Sugarcane Yield Forecasting Model Based on Weather Parameters
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RESEARCH ARTICLE
Sugarcane Yield Forecasting Model Based on Weather Parameters Amit Kumar Verma1 • Pradeep Kumar Garg1 • K. S. Hari Prasad2 Vinay Kumar Dadhwal3 • Sunil Kumar Dubey4 • Arvind Kumar5
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Received: 6 April 2020 / Accepted: 19 September 2020 Society for Sugar Research & Promotion 2020
Abstract A reliable estimate of the crop production prior to harvest is important for determining the prices, import– export decisions, and various food procurement policies that would enable the Government to take advance action in terms of surplus or scarcity production. Crop yield forecasting models could potentially be applied to small areas where all the necessary data are available. For large area data availability becomes critical, and the techniques of regression modeling and remote sensing are favored
& Amit Kumar Verma [email protected] Pradeep Kumar Garg [email protected] K. S. Hari Prasad [email protected] Vinay Kumar Dadhwal [email protected]
over growth simulation modeling. In this study, various weather parameters based statistical models have been developed to forecast the sugarcane yield during autumn and spring planting for Muzaffarnagar District of Uttar Pradesh. Last 35 year historical weather data from 1981 to 2015 were used for analysis. Various weighted and unweighted weather indices have been utilized in developing the statistical model. The developed model using regression techniques for the spring season (Model-S4) and autumn season (Model-A5) showed a good relationship between predicted and observed values of yield. Model-S4 error ranges from - 0.063 to ? 5.81%, whereas Model-A5 error varying from - 3.54 to ? 3.51%. In all the developed models, weighted weather indices have been found to be significantly more effective rather than un-weighted weather indices. Keywords Crop Forecasting Regression Sugarcane Yield
Sunil Kumar Dubey [email protected] Arvind Kumar [email protected] 1
Geomatics Engineering Group, Department of Civil Engineering, Indian Institute of Technology (IIT), Roorkee, Roorkee 247667, India
2
Hydraulics Engineering Group, Department of Civil Engineering, Indian Institute of Technology (IIT), Roorkee, Roorkee 247667, India
3
Indian Institute of Space Science and Technology, Thiruvananthapuram 695547, India
4
Mahalanobis National Crop Forecast Centre, New Delhi 110012, India
5
Narendra Deva University of Agriculture and Technology, Faizabad 224229, India
Introduction Sugarcane is a traditional commercial crop of India that plays a significant role in agriculture and industrial economy of the nation; therefore, a proper forecast of production of such crops is very important (Suresh and Krishna Priya 2011). The development of crop yield models to predict yields, i.e., production per unit area, is an important component in the production forecasting system. The present system of forecasting is based on eye estimate, which is totally subjective. A need was felt to develop a suitable objective methodology for the purpose of correct estimation of y
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