2016

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YIELD FORECAST MODELING FOR WHEAT CROP USING AGRO METEOROLOGICAL DATA
(UMT, Lahore, 2016) MUHAMMAD FAROOQ
This thesis explores the possibility of using remote sensing images and its derived products, Normalized Difference Vegetation Index (NDVI), to assess and measure wheat yield potential. Panel regression models were developed for the Gujranwala division at different stage of spatial aggregation. Through the panel regression analysis, the study identified the relationship between the crop vegetation over the growing period and the final crops yield. The vital goal of the research is to inspect whether the NDVI model can generate precise and timely yield forecasts.The present study primarily focuses on the variation of crop yield through NDVI based panel regression analysis is used as a tool to achieve this objective. The NDVI model is used to represent crop vegetation density or greenness (reflectance of all agro metrological parameter) of the vegetation cover. It proves that, crops yield can be predicted more effectively and accurately by using NDVI base model as an alternative tool against the conventional models. The Redundant and Hausman test which guides about the fixed effect model is suitable in this study. The NDVI base fixed effect model fits the crop yield data extremely well, with high R-square value at about 90%. The results suggest that NDVI for the month of January, February and March were ideal for wheat yield assessment.