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Targeting AGwater Management Interventions (TAGMI)

Targeting AGwater Management Interventions (TAGMI) is a decision support tool that facilitates targeting and scaling-out of three different Agricultural Water Management (AWM) technologies in the Limpopo and the Volta River Basins. This online tool displays the output of a Bayesian network model that assesses the influence of social and bio-physical factors on the likelihood of success of implementing different AWM technologies. The Bayesian network model was developed iteratively, in collaboration with local researchers and experts, and merges knowledge pools from technical experts to local agriculture extension agents.


Last updated on 12 August 2020

TAGMI displays spatially explicit model results at the district scale, based on available data, to determine which districts may be better suited than others for a particular technological intervention in Volta and Limpopo Basin countries. TAGMI helps to answer the question: will an intervention successfully applied in one location have a reasonable chance of success at other locations? The answer, provided with a measurable degree of certainty, suggests a way forward for scaling-out AWM interventions.

TAGMI Assesses the Likelihood of Success. The tool models the relationship between social and bio-physical factors and successful implementation and long-term adoption of agricultural water management technologies. It is intended for non-technological expert users who want to know which parts of the river basins have conditions suitable for successful implementation of a planned AWM intervention. For more detailed information about using the tool see the User Manual.

The tool is Science Based. Taking social and human resources into account reflects the fact that there are further enabling conditions required beyond the purely bio-physical conditions that dictate whether or not a technology is appropriate for introduction. The conceptual framework for the Bayes model is informed by the Sustainable Livelihoods Framework (DFID 1999).

It is Evidence Based. The Bayesian network model makes use of available data on key characteristics in a systematic way to suggest the likelihood of success of an intervention. It estimates how different contextual factors interact to influence success. This model and tool are based on the premise that, while absolute certainty is unobtainable, degrees of certainty are both obtainable and useful when using the available information in a systematic way.

This tool was developed as part of the 3-year CGIAR Challenge Program on Water and Food’s Volta and Limpopo Basin Development Challenges. For more information on the programme click here.

Go to the TAGMI Website

Key Documents:

TAGMI Fact Sheet (English, French) (pdf, 536KB) : 1 page outline of the TAGMI tool’s aim, principles and functionality

TAGMI Information Brief (Volta – English, Volta – French, Limpopo) (pdf, 1.3MB) : 4 page introduction to the TAGMI tool’s rationale, development and use

User Manual (pdf, 409KB) : Brief introduction to Bayes networks and the background to the tool, followed by a step-by-step description of how to use the tool and what the various components mean.

Technical Document (pdf, 800KB) : Details of the technical components of the tool, including parameters of the Bayes network (Available in English)

Meta-data (Volta basin, Botswana, Mozambique, South Africa, Zimbabwe) (pdf, 1.3MB) : Details of data layers used in the tool (Available in English)

SEI Main Contacts:
Joanne Morris


  •  Volta Basin Research Partners:

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 of the Council for Scientific and Industrial Research, Ghana (CSIR-SARI); Département de Géographie de l’Université de Ouagadougou

  •  Limpopo Basin Research Partners:

WaterNet; University of Witwatersrand; International Water Management Institute-South Africa

External website:


Eric Kemp-Benedict
Eric Kemp-Benedict

SEI Affiliated Researcher


Profile picture of Joanne Morris
Joanne Morris


SEI York

Douglas Wang

Software Developer

SEI York

Design and development by Soapbox.