The process of rapid urbanization has intensified the conversion of different land use types, resulting in a substantial loss of ecological land and ecological security being threatened. In the context of China’s vigorous advocacy of an ecological civilization, it is important to explore future land use patterns under ecological security constraints to promote sustainable development. The insufficient consideration of land ecological security in existing land use pattern simulation studies makes it difficult to effectively promote improvement in the ecological security level. Therefore, we developed a land use simulation framework that integrates land ecological security. Taking the sustainable development of land ecosystems as the core, the land ecological security index (LESI) and ecological zoning (EZ) were determined by the pressure–state–response (PSR) model and the catastrophe progression method (CPM). Natural development (ND) and ecological protection (EP) scenarios were then constructed taking the LESI and EZ into consideration. The CA–Markov model was used to simulate the land use pattern of Guangzhou for 2030 under the two scenarios. The results showed that (1) the study area was divided into four categories: ecological core zone, ecological buffer zone, ecological optimization zone, and urban development zone, with area shares of 37.53%, 31.14%, 16.96%, and 14.37%, respectively. (2) In both scenarios, the construction land around the towns showed outward expansion; compared with the ND scenario, the construction land in the EP scenario decreased by 369.10 km², and the woodland, grassland, and farmland areas increased by 337.04, 20.80, and 10.51 km², respectively, which significantly improved the ecological security level. (3) In the EP scenario, the construction land in the ecological core zone, ecological buffer zone, and ecological optimization zone decreased by 85.49, 114.78, and 178.81 km², respectively, and no new construction land was added in the ecological core zone, making the land use pattern of the EP scenario more reasonable. The results of the study have confirmed that the land use pattern simulation framework integrating land ecological security can effectively predict land use patterns in different future scenarios. This study can provide suggestions and guidance for managers to use in formulating ecological protection policies and preparing territorial spatial planning.

The study aims to estimate different land leasing entities’ intentions and drivers to grow non-grain crops. In 2021, following a multistage sampling technique based on non-grain farmland, 264 farmers from the Zengcheng District of China were interviewed using a well-structured questionnaire based on the theory of planned behavior and transaction cost. The structural equation model was used to quantitatively reveal the influence mechanism of the non-grain use of the transferred farmland. The difference in the non-grain use of the transferred farmland was analyzed from the perspective of the differentiation of the renting entities. The results showed that the profit margin of non-grain and food crops, and the follow-up behavior of business entities, all promote the non-grain utilization of transferred farmland; however, the transaction costs of non-grain utilization and the endowment constraints of agricultural businesses inhibit the non-grain utilization of farmland. The non-grain crops in the suburbs are more profitable, and the transaction costs of the farmland leasing entities are low, so they tend to be grain-free; the rents of the farmland in the outer suburbs are low and can be operated on a large scale, and the leasing entities tend to be grain-oriented. Large-scale leasing entities tend to grow grain, while small-scale leasing entities tend to grow non-grain crops. In general, large-scale leasing entities in the outer suburbs have high transaction costs and low land rents and tend to be grain-oriented. The small-scale leasing entities in the suburbs are close to the market, the transaction costs are low, the rental price of farmland is high, and they are more inclined to grow non-grain crops. The non-grain utilization of the leased farmland should be treated separately, the supervision of the grain production capacity of the leased farmland should be carried out, and the rotation of grain and non-grain crops should be encouraged; the moderate scale operation in outer suburbs should be encouraged, and the construction of high-standard basic farmland for grain-growing farmland should be promoted.

The optimization of new cropland allocation is crucial for promoting the efficient use of cropland resources and safeguarding food security. However, existing studies primarily take suitability as the optimization objective and lack the consideration of stable utilization potential, which may lead to subsequent unstable use. To address this gap, this study quantified the stable utilization potential of new cropland using a machine learning model and integrated it with the ant colony optimization (ACO) model to develop a spatial allocation framework. This framework was validated in Guangdong Province, China, a region characterized by diverse resource endowments and pronounced regional heterogeneity. The results indicated that, under the specified objective-weighting scheme and compared with the business-as-usual (BAU) scenario, the optimized scenario achieved regional cropland quantity balance. It also increased the average stable utilization potential of new cropland and overall utility by 34.84% and 12.74%, respectively, while reducing the mean cost and mean ecological benefit loss per unit area by 10.22% and 41.36%, respectively. Overall, under the specified constraints, the proposed framework offers a promising approach for new cropland planning and provides a basis for governments and land management authorities to improve future allocation practice.