A scalable scheme to implement data-driven agriculture for small-scale farmers



https://doi.org/10.1016/j.gfs.2019.08.004
DOI: 
10.1016/j.gfs.2019.08.004
Proveedor: 
Licencia de recurso: 
Attribution / Atribución (CC BY).
Tipo: 
Artículo de revista
Revista: 
Global Food Security
Número: 
December 2019
Páginas: 
256-266
Volumen: 
23
Autor (es): 
Jiménez D.
Delerce S.
Dorado H.
Cock J.
Muñoz L.A.
Agamez A.
Jarvis A.
Editor (es): 
Descripción: 

The Colombian Ministry of Agriculture Colombia, an international research center and a national farmers’ organization developed a data-driven agricultural program that: (i) compiles information from multiple sources; (ii) interprets that data; and (iii) presents the knowledge to farmers through the local advisory services. Data was collected from multiple sources, including small-scale farmers. Machine learning algorithms combined with expert opinion defined how variation in weather, soils and management practices interact and affect maize yield of small-scale farmers. This knowledge was then used to provide guidelines on management practices likely to produce high, stable yields. The effectiveness of the practices was confirmed in on-farm trials. The principles established can be applied to rainfed crops produced by small-scale farmers to better manage their crops with less risk of failure.

Año de publicación: 
2019
Palabras clave: 
Data-driven
Maize
Machine learning
small-scale farmers
Colombia
collaboration
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