耕地非农化

Arable land is a crucial natural resource for human survival and development, which supports food production, ecological services, and material–energy cycling. It is not only an important production resource for agriculture but also a key guarantee for ensuring food security and sustainable agricultural development. Understanding the current utilization of arable land, exploring the spatial–temporal evolution characteristics, and analyzing the driving mechanisms behind its pattern changes are essential for the rational allocation and sustainable utilization of arable land resources. This study focuses on the utilization of arable land in Guangzhou from 2005 to 2018, employing methods such as statistical analysis and spatial econometrics to provide an in-depth analysis of the spatial–temporal distribution characteristics and driving mechanisms of arable land changes. The results show that from 2005 to 2018, the issue of the conversion of arable land to non-agricultural uses was quite severe in Guangzhou, with the primary form being the conversion of arable land into urban residential construction land. Kernel density analysis revealed that non-agriculturization in Guangzhou exhibited spatial clustering, mainly concentrated in areas with lower elevation. Using standard deviation ellipses and centroid migration analysis, it was found that the center of gravity of non-agriculturization in Guangzhou was generally distributed in a southwest–northeast direction, with a more distinct dispersion compared to the northwest–southeast direction. From 2005 to 2010, the rapid increase in the non-agriculturization rate of arable land in Guangzhou was mainly driven by population density and per capita income, both having a positive impact. From 2010 to 2015, the main driving factor shifted to regional GDP. From 2015 to 2018, regional GDP and the value of the tertiary industry became the main driving factors, but unlike the impact of GDP, the tertiary industry exerted a negative influence on non-agriculturization.

… Rather than a simple, uniform depletion of arable land, this non-agriculturalization process is highly dynamic, manifesting through diverse physical transformations across the landscape…

Cropland is a critical component of food security. Under the multiple contexts of climate change, urbanization, and industrialization, China’s cropland faces unprecedented challenges. Understanding the spatiotemporal dynamics of cropland non-agriculturalization (CLNA) and quantifying the contributions of its driving factors are vital for effective cropland management and the optimal allocation of land resources. This study investigated the spatiotemporal dynamics and driving mechanisms of CLNA in Shaanxi Province (SP), a major grain-producing region in China, from 2001 to 2020, using geospatial statistical analysis and machine learning techniques. The results showed that, between 2001 and 2020, approximately 17,200.8 km2 of cropland (8.4% of the total area) was converted to non-cropland, with a pronounced spatial clustering pattern. XGBoost-SHAP attribution analysis revealed that among the 15 selected driving factors, precipitation, road network density, rural population, population density, grain yield, registered population, and slope length exerted the most significant influence on CLNA in SP. Notably, the interaction effects between these factors contributed more substantially than the individual factors. These findings highlight the pronounced regional disparities in CLNA across SP, driven by a complex interplay of multiple factors, underscoring the urgent need to implement water-saving agricultural practices and optimize rural land-use planning to maintain the dynamic balance of cropland and ensure food security in the region.

… Understanding the impact of cultivated land-use changes on China’s grain production potential and policy implications: a perspective of non-agriculturalization, non-grainization, and …

Cultivated land non-agriculturalization is an important manifestation of county-level land use transition. Timely identification of its spatial distribution and phased evolution is essential for farmland protection and rational land resource allocation. Taking Qixian County in Henan Province, China, as the study area, this paper used Sentinel-2 imagery from 2020, 2023, and 2025 on the Google Earth Engine platform. Percentile compositing was employed to generate multi-temporal feature images, and a random forest classifier was used to extract land use information. A unified cultivated land mask for 2020 was then used as a constraint, and post-classification comparison was applied to identify the conversion of cultivated land to non-agricultural uses. The spatiotemporal evolution of non-agriculturalization was analyzed in terms of area, transition type, and spatial distribution. The results show that the overall accuracy of land use classification in the three periods was higher than 83%, and the Kappa coefficient was above 0.77. The areas of cultivated land non-agriculturalization in 2020–2023, 2023–2025, and 2020–2025 were 54.5241 km², 10.3185 km², and 40.6306 km², respectively. Conversion to water dominated in 2020–2023, whereas conversion to orchard/woodland became dominant later. Spatially, non-agriculturalization was concentrated around the county seat, township centers, and transport corridors.

China's rapid urbanization has accelerated Farmland conversion to construction uses, threatening food security. This study assessed cultivated land loss in Jingbian County (2005-2020) using land-use data and high-resolution remote sensing, comparing three classification methods.The Decision Tree method achieved superior performance (>85% accuracy), significantly outperforming Support Vector Machine (>80%) and Maximum Likelihood (60%) approaches. Temporal analysis showed consistent construction land expansion alongside fluctuating cultivated land patterns, with other land types continuously declining.These findings demonstrate that integrating high-resolution remote sensing with Decision Tree classification significantly improves monitoring precision for non-agricultural land conversion, providing crucial scientific support for cropland conservation and sustainable land-use planning.

Understanding the impact of changes in cropland on food production is crucial for economic development and social stability. In recent years, rapid economic growth and frequent population migration in Guangdong Province have significantly changed cropland use and patterns, posing challenges to cropland protection and food security. This study utilized Landsat-4/5/7/8 time-series imagery from the Google Earth Engine and combined it with deep learning techniques to identify long-term cropland use from 1991 to 2020. Then the Global Agro-Ecological Zones model was applied to assess the impact of various cropland use changes on grain production potential (GPP). On this basis, the intrinsic relationship between population, economic development, and food production was further explored using the center of gravity model and spatial mismatch model. The study finds that Guangdong Province’s cropland area has decreased by approximately 34.16%. The annual average loss due to non-agricultural use and abandonment is 2.75 thousand km2 and 3.09 thousand km2, respectively, while the average yearly compensated cropland area is 2.94 thousand km2. The actual annual food loss could meet the needs of about 4.6 million people. Furthermore, non-agriculturalization is the main way of losing GPP, and the reduction of GPP caused by abandonment cannot be underestimated. When considering the GPP loss due to abandonment, new GPP has not fully compensated for lost GPP. Guangdong Province has rapidly decreased the coordination between food production, population, and economic development, leading to considerable contradictions in the social–ecological systems. Finally, the movement of cropland and population centers in opposite directions has intensified the decoupling phenomenon. The results can guide the development of refined cropland protection policies and promote sustainable development of social–ecological systems.

… In response to these challenges, the General Office of the State Council of China issued a notice in September 2020 to curb the non-agriculturalization of farmland. In November of the …

Cropland non-agriculturalization (CNA) threatens food security, ecosystem services, and sustainable development amid accelerating global urbanization. However, existing monitoring methods are often retrospective and lack adequate spatial and temporal resolution for proactive management. This study proposes GS-GAT, a graph-based deep learning framework for predicting CNA susceptibility at the meso-spatial scale. A spatial graph was constructed for the non-central districts of Wuhan, China, and multisource features were extracted across four dimensions: imagery, land cover, topography, and socioeconomics. A comprehensive intensity index is developed to compute susceptibility levels at the street-block level based on multi-year land use data from 2018 to 2022. To address class imbalance, GraphSMOTE is employed to enhance minority node representation. The key model of GS-GAT is trained across four temporal snapshots using attention-based feature aggregation and joint optimization of classification and structural reconstruction losses. Experimental results show that GS-GAT demonstrated an average AUC of 85.6% and an F1 score of 82.6%, which increased to 93% and 91%, respectively, under relaxed evaluation criteria, whereby baseline models such as SVM and XGBoost were outperformed. Ablation studies confirm the contributions of feature fusion and GraphSMOTE to model robustness and minority class detection. The proposed framework offers a scalable and interpretable approach for early identification of cropland conversion risks, supporting more targeted land-use management and cropland protection strategies.

ABSTRACT Changes in cropland use, particularly the transition from agricultural to non-agricultural and non-food crop production, can diversify rural economies but may also pose challenges to regional food security, especially in densely populated and rapidly developing regions such as China. High-precision monitoring of cropland non-agricultural and non-grain conversion is essential for balance regional food security with rural income enhancement. This study focuses on the monitoring cropland non-agricultural and non-grain conversion in the rainy and cloudy regions of southern China. We aim to develop an automated process framework that accurately extracts parcel boundaries and identifies multiple types of changes. Quantitative experiments assessed the effectiveness of various solutions for key modules in the framework, including multisource data fusion, image segmentation, sample generation, and classification feature strategies. Validation using verification samples obtained through visual interpretation and field surveys revealed the following results: (1). The use of both optical and SAR images improved classification accuracy by 1.30% compared to using optical images alone. (2) Under the constraint of vector patch data, segmentation using high-resolution images outperformed both segmentation using medium-resolution images with the same constraint and segmentation using high-resolution images without the constraint, achieving Mean Intersection over Union (MIOU) improvements of 0.28 and 0.24. (3) Samples automatically generated from vector patch data achieved classification accuracy comparable to that of manually selected samples, with only a 0.64% decrease in overall classification accuracy. (4) Classification utilizing time-series feature extraction from reconstructed data outperformed classification based on temporal feature, with an overall accuracy increase of 1.94%. The optimized automated process framework achieved an overall accuracy of 89.00% in monitoring cropland conversion in the complex planting conditions of southern China. This framework represents an effective approach for the automated monitoring of cropland non-agricultural and non-grain conversion with precise parcel boundaries, providing valuable insights for similar monitoring objectives and application scenarios.

Nowadays, most agricultural land is converted into non-agriculture activities in the district of Peshawar, which has caused a food crisis for future generations. Seeing its importance, the present study was carried out in 2024 in the district of Peshawar. The primary objective is to discover why agricultural land is converted into non-agriculture activities in the study area. District Peshawar consists of four towns. One village in each town was randomly selected, namely Qazi Killi, Tarnab, Palosi Maghdazi, and Musa Zai. The total number of respondents was 10496 in the four villages. Yamani formula was used, and the sample size was fixed to 385. Then, through the allocation proportion method, 385 respondents were distributed among the selected villages. Data were collected from the respondents through a questionnaire schedule, and descriptive statistics were used for data analysis. The result revealed that urbanization, low per-acre productivity, unemployment, availability of fewer facilities on the agriculture farm, less security, no awareness of the crisis of food in the future, population pressure and hereditary division are the reasons for agriculture land conversion into non-agriculture activities while above ninety per cent respondents claimed the mentioned reasons for agriculture land conversion to non-agriculture activities.  Based on problems, the study recommends that the government should provide facilities to rural farmers in the village to enhance per acre agriculture productivity; rural industries should be developed for the generation of employment; strict rules should be applied for controlling agricultural land conversion into non-agriculture activities by the government; awareness should be given about future food crisis to the community of the study area.

… Research on the evolution pathways and driving mechanisms of farmland conversion to nonagricultural uses in Gansu Province based on the optimalparameter geographical detector[J/…

Rapid conversion of agricultural land into housing challenges rural livelihoods and food security in many developing countries; however, its implications for agrarian injustice remain underexplored. This study investigates how unjust agricultural land conversion affects farmers’ welfare and household food insecurity in Kediri Subdistrict, West Lombok, Indonesia. Unjust agricultural land conversion relates to unequal land governance, weak spatial planning enforcement, and socioeconomic effects on smallholders. A convergent mixed-method approach is used to integrate multitemporal spatial analysis using Geographic Information Systems, household survey data from 100 farmers, qualitative interviews, and logistic regression modeling based on the Food Insecurity Experience Scale. The results reveal a substantial transformation of rural landscapes, with approximately 31% of productive agricultural land converted into housing between 2012 and 2024. This process undermines farmers’ livelihoods and intensifies food insecurity across availability, accessibility, and utilization dimensions. Logistic regression identified housing-driven land conversion as the strongest predictor of worsening food insecurity. Older and less-educated farmers exhibited significantly higher vulnerability. The findings demonstrate that farmland conversion is a physical land-use change and structural driver of food insecurity through land dispossession, livelihood erosion, and reduced household resilience. This study clarifies that unjust conversion reflects rapid land transformation driven by housing development, weak spatial planning control, and unequal effects on smallholders who experience livelihood loss without adequate protection or compensation. These findings highlight the need to strengthen spatial planning enforcement, protect sustainable agricultural land, and promote more inclusive land governance to safeguard rural livelihoods and food security.

As one of the swiftly advancing megacities globally, Lagos faces significant challenges in managing its urban expansion. Mainly, this study focuses on monitoring and predicting urban growth using a comprehensive approach incorporating Global Land 30 (GL30), satellite-based nighttime light observations, and built-up and population density data. The application of remote sensing techniques, combined with utilizing the GL30 dataset, provides an effective means to monitor and predict urban growth trends and patterns. The major patterns occurred from 2000 to 2020, including increased cultivated land; reductions in grasslands, shrublands, and wetlands; and major urbanization. Predictive models indicate that urbanization will continue. Furthermore, employing the Cellular Automata (CA) Markov model in land-use and land-cover (LULC) change prediction. The findings revealed significant changes in LULC over the two decades. Particularly, the percentage of artificial terrain increased from 17.016% to 25.208%, and the area under cultivation increased significantly, rising from 46,771 km2 (1.238%) in 2000 to 75,283 km2 (1.993%) in 2020. Grasslands fell from 7.839% to 1.875%, while forest cover somewhat increased, climbing from 39.319% to 43.081%. Additionally, marshes fell from 9.788% to 5.646%, while shrublands decreased from 4.421% to 2.640%. Surprisingly, bare ground decreased sharply from 0.677% to 0.003%. To forecast future LULC changes, the study also used a Markov Chain Transition Matrix. According to the data, there is a 3.54% chance that agricultural land will become urban, converting it from being used for agriculture to urban development. On the other hand, just 1.05% of forested regions were likely to become municipal areas. This study offers foundations for the upcoming research to enhance urban growth models and sustainability strategies in the face of rising urbanization and environmental concerns in the region, as well as laying the groundwork for informed decision-making in the region.

To accurately grasp the land and ecological dynamics in the Henan section of the Yellow River Basin (YRB) and provide detailed local data for the ecological protection of the YRB, this article takes the Henan segment within the YRB as the research area, explores the spatio-temporal evolution of land use (LU) and landscape ecological risks (LERS), and predicts LU and LERS under various scenarios in the future based on the PLUS model. We found that: (1) From 2000 to 2020, object types in research area were given priority with cultivated land, forest land, and construction land, with construction land and cultivated land experiencing the largest changes of 5.71% and −6.34%, respectively. Changes in other land types varied within a ±3% range. The expansion of construction land principally encroached upon cultivated land, indicating significant urban sprawl. (2) The high-ecological-risk areas were clustered in the area centered in Zhengzhou, and the low-ecological-risk areas were distributed in the edge of the study area. As risk levels increased, the risk center gradually shifted towards the central regions, particularly around Luoyang and at the junction of Luoyang, Zhengzhou, and Jiaozuo. (3) The LU status in 2030 was projected using the PLUS model under three varied scenarios. The Kappa coefficient of the model was 0.81, and the overall accuracy was about 88.13%. Cultivated land, forest land, and construction land still accounted for the main part, and the area of cultivated land and construction land changed significantly. Based on this analysis of LERS prediction, the distribution of risk levels in different scenarios was different, but in general, high-ecological-risk areas and higher-ecological-risk areas accounted for the main part, while the study area’s edges were where low-ecological-risk zones were situated. Research can offer scientific and technological support for the sensible utilization and administration of resources, along with the protection of the ecological environment and regional sustainable development.

Evaluating the effects of urbanization on land use and landscape patterns is crucial for protecting resources and promoting environmental sustainability. This study examined urbanization in southern China from 1990 to 2020, focusing on urban area growth, GDP, population density, and the Landscape Expansion Index (LEI). It also assessed the impact on land use and landscape patterns through land use structures and metrics. The results reveal that Urban built-up areas have consistently increased over the past three decades, especially between 2010 and 2020. The extent of urban land increased from 20,758 km² in 1990 to 42,939 km² in 2020, reflecting an average annual growth rate of 3.56%, largely driven by the conversion of cultivated land (74.5%) and forest land (18.4%) compared to other land cover types. This expansion coincided with increased population density and GDP. The LEI also indicates a transition from compact urban growth (1990–2010) to a more dispersed pattern (2010–2020). Landscape metrics indicate decreased dominance of a single land-use type, leading to a more balanced structure and greater fragmentation. This emphasizes the need to tackle urbanization’s environmental challenges and the importance of sustainable development in urban planning for harmonious coexistence.

… areas upsurged by 19.2% while cultivated land and bare land decreased by 24.9% and 8.1… development control, culminating in unplanned urban sprawl. Main factors underlying these …

… cultivated land, we can optimize human welfare, safeguard food security, and foster the enduring resilience of both urban and rural areas. … A Ghanaian twist to urban sprawl. …

… To assess the impact of GEC on the fragmentation of both cultivated land and ecological land, we utilized Fragstats to calculate three metrics at the class scale: Patch Density (PD), …

Air pollution has become one of the most pressing environmental challenges globally, with significant consequences for public health and ecosystems. Recently, Gharbia Governorate has undergone remarkable changes in land use/land cover (LULC), leading to a variety of environmental impacts. These transformations have raised concerns about their potential influence on air quality, making it crucial to investigate how shifts in LULC are affecting pollution levels in the region. Understanding this relationship is vital for developing sustainable land management strategies and improving air quality monitoring in the area. This study aims to employ remotely sensed data integrated with Geographic Information System (GIS) to monitor the LULC changes and evaluate their influences on the governorate’s air quality. Two Landsat 8 Operational Land Imager data, as OLI acquired in 2023, 2013, and Landsat 5 Thematic Mapper data, as TM 5 acquired in 2003, were used. A maximum likelihood classifier was used to produce a land cover map and track changes in land cover of the study area. Aqua, Terra, and Sentinel-5P were employed to measure the PM2.5, CO, NO2, and SO2 concentrations. The classification results identified two dominant LULC classes: cultivated land and urban area. Between 2003 and 2023, urban areas increased by 136.2 km2, while cultivated lands declined by 135.94 km2, reflecting significant urban expansion. This land conversion was associated with a marked impact on air quality. PM2.5 concentrations ranged between 7.12 and 8.66 µg/m3 during the study period. Notably, NO2 levels peaked during autumn (3.13 µg/m3), while elevated CO and SO2 concentrations were observed in summer (946.79 µg/m3) and winter (8 µg/m3), respectively. The highest pollutant levels were consistently recorded in the recently expanded urban areas compared to other land use categories. These findings demonstrate that urban sprawl has a significant and direct impact on air quality, highlighting the need for integrated land-use planning and air pollution control. The generated data provides a valuable foundation for sustainable development and environmental decision-making in Gharbia Governorate.

Motivated by China’s new urbanization and ecological civilization construction initiatives, the Shenyang Municipal Committee has recently has proposed an ambitious goal of advancing the construction of a Park City with northern characteristics. The scientifically planned urban landscape is essential for balancing ecological protection with sustainable development,. This plan is crucial for driving the realization of the Park City initiative. This study employed ArcGIS 10.8 and Fragstats 4.2 to systematically examine land use transitions and landscape pattern dynamics in Shenyang’s main urban area (2000–2020). The results indicated that Shenyang’s urban core has experienced significant southward expansion across the Hun River over the last two decades. This expansion resulted in a substantial increase in constructed land of 490.84 km2 (from 15.78% to 29.19% in total coverage). Conversely, cultivated land, forest land, and grassland exhibited negative dynamic rates of −0.99%, −0.54%, and −1.02%, respectively, with 76.89% of cultivated land converted to construction land. Landscape pattern indices revealed intensified fragmentation: the number of patches rose by 163, while the largest patch area, landscape aggregation index, and contagion index decreased by 16.74%, 0.40%, and 5.84%, respectively. However, the landscape division index increased by 0.12%, with Shannon’s diversity index and evenness index increasing by 0.19 and 0.11, respectively. These metrics demonstrated the positive correlation between urbanization intensity and landscape pattern alterations. The examination of the dynamic land use patterns in Shenyang integrated seven crucial indicators to assess the development of the emerging Park City. Results indicated challenges including urban land expansion, cultivated land loss, limited resources, and uneven green space distribution. The findings revealed the negative correlation between land use pattern evolution and Park City requirements. The research suggested strategies at the macro-, meso-, and micro-scales to address these issues and reconcile urbanization pressures with sustainable Park City development in Shenyang.

… built-up land in the core and the peripheral in 2030 is comparable, but cultivated land in the … There is a high proportion of cultivated land and built-up land with strong absolute conflicts. …

Urban expansion has a profound impact on both society and the environment. In this study, VOSviewer 1.6.16 and CiteSpace 6.3.R1 were used to conduct a bibliometric analysis of 2987 articles published during the period of 1992–2022 from the Web of Science database in order to identify the research hotspots and trends of urban expansion and its driving factors. The number of articles significantly increased during the period of 1992–2022. The spatiotemporal characteristics and driving forces of urban expansion, urban growth models and simulations, and the impacts of urban expansion were the main research topics. The rate of urban expansion showed regional differences. Socioeconomic factors, political and institutional factors, natural factors, path effects, and proximity effects were the main driving factors. Urban expansion promoted economic growth, occupied cultivated land, and affected ecological environments. Big data and deep learning techniques were recently applied due to advancements in information techniques. With the increasing awareness of environmental protection, the number of studies on environmental impacts and spatial planning regulations has increased. Some political and institutional factors, such as subsidies, taxation, spatial planning, new development strategies, regulation policies, and economic industries, had controversial or unknown impacts. Further research on these factors and their mechanisms is needed. A limitation of this study is that articles which were not indexed, were not included in bibliometric analysis. Further studies can review these articles and conduct comparative research to capture the diversity.

Urban development model is transitioning from disorderly sprawl to compact growth. In this process, urban growth boundary (UGB) is important for preventing excessive spatial expansion and optimizing land use structure. However, few existing studies have focused on delineation strategies that integrate both rigid and elastic UGBs. Taking Zhengzhou as a case study, we developed a framework for delineating rigid and elastic UGBs involving identification of basic farmland and ecological protection zone, evaluation of land suitability, and multi-scenario simulation of urban development. The results showed that urban space increased significantly by 210.56 km2 from 2000 to 2020, which posed risks of imbalanced land use structure. Identified basic farmland and ecological protection zone covered 41.99 km2 and 57.68 km2, respectively. Their scope was prohibited for urban construction and was used as guidance to delineate rigid UGB, which covered 712.21 km2. Sustainable development scenario was considered as dominant paradigm for urban development. Therefore, it was used as guidance to delineate elastic UGB, which covered 595.55 km2. These findings confirm the effectiveness of a delineation strategy that combines rigid and elastic UGBs in maintaining ecological security and constraining spatial sprawl. Additionally, technical references for delineating UGB are provided for cities facing compact growth demands.

In the Mediterranean regions, habitat degradation, alteration, and even destruction were major causes of the rarefaction of species in their natural environment. In the last decade, these phenomena are further amplified by the reduction of natural vegetation cover, urban sprawl, and climate change. In addition, the degradation of plant cover through cropping, and intensive agricultural practices causes soil erosion, habitat destruction, and loss of biodiversity. While to reduce these risks, it is necessary to assess and monitor Land Use Land Cover changes that will be crucial information for managing natural resources and guiding decision-making processes in their development plans. This is the case of a Mediterranean coastal region, Sousse, Tunisia which is characterized by its landscape diversity and the richness enhanced by the coastal and marine fauna and flora. The methodology is based on the use of Remote Sensing and Geographical Information System where different tools and functions have been employed such as Maximum Likelihood classification validated with field truth, thematic changes using classification change matrix, etc. The establishment of a multi-scalar landscape approach revealed changes in land use on a regional scale between 2007 and 2017. The landscape of Sousse region has been strongly mod-ified by the action of Man where we note a significant growth of built-up areas and a regression of cultivated areas. From 11.84 % to 14.14 %, and from 28.83 % to 21.70 %; respectively. Hence, the growing risk of fragmentation of natural habitats, forests and agricultural areas. The development plan proposed in favor of biodiversity conservation offers a concrete example and can be useful for developing appropriate land use policy for sustainable region in Tunisia and similar Mediterranean regions. This is an essential study for all land use planning proposals as a guide to landscape conservation, enhancement and protection. (cid:1) 2023 National Authority of Remote Sensing & Space Science. Published by Elsevier B

… -up lands and a decrease in agriculture and barren lands, signaling unplanned urban sprawl. … supported by the reduction in planted/cultivated land observed in this study. This research …

Exploring the ecological utility of cultivated land's carbon metabolism offers policy insights for ensuring its healthy operation and promote the dual carbon goals (carbon peak and carbon neutrality). We employed ecological network analysis (ENA) and kernel density estimation to conduct an empirical study, taking Hubei Province from 2000 to 2020 as an example. The results revealed apparent negative effects of carbon metabolic flow on regional carbon balance. Specifically, cultivated land conversion into transportation and industrial land contributed significantly to the harmful carbon flow. Ecological relationships showed fierce competition for carbon storage, leading to overall adverse ecological effects. The ecological utility indicated detrimental impacts on the orderly functioning of land-use carbon metabolism. Cultivated land's carbon metabolism will be essential in achieving land-use carbon neutrality. Therefore, territorial spatial low-carbon optimization should be implemented to realize its green and sustainable development.

Urban areas—cities—are not simply geographic areas for human gathering but are a locus of economic production, cultural and social interactions, and ecological development. Therefore, cities create positive development values when planned and managed on a sustainable footing by considering institutional, governance, environmental, political, economic, coherent policies, cultural, and social conditions and requirements. Sustainable urbanization has multiple benefits including creating more employment opportunities and better incomes, hubs for innovative solutions by attracting competencies, enhancing land utilization efficiency, improving infrastructural performance, providing better services (e.g., education, health, water supply, and electricity), economic growth hub, acting as knowledge centers, better social and cultural life, and providing better living standards. While the impacts of rapid unsustainable urbanization are water stress, scarcity, and high consumption, sanitation wastewater, water pollution, air pollution, climate change, noise pollution, cultivated land depleted, urban sprawl, dust, solid and hazardous wastes, destruction of biodiversity, high energy consumption, traffic congestion, soil pollution, and deforestation. Thus, the 2030 Agenda1 for Sustainable Development—and its sustainable development goals (SDGs)—and New Urban Agenda are key transformative power toward sustainable urbanization development; this development is not at the expense of the environment while leading to prosperity and improving quality of life.

… land price, transportation cost, and urban sprawl under the condition of local governments monopolizing land … increasing land prices and decreasing transportation costs, urban fringes …

China’s rapid urbanization and evolving agricultural practices have driven significant changes in cultivated land utilization, characterized by non-agriculturalization (NA) and non-grain utilization (NGU) transformation. Understanding the spatial patterns and driving mechanisms of these transformations is critical for formulating effective cultivated land management and protection policies. Previous studies have treated the non-agriculturalization (NA) and non-grain utilization (NGU) of cultivated land as distinct phenomena with no correlation. Therefore, this study constructs a theoretical framework to explore the correlation between NA and NGU and examines their interaction patterns using Ezhou City in China as a case study. Spatial econometric models and multinomial logistic regression analyses reveal distinct trade-offs and synergies between NA and NGU, which are shaped by locational, socioeconomic, natural, and policy factors. Urban areas exhibit higher NA rates due to economic development, while rural areas favor NGU for improved land use efficiency and profitability. Suburban zones demonstrate a coordinated transformation, where both processes coexist synergistically. The findings, which are also verified by another two case study areas, highlight the existence of spatial correlations between NA and NGU transformations of cultivated land. They also underscore the necessity for region-specific policies to balance food security with economic growth and dietary transformation. This study helps to elucidate the complex mechanisms underlying different types of cultivated land use transitions and offers new perspectives for the formulation of cultivated land use and protection policies for global cities.

Agrarian land is being converted into a non-agricultural sector, which is a matter of significant concern. This study aims to determine the factors responsible for the agrarian land …

This study analyses the evolving dynamics and sustainability challenges of land use change in India during the 21st century, using Annual Growth Rate (AGR) and Average Annual Growth Rate (AAGR) metrics derived from authoritative secondary data. Rapid urbanization, demographic pressure, and industrialization are driving significant shifts in land use, with far-reaching implications for food security, ecological stability, and inclusive development. The results reveal marked spatial and temporal variability across land use categories. Expansion of non-agricultural land, while reflecting structural transformation, often occurs at the expense of fertile agricultural land and ecologically sensitive zones. Forest areas and common property resources exhibit erratic trends, underscoring the weakness of conservation enforcement. Stagnation in net sown area and volatility in cropping intensity point to difficulties in sustaining agricultural output without expanding cultivated land. Additionally, frequent fluctuations in reporting areas expose institutional weaknesses in land data governance, especially in less-resourced states. Crucially, the study finds that land use growth lacks uniformity, and persistent volatility exacerbates fragmentation, degradation, and marginalization, particularly for smallholder farmers. This instability underscores the urgency of transitioning toward integrated and sustainable land use governance. Policy recommendations emphasize the need for a paradigm shift from horizontal land expansion to vertical intensification and ecological regeneration. Key measures include the adoption of agroecological restoration practices, the promotion of agroforestry and Silvopasture Systems, the implementation of stricter urban zoning regulations, the enhancement of land monitoring through real-time GIS tools, and support for productivity-led agriculture. In sum, India’s current land use trajectory reflects a departure from traditional agrarian models toward more diverse but environmentally contentious practices. Achieving long-term sustainability will require adaptive, inclusive, and ecologically balanced land governance that aligns development imperatives with conservation goals.

Abstract Urbanization increases the proportion of non-agricultural workforce, and changes in land use land cover from agricultural to non-agricultural pattern continuously through time. This study designed to analyse land use land cover (LULC) dynamics in Dukem town using remote sensing & GIS techniques between 2003 and 2019 years. The Primary data sources were collected through observations and secondary data sources were collected using remotely sensed satellite images. The key results of this study revealed that built-up area augmented severely from 698.06 ha (16.45%) in 2003 to 3,091.67 hectare (72.81%) in 2019. Specifically, agricultural land extremely decreased in all years from 2,764.37 hectare (65.12%) in 2003 to 999.05 hectare (23.53%) in 2019. Hence, accordingly, this incident reduced agricultural land and increased built areas dramatically.

To explore the spatiotemporal evolution characteristics of land use function and its correlation with social and economic development levels, taking the Yangtze River Delta region as an example, we quantified the multifunctional land use in the Yangtze River Delta region from 2000 to 2020 on a 5 km × 5 km grid and analyzed its spatiotemporal evolution characteristics. Each city’s comprehensive measure of economic development used the projection tracing method. Person’s method of interpretation was used for correlation between the spatial and temporal evolution of land use functions and the level of economic development and its coupling association. The study shows that: (1) from 2000 to 2020, the agricultural production function > ecological function > living function > non-agricultural production function in the Yangtze River Delta, but the non-agricultural production and living functions were gradually increasing, while the agricultural production and ecological functions were decreasing. In terms of spatial distribution, the agricultural production function decreases significantly around the built-up area due to the expansion of the built-up area. The non-agricultural production function strengthened around the central city in a network pattern and had a path-locking effect. Topography limits life functions, with high north and low south partially overlapping with non-agricultural production functions. Furthermore, the ecological function was high in the south and low in the north and continues to weaken due to the interference of human activities. (2) The spatiotemporal heterogeneity of different functions generated trade-offs/synergies. The trade-off relationship was prominent in agricultural production and non-farm production function and living function, and non-farm production and living function and ecological function during the study period. Conversely, agricultural production and ecological functions and non-farm production and subsistence functions were generally synergistic. Spatially, there was substantial spatial heterogeneity in the trade-off/synergy relationship between the two functions. (3) There was a clear correlation and spatial coupling between land use function indices and economic development levels in the whole region and sample zones. Still, the dynamic and regional nature of the evolution of land use functions results in sudden changes and jumps in different functions in space. Therefore, in the future integration of the Yangtze River Delta, it is necessary to pay comprehensive attention to the morphology of different land use functions and their synergy/trade-off relationship and to adjust the spatial governance strategy promptly according to the local conditions and the situation.

… (14.03%) with a rate of conversion of agricultural land to non-agricultural areas of 264 ha (24 ha year-1). Changes in land use have a very significant effect on the land capability class. …

… In this study, we monitor land use change, a process aimed at mapping the location, patterns, and extent of the conversion of agricultural land to non-agricultural functions …

The increase in population and the increasing flow of urbanization in Central Ternate District make the need for land to live also increase as a result of which there will be inconsistencies or inequalities between land needs and available land, a decrease in environmental carrying capacity and environmental damage in the future. This study aims to analyze changes in land cover due to urban growth in Central Ternate District, Ternate City using The Automata- Markov Chain. Cellular Automata- Markov Chain is used to analyze and predict land cover changes in 2002, 2012, 2022 and 2031. The results showed that residential land will continue to experience an increase in area along with population growth and the high demand for land to settle. The results of this study are expected to be input in policy making related to the arrangement and utilization of space in The Central Ternate District in the future.

Land Use Change occurred in many places in Boyolali Regency, including Boyolali and Mojosongo District. Some factors contributed to that thing, like residential and toll road development. There were some instruments to mitigate, such as Boyolali Regional Law Number 17/2016 and Boyolali Regional Law Number 8/2019. Despite being regulated, the enforcement of these regulations has been suboptimal, leading to changes in agricultural land use to non-agricultural purposes in several areas. Monitoring the law is important to see if it goes well or not. This can be utilized Geographic Information Systems (GIS) with overlay method and on-screen digitization of SPOT-6 imagery. Besides that, Remote Sensing analyzed land use trends using multi-temporal SPOT-6 images of 2018 and 2022. This study aims to determine the extent of land use in 2018 and 2022 as well as to determine the suitability of Sustainable Food Agricultural Land to land use and also evaluate the suitability of Sustainable Food Agricultural Land to the Regional Spatial Plan. Results showed that in 2018, the largest land use class was residential areas, covering 2,437.77 hectares, followed by rice fields and moorland, which covered 1,109.06 and 2,205.74 hectares, respectively. By 2022, residential areas had expanded to 2,625.57 hectares, while rice fields and moorland covered 1,130.54 and 2,145.07 hectares, respectively. Based on the overlay method, the suitability analysis revealed that Sustainable Food Agricultural Land matched 90.47% of land use in 2018 and 90.04% in 2022. Meanwhile, the suitability between the Sustainable Food Agriculture Land and Regional Spatial Plan amounted to 81.03%.

Land is a critical factor in agriculture, especially in soil-based cultivation. The prevailing problem in agricultural land use that is yet to be solved is the rapid conversion to non-agricultural use, which raised concern for agriculture’s existence in the future. Moreover, the population continues to grow despite the receding agricultural land to produce food. Therefore, research and policies are starting to lean towards optimization of marginal land for agricultural activities. Indonesia still has potential marginal land for agricultural expansion. This paper uses secondary data and former studies to summarize the potential and availability of marginal land for agricultural expansion based on the land categories: forest land, dryland, and wetland (tidal swamp and peat). This paper also discusses the government's extensification program and the results of its implementation. We found that various reports about marginal lands utilization emphasized optimizing the target land with appropriate agricultural technology. The presentation of data obtained through literature studies can strengthen the opinion that the potential availability and potential for expansion of agricultural land in Indonesia is real. This paper is expected to provide a comprehensive reference for all Indonesian regional governments, so stakeholders continue optimizing the potential of existing natural resources.

Agricultural land is a key resource for territorial resilience. In the European context, fertile soils are under pressure not only from urbanisation processes, abandonment and the establishment of non-agricultural uses but also from agriculture that is not well adapted to territorial resources. In order to inform urban planning, a methodology is proposed and applied to the Madrid region to analyse the suitability of agricultural land uses with respect to agrological quality. The majority of agricultural uses in the region are well adapted to the agroecological quality of the land; larger areas of over-exploited land are located along some of the region’s rivers and in the Campiña, while under-utilised land is mainly found in the south-west and in the metropolitan comarcas. This methodology is based on official and open-access information, so it can be easily replicated and used to inform land planning. We propose three strategies depending on the suitability of land use: the introduction of crops in priority areas for horticulture or arable crops, agricultural protection areas and ecological regeneration areas.

Land use/land cover is an important component in understanding the interactions of human activities with the environment and is necessary to recognize the changes in order to monitor and maintain a sustainable environment. The main objectives of this study were to analyze changes in land cover in the Nashe-watershed for the period 2010–2020, analyze household demographic and livelihood characteristics and identify the impact of the construction of the DAM and changes in land cover on the environment. Since the dam of the Nashe watershed was built in 2012, the socioeconomic characteristics of the area were used to interpret the causes of land use and land cover changes, which cause changes in their life and environment. Purposively 156 households were selected who were more than 40 years old from the total households (1222) in three kebele and for land use land cover of 2010, Land sat 7 were used whereas for 2020, land sat 8 was used. The socioeconomic data were analyzed with Excel and integrated with biophysical data. The 2010–2020 ten-year period showed that cultivated land and forest land were reduced from 73% to 62% and 18%–14%, respectively, and swampy areas fully converted to Water Bodies, alternately increasing Water Bodies and grazing land also converted from 43.9% to 54.5% and 0.04%–17.96% respectively. The reason for this change was the construction of dams, human encroachment, and expansion of cultivated land which were bringing LULCC in study area. However, government could not gave these people adequate compensation for their lands, properties that conquered by water. Hence, the Nashe watershed is identified as an area highly affected by land use and land cover change, the livelihoods were suffered by Dam construction, also environmental sustainability is hindering still now. Therefore it is necessary to closely monitor land use/land cover, giving consideration for HHs who affected by Dam, and to maintain a sustainable environmental resource for the future sustainable development is a critical issue in the Ethiopia in general, particularly in the study area.

… of land use cover change, across Africa’s sub-regions using integrated remote sensing techniques … provided shows more lands will be converted into built-environment and cultivated …

… The spatio-temporal change in land use due … land within the urban centres or by changing the land use of urban periphery as well as peri-urban settlements including agricultural lands (…

The destabilization of delta's worldwide due to climate change and human activities presents challenges in meeting the growing demands for freshwater and food. The Nile Delta in Egypt is a prime example of a vulnerable region facing various stressors. In order to preserve land and water resources, it is crucial to monitor the spatial and temporal changes in Land Use/Land Cover (LULC), shoreline, and Terrestrial Water Storage (TWS) in these vulnerable regions This study comprehensively investigates the dynamic changes in LULC and their associated water and soil responses in the Eastern Nile Delta under these combined impacts. To achieve this goal, a combination of remote sensing techniques utilizing Landsat (5, 8, and 9), and GRACE datasets, along with field observations and Geographic Information System (GIS) tools, was employed. Accordingly, shoreline changes show coastal erosion rates ranging from 5.28 to 34.92 m/year due to climate change-induced SLR, with continued inland movement predicted for the next 20 years. Moreover, the dynamic changes in urbanization and alterations in agricultural cover have considerable penalties for water demand. Analysis of GRACE data indicates a notable reduction in average TWS by 77.89 mm between 2002 and 2017, with an annual rate, estimated at -5.821 mm/year. Soil sampling in highly vulnerable areas confirms agricultural degradation attributed to elevated salinity levels, with EC values ranging from 3.60 to 190 ds/m. These finds provide valuable insights for stakeholders and policymakers, to make reliable strategies regarding water allocation, land use regulations, and climate change adaptation in the worldwide vulnerable deltas.

… farmland use conflicts among the three main competing functions (production, consumption and protection) … courts’ database in Poland to analyse farmland use conflicts in a quantitative …

… , an overlooked impact is the effective farmland planting area (EFPA), resulting from farmland fragmentation at different stages. We used remotely sensed imagery to quantify farmland …

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… farmland to forest land would increase the trade-off potential and intensity, while urbanization and returning farmland to … land use policies’ impact on ecosystem services trade-offs. …

Despite the Yangtze River Basin (YRB)’s abundant land and forestry resources, there is still a dearth of research on forecasting habitat quality changes resulting from various geographic and environmental factors that drive landscape transformations. Hence, this study concentrates on the YRB as the focal area, with the aim of utilizing the Patch Landscape Upscaling Simulation model (PLUS) and the habitat quality model to scrutinize the spatial distribution of landscape patterns and the evolution of HQ under four scenarios: the natural development scenario (NDS), farmland protection scenario (CPS), urban development scenario (UDS), and ecological protection scenario (EPS), spanning from the past to 2030. Our results show that (1) from 2000 to 2020, the construction land in the YRB expanded at a high dynamic rate of 47.86% per year, leading to a decrease of 32,776 km2 in the cultivated land area; (2) the UDS had the most significant expansion of construction land, followed by the NDS, CPS, and EPS, which had higher proportions of ecologically used land such as forests and grasslands; (3) from 2000 to 2020, the HQ index ranged from 0.211 to 0.215 (low level), showing a slight upward trend, with the most drastic changes occurring in the low-level areas (−0.49%); (4) the EPS had the highest HQ (0.231), followed by the CPS (0.215), with the CPS increasing the HQ proportion of the lower-level areas by 2.64%; (5) and in addition to government policies, NDVI, DEM, GDP, and population were also significant factors affecting landscape pattern and changes in habitat quality.

… function space, thus, corresponding land use policies are necessary to ensure food security. This study unveils the impact of multifactor interactions on land use transitions and offers …

… rates of farmland loss, which in turn poses new challenges to the ecological security of farmland. Taking the Huaihe River Basin as a case, this paper analyzes farmland loss from 2000 …

Arable land is vital to agriculture, and studying cropland fragmentation is key for sustainable resource use. However, research has largely ignored the dynamic nature of this fragmentation, focusing instead on static farmland patterns. This study proposed eight spatial models of cropland fragmentation dynamics, assessed their distribution and evolution in the Yellow and Huaihai grain‐producing regions from 2010 to 2020, and investigated the underlying drivers. It was found that (1) although cropland fragmentation in the study area showed an increasing trend from 2010 to 2020, the rate of increase gradually weakened, and the cropland fragmentation in the southeast coastal region was higher than that in the northwest inland region. (2) LPI↑PD↑LSI↓ mode cropland, as the main cropland fragmentation mode in the study area, is widely distributed in Shandong and Henan Provinces, as well as Jiangsu Province. (3) Except for the LPI↓PD↑LSI↑ model, the main drivers of its models are population density and mechanization level, while the main drivers of the LPI↓PD↑LSI↑ model are natural endowment factors such as topographic relief. The findings emphasize the need to curb the fragmentation of cropland as well as to promote the concentration and connectivity of cropland.

(1) Background: in the context of sustainable development goals (SDGs), this work explores the repair direction of future cultivated land protection policies based on the Smith policy implementation model from the historical evolution to ensure that the cultivated land protection policy does not deviate during the implementation process. (2) Methods: this paper uses literature research and inductive deduction methods to sort out the evolution process of Chinese cultivated land protection policy, summarize the successful experience of transferring the focus of cultivated land protection in chronological order, and conclude the repair direction of Chinese cultivated land protection. (3) Results: although the Chinese cultivated land protection policy in the historical evolution has through three significant phases, there are still certain issues in the development trend of Chinese cultivated land protection policy based on regional division of labor for grain transportation and sales. (4) Conclusion: directly utilizing feedback from farmers as stakeholders will contribute to the maturity and improvement of cultivated land protection policies in the specific implementation of cultivated land protection policies.

This paper examines rural enclosures for industrialized agriculture as a window into the local political economy and territorial politics underlying projects of agricultural modernization. In recent years, agro‐industrial parks with ‘characteristic’ industries have proliferated in China as the government viewed it as a technical solution to problems of declining grain security and land use inefficiency. Comparing these enclosures with previous waves of expropriation for industrial and real estate development, this paper argues that enclosures for agro‐industrialization evince a specific logic of state intervention, which in turn shapes the mechanisms of land assembly and the politics of counter‐enclosure engendered. Through ethnographic fieldwork and in‐depth interviews, this paper interrogates the unequal impact of farmland transfer as villagers found it increasingly difficult to hold onto their land and turned to individualized strategies for survival.

Abstract China has established the world’s most stringent cultivated land protection policies (CLPP) system. However, the key to policy is its implementation. This study constructs two tripartite evolutionary game models for CLPP implementation, and studies behavior strategy evolution with the help of numerical simulation technology. The results show the following: (1) The existence of principal-agent problems will lead to administrative phenomena such as obstruction, flexibility and collusion in CLPP implementation. (2) The evolution of the behavior strategy of the tripartite game depends on factors such as penalties on farmers, rent-seeking compensation given to farmers by local governments, cost of farmers’ safeguarding rights, cost of cultivated land protection, land finance and promotion opportunity, degree of stricter control, political punishment, and possibility of collusion being detected and punished. (3) The behavioral optimization mechanism of CLPP implementation can be used to propose more efficient governance schemes.

… occupation of cropland. Yet, to date, we have not achieved continuous monitoring of the occupation of cropland … up land (OBL) and their occupation of cropland at the county level across …

Land optimization simulation and ecosystem service value (ESV) estimation can better serve land managers in decision-making. However, land survey data are seldom used in existing studies, and land optimization constraints fail to fully consider land planning control, and the optimization at the provincial scale is not fine enough, which leads to a disconnection between academic research and land management. We coupled ESV, gray multi-objective optimization (GMOP), and patch-generating land use simulation (PLUS) models based on authoritative data on land management to project land use and ESV change under natural development (ND), rapid economic development (RED), ecological land protection (ELP), and sustainable development (SD) scenarios in 2030. The results show that construction land expanded dramatically (by 97.96% from 2000 to 2020), which encroached on grassland and cropland. This trend will continue in the BAU scenario. Construction land, woodland, and cropland are the main types of land used for expansion, while grassland and unused land, which lack strict use control, are the main land outflow categories. From 2000 to 2030, the total amount of ESV increases steadily and slightly. The spatial distribution of ESV is significantly aggregated and the agglomeration is increasing. The policy direction and land planning are important reasons for land use changes. The land use scenarios we set up can play an important role in preventing the uncontrolled expansion of construction land, mitigating the phenomenon of ecological construction, i.e., “governance while destruction”, and promoting food security. This study provides a new approach for provincial large-scale land optimization and ESV estimation based on land survey data and provides technical support for achieving sustainable land development.

The unequal distribution of territorial space resources stands out as a leading cause of the human–land contradictions and environmental degradation. These issues are especially pronounced in the Minjiang River Basin, which exhibits significant regional disparities. In pursuit of solutions to these pressing problems and the identification of sustainable developmental pathways, this study presents an innovative territorial space double optimization simulation model. This model integrates quantity structure optimization and distribution pattern optimization, in order to comprehensively consider the optimization of territorial space allocation and build a new territorial space pattern for the Minjiang River Basin in 2030. On this basis, we employed the Patch-generating Land Use Simulation (PLUS) model and scenario analysis method to design the double optimization scenario and natural development scenario. By comparing these two scenarios, and calculating the ecological benefits (EB), economic benefits (ECB), carbon storage (CS), and comprehensive benefits (CB) achieved in different scenarios, the validity of the double optimization model was fully verified. The results indicated that: ① the loss of sub-ecological space (PeS) under the natural development scenario was significantly larger than that under the double optimization scenario, and the loss should be mainly attributed to the large expansion of production space (PS) and living space (LS); ② the area of ecological space (ES) has reduced since 2020, but less area was lost and the retention rate was higher under the double optimization scenario; ③ the natural development scenario made the research region gain more ECB, but it also resulted in the loss of more EB and CS, whereas the Minjiang River Basin under the double optimization scenario was able to effectively balance the relationship among the three, thus achieving the best CB. The research findings provide strong scientific support for alleviating the human–land contradictions, protecting the ecological security in the basin, and promoting the sustainable development of the region.

As a populous country with limited per capita land area, China has implemented the strictest land use regulation to ensure food security. Yet quantitative assessments of how it shapes land use change and the subsequent economic impacts remain insufficient. Land use directly affects land supply for industry and services, thereby impacting local fiscal and tax revenues. Meanwhile, land transfer income serves as a major off-budget revenue source for local governments, with county fiscal capacity laying the foundation for national economic development and public welfare. Therefore, this study integrates county-level statistics with remotely sensed land use data and applies an Intensity Difference-in-Differences (Intensity DID) design to identify policy impacts. Specifically, it examines the effects of land use regulation on county governments’ land transfer activities, land use efficiency, as well as fiscal revenue and public service provision. Empirical results show that tighter land use regulation constrains the new supply of construction land by limiting cultivated land conversion. In response, local governments modify floor area ratios (FARs) and shorten construction cycles, which is conducive to improving land use efficiency. Nevertheless, the policy reduces the land transfer income, tax revenue, and general public budget revenue of county governments, weakening public service provision. Heterogeneity analysis indicates that major grain-producing counties are more severely affected by negative policy shocks than non-major ones. This study provides empirical evidence for optimizing the land use regulation system and offers policy implications for coordinating food security and balanced regional development through horizontal interest compensation in major grain-producing regions.

Abstract. Accurate, detailed, and up-to-date information on cropland extent is crucial for provisioning food security and environmental sustainability. However, because of the complexity of agricultural landscapes and lack of sufficient training samples, it remains challenging to monitor cropland dynamics at high spatial and temporal resolutions across large geographical extents, especially for regions where agricultural land use is changing dramatically. Here we developed a cost-effective annual cropland mapping framework that integrated time-series Landsat satellite imagery, automated training sample generation, as well as machine learning and change detection techniques. We implemented the proposed scheme to a cloud computing platform of Google Earth Engine and generated a novel dataset of China's annual cropland at a 30 m spatial resolution (namely CACD). Results demonstrated that our approach was capable of tracking dynamic cropland changes in different agricultural zones. The pixel-wise F1 scores for annual maps and change maps of CACD were 0.79 ± 0.02 and 0.81, respectively. Further cross-product comparisons, including accuracy assessment, correlations with statistics, and spatial details, highlighted the precision and robustness of CACD compared with other datasets. According to our estimation, from 1986 to 2021, China's total cropland area expanded by 30 300 km2 (1.79 %), which underwent an increase before 2002 but a general decline between 2002 and 2015, and a slight recovery afterward. Cropland expansion was concentrated in the northwest while the eastern, central, and southern regions experienced substantial cropland loss. In addition, we observed 419 342 km2 (17.57 %) of croplands that were abandoned at least once during the study period. The consistent, high-resolution data of CACD can support progress toward sustainable agricultural use and food production in various research applications. The full archive of CACD is freely available at https://doi.org/10.5281/zenodo.7936885 (Tu et al., 2023a).

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

… Nevertheless, spatial-temporal dynamics of land use land … mostly at expense of agricultural lands thus agriculture areas … expense of vegetation and agricultural lands while Bharatpur, …

… urbanization and reveal its impacts on cultivated land. We analyzed the spatial pattern of cultivated land occupied by rural residential land and discuss the distribution characteristics …

This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.

Higher food prices arising from restrictions on exports from Russia or Ukraine have been exacerbated by energy price rises, leading to higher costs for agricultural inputs such as fertilizer. Here, using a scenario modelling approach, we quantify the potential outcomes of increasing agricultural input costs and the curtailment of exports from Russia and Ukraine on human health and the environment. We show that, combined, agricultural inputs costs and food export restrictions could increase food costs by 60–100% in 2023 from 2021 levels, potentially leading to undernourishment of 61–107 million people in 2023 and annual additional deaths of 416,000 to 1.01 million people if the associated dietary patterns are maintained. Furthermore, reduced land use intensification arising from higher input costs would lead to agricultural land expansion and associated carbon and biodiversity loss. The impact of agricultural input costs on food prices is larger than that from curtailment of Russian and Ukrainian exports. Restoring food trade from Ukraine and Russia alone is therefore insufficient to avoid food insecurity problem from higher energy and fertilizer prices. We contend that the immediacy of the food export problems associated with the war diverted attention away from the principal causes of current global food insecurity. The Russia–Ukraine war has impacted food access globally, but the exact drivers behind it and the broader consequences for human and environmental health are unclear. Through scenario analysis, this study assesses the relative importance of higher agricultural input prices and export disruption to food access, and estimates undernourishment and cropland expansion.

Various biotic, abiotic and anthropogenic factors are causing enormous food losses. Burgeoning human population demands for more food, however scarcity and unavailability of natural resources occurring globally. Major factors causing these losses include pests, diseases, pathogens, climatic changes, salinity, drought, loss of arable lands and weeds. Post-harvest losses are also responsible for devastating negative role towards global food losses. Inadequate use of resources leads to the exploitation and loss of arable land. Currently 38% losses to agriculture are solely caused by insect pests while 34% losses are due to weeds. Abiotic factors account for more than 50% agricultural losses. Arable land is decreasing day by day due to increased urbanization and industrialization.  Climate change also potentially decreases 10-25% of agricultural productivity and forecasted to cause more within next 50 years. All these problems are worse in under-developed countries due to uncontrolled measures and lack of awareness among the community. It has been reported that human population will increase to 11 billion within next 80 years, it is crucial now to minimize these losses for in order to ensure food security and sustainable development. Food losses needs to be minimized by considering the current scenario and needs to devise appropriate strategies to enhance food production by exploiting minimum natural resources. Focus of this review article is to convey reasons of food losses worldwide and depletion of natural resources to research and farming community so that appropriate methods for food security and sustainability could be devised and implemented.