In agriculture based economies like Pakistan, farmers often shift from farming to off-farm activities as part of an apparent livelihood transition strategy, despite the fact that most of the workforce depends upon farming. In this paper, we try to uncover insights into how livelihood assets, such as human capital, natural capital, economic capital, and locational characteristics, affect a household’s exit decision from on-farm to off-farm activities as a livelihood transition strategy in rural Pakistan. We analyzed data from 335 farming households from the second largest agricultural producing province in the country, Sindh. Our findings show that more than 19% of households have completely shifted from farming to off-farm activities. Furthermore, we identified that the ‘crop input credit’ is one of the major constraints to farmers converting their previous input-driven small loans into larger loans, where large markups may be imposed if they fail to pay when the harvest is made. The empirical findings from Binary Logistic Regression provide strong evidence for family labor characteristics, particularly for working-age males, working-age females, and working-age children. Surprisingly, the cultivated land size significantly and positively influences farm exit rather than a continuation of farming. Off-farm employment, exogenous shocks, and urbanization also significantly and positively influenced the decision to transition into off-farm work. In contrast, the age of the household head, livestock ownership, and distance to a commercial zone significantly inhibited the decision to exit farming. However, government assistance, including subsidies, strongly encouraged farmers to continue farming. These findings provide new insights into the factors affecting the drivers of both exit and continuation in the farming sector as part of a long-term livelihood transition strategy.

The mixed crop–livestock system is a primary source of livelihood in developing countries. Erratic climate changes are severely affecting the livelihoods of people who depend upon mixed crop–livestock production. By employing the livelihood vulnerability index (LVI), the Intergovernmental Panel on Climate Change LVI (LVIIPCC), and the livelihood effect index (LEI), this study evaluated livelihood vulnerability in southern Punjab, Pakistan. The study provides a range of indicators for national and local policy makers to improve resilience in the face of livelihood vulnerability. By incorporating more major components and subcomponents, this study identifies more specific challenges of livelihood vulnerability for future policy directions. It is interesting to find that credit and cash used for crop inputs are critical financial constraints for farmers. From the estimated indicators, this study also provides some specific policy recommendations for the four study districts of Punjab Province. These results are helpful in identifying and highlighting vulnerability determinants and indicators. Initiating and promoting better adaptive capacity and starting resilience projects for households are urgent actions required by donors and governments to reduce the livelihood vulnerability of mixed crop–livestock households in arid and semiarid areas.

No abstract

Considering the farmer’s decision of quitting cotton plantation due to low economic incentives, the current research is intended to evaluate the economic viability of cotton growers in Punjab. A comprehensively pretested questionnaire was used to gather the information from 240 cotton growers in face-to-face interviews. The cost–benefit ratio was estimated by calculating incurred variable costs, revenue generated, net farm income, and gross margin. The data envelopment analysis was applied to explore the economic, technical, and allocative efficiencies of the cotton producers. The second-stage regression analysis was also conducted to evaluate the socioeconomic factors affecting farmer’s efficiencies by applying the Tobit regression model. The small farmers were found to be most vulnerable group with low returns on investments and low technical and economics efficiencies score. The results also indicate that the financial constraints, difficulty to access agriculture credit, access to extension services, and lack of formal education are the main factors affecting farmer’s efficiency. The government should regulate the input prices and agriculture department should provide formal training to the farmers to adopt better management practices to reduce cost of production.

No abstract