Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks
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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Public Library of Science
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/110045 |
| _version_ | 1855514397297868800 |
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| author | Yet, Barbaros Lamanna, Christine Shepherd, Keith D. Rosenstock, Todd S. |
| author_browse | Lamanna, Christine Rosenstock, Todd S. Shepherd, Keith D. Yet, Barbaros |
| author_facet | Yet, Barbaros Lamanna, Christine Shepherd, Keith D. Rosenstock, Todd S. |
| author_sort | Yet, Barbaros |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace110045 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1100452025-01-24T14:11:54Z Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks Yet, Barbaros Lamanna, Christine Shepherd, Keith D. Rosenstock, Todd S. food security climate change agriculture prioritization 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. 2020-06-05 2020-11-03T23:29:07Z 2020-11-03T23:29:07Z Journal Article https://hdl.handle.net/10568/110045 en https://doi.org/10.1371/journal.pone.0236909 Open Access Public Library of Science Yet B, Lamanna C, Shepherd K, Rosenstock T. 2020. Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks. Plos One 15(7):e0234213. |
| spellingShingle | food security climate change agriculture prioritization Yet, Barbaros Lamanna, Christine Shepherd, Keith D. Rosenstock, Todd S. Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks |
| title | Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks |
| title_full | Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks |
| title_fullStr | Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks |
| title_full_unstemmed | Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks |
| title_short | Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks |
| title_sort | evidence based investment selection prioritizing agricultural development investments under climatic and socio political risk using bayesian networks |
| topic | food security climate change agriculture prioritization |
| url | https://hdl.handle.net/10568/110045 |
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