Farming practices are becoming increasingly affected by the changing climate. Many studies have focused on farmers’ adaptation strategies capable of enhancing vulnerable households’ resilience building capacity. However, few studies have focused on how smart crop-livestock diversification as climate adaptation decisions influence farmers’ living conditions. Based on synthesis from previous research and direct interview with farmers, six climate change adaptation options, each varying at five levels, were compiled and included in this study. In this study, the authors use fractional factorial design to construct five versions of the questionnaire with each five questions, and data were collected from 464 farmers randomly assigned to one version. For each question, respondents were asked to make repeated choices about his best adaptation strategies, and this process is consistent with random utility maximization. Mixed logit model was used to model crop-livestock diversification adaptation actions on farmers’ living conditions. Results indicate that cereal crops such as millet (32%), followed by livestock such as cattle and goat (30%), perennial crops such as Adansonia digitata (17%), and tuber and root crops such as cassava (11%), are the most preferred crop-livestock diversification as climate change adaptation strategies. Results also reveal that farmers’ living conditions would be more improved when this combination is implemented in their production systems. The aggregate annual benefit of introducing smart crop-livestock as climate change adaptation was estimated to be 9,722,763.08 FCFA ($19,445.53). These findings may be useful to build resilience capacity and to address future challenges related to climate change, thereby ensuring sustainable development in the study area.
Climate smart agriculture Crop-livestock diversification Adaptation actions Farmers’ living conditions
This is a preview of subscription content, log in to check access.
Assoumana BT, Ndiaye M, Puje G, Diourte M, Graiser T (2016) Comparative assessment of local farmers’ perceptions of meteorological events and adaptations strategies: two case studies in Niger Republic. J Sustain Dev 9:118–135CrossRefGoogle Scholar
Burnham M, Ma Z (2015) Linking smallholder farmer climate change adaptation decisions to development. Clim Dev 8(4):289–311CrossRefGoogle Scholar
Comoé H (2013) Contribution to food security by improving farmers’ responses to climate change in northern and central areas of Côte D’ivoire. ETH, ZurichGoogle Scholar
Epule T, Bryant C (2016) Small scale farmers’ indigenous agricultural adaptation options in the face of declining or stagnant crop yields in the Fako and Meme divisions of Cameroon. Agriculture 6(2):22CrossRefGoogle Scholar
McFadden D (1974) Conditional logit analysis of qualitative choice behavior. Frontiers in econometrics. Zarembka, New YorkGoogle Scholar
Morris SS, Wodon Q (2003) The allocation of natural disaster relief funds: Hurricane Mitch in Honduras. J World Dev 31(7):1279–1289CrossRefGoogle Scholar
Mugambiwa SS, Tirivangasi HM (2017) Climate change: a threat towards achieving Sustainable Development Goal number two (end hunger, achieve food security and improved nutrition and promote sustainable agriculture) in South Africa. Jàmbá 9(1):a350. https://doi.org/10.4102/jamba.v9i1.350CrossRefGoogle Scholar
Nyong A, Adesina F, Osman Elasha B (2007) The value of indigenous knowledge in climate change mitigation and adaptation strategies in the African Sahel. Mitig Adapt Strateg Glob Chang 12(5):787–797CrossRefGoogle Scholar
Orme B (2010) Getting started with conjoint analysis: strategies for product design and pricing research, 2nd edn. Research Publishers LLC, MadisonGoogle Scholar
Tabbo AM, Amadou Z, Danbaky AB (2016) Evaluating farmers’ adaptation strategies to climate change: A case study of Kaou local government area, Tahoua State, Niger Republic, Jàmbá: Journal of Disaster Risk Studies 8(3), a241. https://doi.org/10.4102/jamba.v8i3.241
Train KE (2009) Discrete choice methods with simulation. Cambridge University Press, New YorkCrossRefGoogle Scholar
Traoré B, Van Wijk MT, Descheemaeker K, Corbeels M, Rufino MC, Giller KE (2015) Climate variability and change in Southern Mali: learning from farmer perceptions and on-farm trials. Exp Agric 51(04):615–634CrossRefGoogle Scholar
Zoysa MD, Inoue M (2014) Climate change impacts, agroforestry adaptation and policy environment in Sri Lanka. J For 4:439–456Google Scholar