Managing the declining yield of non-food crops has opened new strategic challenges amidst global uncertainties. The COVID-19 scenario has increased awareness of natural lifestyle and eco-friendly products, largely dependent on non-food crop material. This strategic shift requires moving beyond traditional farm practices to improve agricultural production efficiency, and developing countries in particular have shown a consistent loss in their self-sufficiency of industrial crops despite being major exporters of non-food crop materials. However, existing studies analyze production efficiencies of non-food crops from general or theoretical aspects often by virtual estimates from breaking down the multiple factors of crop productivity. This study examined multiple factors of crop production to identify “which crop inputs have been inefficiently used overtime” by tracking efficiency changes and various input issues in overall cotton production from practical aspects, i.e., scaling non-constant returns of those multiple factors would allow for the violation of various situations. Accordingly, a stochastic frontier approach was employed to measure the production frontier and efficiency relationship using time-series data of Pakistan’s cotton production from 1971–2018—a specific non-food crop perspective from a top-ranked cotton-producing country that has recently been shifted towards being a non-exporter of cotton due to low yield. The coefficient of area, seed, and labor indicates the positive relationship with cotton production, while fertilizer, irrigation, electricity, and machinery are statistically negative. This implies that policymakers need priority-based strategies for the judicial use of synthetic fertilizers, irrigation, a subsidy policy, and technology adoption, which could significantly improve the efficiencies of cotton productivity from the same land resources. Being adaptable to other developing economies, the analysis would strategically facilitate designing and developing affordable technology-driven solutions and their customized extensions towards sustainable non-food crop production practices and Agri-Resources efficiencies.

The analysis of herding households’ agricultural insurance purchasing behavior under climate risk can help improve the agricultural insurance system in pastoral areas in China.By constructing an analytical framework of “climate change, risk perception, and herding households’ agricultural insurance purchase decisions,” this study matched the survey data from 764 herding households across different types of grasslands in Inner Mongolia with meteorological data from 2019 to 2023. Based on Probit model regression and mediation effect tests, the following conclusions were drawn: Firstly, climate change, especially abnormal fluctuations in precipitation, triggers yield losses and induces loss aversion among herding households, which translates into agricultural insurance purchasing behavior. Secondly, as herding households are divided into production herding households and subsistence herding households, the impact of climate risk on production herding households’ agricultural insurance purchase decisions is more pronounced than that of subsistence herding households. Thirdly, from the perspective of the mechanism of climate change on the agricultural insurance purchasing behavior of production-oriented herding households, climate risk mainly influences herding households’ agricultural insurance purchase through their risk perception. Therefore, with the intensification of global climate change, while further stimulating the agricultural insurance purchasing demand of living herding households through differentiated premium subsidy policies in the future, it is necessary to further enhance the risk perception level of herding households by strengthening the construction of weather forecasting and agricultural and livestock risk data sharing platform, so as to transform the external changes in climate risk into the intrinsic motivation of herding households’ agricultural insurance purchasing behaviors, and to enhance the herding households’ Climate risk coping capacity of herdsmen.

The productivity of the agricultural sector depends on natural resources, and farmers are vulnerable to substantial weather risks globally. For many years, different insurance products have been promoted in China to manage the negative impact and risk associated with unpredictable natural events. Generally, different insurance products correspond to the various premium levels, and farmers are unaware of the insurance policies and premium calculations. This study focuses on farmers’ preferences toward the insurance of corn crop, one of China’s largest grain crop productions. This study uses Heckman’s two‐stage model to focus on the farmers’ willingness to pay for multiple weather‐based insurances, who suffered severe disasters. The contingent valuation method was used to collect the data about farmers’ willingness to pay and preferences from 252 households across three cities of Inner Mongolia, China. The result of empirical estimation indicates that farmers with less experience and high income tend to choose “60% of full‐cost insurance product.” Farmers with a lack of specialization and lower diversified planting tend to choose “full‐cost insurance product.” In contrast, farmers with higher education prefer “output value insurance product.” The empirical result indicates that the promotion of various agricultural insurance could be a valuable strategy to improve protection levels and manage the risk adjustment input in the agricultural sector. Therefore, this study provides insights into risk reduction design that suggests adopting and encouraging different agricultural insurance products.

This research probes the potential of weather derivatives as tools for mitigating the variability of crop yields due to climatic uncertainties in China. Centered on Tongliao City in Inner Mongolia, the study exploits a long short-term memory (LSTM) network to dissect and simulate 32 years of local precipitation data, thereby achieving a simulation of high reliability. Further exploration through a multiple linear regression model confirms a marked positive relationship between rainfall amounts and maize yields. By combining precipitation put options and the total revenue function for farmers, mathematical derivations yield specific expressions for optimal trading quantities and risk hedging efficiency. The research findings show that, using an assumption of a maize price that is 3 CNY/kg, when farmers purchase around 6.22 precipitation put options they can achieve 67.9% risk hedging efficiency. This highlights the significant role of precipitation put options under specific conditions in reducing the risk of decreased maize yields due to reduced precipitation. However, in practical markets, variations in maize prices and the price change unit (λ) are inevitable. Through further analysis, this study reveals that as these factors change, the optimal trading quantity and hedging efficiency also undergo varying degrees of adjustment. The investigation lays a theoretical groundwork for the practical application and empirical validation of weather derivatives within China’s agrarian sector. However, the study underscores the necessity of a holistic approach to market dynamics to refine hedging strategies. Future decision making must integrate market fluctuations, and adopting transparent pricing mechanisms is critical for enhanced risk management and the advancement of sustainable agricultural practices.