Modeling agricultural crop water demands and sustainable water management under extreme climate conditions in Western Kansas - Igwe, Kelechi Emmanuel (Kansas State University, 2023)
Prevalent extreme climate conditions, as well as depleting water resources in semi-arid regions continue to underscore the need for adopting efficient and data-driven management practices to ensure the long-term viability of agriculture. Crop evapotranspiration (ET) data—an indication of the water requirement of crops—varies significantly in response to these prevailing extreme climate conditions. Understanding the primary indicators responsible for the observed variability in crop ET, among the indices chosen to represent these events, as well as the degree to which these events may have an impact on crop ET in the future, is vital. Additionally, it is necessary to investigate water conservation strategies that can assist agricultural producers to become better prepared for the effects of these conditions by increasing the resilience of the maize crop to these extreme conditions, without compromising on the yield output or water use and productivity. The predictive power of a random forest machine-learning algorithm was employed to identify climate extreme indices that most influences crop ET, and to quantify their potential impacts in future climate change scenarios. The Decision Support System for Agrotechnology Transfer-Crop Environment REsource Synthesis (DSSAT-CERES) Maize model was further used to develop and evaluate diverse irrigation strategies based on crop ET data. The aim was to assess their effectiveness in enhancing maize crop’s resilience to extreme climate conditions while minimizing the overuse of limited water resources. Our study revealed that crop evapotranspiration (ET) was primarily influenced by two key indices: the maximum number of consecutive dry days, and the maximum temperature. Model predictions further indicate that these indices have the potential to increase crop ET by 0.4, 3.1 and 3.8% under low greenhouse gas emission scenario, and by 1.7, 5.9 and 9.6% under high greenhouse gas emission scenario in the near, mid and end century, respectively. A comprehensive 30-year simulation utilizing the DSSAT model revealed that in comparison to the commonly practiced full irrigation treatment, an irrigation strategy based on crop evapotranspiration (ET) – specifically, applying 75% of the ET requirement– demonstrated superior effectiveness. Applying 75% of the required ET amount when it reached a 30mm threshold, optimized yield, water usage, and productivity. Yield loss was limited to ~6%, with irrigation water savings of up to 19%, and water productivity decline was limited to a level below 5%, when the maximum temperature or the maximum consecutive dry days increased by up to 2°C or 1 day, respectively. The decline in yield, as well as the losses in irrigation water and productivity, however, were significantly exacerbated when temperatures increased by up to 4°C, thereby highlighting the importance of managing heat stress in order to preserve crop yields and significantly minimize water use in agriculture. Overall, these findings show that the ET-based deficit irrigation strategy adapts well to extreme heat and water stress, bearing important implications for irrigation management decisions in the future.
Keywords: Climate extreme events; Crop evapotranspiration; Machine learning; Deficit irrigation; Groundwater; Water productivity