[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-223":3,"related-223":71},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":65,"slug":68,"view_count":69,"doi":8,"paper":8,"created_at":70},223,"数字经济与实体经济融合对中国农业新质生产力的影响——基于13个粮食主产区的实证","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141","基于2012-2023年中国13个粮食主产区省级面板数据,构建数字-实体融合与农业新质生产力评价指数,采用固定效应模型、中介效应模型和空间计量方法实证检验数字-实体融合对农业新质生产力的影响。研究发现:数字-实体融合与农业新质生产力呈现稳健正相关;农业产业结构升级与农业绿色全要素生产率是显著中介渠道;效应在区域特征、产业结构水平和财政分权水平三个维度上呈现显著异质性;具有显著空间溢出效应;存在显著阈值效应,创新水平和人力资本水平超过临界阈值后促进效应增强。",null,"## 1. Introduction\n\nThe current process of agricultural modernization is accelerating, and the new technological and industrial revolutions are mutually reinforcing. These developments place higher demands on high-quality agricultural laborers, labor materials, and labor objects. Traditional agricultural productivity can no longer meet the strategic requirements for building a strong agricultural nation [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B1-agriculture-16-00141)]. Therefore, there is an urgent need to explore breakthrough paths based on the formation laws of new quality productive forces. The “digital–real integration” examined in this study refers to the process of deep integration between digital technologies and the real economy, while “new quality productive forces” refers to an advanced form of productive forces driven by scientific and technological innovation. Together, they constitute the theoretical framework and practical pathway for contemporary agricultural transformation and upgrading. The former serves as the means, while the latter represents the objective. According to general principles, human practices in transforming nature are the fundamental driving force for the formation of new quality productive forces, and changes in production relations are the key opportunity for qualitative transformation of productivity. Digital–Real integration, through cultivating new development momentum and reshaping new production relations, serves as a critical driver for the formation of new quality productive forces [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B2-agriculture-16-00141)]. In response to the challenges of climate change, resource constraints, and food security, major economies worldwide regard digital technologies as the core driving force for agricultural transformation. The European Union’s “Digital Agriculture Strategy,” the United States’ “Precision Agriculture Initiative,” and Japan’s “Smart Agriculture Promotion Policy” all reflect the international consensus on empowering agricultural transformation through digital technologies. In the 2025 Central Document No. 1, the concept of “agricultural new quality productive forces” is introduced for the first time, and General Secretary Xi Jinping emphasized the importance of “promoting the deep integration of digital technology with the real economy to empower the transformation and upgrading of traditional industries”; this provides clear guidance for the Chinese-style agricultural modernization path. In the agricultural field, the deep integration of digital and real economies, with technology resource sharing as the link [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B3-agriculture-16-00141)], achieves efficient circulation and sharing of new production factors such as technology, data, and knowledge within the industrial chain through digital infrastructure like big data platforms, the Internet of Things, and artificial intelligence. This greatly cultivates new development momentum for agriculture, enhances the skills of laborers, innovates labor materials, and expands labor objects, thus strengthening the material foundation for new quality productive forces. On the other hand, this deep integration breaks traditional agricultural organizational methods and production modes, reshapes agricultural production relations, and gives rise to new business models such as smart farms, cloud-based cooperatives, and direct connection models for order agriculture. These developments provide important institutional foundations and environmental conditions for the “qualitative” leap in agricultural productivity. Therefore, promoting deep integration of the digital and real economies is not only an inherent requirement following the evolution laws of new quality productive forces but also a strategic measure to break through the bottlenecks of agricultural development and accelerate the construction of a strong agricultural nation.\n\nChina’s main grain-producing regions, as the core strategic areas for ensuring national food security, are currently facing deep structural contradictions such as low development quality and efficiency, and delayed transformation and upgrading. Coordinating the scientific protection and sustainable development of agricultural resources in these major grain-producing areas is not only an urgent need to address current challenges but also an inevitable choice to obey the laws of modern agricultural evolution. Cultivating and strengthening agricultural new quality productive forces has become the core driving force for achieving leapfrog development in agriculture and rural economic and social progress. In the new wave of technological revolution, how to effectively integrate high-level human resources with cutting-edge digital technologies, enhance the comprehensive guarantee capacity of major grain-producing areas, optimize the allocation of production factors, and promote the transformation of these areas from traditional development models to modern, high-quality development paradigms, has become a major theoretical and practical issue that urgently requires in-depth research and resolution [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B4-agriculture-16-00141)].\n\nCurrently, the relevant research progress on digital–real integration and new quality productive forces, both domestically and internationally, is as follows [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B5-agriculture-16-00141),[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B6-agriculture-16-00141),[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B7-agriculture-16-00141)]. Digital–real integration is generally defined as “the penetration and application of digital technology in various aspects of the real economy to achieve their coordinated development [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B8-agriculture-16-00141),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B9-agriculture-16-00141),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B10-agriculture-16-00141),[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B11-agriculture-16-00141),[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B12-agriculture-16-00141)],” specifically covering multiple dimensions such as technology integration, factor integration, product integration, and process integration [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B13-agriculture-16-00141)]. Existing studies have analyzed its spatial–temporal evolution characteristics [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B14-agriculture-16-00141)] and explored its impact on enterprises, industries, and economic development [[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B15-agriculture-16-00141),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B16-agriculture-16-00141),[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B17-agriculture-16-00141)], among other aspects. Additionally, international scholars have systematically elucidated from the perspective of digital transformation how digital technologies reshape corporate business models and value creation processes, emphasizing that the deep integration of digital technologies with traditional business operations serves as a key source of a competitive advantage for enterprises [[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B18-agriculture-16-00141)]. Agricultural new quality productive forces are explained as “representing the leap of agricultural productivity driven by technological innovation [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B19-agriculture-16-00141)], with its core being the innovative transformation of agricultural production factors, production processes, and the organization, division of labor, and cooperation along the industrial chain [[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B20-agriculture-16-00141),[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B21-agriculture-16-00141)].” In terms of measurement, scholars have constructed evaluation indicator systems for agricultural new quality productive forces [[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B22-agriculture-16-00141),[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B23-agriculture-16-00141),[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B24-agriculture-16-00141)] and identified the empowering roles of digital technologies and green energy transformation in their development [[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B25-agriculture-16-00141),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B26-agriculture-16-00141),[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B27-agriculture-16-00141)]. Regarding the relationship between digital–real integration and new quality productive forces, existing studies have begun preliminary exploration at the industry and enterprise levels. From the industry perspective, digital–real integration influences new quality productive forces by promoting the rationalization and upgrading of industrial structures [[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B28-agriculture-16-00141)]. Through deep integration of digital technologies with physical manufacturing, the agility, precision control, and market responsiveness of production processes are enhanced. This drives the systemic digital transformation and upgrading of traditional industries, promotes the optimization and collaborative integration of resource allocation, and provides important support for the cultivation and growth of new quality productive forces [[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B29-agriculture-16-00141)]. From the enterprise level, digital–real integration promotes the development of new quality productive forces by reducing production costs and improving management efficiency [[30](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B30-agriculture-16-00141)]. Enterprises use digital tools to achieve precise operations, agile production, and intelligent supply chains, effectively optimizing processes, controlling costs, and improving efficiency [[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B29-agriculture-16-00141)]. Related international research has found that digitalization strategies can enhance firm performance by optimizing resource allocation, improving operational efficiency, and promoting innovation, thereby providing theoretical support for understanding how digital–real integration drives productivity enhancement [[31](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B31-agriculture-16-00141)].\n\nAlthough the existing literature has conducted preliminary exploration on the relationship between the two based on the 30 provinces (municipalities) of China [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B3-agriculture-16-00141),[32](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B32-agriculture-16-00141),[33](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B33-agriculture-16-00141)], which lays a good theoretical foundation for this article, the following shortcomings still exist: (1) The research objects are relatively broad, lacking focused analysis of the major grain-producing regions; (2) many studies construct evaluation indicator systems for new quality productive forces from the traditional “three labor” (labor, labor materials, labor objects) [[34](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B34-agriculture-16-00141),[35](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B35-agriculture-16-00141),[36](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B36-agriculture-16-00141),[37](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B37-agriculture-16-00141)] perspective, with limited innovation; (3) there remains a research gap in conducting in-depth exploration of the mechanisms through which digital–real integration affects new quality productive forces in agriculture, specifically focusing on the 13 major grain-producing provinces as research subjects. Based on this, this paper takes 13 major grain-producing areas as the research object, with the possible marginal contributions reflected in the following. (1) Focus and deepening of the research perspective: the study can more accurately reveal the practical effects and unique patterns of digital and real integration in core agricultural regions by precisely focusing on the 13 major grain-producing areas in China. (2) Innovation and reconstruction of the evaluation indicator system: this study breaks through the limitations of the traditional “three labor” perspective by constructing a comprehensive evaluation indicator system for agricultural new quality productive forces that aligns with the three main characteristics of “high-technology investment, high-efficiency performance, and high-quality development.” This enables a more scientific and comprehensive measurement of their actual development level. (3) In-depth analysis and expansion of action mechanisms: this study takes agricultural industrial structure upgrading and agricultural green total factor productivity as mediating variables to explore the transmission pathways between digital–real integration and new quality productive forces in agriculture. Furthermore, it conducts spatial effects analysis, heterogeneity analysis, and threshold effects analysis, rendering the mechanism research more systematic and comprehensive.\n\n## 2. Study Site Overview\n\nChina’s major grain-producing regions are key agricultural production zones designated by the government to ensure food security, occupying an irreplaceable and central position in the national food security strategy. These regions encompass 13 provinces: Inner Mongolia, Liaoning, Jilin, Heilongjiang, Jiangsu, Anhui, Jiangxi, Shandong, Henan, Hebei, Hubei, Hunan, and Sichuan. Despite accounting for less than half of the nation’s cultivated land, these areas produce nearly 80% of the country’s grain output and supply over 90% of its marketable grain, demonstrating their exceptional production concentration and supply capacity. They play a decisive role in guaranteeing absolute staple food security, stabilizing grain supply, and maintaining market equilibrium, serving as the “ballast stone” of China’s food security system. Given the strategic position of major grain-producing regions within the national food security framework, investigating the mechanisms through which digital–real integration influences new quality productive forces in agriculture holds significant theoretical value and practical implications for exploring pathways toward agricultural modernization and enhancing comprehensive grain production capacity. Therefore, focusing on major grain-producing regions as research subjects is not only representative and typical but also provides critical policy insights and practical guidance for safeguarding national food security ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#fig_body_display_agriculture-16-00141-f001)).\n\n**Figure 1.** Distribution map of China’s major grain-producing areas.\n\n## 3. Theoretical Analysis and Research Hypotheses\n\n### 3.1. Direct Impact of Digital–Real Integration on Agricultural New Quality Productive Forces\n\nDigital–real integration serves as the core driving force for developing new quality productive forces in agriculture. This process extends beyond mere technological application; it represents a profound convergence of the digital economy and the agricultural real economy at the levels of production factors, operational processes, and organizational structures. Its impact manifests across three critical dimensions. First, from the high-tech advancement perspective, digital infrastructure development and agricultural digitalization provide innovative pathways for knowledge generation and dissemination in agriculture. Technologies, such as the Internet of Things (IoT) and remote sensing monitoring, establish comprehensive data perception networks spanning the entire agricultural production cycle, enabling technological innovation to shift from experience-based accumulation to precision-driven iteration grounded in real-time data analytics. This not only directly elevates agricultural technological capabilities but, more importantly, stimulates innovation capacity across diverse business entities by lowering research and development barriers and accelerating knowledge diffusion [[38](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B38-agriculture-16-00141)]. Second, from the high-quality development dimension, digital tools enable refined management of the relationship between agricultural production and resource environments. Precision farming technologies, including variable-rate fertilization and intelligent irrigation systems, dynamically adjust inputs based on crop requirements and environmental conditions, fundamentally reducing excessive application of fertilizers and pesticides and directly mitigating non-point source pollution. Simultaneously, comprehensive data traceability and monitoring systems provide credible management tools for green business models, such as ecological and circular agriculture, driving the transformation of agricultural production toward environmental sustainability [[39](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B39-agriculture-16-00141)]. Third, from the high-efficiency dimension, the digitalization of industries and industrial digitalization reshape value creation and distribution mechanisms in agriculture. Emerging business models, such as e-commerce platforms and smart logistics systems, shorten the farm-to-table distance and enhance circulation efficiency, while technologies, like big data and blockchain, strengthen trust and coordination across all links of the industrial chain, promoting deep integration of primary, secondary, and tertiary industries in rural areas. This systematic collaborative optimization transforms agricultural growth from dependence on yield increases in isolated segments to value appreciation through efficiency improvements across the entire industrial chain [[40](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F2\u002F141#B40-agriculture-16-00141)]. In summary, digital–real integration systematically establishes the foundation for developing new quality productive forces in agriculture by infusing data elements, optimizing production processes, and restructuring industrial organization. Based on this, Hypothesis 1 is proposed:\n\n**Hypothesis 1.**\n\nDigital–real integration can effectively promote the development of agricultural new quality productive forces.\n\n### 3.2. Indirect Impact of Digital–Real Integration on Agricultural New Quality Productive Forces\n\nIn the transmission mechanism through which digital–real integration influences new quality productive forces in agriculture, this study identifies agricultural industrial structure and agricultural green total factor productivity as key mediating variables. Theoretically, the formation of new quality productive forces is inseparable from “structural optimization” and “efficiency enhancement”: industrial structural upgrading embodies profound transformations in production organization methods and resource allocation patterns, while green total factor productivity encompasses elements such as technological progress, efficiency improvement, and environmental protection. Compared to single-factor inputs or traditional productivity indicators, these two variables offer a more comprehensive depiction of the pathways through which digital–real integration drives the development of agricultural new quality productive forces, examining the process from the perspectives of “industrial structural reshaping” and “factor efficiency improvement,” respectively.\n\n#### 3.2.1. Digital–Real Integration, Agricultural Industrial Structure Upgrading, and Agricultural New Quality Productive Forces\n\nAgricultural industrial structure reflects the composition and proportional relationships among various industries within agriculture and serves as an important bridge linking digital–real integration with agricultural new quality productive forces. On the one hand, digital–real integration supports the optimization and upgrading of agricultural industrial structure through promoting agricultural digital transformation. The application of digital tec","Agriculture (MDPI) 2026-01-21","2026-01-21T00:00:00Z","论文",10,false,76,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":20,"comment":21},22,18,13,1,"基于13个粮食主产区实证研究，探讨数字融合对农业新质生产力的影响，数据扎实，但发表时间较早，时效性低。",[23],{"name":10,"url":6},[25,26,27,28,29],"农业新质生产力","数字经济","实体经济","粮食主产区","MDPI Agriculture",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,55,60],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},3,true,[],[48],"空间计量",[],{"code":51,"score":20,"supported":14,"title":52,"abstract":53,"tags":54},"A8",[],[],[28],{"code":56,"score":20,"supported":14,"title":57,"abstract":58,"tags":59},"I6",[],[],[26],{"code":61,"score":20,"supported":14,"title":62,"abstract":63,"tags":64},"J2",[],[],[25],[66,67],"农业新质生产力 粮食主产区 实体经济 数字经济","农业新质生产力 粮食主产区","农业新质生产力粮食主产区实体经济数字经济-223",9,"2026-08-04T23:54:47.725900Z",{"total":72,"page":20,"page_size":72,"items":73},6,[74,144,223,291,354,410],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":8,"content":8,"source_name":79,"source_url":8,"published_at":80,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":88,"tags":90,"primary_topic":95,"secondary_topics":96,"classification":97,"search_phrases":128,"slug":131,"view_count":132,"doi":8,"paper":133,"created_at":143},3320,"人工智能对农业新质生产力的驱动机制与效应——基于278个地级市面板数据（2010—2024）","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165477.html","《农业经济与管理》2026年第03期。钟文、郑林泽、严芝清（江西理工大学）研究表明：人工智能技术创新对农业新质生产力具有显著直接提升效应。机制检验表明，人工智能主要通过强化农业科技创新能力、激发农村地区创业活力、深化农业生产数字化水平三条核心路径驱动农业新质生产力发展。异质性分析表明，人工智能的促进作用具有普遍性但存在显著梯度差异，受区域资源禀赋、发展阶段及农业生产模式多样性深刻影响。空间计量检验进一步发现，人工智能技术创新存在显著正向空间溢出效应，但农村人力资本水平构成关键调节变量，其不足将显著制约溢出效能的有效释放。","《农业经济与管理》2026年第03期","2026-09-17T00:00:00Z",86,{"impact":17,"substance":83,"depth":84,"authority":85,"freshness":86,"relevant":20,"comment":87},23,19,14,8,"基于278个地级市面板数据的实证研究，方法规范、结论有新意，对农业AI政策制定具参考价值，值得进入每日精选。",[89],{"name":79,"url":77},[91,25,92,93,94],"农业人工智能","农业数字化","空间溢出效应","农村创业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":98},{"candidates":99,"agricultural_evidence":45},[100,105,110,114,119,124],{"code":31,"score":101,"supported":45,"title":102,"abstract":103,"tags":104},4,[],[48],[93],{"code":106,"score":20,"supported":14,"title":107,"abstract":108,"tags":109},"G4",[],[],[94],{"code":35,"score":20,"supported":14,"title":111,"abstract":112,"tags":113},[],[],[92],{"code":115,"score":20,"supported":14,"title":116,"abstract":117,"tags":118},"I1",[],[],[92],{"code":120,"score":20,"supported":14,"title":121,"abstract":122,"tags":123},"I2",[],[],[91],{"code":61,"score":20,"supported":14,"title":125,"abstract":126,"tags":127},[],[],[25],[129,130],"人工智能 农业新质生产力 地级市面板","农业新质生产力 农业人工智能 空间溢出效应 农业数字化","人工智能农业新质生产力地级市面板-3320",5,{"doi":8,"openalex_id":8,"authors":134,"venue":8,"cited_by_count":135,"oa_url":8,"card":136,"direction":140,"ingested_from":142},[],0,{"tldr":137,"method":138,"finding":139,"direction":140,"opportunity":141},"基于278个地级市面板数据，实证检验人工智能对农业新质生产力的驱动机制与空间溢出效应。","278个地级市2010—2024年面板数据，机制检验、异质性分析与空间计量模型。","人工智能显著直接提升农业新质生产力，经三条路径驱动，并具正向空间溢出，但受农村人力资本调节制约。","数字乡村与农业信息化","可深入探究农村人力资本门槛下AI空间溢出的非线性衰减机制及跨区域协同提升路径。","agent","2026-09-24T00:04:02.275354Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":8,"source_name":150,"source_url":147,"published_at":151,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":156,"tags":158,"primary_topic":164,"secondary_topics":165,"classification":166,"search_phrases":201,"slug":204,"view_count":135,"doi":205,"paper":206,"created_at":222},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":86,"substance":18,"depth":85,"authority":154,"freshness":69,"relevant":20,"comment":155},12,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[157],{"name":150,"url":147},[159,160,161,162,163],"遥感","土地利用","土壤侵蚀","GIS","水土保持",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":167},{"candidates":168,"agricultural_evidence":45},[169,175,182,189,195],{"code":31,"score":13,"supported":45,"title":170,"abstract":171,"tags":174},[],[172,162,173],"地理信息","geographic information",[162],{"code":176,"score":177,"supported":45,"title":178,"abstract":179,"tags":181},"I3",7,[],[159,180],"remote sensing",[159],{"code":183,"score":72,"supported":45,"title":184,"abstract":185,"tags":188},"H2",[],[186,187],"土壤","soil",[],{"code":190,"score":44,"supported":45,"title":191,"abstract":192,"tags":194},"H3",[],[193],"灌溉",[],{"code":196,"score":197,"supported":14,"title":198,"abstract":199,"tags":200},"H6",2,[],[],[160,163],[202,203],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":205,"openalex_id":207,"authors":208,"venue":150,"cited_by_count":135,"oa_url":8,"card":214,"direction":220,"ingested_from":221},"W7223117733",[209,211],{"name":210,"orcid":8},"MERİÇ KURT",{"name":212,"orcid":213},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":215,"method":216,"finding":217,"direction":218,"opportunity":219},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","农业遥感与作物表型","openalex","2026-10-22T11:20:39.475037Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":8,"source_name":229,"source_url":226,"published_at":230,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":135,"score_detail":231,"sources":233,"tags":235,"primary_topic":239,"secondary_topics":243,"classification":245,"search_phrases":268,"slug":271,"view_count":135,"doi":272,"paper":273,"created_at":290},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":135,"substance":135,"depth":135,"authority":135,"freshness":135,"relevant":135,"comment":232},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[234],{"name":229,"url":226},[236,237,160,238],"机器学习","遥感监测","城市扩张",{"code":120,"slug":240,"name":241,"path":242,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[244],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":246},{"candidates":247,"agricultural_evidence":45},[248,254,260,264],{"code":120,"score":249,"supported":45,"title":250,"abstract":252,"tags":253},15,[251],"machine learning",[236,251],[236],{"code":31,"score":69,"supported":45,"title":255,"abstract":256,"tags":259},[],[257,162,258],"空间分析","spatial analysis",[],{"code":176,"score":177,"supported":45,"title":261,"abstract":262,"tags":263},[],[159,180],[237],{"code":196,"score":20,"supported":14,"title":265,"abstract":266,"tags":267},[],[],[160],[269,270],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":272,"openalex_id":274,"authors":275,"venue":229,"cited_by_count":135,"oa_url":226,"card":285,"direction":220,"ingested_from":221},"W7223022155",[276,279,281,283],{"name":277,"orcid":278},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":280,"orcid":8},"Goanar Jal Dup",{"name":282,"orcid":8},"Weyesa Merga Chemeda",{"name":284,"orcid":8},"Shankar Karuppannan",{"tldr":286,"method":287,"finding":288,"direction":220,"opportunity":289},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":292,"title":293,"url":294,"summary":295,"summary_zh":296,"content":8,"source_name":297,"source_url":294,"published_at":298,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":135,"score_detail":299,"sources":301,"tags":303,"primary_topic":307,"secondary_topics":311,"classification":319,"search_phrases":336,"slug":339,"view_count":135,"doi":340,"paper":341,"created_at":353},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":135,"substance":135,"depth":135,"authority":135,"freshness":135,"relevant":135,"comment":300},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[302],{"name":297,"url":294},[304,237,305,306],"防灾减灾","洪水风险评估","GIS应用",{"code":176,"slug":308,"name":309,"path":310,"parent_code":35},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[312,313],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":314,"slug":315,"name":316,"path":317,"parent_code":318},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":38,"locked":14,"version":39,"method":40,"evidence":320},{"candidates":321,"agricultural_evidence":45},[322,326,330],{"code":176,"score":249,"supported":45,"title":323,"abstract":324,"tags":325},[180],[159,180],[237],{"code":31,"score":85,"supported":45,"title":327,"abstract":328,"tags":329},[162],[172,162],[],{"code":314,"score":86,"supported":45,"title":331,"abstract":332,"tags":335},[],[333,334],"干旱","drought",[305,304],[337,338],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":340,"openalex_id":342,"authors":343,"venue":297,"cited_by_count":135,"oa_url":347,"card":348,"direction":220,"ingested_from":221},"W7222885988",[344],{"name":345,"orcid":346},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":349,"method":350,"finding":351,"direction":220,"opportunity":352},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":8,"source_name":360,"source_url":357,"published_at":298,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":361,"score_detail":362,"sources":364,"tags":366,"primary_topic":369,"secondary_topics":370,"classification":371,"search_phrases":386,"slug":389,"view_count":135,"doi":390,"paper":391,"created_at":409},5323,"Impact of Land Use and Land Cover Change on Vegetation Cover Loss in Rwanda: Evidence from Muhoza Sector, Musanze District (2015–2025)","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26oct696","This study analyzed the impact of land use and land cover (LULC) change on vegetation cover loss in Muhoza Sector, Musanze District, Rwanda, between 2015 and 2025. A quantitative geospatial research design was adopted using multi-temporal Landsat and Sentinel-2 satellite imagery, field-based GPS validation, Geographic Information Systems, and Normalized Difference Vegetation Index (NDVI) analysis. Six land-cover classes—forestland, grassland, cropland, urbanized land, wetland, and water bodies—were mapped using supervised classification and post-classification change detection.","本研究分析了2015年至2025年间卢旺达穆桑泽区穆霍扎地区土地利用与土地覆盖（LULC）变化对植被覆盖丧失的影响。研究采用定量地理空间研究设计，运用多时相Landsat和Sentinel-2卫星影像、基于实地GPS验证、地理信息系统以及归一化植被指数（NDVI）分析。通过监督分类和分类后变化检测，对林地、草地、耕地、城市化用地、湿地和水体六类土地覆盖类型进行了制图。","International Journal of Innovative Science and Research Technology (IJISRT)",55,{"impact":86,"substance":18,"depth":85,"authority":72,"freshness":69,"relevant":20,"comment":363},"基于Landsat与Sentinel-2多时相影像的卢旺达局部土地利用变化与植被损失实证研究，方法规范但属区域案例，国际参考价值有限。",[365],{"name":360,"url":357},[237,160,367,368],"植被覆盖","卢旺达农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":372},{"candidates":373,"agricultural_evidence":45},[374,378,382],{"code":31,"score":72,"supported":45,"title":375,"abstract":376,"tags":377},[],[172,173],[],{"code":176,"score":197,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[367,237],{"code":196,"score":20,"supported":14,"title":383,"abstract":384,"tags":385},[],[],[160],[387,388],"Muhoza Sector 土地利用","卢旺达 NDVI 植被覆盖","MuhozaSector土地利用-5323","10.38124\u002Fijisrt\u002F26oct696",{"doi":390,"openalex_id":392,"authors":393,"venue":360,"cited_by_count":135,"oa_url":403,"card":404,"direction":220,"ingested_from":221},"W7222817253",[394,396,398,400],{"name":395,"orcid":8},"Vincent Hategekimana",{"name":397,"orcid":8},"Richard Mind J. E.",{"name":399,"orcid":8},"Aboubakar Gasirabo",{"name":401,"orcid":402},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":405,"method":406,"finding":407,"direction":220,"opportunity":408},"分析卢旺达Muhoza地区2015–2025年土地利用变化对植被覆盖丧失的影响。","多时相Landsat与Sentinel-2影像、GPS实地验证、GIS与NDVI","六类地物制图揭示建设用地扩张等LULC变化是植被覆盖减少的主要驱动。","可结合高分辨率时序遥感与驱动因子模型，量化小农户尺度城镇化对耕地与植被的胁迫。","2026-10-19T23:30:23.040351Z",{"id":411,"title":412,"url":413,"summary":414,"summary_zh":415,"content":8,"source_name":416,"source_url":413,"published_at":417,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":418,"score_detail":419,"sources":421,"tags":423,"primary_topic":427,"secondary_topics":428,"classification":430,"search_phrases":459,"slug":462,"view_count":135,"doi":463,"paper":464,"created_at":482},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex","2026-10-18T00:00:00Z",60,{"impact":86,"substance":18,"depth":249,"authority":13,"freshness":69,"relevant":20,"comment":420},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[422],{"name":416,"url":413},[159,162,424,425,426],"农用地适宜性","土地覆被","孟加拉农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[429],{"code":176,"slug":308,"name":309,"path":310,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":431},{"candidates":432,"agricultural_evidence":45},[433,437,441,448,455],{"code":31,"score":18,"supported":45,"title":434,"abstract":435,"tags":436},[162],[172,162,173],[162],{"code":176,"score":249,"supported":45,"title":438,"abstract":439,"tags":440},[180],[159,180],[159],{"code":442,"score":72,"supported":45,"title":443,"abstract":444,"tags":447},"A1",[],[445,446],"稻米","rice",[],{"code":449,"score":72,"supported":45,"title":450,"abstract":451,"tags":454},"G",[],[452,453],"农村发展","rural development",[],{"code":183,"score":72,"supported":45,"title":456,"abstract":457,"tags":458},[],[186,187],[],[460,461],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":463,"openalex_id":465,"authors":466,"venue":476,"cited_by_count":135,"oa_url":413,"card":477,"direction":220,"ingested_from":221},"W7222895468",[467,469,471,473],{"name":468,"orcid":8},"Raju Ahmed",{"name":470,"orcid":8},"Shaikh Mehdee Mohammad",{"name":472,"orcid":8},"Ummul Momanin Coalee",{"name":474,"orcid":475},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":478,"method":479,"finding":480,"direction":220,"opportunity":481},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z"]