Prediction Model of Loan Support Limit of Plows and Rotavators Used in South Korea by Regression Analysis

초록

PurposeThis study predicts the loan support limit using main variables of plows and rotavators by regression analysis.MethodsThe study collected and analyzed the data on loan support limit, rotary tilling width, the power required, and the number of rotary blades from 360 units of commercial rotavators in South Korea. Additionally, loan support limit, plowing width, the power required, and weight were investigated for 80 units of plows commercially available in South Korea. Simple and multiple linear regression analyses were conducted to examine the effect of variables on the loan support limit.ResultsSimple and multiple regression models for the prediction of the loan support limit of plows and rotavators were developed. The coefficient of determination (R2) and root mean square error (RMSE) were derived to evaluate the accuracy of each regression model. In the case of plows, the R2 and RMSE of the weight-based simple regression model showed the highest performance of prediction for the loan support limit, followed by the models based on power required and plowing width variables. In the case of rotavators, R2 and RMSE of a simple regression model based on rotary tilling width showed the highest prediction accuracy of loan support limit, followed by the models based on power required and the number of rotary blades variables.ConclusionsFor plows and rotavators, multiple regression models have a higher R2 and a smaller RMSE compared to the simple regression models, demonstrating their adequacy for the prediction of the loan support limit. The developed prediction model can be helpful to save time and make a suitable decision for farmers to purchase tractor implements.

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