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Downscaling Regional Crop Yields to Local Scale Using Remote Sensing

Author

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  • Paresh B. Shirsath

    (CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Borlaug Institute for South Asia (BISA), International Maize and Wheat Improvement Centre (CIMMYT), New Delhi 110012, India)

  • Vinay Kumar Sehgal

    (Division of Agricultural Physics, Indian Agricultural Research Institute (IARI), ICAR, New Delhi 110012, India)

  • Pramod K. Aggarwal

    (CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Borlaug Institute for South Asia (BISA), International Maize and Wheat Improvement Centre (CIMMYT), New Delhi 110012, India)

Abstract

Local-scale crop yield datasets are not readily available in most of the developing world. Local-scale crop yield datasets are of great use for risk transfer and risk management in agriculture. In this article, we present a simple method for disaggregation of district-level production statistics over crop pixels by using a remote sensing approach. We also quantified the error in the disaggregated statistics to ascertain its usefulness for crop insurance purposes. The methodology development was attempted in Parbhani district of Maharashtra state with wheat and sorghum crops in the winter season. The methodology uses the ratio of Enhanced Vegetation Index (EVI) of pixel to total EVI of the crop pixels in that district corresponding to the growth phase of the crop. It resulted in the generation of crop yield maps at the 500 m resolution pixel (grid) level. The methodology was repeated to generate time-series maps of crop yield. In general, there was a good correspondence between disaggregated crop yield and sub-district level crop yields with a correlation coefficient of 0.9.

Suggested Citation

  • Paresh B. Shirsath & Vinay Kumar Sehgal & Pramod K. Aggarwal, 2020. "Downscaling Regional Crop Yields to Local Scale Using Remote Sensing," Agriculture, MDPI, vol. 10(3), pages 1-14, March.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:3:p:58-:d:327058
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    References listed on IDEAS

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    1. Aggarwal, P.K. & Banerjee, B. & Daryaei, M.G. & Bhatia, A. & Bala, A. & Rani, S. & Chander, S. & Pathak, H. & Kalra, N., 2006. "InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. II. Performance of the model," Agricultural Systems, Elsevier, vol. 89(1), pages 47-67, July.
    2. Naresh Soora & P. Aggarwal & Rani Saxena & Swaroopa Rani & Surabhi Jain & Nitin Chauhan, 2013. "An assessment of regional vulnerability of rice to climate change in India," Climatic Change, Springer, vol. 118(3), pages 683-699, June.
    3. Singh, Anil Kumar & Tripathy, Rojalin & Chopra, Usha Kiran, 2008. "Evaluation of CERES-Wheat and CropSyst models for water-nitrogen interactions in wheat crop," Agricultural Water Management, Elsevier, vol. 95(7), pages 776-786, July.
    4. Aggarwal, P.K. & Kalra, N. & Chander, S. & Pathak, H., 2006. "InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description," Agricultural Systems, Elsevier, vol. 89(1), pages 1-25, July.
    5. Kattarkandi Byjesh & Soora Kumar & Pramod Aggarwal, 2010. "Simulating impacts, potential adaptation and vulnerability of maize to climate change in India," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(5), pages 413-431, June.
    6. Prasun K. Gangopadhyay & Arun Khatri-Chhetri & Paresh B. Shirsath & Pramod K. Aggarwal, 2019. "Spatial targeting of ICT-based weather and agro-advisory services for climate risk management in agriculture," Climatic Change, Springer, vol. 154(1), pages 241-256, May.
    7. Timsina, J. & Godwin, D. & Humphreys, E. & Yadvinder-Singh & Bijay-Singh & Kukal, S.S. & Smith, D., 2008. "Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 95(9), pages 1099-1110, September.
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    Cited by:

    1. Usha Poudel & Haroon Stephen & Sajjad Ahmad, 2021. "Evaluating Irrigation Performance and Water Productivity Using EEFlux ET and NDVI," Sustainability, MDPI, vol. 13(14), pages 1-26, July.

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