Abstract:
The agriculture sector is most sensitive to the environment and it is directly affected by extreme weather conditions, For the last decade we have seen a sudden shift in weather patterns toward the extreme, increased number of floods, droughts, and pest attacks have damaged the crops. The objective of the research is to assess the agriculture, socio-demographic, and livelihood vulnerability levels of farmers, the channel is chosen for the research since no similar study has been carried out in the field before. it is vital to study the condition of farmers and climate fluctuations at the local level and to improve the farmers and agriculture vulnerability. Small-hold farmers account for around 85% of the world and their fate will determine if the world can reduce poverty and hunger to reach the Millennium Development goal. A total of 200 respondents were randomly selected and interviewed from 4 villages, between February to March 2019. 50 respondents were randomly chosen from each village. The questionnaire included questions related to the farmer's livelihood, socio-demographic adaptation strategies, and exposure to climate changes to determine the vulnerability according to the Intergovernmental Panel on Climate Change framework (IPCC). Two main methods were chosen, Livelihood Vulnerability Index (L.VI) and Livelihood Vulnerability Index-Intergovernmental Panel on Climate Change (LVI-IPCC) to determine and calculate vulnerability. To calculate LVI statistically and by mathematical formulae, we standardized the sub-indicators of major components of each village and for the second step LVI-IPCC method, we used the vulnerability formula by calculating adaptive capacity, sensitivity, and exposure scores of the selected major components and their sub-indicators in the study areas. We observed considerable variation in results, Lodhey is more vulnerable (0.074) due to its low scores in adaptive capacity (socio-demographic and socio-economic) and high scores in sensitivity contributing factors, and Kot Jhando is the least vulnerable (0.025) village because the opposite scores are true for sensitivity and adaptive capacity. However; Changa and Jahangir showed moderate scores with (0.036) and (0.032) respectively. The major component values differ in Socio-demography and Socio-economic components but found similar in Agriculture assets and Climate exposure for all four villages. The study helps to find the vulnerable segment of the society and proper intervention can be done by policymakers to reduce the vulnerability. Furthermore, large-scale research can be done on the district level by using this methodology.
