VULNERABILITY ASSESSMENT OF AGRICULTURAL COMMUNITIES TO CLIMATE CHANGE

Abstract

Agriculture is highly vulnerable to climate change, and especially in developing countries, where farming is a large part of the livelihoods. Pakistan is one of the highly vulnerable countries to climate change even though it has a very low contribution in the global greenhouse gas emission. Increasing temperatures, frequently followed by droughts and irregular rainfall, floods and pest outbreaks in the region of Southern Punjab are all jeopardizing the agricultural productivity and rural livelihoods. This study evaluated the vulnerability of agricultural populations to climate change in four major agricultural districts of Southern Punjab (Muzaffargarh, Multan, Bahawalpur and Rahim Yar Khan) within the framework developed by Intergovernmental Panel on Climate Change (IPCC), which is based on three components: exposure, sensitivity and adaptive capability. A cross sectional survey was carried out with 160 farmers selected at random, with a structured questionnaire. Descriptive statistics, reliability analysis, confirmatory factor analysis (CFA), correlation analysis, multiple linear regression, one-way ANOVA, bootstrapped mediation analysis, and structural equation modeling (SEM) were used for data analysis. The measurement model was found to be reliable and valid, with c range from 0.89 to 0.92 and CFI and RMSEA measure of model fit indices were acceptable. The level of farmers' exposure (M = 4.12), sensitivity (M = 3.88) and adaptive capacity (M = 3.19) were high, moderate and relatively moderate, respectively. The results of correlation and SEM showed that exposure (β = 0.52; p < 0.001) and sensitivity (β = 0.33; p < 0.001) significantly increased climate change vulnerability while adaptive capacity significantly reduced vulnerability (β = –0.47; p < 0.001). Multiple regression results showed that education, farm size, extension services and access to climate information were significant negative predictor variables of vulnerability while age had a positive association with vulnerability. The model accounted for 66% of the variability in the vulnerability (Adjusted R² = 0.64). There were significant spatial variations between districts (F = 19.81, p < 0.001) with Muzaffargarh showing the highest vulnerability and Rahim Yar Khan showing the lowest. Additionally, mediation analysis showed that extension services partially mediated the relationship between extension services and climate change vulnerability. The results highlight the need to bolster adaptive capacity to climate change to decrease farmers vulnerability to climate change, such as by enhancing agricultural extension services, providing timely climate information, farmer education, institutional support, and climate resilient agricultural policies. The study offers empirical evidence in favor of adaptation planning and policy interventions at the district level for improving the resilience of agriculture communities in climate sensitive areas of Pakistan.

Key Words: Climate, Vulnerability, Impact, Community