Land Degradation & Development最新文献

{"title":"Decipher the Persistence of Soil Dissolved Organic Matter Under Different Land Use Types Using Potential Molecular Transformations","authors":"Aoping Mao, Jingming Zheng, Ziteng Wang, Fuhong Sun, Yiwen Sang, Xiuyuan Chen, Yimiao Li, Anning Miao","doi":"10.1002/ldr.70156","DOIUrl":"https://doi.org/10.1002/ldr.70156","url":null,"abstract":"Soil dissolved organic matter (SDOM), as the largest active carbon pool in terrestrial ecosystems, understanding its chemical composition and molecular transformation is of great significance for the global carbon cycle. However, due to the inherent complexity and dynamic nature of DOM in different ecosystems, the differences in molecular transformations under different land use types and the main factors affecting the transformations remain unclear. To address this issue, we employed ultrahigh‐resolution Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR MS) to analyze molecular transformation differences and their influencing factors in SDOM from five land use types: farmland (FL), forest (FR), grassland (GL), urban area (UA), and wetland (WL). The results showed that in human‐affected land use types, the transformations of aliphatic/peptide‐like compounds in FL and condensed aromatic compounds in UA were more abundant than in natural soil systems (FR, GL, and WL); the proportions accounted for 3–5 times and 1.5–100 times those of other land types, respectively. Random forest analysis identified two key factors influencing the intensity of transformation‐related molecules: Total phosphorus (TP) and Shannon index. Increased TP led to a decrease in intensity for unstable molecules involved in transformation, and enhanced Shannon index resulted in increased intensity of stable molecules (lignin compounds and condensed aromatic compounds) but decreased intensity of unstable molecules (aliphatic/peptide compounds). Compared to molecular intrinsic characteristics, the influence of external environmental factors on SDOM molecular transformation cannot be overlooked. Our findings highlight the importance of external environmental factors and chemodiversity in influencing molecular transformations, providing new insights for understanding the dynamic changes in soil carbon pools.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"10 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Trade‐Offs and Synergies Among Agricultural Land Use Benefits and Their Principal Influencing Factors: Implications for Sustainable Agricultural Development","authors":"Zhaojun Wang, Ying Wang, Haiyang Li, Xiuyu Huang","doi":"10.1002/ldr.70145","DOIUrl":"https://doi.org/10.1002/ldr.70145","url":null,"abstract":"Agricultural land plays a pivotal role in safeguarding food security and improving farmers' living standards. However, its utilization faces sustainability challenges due to greenhouse gas emissions and associated environmental impacts. Improving agricultural land use benefits (ALUB) has become imperative for achieving sustainable agricultural development. This study combines the Sustainable Development Goals (SDGs) with agricultural land systems to construct a novel “element‐structure–function‐benefit‐interaction” theoretical model for understanding ALUB. Methodologically, using county‐level data (2000–2020) from Hubei Province, we first quantified the ALUB indices for 75 counties through the evaluation index system. Then, we analyzed the spatiotemporal evolution patterns of trade‐off and synergy relationships among ALUB dimensions via a Mechanical Equilibrium Model. Finally, we identified key influencing factors using the Optimal Parameters Geographical Detector. Results show: (1) Economic, social, and comprehensive benefits showed fluctuating growth, whereas ecological benefits remained relatively stable; (2) The 2020 average coordination level (0.378) indicated basic coordination, with stable non‐high‐coordination spatial patterns; (3) Socioeconomic factors (e.g., GDP density, rural labor force) predominantly influenced trade‐off/synergy relationships. Through deviation distribution analysis, the counties were categorized into three distinct typologies: excellent ecological benefits but weak economic and poor social benefits (Quadrant I), excellent economic benefits but weak ecological and poor social benefits (Quadrant II), and excellent ecological benefits but weak social and poor economic benefits (Quadrant VI). Based on these classifications, tailored optimization strategies were proposed to enhance coordination among ALUB dimensions. This study not only enriches the conceptual framework of ALUB formation and its interrelationships but also provides practical guidance for sustainable agricultural land management.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"52 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Site Index Drives the Soil Microbial Network Complexity and Function in Chinese Fir Mixed Plantations After Near‐Natural Transformation","authors":"Jie Lei, Ziqing Lv, Aiguo Duan, Congwei Xiang, Jianguo Zhang","doi":"10.1002/ldr.70160","DOIUrl":"https://doi.org/10.1002/ldr.70160","url":null,"abstract":"The site index (SI), an important indicator of forest health, has been well‐documented due to its impact on tree growth. Soil microorganisms, as decomposers that control nutrient cycling in forest ecosystems, usually respond significantly to changes in site index. However, most of the existing studies have focused on the direct effects of SI on tree growth, while the response mechanism of the microbial community network and its function has rarely been explored. In view of this, this study investigated the co‐occurrence network and function of soil microbial communities under different site index (SI‐14.96, SI‐15.70, and SI‐16.90) and soil depths (0–20 cm, 20–40 cm, and 40–60 cm) within a mixed Chinese fir plantation. The increase of site index significantly improved the soil physical and chemical properties of the Chinese fir plantation, including total phosphorus, total nitrogen, exchangeable magnesium, and soil water content. Dominant bacterial communities included Acidobacteria, Chloroflexi, and Proteobacteria, while Ascomycota and Basidiomycota dominated the fungal community. The variation in bacterial community structure was mainly driven by soil depth (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 37.33%), while the fungal community structure was influenced primarily by the site index (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 20.80%). Soil phosphorus, organic carbon, and soil water content drove microbial community variation. In the relatively high site index, the topological properties of the bacterial and fungal co‐occurrence network, including nodes, edges, and the average clustering coefficient, reached the highest, showing the highest network complexity, and the keystone taxa were more abundant in the surface soil. Functional annotation analysis further indicated that bacterial functions related to nitrogen cycling and arbuscular mycorrhizal fungi were both significantly highest at SI‐16.90. In general, a relatively high site index (SI‐16.90) for Chinese fir plantations can improve the complexity of the soil microbial network, enhance the abundance of keystone taxa, and optimize the nitrogen cycle and the function of arbuscular mycorrhizal fungi. These findings are of great significance to the shaping of soil microbial diversity and ecological functions and provide a practical basis for improving soil ecology with a high site index in forest management.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"9 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altitudinal Patterns of Soil Organic Carbon and Its Drivers in the Mountains of Southeastern Tibet","authors":"Zhiwei Liu, Wenting Zhou, Xiaodong Wu, Xin Xiong, Quanlian Li, Huhu Kang, Tanuj Shukla, Qianggong Zhang, Shichang Kang, Xiufeng Yin","doi":"10.1002/ldr.70149","DOIUrl":"https://doi.org/10.1002/ldr.70149","url":null,"abstract":"Understanding the distribution and drivers of soil organic carbon (SOC) in mountain ecosystems is essential for evaluating carbon stability and climate change responses. This study investigates the spatial patterns and driving mechanisms of SOC and its two main components—particulate organic carbon (POC) and mineral‐associated organic carbon (MAOC)—along altitudinal gradients in five Himalayan valleys. SOC in 0–10 cm soil peaks at mid‐elevations (1000–3500 m) and declines at higher elevations. SOC content varies markedly across land cover types, highest in forests (71.34 ± 62.36 mg/g), followed by grasslands, and lowest in deserts (12.40 ± 3.24 mg/g). POC is the main component of SOC in most ecosystems, especially forests, as it is closely influenced by vegetation type, biomass input, and microbial activity. In contrast, MAOC increases with elevation and is primarily controlled by soil mineral interactions and physicochemical properties. SOC components are co‐regulated by biotic and abiotic drivers. POC formation is closely linked to plant productivity and microbial processes, whereas MAOC accumulation is largely determined by soil physicochemical properties, including soil texture, pH, moisture, and oxides. Under climate warming, significant vegetation shifts—particularly the encroachment of alpine shrubs into meadow areas—have altered SOC composition. Shrub expansion favors POC accumulation, which is less stable and more prone to decomposition, whereas alpine meadows support the formation of more stable MAOC. Although meadows are at risk of degradation, longer growing seasons may enhance SOC storage. These findings reveal the spatial dynamics and controls of SOC in the Himalayas, offering crucial insights for understanding mountain carbon cycles and informing climate adaptation and carbon management strategies.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"5 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Water‐Induced Soil Erosion Using Machine Learning: XGBoost as the Most Effective Model","authors":"Ihtisham Khan, Kashif Khan, Kazimierz Bęcek, Muhammad Fahad Bilal","doi":"10.1002/ldr.70152","DOIUrl":"https://doi.org/10.1002/ldr.70152","url":null,"abstract":"Soil erosion is a significant environmental concern that threatens agricultural activities, reduces soil fertility, and eventually impacts productivity. Assessing soil erosion is essential for effective planning and conservation initiatives in a basin or watershed. This study evaluates water‐induced soil erosion susceptibility using machine learning models, with a focus on the comparative performance of Random Forest (RF), k‐Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost). Unlike conventional approaches, this study emphasizes the effectiveness of ML‐based predictive modeling, rather than re‐identifying well‐established erosion‐controlling factors. A comprehensive dataset comprising topographic, climatic, and land use parameters was used to train and validate the models (80% training, 20% testing). The models were assessed based on multiple performance metrics, including sensitivity, specificity, Kappa coefficient, and area under the curve (AUC). Among the tested models, XGBoost demonstrated the highest predictive performance with an AUC of 0.91, sensitivity of 0.91, specificity of 0.89, and a Kappa index of 0.80. RF and kNN also performed well, with AUC values of 0.87 and 0.89, and Kappa values of 0.80 and 0.73, respectively. Field validation showed that XGBoost correctly predicted 78.7% of high‐risk erosion sites. The final susceptibility map classified 21.3% of the area as high‐risk, mainly concentrated in steep, sparsely vegetated uplands. These findings confirm the effectiveness of machine learning—particularly XGBoost—for accurate erosion risk mapping in data‐scarce, topographically diverse regions. The findings contribute to sustainable land management strategies, offering a scalable and adaptable approach for erosion risk assessment in diverse environmental settings.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"53 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding Soil Constraints in Queensland, Australia: Strategies for Precision Management to Enhance Crop Productivity","authors":"Tong Li, Lizhen Cui, Bernhard Wehr, Yunru Lai, Hongdou Liu, Cong He, Caixian Tang, Vilim Filipović, Ranjay K. Singh, Timothy I. McLaren, Ram C. Dalal, Yash P. Dang","doi":"10.1002/ldr.70111","DOIUrl":"https://doi.org/10.1002/ldr.70111","url":null,"abstract":"Soil constraints significantly impact crop productivity, yet their direct relationships to yield remain unclear. This limits the development of targeted soil management strategies for improving agricultural output. This study aims to clarify the influence of key soil constraints on crop productivity by examining soil chemical indicators across distinct productivity zones in Queensland. Soil samples were collected from 21 farms across three productivity zones (consistently low, inconsistent, and consistently high) and five soil layers (D1–D5, 0–10, 10–30, 30–60, 60–90, and 90–120 cm). We utilized the Constraint ID tool alongside mixed‐effects models, principal component analysis (PCA), and machine learning to evaluate indicators including nitrate (NO<jats:sub>3</jats:sub><jats:sup>−</jats:sup>), electrical conductivity of the saturated soil extract (ECe), pH, chloride (Cl), exchangeable sodium percentage (ESP), and exchangeable cations (Ca, K, and Mg). This integrated approach—among the first in Queensland—enabled depth‐specific and spatially explicit analysis of soil constraint impacts. ECe, pH, Cl, and ESP are critical factors influencing soil fertility, particularly in subsoil layers (D3–D5). Low‐yielding zones (Zone L) exhibited high pH (up to 8.41), Cl (up to 151 mg/kg), ESP (up to 8.64), and ECe (exceeding 4 dS/m), indicating salinity, alkalinity, and sodicity issues. These subsoil constraints are difficult to remediate, highlighting the need for surface‐level strategies that support whole‐profile soil health. This study underscores the necessity of site‐specific, surface‐focused interventions that address constraints across the entire soil profile. The findings offer actionable insights for tailoring soil management and support regional decision‐making to optimize crop yields in Queensland's agricultural systems.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"10 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Dynamics and Driving Mechanisms of Soil Salinization in Northwest China's Oasis–Desert Regions: A 20‐Year Remote Sensing and Machine Learning Analysis (2000–2020)","authors":"Guojun Han, Haiping Luo, Leyuan Ma, Peng Qi, Peihao Wang, Chunbin Li","doi":"10.1002/ldr.70157","DOIUrl":"https://doi.org/10.1002/ldr.70157","url":null,"abstract":"Soil salinization is a major threat to land productivity, food production, and ecosystem balance in Northwestern China. However, in the oasis–desert transition zone, research on the remote sensing monitoring of saline–alkaline land over longer time scales that integrate natural and anthropogenic factors is relatively lacking. Moreover, the application of machine learning for the quantitative analysis of the multifactor interactions of salinization remains limited. In this study, a soil salinization detection index was constructed, and a random forest (RF) algorithm was used to systematically investigate the spatiotemporal evolution patterns and dominant driving mechanisms of regional soil salinization. From a methodological perspective, we innovatively fused spectral feature indices with machine learning regression algorithms and employed multidimensional data analysis to quantify the effects of various environmental factors on salinization processes. The results revealed that severely salinized soils dominated the study area in 2000, covering 167,445.8 km<jats:sup>2</jats:sup>. The extent of salinized areas decreased from 2000 to 2010 but increased from 2010 to 2020. During 2000–2010, the overall salinization level improved, with increases in nonsalinized, lightly, and moderately salinized areas and a decrease in severely salinized areas. However, from 2010 to 2015, salinization significantly deteriorated in the northern region, with a rise in severe salinization. From 2015 to 2020, the area of severe salinization continued to increase, while the areas of other salinization levels decreased, indicating an overall increasing trend. Spatial analysis revealed divergent trends across geographic sectors, with the western and central regions demonstrating significant improvement in soil quality metrics, whereas the southern and northern zones exhibited progressive degradation patterns. Despite these changes, the overall salinization level improved due to the reduction in moderately and severely salinized areas. The RF model identified sunshine hours as the primary driver of salinization, followed by temperature, evaporation, relative humidity, and precipitation. In contrast, GDP, wind speed, and population density had relatively minor effects.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"23 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio‐Temporal Evolution Characteristics and Driving Mechanisms of Habitat Quality on the Qinghai‐Tibet Plateau: A Multi‐Source Data Approach","authors":"Gao Jie, Shan Wenfei, Sun Yongxiu, Liu Shiliang, Xu Xiaoling, Niu Zhirui, Teng Yanmin, Cheng Fangyan","doi":"10.1002/ldr.70158","DOIUrl":"https://doi.org/10.1002/ldr.70158","url":null,"abstract":"The Qinghai‐Tibetan Plateau (QTP), serving as a crucial ecological security barrier in China, exhibits habitat quality patterns that profoundly influence regional ecological stability and human welfare. Although previous studies have examined this relationship, comprehensive investigations on habitat quality dynamics and their underlying mechanisms remain limited. This study presents a multi‐dimensional assessment framework that integrates ecosystem services values, vegetation characteristics, and external threat factors to evaluate spatiotemporal variations in habitat quality. Using a methodological approach combining structural equation modeling (SEM) and geographically weighted regression (GWR), we systematically analyzed the driving factors and their spatial heterogeneity effects. Key findings include: (1) temporal analysis revealed relative stability in overall habitat quality during 1990–2015, followed by a marked decline during 2015–2020, (2) spatially, habitat quality demonstrated a distinct northwest‐to‐southeast increasing gradient, with &gt; 50% of the northern regions maintaining a low‐quality status. Degradation hotspots during 1990–2020 clustered in the central, western, and northern zones, while 38% of the southeastern areas showed improvement, (3) multivariate analysis identified interactive climate‐topography‐anthropogenic effects, with standardized total impact coefficients of 0.76 (climate), 0.28 (topography), and 0.14 (human activities), establishing climate as the predominant driver, and (4) GWR results revealed significant spatial non‐stationarity in factor influences. This study advances methodological integration for ecosystem assessment and provides actionable insights for managing fragile alpine environments.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"4 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Skid Trails and Evaluating Soil Disturbance From UAV‐Based LiDAR Surveys in Mediterranean Forests","authors":"Francesco Latterini, Marcin K. Dyderski, Rodolfo Picchio, Rachele Venanzi, Raffaele Spinelli, Natascia Magagnotti, Janine Schweier, Sunni Kanta Prasad Kushwaha, Nicolò Camarretta, Michael S. Watt","doi":"10.1002/ldr.70162","DOIUrl":"https://doi.org/10.1002/ldr.70162","url":null,"abstract":"Soil disturbance resulting from forest harvesting activities can have significant and lasting environmental consequences, particularly in sensitive ecosystems such as Mediterranean forests. Skid trails, the routes used by machinery to extract timber, are among the most critical areas of impact, and their detection is critical for assessing post‐harvest impacts and informing future planning. Traditional ground‐based methods for detecting these trails are often labor‐intensive and inefficient. This study evaluates the potential of Unmanned Aerial Vehicle (UAV)‐based Laser Scanning (ULS) surveys for the accurate detection of skid trails across five Mediterranean forest sites subjected to different silvicultural treatments. Four analytical techniques were tested: Hillshading (Hill), Local Relief Model (LRM), Relative Density Model (RDM), and the machine learning‐based SkidRoad_Finder (SRF). Accuracy was assessed using a ground‐truth dataset obtained through Global Navigation Satellite System (GNSS) field mapping. Of the tested techniques, RDM performed best overall, achieving approximately 73% accuracy, 66% sensitivity, and a Cohen's kappa value of 0.50. LRM performed best in even‐aged beech forests, due to its ability to capture microtopographic changes. In contrast, SRF consistently underperformed, likely due to its reliance on training data not representative of the Mediterranean context. Our findings highlight that former skid trails and related soil disturbance can be effectively detected using ULS data. However, the accuracy and reliability of detection vary depending on site‐specific factors such as forest type, vegetation structure, and terrain complexity. Overall, this study underscores the utility of ULS for operational forest monitoring and supports the tailored selection of detection techniques based on local stand characteristics and available computing resources.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"54 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Spatio‐Temporal Variability, Interrelationships and Ecological Sustainability of Cropland Fragmentation and Habitat Quality: A Case Study of Northwest China","authors":"Yujie Zhou, Yiheng Zhang, Wanying Li, Juan Li","doi":"10.1002/ldr.70141","DOIUrl":"https://doi.org/10.1002/ldr.70141","url":null,"abstract":"Cropland fragmentation (CLF) has profound implications for both ecological functions and agricultural production security. The northwest region of China is a critical area for agricultural development; however, the CLF phenomenon in this region is complex, and its ecosystem is highly vulnerable. Therefore, a comprehensive understanding of the spatial and temporal patterns of CLF and habitat quality (HQ) in this region, as well as their interrelationships, is essential for preserving regional ecological sustainability and ensuring food security. Based on landscape indicators and socio‐economic attributes, we developed a comprehensive evaluation index system for CLF, which includes scalability (SPI), natural endowment (NPI), aggregation (API), and convenience (CPI). Regional HQ was evaluated using the Integrated Assessment of Ecosystem Services and Tradeoffs (InVEST) model. The study explores the spatial distribution characteristics, spatial relationships, and the driving mechanisms of CLF and HQ from 2000 to 2022. The results indicated that the spatio‐temporal variation of CLF exhibited significant heterogeneity and complexity, with high values concentrated in the northwestern region characterized by higher elevation, and low values clustered in the northeastern region. CLF showed a substantial increase from 2000 to 2010, followed by a decline and eventual stabilization from 2010 to 2022. Poor‐level HQ was predominantly found in urban aggregations and desert areas, whereas excellent‐level HQ was mainly located in grassland and forest ecosystems. Overall, HQ exhibited a declining trend over the past 22 years, and a strong positive correlation was observed between CLF and HQ, both showing significant spatial clustering. The slope was identified as the most influential predictor of HQ. The research findings can offer valuable reference and theoretical support for the management of cultivated land use, the protection of sustainable agricultural development, and the implementation of targeted measures at the regional scale.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"25 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}