Cultivated land use layout adjustment (CLULA) based on crop planting suitability is the refinement and deepening of land use transformation, which is of great significance for optimizing the allocation of cultivated land resources and ensuring food security. At present, people rarely consider the land suitability of crops when using cultivated land, resulting in an imbalance between crop distribution and resource conditions such as water, heat, and soil, and adversely affects the ecological security and utilization efficiency of cultivated land. To alleviate China’s grain planting structural imbalance and efficiency loss, this paper based on the planting suitability of main food crops (rice, soybean, and maize) to adjust and optimize the cultivated land use layout (CLUL) in the typical counties of the main grain production area in Northeast China, using the agent-based model for optimal land allocation (AgentLA) and GIS technology. Findings from the study show that: (1) The planting suitability of rice, soybean, and maize in the region is obviously different. Among them, the suitability level of soybean and maize is high, and that of rice is low. The current CLUL of the food crops needs to be further optimized and adjusted. (2) By optimizing the layout of rice, soybean, and maize, the planting suitability level of the food crops and the concentration level of the CLUL spatial pattern have been improved. (3) The plan for CLULA is formulated: The study area is divided into rice stable production area, maize-soybean rotation area, maize dominant area, and soybean dominant area, and town or village is identified as the implementation unit of CLULA. The plan for CLULA will be conducive to the concentrated farming of food crops according to the suitable natural conditions and management level. The research realized the optimization of spatial structure and cultivated land use patterns of different food crops integrating farming with protecting land. The significance of the study is that it provides a scientific basis and guidance for adjusting the regional planting structure and solving the problem of food structural imbalance.

Previous studies focused on the status and driving factors of non-grain cultivated land (NGCL), but lacked research on the impact mechanism of NGCL on sustainable agricultural development from the perspective of farmers’ household livelihoods and agricultural production factor allocation. Therefore, the concept of resilience was introduced. According to official statistics of China from 2010 to 2021, such as the local statistical yearbooks, the impact of NGCL on agricultural development resilience (ADR) in the main grain-producing area of Northeast China was explored based on the threshold effect model and the spatial lag model. The results indicate that: (1) the overall level of NGCI in the study area from 2011 to 2020 show an upward followed by a downward trend; (2) the size of agricultural labor force and average area per labor constrain the impact of NGCI on ADR, and the change can be characterized by negative to positive, increasing and then decreasing respectively, and the former is more constrained than the latter; (3) a negative effect of the NGCI trend on ADR exists without spatial spillover effect. The expansion of food production exacerbates the risk of factor mismatch, which is accentuated by the governance environment that pursues food production excessively. Establishing the NFP governance standards should consider the transformation of farmers’ livelihoods and the optimization of production factor allocation. Constructing a resilient risk management mechanism, promoting moderate scale operation and optimizing agricultural labor scale are specific paths for improving the governance mechanisms of NGCI. This study provides a theoretical reference for the development of policies and governance strategies for NGCI in underdeveloped areas.

Research on land resource carrying capacity (LRCC) focuses on the population that regional land resources can support as well as the grain output they can deliver. China’s major grain-producing areas consist of 13 provinces, and the grain produced in these areas makes up 75% of the country’s gross grain output. To boost the land carrying capacity of major grain-producing areas and to ensure national food security, it is crucial to examine the spatial–temporal evolution patterns of LRCC and to devise optimal regulatory strategies. From the perspective of human–grain relationships, this paper looks into the evolutionary features of the spatial–temporal patterns of the LRCC of China’s major grain-producing areas based on a land resource carrying capacity model, a land resource carrying capacity index model, and a land resource limitation model. We obtain three main results: (1) On the temporal scale, the land resource carrying capacity index (LRCCI) of China’s major grain-producing areas as a whole tapered off over a period from 1980 to 2020, whereas the overall LRCC increased in this period, indicating that the human–grain relationship in China’s major grain-producing areas is improving. (2) On a spatial scale, China’s major grain-producing areas ranked by LRCC from the greatest to the lowest, in 2020, were North China, the middle and lower reaches of the Yangtze River, Northeast China, and other regions. In terms of the carrying state of land resources, provinces with grain surpluses significantly rose during 1980–2020, the growth of LRCC of the aforementioned four major regions markedly slowed down in 2015–2020, and a large gap exists in LRCCI between the 13 provinces, revealing an unbalanced, insufficient development of LRCC in each province. (3) From 2000 to 2020, the limit of land resources on population aggregation in most major grain-producing areas was negative, and its absolute value continued to increase; this suggests that the land resources of major grain-producing provinces set small limits on population aggregation, with great potential for increasing LRCC. Taking into account the research results, this paper gives strategies for regulating the LRCC of China’s major grain-producing areas in a bid to further augment the human–grain carrying capacity of land resources in China’s major grain-producing areas and to guarantee national food security.

Analyzing the interaction between land use patterns (LUPs) and socioeconomic factors (SEFs) could provide a basis for regional land spatial planning and management decisions in the future. In this study, population, gross domestic product (GDP) and land use intensity were selected to explain the relationship between SEFs and LUPs. The study designed a new method of sample line acquisition for wavelet analysis, and identified the interaction grid scales of LUP changes with SEFs in 1991, 2005 and 2019 by using cross wavelet transform analysis (XWT). Wavelet transform coherent analysis (WTC) was used to reveal the interaction direction and impact strength between LUPs and SEFs. The results showed that: (1) There were two ranges of 2978–5008 m and 24,400–29,738 m in which the grid scales showing interaction between LUPs and SEFs (population, GDP and land use intensity) from 1991 to 2019 were overlapping. (2) The interaction direction between LUPs and SEFs from 1991 to 2019 was almost negative on all sample lines, while the interaction directions of the middle sample line of population and GDP from 1991 to 2019, the end sample line of GDP in 2019, and the start sample line of land use intensity in 1991 were positive. (3) Dry land, grassland and construction land were most affected by SEFs, followed by paddy fields, forest land and other land, and the least affected were water areas during 1991 to 2019. The impact of population and GDP on LUPs was reduced, while the impact of land use intensity on LUPs was increased from 1991 to 2019. Overall, population, GDP and land use intensity were the important SEFs in the changes of LUPs, and were important factors for social progress and economic development.