• Linear Mixed Modeling for Mustard Yield Prediction in Haryana State (India)

    U. Verma, H. P. Piepho, K. Hartung, J. O. Ogutu, A. Goyal

    Abstract: Crop forecasting is a formidable challenge. Such predictions before harvest are needed by the national and state governments for various policy decisions relating to storage, distribution, pricing, marketing, import-export, etc. This study deals in developing a methodology for pre-harvest crop yield prediction of major mustard growing districts in Haryana (India). Zonal yield models using agro-meteorological parameters were generated using multiple linear regression and mixed model procedures. The common weather-based approach to yield forecast is linear regression with constant coefficients over time. This may be restrictive and of limited prediction power since it does not account for the year-to-year dependence in the yield variable. A mixed model procedure provided a flexible way to fit a multi-level model for crop yield prediction. The linear mixed effects models with random time/weather effects at district, zone and state level were fitted for crop yield estimation. The percent deviation(s) of district-level yield forecasts from the real time yield(s) data show a preference for using linear mixed models. The purpose of this paper is also to show the usefulness of the mixed model framework for pre-harvest crop yield forecasting.

    Keywords: Multiple linear regression, linear mixed model, weather variables, pre-harvest crop forecast and percent relative deviation.

    Pages: 96 – 105 | Full PDF Paper