Agriculture is considered the backbone of any economy and it is also the most important sector of Pakistan’s economy. The purpose of this research is to analyze the Pakistan’s agriculture and its impact on economy. This study also highlights the agricultural challenges and its possible solutions. Time-series data is used in this paper and data is collected from different articles, websites and reports. The agriculture sectors i.e. major and minor crops, fruits, livestock, fisheries and forestry are studied in this research paper. There is some agriculture problems i.e.; limited water, poor management, natural calamities and others which have negative impact on Pakistan’s economy. The findings shows that due to agriculture problems there is fluctuations in Pakistan’s economic growth (GDP) thus, Pakistan’s economic growth is going to slowdown. Total factor of production is also going to slow down due to these instabilities.

Incorporating adaptation into subsistence farming systems is an important strategy to reduce damages related to climate change and to protect livelihoods in developing countries. Using a dataset of 450 farm households collected from three agro-ecological zones, this study examines rural networks, assesses farm-level institutional support and documents any existing structural gaps on climate change adaptation in the agricultural sector of Pakistan. For this purpose, a social network analysis method is used. The study findings reveal that farmers reported a decrease in crop production and increase in pests and diseases due to climate change. Further, changing crop varieties, sowing dates, input mixes and planting trees are the key measures adopted by farmers. Lack of information, finances and resources are the key adaptation constraints. The study findings show that only 28% and 13% of the respondents do not have access to financial services and climate adaptation knowledge, respectively. Support to farmers mainly consists of marketing information and farm equipment from community-based organizations, while private institutions offer weather forecasting services. Public institutions are poorly represented in the network analysis. We also found that extension services are key institutions in the climate adaptation network, while agricultural credits, post-harvest services and marketing of produce were dominant but weakly connected in the financial support network. We also found that with an increase in the provision of services at the farm level, farmers not only adapt more but also move from low-cost and short-term measures to advanced measures. This study proposes an integrated framework to improve the stakeholders’ networking through different kind of partnerships and better adaptation to climate change.

Pakistan is vulnerable to climate change, and extreme climatic conditions are threatening food security. This study examines the effects of climate change (e.g., maximum temperature, minimum temperature, rainfall, relative humidity, and the sunshine) on the major crops of Pakistan (e.g., wheat, rice, maize, and sugarcane). The methods of feasible generalized least square (FGLS) and heteroscedasticity and autocorrelation (HAC) consistent standard error were employed using time series data for the period 1989 to 2015. The results of the study reveal that maximum temperature adversely affects wheat production, while the effect of minimum temperature is positive and significant for all crops. Rainfall effect towards the yield of a selected crop is negative, except for wheat. To cope with and mitigate the adverse effects of climate change, there is a need for the development of heat- and drought-resistant high-yielding varieties to ensure food security in the country.

Climate change remains one of the most pressing, if not the most pressing, medium-to-long-term global challenges, despite the emergence of multiple additional global crises, including the COVID-19 crisis, refugee crisis and financial crisis. Environmental leaders and pioneers have long been identified as crucial agents of change who are of central importance to successful, innovative climate change measures at all levels of governance. Much of the existing literature on environmental and climate leaders and pioneers has focused primarily on countries in the Global North. We know relatively little about the climate leadership and pioneership in and by countries of the Global South. This volume tries to help close this gap in the literature, while also providing new empirical findings and novel analytical insights about climate leadership and pioneership in and by Global North countries. The success of climate governance at multiple levels depends not only on leaders and pioneers but on followers which have remained largely under-researched. This volume also identifies followers and assesses occurrences of climate followership in various multilevel and polycentric climate governance structures. As explained in this volume, climate leaders and pioneers have arguably become even more important since the 2015 Paris Agreement, which is based on a bottomup approach and demands that the acceding states put forward voluntary statelevel pledges via Nationally Determined Contributions (NDCs). The Agreement refers explicitly to the need for and the exchange of information, experiences and best practices. Learning from innovative climate governance measures of climate leaders and pioneers and, if possible, emulating their best practices, have therefore become even more central to global climate efforts.

The Agricultural Model Intercomparison and Improvement Project (AgMIP) assesses the impact of climate change on crop production by comparing multiple crop models. This study contrasts two key models—APSIM and DSSAT—analyzing their responses for crops like maize, wheat, rice, and peanuts under variables such as CO₂ levels, temperature, water, and nitrogen (CTWN). APSIM integrates modules for various crops, using a radiation-use efficiency (RUE) approach and dynamic soil water management, while DSSAT employs a similar RUE-based approach but includes additional parameters like evapotranspiration and soil carbon balance. Regional data from farm surveys in diverse locations like Africa, South Asia, and Pakistan helped simulate realistic conditions, though limitations in specific soil and initial conditions posed challenges. This comparative analysis reveals the unique sensitivities of each model, providing insights into the effects of climate variables on crop yield and adaptation strategies for diverse farming systems.The study examines the sensitivity of crop models (APSIM and DSSAT) to nitrogen (N) fertilization, CO₂, and rainfall across various crop types. Model behavior is analyzed in terms of soil organic matter (SOM) settings, highlighting that correct calibration of soil carbon pools is crucial for mimicking non-legume crops' response to N, especially in degraded soils. CO₂ responses are unique for C-3 crops (e.g., wheat, rice) compared to C-4 crops (e.g., maize, sorghum), with C-3 types generally showing higher CO₂ responsiveness. The research reveals that N availability significantly influences CO₂ responses, showing reduced effects under low-N conditions due to nutrient limitations on growth. Moreover, water availability strongly impacts crop yield, especially under high N fertilization, with irrigated or high-rainfall sites showing more stable yields than rainfed, water-limited conditions.This study explores crop yield responses to rainfall and nitrogen (N) fertilization across various soil and climate conditions, primarily in Africa, using APSIM and DSSAT crop models. For rainfed crops in areas with low-N and degraded soils, rainfall increases show limited yield improvements, with leaching of N under high rainfall resulting in lower yields for maize, millet, and sorghum. In well-fertilized soils, such as in South Africa, yields of maize and other crops improve significantly with more rainfall. Model comparisons reveal differing sensitivities in yield to rainfall, N levels, and CO₂, especially for maize, peanut, and millet. Temperature sensitivity varies between APSIM and DSSAT due to differences in parameterizations for processes like leaf expansion, reproductive progression, and grain growth. For example, DSSAT exhibits a stronger yield reduction in high temperatures due to its grain-filling rate sensitivity. These findings underline the complex interactions among rainfall, soil fertility, N management, and temperature, emphasizing the need for localized model adjustments in climate impact assessments.The CROPGRO-Peanut and APSIM-Peanut models in DSSAT (Decision Support System for Agrotechnology Transfer) offer distinct approaches to simulate peanut crop growth under varying climate conditions. CROPGRO-Peanut focuses on leaf-to-canopy assimilation with hourly temperature impacts on seed development, while APSIM-Peanut uses radiation use efficiency (RUE) for yield prediction and responds differently to temperature. Temperature sensitivity varies significantly between models, with CROPGRO demonstrating a higher sensitivity at elevated temperatures, especially in regions like Senegal. Both models, however, generally align on CO₂ responsiveness but vary in sensitivity to rainfall and nitrogen fertilization, showing greater complexities under nutrient-deficient conditions. This study highlights key insights into model calibration, especially for low-input agricultural systems, and emphasizes the importance of accounting for soil degradation and nutrient limitations in climate impact projections to avoid overly optimistic yield forecasts in nutrient-poor regions.

Cotton production is most vulnerable to climate change particularly in Pakistan, and sustainable cotton yield is critical to accomplish the future demand of the country. Climate change has negative impact on cotton production in major parts of the cotton-growing regions. It hampers not only the yield but also quality of fiber and has negative impact on socioeconomic conditions of farmers. Climate, crop, and economic multidisciplinary modeling approach are being used to assess the impact of climate change and development of adaptation strategies for sustainable cotton production. Climate change scenarios revealed the increase in both maximum and minimum temperature and uncertain rainfall patterns throughout the world and especially in dry and arid areas of the world like Pakistan. Rainfall would increase and decrease as projected by multi-GCMs and RCPs, and it is fact that these changes in climate would lead to negative effect on cotton crop production, and sustainable cotton production in the future is under threat due to climate variablity. Generally, mostly general circulation model (GCM) scenario projected the reduction in cotton yield as compared with the baseline during both timer periods and RCPs tested. Adaptation strategies can minimize the negative impact of climate change. So, changes in crop management practices (sowing, planting density, irrigation, and plant protection) may be good adaptation strategies for sustainable cotton production under changing climate scenarios of the world. Climate resilient cotton production system has potential to minimize the negative impacts of climate change on cotton crop by developing heat and drought resilient germplasm, mitigation technology to reduce GHG emission, and application of decision support system (DSS) and use of ICT-based technologies for sustainable cotton crop production. It is time to adopt climate, energy, and water smart cotton production technologies and practices for sustainable cotton production in the future.

The rice-wheat farming system in Punjab, Pakistan, faces significant threats from climate change, including rising temperatures, extreme rainfall, and water shortages due to glacial melt. By 2050, temperatures in Punjab are projected to increase by 2°C, causing seasonal shifts with heavier wet-season rainfall and drier dry seasons, which could intensify flooding and droughts. This poses a risk to food security, as yields of essential crops like rice, wheat, and cotton plateau, potentially reducing rice yields by 8-30% and wheat by 6-19%, increasing poverty by 6%. To mitigate these impacts, a study by local researchers under AgMIP assessed adaptation strategies such as early sowing, improved crop varieties, increased sowing density, and enhanced fertilizer use. These measures could substantially reduce projected poverty rates and support resilience in small-holder farming, emphasizing the need for collaborative efforts to sustain food security in the face of climate challenges. The study focuses on the calibration and evaluation of the DSSAT crop models for various crops (wheat, rice, maize, and cotton) using field experiment data to predict crop growth and productivity under local climatic conditions. The models were parameterized with crop-specific data, including growth stages, soil, and weather conditions. The study also assessed the impact of different agronomic practices and nitrogen levels on crop yield using simulations from the DSSAT platform. Additionally, future climate scenarios based on the HAPPI project were statistically downscaled to predict temperature and rainfall changes, showing significant warming and altered rainfall patterns, which were then incorporated into the DSSAT models to assess climate change impacts on crop yields. The study focuses on calibrating and evaluating the CSM-CERES-Wheat model for wheat cultivars Faisalabad-2008, Lasani-2008, and Sahar-2006 under varying nitrogen treatments in Faisalabad, Pakistan. The model accurately predicted phenology, leaf area index (LAI), and grain yield, with minimal errors in simulated versus observed data, particularly for anthesis and maturity dates. However, discrepancies were noted in the model's response to different nitrogen levels, as it did not account for nitrogen-induced delays in anthesis and maturity. Despite this, the model performed well for grain yield prediction, with the best results for Sahar-2006. The calibration process involved estimating genetic coefficients, which were found to be robust, allowing the model to simulate growth and yield effectively under irrigated, semi-arid conditions. Overall, the model is suitable for predicting wheat growth and yield, guiding agronomic practices in similar environments. The calibration of the CERES-Maize model for three maize hybrids (Pioneer-1543, Monsanto-DK6103, and Syngenta-NK8711) focused on phenological and growth parameters, including thermal time, kernel growth, and leaf area index (LAI). Monsanto-DK6103 required more thermal time from seedling emergence to juvenile phase (P1) and from silking to maturity (P5) compared to the other hybrids, while Syngenta-NK8711 was a shorter duration cultivar. The model showed good predictions for phenology, growth, and grain yield, with the best fit observed in the long-duration hybrids, Monsanto-DK6103 and Pioneer-1543, which also exhibited higher grain yields. However, slight under simulations were noted, particularly in leaf area index (LAI) and biomass, but the model's performance was generally deemed acceptable. The simulations indicated that early sowing dates yielded the highest grain production, especially for longer-duration hybrids, highlighting the model's ability to predict the impact of planting date on yield. The study explores the impact of climate change on agricultural yields in Punjab, Pakistan, focusing on rice, maize, and cotton. In rice, significant yield reductions were observed in southern districts like Bahawalpur (up to 26%), while northern regions like Sialkot experienced positive impacts with a 5.3% increase in yield. Maize showed less drastic reductions, with some districts like Bahawalpur and Sialkot seeing yield increases due to favorable soil and rainfall conditions. Cotton yields, however, were negatively affected by rising temperatures across Punjab, particularly in the south, where reductions of 18% were recorded. The study highlights the varying regional impacts and emphasizes the need for adaptation measures to mitigate the negative effects of climate change on crop production.

Climate change is a significant threat to food security in developing nations, particularly in Pakistan, where agriculture underpins livelihoods and economic stability. Key crops like wheat and cotton are especially vulnerable to temperature shifts, water scarcity, and extreme events like floods and droughts. The Agricultural Model Intercomparison and Improvement Project (AgMIP) addresses these issues by developing climate adaptation strategies for Pakistan's cotton-wheat cropping system. AgMIP uses climate, crop, and economic modeling to assess impacts and suggest measures for adaptation. Engaging farmers, policymakers, and researchers, AgMIP’s study applies a demand-driven approach, aiming to refine research outcomes and promote adaptation strategies that mitigate negative impacts and build resilience. The study examines baseline climate trends in Southern Punjab across five districts, focusing on maximum and minimum temperatures, precipitation, and solar radiation from 1981 to 2010, revealing how high seasonal temperatures and precipitation variability affect cotton yields. For future climate projections, 25 selected global climate models (GCMs) simulate potential impacts on crop productivity, addressing monsoon patterns and climate variability. A comprehensive assessment through farm surveys and crop modeling (DSSAT and APSIM) highlights increased temperatures and altered rainfall could significantly reduce cotton yields by mid-century, especially under extreme scenarios. Adaptation strategies, including optimized nitrogen use, improved planting techniques, and heat- and drought-resistant crops, show some yield improvements but underscore the need for sustainable practices to maintain resilience in Punjab’s cotton-wheat system. Using the TOA-MD model, the study analyzes socio-economic impacts, showing that, without adaptation, climate effects on farm returns could raise poverty among farm households. Adaptation strategies like drought-resistant crops, water conservation, and improved irrigation can offset losses, enhance resilience, and reduce poverty. Supported by UK DFID, ICRISAT, and Columbia University's AgMIP Coordination Unit, the research emphasizes integrated policy measures for sustainable adaptation in Punjab's cotton-wheat agriculture under climate scenarios (RCP 4.5 and 8.5). The findings underscore the urgent need for strategies that protect agricultural productivity and enhance resilience in the face of climate change.

Agriculture is very vulnerable to temperature and drought in semi-arid and arid regions. Farming communities are especially vulnerable to the potential impact of climate change on crop and livestock. For Pakistan, a potential increase of 2.8°C for the maximum day temperature and 2.2°C decrease in night temperature by the mid- century has been reported. The goal of this chapter is to introduce climate-smart interventions as mitigation and adaptation strategies coupled with crop diversifica- tion through the introduction of climate resilient crops in existing cropping sys- tems. Firstly, it describes the impacts of climate change in context to current food security situation in Pakistan and, secondly, potential climate smart interventions to combat changes in the country. Crop models, their application for developing adaptations, modeling technique and its integration with breeding, remote sensing and its application, policy interventions and resource smart interventions in con- text to changing climate are imperative means to favor the farming community in future farming. Introducing climate resilient crops can be rescued and recognized in dry and hot areas of Pakistan using climate smart interventions and resource use efficiency may be determined with the aid of computer and decision support IT tools in resource inefficient areas.

Climate change is a serious threat to agriculture and food security. Extreme weather conditions and changing patterns of precipitation lead to a decrease in the crop productivity. High temperatures and uncertain rainfall decrease the grain yield of crops by reducing the length of growing period. Future projections show that temperature would be increased by 2.5°C up to 2050. The projected rise in tempera- ture would cause the high frequent and prolong heat waves that can decline the crop production. The rise in temperature results in huge reduction in yield of agronomic crops. Sustaining the crop production under changing climate is a key challenge. Therefore, adaptation measures are required to reduce the climate vulnerabilities. The adverse effect of climate change can be mitigated by developing heat tolerant cultivars and some modification in current production technologies. The develop- ment of adaptation strategies in context of changing climate provides the useful information for the stakeholders such as researchers, academia, and farmers in mitigating the negative effects of climate change.