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.

Farmland fragmentation is a critical structural constraint hindering the improvement of farmers' chemical fertilizer use efficiency. However, crop‐specific micro‐level evidence focusing on maize growers, along with systematic empirical verification of the moderating roles of mechanized fertilization and labor input, remains lacking. Based on micro‐survey data of 1213 maize‐growing households across seven major maize‐producing provinces from the 2020 China Rural Revitalization Survey, this paper applies Stochastic Frontier Analysis to measure chemical fertilizer use efficiency, combined with Ordinary Least Squares estimation and ‐based Shapley value decomposition for empirical verification. The results show that the average fertilizer use efficiency of the sample households is 0.420, representing a potential model‐based fertilizer reduction space of approximately 57.97%. Each additional plot per hectare decreases efficiency by approximately 11.9% on average. Mechanized fertilization exacerbates efficiency loss under conditions of high fragmentation, whereas labor input dedicated to fertilization acts as a buffering mechanism. Farmland fragmentation explains 25.60% of the explained variation in fertilizer use efficiency, making it the second most critical explanatory factor after geographic and environmental characteristics. Accordingly, on the premise of safeguarding farmers' rights and balancing ecological trade‐offs, priority should be given to promoting differentiated land consolidation and contiguous management, complemented by scale‐appropriate machinery, agricultural service provision, and precision nutrient management tailored to small parcels.

Accurately characterizing the spatial deviation of hog slaughter across prefecture-level cities within a province relative to the agricultural production base is essential for understanding adjustments in hog-production capacity and for advancing differentiated regional governance. Using prefecture-level hog slaughter, the gross output value of agriculture, forestry, animal husbandry and fishery, and provincial farm-size structure data for 30 mainland Chinese provinces from 2007 to 2023, this study constructs an intra-provincial Ellison-Glaeser (EG) index. Markov transitions, spatial Markov analysis, club convergence and the optimal-parameters-based geographical detector (OPGD) are then used to examine the dynamic evolution of intra-provincial spatial organization and its influencing factors. The results show that: (1) the national mean EG index increased with fluctuations, whereas the median remained broadly stable, indicating that the rise in relative spatial deviation was driven mainly by a small number of high-EG provinces; (2) Qinghai, Xinjiang, Shanghai and Inner Mongolia recorded relatively high EG values, whereas the traditional hog-producing provinces of Sichuan, Hunan, Henan and Hebei recorded relatively low values. The net-exporting areas also had lower EG levels than the major consuming and balanced production-consumption areas, suggesting a closer spatial match between hog slaughter and the agricultural production base in traditional producing regions; (3) EG states displayed strong path dependence and spatial association, with conditional transition probabilities varying across neighboring EG environments, while provinces formed three relatively stable high-, medium- and low-EG evolutionary tiers rather than converging as a whole; and (4) the leading factor varied by period: agricultural industrial structure was most prominent in 2007–2012, mean annual temperature in 2013–2018, and the corn-production base in 2019–2023, although the evidence for the last period was weak. The study extends hog-industry agglomeration research from an intra-provincial relative-spatial-organization perspective and provides evidence for improving production layout and differentiated regional governance.