Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Ahmad, Tauqueer
- Spatial Approach for the Estimation of Average Yield of Cotton Using Reduced Number of Crop Cutting Experiments
Abstract Views :48 |
PDF Views:40
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.Full Text
References
- Ahmad, T., Sud, U. C., Rai, A. and Sahoo, P. M., An alternative sampling methodology for estimation of cotton yield using double sampling approach. J. Indian Soc. Agric. Stat., 2020, 74(3), 217–226.
- Moury, P. K., Estimation of finite population total using robust geographically weighted regression approach. Ph.D. thesis, ICAR-IARI, New Delhi, 2020.
- Ahmad, T., Bhatia, V. K., Sud, U. C., Rai, A. and Sahoo, P. M., Study to develop an alternative methodology for estimation of cotton production. Project Report, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 2013.
- Brunsdon, C., Fotheringham, A. S. and Charlton, M. E., Geographically weighted regression: a method for exploring spatial non-stationarity. Geogra. Anal., 1996, 28, 281–298.
- Brunsdon, C., Fotheringham, S. and Charlton, M., Geographically weighted regression-modelling spatial non-stationary. Statistician, 1998, 47(3), 431–443.
- Fotheringham, A. S., Brunsdon, C. and Charlton, M., Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley, England, UK, 2002, pp. 52–64.
- Cressie, N. A. C., Statistics for Spatial Data, Wiley, New York, USA, 1991, pp. 105–143.
- Pang, S., Li, T., Wang, Y., Yu, H. and Li, X., Spatial interpolation and sample size optimization for soil copper (Cu) investigation in cropland soil at county scale using cokriging. Agric. Sci. China, 2009, 8(11), 1369–1377.
- Donald, S., A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 Association for Computing Machinery National Conference, Association for Computing Machinery, New York, United States, 1968, pp. 517–524.
- Biswas, A., Rai, A., Ahmad, T. and Sahoo, P. M., Spatial estimation and rescaled spatial bootstrap approach for finite population. Commun. Stat. – Theory Method., 2017, 46(1), 373–388.
- Breiman, L., Bagging predictors. Mach. Learn., 1996, 24, 123–140.
- Breiman, L., Random forests. Mach. Learn., 2001, 45, 5–32.
- Hengl, T. et al., Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 2018, 6, 1–49; doi:10.7717/peerj.5518.
- Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M. and Bajat, B., Random forest spatial interpolation. Remote Sensing, 2020, 12, 1687.
- Rasmussen, C. E. and Williams, C. K. I., Gaussian Processes for Machine Learning, MIT Press, Cambridge, Massachusetts, USA, 2006.
- Erickson, C. B., Ankenman, B. E. and Sanchez, S. M., Comparison of Gaussian process modeling software. Eur. J. Oper. Res., 2018, 266(1), 179–192.
- Nikitin, A. et al., Bayesian optimization for seed germination. Plant Method., 2019, 15, 43.
- Feng, L., Wang, Y., Zhang, Z. and Du, Q., Geographically and temporally weighted neural network for winter wheat yield prediction. Remote Sensing Environ., 2021, 262, 1–15.
- Leung, Y., Mei, C. L. and Zhang, W. X., Statistical tests for spatial non-stationarity based on the geographically weighted regression model. Environ. Plann. A, 2000, 32(1), 9–32.
- Wang, J., An intuitive tutorial to Gaussian processes regression. 2020, arXiv:2009.10862.
- Du, Z., Wang, Z., Wu, S., Zhang, F. and Liu, R., Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. Int. J. Geogr. Inf. Sci., 2020, 34(7), 1353–1377.