CIP Study Claims Africa Potato Production Can Increase 140%
A study conducted by the International Potato Center (CIP) and its partners in 10 Sub Saharan African (SSA) countries showed that farmer’s yield gap (24 t/ha) exceeds the current farmer’s yield (8 t/ha). In other words, farmers are getting only a fourth of what they could produce on the same piece of land.
In this study, the research yield gap is defined as the difference between the research yield and the potential yield, whereas the absolute yield gap is derived from the difference between the average farmer’s yield and the potential yield.
The difference between the average farmer’s yield and research yield is named farmer’s yield gap.
A six-month online survey looking into what is causing this outlined 12 most important yield gap challenges out of an initial list of 30.
Poor quality seed was the top-ranked yield gap cause identified by survey respondents, followed by bacterial wilt.
Yield gap reflects quantitative differences between a base-line yield (generally: average farmers’ yield) and either attainable (generally: experiment-based yield) or potential yield over some specified spatial and temporal scale. Thus, several yield gap types can be discerned.
The study used a novel and unique participatory methodology, which consisted of three major components. First saw the development of site-specific crop modeling tools and the acquisition of agricultural statistics. Secondly, regional workshops for modeling and yield gap assessment were undertaken. Thirdly, the online survey on yield gap drivers looked for answers.
To this end, a community of practice (CoP) was established in 13 SSA countries: Nigeria in West Africa; Burundi, Rwanda, Kenya, Uganda, Tanzania, Democratic Republic of Congo and Ethiopia in Eastern and Central Africa; Cameroon in Central Africa; and Angola, Malawi, Madagascar and Mozambique in Southern Africa. The CoP revealed to be a powerful tool for data and knowledge sharing, especially in the context of scarcity of experimental information required for crop modeling.
This study was funded by three CGIAR Research Programs (CRPs).