Real-time noninvasive monitoring of crop water information is an important basis for water-saving irrigation and precise management. Nano-electronic technology has the potential to enable smart plant sensors to communicate with electronic devices and promote the automatic and accurate distribution of water, fertilizer, and medicine to improve crop productivity. In this work, we present a new flexible graphene oxide (GO)-based noninvasive crop water sensor with high sensitivity, fast responsibility and good bio-interface compatibility. The humidity monitoring sensitivity of the sensor reached 7945 Ω/% RH, and the response time was 20.3 s. We first present the correlation monitoring of crop physiological characteristics by using flexible wearable sensors and photosynthesis systems, and have studied the response and synergistic effect of net photosynthetic rate and transpiration rate of maize plants under different light environments. Results show that in situ real-time sensing of plant transpiration was realized, and the internal water transportation within plants could be monitored dynamically. The synergistic effect of net photosynthetic rate and transpiration of maize plants can be jointly tested. This study provides a new technical method to carry out quantitative monitoring of crop water in the entire life cycle and build smart irrigation systems. Moreover, it holds great potential in studying individual plant biology and could provide basic support to carry out precise monitoring of crop physiological information.

In 2013, the government officially approved the construction task of developing high-standard farmland, which had been written into the outline of the “12th Five-Year Plan”, the “13th Five-Year Plan” and the “14th Five-Year Plan”, effectively ensuring the sustainable development of farmland with high and stable yield in China. Moreover, with the rapid progress of urbanization and industrialization, the quality and usage of cultivated land have changed greatly, and the relationship between the economic value, social value and ecological value of land has become increasingly prominent. Whether the development of high-standard farmland, especially the high-standard farmland used for grain production, has achieved the goals of increasing farmers’ income, agricultural output and rural development is not clear. Therefore, it is necessary to evaluate the comprehensive benefits of high-standard farmland development in grain production, so as to scientifically measure the results of the development. From the perspective of economic, social and ecological benefits, this paper establishes an entropy weight evaluation index system and a model to evaluate the level and effectiveness of high-standard farmland development from 2013 to 2020 in China. The results show that the high-standard farmland development project has improved the yield of grain and the basic productivity of cultivated land, effectively increased the yields of land in the project area and promoted the protection and improvement of cultivated land quality, which includes soil quality improvement, soil fertility enhancement, pollution control and soil remediation. The project also helped raise the farmers’ income levels and improved farmers’ agricultural knowledge and skills in the project area. The projects are very beneficial for agricultural production, the farmers’ income and rural development. However, there is still a certain gap between the national average level of improvement and the original goal set in the policy. The average grain yield per mu (Note: 1 mu ≈ 0.0667 ha, similarly hereinafter) was expected to be increased by less than 100 kg (the national average was a 40 kg increase), and the degrees of improvement in economic, social, ecological and comprehensive benefits in different project types were also different. In the future, we suggest that the project should be implemented according to local conditions and the features of each region. We should pay attention to the protection of basic farmland quality and further improve grain output to achieve the goal of stabilizing and increasing production.

As the source of data acquisition, sensors provide basic data support for crop planting decision management and play a foundational role in developing smart planting. Accurate, stable, and deployable on-site sensors make intelligent monitoring of various planting scenarios possible. Recent breakthroughs in plant advanced sensors and the rapid development of intelligent manufacturing and artificial intelligence (AI) have driven sensors towards miniaturization, intelligence, and multi-modality. This review outlines the key technologies in developing new advanced sensors, such as micro-nano technology, flexible electronics technology, and micro-electromechanical system technology. The latest technological frontiers and development trends in sensor principles, fabrication processes, and performance parameters in soil and different segmented crop scenarios are systematically expounded. Finally, future opportunities, challenges, and prospects are discussed. We anticipate that introducing advanced technologies like nanotechnology and AI will rapidly and radically revolutionize the accuracy and intelligence of agricultural sensors, leading to new levels of innovation.

Agricultural insurance is instrumental in consolidating the gains of poverty alleviation and advancing rural revitalization. It significantly aids in the efficient allocation of agricultural production factors, which in turn enhances agricultural output and bolsters the evolution of modern agriculture. Therefore, utilizing data from 583 household surveys and employing endogenous transformation and intermediary effect models, this paper analyzes the production factor allocation effect and specific mechanism of agricultural insurance. It focuses on small-scale farmers and new agricultural operators, exploring how insurance contributes to the advancement of modern agricultural practices. The results show the following: (1) Agriculture insurance can significantly affect the agricultural scale input behavior of farmers such as land input scale and input scale, agricultural machinery application behavior such as the degree of mechanization and water conservancy application, agricultural technology adoption behavior, and planting structure selection behavior, thereby helping to modernize agriculture. (2) There is heterogeneity in the impact of agriculture insurance on the allocation of production factors for small farmers and new agricultural operators. For small farmers, agriculture insurance has a significant promoting effect on their agricultural machinery application behavior, agricultural technology adoption behavior, and planting structure selection behavior. For new agricultural operators, agriculture insurance significantly promotes their agricultural scale input behavior, agricultural machinery application behavior, and agricultural technology adoption behavior. (3) In terms of the mechanism of action, agriculture insurance mainly promotes agricultural scale input behavior through land transfer, facilitates agricultural machinery application behavior by purchasing agricultural machinery equipment and services, encourages agricultural technology adoption behavior by strengthening agricultural technology training, and enhances professional production levels by increasing the scale of insured planting, thereby contributing to the development of modern agriculture. Based on this, several policy suggestions have been proposed. These include enhancing the directionality of agriculture insurance policies, improving the collaborative interaction mechanism between agriculture insurance and agricultural credit financing, and adopting certain reward and punishment measures to curb moral hazard.

Studying the risk propagation mechanisms in agricultural systems is crucial for maintaining agricultural stability and promoting sustainable development. This research analyzes the risk effects and risk propagation mechanisms in agricultural systems using the DCC-t-Copula-CoVaR model, multi-layer network structures, and the mixed-frequency regression MIDAS model. The study finds that there is significant heterogeneity in risk spillover and absorption in agricultural systems; the risk propagation in agricultural systems is stable, and the stronger the connectivity of industry nodes, the greater the risk. Taking the seed industry as an example, its structural indicator values consistently range between 1.0 and 1.1, with fluctuations closely linked to industry development and policy adjustments. Major risks are caused by risk resonance across multiple industries, not triggered by a single industry alone; the interconnections between industries within the agricultural system can disperse risks, forming a collective risk-sharing mechanism. Understanding these dynamics is essential for developing resilient agricultural practices that support long-term sustainability, ensuring food security, and mitigating environmental impacts. By addressing risk propagation and fostering interconnected risk-sharing mechanisms, agricultural systems can better adapt to challenges such as climate change, resource scarcity, and market volatility, ultimately contributing to a more sustainable and stable global food system.

The grain price, the base of all other prices, is relatively sensitive to policy adjustments. The too high minimum purchase prices will lead to the imbalance of the grain market, while the decline of the minimum purchase prices will exert a direct influence on business entities’ expectation of the market prices in the following year. With the early long-grain nonglutinous rice, the middle-late long-grain nonglutinous rice, the round-grain nonglutinous rice and wheat as instances, this article applies the stepwise regression model and the data between 2004 and 2014 to simulate the effect of the minimum grain purchase prices of 2018 on the cultivated area and the yield of the early long-grain nonglutinous rice, the middle long-grain nonglutinous rice, the round-grain nonglutinous rice and wheat as well as farmers’ corresponding revenues. As suggested by the research, the minimum purchase prices play a significantly positive role in the fluctuation of the rice and wheat prices. In case of the same decline rate of minimum purchase prices, the round-grain nonglutinous rice sees the greatest decline rate of the cultivated area and the yield, followed by the middle-late long-grain nonglutinous rice, while the cultivated area and the yield of the early long-grain nonglutinous rice show the smallest decline. However, when deciding to plant rice and wheat, farmers not only consider about the changes of the minimum purchase prices of rice and wheat, but also are subject to the technical input, the revenue and the cost etc. The government may appropriately adjust the minimum purchase prices of rice and wheat to adjust the cultivated area of rice and wheat, thus to stabilize the cultivated area of rice and wheat in China and realize a win-win situation between society and farmers.