Some Statistical Models for Crop Yield Forecasting - Garde, Yogesh
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Crop yield forecasting is an important aspect for a developing economy so that adequate planning exercise is undertaken for sustainable growth and overall development of the country. Weather fluctuations affect crop yield significantly during different stages of crop growing season, therefore several studies have been carried out to forecast crop yield using weather parameters. However, such forecast studies based on statistical models need to be done on continuing basis and for different agro-climatic zones, due to visible effects of changing environment conditions and weather shifts at…mehr

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Produktbeschreibung
Crop yield forecasting is an important aspect for a developing economy so that adequate planning exercise is undertaken for sustainable growth and overall development of the country. Weather fluctuations affect crop yield significantly during different stages of crop growing season, therefore several studies have been carried out to forecast crop yield using weather parameters. However, such forecast studies based on statistical models need to be done on continuing basis and for different agro-climatic zones, due to visible effects of changing environment conditions and weather shifts at different locations and areas. Therefore, present study was undertaken for forecasting yield of two major crops viz. rice and wheat based on time series data for 27 years (w.e.f.1981-82 to 2007-08) of yield and weather parameters obtained from G. B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar,Uttarakhand, India. This study reveals that stepwise Multiple Linear Regression techniques (MLR) can be successfully used for pre-harvest crop yield forecasting. This model was most consistent and can be apply on zone or state level.
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Autorenporträt
A Master and Doctorate in Agricultural Statistics, is currently working as Agriculture expert, at Mahalanobis National Crop Forecast Center, New Delhi, India. He has experience on different crop yield forecast models and worked out different strategies to forecast the crop yield.