Resilient Capacity of Farm Households to Climate Change along the Floodplain of River Niger in Anambra State, Nigeria
Received Date: Jun 18, 2020 / Accepted Date: Jul 23, 2020 / Published Date: Jul 30, 2020
Abstract
Rising threats of flood present a precarious future for households especially the poor farmers, who often live in fragile environment and are reliant on climate-sensitive agriculture for their livelihoods. The study assessed the resilience capacity of farm households to climate change along the floodplain of River Niger in Anambra State of Nigeria. Multistage and purposive sampling techniques were used in selecting 100 household-heads. Data were collected using semi-structured interview schedule and were analyzed and presented with percentage, mean score, Spearman Rho rank order correlation and linear regression model. Results show that households’ perceived resilience capacity assets were water sources available to households (M=2.84), family members as source of social capital (M=2.80), and health of household members (M=2.15). There was a significant (rho = 0.385; p = 0.000) positive correlation between household resilient capacity assets and their perceived resilient capacity. Majority (96.8%) of the respondents had very low resilience capacity to climate change. Number of years spent in school (t=0.030; p≤0.005), and farming experience (t-0.003; p≤0.05) had significant positive relationship with household resilience capacity. Improvements in availability and quality of infrastructural and social resilient assets will advance climate change resilience capacity of households in the area.
Keywords: Climate change, Resilience capacity, Farm households, Floodplain, River Niger
Citation: Ebere NC, Luke UC, Nicholas O, Uzoamaka AG, Chukwuma OJ (2020) Resilient Capacity of Farm Households to Climate Change along the Floodplain of River Niger in Anambra State, Nigeria. Environ Pollut Climate Change. 4: 176. Doi: 10.4172/2573-458X.1000176
Copyright: © 2020 Ebere NC, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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