Abstract
Multiple regression approach has been used to forecast the crop production widely. This study has been undertaken to evaluate the performance of stepwise and Lasso (Least absolute shrinkage and selection operator) regression technique in variable selection and development of wheat forecast model for crop yield using weather data and wheat yield for the period of 1984-2015, collected from IARI, New Delhi. Statistical parameters viz. R2, RMSE, and MAPE were 0.81, 195.90 and 4.54 per cent respectively with stepwise regression and 0.95, 99.27, 2.7 percentage, respectively with Lasso regression. Forecast models were validated during 2013-14 and 2014-15. Prediction errors were -8.5 and 10.14 per cent with stepwise and 1.89 and 1.64 percent with the Lasso. This shows that performance of Lasso regression is better than stepwise regression to some extent.
Highlights
Reliable and routine forecast of crop production play a vital role for advance planning, formulation and implementation of a number of policies dealing with food procurement its distribution, pricing structure, import and export decisions and for exercising several administrative measures related to the storage and marketing of the agricultural commodities
The multiple regression models based on agrometeorological parameters are used to predict the crop production which is easier to use, it is likely to be more accurate than the simulation model approach
Variable selection in multi regression model plays important role in prediction accuracy as (i) it makes the model easier to interpret, removing variables that are redundant and do not add any information (ii) reduces the size of the problem to enable algorithms to work faster, making it possible to handle with high-dimensional data and (iii) reduces over fitting in model.Many techniques have been developed for selection of predictor variables such as ordinary least square (OLS), stepwise regression, ridge regression, Lasso regression and elastic net regression
Summary
Reliable and routine forecast of crop production play a vital role for advance planning, formulation and implementation of a number of policies dealing with food procurement its distribution, pricing structure, import and export decisions and for exercising several administrative measures related to the storage and marketing of the agricultural commodities. Two main approaches to predicting crop yield based on weather conditions are simulation models and multiple regression models (Lee, 2011). Other issue is that when estimating the degrees of freedom, the number of the candidate independent variables from the best fit selected maybe smaller than the total number of final model variables, causing the fit to appear better than it is when adjusting the r2 value for the number of degrees of freedom. Another variable selection Lasso (Least Absolute Shrinkage and Selection Operator) - was first formulated by Tibshirani(1996). Regularization techniques are used to prevent statistical overfitting in a predictive model.In general
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