[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1145":3,"related-1145":53},{"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":23,"tags":25,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":47,"slug":50,"view_count":51,"doi":8,"paper":8,"created_at":52},1145,"PNS-WINGS-AHP–EWM集成框架:数字农业多准则决策分析方法","https:\u002F\u002Fwww.mdpi.com\u002F2073-8994\u002F18\u002F9\u002F1435","山东农业大学经济管理学院与山东女子大学商学院发表于《Symmetry》2026年第18卷第9期1435(7月7日投稿、8月21日修订、8月25日接收、8月27日发表)。针对数字农业多准则决策分析(MCDA)中难以表示不确定专家判断、捕捉因子间影响结构、整合互补权重信息的难题,构建集成Pythagorean Neutrosophic Sets(PNS)–Weighted Influence Non-linear Gauge System(WINGS)–Analytic Hierarchy Process(AHP)–Entropy Weight Method(EWM)框架。应用于数字农业案例识别关键影响因素并确定发展优先级,结果显示经济发展水平(W1)排名第一,其次是数字技术水平(W6)和数字基础设施(W5);WINGS分析揭示因子间感知影响结构,数字基础设施(W5)为网络中最强净影响因子。",null,"Loading [MathJax]\u002Fjax\u002Foutput\u002FHTML-CSS\u002Fjax.js\nJournals Topics Information Author Services Initiatives About\nSign In \u002F Sign Up Submit\n \nSearch for Articles:\nSymmetry\nAll Article Types\nAdvanced\n \nJournals  Symmetry  Volume 18  Issue 9  10.3390\u002Fsym18091435 \nSubmit to this Journal Review for this Journal Propose a Special Issue\nArticle Menu\nAcademic Editor\nJie Yang\nRecommended Articles\nRelated Info Link\nMore by Authors Links\nArticle Views\n104\nTable of Contents\nAbstract\nIntroduction\nLiterature Review\nFundamental Concepts and Procedures\nPythagorean Neutrosophic WINGS-AHP–EWM Combination Method\nResults\nDiscussion\nConclusions\nAuthor Contributions\nFunding\nData Availability Statement\nConflicts of Interest\nAppendix A\nAppendix B\nReferences\nshare\nShare\nannouncement\nHelp\nformat_quote\nCite\nquestion_answer\nDiscuss in SciProfiles\nfirst_page\nsettings\nOrder Article Reprints\nOpen AccessArticle\nPNS-WINGS-AHP–EWM Integrated Framework for Multi-Criteria Decision Analysis: An Application-Oriented Decision Framework in Digital Agriculture\nby Yawen Wang 1 andKecheng Zhang 2,*\n1\nCollege of Economics and Management, Shandong Agricultural University, Tai’an 271018, China\n2\nSchool of Business Administration, Shandong Women’s University, Jinan 250300, China\n*\nAuthor to whom correspondence should be addressed.\nSymmetry 2026, 18(9), 1435; https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18091435\nSubmission received: 7 July 2026 \u002F Revised: 21 August 2026 \u002F Accepted: 25 August 2026 \u002F Published: 27 August 2026\n(This article belongs to the Special Issue Symmetry\u002FAsymmetry in Fuzzy Multi-Criteria Decision-Making: Application in Management and Engineering)\n\nDownload\nkeyboard_arrow_down\n Browse Figures Versions Notes\n\nAbstract\nDigital-agriculture development increasingly requires multi-criteria decision analysis (MCDA) approaches to evaluate complex and interrelated influencing factors. However, existing methods often face difficulties in representing uncertain expert judgments, capturing inter-factor influence structures, and integrating complementary weighting information. To address these challenges, this study develops an integrated Pythagorean Neutrosophic Sets (PNS)–Weighted Influence Non-linear Gauge System (WINGS)–Analytic Hierarchy Process (AHP)–Entropy Weight Method (EWM) framework for digital-agriculture factor evaluation. Specifically, PNS is employed to represent uncertain linguistic assessments through truth, indeterminacy, and falsity information; WINGS is applied to identify the direction and intensity of expert-perceived influence relationships among factors; and a coordinated weighting mechanism integrates AHP-based preference information with entropy-based cross-expert dispersion information. The proposed framework is applied to a digital-agriculture case to identify key influencing factors and determine development priorities. The results indicate that Economic Development Level (W1) ranks first in the final composite priority assessment, followed by Digital Technology Level (W6) and Digital Infrastructure (W5). Furthermore, the WINGS analysis reveals the perceived influence structure among factors and identifies Digital Infrastructure (W5) as the strongest net-influencing factor within the network. Comparative analyses suggest that the proposed framework provides a systematic representation of uncertainty, influence relationships, and complementary weighting information for digital-agriculture factor evaluation. This study highlights the application potential of integrating established MCDM techniques to support complex agricultural decision-making problems.\nKeywords: multi-criteria decision analysis; Pythagorean neutrosophic sets; weighted influence nonlinear gauge system; Analytic Hierarchy Process; Entropy Weight Method; digital agriculture\n\n1. Introduction\nDigital agriculture, emerging from the convergence of information technology and agricultural production, has become an important driver of agricultural transformation worldwide. Meanwhile, population growth and accelerated urbanization continue to increase the demand for agricultural products. However, agricultural systems face multiple challenges, including limited land availability, labor constraints, climate variability, and changing socioeconomic conditions. These challenges have accelerated the transition toward digitalization, intelligent management, and sustainable development, positioning digital agriculture as an important topic in both scientific research and policy discussions [1,2].\nTo support this transition, extensive studies have investigated the factors influencing digital-agriculture development. Existing research has identified several important determinants, including economic development level, social capital participation, policy support, human capital quality, digital infrastructure, digital technology level, operational scale, profitability, and openness to global markets. Understanding the relative importance of these factors is essential for designing effective development strategies. However, existing analytical approaches still face several limitations that restrict their ability to provide comprehensive decision support.\nFirst, many studies analyze influencing factors separately and treat them as independent criteria within conventional multi-criteria decision-making (MCDM) frameworks [3,4]. Such approaches may overlook the complex interactions, feedback effects, and interdependencies among factors in digital-agriculture systems. Consequently, the resulting rankings often provide a static evaluation of factor importance but may not reveal the relative influence positions of different factors during the digital transformation process [5]. Second, many existing methods rely on precise numerical evaluations and provide limited support for representing uncertainty, hesitation, and inconsistency in expert judgments [6]. As expert assessments of digital-agriculture development often involve subjective perceptions and incomplete information, directly converting linguistic judgments into crisp values may reduce the richness of the original evaluation information [7].\nDespite considerable methodological progress, existing studies remain fragmented across four interconnected analytical dimensions. At the information representation level, expert judgments are frequently transformed into numerical scores, which may fail to preserve linguistic uncertainty and indeterminacy. At the structural analysis level, conventional weighting and ranking approaches generally assume criterion independence and therefore have limited capability to capture direct, indirect, and self-influence relationships among digital-agriculture factors [3,4,5]. At the weighting level, the Analytic Hierarchy Process (AHP) incorporates expert preferences but may be affected by subjective judgment, whereas the Entropy Weight Method (EWM) reflects data variation but does not consider expert preference information or network relationships [8,9]. At the integration level, uncertainty representation, influence analysis, and weight determination are often conducted separately. Therefore, existing approaches may identify factor importance or influence structures individually, but they rarely establish a continuous analytical process connecting uncertain expert assessments, inter-factor relationships, and final priority determination.\nMotivated by these limitations, this study integrates Pythagorean Neutrosophic Sets (PNS), the Weighted Influence Non-linear Gauge System (WINGS), AHP, and EWM into a sequential and functionally differentiated analytical framework. Rather than simply combining multiple techniques, the proposed framework assigns each method a specific analytical role. PNS is used at the input stage to represent linguistic evaluations through truth, indeterminacy, and falsity components. After aggregation and score transformation, the resulting influence matrix is used as the input for WINGS, which analyzes direct, indirect, and self-influence relationships and identifies network prominence and net-influence positions. Subsequently, AHP and EWM provide complementary perspectives on factor importance: AHP reflects expert preference information, whereas EWM captures cross-expert dispersion information derived from the individual AHP priority distributions. Their results are integrated through a combination-weighting procedure, and the resulting weights are further linked with WINGS-based network prominence to generate composite priorities.\nThe contribution of this study lies in establishing and applying this connected analytical workflow for digital-agriculture factor evaluation rather than developing entirely new individual methods. The framework provides both an expert-perceived influence structure and a composite priority ranking. Moreover, the potential effects of multi-stage processing, including possible information compression, are examined through subsequent comparative and sensitivity analyses.\nThe remainder of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 introduces the fundamental concepts of PNS. Section 4 presents the integrated methodology. Section 5 reports the empirical results and analysis. Section 6 discusses the comparative analysis, theoretical implications, policy implications, limitations, and future research directions. Finally, Section 7 summarizes the main findings and conclusions of this study.\n2. Literature Review\n2.1. Evolution of Digital Agriculture and Its Influencing Factors\nCertain stages in agricultural development have involved profound transformations and have therefore been described as agricultural revolutions, such as the Green Revolution [10,11]. The emergence of digital technologies is expected to promote a new stage of agricultural transformation by improving the efficiency, sustainability, and management capability of both crop production and livestock systems. This transition has attracted increasing attention from researchers, policymakers, and practitioners [12].\nThe concept of digital agriculture was introduced in 1997 as a data-driven agricultural development approach that integrates geospatial technologies and information-based systems [13]. By leveraging digital tools and resources, digital agriculture emphasizes the application of digital technologies throughout the agricultural production process, with the objectives of improving productivity, increasing farmers’ income, and enhancing the competitiveness of agricultural products [14]. In recent years, digital agriculture has also been associated with concepts such as precision agriculture and smart farming, which originated from technology-enabled crop management practices in the 1990s [15,16]. Accordingly, researchers have proposed various definitions of digital agriculture from different perspectives, reflecting its multidisciplinary characteristics [17,18,19].\nRecent studies have increasingly focused on identifying the factors that influence digital-agriculture development. Existing research has highlighted the importance of technological progress, economic conditions, digital infrastructure, policy support, operational scale, profitability, social capital participation, human capital quality, and market openness. Specifically, Shepherd emphasized the role of technological advancement in transforming traditional agricultural management models [20]. Shen demonstrated that regional economic conditions significantly influence the development level of digital agriculture [21]. Luo highlighted the importance of digital infrastructure in supporting digital transformation [22], while Tian examined the role of governmental policy support in facilitating agricultural digitalization [23]. Chen further suggested that the adoption of digital technologies is closely associated with the expansion of agricultural operation scale [24]. In addition, other studies have identified profitability, global market openness, social capital participation, and human capital quality as important determinants of digital-agriculture development [25,26,27,28].\nAlthough previous studies have identified numerous influencing factors, an important research question remains: how can the relative importance of these factors and their interrelationships be systematically evaluated? Existing studies often examine individual factors separately, providing limited insights into the interactions among multiple dimensions of digital-agriculture development. Therefore, a comprehensive quantitative framework is required to identify key factors and reveal their structural relationships. Furthermore, because evaluations of digital-agriculture development frequently involve expert knowledge, incomplete information, and subjective judgment, decision-making approaches capable of handling uncertainty are needed.\nIn this study, the decision problem is defined as a regional-level prioritization problem within the Chinese digital-agriculture context. The evaluation object is not an individual farm, enterprise, or specific technology project, but rather the regional enabling environment that supports digital-agriculture development. The primary decision stakeholders are regional agricultural authorities and public development agencies, which need to determine the relative priorities of policy attention, infrastructure investment, capability development, and implementation resources. The resulting ranking aims to support medium-term planning over an approximately three-to-five-year horizon by identifying structural development priorities rather than predicting short-term agricultural performance or determining specific budget allocations.\n2.2. Methodological Foundation: From Fuzzy Sets to Pythagorean Neutrosophic Sets\nTo address uncertainty in complex decision-making problems, fuzzy set theory has been widely adopted as an effective approach for representing imprecise information. Zadeh introduced fuzzy sets in 1965, using membership functions to describe uncertain and vague information [29]. However, because fuzzy sets rely on a single membership degree, they may have limited capability in representing situations involving hesitation or conflicting information [30].\nTo overcome this limitation, Atanassov proposed intuitionistic fuzzy sets (IFS), which extend fuzzy sets by incorporating membership and non-membership degrees as well as hesitation information [31]. Nevertheless, IFS requires that the sum of membership and non-membership degrees does not exceed one, which may restrict its applicability in certain complex decision environments [32,33]. Subsequently, Yager introduced Pythagorean fuzzy sets (PFS), allowing the squared sum of membership and non-membership degrees to satisfy a less restrictive condition and thereby expanding the representation space of uncertain information [34,35,36,37]. Meanwhile, Smarandache proposed neutrosophic set theory, which explicitly introduces an indeterminacy dimension in addition to truth and falsity memberships, enabling a more comprehensive description of uncertain and incomplete information [38,39,40,41].\nBuilding on these developments, Jansi et al. introduced the concept of Pythagorean neutrosophic sets (PNS) in 2019 [7]. PNS integrates truth, falsity, and indeterminacy components, providing a flexible representation of information that involves uncertainty, ambiguity, and incomplete knowledge [42,43]. This characteristic is particularly relevant for digital-agriculture factor evaluation, where expert judgments regarding factor relationships may contain both supportive and uncertain perceptions. Compared with conventional fuzzy sets and intuitionistic fuzzy sets, PNS provides a broader representation space by explicitly incorporating indeterminacy information [44]. Therefore, PNS has been applied in various uncertain decision-making contexts, including multi-criteria decision analysis, artificial intelligence, and decision-support systems [45,46,47].\nExisting PNS research has gradually expanded from theoretical development toward practical applications. Early studies mainly focused on mathematical properties and operational rules [48,49], whereas recent studies have explored its application in complex decision-support problems [50]. By retaining multiple dimensions of uncertain information, PNS provides a useful foundation for decision frameworks that require the representation of expert uncertainty.\nRecent MCDM studies have also explored the integration of uncertainty modeling and structural analysis. Muyulema-Allaica et al. combined FAHP with DEMATEL to identify causal and effect factors in industrial symbiosis resource optimization [51]. Petrović et al. incorporated triangular fuzzy numbers into AHP to address uncertainty in weight determination under expert subjectivity [52]. Turgay et al. developed a decision model based on the T-spherical hesitant fuzzy rough set to represent incomplete and conflicting information using truth, falsity, and indeterminacy dimensions [53]. These studies demonstrate the importance of uncertainty-aware decision models. However, existing approaches generally focus on specific analytical aspects, such as uncertainty representation or influence analysis, rather than integrating uncertainty modeling, influence-network analysis, and hybrid weighting within a unified decision process.\nTo clarify the methodological positioning of the proposed framework, Table 1 compares representative MCDM approaches from the perspectives of uncertainty representation, influence analysis, and weight determination.\nTable 1. Analytical comparison of representative MCDM methods related to uncertainty quantification, influence-structure analysis, and hybrid weighting.\n2.3. Analytical Methods: WINGS, AHP, and EWM\nThe Decision-Making Trial and Evaluation Laboratory (DEMATEL) is a widely used MCDM method for analyzing complex systems through influence relationship modeling [54]. Building upon DEMATEL, Michnik proposed the Weighted Influence Non-linear Gauge System (WINGS), which extends the analysis by incorporating self-influence and considering broader network characteristics beyond pairwise relationships [55]. Previous studies have applied WINGS to analyze complex inter-factor relationships and identify influential elements in different decision contexts. For example, Wang et al. demonstrated its capability in analyzing interconnected factors and their internal relationships [56]. Kannan et al. applied WINGS to prioritize barriers in decision systems [57], while Kumar et al. used it to investigate factors affecting blockchain technology adoption in supply chains [58]. However, WINGS generally requires numerical input information and therefore has limited capability in directly handling linguistic uncertainty [59].\nThe Analytic Hierarchy Process (AHP) is a classical MCDM approach that combines qualitative judgments with quantitative analysis and has been widely applied in complex decision-making problems since the 1970s [60,61]. By incorporating expert preferences, AHP provides an effective approach for subjective weight determination. However, its results may be influenced by expert judgment and consistency issues [8].\nIn contrast, the Entropy Weight Method (EWM) determines indicator weights according to information entropy and the dispersion of observations [9]. Although EWM is commonly classified as an objective weighting method because its weights follow predefined mathematical rules once the input matrix is fixed, the informational meaning of the resulting weights depends on the nature of that input matrix. In the present study, the EWM input consists of expert-specific AHP priority distributions; therefore, EWM is interpreted as extracting cross-expert dispersion information rather than as introducing an independent objective data source. EWM has been applied in various evaluation and optimization problems, including industrial efficiency assessment, risk evaluation, ","《Symmetry》2026, 18(9), 1435","2026-08-27T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,18,4,1,"论文提出数字农业多准则决策新框架，方法新颖，但时效性稍差。",[24],{"name":10,"url":6},[26,27,28],"数字农业","多准则决策","Pythagorean Neutrosophic Sets Symmetry",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":43},[42],{"code":30,"score":17,"supported":43,"title":44,"abstract":45,"tags":46},true,[26],[26],[26],[48,49],"多准则决策 数字农业 Pythagorean Neutrosophic Sets Symmetry","多准则决策 数字农业","多准则决策数字农业PythagoreanNeutrosophicSetsSymm-1145",2,"2026-08-31T00:08:57.223880Z",{"total":54,"page":21,"page_size":54,"items":55},6,[56,113,170,215,270,322],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":8,"content":8,"source_name":61,"source_url":59,"published_at":62,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":63,"score_detail":64,"sources":67,"tags":69,"primary_topic":73,"secondary_topics":74,"classification":75,"search_phrases":89,"slug":92,"view_count":93,"doi":94,"paper":95,"created_at":112},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square","Research Square","2026-10-21T00:00:00Z",38,{"impact":65,"substance":54,"depth":65,"authority":54,"freshness":13,"relevant":21,"comment":66},8,"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[68],{"name":61,"url":59},[26,70,71,72],"农业信息化","遥感","地图评估",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":76},{"candidates":77,"agricultural_evidence":43},[78,84],{"code":30,"score":79,"supported":43,"title":80,"abstract":82,"tags":83},13,[81],"digital agriculture",[81],[70,26],{"code":85,"score":21,"supported":14,"title":86,"abstract":87,"tags":88},"I3",[],[],[71],[90,91],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579",0,"10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":94,"openalex_id":96,"authors":97,"venue":61,"cited_by_count":93,"oa_url":59,"card":8,"direction":110,"ingested_from":111},"W7223099987",[98,101,104,107],{"name":99,"orcid":100},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":102,"orcid":103},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":105,"orcid":106},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":108,"orcid":109},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","openalex","2026-10-22T11:20:47.463814Z",{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":8,"source_name":119,"source_url":116,"published_at":62,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":126,"tags":128,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":152,"slug":155,"view_count":93,"doi":156,"paper":157,"created_at":169},5578,"Unlocking digital agriculture through new quality productive forces: nonlinear thresholds and spatial spillovers in China","https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-09510-z","New quality productive forces (NQPF) critically enable agricultural digitisation. Using China as a case study, this paper employs panel data from 284 prefecture-level cities in China from 2011 to 2022. The results show that NQPF has a significant promoting effect on agricultural digital transformation and exhibits positive spatial spillover effects, driving not only local transformation but also the development of surrounding regions. Labour skills and innovation capacity play mediating roles in this process. Notably, the enabling effect of NQPF on agricultural digitisation displays significant regional heterogeneity. Threshold analysis indicates that its promoting effect follows a non-linear pattern, characterised by a double threshold effect. Therefore, the development of NQPF should be accelerated, with emphasis placed on regional coordination and talent cultivation, to promote the sustained and in-depth advancement of agricultural digital transformation in accordance with local conditions.","新质生产力（New Quality Productive Forces, NQPF）对农业数字化具有关键赋能作用。本文以中国为案例，采用2011—2022年中国284个地级市的面板数据。结果表明，新质生产力对农业数字化转型具有显著促进作用，并呈现正向空间溢出效应，不仅推动本地转型，也带动周边地区发展。劳动技能与创新能力在这一过程中发挥中介作用。值得注意的是，新质生产力对农业数字化的赋能效应表现出显著的区域异质性。门槛分析表明，其促进作用呈非线性特征，表现为双重门槛效应。因此，应加快新质生产力发展，注重区域协调与人才培养，因地制宜推动农业数字化转型持续深入推进。","Humanities and Social Sciences Communications",81,{"impact":19,"substance":122,"depth":19,"authority":123,"freshness":124,"relevant":21,"comment":125},22,14,9,"基于284个地级市面板数据的实证研究，揭示新质生产力对农业数字化的非线性门槛与空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[127],{"name":119,"url":116},[26,129,130,131,132],"新质生产力","空间溢出","区域协调","农业数字化转型",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":136},{"candidates":137,"agricultural_evidence":43},[138,142,147],{"code":30,"score":13,"supported":43,"title":139,"abstract":140,"tags":141},[81],[],[132,26],{"code":143,"score":21,"supported":14,"title":144,"abstract":145,"tags":146},"I4",[],[],[130],{"code":148,"score":21,"supported":14,"title":149,"abstract":150,"tags":151},"J2",[],[],[129],[153,154],"新质生产力 农业数字化","地级市 面板数据","新质生产力农业数字化-5578","10.1057\u002Fs41599-026-09510-z",{"doi":156,"openalex_id":158,"authors":159,"venue":119,"cited_by_count":93,"oa_url":116,"card":164,"direction":110,"ingested_from":111},"W7221723385",[160,162],{"name":161,"orcid":8},"Qingqing Huang",{"name":163,"orcid":8},"Wenjing Guo",{"tldr":165,"method":166,"finding":167,"direction":110,"opportunity":168},"基于中国284个地级市面板数据，检验新质生产力对农业数字化的促进、空间溢出与门槛效应。","2011—2022年284个地级市面板数据，空间计量与门槛回归，中介效应检验。","新质生产力显著促进农业数字化并具正向空间溢出，存在双门槛非线性效应与区域异质性。","可深入探究新质生产力影响农业数字化的微观机制，以及不同区域门槛条件下的差异化政策路径。","2026-10-22T11:20:47.354744Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":8,"content":8,"source_name":175,"source_url":173,"published_at":176,"category":12,"cover_url":8,"hotness":177,"is_selected":14,"score":178,"score_detail":179,"sources":181,"tags":185,"primary_topic":188,"secondary_topics":189,"classification":190,"search_phrases":197,"slug":200,"view_count":93,"doi":201,"paper":202,"created_at":214},5396,"Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex","2026-10-19T00:00:00Z",25,37,{"impact":65,"substance":54,"depth":65,"authority":54,"freshness":124,"relevant":21,"comment":180},"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[182,183],{"name":175,"url":173},{"name":175,"url":184},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[26,186,70,187],"智慧农业","中非",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":191},{"candidates":192,"agricultural_evidence":43},[193],{"code":30,"score":123,"supported":43,"title":194,"abstract":195,"tags":196},[81],[81],[70,26,186],[198,199],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":201,"openalex_id":203,"authors":204,"venue":8,"cited_by_count":93,"oa_url":173,"card":8,"direction":110,"ingested_from":111},"W7222930115",[205,207,209,212],{"name":206,"orcid":8},"Anani Ogou",{"name":208,"orcid":8},"Henry Chotangui",{"name":210,"orcid":211},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":213,"orcid":8},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":8,"source_name":221,"source_url":218,"published_at":176,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":222,"score_detail":223,"sources":227,"tags":229,"primary_topic":234,"secondary_topics":235,"classification":236,"search_phrases":248,"slug":251,"view_count":93,"doi":252,"paper":253,"created_at":269},5370,"Towards digital agriculture: uncovering the drivers and barriers to the adoption of digital agricultural technologies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1894648","Accurately identifying the driving and hindering factors of agricultural digital technology adoption is the key to overcoming the “last mile” challenge in agricultural technology diffusion. Based on the evolutionary game theory of scale-free complex networks, this paper constructs an actor network for agricultural technology adoption to analyze its multidimensional mechanisms of influence in depth. The results indicate that while government subsidies provide short-term incentives for technology diffusion, their sustainability is limited. Agricultural technology advantages serve as the core driver, transforming exogenous technology into an endogenous growth engine. Conversely, the information gap, the perception of high risk, and high costs hinder farmers’ willingness to adopt these technologies. This study expands the application boundaries of complex network theory in agricultural technology adoption, reveals the operational laws governing driving and hindering factors, and provides a valuable reference for optimizing agricultural technology adoption strategies.","准确识别农业数字技术采用的驱动与阻碍因素，是破解农业技术扩散“最后一公里”难题的关键。本文基于无标度复杂网络的演化博弈理论，构建农业技术采用的行为者网络，深入剖析其多维影响机制。结果表明，政府补贴虽能在短期内激励技术扩散，但其可持续性有限。农业技术优势是核心驱动力，可将外生技术转化为内生增长引擎。相反，信息鸿沟、高风险感知和高成本则阻碍农户的采用意愿。本研究拓展了复杂网络理论在农业技术采用中的应用边界，揭示了驱动与阻碍因素的运行规律，为优化农业技术采用策略提供了有价值的参考。","Frontiers in Sustainable Food Systems",78,{"impact":19,"substance":224,"depth":225,"authority":79,"freshness":124,"relevant":21,"comment":226},21,17,"该研究基于复杂网络演化博弈，揭示数字农业技术采纳的驱动与阻碍机制，方法新颖、结论有参考价值，但属理论模型研究，产业影响有限。",[228],{"name":221,"url":218},[26,230,231,232,233],"技术采纳","农业技术扩散","复杂网络","政府补贴",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":237},{"candidates":238,"agricultural_evidence":43},[239,243],{"code":30,"score":124,"supported":43,"title":240,"abstract":241,"tags":242},[81],[],[26],{"code":244,"score":21,"supported":14,"title":245,"abstract":246,"tags":247},"G1",[],[],[230],[249,250],"农业数字技术 采纳 驱动因素","复杂网络 农业技术扩散","农业数字技术采纳驱动因素-5370","10.3389\u002Ffsufs.2026.1894648",{"doi":252,"openalex_id":254,"authors":255,"venue":221,"cited_by_count":93,"oa_url":262,"card":263,"direction":268,"ingested_from":111},"W7222935377",[256,258,260],{"name":257,"orcid":8},"Xinrui Gao",{"name":259,"orcid":8},"Yangyang Zheng",{"name":261,"orcid":8},"Rumiao Li","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1894648\u002Fpdf",{"tldr":264,"method":265,"finding":266,"direction":110,"opportunity":267},"用无标度复杂网络演化博弈分析农业数字技术采纳的驱动与阻碍因素。","基于无标度复杂网络的演化博弈模型，构建技术采纳主体网络。","补贴短期有效但难持续，技术优势是核心驱动，信息差、高风险与高成本阻碍采纳。","可结合实证数据校准网络博弈参数，研究补贴退出后技术优势驱动的长效扩散机制。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:06.657514Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":8,"content":8,"source_name":275,"source_url":8,"published_at":276,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":222,"score_detail":277,"sources":279,"tags":281,"primary_topic":285,"secondary_topics":286,"classification":287,"search_phrases":308,"slug":311,"view_count":93,"doi":8,"paper":312,"created_at":321},5299,"刘宝涛等《统计与决策》:数字农业与粮食绿色生产效率的耦合协调特征及障碍机制","https:\u002F\u002Fguoyanwang.clcn.net.cn\u002FDRCNet.Mirror.Documents.Web\u002FDocSummary.aspx?DocID=8315929&leafID=18927","吉林农业大学经济管理学院刘宝涛副教授等基于2011—2023年中国31个省份面板数据,运用熵权TOPSIS法、超效率SBM模型测算数字农业发展水平和粮食绿色生产效率;研究期内数字农业发展水平呈\"小幅波动、平稳上升\"时序演化趋势,空间格局呈东南高、西北低、中部均衡;耦合协调度表现为波动上升趋势、由失调衰退向协调发展;粮食产量、科学研究和技术服务业固定资产投资额是主要障碍因子。","统计与决策(2026,11)","2026-10-15T00:00:00Z",{"impact":19,"substance":122,"depth":19,"authority":123,"freshness":54,"relevant":21,"comment":278},"基于31省11年面板数据的实证研究，方法规范、结论有政策参考价值，但属学术论文，公共影响有限，可入精选但非头条。",[280],{"name":275,"url":273},[26,70,282,283,284],"粮食安全","耦合协调","绿色生产",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":288},{"candidates":289,"agricultural_evidence":43},[290,294,299,304],{"code":30,"score":79,"supported":43,"title":291,"abstract":292,"tags":293},[26],[26],[70,26],{"code":295,"score":21,"supported":14,"title":296,"abstract":297,"tags":298},"A8",[],[],[282],{"code":300,"score":21,"supported":14,"title":301,"abstract":302,"tags":303},"H8",[],[],[284],{"code":143,"score":21,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[283],[309,310],"刘宝涛 数字农业 粮食绿色生产效率","吉林农业大学 数字农业 耦合协调","刘宝涛数字农业粮食绿色生产效率-5299",{"doi":8,"openalex_id":8,"authors":313,"venue":8,"cited_by_count":93,"oa_url":8,"card":314,"direction":318,"ingested_from":320},[],{"tldr":315,"method":316,"finding":317,"direction":318,"opportunity":319},"测度2011—2023年中国31省数字农业与粮食绿色生产效率的耦合协调及障碍因子。","熵权TOPSIS法、超效率SBM模型，31省面板数据。","耦合协调度波动上升、由失调转向协调；粮食产量与科研投资是主要障碍因子。","农业绿色发展与碳","可深入探究障碍因子的省际异质性与动态传导机制，并检验数字农业对绿色效率的门槛或空间溢出效应。","agent","2026-10-19T00:07:48.451681Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":8,"source_name":328,"source_url":325,"published_at":276,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":333,"tags":335,"primary_topic":340,"secondary_topics":341,"classification":342,"search_phrases":355,"slug":358,"view_count":21,"doi":359,"paper":360,"created_at":377},5103,"Assessment of the efficiency of resource potential utilization in the agricultural sector of the economy under investment and innovation development","https:\u002F\u002Fdoi.org\u002F10.32782\u002Fnvlvet-e10701","Ukraine’s agricultural sector is undergoing transformation under the influence of wartime challenges, economic restructuring, European integration, digitalization, and the growing need for sustainable resource management. Improving the efficiency of resource potential utilization has become a prerequisite for strengthening the competitiveness and sustainability of agricultural enterprises. The purpose of this study is to provide a comprehensive assessment of the efficiency of resource potential utilization in the agricultural sector of the economy under investment and innovation development and to substantiate organizational and economic measures for its improvement. The research is based on a systems approach using economic-statistical, comparative, analytical, economic-mathematical, graphical, forecasting, and composite assessment methods. The empirical analysis covers agricultural enterprises in the Poltava region during 2015–2025. Resource potential utilization efficiency was comprehensively assessed by integrating economic, investment, innovation, environmental, and managerial indicators. The results show that technological modernization, digital transformation, investment activity, and strategic management are the principal drivers of efficient resource utilization and sustainable enterprise development. An integrated organizational and economic model for resource potential development is proposed, combining precision agriculture, digital monitoring, resource-saving technologies, and human capital development. Forecasting confirms the positive impact of the proposed measures on labour productivity, capital productivity, profitability, investment activity, digitalization, and the integrated indicator of resource utilization efficiency. The scientific novelty lies in improving a comprehensive methodological approach to assessing the efficiency of resource potential utilization in the agricultural sector of the economy through integrating economic, investment, innovation, environmental, and managerial components. The practical significance lies in applying the proposed approach to strategic planning for agricultural enterprise development, investment decision-making, and improving resource management efficiency.","乌克兰农业部门正处在战时挑战、经济结构调整、欧洲一体化、数字化以及可持续资源管理需求日益增长等多重因素影响下的转型进程之中。提高资源潜力利用效率已成为增强农业企业竞争力和可持续性的先决条件。本研究旨在对投资与创新发展背景下经济中农业部门的资源潜力利用效率进行综合评估，并为其改进提供组织与经济措施的依据。研究基于系统方法，运用了经济统计、比较、分析、经济数学、图形、预测及综合评估等方法。实证分析涵盖2015—2025年波尔塔瓦地区的农业企业。通过整合经济、投资、创新、环境和管理指标，对资源潜力利用效率进行了综合评估。结果表明，技术现代化、数字化转型、投资活动和战略管理是高效资源利用与企业可持续发展的主要驱动力。研究提出了一个资源潜力发展的综合组织与经济模型，将精准农业、数字监测、资源节约技术和人力资本开发相结合。预测结果证实了所提措施对劳动生产率、资本生产率、盈利能力、投资活动、数字化以及资源利用效率综合指标的积极影响。本研究的科学新颖性在于，通过整合经济、投资、创新、环境和管理等组成部分，改进了评估经济中农业部门资源潜力利用效率的综合方法论。其实践意义在于将所提方法应用于农业企业发展的战略规划、投资决策以及提高资源管理效率。","Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies",63,{"impact":65,"substance":19,"depth":331,"authority":17,"freshness":124,"relevant":21,"comment":332},16,"区域实证研究，方法综合但影响限于乌克兰波尔塔瓦地区，对国内三农信息化参考价值有限。",[334],{"name":328,"url":325},[26,336,337,338,339],"精准农业","农业投资","乌克兰农业","农业资源利用",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":343},{"candidates":344,"agricultural_evidence":43},[345,350],{"code":30,"score":65,"supported":43,"title":346,"abstract":347,"tags":349},[],[336,348],"precision agriculture",[26,336],{"code":351,"score":21,"supported":14,"title":352,"abstract":353,"tags":354},"G3",[],[],[337],[356,357],"波尔塔瓦 农业企业 资源潜力","乌克兰 数字化 投资","波尔塔瓦农业企业资源潜力-5103","10.32782\u002Fnvlvet-e10701",{"doi":359,"openalex_id":361,"authors":362,"venue":328,"cited_by_count":93,"oa_url":325,"card":372,"direction":110,"ingested_from":111},"W7221969015",[363,366,369],{"name":364,"orcid":365},"Olena Bukhalo","https:\u002F\u002Forcid.org\u002F0000-0001-7776-6715",{"name":367,"orcid":368},"Андрій Петрович Дорошенко","https:\u002F\u002Forcid.org\u002F0000-0002-6314-1586",{"name":370,"orcid":371},"Yuri Nesterenko","https:\u002F\u002Forcid.org\u002F0009-0000-5831-4465",{"tldr":373,"method":374,"finding":375,"direction":110,"opportunity":376},"评估乌克兰波尔塔瓦地区农业企业资源潜力利用效率并提出改进措施。","系统方法结合经济统计、比较、预测与复合评估，分析2015-2025年农业企业数据","技术现代化、数字化、投资与战略管理是资源高效利用和可持续发展的主要驱动力。","可延伸至战后或转型期农业数字化投资对资源效率的因果机制与动态监测研究。","2026-10-16T23:30:33.662938Z"]