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 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 un-weighted 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.
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Acknowledgements
The authors wish to acknowledge Director, Sugarcane Development Centre, Muzaffarnagar, for providing the meteorological dataset from 1981 to 2015. We are also grateful to the Director, Directorate of Agriculture, Lucknow, Uttar Pradesh, for the District level sugarcane yield data. Help and support received from farmers during field visits, is also acknowledged.
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Verma, A.K., Garg, P.K., Hari Prasad, K.S. et al. Sugarcane Yield Forecasting Model Based on Weather Parameters. Sugar Tech 23, 158–166 (2021). https://doi.org/10.1007/s12355-020-00900-4
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DOI: https://doi.org/10.1007/s12355-020-00900-4