Targeting investments in small-scale groundwater irrigation using Bayesian networks for a data-scarce river basin in Sub-Saharan Africa. (August 2016)

• Record Type: Journal Article
• Authors: Katic, Pamela; Morris, Joanne
• Is Part Of: Environmental modelling & software. Volume 82(2016:Aug.)
• Page Start: 44
• Page End: 72
• Publication Date: 2016-08
• Subjects: Smallholders -- Irrigation technologies -- Outscaling -- Decision-support; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.04.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Irrigation for smallholder farming systems is an important approach for sustainable intensification and increased productivity in Sub-Saharan Africa, provided investments in irrigation are properly targeted and accompanied by complementary improvements. Many GIS-based tools have been developed to identify suitable areas for investments in different types of small scale irrigation (SSI), but they do not explicitly address uncertainty on the data input and on the determination of factors that affect success of an investment in a given context. This paper addresses this problem by presenting an application of a decision-support targeting tool based on Bayesian networks (BNs) that can be used by non-expert policy-makers and investors to assess the potential success of specific technologies used for groundwater-based SSI. A case study application for the White Volta Basin in West Africa is presented to illustrate the BN approach. Highlights: A Bayesian network decision tool is tested for targeting irrigation technologies. It includes socio-economic and biophysical factors driving technology success. The development of the tool is illustrated in the White Volta Basin in West Africa. Water, market access and skills are the most influential factors.