Arable land is a multiple complex system with food production as its core function and multiple functions such as living, ecology and culture, under the combined effect of natural resource elements and human activities. Currently, the spatial pattern and utilization of arable land in China are changing, showing five phenomena: "non-agricultural", "non-grain", "fragmentation", "marginalization" and "ecological degradation". Previous studies on cultivated land resources cognition emphasize the explicit use of arable land, and mainly focus on state assessment. To a certain extent, they ignore the explicit and implicit comprehensive use of arable land and their mutual feed mechanism with arable land ecosystem, which makes it difficult to quantitatively represent the "regional suitable arable land use intensity and pattern"; early warning "order parameters and their triggering mechanism and inflection point affecting regional arable land health"; trade-off the spatial pattern of arable land production, living and ecological function. The essence of arable land as a kind of resource is its "spatial availability". The unique geographic perspective of the interplay of elements, space and time provides a significant support for understanding the spatial patterns, spatial-temporal dynamic changes, impacts and driving factors of arable land resources. The development of theories and methods for the cognition of arable land resource from a geospatial perspective is the key to exploring a coupled synergistic path of arable land conservation and utilization. In this study, we explored the connotations of arable land resources, based on existing research on the quality and value of arable land resources, including analyzing the "element-function-value" cascade of the natural, livelihood, institutional and consciousness layers, combing the multi-scale, holistic, regional and dynamic characteristics. On this basis, we proposed a theoretical framework for the cognition of arable land resources, suggesting a comprehensive cognition of the pattern of arable land resources from three aspects of arable land resource utilization-noumenon attributes-benefit; developing a coupled utilization-noumenon attributes-benefit model of arable land resources process; and analyzing the influencing mechanism of cultural traditions and socio-economic development on the future demand for crop production. The theoretical basis for the cognition of arable land resources contains a series of theories such as resource carrying capacity, arable land quality, geographical trade-offs and synergies, geographical coupling and integration, and complex geosystems. These theories provide theoretical and methodological support for quantitatively describing the state of utilization, noumenon attributes and benefit of arable land resources, and clarifying the reciprocal effect among them. In particular, the theory of complex geographic systems provides a new way to recognize the evolution of cultivated land systems. Finally, the key technical system and challenges of the cognition of arable land resources have been discussed from four aspects, including space-air-ground integrated arable land resource perception, high-performance spatial-temporal data processing, spatial-temporal pattern and process analysis, and multi-scenario spatial simulation and optimization. China's 2023 “No. 1 central document” sets out goals in terms of strengthening arable land protection and use control, strengthening high-standard farmland construction, and promoting green agricultural development. The theoretical framework of comprehensive cognition of arable land resources proposed in this study can provide support for the realization of these goals in terms of optimizing the spatial pattern of arable land, delimiting the red line of arable land protection, warning the degradation of farmland ecosystem, analyzing the core short board factors of regional high-standard farmland construction, and evaluating the potential of regional arable land to reduce fertilizer (and pesticide) inputs and increase outputs. In the future work, empirical studies on the feed-back mechanism of arable land resources utilization, noumenon attributes and benefit under specific scenarios should be carried out in different regions and scales. Another important direction is to integrate dissipative structure, synergetic, self-organization, catastrophe theory and other complex system theories and methods to study the measurement method of "entropy" and "order" of arable land complex system; and explore the emergence process, order parameters and critical conditions of "entropy change" and "phase change" of macroscale arable land system by micro-scale utilization and its change.

Spatial patterns and temporal variation of arable land resources can greatly contribute to regional protection and sustainable utilization, particularly for food security and social stability in the Qinghai-Tibet Plateau. A spatial sampling with fewer points is required to comprehensively characterize the overall features of regional arable land. Long-term observation is also of great significance in evaluating the quality of arable land resources. In this study, a data-driven spatial sampling was presented to determine the indicators of arable land quality in the Tu Autonomous Country of Huzhu, Qinghai Province, China. The grid units of the sample population were set as 1 km of arable land, with a total of 790 points. 21 indicators were extracted from three dimensions of topographic features, soil properties, and tillage technical conditions. An indicator system was then constructed for the sampling of arable land at the point scale, such as the slope, soil bulk density, organ carbon content, and agriculture mechanization level. The performance of spatial sampling was quantified by the natural quality, technical level, and arable land productivity index, which were collected from the pilot project of the Ministry of Natural Resources of China. Local spatial heterogeneity was represented to simulate the accuracy of the overall quality of arable land. Multiple indicators were also calculated from five dimensions of topographic features, soil properties, tillage technical conditions, environmental conditions and biological characteristics, in order to evaluate the quality and productivity of arable land. RSM (random sampling), SPCOSA (spatial coverage sampling and random sampling), CLHS (conditioned Latin hypercube sampling), XY_CLHS (CLHS with x and y coordinates as covariates), and SPCOSA_CLHS (spatial coverage sampling and random sampling-conditioned Latin hypercube sampling) were compared from the perspectives of information entropy, Kullback–Leibler divergence, similarity distance, the spatial heterogeneity and distribution uniformity of samples in the overall quality of arable land. A survey of observation points was carried out on the indicators of arable land quality in the county-level areas. Entropy-based tests were also performed on each sampling using the nine groups with the multigroup sample sizes: 10, 20, 30, 40, 50, 75, 100, 150, and 200. A suitable number of points were explored in the study area. The results show that the SPCOSA_CLHS model integrated the SPCOSA into the CLHS model, and then represented the spatial heterogeneity with the lower spatial constraints. A better performance was achieved to express the overall index attribute and spatial heterogeneity of arable land quality. When the number of sampling points was between 100 and 200, five sampling models shared similar applicability for the attribute in the overall quality indicators of arable land. Once the sample size dropped below 100, there were the greatest differences among sampling models, where the SPCOSA and RSM offset most. A better performance of SPCOSA_ CLHS was also obtained in the information entropy, KL divergence, and similarity distance, in terms of convergence and stability. When the number of sample points was 40-50, a similar spatial sampling was observed as the sample size of 100-200. Therefore, SPCOSA_CLHS can be expected to describe the spatial heterogeneity of arable land quality indicators, the spatial uniformity of sample point distribution, and the simulated accuracy of the overall quality. This finding can provide strong support to the survey and monitoring of arable land quality in the Qinghai-Tibet Plateau. In turn, the evolution of arable land quality can also be used to explore the sustainable use pathways of arable land.

A systematic analysis was performed on the logical framework underlying fallow system in the typical countries and regions, along with its implications in China. This study aims to examine the current regional layout and practical effectiveness of the fallow practices in China. Subsequently, a differentiated fallow framework was proposed to tailor for the specific national conditions. The results revealed that: 1) There were the significant differences of the fallow systems in the various countries, in terms of the implementation background, design goals, implementation models and performance. Furthermore, the differentiated implementation logics were adopted by each country, in the key dimensions of the system design. Four operational logics were also found behind its fallow practice: the goal orientation, system adaptation, compensation design and implementation constraint logic. 2) Five key insights were emerged from the international fallow experiences applicable to China. The fallow space planning was required to integrate the diagnostics of the arable land’s natural resource endowments and regional ecological conditions; The scale of the fallow was estimated to fully meet the baseline requirements for the stable grain production and ecological civilization construction; The fallow model was selected to balance the long-term ecological restoration with the cyclic utilization of agricultural functions; The compensation standards were necessary to coordinate the linkage between economic returns and ecological protection benefits; The fallow policy was required for the synergistic advancement with the national rural revitalization and regional agricultural transformation. 3) The arable land fallow was evolved into the four conceptual phases in China: the “migratory” model that characterized by cyclical exploitation, the “rotational” model featuring planned alternation between cultivation and abandonment, the “seasonal” model driven by empirical practices, and the contemporary “storing grain in the ground and storing grain through technology” model in the sustainable development. Fallow behavior was observed from the early spontaneous, sporadic individual behavior to the institutional lead fallow behavior. 4) The specific variables were considered, such as the cultivated land pressure index, soil quality, climatic characteristics, the bottom line of food security, and the red line of cultivated land protection. There were the regional variations in the natural conditions, soil quality, climatic features, and socioeconomic development levels. The regional adaptability was also explored to implement the fallow policies under the dual goals of the ecological protection and food security. The finding can provide a strong reference to promote the coordinated development of the "quantity-quality-ecology" of the cultivated land for the long-term food security of the country. Some insights were also offered to promote the ecological protection and sustainable utilization of the cultivated land resources.

Cultivated land fragmentation is often considered to reduce agricultural productivity, leading to threatening food security. The pattern and drivers of cultivated land fragmentation can be expected to serve as an important prerequisite for scientific management. Taking Jiangsu Province as the study area, this study aims to assess the cultivated land fragmentation at the county level from four dimensions, including PD (patch density), AWMSI (area-weighted mean shape index), SHDI-S (Shannon's diversity index of area), and COHESION (patch cohesion index). A systematic analysis was made on the clustering features of cultivated land fragmentation in different dimensions using the K-means. MGWR (multiscale geography weighted regression) model was applied to explore the driving factors of cultivated land fragmentation in each county from five dimensions: topographic site, soil properties, urbanization, utilization intensity, and protection strength. The results showed that there were north-south differences in the cultivated land, where the PD and AWMSI were higher in the south than those in the north, the SHDI-S was higher in the north than in the south, and the COHESION was higher in the west. The four dimensions of the county-level cropland fineness index showed three types of clustering: Type A and Type B counties were located in the southern part of the province, both of which shared high patch density, irregular shape, and high area diversity. Among them, the Type A counties were higher connectivity of cultivated land than Type B; Type C counties shared lower patch density, regular shape, low area diversity, and high cropland connectivity, which were distributed mainly in the central and northern parts of the province. In addition, spatial heterogeneity was observed in the driving factors of cultivated land fragmentation in different types of regions. Differentiated fragmentation management should be adopted for the different regions. Among them, cultivated land fragmentation in the southern counties and districts (Type A and Type B) was mainly driven by natural factors (elevation, slope, and soil capacity). There were the more complex factors in the central and northern counties (Type C), including cropland allocation and urbanization. Cultivated land in the Type A counties exhibited a high density and irregular shape of patches, but a high degree of connectivity, which was a greater potential for management. Some considerations were given to promote the merging of adjacent cultivated land for large-scale production when the soil properties were similar in the topographic conditions. A low degree of contiguous cultivated land was observed in Type B counties, indicating the more complex driving factors are more difficult to manage. More attention should be paid to protecting the existing cultivated land for future urbanization. Cultivated land in Type C counties shared a low density and regular shape of patches, with the large cultivated land and continuous distribution. Cultivated land in Type C counties was the most affected by the topographic slope and protection. The central and northern regions were mainly plains with flat terrain and arable land, indicating the low potential for management. However, some attention should still be paid to regulating the use of cultivated land. The finding can provide a strong reference to managing the cultivated land fragmentation in Jiangsu Province. The cropland fine fragmentation model can also be optimized to develop the sustainable use of cropland.