July 2024
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With unprecedented challenges to achieve sustainable crop productivity under climate change and varying soil conditions, adaptive management strategies are required for optimizing cropping systems. Using sensors, cropping systems can be continuously monitored and the data collected by them can be analyzed for making informed adaptive management decisions to enhance productivity and environmental sustainability. But sensors reflect present conditions or provide some history, yet decisions should also consider what is yet to occur. This study leverages the use of the state-of-the-art biophysical model, Agricultural Production System sIMulator (APSIM), which takes the genetics (G), environmental (E), and management (M) data, to predict the growth and yield of corn (Zea Mays L.), a major crop for United States. Using digital twin models, we can project outcomes of different management decisions under varying environmental conditions and soil types and in context of climate change. The key objectives of this research were to elucidate the impacts of varying soil conditions and climate scenarios on corn growth and yield and further identify the best optimum practices (planting date, amount of nitrogen fertilizer, and amount of irrigation) to improve yield and profitability. In doing so, we characterize system resilience by running simulations over 38 years of past weather data for four locations having four different soil types and under two different climate scenarios.