A scalable scheme to implement data-driven agriculture for small-scale farmers - Jiménez, Daniel; Delerce, Sylvain Jean; Dorado, Hugo Andres; Cock, James H.; Muñoz, Luis Armando; Agamez, Alejandro; Jarvis, Andy (Elsevier, 2019)
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.