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Spatial Approach for the Estimation of Average Yield of Cotton Using Reduced Number of Crop Cutting Experiments


Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
2 Indian Council of Agricultural Research, New Delhi 110 001, India
 

In India, cotton yield estimates are done using crop cutting experiments (CCEs) conducted within the framework of the general crop estimation surveys (GCES) methodology. In recent times, for obtaining reliable estimates at levels lower than the district, the number of CCEs has increased in comparison to the existing set-up of GCES. This puts an additional financial burden on Government agencies. There is a possibility of reducing the number of CCEs under the GCES methodology and predicting the remaining CCE points using an appropriate spatial prediction model. In this article, the predictive performance of different spatial models has been compared. Furthermore, district-level estimate of average productivity of cotton has been determined using the geographically weighted regression (GWR) technique and the results compared with those obtained using the traditional GCES methodology. The proposed spatial estimator of the average yield of cotton obtained using the GWR approach is more efficient and the results are comparable with the estimates obtained using the GCES methodology. The developed methodology can be utilized to reduce the number of CCEs and capture the spatial non-stationarity present in the cotton crop yield.

Keywords

Cotton Yield, Crop Cutting Experiments, District Level, Geographically Weighted Regression, Spatial Non-Stationarity.
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  • Spatial Approach for the Estimation of Average Yield of Cotton Using Reduced Number of Crop Cutting Experiments

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Authors

Nobin Chandra Paul
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Anil Rai
Indian Council of Agricultural Research, New Delhi 110 001, India
Tauqueer Ahmad
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Ankur Biswas
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Prachi Misra Sahoo
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India

Abstract


In India, cotton yield estimates are done using crop cutting experiments (CCEs) conducted within the framework of the general crop estimation surveys (GCES) methodology. In recent times, for obtaining reliable estimates at levels lower than the district, the number of CCEs has increased in comparison to the existing set-up of GCES. This puts an additional financial burden on Government agencies. There is a possibility of reducing the number of CCEs under the GCES methodology and predicting the remaining CCE points using an appropriate spatial prediction model. In this article, the predictive performance of different spatial models has been compared. Furthermore, district-level estimate of average productivity of cotton has been determined using the geographically weighted regression (GWR) technique and the results compared with those obtained using the traditional GCES methodology. The proposed spatial estimator of the average yield of cotton obtained using the GWR approach is more efficient and the results are comparable with the estimates obtained using the GCES methodology. The developed methodology can be utilized to reduce the number of CCEs and capture the spatial non-stationarity present in the cotton crop yield.

Keywords


Cotton Yield, Crop Cutting Experiments, District Level, Geographically Weighted Regression, Spatial Non-Stationarity.

References





DOI: https://doi.org/10.18520/cs%2Fv125%2Fi5%2F518-529