Indentifying farmers’ preferences and constraints to maize production in two agro‑ecological zones in Burkina Faso
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Date
2015Author
Dao, Abdalla
Sanou, Jacob
Gracen, Vernon
Danquah, Eric Y.
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Background: The low adoption of technologies, developed by research institutions, by farmers substantiates the
need for client orientation in research and development. Hence, to bridge the gap between breeders and farmers
and to ensure that new varieties fit farmers’ preferences and suit their socioeconomic situations, Participatory Rural
Appraisals (PRA) were undertaken in two agro-ecological zones (North-Sudan and South-Sudan) to determine maize
varieties farmers grow, farmers’ preferences in choice of the varieties and to evaluate farmers’ perceptions of constraints
to maize production.
Results: Farmers grow both local and improved varieties to meet their multiple objectives in maize farming. Nearly
63 % of the farmers grow local maize varieties whilst about 37 % grow improved varieties. All the different varieties
are grown in both northern and southern zones but, in general, early maturing maize varieties are grown in northern
zones while late maturing maize varieties are planted in southern zones. This study revealed that farmers preferred
varieties that carry traits like high yield potential, early maturity, tolerance to drought, Striga and low soil fertility in all
districts. Maize production in the two zones is constrained by a variety of related factors, the extent of contribution of
each factor varies across the districts. The main constraints to maize production are poor cash flow, inadequate farm
tools, poor extension service, drought and pests. Striga is considered as the most important pest followed by termites.
Conclusions: Few studies have been conducted on models on Burkina Faso agriculture and technology adoption.
This study identified agro-ecological zones and others key factors in adoption decision of new maize variety. It is
expected that future studies will include these factors as some of the explanatory variables in adoption models.
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