There are numerous anticipated effects of climate change (CC) on agriculture in the developing and the developed world. Pakistan is among the top ten most prone nations to CC in the world. The objective of this analysis was to quantify the economic impacts of CC on the agricultural production system and to quantify the impacts of suggested adaptation strategies at the farm level. The study was conducted in the Punjab province’s rice-wheat cropping system. For this purpose, climate modeling was carried out by using two representative concentration pathways (RCPs), i.e., RCPs 4.5 and 8.5, and five global circulation models (GCMs). The crop modeling was carried out by using the Agricultural Production Systems Simulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation models (CSMs), which were tested on the cross-sectional data of 217 farm households collected from the seven strata in the study area. The socio-economic impacts were calculated using the Multidimensional Impact Assessment Tradeoff Analysis Model (TOA-MD). The results revealed that CC’s net economic impact using both RCPs and CSMs was negative. In both CSMs, the poverty status was higher in RCP 8.5 than in RCP 4.5. The adaptation package showed positive results in poverty reduction and improvement in the livelihood conditions of the agricultural households. The adoption rate for DSSAT was about 78%, and for APSIM, it was about 68%. The adaptation benefits observed in DSSAT were higher than in APSIM. The results showed that the suggested adaptations could have a significant impact on the resilience of the atmospheric changes. Therefore, without these adaptation measures, i.e., increase in sowing density, improved cultivars, increase in nitrogen use, and fertigation, there would be negative impacts of CC that would capitalize on livelihood and food security in the study area.

Urban migration unlocks new employment opportunities for rural dwellers in a productive manner. This study assessed the quality of employment of migrant workers, and its effect on rural households’ welfare. To this end, we used primary data collected from the four major districts of Lahore, Gujranwala, Faisalabad, and Sialkot in Punjab, Pakistan. These data include 504 immigrant and non-immigrant families in rural areas, and 252 migrant workers in urban destinations. We use IV probit and two-step sequential estimation methods for the empirical analysis. The study provides new insights for migration in Pakistan. First, migrant workers are better off in their new urban settings in terms of improved incomes and living conditions, but their social protection status is still poor. Second, the results of the employment quality models show that migration is a successful strategy for rural households to improve the quality of their employment. In addition, the characteristics of migrants and native households affect the relative improvement in the quality of employment and migrants’ conditions. Third, the results of the propensity score matching technique suggest that migration has a positive impact on rural households’ income, and these impacts are more pronounced in large cities. Based on the findings, the study recommends that the government should invest in quality education in rural areas, and ensure that social security schemes are provided for migrant workers in urban areas.

The rate of urbanization in Pakistan especially in Punjab is quite high. The reason behind this is the high population growth that is about 2.4 percent in the last decade. This causes a burden on the farm size and in rural areas people have no choice except to move in the urban areas for their livelihood. The main objective of this study was to identify the key influential factors that affect the decision to migrate. Study evaluated the impact of economic, social, demographic, natural and climatic factors on the welfare of the migrants and non-migrant’s households. For this purpose, an extensive survey from 504 respondents was carried out in four districts of Punjab, Pakistan. Due to the dichotomous nature of the dependent variable i.e. migrant and non-migrant, logistic regression was employed on the collected data using Stata. Results revealed that unemployment, educational and health facilities, family conflicts, small farm size for agricultural activities, and greater family size are the main influencing factors affecting migration decision from rural to urban areas. This creates the strong implications i.e. putting burden on the urban areas due to the high rate of urbanization. So, it is however recommended to stem down the rate of migration all necessary facilities should be provided in the rural areas and Agro-based must be set up near the rural areas providing employment opportunities for the rural dwellers.

The basic purpose of this study was to look at the position of Better Management Practices (BMPs) of cotton crop in order to reduce the burden on our natural scarce resources. Relevant information was taken from 150 respondents from the district Khanewal, Punjab. The results of this study revealed that the education level and landholding size of respondents have a positive impact on the adoption of sustainable agricultural practices. While the farmer’s age and farming experience were found a negative impact on BMPs adoption rate. Similarly, contact of respondents with the staff of farmer’s field schools and the number of visits of farmer’s field school staff were found positively correlated to BMPs adoption rate. The results of the regression analysis revealed the impact of different agriculture inputs and BMPs on cotton yield. The results showed that the age of the farmers, education, fertilizer, irrigations, water scouting, cotton area, farmyard manure, and hybrid seed variety and sowing method had a positive sign while the sign of pesticide coefficient was negative. The gross margins of BMPs adopter and non-adopter were 45,955 PKR and 28,790 PKR per acre respectively. The model also indicated that the production function fits well with the given data set. Therefore, the results of this study conveyed policy messages for the private and public organizations to promote BMPs for the betterment of the farming community.

Objectives: To measure the impacts of climate change sensitivity and how it is affecting economic conditions of farmers in current rice wheat cropping system. Methods/Statistical analysis: Cross-sectional data of 210 farmers from the seven different strata were collected from Punjab, Pakistan. Climate data of baseline (1980-2010) and future (2039-2040) under representative concentration pathways 4.5 and 8.5 for five global circulation models were collected from secondary sources. The climate scenarios were used in two crop simulation models, i.e., DSSAT and APSIM. Tradeoff Analysis Model for Multidimensional Impact Assessment (TOA-MD) was used for the economic analysis. Findings: The crop modeling results of the study using different GCMs and RCPs show that there was negative impact of climate change on the yields of both major crops i.e., rice and wheat. The comparison of both CSMs given the insight that the percent losses were higher in APSIM as compared to DSSAT. The economic analysis endorsed the negative impacts of climate change on farming community. The major economic indicators (net returns, per capita income and poverty) of the study area expressed the declining trend in both RCPs (4.5 and 8.5) and all five GSMs. The observed household vulnerability to climate change percentage was more intense in RCP 8.5 as compared to RCP 4.5, however, among GCMs the figures shown higher vulnerability in hot dry climate conditions and lower in cool wet. The poverty of the study area increased with climate change and it was more prominent while using RCP 8.5 as compared with RCP 4.5.The highest increase in poverty was observed using APSIM crop model for hot-dry conditions. Application/Improvements: The study concluded that to ensure food security, poverty alleviation and to minimize climatic risks there is the need to update agronomic practices and develop adaptation strategies.