A meteorological‐based crop coefficient model for estimation of daily evapotranspiration

Climate change has an impact on soybean production in the USA, necessitating thorough impact studies across broad geographic areas and extended periods to develop appropriate coping strategies. This study investigates the simulated response of soybean in ten major soybean-growing states of the USA under Climate Model Intercomparison Project Phase 5 based on multiple global climate models, two representative concentration pathways [RCP8.5 and RCP4.5] under rainfed and irrigated conditions for 2013–2039, 2043–2069, 2063–2099. The future climate series was developed using Agricultural Model Intercomparison and Improvement Project protocol by applying mean and variability, and CROPGRO-soybean model was explored for soybean simulation under 400 ppm CO2 level and a set of management. Under future climate, intense changes in temperature, precipitation amount and variability are anticipated under RCP8.5 than RCP4.5. As a result, a shorter life cycle, more evapotranspiration, lower grain production, higher water consumption and water productivity were expected under RCP8.5 than RCP4.5 scenarios. A higher reduction in grain yield and water productivity is expected under rainfed than irrigated conditions and intensity increases with advancement towards end of the century. Irrigation tends to decrease adverse climate change effects; however, the marginal economy for irrigation water must be assessed. Since the northern states under study are likely to experience increased grain yields or lower negative impacts, these areas could be the major production zones for soybean production in the future if only climate change is taken into account. Before reaching a convincing conclusion, different adaptation strategies must be thoroughly investigated.

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A meteorological‐based crop coefficient model for estimation of daily evapotranspiration

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References 53 publications

Ecohydrological Model for Grassland Lacking Historical Measurements I: Downscaling Evaporation Data Based on Dynamic Sensitive Parameters and Deep Learning

Ecohydrological Model for Grassland Lacking Historical Measurements I: Downscaling Evaporation Data Based on Dynamic Sensitive Parameters and Deep Learning

Combining the FAO-56 method and the complementary principle to partition the evapotranspiration of typical plantations and grasslands in the Chinese Loess Plateau

Soybean response under climatic scenarios with changed mean and variability under rainfed and irrigated conditions in major soybean-growing states of the USA

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