Optimizing Corn and Soybean Yield Predictions in Illinois Using the AquaCrop Model

Crop yield is important for agricultural productivity and country’s economy. Accurate crop yield estimation is critical for policymakers, farmers, and governments because it allows better management techniques, decision making and the implementation of practicable agricultural policies. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. In this study, we used the Food and Agriculture Organization (FAO) of the United Nations developed AquaCrop model to estimate the crop yields of corn and soybean crops in Illinois, United States (US). Data of various meteorological parameters as precipitation, maximum and minimum temperature, relative humidity, wind speed, solar radiation datasets were collected from NASA Prediction of Worldwide Energy Resources (POWER), for a period of 25-years from 2000 to 2024. Whereas, reference evapotranspiration was calculated by using the modified Hargreaves method. The objective of this study is to assess the accuracy of yield estimation of corn and soybean by using the AquaCrop model. The AquaCrop model was simulated for the growing period of corn and soybean from May to September. Using the AquaCrop model, the maximum and minimum corn yields were found to be 14.49 tons/ha in the year 2022 and 7.60 tons/ha in the year 2005, respectively. Similarly, the maximum yield of soybean was found to be 4.33 tons/ha in the year 2022, while the minimum yield was 2.26 tons/ha in the year 2012. The coefficient of determination (R²) values of 0.72 for maize and 0.76 for soybean, gives a satisfactory level of model accuracy. The model's performance can be improved further by incorporating more ground-truth data and appropriate parameters. This study demonstrates the AquaCrop model's ability to estimate crop production with few input parameters, as well as suggest opportunities for improvement. To improve prediction accuracy and promote informed agricultural planning and food security, future study might use sophisticated methodologies, localized farming practices, crop phenology, and specific soil data. 

 

Keywords:  AquaCrop, Crop yield, Illinois, Yield Predictions.