Comparison of weather-based wheat yield forecasting models for different districts of Uttarakhand using statistical and machine learning Techniques

Authors

  • PARUL SETIYA Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India https://orcid.org/0000-0001-5454-6891
  • ANURAG SATPATHI Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
  • AJEET SINGH NAIN Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
  • BAPPA DAS ICAR Central Coastal Agricultural Research Institute, Old Goa, India

DOI:

https://doi.org/10.54386/jam.v24i3.1571

Keywords:

Forecast Model, Stepwise Multiple Linear Regression, Artificial Neural Network, Least Absolute Shrinkage and Selection Operator, Elastic Net, Ridge Regression

Abstract

The prediction of crop yield before harvest is crucial for facilitating the formulation and implementation of policies about food safety, transportation cost, and import-export, storage and marketing of agro-products. The weather plays a crucial role in crop growth and development. Therefore, models using weather variables can provide reliable forecasts for crop yield and choosing the right model for crop production forecasts can be difficult. Therefore in the present study, an attempt was made to find the best model for wheat yield forecast by using five different techniques viz. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET) and Ridge regression. Historical wheat yield data (taken from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare) and weather data of past 18-20 years were collected for seven different districts of Uttarakhand. Analysis was carried out by fixing 80% of the data for calibration and remaining dataset for validation. The present study concluded that the performance of ANN was good for crop yield forecasting as compared to the other models based on the value of RMSE (0.005 - 0.474) and nRMSE (0.166 - 26.171).

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Published

31-08-2022

How to Cite

SETIYA, P. ., SATPATHI, A. ., NAIN, . A. S. ., & DAS, B. . (2022). Comparison of weather-based wheat yield forecasting models for different districts of Uttarakhand using statistical and machine learning Techniques. Journal of Agrometeorology, 24(3), 255–261. https://doi.org/10.54386/jam.v24i3.1571

Issue

Vol. 24 No. 3 (2022): September

Section

Research Paper

License

Copyright (c) 2022 PARUL SETIYA, ANURAG SATPATHI , AJEET SINGH NAIN , BAPPA DAS

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