Downscaling Regional Crop Yields to Local Scale Using Remote Sensing
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 coe cient of 0.9.
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2020-03-02
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Subjects: | climate change, agriculture, food security, crop yield, |
Online Access: | https://hdl.handle.net/10568/107424 https://doi.org/10.3390/agriculture10030058 |
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dig-cgspace-10568-1074242023-12-08T19:36:04Z Downscaling Regional Crop Yields to Local Scale Using Remote Sensing Shirsath, Paresh Bhaskar Sehgal, Vinay K Aggarwal, Pramod K. climate change agriculture food security crop yield 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 coe cient of 0.9. 2020-03-02 2020-03-09T14:49:05Z 2020-03-09T14:49:05Z Journal Article Shirsath PB, Sehgal VK, Aggarwal PK. 2020. Downscaling Regional Crop Yields to Local Scale Using Remote Sensing. Agriculture 10(3):58. 2077-0472 https://hdl.handle.net/10568/107424 https://doi.org/10.3390/agriculture10030058 en CC-BY-4.0 Open Access 58 MDPI Agriculture |
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climate change agriculture food security crop yield climate change agriculture food security crop yield Shirsath, Paresh Bhaskar Sehgal, Vinay K Aggarwal, Pramod K. Downscaling Regional Crop Yields to Local Scale Using Remote Sensing |
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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 coe cient of 0.9. |
format |
Journal Article |
topic_facet |
climate change agriculture food security crop yield |
author |
Shirsath, Paresh Bhaskar Sehgal, Vinay K Aggarwal, Pramod K. |
author_facet |
Shirsath, Paresh Bhaskar Sehgal, Vinay K Aggarwal, Pramod K. |
author_sort |
Shirsath, Paresh Bhaskar |
title |
Downscaling Regional Crop Yields to Local Scale Using Remote Sensing |
title_short |
Downscaling Regional Crop Yields to Local Scale Using Remote Sensing |
title_full |
Downscaling Regional Crop Yields to Local Scale Using Remote Sensing |
title_fullStr |
Downscaling Regional Crop Yields to Local Scale Using Remote Sensing |
title_full_unstemmed |
Downscaling Regional Crop Yields to Local Scale Using Remote Sensing |
title_sort |
downscaling regional crop yields to local scale using remote sensing |
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MDPI |
publishDate |
2020-03-02 |
url |
https://hdl.handle.net/10568/107424 https://doi.org/10.3390/agriculture10030058 |
work_keys_str_mv |
AT shirsathpareshbhaskar downscalingregionalcropyieldstolocalscaleusingremotesensing AT sehgalvinayk downscalingregionalcropyieldstolocalscaleusingremotesensing AT aggarwalpramodk downscalingregionalcropyieldstolocalscaleusingremotesensing |
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