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

Authors
  • Jiménez, Daniel
  • Delerce, Sylvain
  • Dorado, Hugo Andres
  • Cock, James
  • Muñoz, Luis Armando
  • Agamez, Alejandro
  • Jarvis, Andy
Publication Date
Sep 10, 2019
Source
CGSpace
Keywords
Language
English
License
Unknown
External links

Abstract

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.

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