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

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

Katic, Pamela ORCID: 0000-0001-7594-1081 and Morris, Joanne (2016) Targeting investments in small-scale groundwater irrigation using Bayesian networks for a data-scarce river basin in Sub-Saharan Africa. Environmental Modelling and Software, 82. pp. 44-72. ISSN 1364-8152 (doi:https://doi.org/10.1016/j.envsoft.2016.04.004)

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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.

Item Type: Article
Uncontrolled Keywords: Smallholders; Irrigation technologies; Outscaling; Decision-support
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Development Studies Research Group
Faculty of Engineering & Science > Natural Resources Institute > Livelihoods & Institutions Department
Last Modified: 16 May 2019 15:58
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT a
Selected for GREAT 2019: GREAT 1
URI: http://gala.gre.ac.uk/id/eprint/17658

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