Modelling Cropping Plan Strategies: What Decision Margin for Farmers in Burkina Faso?

In a context of strong climatic and economic uncertainty in West Africa, agricultural statistics reveal interannual variations in the proportions of crops. The underlying causes of these variations remain, however, poorly documented although their understanding is essential for crop production monitoring and agricultural policy-related decision-support. Regional scale cropping plan fluctuations arise directly from multiple individual farmer decisions. The purpose of this study is to understand the decision-making processes involved when farmers choose their cropping plans, in order to assess the respective weight of the different factors underlying the observed fluctuations in cropped areas. The study zone is the Tuy Province, occupying around 6000 km(2) in central-western Burkina Faso. An initial field work showed how farmers' decision-making processes depend on external factors. This led to a separation and prioritization of the decisions taken in response to the physiological needs of the family (primary objectives) from those taken in response to other needs (secondary objectives). Four decision-influencing external factors were identified: i) climate, ii) price of cash crops, iii) incentive measures and dissemination processes and iv) credits for inputs. Decision-making rules were then determined by combining the objectives with the external factors. A decision model built on these rules was applied to Tuy Province between 2002 and 2014 to simulate every year the decision-making process of each farmer depending on several influencing factors. The model was verified by annually comparing the proportions of each crop grown in each cultivated area with those recorded in the agricultural statistics. The annual weight of each of these factors was then assessed: over the study period, 55% of the cropping plans satisfied unavoidable primary needs (the factors involved being internal determinants and credit), and 45% concerned secondary objectives (influenced by prices and promotion drives). With this approach, we evaluated the weight of the price factor to be only 6%. This result did not tally with the literature where the price factor is seen as a major element influencing cropping plan decisions. It is then discussed and considered in the specific context of this study. Sixty percent of the areas planted in cotton were linked to the access to credit granted by the cotton company in the zone, and tallied with the primary objectives of the farmers. The farms were therefore fully dependent on the cotton company. This study also illustrates the merits of modelling to assess how the respective weights of factors change over time, and to provide some major methodological perspectives for using spatial models to strengthen and validate typologies and processes arising from field analysis.