Development of Rice Yield Forecasting Model Using Linear Regression for Imphal West District, Manipur, India

Abstract

Rice is a staple food crop and India’s principal food grain. It is generally grown under completely flooded conditions and any changes in weather parameters might affect the rice productivity thereby impacting the food security of the ever-increasing population Prevailing weather conditions during the crop growth period determine the yield of Rice. Hence, the crop yield forecasting models based on weather parameters will be an appropriate option for policymakers and researchers to develop sustainable cropping strategies. The present study examines the application of stepwise multiple linear regression for rice yield prediction using long-term weather data. Analysis was carried out by fixing data from 1998-99 to 2016-17 for calibration and the remaining 2017-18 to 2020-21 data for validation. The accuracy of these models was estimated by R2 (coefficient of determination) and the performance by Mean Square error (MSE), Root mean square error (RMSE), and Normalised root mean square error (NRMSE). The R2 of the developed models ranged from 0.27 – 0.95. The best-performing model was the 5th model with R2 (0.95) with MSE (0.03%), RMSE (0.17%), and NRMSE (0.05%) during validation.

Effect of Weather Parameters on Wheat Productivity: A Statistical Analysis Using SPSS

“…SPSS provides a range of tools and techniques for data analysis, making it a valuable tool for conducting agricultural research (Tega and Bojago 2023). By employing SPSS, researchers can explore the relationship between weather variables and crop productivity, enabling policymakers and farmers to make informed decisions regarding agricultural practices, resource allocation, and risk management (Wangkheimayum and Paliwal, 2023). SPSS enables diverse statistical analyses: correlation, regression, and time series (Das and Kumar, 2019).…”