The project will use multiple research approaches to develop and apply a decision support tool for scaling out successful agricultural water management (AWM) interventions. The tool will be constructed in a consultative research process engaging with national and local stakeholders.
By incorporating data on existing cases of farming system development in the basin, the tool will be used to propose similarity where specific approaches can be adopted. This prediction will be associated with a level of uncertainty depending on input data quality.
The tool is intended for non-expert users and will be available via the World Wide Web and CD and through trainings during the project. This tool improves on existing out scaling models in two ways: first, by using a Bayes network modelling approach, the tool helps users account for uncertainties in joining data and information layers. It also enables inclusion of various sources of expertise in a spatial manner.
Secondly, the tool includes dimensions of social and human capital known to be important for adoption and uptake of improved agricultural water management strategies among smallholder farmers, thus providing more realistic decision support.
The project will develop an evidence and knowledge-based tool to assess and map the likelihood that a given intervention will be successful in given locations, at the basin scale. The project seeks to answer the question of what works, where and why.
Improved knowledge of where potential interventions have highest opportunity for successful out-scaling through the application of the decision support tool.
Institut National de l’Environnement et de Recherches Agricoles (INERA); Civil Engineering Dept. of the Kwame Nkrumah University of Science and Technology (KNUST); Savanna Agricultural Research Institute (SARI); Département de Géographie de l’Université de Ouagadougou.
Jennie Barron (email@example.com).