Assessment of smallholder farmers' adaptive capacity to climate change: Use of a mixed weighting scheme

J Environ Manage. 2020 Dec 15:276:111275. doi: 10.1016/j.jenvman.2020.111275. Epub 2020 Sep 4.

Abstract

Weighting scheme definition represents an important step in assessment of adaptive capacity to climate change with indicator approach since it defines the trade-offs among indicators or components and can be source of uncertainty. This study aims to assess smallholder farmers' adaptive capacity to climate change by using a mixed weighting scheme that reflect farmers' perceived importance of adaptive capacity components to inform policy makers. To achieve that objective, the sustainable livelihood framework was adopted and indicator approach was used for the assessment. The mixed weighting scheme were defined by using both equal weights and experts judgement methods during the assessment process. The mixed weighting scheme index is compared to the case where equal weights are applied in the assessment process and an uncertainty analysis was performed on relative standard deviation through a Monte Carlo simulation. Primary Data were collected from 450 farmers in two communities in northern Benin with a structured questionnaire and through focus groups discussion. The results show that smallholder farmers in both communities do not have the same perceived importance of adaptive capacity components. The index scores show that farmers have in majority low adaptive capacity. When weighted product aggregation method is used, there is more uncertainty related to the index computed with the mixed weighting scheme, but it leads to the same characterisation when compared with the index computed with the equal weights. It is recommended that mixed weighting scheme should be preferred for the assessment of adaptive capacity and weighted product aggregation method should be used.

Keywords: Adaptive capacity; Climate change; Smallholder farmers; Weighting scheme.

MeSH terms

  • Agriculture
  • Benin
  • Climate Change*
  • Farmers*
  • Humans
  • Surveys and Questionnaires