Abstract
Recent studies highlight a growing concern over the limited adoption of climate smart agricultural (CSA) practices despite their potential benefits on adaptation, mitigation and productivity. Literature indicates several factors behind the lack of adoption including socio-demographic and economic conditions, agro-ecological scales and the nature of the practices. This paper examines to what extent and under which conditions such factors influence the adoption of CSA practices at farm, household and community level across three study sites in different continents: Vietnam, Nicaragua and Uganda. While cost benefit analysis (CBA) is employed to assess the farm-level profitability of CSA practices, the aggregate community impact disaggregated by different groups of farmer typologies with specific socio-economic features is derived from the adoption rate estimated by the relative advantage of practices and the income level of each group. Our main findings show great variation of farm-profitability of CSA practices across scales. Similar practices could generate different profitability depending on crop typologies, input access and prices, household types and local context. Regarding the aggregate profitability of CSA practices at regional scale, we found that under particular conditions, relevant factors of adoption matter to the adoption pattern and thereby affects the ranking. Such conditions include (i) high income inequality, (ii) large profitability gap of prioritized CSA practices, and (iii) large proportion of cost and benefit of the practices in the level of income. This study contributes to enhancing the prioritization process of CSA practices and provides practical guidance for research and policy to tailor the investment to appropriate end-users to assure the greatest impact for the community.
Original language | English |
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Article number | e0207700 |
Number of pages | 21 |
Journal | PLoS One |
Volume | 13 |
Issue number | 11 |
DOIs | |
Publication status | Published - 19 Nov 2018 |
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03. VNM_without_CBA-1.tab
Le, L. (Creator), Sain, G. (Creator), Czaplicki, S. (Creator), Guerten, N. (Creator), Shikuku, K. M. (Creator), Grosjean, G. (Creator), Läderach, P. (Creator) & Methods, C. D. A. R. (Contributor), Harvard Dataverse, 8 Jan 2019
DOI: 10.7910/dvn/6uyi0x/pz8lmb, https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/6UYI0X/PZ8LMB
Dataset
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01a. NIC_dict.tab
Le, L. (Creator), Sain, G. (Creator), Czaplicki, S. (Creator), Guerten, N. (Creator), Shikuku, K. M. (Creator), Grosjean, G. (Creator), Läderach, P. (Creator), Methods, C. D. A. R. (Contributor) & Agriculture, I. C. F. T. (Contributor), Harvard Dataverse, 8 Jan 2019
DOI: 10.7910/dvn/6uyi0x/xhb0jg, https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/6UYI0X/XHB0JG
Dataset
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01b. VNM_dict.tab
Le, L. (Creator), Sain, G. (Creator), Czaplicki, S. (Creator), Guerten, N. (Creator), Shikuku, K. M. (Creator), Grosjean, G. (Creator), Läderach, P. (Creator) & Methods, C. D. A. R. (Contributor), Harvard Dataverse, 8 Jan 2019
DOI: 10.7910/dvn/6uyi0x/svu6ga, https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/6UYI0X/SVU6GA
Dataset