[Objective] Data across the agricultural industrial chain are generated throughout production, processing, storage, logistics, sales and supervision. Such data feature dispersed stakeholders, long‑link processes, multi‑source heterogeneity, and high privacy‑ and business‑information sensitivity. Nevertheless, current agricultural big‑data platforms and standalone technologies including blockchain‑enabled traceability, privacy‑preserving computation and federated learning cannot systematically resolve bottlenecks in cross‑entity data circulation, such as data ownership confirmation, controlled utilization, audit tracing and value‑oriented collaboration. Accordingly, this paper proposes a trusted data‑space framework tailored for agricultural production, circulation, supervision and service collaboration scenarios to satisfy requirements for trusted cross‑entity data circulation. It elaborates the framework's layered architecture, core operational mechanisms and implementation paths, offering systematic references for agricultural data sovereignty protection, controlled data sharing, audit tracing and value collaboration. [Methods] Based on the national standard Technical Architecture of Trusted Data Space and the characteristics of the agricultural industry chain, including multiple stakeholders, long-chain processes, limited computing capacity at edge nodes, and data heterogeneity, a domain-adaptation approach was adopted to construct the overall framework and analyze its operating mechanisms. The framework design focused on semantic interoperability, connector-based controlled interaction, privacy-preserving computation, and blockchain-based evidence preservation. A self-developed integrated service platform was then used for preliminary scenario-based analysis, combining about 120 000 food safety sampling and monitoring records from a city during 2023－2025 with the business workflow of an organic farm. The analysis covered standardized data access and governance, risk profiling, on-chain evidence preservation, trusted traceability, permission control, abnormal request interception, and audit tracing. [Results and Discussions] A four-layer architecture was established, consisting of an infrastructure and data resource layer, a trusted data space core layer, a data capability support layer, and an agricultural industry chain application ecosystem layer. The operating mechanism formed a closed loop jointly driven by deep semantic interoperability, connector-based controlled interaction, value co-creation through privacy-preserving computation, and dynamic trust supported by blockchain and smart contracts. Under this framework, ontology models, metadata, and knowledge graphs supported concept alignment, structural mapping, and contextual disambiguation; connectors, digital contracts, and usage control policies constrained usage purposes, invocation frequency, field scope, output forms, and prohibited behaviors; privacy-preserving computation and federated learning enabled cross-entity collaborative analysis without centralizing raw plaintext data; and blockchain recorded contract hashes, log hashes, result digests, and abnormal interception records for auditable tracing. The platform-based scenario description showed that the framework could support standardized data access, risk profiling, on-chain evidence preservation, role-based permission control, abnormal operation interception, and audit tracing in agricultural and food safety risk governance scenarios. In the organic-farm workflow, the mechanism was reflected by keeping raw data off-chain, storing key digests on-chain, controlling data use within authorized environments, and retaining auditable records of the process. Compared with conventional centralized agricultural data platforms, this framework emphasized physical distribution with logical integration and extends security protection from access control to continuous usage control after cross-entity interaction. The four-dimensional implementation pathway further indicated that institutional rules, lightweight connectors and privacy-preserving components, specialized data intermediaries, and progressive pilot deployment should advance in coordination. [Conclusions] The proposed trusted data space framework provides a systematic approach to controlled data circulation, data sovereignty protection, auditability, and value collaboration among multiple stakeholders in the agricultural industry chain without requiring centralized aggregation of raw plaintext data. It can serve as a reference for the circulation of agricultural data elements and the collaborative transformation of the agricultural industry chain, while providing a basis for integration with the national data infrastructure system.
[Objective] The ecological agricultural product industry chain is subject to deep-seated vulnerabilities during scale expansion, quality improvement, and premium growth, yet its structural roots remain inadequately understood. Although digital technologies are expected to relieve these vulnerabilities, recent evidence suggests that digitalization may also induce new suppression loops, resulting in smallholder exclusion, profit squeeze, and homogenized competition. Existing studies have largely addressed this subject from isolated perspectives such as brand governance, industrial chain resilience, or policy evaluation, without offering a systematic diagnosis of the evolutionary mechanism of vulnerability. The aim of this study is to identify the operational logic of growth ceiling archetypes under digital empowerment, exploring how digital technologies interact with feedback structures, and proposing resilience governance strategies that move beyond "technological fixes". [Methods] The Limits to Growth archetype was employed as the analytical framework. Variable selection followed a three-stage procedure integrating theoretical deduction, bibliometric analysis, and case validation. First, constraining factors were identified across multiple dimensions, including natural resource endowment, ecological carrying capacity, market saturation, and trust thresholds, grounded on the theoretical proposition that growth processes endogenously activate restraining forces. Second, a systematic literature search was conducted using search terms such as ecological agriculture, agricultural product industry chain, and system dynamics. Candidate variables that appeared with high frequency in relevant empirical studies were screened, yielding an initial pool of over 20 variables. Third, field investigations of typical cases such as Gannan navel orange, Wuchang rice, and Hengzhou digital jasmine were performed to test, consolidate, and refine the initial pool, ultimately producing 14 core variables. Causal linkages among these variables were established upon three forms of evidence, namely theoretical logic derived from established economic principles, literature evidence drawn from prior empirical findings, and case facts observed during field investigations. Loop polarities were determined by the parity of negative causal chains, where even numbers indicate reinforcing loops (R) and odd numbers indicating balancing loops (B). On this basis, and in combination with field data from the typical cases, four reinforcing loops that drive growth and seven balancing loops that constrain growth were identified. The moderating effects of three categories of digital tools, namely digital agricultural technology platforms, quality traceability systems, and e-commerce platforms, on these loops were then separately assessed. [Results and Discussions] The results indicated that the sustained growth of the ecological agricultural product industry chain was underpinned by four positive feedback loops: the scale-income loop, the quality-demand loop, the premium-income loop, and the quality-reputation loop. At the same time, however, the growth process activated seven negative feedback loops that imposed constraints from multiple directions, including cost erosion, quality dispersion, market saturation, homogenized competition, low-price substitute diversion, trust erosion, and reputation damage—thus locking the system into three types of Limits to Growth dilemmas relating to scale expansion, quality improvement, and premium growth. Digital technology empowerment enhanced the operational efficiency of the industrial chain by reinforcing the positive loops and weakening the negative ones, yet it failed to remove the structural roots of the growth ceiling. More importantly, digitalization unexpectedly triggered three new suppression loops, namely the smallholder exclusion loop, driven by digital and certification barriers, illustrated by the Hengzhou digital jasmine case where elderly flower farmers were marginalized due to the digital divide; the profit squeeze loop, driven by platform cost transference, as shown in the Shanghai Hema village cooperative case, where high equipment costs and loss rates eroded profit margins; and the homogenized competition loop, driven by standardization orientation, as evidenced in the Gannan navel orange case where farmers abandoned flavor-differentiated production practices to comply with platform specifications. These three loops respectively weakened the operational foundations of the scale-income, premium-income, and quality-reputation loops, thereby shifting the locus of vulnerability from traditional constraints to digitally-induced risks. These findings revealed a dual effect of digital technology empowerment, as mitigating certain existing constraints, digital technologies may simultaneously generate new vulnerabilities through digital divides, cost transference, and standardization pressures. The essential task for resilience governance, therefore, lay in identifying and intervening in the dominant balancing loops that constrains growth, rather than relying exclusively on technological inputs. [Conclusions] First, the three limits to growth archetypes, namely scale expansion, quality improvement, and premium growth, constitute the shared structural roots of vulnerability in the ecological agricultural product industry chain. Second, digital technology empowerment cannot eradicate these structural roots and may give rise to new vulnerabilities. Third, enhancing industrial chain resilience should be grounded in leverage point interventions derived from the system's feedback structure. Corresponding governance strategies for the three types of newly identified risks, namely smallholder exclusion, profit squeeze, and homogenized competition, should include lowering technological entry barriers, establishing benefit-sharing mechanisms, and strengthening differentiated certification and geographical indication protection. Overall, this study provides a system dynamics-based analytical tool and policy leverage points for resilience governance of the ecological agricultural product industry chain in the context of digital technology empowerment.
[Objective] In the agricultural domain, user demand texts serve as essential primary sources for agricultural extension, production management, and policy services. However, these texts typically contain highly specialized terminology, exhibit non-standard, colloquial, and diverse linguistic expressions, present fragmented semantics, and rely heavily on contextual reasoning. Such characteristics make them difficult to parse accurately using traditional rule-based approaches or shallow machine learning models. Consequently, these limitations often lead to biased demand classification and incomplete extraction of key factors, thereby constraining the quality of data available for intelligent agricultural decision-making. To address these challenges, the aim of this research is to develop a robust, domain-adapted, and highly interpretable structured analysis method for agricultural user demands. [Methods] Agri-NeedAgent, an agricultural user demand analysis framework, was proposed based on a "three-stage training + multi-agent collaboration" paradigm. First, during the domain knowledge pretraining stage, 80 000 agriculture-related texts, including crop cultivation manuals, pest and disease control guides, agricultural policy documents, and farmer consultation records, were used to construct domain-specific semantic understanding, thereby enhancing the model's capability to interpret agricultural terminology, dialectal expressions, contextual logic, and implicit semantics. Second, in the instruction fine-tuning stage, 6 320 annotated samples in an "instruction-input-output" format were employed to establish an explicit mapping from raw demand texts to structured outputs. Third, in the agricultural knowledge low-rank adaptation stage, Low-rank Adaptation (LoRA) was applied to perform lightweight parameter tuning on task-specific agents, enabling targeted adaptation for demand classification and key-factor extraction tasks. Built upon the above training process, a multi-agent collaborative framework was constructed, in which the manager agent was responsible for task scheduling and quality control, while task agents were designed to perform demand classification, key-factor extraction, and explanation generation, respectively. Through this division of labor and collaborative mechanism, the framework achieved efficient and structured analysis of agricultural user demands. [Results and Discussions] Experimental results demonstrate that the proposed Agri-NeedAgent achieved a demand classification accuracy of 84.6%, a key-factor extraction F1-Score of 85.2%, a structured interface compliance rate of 94.2%, and an interpretability score of 90.2.These results showed clear improvements over traditional deep learning models such as Bidirectional Encoder Representations from Transformers (BERT) as well as general-purpose large language models (LLMs) without domain adaptation. The findings confirmed the critical role of domain knowledge injection, explicit task alignment, and multi-agent specialization in enhancing semantic understanding and structured analysis of agricultural texts. Ablation experiments further validated the effectiveness of each component. Removing domain pretraining or LoRA fine-tuning resulted in substantial performance degradation in classification and key-factor extraction, indicating the necessity of domain adaptation and task-specific optimization for handling non-standard agricultural expressions. Moreover, eliminating the manager agent or the Reasoning and Acting (ReAct) mechanism significantly reduced structured interface compliance and interpretability, highlighting the importance of task coordination, intermediate verification, and multi-step reasoning for ensuring logical consistency and output completeness. Additionally, removing the external knowledge base reduced the interpretability score from 90.2 to 77.6, underscoring its essential role in providing theoretical grounding, reasoning support, and professional explanations. Although the multi-agent collaboration introduced an additional inference overhead of approximately 140 ms, the overall per-sample inference time remained within 225 ms, meeting the real-time requirements of agricultural consultation scenarios. [Conclusions] Supported by a "three-stage training + multi-agent collaboration" framework, LLMs can effectively address challenges posed by non-standard expressions, semantic fragmentation, and multi-factor reasoning in agricultural user demand texts. The proposed method demonstrated significant improvements in demand classification, key-factor extraction, structured output compliance, and interpretability, providing high-quality and traceable structured data for intelligent agricultural decision-making. After domain adaptation and task-specific tuning, the model not only gains enhanced capability for deep semantic analysis of agricultural user demands but also ensures the completeness and interpretability of outputs through multi-agent coordination. Although the current workflow still requires optimization in terms of data preparation, staged training, and knowledge-base updating, future work will focus on expanding region-specific and emerging-technology-related demand data, developing a dynamically updated agricultural knowledge system, improving multi-agent coordination efficiency, and exploring cross-lingual agricultural demand analysis to further promote the application and deployment of agricultural large models across broader scenarios.
