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



Voir les résultats en:
https://doi.org/10.1016/j.gfs.2019.08.004
DOI: 
10.1016/j.gfs.2019.08.004
Provider: 
Licence de la ressource: 
Creative Commons Attribution (CC BY)
Type: 
Article de journal
Journal: 
Global Food Security
Nombre: 
December 2019
Pages: 
256-266
Volume: 
23
Auteur: 
Jiménez D.
Delerce S.
Dorado H.
Cock J.
Muñoz L.A.
Agamez A.
Jarvis A.
Editeur(s): 
Description: 

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.

Αnnée de publication: 
2019
Μots-clés: 
Data-driven
Maize
Machine learning
small-scale farmers
Colombia
collaboration