Forecasting of Productivity of Pulse Crops in India: A Nonlinear Approach
Sanju
Department of Mathematics and Statistics, College of Basic Science and Humanities, CCSHAU, Hisar, Haryana, India.
Pooja Rawat
Department of Mathematics and Statistics, College of Basic Science and Humanities, CCSHAU, Hisar, Haryana, India.
Ajay Sharma *
Department of Mathematics and Statistics, College of Basic Science and Humanities, CCSHAU, Hisar, Haryana, India.
Mohit Godara
Department of Agricultural Meteorology, College of Agricultural, CCSHAU, Hisar, Haryana, India.
*Author to whom correspondence should be addressed.
Abstract
India is the world's top producer and consumer of pulse crops. Pulse crops are an essential source of protein, providing amino acids, vitamins, and minerals to supplement diets. These contain 22-24 percent protein, about twice as much as wheat. This research uses a non-linear growth approach to conduct an analytical evaluation of total pulse productivity in India from 1949-2020. For total pulse productivity in India, four different non-linear growth models were fitted: Logistic, Gompertz, Monomolecular, and Von Bertalanffy. Goodness of fit statistics such as Coefficient of determination, Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error were used to choose the best model. The Logistic model was determined to be the best fit for productivity of total pulse crops grown in India, followed by the Gompertz model. Finally, the Logistic and Gompertz models were used to forecast India's total pulse productivity from 2020-21 to 2025-26.
Keywords: Gompertz, logistic, monomolecular, non-linear growth model, von bertalanffy