop model for paddy rice to optimize a new set of regional-specific parameters and quantify the uncertainty of yield estimation associated with model parameters. The developed large-scale model is process-based and up-scaled from a conventional field-scale model to meet the intended spatial-scale of the large-scale model to the typical grid size of high-resolution climate models. The domain of the large-scale model covers all of Japan, but the crop simulation is conducted for each local governmental area in Japan. The MCMC technique exhibits powerful capability to optimize multiple parameters in a nonlinear and fairly complex model. The application of the Bayesian approach is useful to quantify the uncertainty of model parameters in a comprehensive manner when researchers on crop modeling analyze the uncertainty of yield estimation associated with model parameters under given observations. A sensitivity analysis of the large-scale model was conducted with the obtained posterior distribution of parameters and warming conditions that have never been experienced before to demonstrate the change in the uncertainty of yield estimation associated with the uncertainty of parameters of the large-scale model. The uncertainty of yield estimation under warming conditions was larger than that obtained under climate conditions that have been experienced before. This raises a concern that the uncertainty of impact assessment on crop yield may increase if future climate projections are fed to crop models with parameters optimized under current climate conditions. [Copyright &y& Elsevier]

• Systematically examines climatic forcing data in agricultural model performance • Explores uncertainty across up to 91 climate data / crop model combinations • Isolates key climatic features driving interannual yield variation in each region • Quantifies performance of climatic forcing datasets for top countries and crop species • More extensive bias correction improves climatic forcing datasets for crop models We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region. [ABSTRACT FROM AUTHOR]