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Forecasting yield of rapeseed and mustard using multiple linear regression and ANN techniques in the Brahmaputra valley of Assam, North East India
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Forecasting yield of rapeseed and mustard using multiple linear regression and ANN techniques in the Brahmaputra valley of Assam, North East India
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Forecasting yield of rapeseed and mustard using multiple linear regression and ANN techniques in the Brahmaputra valley of Assam, North East India
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Journal Article
Forecasting yield of rapeseed and mustard using multiple linear regression and ANN techniques in the Brahmaputra valley of Assam, North East India
2022
Overview
Crop yield forecasting is the art of predicting yield before harvest and is crucial for sound planning and policy making at various levels. Rapeseed and mustard (R&M) is the predominant oilseed crop in Assam and is very much sensitive to climatic factors compared to other crops grown during rabi season. In the context of recent climate variability observed in the Brahmaputra valley, development of efficient yield forecasting models for R&M based on weather parameters would certainly help the policy makers in formulating appropriate policies and decision-making. The present study was undertaken to develop R&M pre-harvest yield forecast models at F1 (flowering), F2 (siliqua formation) and F3 (siliqua development & maturity) stages for 15 districts of Assam using stepwise multiple linear regression (SMLR) and artificial neural network (ANN) techniques by analysing the yield and weather data for 27 years and to investigate the impact of weather factors in determining the yield of R&M. The results were enumerated based on coefficient of determination (R2), root mean square error (RMSE) and normalized root mean square error (RMSEn) values. ANN models were found to have greater R2 values over SMLR at all the three stages of forecast except in 2–3 districts. Similarly, profound improvement was observed in RMSE and RMSEn values of the forecast models using ANN technique. Analysing the percent error between observed and forecasted yield, ANN weather-based models were found to give more accurate pre-harvest yield prediction of R&M in the Brahmaputra valley of Assam. Temperature and relative humidity were found to be the most significant parameters in affecting R&M yield in most of the districts during all the three stages of forecast.
