Forecasts are valuable to countries to make informed business decisions and develop data-driven strategies. The production of pulses is an integral part of agricultural diversification initiatives because it offers promising economic opportunities to reduce rural poverty and unemployment in developing countries. Pulses are the cheapest source of protein needed for human health. India's pulses production guidelines must be based on accurate and best forecast models. Comparing classical statistical and machine learning models based on different scientific data series is the subject of high-level research today. This study focused on the forecasting behaviour of pulses production for India, Karnataka, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh. The data series was split into a training dataset (1950-2014) and a testing dataset (2015-2019) for model building and validation purposes, respectively. ARIMA, NNAR and hybrid models were used and compared on training and validation datasets based on goodness of fit (RMSE, MAE and MASE). This research demonstrates that due to the diverse agricultural conditions across different provinces in India, there is no single model that can accurately predict pulse production in all regions. This study's highest accuracy model is ARIMA. ARIMA outperforms NNAR, a machine learning model. Pulse production in India, Rajasthan, and Madhya Pradesh will expand by 26.11%, 12.62%, and 0.51% from 2020 to 2030, whereas it would decline by - 6.5%, - 6.21%, and - 6.76 per cent in Karnataka, Maharashtra, and Uttar Pradesh, respectively. The current forecast results could allow policymakers to develop more aggressive food security and sustainability plans and better Indian pulses production policies in the future.
Keywords: ARIMA; Future food; Hybrid model; NNAR; Pulses; Sustainable development goal (SDG) 2.
© The Author(s), under exclusive licence to The National Academy of Sciences, India 2023.