Vol. 19 No. 1
Forecasting cash crop production with statistical and neural network model
Author(s): S. RAY, 1A. M. G. AL KHATIB, B. KUMARI, T. BISWAS, A. C. NUTA AND P. MISHRA
Abstract: Countries can use forecasts to establish data-driven strategies and make educated commercial decisions. In order to minimize rural poverty and unemployment in developing nations, the development of cash crops is a crucial component of agricultural diversification projects. A comparison of the ARIMA, ETS, and NNAR models for forecasting area, production, and productivity of wheat, paddy, maize, jowar and cotton crops is presented in this study. We have used data from 1980 to 2010 to estimate using models (training) and 2011 to 2020 to test the model’s validity (testing). On the basis of goodness of fit, the models were contrasted using training and validation data sets (RMSE, MAE and MASE). Forecast values for the years up to 2027 were derived by choosing the best model. Wheat, paddy, and cotton production are predicted to rise, but jowar and maize production are predicted to fall. The outcomes of the current forecast may enable policymakers to create future strategies that are more aggressive in terms of food security and sustainability, as well as better in terms of Indian cash crop production.
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