The increasing trend of atmospheric carbon dioxide (CO) is the main cause of harmful anthropogenic greenhouse gas emissions, which may result in environmental pollution, global warming, and climate change. These issues are expected to adversely affect the agricultural ecosystem and well-being of the society. In order to minimize food insecurity and prevent hunger, a timely adaptation is desirable to reduce potential losses and to seek alternatives for promoting a global knowledge system for agricultural sustainability. This paper examines the causal relationship between agricultural ecosystem and CO emissions as an environmental pollution indicator in Pakistan from the period 1972 to 2014 by employing Johansen cointegration, autoregressive distributed lag (ARDL) model, and Granger causality approach. The Johansen cointegration results show that there is a significant long-run relationship between the agricultural ecosystem and the CO emissions. The long-run relationship shows that a 1% increase in biomass burned crop residues, emissions of CO equivalent of nitrous oxide (NO) from synthetic fertilizers, stock of livestock, agricultural machinery, cereal production, and other crop productions will increase CO emissions by 1.29, 0.05, 0.45, 0.05, 0.03, and 0.65%, respectively. Further, our finding detects that there is a bidirectional causality of CO emissions with rice area paddy harvested, cereal production, and other crop productions. The impulse response function analysis displays that biomass-burned crop residues, stock of livestock, agriculture machinery, cereal production, and other crop productions are significantly contributing to CO emissions in Pakistan.

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This study aimed to investigate factors influencing the adoption of improved cultivars (ICs) in peach production in Khyber Pakhtunkhwa province of Pakistan. A total of 270 respondents were randomly selected from the three different cultivated areas of Khyber Pakhtunkhwa, namely, Peshawar, Nowshera and Swat. Binary choice model was used in this study to categorise the ICs of peach farmers into adoption and non-adoption. The study identifies that socio-economic, institutional farm resources, and climatic factors are influencing the adoption of ICs of peach production. Results of the estimated model reveal that farmer’s age, education, household size, membership, cell phone, farm size, extension services and the role of the non-government organization have a positive effect on adoption of ICs. In addition, farmer’s experience, off-farm income, livestock and machinery ownership, credit access and inputs prices have a positive and significant impact on ICs adoption. Moreover, results of the logit model demonstrate that climatic related factors have a highly significant and positive impact on the adoption of ICs. These results suggested that institutional services should be strengthened to provide managerial and technical skills on ICs technology adoption and on time provision of financial services to enhance the productivity of peach farmers.