Anuj Kumar Dwivedi*, C.S.P. Ojha and Vijay P. Singh
The Indian economy depends on agriculture, and wheat is one of the most important grains of the country. The northern states of India, like Punjab, Haryana, Madhya Pradesh, Rajasthan, Uttar Pradesh, and Uttarakhand contribute a lot to the production of wheat but they lie in different agro-climatic zones. Climatic parameters greatly impact agricultural productivity. Using agro-meteorological and satellite parameters, the present study develops regression models for predicting crop yield in three different agro-climatic zones of India. Remote sensing approaches are being increasingly utilized these days. The present study develops a wheat yield models using the Normalized Difference Vegetation Index (NDVI) as well as agro meteorological data like rainfall, temperature, and humidity for the districts of Allahabad, Haridwar, and Ludhiana in the states of Uttar Pradesh, Uttarakhand and Punjab, respectively. It also deals with the development of models to forecast crop yield based on only agro meteorological and NDVI based agro meteorological models three times during the crop period. The maximum NDVI, maximum temperature, minimum temperature, rainfall, and humidity data of 21 years were considered as an input variable to develop a regression model for the districts of Allahabad, Haridwar, and Ludhiana. Comparison of several approaches indicated the better performance of coupled crop yield model which utilized NDVI and hydro-meteorological data simultaneously. Further, models using other temperature indices were also evaluated for their effectiveness over conventionally used temperature terms.
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Irrigation & Drainage Systems Engineering received 835 citations as per Google Scholar report
