Comparison of phenological weather indices based statistical, machine learning and hybrid models for soybean yield forecasting in Uttarakhand

Abstract

Early information exchange regarding predicted crop production could play a role in lowering the danger of food insecurity. In this study total six multivariate models were developed using past time series yield data and weather indices viz. SMLR, PCA-SMLR, ANN, PCA-ANN, SMLR-ANN and PCA-SMLR-ANN for three major soybean producing districts of Uttarakhand viz. Almora, Udham Singh Nagar and Uttarkashi. Further analysis was done by fixing 80% of the data for calibration and the remaining dataset for validation to predict soybean yield. Phenology wise average values were computed using the daily weather data. These average values are subsequently employed in the computation of both weighted and unweighted weather indices. The PCA-SMLR-ANN, SMLR-ANN and PCA-ANN models were found to be the best soybean yield predictor model for Almora, Udham Singh Nagar and Uttarkashi districts, respectively. The overall ranking based on the performances of the models for all locations can be given as: SMLR-ANN > PCA-ANN > PCA-SMLR-ANN ≈ ANN > PCA-SMLR > SMLR. The study results indicated that hybrid models outperformed the individual models well for all the study regions.