Enhancing crop production in Kansas via in-silico studies and environmental characterization to improve management decisions

Abstract

Increasing climate variability and crop demand require sustainable intensification of rainfed cropping systems. This thesis addresses this challenge in Kansas by integrating crop growth model with environmental characterization to improve crop production strategies. Two complementary approaches were used: (1) evaluating the feasibility of intensifying rainfed wheat (Triticum aestivum L.) -based cropping systems by adding a summer crop using long-term in-silico simulations; and (2) characterizing rainfed maize (Zea mays L.) productivity environments across Kansas using long-term historical climate, yield and crop phenology data. Results from the simulations indicated that double-cropping can increase annual productivity, but success depends on crop specie, cycle length choice and sowing timing. Soybean (Glycine max L.) as a second crop after wheat consistently showed higher success rates and more stable yields than maize, with earlier summer planting maximizing yield (but increasing drought risk) and later planting avoiding drought (but increasing frost risk). Accordingly, wheat-soybean rotations were consistently more productive and profitable, whereas wheat-maize rotations showed greater yield variability and risk. In parallel, cluster analysis of the 1993–2021 climate and yield records delineated ten distinct rainfed maize production regions in Kansas, with average yields ranging from ~3.5 to ~7.5 Mg ha-1. Correlation analysis revealed that extreme heat (daily accumulated degree-days > 35 °C) and high vapor pressure deficit during flowering stage were the primary yield-limiting climatic factors, with each additional degree-day > 35 °C around flowering reducing yield by ~46 kg ha-1, with the stress being most severe in southeastern Kansas. These findings highlight that optimizing summer crop selection and planting schedule can enhance productivity in wheat-based systems, while spatial climate analysis enables region-specific management strategies. By combining long-term modeling with crop environmental characterization, this study provides insights to inform adaptive management strategies to improve crop productivity and resilience in Kansas. This regionalization approach could be easily extrapolated to other crops and regions.