This article aims at exploring the performance of the price discovery function of cornstarch futures market in China. In order to test the stationarity of the cash and futures prices of cornstarch, the augmented Dickey–Fuller test is applied. Both prices are integrated of order one. Then, the Johansen cointegration test is conducted to test the cointegrating relationship between those two prices. Finally, the Granger causality test is performed to observe the direction of causality. The evidence shows that there is a long-run relationship between cash and futures prices and the futures price Granger causes cash price. As a whole, price discovery of cornstarch market in China is present although it is a newly emerged market.

This study examines economic, demographic, and political factors influencing Afghanistan's wheat import trade from 1996 to 2023 with a focus on agriculture production. Using multiple regression models and international government data, the analysis highlights the impact of conflict, climate change, and economic instability on wheat imports. Despite cultivating 2.7 million hectares, Afghanistan remains import-dependent, particularly on Kazakhstan, India, and Uzbekistan, which supplied $63.1M, $11.9M, and $1.24 M worth of wheat from 2021 to 2023. The Russia–Ukraine war has exacerbated food insecurity by increasing wheat prices, with Afghanistan potentially importing 2–2.5 million metric tonnes of Kazakhstan wheat flour to meet demand. findings indicate a 1% GDP (gross domestic product) growth resulting in a 24.764 metric-tonne rise in wheat imports while adverse weather may reduce autumn wheat yield by 20-30 % The study underscores the need for wheat sector improvement, price stabilisation, and food security policy to enhance agricultural resilience and economic analysis.

This study assesses the technical efficiency of wheat production and its determining factors among 384 smallholder farmers in the Imam Sahib, Ali Abad, and Khan Abad districts of Kunduz Province, Afghanistan. Agriculture in this region is the primary livelihood and essential for food security, yet wheat yields remain suboptimal due to inefficient input use and structural challenges. Using a stochastic frontier analysis (SFA) based on the Cobb-Douglas production function, the study estimates farm-level efficiency while accounting for random variation. A Tobit regression model is employed to identify socio-economic and institutional factors affecting technical efficiency (TE), given that TE scores fall between 0 and 1. The findings reveal an average TE score of 77.89%, suggesting that farmers could boost output by 22.11% using the same inputs more efficiently. Key production inputs, land, labor, improved seeds, fertilizer, and pesticides, significantly influenced output. Additionally, higher education levels, better access to extension services, formal credit, mobile phone use, training participation, and market proximity positively impacted TE. These results point to considerable inefficiencies and highlight the need for targeted policy interventions to improve education, rural infrastructure, and institutional support. Promoting digital tools and effective extension services can also enhance productivity. The study provides practical insights to inform strategies aimed at strengthening agricultural performance and rural livelihoods in resource-constrained, post-conflict regions of northern Afghanistan.

Against the backdrop of global food crises and intensifying climate risks, farmers’ production anxiety has become a significant psychological factor threatening food security. Based on the Theory of Planned Behavior and the FAO Sustainable Agriculture Risk Management Framework, this paper utilizes micro-level survey data from 508 grain-growing households in Jilin Province. The dependent variable was farmers’ production anxiety that was measured based on a composite assessment of their planting intentions and willingness to manage farmland independently; the mediating variable was farmers’ future willingness to participate in the “Insurance + Futures” model; the moderating variable was satisfaction with government subsidies. Through Logit models and tests for mediating and moderating effects, it systematically analyzes the impact mechanism of the “Insurance + Futures” model on farmers’ production anxiety and compares it with international programs such as the U.S. Crop Insurance Program and India’s PMFBY. The results show: (1) The “Insurance + Futures” model significantly reduces farmers’ production anxiety, with its intervention effect (OR = 0.318) compares favorably with the reported effect of the U.S. Federal Crop Insurance Program (OR = 0.62, United States Department of Agriculture, 2022) from existing literature, suggesting potential differences in the psychological impacts of risk management tools across institutional contexts. The core mechanism lies in the dual safeguards of “price insurance locking in returns + futures market hedging risks”; (2) Farmers ‘future participation willingness partially mediates the relationship between the model and anxiety (mediation effect accounted for 43.2%), validating the transmission path of “policy experience-satisfaction-behavioral willingness-psychological perception”; (3) Government subsidy satisfaction positively moderates the relationship between “participation willingness-anxiety alleviation,” with higher policy trust leading to more pronounced psychological intervention effects of market-based tools. The study provides China’s experience for building a global agricultural risk management system of “policy guidance + market operation,” suggesting that the UN Food and Agriculture Organization consider the “Insurance + Futures” model as a reference example for developing countries in the formulation of global food security governance strategies, thereby providing empirical insights for relevant policy discussions.

The main objective of the present study is to analyze the determinants of the adoption of green energy products in farming by farmers of the district Swabi, Khyber Pakhtunkhwa. A sample size of 460 was determined by using the Cochran formula. Data were collected using a structured questionnaire through non-probability sampling methods, including convenience and snowball sampling techniques. Data has been analyzed using simple descriptive statistics and Robit regression. The key results indicate that the use of green energy products declines when farmers are uncomfortable using them, are illiterate, or are completely unaware of these technologies. The use of green energy products has shown an increasing effect with higher income, greater farming experience, greater family support during peak seasons, and higher awareness of green energy products among the farmers. Following the study, the government is required to provide agriculture-specific education and training. Incentives and support from the government for installation, maintenance, upgradation, and expansion can significantly lower the barriers to adopting green energy.