Specialty crop industries generate heterogeneous knowledge across cultivation, pest and disease management, post-harvest handling, quality control, and marketing, yet this knowledge is often dispersed across technical documents, web resources, relational datasets, and expert experience. Taking the jujube (Ziziphus jujuba Mill.) industry as a case study, this study develops a reproducible ontology-constrained knowledge-engineering framework. A lifecycle-oriented domain ontology and a curated knowledge graph prototype were constructed from verified ontology-aligned records, while unstructured texts were used to evaluate prospective information-extraction components. Four BERT-based named entity recognition models were compared under leakage-controlled five-fold cross-validation, and versioned ontology-constrained trigger rules were evaluated under a gold-entity setting. The final ontology contained 457 RDF triples, 38 classes, 13 object properties, and 71 data properties, and logical consistency checks detected no inconsistent named classes. BERT-Softmax obtained the highest mean NER F1-score (0.328 ± 0.146) in this small corpus, but the fold variability does not support a general claim of superiority over CRF-based architectures. Under gold entities, the refined trigger rules achieved an F1-score of 0.683 ± 0.142; in a stricter diagnostic using automatically predicted fine-grained entities, end-to-end relation extraction declined to 0.035 ± 0.054. On a 93-question benchmark generated from verified graph triples, the rule-graph QA engine achieved a token-level F1-score of 0.973 and a tail-set exact accuracy of 98.9%, primarily reflecting relation mapping and graph retrieval correctness. A separate 20-question human-authored challenge audit found three partially supported questions and no fully supported question, illustrating the boundary beyond graph-grounded retrieval. The results provide case-specific, exploratory evidence that explicit ontology constraints can support reproducible knowledge organization and verifiable retrieval under limited domain data; they do not establish deployment-level or unrestricted agricultural QA performance.

Data classification and grading is an information governance mechanism through which information resources are categorized, assigned responsibilities, linked to differentiated controls, and updated over time. Using China as the main case, this review examines how classification and grading has been operationalized across regulatory, local public data, and sectoral settings, and where its implementation remains incomplete. This study adopts a structured analytical review design, synthesizing academic studies, national laws and regulatory documents, standards and technical guidance, local public data normative documents, and sectoral implementation instruments, with a focused comparison of governance approaches in China, the United States, and the European Union. The review develops a five-dimensional information governance framework covering information stewardship roles, protected information assets and governance concerns, information categorization criteria and risk logics, differentiated information control mechanisms, and assessment and adaptive updating. The findings show that China has rapidly expanded regulatory and sector-specific arrangements for data classification and grading, especially in public data governance and in sectors such as telecommunications, finance, healthcare, and industry. However, significant operationalization gaps remain, including fragmented standards, weak cross-sector interoperability, uneven information stewardship capacity, limited translation of categories into access, handling, sharing, lifecycle, and audit controls, and underdeveloped feedback and updating mechanisms. By repositioning data classification and grading as information governance rather than only regulatory compliance, this review contributes to research on differentiated data management, information stewardship, and adaptive governance practice.

In recent years, various agricultural research institutions in China have begun to increase the investment in informatization construction, so informatization brings great convenience to scientific research and management, but corresponding cybersecurity issues have become more and more important. Relying on the cybersecurity classified protection work, in-depth analysis of each weak link in the cyber system construction of agricultural scientific research institutions, to improve the information system security and avoid information security incidents has been placed in the prominent position of agricultural research institutions.[Method/Process] This article discusses the content of strengthening the cybersecurity management of agricultural scientific research institutions based on National Cybersecurity Classified Protection 2.0. First of all, this article introduces the importance of cybersecurity classified protection. Secondly, the difference between National Cybersecurity Classified Protection 2.0 standard and National Cybersecurity Classified Protection 1.0 standard is analyzed. Thirdly, the system of cybersecurity management and the content of cybersecurity management technology of Chinese Academy of Agricultural Sciences were analyzed in detail. Finally, it discusses and summarizes the effective strategies to strengthen the information security construction of agricultural research institutions. [Results/Conclusions] Taking the Chinese Academy of Agricultural Sciences as an example, this paper discusses its related practices in detail, hope to provide reference for agricultural research institutions to carry out cybersecurity level protection and security construction.

Background: Maize industry security is defined in this study as an integrated, sustainability-oriented food-security posture of the maize sector, reflecting how basic security level, market operation situation, technological innovation capacity, and resource-environment conditions jointly shape the availability, stability, and long-term viability of maize production and use. Methods: To construct a composite Maize Industry Security Index for China, data were synthesized from authoritative national statistical yearbooks and official bulletins covering the period 2011–2020. Twelve indicators were operationalized into four sub-indices: basic security level, market operation situation, technological innovation capacity, and resource-environment conditions. Value-type variables were deflated by CPI, positive and negative indicators were normalized using differential methods, and composite scores were aggregated with expert-assigned weights. Robustness was further checked with alternative weighting and normalization approaches. Results: The overall index remained within the basically secure or secure range, but displayed marked cyclical fluctuations, rising from 84.82 in 2011 to a peak of 97.33 in 2017 before easing to 91.79 in 2020. Basic security initially improved and then weakened after planting-structure adjustment. Market operation demonstrated the highest short-term volatility, with distinct troughs observed in 2015 and 2020. Technological innovation capacity remained comparatively stable, supported by mechanization, variety approval, and productivity gains. Resource-environment conditions improved gradually over the study period, accompanied by a decline in carbon-emissions intensity per unit of output. Implications: The findings indicated that maize industry security could not be judged solely by output growth or self-sufficiency. A coordinated policy mix that stabilizes supply incentives, strengthens market-risk governance, sustains breeding and digital-agriculture innovation, and promotes low-carbon, resource-efficient production was therefore considered necessary for enhancing maize industry security in China.

With the rapid development of agriculture information technology and Internet, the agriculture digital publishing has become one of the most dynamic industries in China. Currently, agriculture digital publishing just begun and the related laws and regulations are being improved. However, agriculture copyright protection for digital publishing still faces many problems and challenges. It is very important to strengthen agriculture copyright protection in digital publishing industry. In this paper, the main concepts and challenges for agriculture digital publishing and its copyright protection are analyzed and some suggestions are also proposed.