Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Abstract

Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.

Climate Change Impacts on Rainfed Maize Yields in Kansas: Statistical vs. Process-Based Models

“…It has been found that a 1 • C rise in the maximum daily temperature (T max ) has reduced maize and rice yields in the southeastern USA by 34% and 8.3%, respectively [23], and results from a statistical model have indicated an 8.3% maize yield reduction globally with a 1 • C rise in temperature [24]. A recent study [25] conducted in Kansas found that temperature has a more pronounced influence on maize production in the region than rainfall. Maize yield reductions in the US of 43% to 44% have been predicted under a slow-warming scenario (assuming resource-efficient technology) and 74% to 79% yield reductions in a fast-warming scenario (assuming the continued use of fossil fuels, which results in the largest increase in CO 2 concentrations and temperatures [26,27]).…”