Smallholder maize yield estimation using satellite data and machine learning in Ethiopia
The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.
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Format: | Article biblioteca |
Langue: | English |
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Elsevier
2023
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Sujets: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Sentinel-2, Smallholder Agriculture, Yield Prediction, INTENSIFICATION, SMALLHOLDERS, AGRICULTURE, YIELD FORECASTING, Sustainable Agrifood Systems, |
Accès en ligne: | https://hdl.handle.net/10883/23047 |
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dig-cimmyt-10883-230472024-02-14T14:45:06Z Smallholder maize yield estimation using satellite data and machine learning in Ethiopia Guo, Z. Chamberlin, J. Liangzhi You AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Sentinel-2 Smallholder Agriculture Yield Prediction INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING Sustainable Agrifood Systems The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data. 165-174 2024-02-13T21:30:14Z 2024-02-13T21:30:14Z 2023 Article Published Version https://hdl.handle.net/10883/23047 10.1016/j.crope.2023.07.002 English Nutrition, health & food security Excellence in Agronomy Resilient Agrifood Systems Bill & Melinda Gates Foundation (BMGF) Federal Ministry for Economic Cooperation and Development (BMZ) Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH CGIAR Trust Fund https://hdl.handle.net/10568/131128 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access Africa China Elsevier 4 2 2773-126X Crop and Environment |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Sentinel-2 Smallholder Agriculture Yield Prediction INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING Sustainable Agrifood Systems AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Sentinel-2 Smallholder Agriculture Yield Prediction INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING Sustainable Agrifood Systems |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Sentinel-2 Smallholder Agriculture Yield Prediction INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING Sustainable Agrifood Systems AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Sentinel-2 Smallholder Agriculture Yield Prediction INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING Sustainable Agrifood Systems Guo, Z. Chamberlin, J. Liangzhi You Smallholder maize yield estimation using satellite data and machine learning in Ethiopia |
description |
The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Sentinel-2 Smallholder Agriculture Yield Prediction INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING Sustainable Agrifood Systems |
author |
Guo, Z. Chamberlin, J. Liangzhi You |
author_facet |
Guo, Z. Chamberlin, J. Liangzhi You |
author_sort |
Guo, Z. |
title |
Smallholder maize yield estimation using satellite data and machine learning in Ethiopia |
title_short |
Smallholder maize yield estimation using satellite data and machine learning in Ethiopia |
title_full |
Smallholder maize yield estimation using satellite data and machine learning in Ethiopia |
title_fullStr |
Smallholder maize yield estimation using satellite data and machine learning in Ethiopia |
title_full_unstemmed |
Smallholder maize yield estimation using satellite data and machine learning in Ethiopia |
title_sort |
smallholder maize yield estimation using satellite data and machine learning in ethiopia |
publisher |
Elsevier |
publishDate |
2023 |
url |
https://hdl.handle.net/10883/23047 |
work_keys_str_mv |
AT guoz smallholdermaizeyieldestimationusingsatellitedataandmachinelearninginethiopia AT chamberlinj smallholdermaizeyieldestimationusingsatellitedataandmachinelearninginethiopia AT liangzhiyou smallholdermaizeyieldestimationusingsatellitedataandmachinelearninginethiopia |
_version_ |
1792501537875951616 |