Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks

PLoS One. 2020 Jun 5;15(6):e0234213. doi: 10.1371/journal.pone.0234213. eCollection 2020.

Abstract

Agricultural development projects have a poor track record of success mainly due to risks and uncertainty involved in implementation. Cost-benefit analysis can help allocate resources more effectively, but scarcity of data and high uncertainty makes it difficult to use standard approaches. Bayesian Networks (BN) offer a suitable modelling technology for this domain as they can combine expert knowledge and data. This paper proposes a systematic methodology for creating a general BN model for evaluating agricultural development projects. Our approach adapts the BN model to specific projects by using systematic review of published evidence and relevant data repositories under the guidance of domain experts. We evaluate a large-scale agricultural investment in Africa to provide a proof of concept for this approach. The BN model provides decision support for project evaluation by predicting the value-measured as net present value and return on investment-of the project under different risk scenarios.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture / economics*
  • Bayes Theorem
  • Climate*
  • Investments / statistics & numerical data*
  • Models, Statistical
  • Politics*
  • Risk
  • Uncertainty

Grants and funding

The research was implemented under the Adaptation of African Agriculture Initiative as part of the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) and its Partnerships for Scaling Climate-Smart Agriculture (P4S) project. CCAFS is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. Part of this research was also carried out under the CGIAR Research Program on Water, Land and Ecosystems with support from CGIAR Fund Donors (http://www.cgiar.org/about-us/our-funders/). This work specifically was developed in collaboration with and supported financially by the World Bank and the International Center for Tropical Agriculture (CIAT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.