Analysis of yields for majorly grown agriculture produces in the arid districts of Rajasthan

Rahul Priyadarshi, Srikanta Routroy and Girish Kant Garg

International Journal of Operational Research, 2023, vol. 48, issue 4, 593-606

Abstract: The three-point moving average, exponential smoothing and MATLAB regression learner tools were utilised to obtain the yield forecasts for majorly grown agriculture produces of arid districts of Rajasthan, India. The yield forecasts were verified on the basis of R² values and the relative error percentage values to draw conclusions. The results illustrated that the adopted forecasting methodology worked better for produces such as cumin, wheat and rapeseed and mustard. These produce can grow with bare minimum water available in arid and hyper-arid regions. However, it was complex to predict the future yields quantity for certain crops that require more rainfall for enhanced yields such as pearl millets, moth bean, sesame and cluster beans. The results could also be utilised to observe cultivation and irrigation patterns in arid and hyper arid regions, demand and supply ratio and economic planning.

Keywords: agricultural yield forecasting; moving average; machine learning; exponential smoothing; forecasting error analysis. (search for similar items in EconPapers)
Date: 2023
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