Predicting rice yield based on weather variables using multiple linear, neural networks, and penalized regression models

Rice is one of the most important cereal foods not only for India but also for the world. The production of crop depends upon the favorable climatic conditions. Farmers’ access to more accurate data on crop yields in various climate conditions can aid in crucial agronomic and crop selection decisions. Taking this into account, the motive of the present research was to find the best method of predicting rice crop yield in seven important rice producing districts of Uttarakhand, namely Udham Singh Nagar, Nainital, Haridwar, Dehradun, Champawat, Tehri-Garhwal, and Pauri Garhwal. Data on the weather variables for the crop-growing season (27th to 44th SMW) for 19 years was gathered from the respective district and the NASA power website, while rice production data for the research period was gathered from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare. Stepwise multiple linear regression (SMLR), least absolute shrinkage and selection operator (LASSO), ridge regression, elastic net (ELNET), and artificial neural network (ANN) were employed for the model’s development. The 80% data of the total datasets was utilized to calibrate the models, while the remaining 20% data was allocated for the model validation. On examining these models, LASSO was found to be the finest performing model followed by ELNET, while SMLR was the worst performing model during calibration stage. During validation stage, ANN performed better for Champawat, Dehradun, Haridwar, Pauri Garhwal, and Udham Singh Nagar. The performance of ELENT and LASSO was found to be best for Nainital and Tehri Garhwal, respectively. The performance of ridge regression and SMLR were found to be poor as compared to the other models for the rice yield forecasting.