Abstract
Sugarcane is one of India's most important cash crops and one of the major crops of Uttarakhand state. Accurate crop yield forecasting is essential for making appropriate government policies. Statistical regression method using meteorological parameters is one of the most widely used crop yield forecasting methods. With the help of statistical regression, it is possible to forecast the sugarcane yield a few months before the harvest. But there is no direct cause–effect relationship between meteorological parameters and crop yield, so uses of other independent parameters can increase the crop yield accuracy. Evapotranspiration is one of the most crucial independent parameters, which can be easily estimated using remote sensing. The benefit of remote sensing over other fields and empirical methods for evapotranspiration is the easy availability of data over a large area as data availability becomes critical in other methods. Crop water efficiency can be easily found by crop water productivity. The developed Sugarcane yield actual evapotranspiration (AET) model using regression techniques for the F2 stage and both with and without AET model for F3 stage except 2019–20 in Haridwar district and the developed sugarcane yield model with and without AET using regression techniques for the F2 and F3 stage in Dehradun district showed a good relationship between predicted and observed values of yield which is below 5% deviation. From the study of crop water productivity, we can easily mark the areas with low water productivity and used different planning to increase the water efficiency to fulfill the need of people in reducing water availability.
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Acknowledgements
The authors wish to acknowledge the Ministry of Earth Science (MoES) for providing the funds and to the Directorate of Agriculture, Uttarakhand, for providing historical crop yield data. We are also grateful to AMFU Roorkee, IIT Roorkee, and Indian Meteorological Department (IMD) for providing weather data.
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Appendix 1
Appendix 1
Weather Indices
Weather variable | Weather indices | |
|---|---|---|
Unweighted weather indices | Weighted weather indices | |
Tmax | Z10 | Z11 |
Tmin | Z20 | Z21 |
Rain | Z30 | Z31 |
RHmax | Z40 | Z41 |
RHmin | Z50 | Z51 |
AET | Z60 | Z61 |
Tmax_Tmin | Z120 | Z121 |
Tmax_Rain | Z130 | Z131 |
Tmax_RHmax | Z140 | Z141 |
Tmax_RHmin | Z150 | Z151 |
Tmax_AET | Z160 | Z161 |
Tmin_Rain | Z230 | Z231 |
Tmin_RHmax | Z240 | Z241 |
Tmin_Rhmin | Z250 | Z251 |
Tmin_AET | Z260 | Z261 |
Rain_RHmax | Z340 | Z341 |
Rain_RHmin | Z350 | Z351 |
Rain_AET | Z360 | Z361 |
RHmax_Rhmin | Z450 | Z451 |
RHmax_AET | Z460 | Z461 |
RHmin_AET | Z560 | Z561 |
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Bhoutika, K., Das, D.P., Kumar, A., Pandey, A. (2022). Application of Remote Sensing and GIS in Crop Yield Forecasting and Water Productivity. In: Pandey, A., Chowdary, V.M., Behera, M.D., Singh, V.P. (eds) Geospatial Technologies for Land and Water Resources Management. Water Science and Technology Library, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-030-90479-1_13
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