Título

Smallholder maize yield estimation using satellite data and machine learning in Ethiopia

Autor

Zhe Guo

Jordan Chamberlin

Liangzhi You

Nivel de Acceso

Acceso Abierto

Resumen o descripción

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.

Fecha de publicación

2023

Tipo de publicación

Artículo

Recurso de información

Formato

application/pdf

Idioma

Inglés

Cobertura

Africa

Audiencia

Investigadores

Repositorio Orígen

Repositorio Institucional de Publicaciones Multimedia del CIMMYT

Descargas

0

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