Land Degradation & Development最新文献

{"title":"An Explainable Machine Learning and Cloud‐Based Remote Sensing Framework for Monitoring Terrace Degradation and Restoration in Mountain Landscapes","authors":"Gadisa Fayera Gemechu, Wei Wei","doi":"10.1002/ldr.70163","DOIUrl":"https://doi.org/10.1002/ldr.70163","url":null,"abstract":"Terrace systems play a critical role in mitigating soil erosion, stabilizing slopes, and supporting agricultural production in mountainous regions. Despite their importance, the degradation and restoration of terraces remain insufficiently quantified, particularly in ecologically sensitive and fragmented landscapes such as China's Yellow River Basin (YRB). This study investigates terrace abandonment patterns in the Zulihe River Basin—a representative watershed in the YRB—by integrating explainable machine learning with multi‐source remote sensing and cloud‐based processing. We employed Sentinel‐1 and Sentinel‐2 imagery, DEM‐derived topographic features, and land cover products, supported by high‐resolution Google Earth reference data. A Google Earth Engine–Colab workflow enabled efficient data integration and classification. An ensemble feature selection (EFS) approach combining Gini importance and recursive feature elimination (RFECV) was implemented for optimal feature selection, followed by classification using Random Forest and LightGBM models. The SHAP (SHapley Additive exPlanations) algorithm was applied to enhance interpretability, revealing geomorphic (37.2%) and SAR texture (27.6%) features as dominant predictors. Model performance exceeded 91%, with F1‐scores > 0.96. Our results indicate that agricultural terraces declined by over 20% between 2015 and 2024, with the most significant losses occurring between 2018 and 2021. These areas consistently exhibited low C‐band SAR backscatter (VV/VH), reflecting structural degradation and fluctuations in vegetation cover. In contrast, grassy terraces expanded on convex slopes, suggesting partial ecological restoration. This study introduces a robust and scalable framework for terrace monitoring, offering interpretable insights into land degradation dynamics. The approach can support sustainable land management strategies and inform policy responses in erosion‐prone regions.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"30 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145235374","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":"Long‐Term Land‐Use‐Driven Changes in Nutrient Storage (Macro and Micro), Biological Health, and Carbon Pools in the Eastern Himalayas, India","authors":"M. A. Ansari, B. U. Choudhury, M. H. Ansari, Ch. Bungbungcha Meitei, Kl. Levish Changloi, Anup Das, A. Dembalar, Meenu Rani, Jayanta Layek, V. K. Mishra, M. Shamim, N. Ravisankar, Sunil Kumar","doi":"10.1002/ldr.70236","DOIUrl":"https://doi.org/10.1002/ldr.70236","url":null,"abstract":"Long‐term land‐use transitions significantly alter soil nutrient dynamics, microbiological functions, and carbon (C) pool distributions in the soil profile. This study assessed the long‐term effects of converting a 50‐year‐old primary forest into cultivated land‐use systems, namely, agriculture (AGLU), horticulture (HOLU), and agroforestry (AFLU), over a period of 20–26 years in the Eastern Himalayas, India. The soil was taken to a depth of 1.0 m, with increments of 0.15 m until 0.60 m and 0.20 m until 1.0 m. The evaluation was carried out to assess macro‐ and micronutrient storage, microbial biomass, enzymatic activities, and total and fractionated organic carbon (C) pools. The depletion of nutrients (macro: −52.6% to −59.2%, micro: −20.4% to −61.6%) and biological properties (SMBC: −40.7%, enzymes: −25.5% to −40.2%) was the most severe in the top soil (0.15 m) under agricultural land use. In contrast, AFLU and HOLU retained higher nutrient levels and C‐pools, both in surface (0–15 cm) and subsoil layers (15–100 cm). Cultivation significantly (<jats:italic>p</jats:italic> &lt; 0.05) reduced soil organic carbon and its fractions in both surface and sub‐surface soils when compared to primary forest (FOLU). The degradation index confirmed greater resilience of tree‐based systems compared to seasonal cropping. These findings support the promotion of agroforestry and perennial horticulture, which can help restore degraded soils in upland ecosystems.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"32 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228857","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":"Balancing Agricultural Growth and Land Degradation: The Key Role of Water Resources in Sustainable Farmland Management","authors":"Kun Wang","doi":"10.1002/ldr.70231","DOIUrl":"https://doi.org/10.1002/ldr.70231","url":null,"abstract":"Agriculture plays a vital role in meeting global food demands, yet its expansion is often linked to challenges such as land degradation and water scarcity. It is important to identify changes in cropping patterns, their interaction with soil condition and water availability. This study investigates the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), and Land Use Land Cover (LULC) by using multi‐temporal Sentinel‐2 satellite imagery during the winter cropping season (2018–2024). Results highlight pronounced temporal variability, with dense vegetation covered in 2020, while peak soil moisture was in 2022, reflecting the sensitivity of cropping systems to changing conditions. Furthermore, BSI index results show that the lowest values were observed in 2020, which indicate the highest vegetation cover in this year. However, the results of LULC reported that the cropping area decreased by −27.18%, while the built‐up area increased by 33.41%. This trend underscores the dual pressure of cropland loss and urban growth. The water bodies' covered area showed a minor increasing trend of 0.56%. The study emphasizes the complex association between the availability of water, the health of vegetation, and land degradation. To ensure food security and environmental sustainability in agricultural regions that have rapidly urbanized, it is crucial to implement integrated water and land management techniques.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"120 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228859","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":"Predicting the Onset Day of California Wildfires Using Deep Learning Methods","authors":"Jinlin Xie, Wei Zhong","doi":"10.1002/ldr.5655","DOIUrl":"https://doi.org/10.1002/ldr.5655","url":null,"abstract":"The increasing frequency and severity of wildfires in California, exacerbated by climate change and human activities, demand advanced predictive tools for effective mitigation. This study employs deep learning (DL) and machine learning (ML) models—convolutional neural networks (CNN), long short‐term memory (LSTM), random forest (RF), and decision trees (DT)—to predict wildfire onset days using a dataset of 14,989 data points (1984–2025) that incorporates historical and projected climate variables such as precipitation, temperature extremes, wind speed, and seasonal patterns. Among the tested models, CNN demonstrated the highest accuracy, achieving a mean absolute error (MAE) of 0.012, mean absolute relative error (MARE) of 0.597%, root mean squared error (RMSE) of 2.432, and an <jats:italic>R</jats:italic><jats:sup>2</jats:sup> of 0.991. The novelty of this study lies in the customized application of CNN for spatiotemporal wildfire prediction, where climate variables are treated as multi‐channel temporal–spatial tensors, enabling the model to learn both short‐term and long‐term dependencies within structured climate data. This approach goes beyond the conventional use of CNN in image processing by integrating dilated convolutions and optimized kernel architectures to detect rare, high‐impact events like heatwaves and wind bursts that often precede wildfire occurrences. These architectural enhancements allow CNN to extract deep, nonlinear patterns from interdependent climate features while maintaining parameter efficiency and reducing overfitting, marking a significant advancement over standard ML and DL approaches. Despite its strong performance, the model's reliance on projected climate data introduces inherent uncertainties, and the lack of real‐time human activity variables, such as land‐use changes and policy interventions, may limit operational applicability. Future improvements should focus on integrating real‐time sensor networks, refining climate projections, and validating across diverse geographic regions to strengthen the model's reliability and scalability. Ultimately, CNN‐based models have the potential to become crucial tools in proactive wildfire management, enabling timely resource allocation and reducing environmental and human impacts amid a rapidly changing climate.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"115 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203750","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":"Continuous Four‐Year Biogas Slurry Application Regulates the Soil Fertility, Microbial Communities, and Nitrogen Cycling Functions in a Farmland Ecosystem","authors":"Binbin Yu, Keming Yang, Yang Zhang, Xiaoqing Qian, Xiaojing Li, Shaohu Ouyang, Zhongzhi Chen","doi":"10.1002/ldr.70226","DOIUrl":"https://doi.org/10.1002/ldr.70226","url":null,"abstract":"Biogas slurry (BS) is widely applied as a crop fertilizer due to its high available nitrogen content. However, the effects of continuous BS application on soil fertility and nitrogen cycling in the farmland ecosystem remain unclear. In this study, we investigated the continuous four‐year BS application on soil properties, enzyme activity, microbial communities, and nitrogen cycling functions in a farmland field. The results showed that BS could increase soil alkali‐hydrolyzable nitrogen, total and available phosphorus, available potassium, organic matter, and the soil NH<jats:sub>4</jats:sub><jats:sup>+</jats:sup>‐N and NO<jats:sub>3</jats:sub><jats:sup>−</jats:sup>‐N concentrations over time. Moreover, BS application also changed the activities of key soil enzymes (e.g., urease, nitrate reductase, catalase, and phosphatase). Furthermore, the high‐throughput sequencing revealed an increase in the relative abundance of <jats:italic>Proteobacteria</jats:italic>, and decreases in the relative abundance of <jats:italic>Bacteroidetes</jats:italic>, <jats:italic>Acidobacteria</jats:italic>, <jats:italic>Actinobacteria</jats:italic>, <jats:italic>Planctomycetes</jats:italic>, and <jats:italic>Nitrospiraceae</jats:italic> with continuous BS application. With the BS application years increasing, the abundance of <jats:italic>amoA</jats:italic>‐AOB initially increased and then declined, while the nitrogen cycling genes (e.g., <jats:italic>narG</jats:italic>, <jats:italic>nirS</jats:italic>, <jats:italic>norB</jats:italic>, and <jats:italic>nosZ</jats:italic>) exhibited significant alterations. Overall, these shifts indicate that continuous BS improves soil nutrient status and enzyme‐mediated processes while reshaping bacterial community structure and the expression of key nitrogen‐functional genes. This work provides new insights into the fertility benefits and the microbial‐ecological trade‐offs of repeated BS fertilization.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"32 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203749","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":"Does Green Energy and Technological Innovations Induce Agriculture and Land Sustainability: Contextual Evidence From Climate Resilient Practices","authors":"Lidi Yuan, Judit Koltai, Karolina Kowalczyk, Sala Dariusz, Agnieszka Majewska","doi":"10.1002/ldr.70219","DOIUrl":"https://doi.org/10.1002/ldr.70219","url":null,"abstract":"With the growing environmental concerns, the existing literature mostly highlights the industrial pollution while neglecting the factors associated with the agriculture-related greenhouse gas emissions. Regarding this, the study explores the impacts of green energy, tech development, and urbanization on agriculture's greenhouse gas emissions. The prime objective of the current research is to unveil the green energy, tech innovations, and environmental sustainability nexus to draw novel implications in the context of Sustainable Development Goals (SDGs). In doing so, the authors employ the quarterly data of China from 1990Q1 to 2020Q4. For the long-run empirical analysis, the authors utilize various time-series estimating approaches, such as Quantile regression, which performs better in testing the nexus at different quantiles. However, the Fully Modified OLS, Dynamic OLS, and Canonical Cointegration Regression methods are used as robustness tools to authenticate the estimate of the primary approach. The results suggest that greener energy and technological innovations significantly reduce agriculture sector emissions. Furthermore, the presence of green energy transforms its negative influence into a positive one. Contrastingly, the use of traditional fossil fuel energy, urbanization, and financial development are significant drivers of emissions. This study's findings support SDGs, particularly SDG-2, which supports the stance of sustainable agriculture and encourages green energy use. Overall, the study discourses policy-related suggestions in the sustainability's context.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"94 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209350","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":"Research Progress of Using Ornamental Plants to Cope Approach Environmental Pollutants, a Breakthrough of Bioremediation Technology","authors":"Rizwan Ali, Minghao Gao, Jianv Liu, Jiayin Guo, Yun Li","doi":"10.1002/ldr.70193","DOIUrl":"https://doi.org/10.1002/ldr.70193","url":null,"abstract":"Ornamental plants (OPs) represent an innovative, sustainable strategy for multi-media phytoremediation, addressing soil, water, and air pollution while circumventing food chain risks. This review synthesizes advances in OPs' physiological, biochemical, and molecular tolerance mechanisms, which enable efficient pollutant extraction, degradation, and sequestration. Beyond their remediation potential, OPs enhance urban biodiversity, revitalize degraded landscapes, and generate socioeconomic value through commercial by-products. Critically, we unveil their dual-function role in emerging carbon neutrality frameworks: photosynthetic carbon capture synergizes with contaminant immobilization, positioning OPs as linchpins in climate-smart remediation. While OPs exhibit remarkable resilience, their efficacy is modulated by environmental variables and site-specific stresses. We identify key challenges—optimizing stress-tolerant traits via genetic engineering, leveraging plant–microbe partnerships to amplify degradation pathways, and sustainable biomass management—that must be addressed to scale applications. By integrating OPs into urban green infrastructure and industrial buffer zones, this approach transcends traditional remediation. This work not only redefines ornamental species as dynamic agents of environmental repair but also provides a roadmap for their deployment in achieving pollution mitigation and net-zero targets.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"102 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209662","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":"Grassroots Innovation and Policy Evolution: The Agricultural Production Responsibility System in Shangtang, China","authors":"Xin He, Jing Wan","doi":"10.1002/ldr.70225","DOIUrl":"https://doi.org/10.1002/ldr.70225","url":null,"abstract":"This study examines the transformative effects of the household contract responsibility system on agricultural productivity, income levels, labor participation, and rural development in Shangtang Community, Jiangsu Province, during China's early rural reforms from 1974 to 1984. Utilizing a qualitative case study approach grounded in extensive archival research and policy document analysis, the study reconstructs the reform trajectory and assesses its impacts through both qualitative insights and key quantitative indicators such as grain output, labor participation, and income growth. Findings reveal that following the reform, grain production nearly doubled, per capita income increased more than fivefold, and labor participation surged significantly, contributing to rural revitalization and economic resilience. The research highlights the critical role of institutional innovation and grassroots leadership in driving sustainable land management and rural socioeconomic transformation. These outcomes validate the household contract responsibility system as an effective policy model and offer lessons for contemporary sustainable rural development strategies.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"19 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194948","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":"Unlocking Mechanisms for Soil Fertility Enhancement in Tropical Forests Restored From Non‐Native Rubber Plantations: Microbes as the Key Drivers","authors":"Xiaoyi Cai, Chunfeng Chen, Ashutosh Kumar Singh, Xiaojin Jiang, Wenjie Liu","doi":"10.1002/ldr.70215","DOIUrl":"https://doi.org/10.1002/ldr.70215","url":null,"abstract":"Forest restoration is a well‐established approach for effective soil rehabilitation, yet how soil microorganisms influence soil fertility at the soil aggregate microscale during tropical forest restoration remains unclear. We investigated the changes in soil microbial diversity and composition across four forest types: a tropical rainforest, a rubber monoculture plantation, and two restored types (natural restoration of rubber monoculture and natural restoration of rubber tree with tea tree intercropping). Results showed that soil fertility (soil organic C, total N, and total P), pH, and electrical conductivity (EC) exhibited increasing trends following forest restoration or decreasing soil aggregate fractions. Forest restoration and soil aggregate fractions were identified as the key predictors of microbial community structure. This relationship may be attributed to enhanced resource availability caused by increased plant diversity, pH, and EC in the restored forests. Smaller aggregates provide physical protection and retain more nutrients, thereby promoting microbial activity and diversity. PLS‐PM showed that microbes constituted the primary contributor among all the factors driving soil fertility. Strong positive correlations were observed between soil fertility and microbial communities, particularly in the dominant phyla and microbial networks. Specifically, the abundance of r‐strategy bacteria (Bacteroidota, Actinobacteria, and Proteobacteria) increased with forest restoration and decreasing aggregate size fractions. Similarly, fungal K‐strategists (Basidiomycota) increased following forest restoration, whereas fungal r‐strategists (Mortierellomycota) increased in the smaller aggregate size fractions. Microbial networks became more complex and tighter with forest restoration and decreasing aggregate size fractions. These shifts in microbial life strategies and co‐occurrence patterns likely enhance the formation of microbial‐derived organic matter, improve the efficiency of resource allocation and ecological signal transmission, and thereby promote soil fertility accumulation. Overall, this study highlights the critical role of forest restoration in abandoned rubber plantations in reshaping soil microbial communities and emphasizes the potential of soil microbes as indicators of soil resilience and health.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"28 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145188782","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":"Decoupling Alfalfa Biomass and Soil Function: The Dominant Role of Nutrient Stoichiometry in Degraded Land Restoration","authors":"Zi‐Qiang Yuan, Bo Wang, Rui Wang, Ruo‐Nan Sun","doi":"10.1002/ldr.70224","DOIUrl":"https://doi.org/10.1002/ldr.70224","url":null,"abstract":"Introducing alfalfa (<jats:styled-content style=\"fixed-case\"><jats:italic>Medicago sativa</jats:italic></jats:styled-content> L.) into degraded lands is a cost‐effective strategy to restore ecosystem functioning. However, the long‐term persistence of alfalfa can diminish productivity and disrupt soil nutrient balance, raising concerns about the sustainability of these systems. Here, we quantified soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry, identified its environmental determinants, and evaluated its role in regulating soil multifunctionality (SMF) across 112 alfalfa grassland sites on the Loess Plateau of China. Average soil C:N, C:P, and N:P ratios (0–20 cm) were 8.6 ± 0.8, 14.4 ± 4.5, and 1.7 ± 0.5, respectively. The C:P and N:P ratios were positively correlated with vegetation cover, aboveground biomass of native species, species richness, soil organic C, but not with alfalfa or total plant biomass. Structural equation modeling revealed that species richness, microbial biomass C, and soil moisture were the dominant drivers of soil C:P and N:P ratios. Soil stoichiometry, particularly the C:P ratio, exerted a stronger influence on SMF than alfalfa biomass, with both direct effects and indirect effects mediated through plant abundance and species richness. These findings identify soil stoichiometry as a key mechanism linking vegetation and microbial processes to SMF. We argue that improving soil stoichiometry—through practices such as balanced fertilization and enhanced plant diversity—will be essential to optimize nutrient use efficiency and sustain soil functioning in alfalfa grasslands.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"93 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194947","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}