Search Results (138)

Against the backdrop of rapid urbanization in western China, which has triggered remarkable land-use changes and habitat degradation, Chengdu, as a developed city in China, plays a demonstrative and leading role in the economic and social development of China during the transition period. Therefore, integrated modeling approaches are required to balance development and conservation. This study responds to this need by conducting a scenario-based assessment of urbanization-induced land-use changes and regional habitat quality dynamics in Chengdu (1990–2030), using the FLUS-InVEST model. By integrating remote sensing-derived land-use data from 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we simulate future regional habitat quality under three policy scenarios: natural development, ecological priority, and cropland protection. Key findings include the following: (1) From 1990 to 2020, cropland decreased by 1917.78 km², while forestland and built-up areas increased by 509.91 km² and 1436.52 km², respectively. Under the 2030 natural development scenario, built-up expansion and cropland reduction are projected. Ecological priority policies would enhance forestland (+4.2%) but slightly reduce cropland. (2) Regional habitat quality declined overall (1990–2020), with the sharpest drop (ΔHQ = −0.063) occurring between 2000 and 2010 due to accelerated urbanization. (3) Scenario analysis reveals that the ecological priority strategy yields the highest regional habitat quality (HQ_mean = 0.499), while natural development results in the lowest (HQ_mean = 0.444). This study demonstrates how the FLUS-InVEST model can quantify the trade-offs between urbanization and regional habitat quality, offering a scientific framework for balancing development and ecological conservation in rapidly urbanizing regions. The findings highlight the effectiveness of ecological priority policies in mitigating habitat degradation, with implications for similar cities seeking sustainable land-use strategies that integrate farmland protection and forest restoration. Full article

To promote high-quality agricultural development and implement the “storing grain in the land” strategy, the construction of Well-Facilitated Farmland (WFF) plays a critical role in enhancing grain production capacity and optimizing the spatial distribution of food supply, thereby contributing to national food security. However, accurately assessing the potential impact of WFF construction on China’s grain production and regional self-sufficiency by 2030 remains a significant challenge. Existing studies predominantly focus on the provincial level, while fine-grained analyses at the city level are still lacking. This study quantifies the potential increase in grain production in China under the 2030 WFF construction target by employing effect size analysis, multi-weight prediction, and Monte Carlo simulation across multiple spatial scales (national, provincial, and city levels), thereby addressing the research gap at finer spatial resolutions. By integrating 2030 population projections and applying a grain self-sufficiency calculation formula, it further evaluates the contribution of WFF to regional grain self-sufficiency: (1) WFF could generate an additional 31–48 million tons of grain, representing a 5.26–8.25% increase; (2) grain supply in major crop-producing regions would expand, while the supply–demand gap in balanced regions would narrow; and (3) the number of cities with grain self-sufficiency ratios below 50% would decrease by 11.1%, while those exceeding 200% would increase by 25.5%. These findings indicate that WFF construction not only enhances overall grain production potential but also facilitates a transition from “overall supply-demand balance” to “structural security” within China’s food system. This study provides critical data support and policy insights for building a more resilient and regionally adaptive agricultural system. Full article

To address the issue of agricultural robot loss of control caused by GNSS signal degradation or loss in complex agricultural environments such as farmland and orchards, this study proposes a neural network-based SLAM/GNSS fusion localization algorithm aiming to enhance the robot’s localization accuracy and stability in weak or GNSS-denied environments. It achieves multi-sensor observed pose coordinate system unification through coordinate system alignment preprocessing, optimizes SLAM poses via outlier filtering and drift correction, and dynamically adjusts the weights of poses from distinct coordinate systems via a neural network according to the GDOP. Experimental results on the robotic platform demonstrate that, compared to the SLAM algorithm without pose optimization, the proposed SLAM/GNSS fusion localization algorithm reduced the whole process average position deviation by 37%. Compared to the fixed-weight fusion localization algorithm, the proposed SLAM/GNSS fusion localization algorithm achieved a 74% reduction in average position deviation during transitional segments with GNSS signal degradation or recovery. These results validate the superior positioning accuracy and stability of the proposed SLAM/GNSS fusion localization algorithm in weak or GNSS-denied environments. Orchard experimental results demonstrate that, at an average speed of 0.55 m/s, the proposed SLAM/GNSS fusion localization algorithm achieves an overall average position deviation of 0.12 m, with average position deviation of 0.06 m in high GNSS signal quality zones, 0.11 m in transitional sections under signal degradation or recovery, and 0.14 m in fully GNSS-denied environments. These results validate that the proposed SLAM/GNSS fusion localization algorithm maintains high localization accuracy and stability even under conditions of low and highly fluctuating GNSS signal quality, meeting the operational requirements of most agricultural robots. Full article

As an ecological transition zone, the ecosystem of the Qin River Basin in the middle reaches of the Yellow River is of great significance to the regional ecological balance. With the rapid socio-economic development, land use changes are significant, and the spatial and temporal patterns of ecosystems are evolving. Exploring its dynamics and driving mechanisms is crucial to the ecological protection and sustainable development of watersheds. This research systematically examines the spatiotemporal dynamics and driving mechanisms of ecosystem patterns in the middle Yellow River’s Qin River Basin (1990–2020). Quantitative assessments integrating ecosystem transition metrics and redundancy analysis reveal three critical insights: (1) dominance of agricultural land and woodland (74.81% combined coverage), with grassland (18.58%) and other land types (6.61%) constituting secondary components; (2) dynamic interconversion between woodland and grassland accompanied by urban encroachment on agricultural land, manifesting as net reductions in woodland (−13.74%), farmland (−6.60%), and wetland (−38.64%) contrasting with grassland (+43.34%) and built-up area (+116.63%) expansion; (3) quantified anthropogenic drivers showing agricultural intensification (45.03%) and ecological protection measures (36.50%) as primary forces, while urbanization account for 18.47% of observed changes. The first two RDA ordination axes significantly (p < 0.01) explain 68.3% of the variance in ecosystem evolution, particularly linking land-use changes to socioeconomic indicators. Based on these findings, the study proposes integrated watershed management strategies emphasizing scientific land-use optimization, controlled urban expansion, and systematic ecological rehabilitation to enhance landscape stability in this ecologically sensitive region. The conclusions of this study have important reference value for other ecologically sensitive watersheds in land use planning, ecological protection policy making, and ecological restoration practice, which can provide a theoretical basis and practical guidance. Full article

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

Citrus is one of the most widely consumed fruits in the world, and its cultivation industry continues to develop rapidly. However, the roles of soil protistan communities during citrus growth are not yet fully understood, despite the potential significance of these communities to the health and quality of citrus. In this study, we examined the soil properties and protistan communities in Eureka lemon farmlands located in Chongqing, China, during the flowering and fruiting stages of cultivation, both in greenhouse and open-field settings. In general, the majority of the measured soil properties (including nutrients and enzyme activities) exhibited higher values in open-field farmlands in comparison to those observed in greenhouse counterparts. According to the results of high-throughput sequencing based on the V9 region of eukaryotic 18S rRNA gene, the diversity of soil protistan communities was also higher in open-field farmlands, and both lemon growth stage and cultivation modes showed significant effects on soil protistan compositions. The transition from traditional agricultural practices to greenhouse farming resulted in a significant transformation of the soil protistan community. This transformation manifested as a shift towards a state characterized by diminished nutrient cycling capabilities. This decline was evidenced by an increase in phototrophs (Archaeplastida) and a concomitant decrease in consumers (Stramenopiles and Alveolata). Community assembly analysis revealed deterministic processes that controlled the succession of soil protistan communities in lemon farmlands. It has been established that environmental associations have the capacity to recognize nitrogen in soils, thereby providing a deterministic selection process for protistan community assembly. Furthermore, a production index was calculated based on 12 quality parameters of lemons, and the results indicated that lemons from greenhouse farms exhibited a lower quality compared to those from open fields. The structure equation model revealed a direct correlation between the quality of lemons and the cultivation methods employed, as well as the composition of soil protists. The present study offers insights into the mechanisms underlying the correlations between the soil protistan community and lemon quality in response to changes in the cultivation modes. Full article

As the global population continues to rise, the impact of urbanization on land utilization and ecosystems are growing more pronounced, particularly within the expanding area of Asia. The land use/land change (LULC) brought by urban expansion directly impacts plant growth and ecological productivity, altering the carbon cycle and climate regulation functions of the region. This research focuses on Harbin City as a case study, employing an enhanced version of the Carnegie–Ames–Stanford Approach (CASA) model to analyze the spatial–temporal variations in vegetation Net Primary Productivity (NPP) across the area from 2000 to 2020. The findings indicate that Net Primary Productivity (NPP) in Harbin exhibited notable interannual variability and spatial heterogeneity. From 2000 to 2005, a decline in NPP was observed across 60.75% of the area. This reduction was predominantly concentrated in the central and eastern areas of the city, where forested landscapes are the dominant feature. In contrast, from 2010 to 2015, 92.12% of the region saw an increase in NPP, closely related to the overall improvement in NPP across all land-use types. Land-use change significantly influenced NPP dynamics. Between 2000 and 2005, 54.26% of NPP increases stemmed from the transition of farmland into forest, highlighting the effectiveness of the “conversion of farmland back to forests” policy. From 2005 to 2010, 98.6% of the area experienced NPP decline, mainly due to forest and cropland degradation, especially the unstable carbon sink function of forest ecosystems. Between 2010 and 2015, NPP improved across 96.86% of the area, driven by forest productivity recovery and better agricultural management. These results demonstrate the profound and lasting impact of land-use transitions on the spatiotemporal dynamics of NPP. Full article

Rural settlements in agro-pastoral ecotones reflect the complex interplay between natural constraints and human land use, particularly in ecologically sensitive and climatically transitional regions. This study investigated the agro-pastoral ecotone of eastern Inner Mongolia, a representative region characterized by environmental heterogeneity and competing land use functions. Landscape pattern indices, ordinary least squares (OLS) regression, and geographically weighted regression (GWR) were employed to analyze settlement morphology and its environmental determinants. The results reveal a distinct east–west spatial gradient: settlements are larger and more concentrated in low-elevation plains with favorable hydrothermal conditions, whereas those in mountainous and pastoral areas are smaller, sparser, and more fragmented. OLS regression revealed a strong positive correlation between arable land and settlement density (r > 0.8), whereas elevation and slope were significantly negatively correlated. GWR results further highlight spatial non-stationarity in the influence of key environmental factors. Average annual temperature generally shows a positive influence on settlement density, particularly in the central and eastern agricultural areas. In contrast, forest cover is predominantly negative, especially in the Greater Khingan Mountains. Proximity to water resources consistently enhances settlement density, although the magnitude of this effect varies across regions. Based on spatial characteristics and land use structure, rural settlements were categorized into four types: alpine pastoral, agro-pastoral transitional, river valley agricultural, and forest ecological. This study provides empirical evidence that natural factors (topography, climate, and hydrology) and land use variables (farmland, pasture, and woodland) collectively shape rural settlement patterns in transitional landscapes. The findings offer methodological and practical insights for targeted land management and sustainable rural development in agro-pastoral regions under ecological and socioeconomic pressures. Full article

Confronting escalating urban ecological risks, this study investigates ecosystem resilience evolution in the Jinan metropolitan area’s mountain–plain structure. We establish a Resistance–Adaptability–Resilience (RAR) framework integrating ecosystem service value and landscape patterns. Using Optimal Multi-layered Geo-Detector and Markov–FLUS modeling, we quantify natural–socio-economic interactions and simulate resilience under three scenarios: inertial development, cultivated land protection, and ecological priority. The results show fluctuating resilience (0.1863→0.1876→0.1863) with functional intensification in high-value areas and escalating vulnerability in low-value regions, alongside the spatial dichotomy between the resilient southern mountains and northern plains, dominated by natural factors. Cultivated land protection degrades mountain resilience via slope farming, while ecological priority stabilizes it through transitional controls. The proposed “resilience red line–development permit” mechanism demonstrates terrain and policy integration optimizing resilience allocation. This framework offers strategies to reconcile ecological conservation and farmland security in urbanizing regions. Full article

In agro-pastoral transitional zones, monitoring vegetation fraction coverage (FVC) and understanding its relationship with land use and climate change are crucial for comprehending how complex land-use/land-cover change (LUCC) improves ecological restoration and land management. This study focuses on the agro-pastoral transitional zone of Inner Mongolia, aiming to analyze vegetation cover changes from 2000 to 2020 using the Mann–Kendall (MK) significance test, Theil–Sen median trend analysis, and coefficient of variation (CV) analysis. Additionally, the study explores the impacts of LUCC, precipitation, and temperature on vegetation cover using methods such as geo-detector, pixel-based statistical analysis, and univariate linear regression. Based on the PLUS land-use prediction model and linear regression results, vegetation cover was simulated under different land-use scenarios for the future. The main findings are as follows: first, from 2000 to 2020, the spatial distribution of vegetation cover in the study area showed a distinct pattern of higher vegetation cover in the east compared to the west, with significant spatiotemporal heterogeneity. Although the overall vegetation cover slightly increased, there were notable differences in the trend across regions, with some areas experiencing a decrease in FVC. Second, LUCC is the most significant explanatory factor for vegetation cover changes, and the interactions between LUCC and other factors have a particularly notable impact on vegetation cover. Third, scenario simulations based on the PLUS model indicate that, by 2040, vegetation cover will perform optimally under the farmland protection and sustainable development scenarios. Particularly under the farmland protection scenario, the conversion of cropland, forestland, and grassland is notably suppressed. In contrast, the unmanaged natural development scenario will lead to a decline in vegetation cover. The results of this study show that vegetation cover in the agro-pastoral transitional zone of Inner Mongolia exhibits substantial fluctuations due to land-use change. Future ecological restoration policies should incorporate land-use optimization to promote vegetation recovery and address ecological degradation. Full article

Rapid socio-economic transition is often accompanied by intensive anthropogenic activities, leading to a significant build-up of heavy metals within farmland soils. However, this unwanted outcome may not be fully uniform but exhibit spatial variability, particularly involving different land uses. Based on 1839 topsoil samples from China’s Sunan Economic Region, this study estimated the contamination profiles and associated ecological risks posed by five heavy metals (As, Cd, Cr, Pb, and Hg) across cash-crop and cereal-crop soils. Further, we applied a combination of geostatistics and positive matrix factorization (PMF) model to identify the targeted zones, priority pollutants, and their underlying sources to pave the way for formulating detailed and fine-scale risk-mitigation strategies. Our results revealed that heavy metal pollution in Sunan displayed significant spatial variability, predominantly influenced by localized Hg and Cd accumulation, with more severe contamination observed in cash-crop soils compared to cereal-crop soils. The 232,532 ha of agricultural land could be designated as the targeted zones in which excessive Hg and Cd accumulation can be identified as the priority pollutants contributing to potential ecological risk. PMF modeling also suggested that within targeted zones, Cd accumulation was predominantly driven by intensive agrochemical application, whereas multiple sources simultaneously determined Hg accumulation. Our findings offer valuable guidance for optimizing land management strategies aimed at mitigating agricultural soil degradation driven by intensive anthropogenic activities. In addition, the integrated approach highlighted the crucial values in aspects to spatially identify risk-targeted zones and priority pollutants. Full article

Background/Objectives: Globally, antibiotic contamination has become an emerging issue in agricultural lands. The presence of antibiotic residues in farmlands, especially through the application of manure fertilizers containing veterinary antibiotics, e.g., tetracycline (TC), can cause severe toxicity, which inhibits crop growth and performance, subsequently threatening human health via consumption of contaminated products. This study was conducted to evaluate the phytotoxicity of TC on Kimchi cabbage (Brassica campestris L.) during seed germination, seedling, and vegetative growth stages, along with its physiological responses and bioaccumulation under TC stress. Methods: The responses of cabbage plants to TC stress were assessed through a germination test and a pot experiment, conducted for three days and six weeks, respectively, under different doses of TC (0, 5, 10, 25, and 50 mg/L). Results: As a result of the germination test, higher TC doses (25 and 50 mg/L) tended to delay seed germination, but all treatments achieved a 100% germination percentage by Day 3 after sowing. Eight days after sowing, the length of shoots and roots of seedlings exhibited a TC dose-dependent decline, specifically under 50 mg TC/L, showing a considerable decrease of 24% and 77%, respectively, compared to control. Similar results were observed in the plants transitioning from the seedling to vegetative stages in the pot experiment. Four and six weeks after sowing, the 50 mg TC/L dose showed the strongest phytotoxicity in cabbage plants with physiological parameters, such as the maximum photosystem II quantum yield (F_v/F_m), pigment content (chlorophyll and carotenoid), biomass, and leaf number, significantly reduced by 26 to 60% compared to control. Interestingly, at lower TC doses (5 and 10 mg/L), a hormesis effect was observed in the phenotype and biomass of the plants. In addition, the degree of TC accumulation in the plants was highly dose-dependent at Week 4 and Week 6, but a temporal decline in TC accumulation was noted between these time points in all TC treatments. This phenomenon might affect the value of the bio-concentration factor (BCF) as an indicator of the plant’s tendency to uptake TC. That is, in Week 6, the dose-dependent reduction in BCF for TC in the plants was likely attributed to a dilution effect caused by plant biomass increase or a degradation mechanism within the plant. Conclusions: Overall, our findings suggest that tetracycline toxicity induces seed germination delay and influences seedling elongation and photosynthetic functions, ultimately impairing crop growth and performance. Also, the antibiotic dynamics related to accumulation and degradation in plants were identified. These results will not only suggest the toxicity threshold of TC for cabbage but also provide insights into effective soil management strategies for food production safety and agroecosystem sustainability in antibiotic-contaminated soils. Full article

With China’s urbanization process and changes in rural family structures, the abandonment of farmland in scattered villages within hilly mountainous regions is becoming an increasingly serious issue, restricting the improvement of land use efficiency. This study analyzes the basic characteristics and variations in abandoned farmland by conducting surveys and interviews with peasants in a scattered village in southern China. Using the Heckman two-stage model, we perform empirical analysis on the factors influencing farmland abandonment, addressing potential sample selection bias. The findings show the following: peasants with better health and higher education levels are more likely to transition to non-agricultural occupations which contributes to an increased abandonment of farmland. However, larger and more integrated land parcels, along with favorable farming conditions, help reduce abandonment. Additionally, rural land transfer and agricultural subsidies are important factors that enhance farmland utilization and mitigate abandonment. These results provide a reference for addressing the abandonment of farmland and improving both the farming environment and social policies in rural villages. Full article

The gullied Loess Plateau, a region characterized by the overlapping of ecological fragility and energy abundance in China, requires urgent analysis of its territorial spatial conflict mechanisms to harmonize human–environment relationships. This study integrated multi-temporal remote sensing data (1990–2020) to develop a Comprehensive Spatial Conflict Index (CSCI) and applied the Optimal Parameter-based Geographical Detector (OPGD) to unravel the driving mechanisms of territorial spatial evolution in Qingyang City, Gansu Province. The results revealed that: (1) Territorial spaces exhibit a transition pattern of ecological restoration, urban expansion, and agricultural contraction. Forest and grassland ecological spaces increased by 1.42 percentage points (to 13.14%) and 1.26 percentage points (to 49.29%), respectively, while industrial-mining production spaces expanded sevenfold (0.01% to 0.08%), and agricultural production spaces decreased by 3.36 percentage points. (2) Spatial conflicts transitioned through three phases: ① A low-intensity stabilization phase (1990–2000), with 90.55% of areas under weak and moderately weak conflict (CSCI ≤ 0.4); ② A moderate conflict contraction phase (2000–2010), where weak conflict zones surged by 28.18 percentage points (13.06% → 41.24%), with moderate and moderately weak spatial conflict (0.2–0.6) decreasing by 28.27 percentage points (86.06% → 57.79%); ③ A moderately strong to strong expansion phase (2010–2020), with moderate and moderately strong conflict areas rising to 16.82%. Strong conflict zones (CSCI ≥ 0.8) expanded to 0.61%, spatially clustered in the Xifeng urban area and the Malian–Pu River corridor, showing significant positive correlations with gully density (>3.5 km∙km⁻²) and nighttime light index (NL). (3) The interaction between NDVI and land use intensity (LUI) dominated conflict patterns (q = 0.2583). In northern energy development zones (Huanxian County), LUI and precipitation (PRE) synergistically intensified landslide risks, while facility agriculture in central plateau farmlands (Ningxian County) triggered groundwater overexploitation. The coupling of road density (RND) and population (POP) factors (q = 0.1892) formed a transportation–population axial belt compression. Policy interventions exhibited spatial heterogeneity: the Grain-for-Green Program increased weak conflict zones by 28.18 percentage points, whereas wind power development in the Huanxian–Huachi northern belt escalated moderately strong to strong conflict zones by 3.6 percentage points. A three-dimensional governance framework integrating geomorphological adaptation, development phasing, and ecological compensation is proposed to optimize territorial spatial planning in the gullied Loess Plateau. Full article

Southwest China’s karst region represents a global hotspot for ecological restoration, with natural succession on abandoned farmland emerging as a pivotal mechanism under recent land-use transitions. Despite its ecological significance, empirical data remain scarce regarding tree growth characteristics in this fragile ecosystem. This seven-year study (2018–2024) at Puding Karst Ecosystem Research Station quantified the spatiotemporal patterns of tree growth through monthly diameter at breast height (DBH) measurements for dominant species, coupled with microhabitat characterization (rock exposure, competition indices, and canopy architecture). Key findings revealed that the mean annual DBH increment was 5.74 mm/a, while biomass accumulation averaged 9.38 kg/a; growing-season drought duration significantly modulated interannual growth variation; and microhabitat heterogeneity and tree size significantly influenced the spatial variance of tree growth. These results substantiate natural succession as an effective carbon sequestration strategy, particularly in nutrient-depleted karst terrains. We advocate for the policy prioritization of passive restoration over active afforestation in marginal croplands. Full article