Scientific Publication

How big is the potato (Solanum tuberosum L.) yield gap in Sub-Saharan Africa and why? A participatory approach

author(s) Harahagazwe, D. - Condori, B. - Barreda, C. - Bararyenya, A. - Byarugaba, A.A. - Kude, D.A. - Lung'aho, C. - Martinho, C. - Mbiri, D. - Nasona, B. - Ochieng, B. - Onditi, J. - Randrianaivoarivony, J.M. - Tankou, C.M. - Worku, A. - Schulte-Geldermann, Elmar - Mares, V. - Mendiburu, F. de - Quiróz, R.
from CGIAR - Research Programs and Platforms
permalink open
themes
regions
  • Africa
  • World
citation

Harahagazwe, D.; Condori, B.; Barreda, C.; Bararyenya, A.; Byarugaba, A.A.; Kude, D.A.; Lung'aho, C.; Martinho, C.; Mbiri, D.; Nasona, B.; Ochieng, B.; Onditi, J.; Randrianaivoarivony, J.M.; Tankou, C.M.; Worku, A.; Schulte-Geldermann, E.; Mares, V. (CIP); Mendiburu, F. de.; Quiroz, R. (CIP). 2018. How big is the potato (Solanum tuberosum L.) yield gap in Sub-Saharan Africa and why? A participatory approach.Journal Article Open Agriculture. (Poland). ISSN 2391-9531. 3(2):180-189.
How big is the potato (Solanum tuberosum L.) yield gap in Sub-Saharan Africa and why? A participatory approach

Publications and datasets provided via GARDIAN

Abstract

According to potato experts from ten Sub-Saharan Africa (SSA) countries working together in a community of practice (CoP) over a 3-years period, potato farmers across SSA can increase their current annual production of 10.8 million metric tons by 140% if they had access to high quality seed along with improved management practices. This paper describes this innovative new methodology tested on potato for the first time, combining modelling and a comprehensive online survey through a CoP. The intent was to overcome the paucity of experimental information required for crop modelling. Researchers, whose data contributed to estimating model parameters, participated in the study using Solanum, a crop model developed by the International Potato Center (CIP). The first finding was that model parameters estimated through participatory modelling using experts’ knowledge were good approximations of those obtained experimentally. The estimated yield gap was 58 Mg ha-1, of which 35 corresponded to a research gap (potential yield minus research yield) and 24 to farmers’ gap (research yield minus farmer’s yield). Over a 6-month period, SurveyMonkey, a Web-based platform was used to assess yield gap drivers. The survey revealed that poor quality seed and bacterial wilt were the main yield gap drivers as perceived by survey respondents.

Open original