Pastoralists’ and agro-pastoralists’ livelihood resilience to climate change-induced risks in the Borana zone, south Ethiopia: Using resilience index measurement approach

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Abstract

This study was conducted in the Borana zone, Oromia region, southern Ethiopia, with the aim of analysing the livelihood resilience of pastoralists’ and agro-pastoralists’ to climate change-related risks. A household survey was used to collect quantitative data, whereas qualitative data were collected via focus group discussions, expert group discussions, and personal observations. Descriptive statistics were used to analyse quantitative data, and content analysis was used to analyse qualitative data. The results show that frequent droughts, rising temperatures, and reduced rainfall are the major climate change-induced risks affecting the livelihoods of pastorals and agro-pastorals in the study area. Among the parameters tested, the greatest impacts of climate change-induced risks on pastoral and agro-pastoral livelihood systems were recorded for food [100%] and animal feed or pasture [99%], followed by livestock loss [95%], a decline in species dynamics [95%], and agro-pastoral land degradation [95%]. The average climate resilience index score of the Borena zone is found to be 0.328, implying that 32.8% of the respondents are resilient, with 0.163 [16.3%] pastoralists and 0.417 [41.7%] agro-pastoralists being resilient. However, all the results are below the scale of the minimum threshold [0.5 or 50%], implying that livelihoods and their households are poorly resilient. For the low resilience in Borena, limited access to basic services [0.26] and the adaptive capacity [0.29] of the pastoralists and agro-pastoralists contributed the highest share for resilience capacity compared to other major building blocks of resilience. However, of the two livelihood systems, pastoralists were found to have less resilience [0.249] compared to agro-pastoralists [0.407]. Besides the climatic factors, the resilience of pastoralists’ and agro-pastoralists’ livelihoods was affected positively and significantly by adaptive capacity and negatively and with statistical significance by the asset endowments of the households at P < 0.05. Therefore, policymakers should give pertinent attention to the reduction of the effects of climatic risks and increase the resilience of pastoral and agro-pastoral livelihood systems. Furthermore, actions that focus on increasing access to water, improving the rangeland generative capacity, diversifying the income sources, and providing timely and accurate early warning information are indispensable to building resilient livelihoods among the pastoral and agro-pastoral communities.

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Principal component analysis: a review and recent developments.

Ian Jolliffe, Jorge Cadima (2016)

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.