Abstract
Current agricultural research is relevant to crop yield prediction. While there are many mathematical methods for predicting agricultural yields, regression analysis is still one of the more popular ones. The effectiveness of the prediction model is crucial, and it is greatly influenced by the selection of the target function. The purpose of this study is to determine the most effective regression model for predicting the production of grain corn, soybeans, and winter wheat. Data on actual crop yields and water use were gathered within 1970-2020 at the Institute of Climate-Smart Agriculture's test plots in the Kherson region of Ukraine. The best subsets regression technique was used to evaluate 145 data pairs to identify the model that provided the greatest fitting quality and prediction accuracy. Microsoft Excel and BioStat were used to conduct all the calculations. The best accuracy is recorded for the hyperbolic (reverse) function in soybeans, quadratic and hyperbolic functions in winter wheat, and cubic function in grain corn. To sum up the study's findings, it is advised that cubic regression function should be employed to estimate crop yields in agricultural studies.
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