Abstract. Climate change is a global environmental threat to all economic sectors, particularly the agricultural sector. Pakistan is one of the countries negatively affected by climate change due to its high exposure to extreme events and low adaptive capacity. In Pakistan, farmers are the primary stakeholders in agriculture and are more at risk due to climate vulnerability. Based on farm household data from 450 households collected from three districts in three agroecological zones in the Punjab province of Pakistan, this study examines how farmers perceive climate change and how they adapt their farming in response to perceived changes in climate. The results demonstrate that awareness of climate change is widespread throughout the area, and farm households make adjustments to adapt their agriculture in response to climatic change. Overall 58 % of the farm households adapted their farming to climate change. Changing crop varieties, changing planting dates, planting of shade trees and changing fertilizers were the main adaptation methods implemented by farm households in the study area. The results from the binary logistic model reveal that education, farm experience, household size, land area, tenancy status, ownership of a tube well, access to market information, information on weather forecasting and agricultural extension services all influence farmers' choices of adaptation measures. The results also indicate that adaptation to climate change is constrained by several factors such as lack of information, lack of money, resource constraints and shortage of irrigation water in the study area. Findings of the study suggest the need for greater investment in farmer education and improved institutional setup for climate change adaptation to improve farmers' wellbeing.

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The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Abstract. Climate change is a global environmental threat to all economic sectors, particularly the agricultural sector. Pakistan is one of the negatively affected countries from climate change due to its high exposure to extreme events and low adaptive capacity. In Pakistan, farmers are the primary stakeholders in agriculture and are more at risk due to climate vulnerability. Based on farm household data of 450 households collected from three districts in three agro-ecological zones in Punjab province of Pakistan, this study examined how farmers perceive climate change and how they adapt their farming in response to perceived changes in climate. The results demonstrate that awareness to climate change persists in the area, and farm households make adjustments to adapt their agriculture in response to climatic change. Overall 58% of the farm households adapted their farming to climate change. Changing crop varieties, changing planting dates, plantation of trees and changing fertilizer were the main adaptation methods implemented by farm households in the study area. Results from the binary logistic model revealed that education, farm experience, household size, land area, tenancy status, ownership of tube-well, access to market information, information on weather forecasting and extension all influence the farmers' choice of adaptation measures. Results also indicate that adaptation to climate change is constrained by several factors such as lack of information; lack of money; resource constraint and shortage of irrigation water in the study area. Findings of the study suggest the need of greater investment in farmer education and improved institutional setup for climate change adaptation to improve farmers' wellbeing.

Cotton is the second largest crop of Pakistan in terms of area after wheat and is being suffered by multiple shocks over the time due to conventional agricultural management practices, climate change, and market failures. Climate Smart Agriculture (CSA) was introduced by the Food and Agricultural Organization (FAO) in 2010, as an innovative cleaner production alternative to conventional farming that aimed at increasing the efficiency of natural resources, resilience, and productivity of agricultural production system, while reducing greenhouse gas emissions. The adverse effects of climate change on cotton production at the farm and regional level can be minimized by using CSA practices and technologies. The present study investigated the financial performance and explored the impact of CSA through sustainable water use management on cotton production in Lower Bari Doab Canal (LBDC) irrigation system of Punjab, Pakistan by using Cobb-Douglas production function. The adopters of CSA in cotton cultivation were identified by conducting six focus group discussions. Data were collected through well-structured questionnaire from 133 adopters of CSA and 65 conventional cotton growers for the cropping season 2016-2017. It was found that water-smart (raising crops on bed, laser land levelling, conjunctive use of water and drainage management), energy-smart (minimum tillage), carbon-smart (less use of chemicals) and knowledge-smart (crop rotation and improved varieties i.e., tolerant to drought, flood and heat/cold stresses) practices and technologies of CSA were adopted by the cotton farmers in the study area. Most of the farmers were of the view that they are adopting CSA practices and technologies due to the limited supply of canal water, climate change, drought-prone, massive groundwater extraction, rapidly declining groundwater table and increasing soil salinity over the time. Results revealed that uniform germination, higher yield and financial returns, the concentration of inputs and increase in resource use efficiency are the main advantages of CSA. The econometric analysis showed that implementation of CSA practices and technologies as judicious use of water and fertilizer, groundwater quality, access to extension services, and appropriate method and time of picking have a significant impact on the gross value of cotton product (GVP). The findings of the study would be helpful for policy makers to formulate policies that can minimize farmer's financial burden to adopt CSA technologies and implement for scaling out in Punjab and beyond.

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Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Liviidae), adults were collected from eight citrus groves across central Florida, and the level of insecticide resistance to ten insecticides was measured using a bottle bioassay. The gene expression of five cytochrome P450 CYP4 (CYP4C67, CYP4DA1, CYP4DB1, CYP4G70 and CYP4C68) and three glutathione S‐transferase (GSTD1, GSTE2 and GSTE1) genes was characterized in seven field populations of D. citri and compared with a laboratory population. Additionally, we reared four neonicotinoid insecticide resistant field populations in the laboratory and observed susceptibility changes without exposure to insecticides over multiple generations. The eight field populations of D. citri adults showed no and very low levels of resistance (RR = 1 and 2–10) to dimethoate, chlorprifos, carbaryl, fenpropathrin, bifenthrin, flupyradifurone and spinetoram. Very low to low resistance was found to imidacloprid and cyantraniliprole (RR = 2–10 and RR = 10–20). Moderate to high resistance was found for thiamethoxam (RR = 20–50 and RR = 50–100). The CYP4G70 and CYP4C68 genes were expressed at a higher level in field populations as compared with the laboratory population. Also the Davenport, Florida field population exhibited higher expression of all target genes compared to the laboratory population. Susceptibility to imidacloprid and thiamethoxam increased by 6.62‐ and 6.42‐fold, respectively, compared to the levels initially observed in the field over six generations of breeding without exposure. These results indicate that insecticide resistance may reverse in the field if insecticide selection pressure is removed from the spray schedule or with use of a rotational scheme with insecticides of different modes of action. Also, the results support use of insecticide resistance survey program combined with effective rotation for integrated insecticide resistance management of D. citri where huanglongbing (HLB) management includes vector suppression with insecticides.