Hongbin Liu

To address the dilemma of ‘non-grain use of cultivated land’ and support China’s requisition–compensation balance policy, this study developed a multi-dimensional assessment framework integrating the production, ecological, and economic dimensions (3D evaluation model), using Shenyang City as a case study to demonstrate the framework’s operational application and policy relevance. Based on 34,704 Third National Land Survey (TNLS) parcels (27,408.39 ha), we applied the constraint factor assessment method and entropy-weighted composite index model. The results show that non-cultivated agricultural land (NCAL) is generally marginally suitable (citywide average score: 2.50/4), with highly suitable areas accounting for only 4.04% (1106.30 ha). These areas exhibit a triangular spatial pattern distributed across northeastern Faku County, central Sujiatun District, and southern Xinmin City. Sensitivity tests using equal weights and ±20% dimension-weight perturbations confirm that high-suitability area remains limited (3.37–5.63% under entropy-weight scenarios; 8.54% under equal weights). Primary limiting factors include severe organic matter deficiency (average 19 g/kg), shallow soil depth, unfavorable pH, land requiring engineering restoration (94%), and punctiform heavy metal contamination (7.53% of plots, 2065.05 ha as spatially excluded areas). Consequently, we propose a five-tier sequential restoration framework: (1) near-term priority recultivation of highly suitable areas; (2) mid-term topsoil reconstruction for moderately suitable areas; (3) medium-to-long-term topsoil stripping and thickening for low-suitability areas; (4) long-term soil amelioration and slope-to-terrace conversion for marginally suitable areas; and (5) strict prohibition of restoration in unsuitable areas. This study establishes a spatially explicit decision-making system integrating “evaluation–classification–sequencing”, and distinguishes technical suitability from economic, institutional, and policy feasibility, providing a decision-support framework for scientifically implementing the cultivated land requisition–compensation balance policy. Future empirical studies using post-restoration monitoring data are needed to test its predictive accuracy against observed restoration outcomes.

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems.

This study aims to systematically examine the concept and characteristics of medium–low yield farmland (MLYF), to identify the key factors influencing the coordination between soil carbon sequestration (SCS) and production capacity (PC) in MLYF, and develop an evaluation indicator system (EIS) to provide innovative approaches for transforming MLYF to enhance food security and emission reduction capabilities. Focusing on the synergistic improvement of SCS and PC in MLYF, this research employs theoretical analysis, systematic inference, and inductive deduction to analyze the relationships between these factors and construct the EIS. The findings reveal that (1) MLYF is characterized by inherent limitations and suboptimal management practices, resulting in low grain PC but significant potential for yield improvement. (2) A positive correlation exists between the soil organic carbon (SOC) content and crop yield, with MLYF demonstrating substantially greater potential for synergistic improvement than high-yield fields. (3) On the basis of soil science principles, the key factors affecting the synergistic enhancement of carbon sequestration and productivity in MLYF include climatic conditions, soil properties, and biological factors. (4) A comprehensive “Demand-Function-Dimension-Element-Indicator” framework was established, incorporating an EIS designed for national, provincial, and city/county levels to address the management requirements of MLYF across various scales, thereby facilitating comprehensive quality improvement. This research contributes to the theoretical understanding of MLYF transformation, offering valuable insights for ensuring national food security and achieving carbon emission reduction goals.

Fertilizer carbon emissions contribute the largest proportion to agricultural carbon emissions in China, while the extension of low-carbon fertilization technologies (LCFTs) is an effective measure to address this issue. Research suggests that the relational networks surrounding farmers significantly influence their carbon reduction behavior. This study conducted a field survey of 239 large-scale grain producers in August 2022 on China’s Poyang Lake Basin, which is the nation’s largest freshwater lake and a vital agricultural production area. Using cross-sectional data, probit and ordered probit models were employed to analyze the impacts of multidimensional relational networks (market, government, and social networks) on the adoption of LCFTs by large-scale grain producers. Additionally, a mediating-effect model was used to examine the pathways through which relational networks influence LCFT adoption. The findings indicated that relational networks not only increased the likelihood of large-scale grain producers adopting LCFTs but also enhanced the intensity of adoption. However, the effects of different relational networks on low-carbon behavior varied. The market network exerted the most prominent influence on LCFT adoption, followed by the social and government networks. A mediation analysis identified information sharing, demonstration effects, and resource guarantees as the mediating pathways between multidimensional relational networks and LCFT adoption by large-scale grain producers. Furthermore, a heterogeneity analysis revealed that the effects of multidimensional relational networks on LCFT adoption differed across generations and carbon intensity levels. The impact was greater among older grain producers than the younger generation, and those in the high-carbon-intensity group exhibited a stronger incentive compared to the medium- and low-carbon-intensity groups.

As the main service mode and management mode of socialized service, agricultural production trusteeship services (APTS) are an important way to realize the tight link between farmers and modern agricultural development, which is highly important for ensuring national food security. By constructing a utility model of farmers, this paper deduces the decision-making mechanism of farmers’ APTS from the dual mechanism of natural risk and non-agricultural employment income (NAEI) and uses the survey data of 956 farmers in the three northeastern provinces to verify the empirical results by using multivariate logit (Mlogit) and propensity score matching (PSM) methods. The research shows that natural risk hinders farmers’ choice of the APTS; NAEI has a positive impact on APST, and an increase in NAEI weakens the negative effect of natural risk on the choice of the APTS, and has an enhanced moderating effect. In addition, the scale heterogeneity analysis revealed that the impacts of natural risk and NAEI on farmers of different scales are significantly different. The NAEI adjustment effect has the greatest impact on small-scale farmers, followed by medium-scale farmers. In view of this, the government should stabilize the nonagricultural employment market and improve the nonagricultural employment opportunities of farmers. APTS organizations should transfer natural risks and improve the agricultural insurance system. According to the differences of different scales of farmers, different promotion policies are formulated, and the expansion of management and deepening services is proposed to further promote the development of APTS and consolidate the foundation of food security in China.

Agricultural producer service (APS) plays a crucial role in the sustainable development of modern agriculture. Enhancing the stability of contracts between farmers and APS is the key to promoting the high-quality development of the latter. This research aimed to explore the decision-making and moderator effects of transaction costs, service satisfaction, and the stability of APS contracts by constructing a theoretical framework. Based on survey data from 893 farmers in Northeast China’s black soil area, we employed the Mvprobit model to examine the relationship between transaction costs, service satisfaction, and contract stability. The key findings are as follows: Firstly, transaction costs have a dual impact on contract stability in agricultural productive services, acting as both inhibitors and promoters. Higher information and execution costs reduce farmers’ willingness to maintain current cooperative relationships, while higher negotiation costs make farmers more inclined to stick with the status quo. Secondly, farmers’ satisfaction with services positively moderates the influence of transaction costs on contract stability, with highly satisfied farmers being more affected than those with lower satisfaction levels. Lastly, farmers’ personal, family, and social characteristics all shape their preferences during the formation process. To mitigate cooperation risks and bolster cooperation contract stability, this study suggests that the government implement supervision and incentives to reduce transaction costs for farmers when procuring services and enhance the efficiency of farmer–service provider connections.

The act of land transfer in rural areas is an important decision-making mechanism for farmers, to enhance resource allocation efficiency and promote capital mobility, and this act is of strategic importance in promoting the level of agricultural scale and mechanization, land system change, and, thus, the sustainable development of livelihoods and production in China. This study aims to explore farmers’ decision-making mechanisms in the process of land transfer in rural areas, by constructing a theoretical framework. Structural equation modeling was used, based on data from a survey of rural families in the Liaoning Province area of Northeastern China. The main findings are as follows: (1) The seven types of exogenous latent variables, including environmental vulnerability, policy, and five main livelihood assets (natural capital, physical capital, financial capital, human capital, and social capital), are intermediary in farmers’ land transfer behavior, which then positively affect farmers’ livelihood outcomes. (2) Among the exogenous latent variables affecting farmers’ land transfer, human assets have the most significant positive effect, followed by social assets and physical assets, family labor force share, frequency of work information exchange, and number of production tools, greatly affect the corresponding variable. (3) Natural assets play the most important role and have a negative effect on farmers’ land transfer decision; contracted area of land per family is the greatest impacted measurable variable of this. The results of the study suggest that the government should strengthen skills training for farmers, improve the land transfer policy system, and provide appropriate subsidies in a regionally targeted manner. Thus, it can promote the transformation of Chinese-style agricultural modernization and achieve rural revitalization.

Along with the increasing prominence of environmental risks such as soil surface source pollution and declining quality grade of arable land, the issues of how to address irrational fertilizer application and enhance the safety of agricultural products have attracted widespread attention. In this context, clarifying the main factors affecting farmers’ use of soil testing and formulated fertilization technology (STFFT) can further improve the technology adoption rate and fertilizer utilization efficiency, promote standardized agricultural production and maintain the health and stability of soil ecology in black soil areas. This is of great significance to the construction of green agriculture, national dietary health and national food security. This study builds an “external environmental stimuli-perceived characteristics-adoption behavior” theoretical framework to investigate the decision-making and the dynamic influence mechanisms of farmers’ adoption behavior of STFFT. Based on farmer survey data, the logistic-ISM model has been applied. The main findings are as follows. First, five types of influencing factors, namely individual characteristics, family characteristics, business characteristics, cognitive characteristics and external environmental characteristics, had significant “push” effects on farmers’ STFFT adoption behavior. Among them, planting scale and technical training are the key factors influencing farmers’ adoption of scientific fertilizer application technology. Second, both farmers’ perceived ease of use and perceived usefulness play a significant role in farmers’ decision-making process, and the easier farmers perceive STFFT to be to master and the greater the benefits it brings, the more pronounced the tendency to adopt the technology, all other influencing conditions being equal. Third, the main influencing factors of farmers’ STFFT adoption behavior are intrinsically related and divided into four categories based on the magnitude of influence: deep-rooted, medium indirect, shallow indirect and superficial direct. In order to reduce further degradation of black soil caused by farmers’ irrational production habits and to improve resource utilization efficiency, this study recommends the government to further regulate the land transfer market, strengthen the propagation of soil-conservation-type technologies in black soil areas, expand the breadth of agricultural technology training and enhance farmers’ understanding and trust in STFFT. Thus, the maintenance of soil ecosystem in black soil areas, effective guarantee of food security and sustainable development of agriculture can be achieved.

The modernization of agriculture in China has begun to take shape, but some groups of farmers still have relatively weak access to welfare and live in poverty, which is closely related to national food security and sustainable agricultural development. Based on survey data from northeastern China, this study analyzes the logical framework of “Agricultural Productive Services (APS) Embedded—Farmers’ Family Economic Welfare Enhancement” by the endogenous switching regression model. The main findings are as follows. First, the purchase of APS brings a significant positive impact on farmers’ family economic welfare improvement, mainly through the income effect and the substitution effect. Second, with the income effect, there are significant differences in the strength of the “pulling power” of different forms of APS for farmers’ family economic welfare. Compared with farmers who purchase APS for the entire production chain, farmers who purchase only some of the services are more likely to increase their welfare in the later stages. Third, with the substitution effect, the re-employment behavior of the rural surplus laborers who are “squeezed out” under the embedding of APS is promoted, which makes farmers’ income channels, income types, and income opportunities diversify, and their welfare will be more obviously strengthened after purchasing services. In order to enhance farmers’ welfare more efficiently and meet their needs for a better life, this study suggests a combination of farmer information archiving and labor market information transparency to reduce service risks, expand employment channels, and enhance the effectiveness of the linkage between farmers and service providers.

Regulating farmers’ choice of agricultural production service (APS) contracts can help maintain the stability of transactions, enhance agricultural production efficiency, protect farmers’ rights and welfare, stimulate the healthy and sustainable development of service organizations, and promote the economic benefits of agriculture. Under the uncertainty of risk (RU), farmers’ willingness to sign a regulated service contract after purchasing APS is a key factor in reaching an efficient and high-quality partnership. Based on the survey data of the black soil area in northeastern China, this study uses the Heckman two-stage model and analyzes it under the logical framework of APS purchases and contractual choice behavior (CCB) under the role of RU. The main findings are as follows. First, the RU has a significant “inducing” effect on farmers’ CCB, and the higher the RU, the more farmers tend to make formal written contracts; among them, each unit increase in business risk increases farmers’ choice of written contracts by 0.797 units, which is an important factor affecting CCB. Second, farmers’ personal trust level played a positive moderating role in influencing CCB. Compared to the low trust level group, farmers in the high trust level group had a diminished degree of influence on CCB, but the intensity was relatively weak. Third, farmers’ personal, family, and social characteristics all influence their behavioral decision-making processes. In order to reduce the risk level of cooperation between farmers and service providers, which can promote the standardization of cooperation contracts, this study suggests that the government should use the publication of manuals and other forms to carry out legal literacy, promote labor market information transparency, reduce service risks, and enhance the effectiveness of the linkage between farmers and service providers.