Probabilistic Assessment of Investment Options in Honey Value Chains in Lamu County, Kenya
| Autor: | Yusuf Karimjee, Eike Luedeling, Caroline Muchiri, Jan W. de Leeuw, Joshua Wafula, Josephat Nyongesa, Keith D. Shepherd, Grace Koech, Geoffrey Malava, Yvonne Tamba |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Value of information 010504 meteorology & atmospheric sciences Impact evaluation Farm income 010501 environmental sciences 01 natural sciences Value chains Baseline (configuration management) Uncertainity 0105 earth and related environmental sciences Applied Mathematics lcsh:T57-57.97 Probabilistic projection Environmental economics Investment (macroeconomics) Variable (computer science) Incentive lcsh:Applied mathematics. Quantitative methods Business lcsh:Probabilities. Mathematical statistics lcsh:QA273-280 Decision outcomes Decision model ISRIC - World Soil Information |
| Zdroj: | Frontiers in Applied Mathematics and Statistics, Vol 4 (2018) Frontiers in Applied Mathematics and Statistics, 4(Article 6) Frontiers in Applied Mathematics and Statistics 4 (2018) Article 6 |
| ISSN: | 2297-4687 |
| DOI: | 10.3389/fams.2018.00006 |
| Popis: | Designing and implementing biodiversity-based value chains can be a complex undertaking, especially in places where outcomes are uncertain and risks of project failure and cost overruns are high. We used the Stochastic Impact Evaluation (SIE) approach to guide the Intergovernmental Authority on Development (IGAD) on viable investment options in honey value chains, which the agency considered implementing as an economic incentive for communities along the Kenya-Somalia border to conserve biodiversity. The SIE approach allows for holistic analysis of project cost, benefit, and risk variables, including those with uncertain and missing information. It also identifies areas that pose critical uncertainties in the project. We started by conducting a baseline survey in Witu and Awer in Lamu County, Kenya. The aim of the survey was to establish the current farm income from beekeeping as a baseline, against which the prospective impacts of intervention options could be measured. We then developed an intervention decision model that was populated with all cost, benefit and risk variables relevant to beekeeping. After receiving training in making quantitative estimates, four subject-matter experts expressed their uncertainty about the proposed variables in the model by specifying probability distributions for them. We then used Monte Carlo simulation to project decision outcomes. We also identified variables that projected decision outcomes were most sensitive to, and we determined the value of information for each variable. The variable with the highest information value to the decision-maker in Witu was the honey price. In Awer, no additional information on any of the variables would change the recommendation to invest in honey value chains in the region. The analysis demonstrates a novel and comprehensive approach to decision-making for different stakeholders in a project where decision outcomes are uncertain.Introduction |
| Databáze: | OpenAIRE |
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