Reducing carbon emissions in crop production not only aligns with the goal of high-quality agricultural development but also contributes to achieving the “dual carbon goals”. Based on panel data from 31 provinces in China between 2010 and 2019, this paper explores the impact of Agricultural Socialized Services on carbon emissions in China’s crop production. Utilizing the classical IPCC carbon emission calculation model and spatial econometrics models, this study analyzes the temporal and spatial distribution characteristics of crop production carbon emissions and their driving factors, with a particular focus on evaluating the role of Agricultural Socialized Services in reducing carbon emissions in crop production. The empirical results reveal a “reverse U-shaped” curve for carbon emissions in crop production from 2010 to 2019, with a peak in 2015. Agricultural Socialized Services significantly reduced carbon emissions in crop production, especially in terms of emissions reductions from fertilizer and pesticide use, although the impact on other carbon sources such as plastic mulch, diesel, and tillage was relatively limited. Furthermore, Agricultural Socialized Services exhibited significant spatial spillover effects, effectively reducing local carbon emissions and generating positive carbon reduction effects in neighboring regions through cross-regional services. Based on these findings, the paper suggests improving the Agricultural Socialized Services system according to regional conditions to fully leverage its positive role in reducing carbon emissions in crop production. It also advocates accelerating the innovation of low-carbon agricultural technologies, encouraging farmers’ participation, and utilizing the organizational advantages of village collectives to jointly promote the development of Agricultural Socialized Services and achieve carbon reduction goals.

Against the backdrop of intensifying climate change, maize, as a critical global food and feed crop, plays a pivotal role in ensuring food security and promoting sustainable agricultural development. This study utilizes panel data from 116 prefecture-level cities across nine provinces in China from 2003 to 2019 to analyze the dynamic impacts of maize prices, fertilizer price indices, and monthly precipitation on maize planting area and yield, employing fixed-effects models and the GMM estimation method. The empirical model incorporates key variables such as the previous year’s maize price, fertilizer price index, precipitation from March to May (affecting planting area), and monthly precipitation from April to September (affecting yield), while also accounting for control variables including urban population density, effective irrigation rate, industrial structure, and mechanization level. The results indicate that the previous year’s maize price and fertilizer price index exert significant positive effects on yield, while the fertilizer price index positively influences planting area, and maize price shows a significant negative effect on planting area. The impact of precipitation varies by month: precipitation in July and August enhances yield, whereas precipitation in April and September negatively affects yield. Additionally, March precipitation has a significant negative effect on planting area, while April precipitation positively affects planting area. Robustness tests further confirm the reliability of these findings. Moreover, the study identifies a diminishing marginal effect of agricultural technological progress and proposes policy recommendations, including optimizing irrigation infrastructure, promoting stress-resistant crop varieties, and fostering technological innovation. These findings provide theoretical insights and empirical support for improving maize production efficiency and ensuring food security.

Food legumes play a pivotal role in China’s food security, nutritional health, and green development strategies due to their unique advantages. This paper presents an empirical study on the economic evaluation of scientific research on pest and disease control for food legumes. Using panel data from 31 Chinese provinces from 2008 to 2023, we employ a Double Machine Learning (DML) approach to identify the impact of investment in plant protection research on food legume outputs. The results indicate a steady increase in China’s investment in this field, with an average annual growth rate of 5.19% from 2008 to 2023, and the total investment in 2023 was 2.14 times that of 2008. Investment in plant protection research effectively mitigates output losses and leads to significant production increases. Specifically, a 1% increase in research investment corresponds to a 0.2% increase in food legume output. This effect remains robust across various algorithms, time windows, and control variable settings. Based on these findings, we recommend: (1) increasing financial support and talent acquisition for research on food legume pests and diseases to enhance the stability and sustainability of research investment; (2) strengthening cooperation mechanisms between research institutions and enterprises to leverage their respective strengths and promote the commercialization of research outcomes and regional variety extension; (3) establishing a diversified research investment system that explores a co-construction model guided by the government, involving enterprises, and utilizing public–private partnerships to reconcile the conflict between long research cycles and market demands; (4) fostering a dual-track linkage between regional technological innovation and enterprise product commercialization to improve the efficiency of technology transfer and application; and (5) strengthening R&D in cutting-edge fields like Artificial Intelligence to improve the efficiency and precision of pest and disease control.

Enhancing technical efficiency in food legume production is essential, since the scope for expanding factor inputs is limited under tightening resource constraints. Higher technical efficiency improves resource allocation, ensures food supply stability, and boosts farm income. To strengthen production performance, using survey data from 817 food legume farm households in five major producing regions of China in 2024, this study employs a two-stage DEA-Tobit model to measure farmers’ technical efficiency in food legume production and to empirically identify its driving factors. The results indicate that: (1) technical efficiency in food legume production shows pronounced regional disparities and substantial within-region heterogeneity; (2) technical efficiency in food legume production improves over time, yet substantial space for efficiency gains remains relative to other staple crops; (3) farm households located at different stages of returns to production inputs show distinct production and management patterns. (4) human capital accumulation, full-time farming status, and participation in food legume cooperative economic organizations exert significant positive effects on technical efficiency. Meanwhile, planting scale exhibits a significant inverted U-shaped relationship with technical efficiency. The findings provide household-level empirical evidence to explain disparities in technical efficiency and identify pathways for improving food legume production.

Maize is a critical global food and feed crop, and China is one of the world's largest producers and consumers of maize (Su, 2024;Wang et al., 2022). Within China's agricultural system, maize ranks alongside rice and wheat as one of the three major staple grains, playing a vital role in ensuring food security. Maize has consistently accounted for a high proportion of the total agricultural output value, particularly in Northeast China and the Huang-Huai-Hai region, where maize planting areas exceed 50% of the total grain crop area, making it a pillar OPEN ACCESS EDITED BY Niaz Ali, Hazara University, Pakistan of the local agricultural economy. Maize is not only an important supplement to food production but also the primary source of feed grain for the livestock sector, accounting for over 60% of national feed grain consumption (Wang et al., 2022). Its stable supply is crucial for the sustained production of meat and dairy products. Due to its suitability for mechanized and large-scale cultivation, maize has become a key sector in China's agricultural modernization, significantly promoting the dissemination of agricultural technologies and improvements in production efficiency.In recent years, driven by population growth and economic development, maize demand has continued to rise. Between 2013 and 2023, China's maize production increased by 46.9%, growing from 209 million tons to 307 million tons (Wang et al., 2021;Liu and Liu, 2023). The growth rate of maize-sown area has outpaced that of other staple crops, but this expansion has also revealed structural contradictions such as inventory surpluses, insufficient supply of high-quality maize, and deepening dependence on imports (Liu and Liu, 2023;Ye et al., 2024;Wang et al., 2024). Currently, maize has become China's secondlargest imported agricultural product, with imports reaching 27.13 million tons in 2023, exacerbating market risks due to over-quota imports (Si et al., 2025). The 2009 natural disaster in Northeast China triggered China's transition from a net exporter to a net importer of maize, and dependence on the international market has continued to intensify since then (Song and Wei, 2024).Against the backdrop of climate change and global geopolitical instability, the strategic significance of maintaining a stable maize supply for national food security and livestock sector development has greatly increased (Song et al., 2020). Price volatility and precipitation uncertainty in maize production present challenges similar to those faced by other staple crops. Based on panel data from 116 prefecturelevel cities between 2003 and 2019, this study quantitatively analyzes the combined effects of maize prices, fertilizer price indices, and monthly precipitation on planting area and yield. It reveals the vulnerabilities of the food production system under the dual pressures of market and climate change, as well as the need for adaptive adjustments. The findings are not only applicable to the maize system in China but also provide important references for addressing common challenges in the construction of sustainable global food systems.Agriculture is one of the sectors most sensitive and vulnerable to climate change (Qian et al., 2014). Some scholars have combined crop models with tools such as Global Climate Models (GCM) and Regional Climate Models (RCM) to analyze the impacts of climate change on agricultural production (Jin, 1991;Lin et al., 1997;Gao, 1991). The impacts of climate change on maize production are primarily reflected in planting area and yield, with key driving factors including precipitation, temperature, and extreme weather events. Changes in precipitation patterns and droughts directly affect the stability of maize yields. Chen and Su (2022) found that precipitation in major maize production areas of Henan Province exhibits nonlinear effects on maize yields, with excessive or insufficient rainfall during the silking stage leading to yield reductions. Ma et al. (2015) indicated that drought significantly reduces maize yields in Hebei, while an increase of 10 millimeters of precipitation can boost yield by 0.21%. Ray et al. (2015) demonstrated that one-third of global maize yield variability can be attributed to climate variability. Temperature is another critical factor influencing maize yield. Studies by Mao et al. (2022) and Cui et al. (2011) showed that moderate increases in temperature can extend the growing period and enhance yields in North China and Northeast China, while high temperatures during the silking and grain-filling stages have significant negative effects. International research, such as Zhao et al. (2017), pointed out that global warming has already led to stagnation in maize yield growth in some regions, particularly in tropical and subtropical areas. Climate change also alters the climatic suitability of land, thereby influencing the spatial distribution of maize planting areas and total production. Research by Ge et al. (2015), based on the GAEZ model, suggested a general decline in maize suitability in China, although the Northeast Plain has seen improved suitability due to rising temperatures and better hydrothermal conditions, while North China has faced constraints from warming and drying trends. Nationwide, climate change has led to a trend of "potential reduction-regional divergence" in maize production (Zhu, 2024), with yields increasing in Northeast China but growth slowing or even declining in North China due to intensified extreme weather events. Regional disparities and adaptation strategies have become focal points of research. In Northeast China, rising temperatures and improved precipitation have led to positive trends in planting area and yield (Ge et al., 2015;Cui et al., 2011). However, in North China, worsening warming and drying trends have progressively deteriorated production conditions (Mao et al., 2022), making adaptation strategies especially important. Chen and Zhou...