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Biswas, Ankur
- District-Level Crop Yield Estimation Using Calibration Approach
Abstract Views :371 |
PDF Views:110
Authors
Affiliations
1 Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
1 Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 112, No 09 (2017), Pagination: 1927-1931Abstract
Estimation of major crop yield rates at the district level using calibration estimation technique is reported here when auxiliary information is available at the unit level only for the selected villages within each district and when the sampling design under consideration is two-stage equal probability without replacement. An estimator was developed for the complex sampling design under consideration using the calibration approach. Through evaluation using real data collected from a pilot survey, we found that the proposed calibration estimator performs better than the usual design-based Horvitz-Thompson estimator under two-stage sampling design.Keywords
Calibration Estimation Technique, Crop Yield, Two-Stage Sampling.References
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- Spatial Approach for the Estimation of Average Yield of Cotton Using Reduced Number of Crop Cutting Experiments
Abstract Views :202 |
PDF Views:78
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 Indian Council of Agricultural Research, New Delhi 110 001, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 Indian Council of Agricultural Research, New Delhi 110 001, IN
Source
Current Science, Vol 125, No 5 (2023), Pagination: 518-529Abstract
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
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