Cultivating Talents at Tertiary Agricultural Institutions in China for Sustainable and Intelligent Development

Abstract

In response to the dual challenge of global agricultural greening and digital transformation, it is imperative for agricultural colleges and universities in China to restructure talent cultivation models to support the development of sustainable and intelligent agriculture. This study combines literature analysis, case studies, and questionnaire surveys to identify misalignments between the current agricultural education system and industry needs. Focusing on educational objectives, curricula, practical training, and faculty expertise, the authors propose a novel four-dimensional collaborative cultivation model, “Objectives–Curriculum–Practice–Faculty”. This model centers on interdisciplinary course clusters (e.g., agricultural artificial intelligence and blockchain traceability), industry–academia-integrated training platforms (e.g., smart agriculture innovation centers), and a Dynamic Adjustment Mechanism (DCAM). To support the implementation of this model, this study advances policy recommendations from three perspectives. First, governments should accelerate reforms by providing special funding support and formulating legislation on industry–academia integration. Second, universities must establish early-warning response mechanisms. Third, enterprises must participate in developing education on ecosystems. This paper establishes both a theoretical framework and a practical pathway to transform agricultural education, offering significant referential value for global agricultural institutions adapting to technological revolutions.

1. Introduction

Agriculture is a foundational industry for the national economy and is vital for maintaining social stability and promoting economic growth [1,2]. However, current global agricultural systems are facing unprecedented pressure, with intensified climate change, natural resource constraints, and sustained population growth being the primary challenges [3]. Specifically, extreme fluctuating temperatures and humidity have reduced the yield and quality of crops while also complicating pest and disease control; heavy rain and snowstorms have also damaged agricultural infrastructure [4]. Furthermore, excessive resource depletion, primarily manifested as aquifer exhaustion resulting from the overexploitation of groundwater, soil salinization, and cropland degradation, has been identified as a predominant cause of stagnating yields in irrigated agriculture [5]. Moreover, according to projections from the United Nations, the global population will range from 8.3 billion to 10.9 billion by 2050, necessitating a 50% to 75% increase in food production across diverse regions worldwide [6]. The transformation pressure faced by Chinese agriculture stems not only from this global consensus but, more fundamentally, from its internal demographics: the aging of the rural population and loss of a high-quality labor force are fundamental challenges to the sustainable development of agricultural production [7]. Against this backdrop, agriculture, as the core sector ensuring food security and ecological balance, must urgently transition towards sustainable and intelligent processes.

China, globally recognized as an agricultural powerhouse, sees its agricultural output value occupying a pivotal position in the national economy [8]. Nevertheless, its traditional agricultural production models still grapple with persistent issues of resource waste, environmental pollution, and operational inefficiency. Concurrently, the global revolution in agricultural technology is accelerating into an era of intelligent agriculture, integrating emerging technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI) with agriculture to achieve intelligent information perception, analytical decision-making, automated control, and precision services [9]. Against this backdrop, China has launched its “Rural Vitalization” and “Digital Village” strategies, explicitly prioritizing agricultural sustainability and intelligence as national development imperatives. However, the implementation of these strategies faces a critical constraint: an insufficient reserve of skilled professionals. This shortage is exemplified by a projected deficit of 4 million AI developers by 2030, a gap that severely hampers the integration of intelligent technologies into agriculture [10,11]. In light of this, research focused on reconstructing talent cultivation models in agricultural institutions to support national strategic imperatives holds significant practical value and is a valuable theoretical reference for global innovation in agricultural education.

As depicted in Figure 1, agricultural sustainability emphasizes the integration of environmental, economic, and social dimensions, with S forming a trilateral model that balances resource–environmental sustainability, economic viability, and social acceptability [12,13]. This leads to low-carbon development in ecological circular farming, organic cultivation, and precision resource management. The United Nations’ (UN) 2030 Sustainable Development Goals (SDGs) explicitly mandate that nations reduce agricultural carbon emissions and enhance sustainable land utilization. At the 75th UN General Assembly, China announced its commitment to the Dual-Carbon Goals, namely, reaching a carbon emissions peak by 2030 and achieving carbon neutrality by 2060 [14]. Chinese agriculture has evolved from Labor-Based Agriculture 1.0 and Mechanization-Led Agriculture 2.0 to initial Automation and IT-Driven Agriculture 3.0, with each phase reshaping production modes [15].

Currently, Internet-based technologies are driving the transition toward Agriculture 4.0, characterized by the integration of intelligence, precision, socialization, and mechanization [10]. Within this framework, intelligent agriculture optimizes entire industrial chains through intelligent equipment, data analytics, and decision systems [16,17]. Empirical evidence suggests that the implementation of smart technologies can increase average yields by over 30% per hectare [18] while simultaneously reducing chemical inputs by 33% [19]. Meanwhile, policy acceleration is evident as exemplified by the Ministry of Agriculture and Rural Affairs’ 14th Five-Year Plan for National Agricultural and Rural Informatization Development, which proposes the establishment of a Space–Air–Ground Integrated Digital Agriculture Monitoring System and the promotion of technologies such as agricultural robotics and blockchain traceability in industrial applications [20]. This implies that the future competitiveness of agriculture will depend on innovative synergies in greening and digitalization, highlighting the importance of merging sustainability and intelligence.

Despite synergistic technological and policy advancements injecting new momentum into agricultural development, tertiary agricultural institutions in China continue to confront multidimensional and deep-rooted challenges in talent cultivation. Traditional agricultural education is dominated by single disciplines (e.g., crop science, animal husbandry), with curricula poorly integrated with emerging fields like information science and artificial intelligence, leaving course content lagging behind technological developments. Concurrently, the transition towards intelligent agriculture requires practitioners to possess both solid theoretical foundations and practical skills that integrate technical application and field management. However, existing pedagogical systems prioritize theoretical knowledge over practical skills, with inadequate industry–academia integration and obsolete training facilities. This prevents students from mastering core competencies such as smart agricultural machinery operation and agricultural big data analysis. Moreover, the limitations of existing educational models are particularly evident in three key areas: insufficient interdisciplinary integration, weak practical training systems, and a constrained international collaboration platform. This, coupled with significant disparities in salary and societal recognition for “New Professional Farmers”, has led to a severe brain drain in the agricultural sector. This is concretely evidenced by employment data showing that approximately 95% of agriculture-related graduates choose urban non-agricultural positions [21], thereby depriving the sector of its most highly educated workforce and weakening its capacity for innovation. These issues become clear when addressing transnational challenges like food security crises and climate change, which require globally competent professionals. Limited international student platforms restrict engagement in high-level research, conferences, and internships, impeding cross-cultural communication, technological insight, and innovative thinking about global issues. This study innovatively constructs a “Technology-driven, Industry-oriented, and Education-responsive” triple-helix analytical framework. By integrating the ecological ethics of sustainable agriculture with the technological logic of intelligent agriculture, this framework transcends traditional single-discipline perspectives. It further proposes a collaborative education mechanism featuring “disciplinary clusters, project-based learning, and industry–academia integration,” and designs a Dynamic Curriculum Adjustment Model (DCAM) to facilitate a fundamental transition from “Reactive Adaptation” to “Proactive Leadership.” By integrating the ecological ethics of sustainable agriculture with the logic of intelligent agriculture, this framework transcends traditional single-discipline-focused education. It further proposes a collaborative education mechanism featuring “disciplinary clusters, project-based learning, and industry–academia integration,” and designs a Dynamic Curriculum Adjustment Model (DCAM) to facilitate the fundamental transition from “Reactive Adaptation” to “Proactive Leadership.” The research results will provide empirical support for the construction of a new agricultural talent cultivation model, facilitating the strategic transformation of agricultural universities in China from “catching up” to “leading” innovation.

2. Materials and Methods

2.1. Literature Analysis

This study involved a systematic review of the theories and practices of sustainable agricultural education, the application of smart agricultural technology, and innovative talent cultivation models both domestically and internationally.

Data Sources: We assessed academic journals and policy reports on agricultural education reform from the past decade in scholarly databases like Web of Science and the China National Knowledge Infrastructure (CNKI), as well as white papers and strategic documents on agricultural technology development released by international organizations such as the FAO and World Bank.

2.2. Case Analysis

This study analyzed successful practices of typical domestic agricultural universities, such as Yangzhou University, focusing on interdisciplinary curriculum design and collaboration with the industry for talent cultivation.

Data Sources: We obtained publicly available curriculum syllabi, training programs, and collaborative project materials from 10 agricultural universities in China, as well as cases of industry–education integration demonstration projects released by governments and institutions (e.g., “Digital Agriculture Talent Base” of the Ministry of Agriculture and Rural Affairs).

2.3. Questionnaire Survey

This study quantified the satisfaction levels of students, faculty members, and employers regarding the current talent cultivation model, along with gaps between their demands and actual outcomes.

Data Sources: A five-point Likert questionnaire was designed, covering three dimensions: knowledge structure (e.g., ecology and information technology), practical skills (e.g., smart equipment operation), and professional competencies (e.g., innovative thinking). Participants included in the survey were selected using a stratified sampling method to ensure representativeness. A total of 800 undergraduate and postgraduate students were selected from 5 agricultural universities, with 1–2 institutions chosen from eastern, central, and western regions of China to reflect geographical diversity. Additionally, 50 agricultural technology enterprises were included to provide industry perspectives. The selection criteria for students included (1) enrollment in agriculture-related majors; (2) at least two years of university experience; and (3) voluntary participation. Enterprise participants were required to have at least three years of operation in the agricultural sector and engagement in technology R&D or production services.

The triangulation method was employed to cross-analyze data from the literature, cases, questionnaires, and interviews, ensuring the reliability and generalizability of the research findings. Excel and SPSS 26.0 were used for statistical analysis on the collected data, including validity and reliability analysis. GraphPad Prism 10 and Excel were utilized to generate figures and tables.

2.4. Qualitative Data Collection and Analysis

To triangulate quantitative findings and gain in-depth contextual insights, this study incorporated complementary qualitative methods. Semi-structured interviews were conducted with 25 key stakeholders, including senior academics (e.g., deans), industry leaders (e.g., CTOs from agri-tech firms), and policymakers, to explore their perspectives on competency gaps and systemic reform barriers; the interview data were transcribed and subjected to thematic analysis using NVivo 12. Concurrently, text mining techniques utilizing Python (version 3.8,with Jieba and Scikit-learn libraries) were applied to analyze a corpus of national policy documents and university training programs, employing keyword frequency analysis and topic modeling that could objectively identify thematic misalignments between strategic priorities and curricular content.

3. Analysis of Current Talent Cultivation in Tertiary Agricultural Institutions in China

3.1. Overview of Tertiary Agricultural Institutions in China

As of 2023, China had approximately 120 undergraduate institutions and 200 higher vocational colleges specializing in agriculture, covering disciplines including agriculture, forestry, fisheries, and animal husbandry. Geographically, the eastern regions, with representatives like China Agricultural University, Nanjing Agricultural University, and Yangzhou University, account for approximately 45% of the nation’s top-tier agricultural universities. In contrast, central and western institutions, typified by Huazhong Agricultural University and Northwest A&F University, mainly serve regional agricultural economies. In terms of disciplinary structures, traditional fields like agronomy, animal science, and plant protection remain dominant. Although emerging interdisciplinary programs such as intelligent agriculture and agricultural artificial intelligence have been introduced in recent years, the integration of these disciplines remains superficial, with some universities persistently employing single-discipline classification systems.

3.2. Current Status of Agricultural Talent Cultivation Models

3.2.1. Analysis of Current Educational Objectives

Current cultivation objectives in China’s tertiary agricultural institutions predominantly center on “serving traditional agricultural production”, emphasizing foundational skills such as crop cultivation and livestock farming. For instance, 80% of the course objectives of one university’s agronomy program are focused on increasing grain yields, with only 10% addressing resource recycling or agricultural information technology. Although some institutions advocate slogans like “cultivating interdisciplinary agricultural talents”, specific competency metrics conspicuously lack explicit requirements for sustainable agriculture (e.g., low-carbon management and ecological restoration) or intelligent agriculture (e.g., data analytics and intelligent equipment operation).

3.2.2. Curricula Systems

Based on the analysis of curricula syllabi from ten case universities, agricultural institutions structurally contradict the “Three Excesses and Three Deficiencies”, as illustrated in Figure 2. This creates systemic imbalances in disciplinary distribution, the nature of courses, and textbook content. Specifically, traditional courses (e.g., soil science and breeding science) dominate at approximately 76%, whereas emerging interdisciplinary courses like big agricultural data and intelligent agricultural machinery technology account for only 8% of teaching. The latter type is often offered as electives or short-term workshops, indicating insufficient alignment with trends in smart agriculture technology. Theoretical courses make up 79% of education, while practical application represents merely 21%. Practical content is largely restricted to basic laboratory operations, with weak links to modern applications such as intelligent greenhouse control and agricultural robotics operation, which are excluded from industrial technological upgrades. Domestically compiled textbooks dominate usage at 84%, while imported textbooks account for only 16%. While domestic texts effectively deliver established agronomic principles, their extensive update cycles (averaging 5 to 8 years) may present challenges in synchronizing with the rapid pace of intelligent agriculture technologies. This structural reliance on traditional disciplines, coupled with limited access to international perspectives, suggests a need for greater diversification of resources to ensure the relevance of curricula amid global technological advancement.

3.2.3. Analysis of Current Practical Training

The questionnaire results reveal that the current cultivation of agricultural talent is confronted with the acute challenge of weak practical training. This is reflected in two aspects. Firstly, the development of smart agriculture training bases is insufficient, with only 30% of colleges and universities having established facilities. Most of them still rely on traditional farmland or greenhouses, failing to meet the demand for developing smart agricultural skills. Secondly, the integration of industry and academia is superficial. While around 80% of universities have signed enterprise agreements, the actual cooperation primarily focuses on internship placements, lacking deeper synergies like joint R&D and technological problem-solving. This results in severe deficiencies in cultivating students’ innovation capabilities. Regarding practical outputs, “imitative experiments” dominate, whereas innovative projects addressing cutting-edge issues (e.g., agricultural carbon neutrality and digital farm design) account for less than 5%. This tendency for “emphasizing theory over practice” limits the cultivation of talent and significantly undermines graduate employability. A contributing factor is the current pace of technological adoption in the broader agricultural sector. As intelligent production technologies are not yet widespread, the industry often lacks the advanced, real-world training environments necessary for university–enterprise collaboration, presenting a structural challenge to build a robust practical training ecosystem. A SWOT analysis on international research [22] also corroborates that weak practical training is a common critical factor impairing agricultural students’ employability.

3.2.4. Analysis of Current Faculty Structure

An analysis of the 10 case universities indicates that current faculty structures are significantly constrained due to three aspects. First, faculty members have homogeneous disciplinary backgrounds. For instance, approximately 85% of faculty members from the selected institutions graduated from traditional agronomy programs, while less than 15% originated from interdisciplinary fields like information science and environmental engineering. This largely restricts the integration of cross-disciplinary knowledge. Second, a significant number of faculty members lack hands-on experience, with up to 70% lacking practical experience in intelligent agriculture technology R&D and industrialization projects. Thus, teaching content is severely disconnected from industrial technological developments. Third, the international profile of this faculty is currently limited, with fewer than 20% participating in substantial overseas research or having sufficient teaching experience. Diverse international exposure is widely recognized as a significant way to enrich pedagogical approaches and gain access to global research networks and innovation hubs in smart agriculture. Enhancing the international diversity of faculty experience could be beneficial for broadening the educational perspectives available to students and more fully integrating global discourse and best practices into the learning environment.

3.3. Major Existing Problems

3.3.1. Misalignment Between Educational Objectives and the Demands of Sustainable and Intelligent Agriculture Development

Existing educational objectives fail to adequately incorporate the requirements of the UN SDGs and the Agriculture 4.0 technological framework. For instance, our analysis of training programs from the case universities shows that only 12% of selected institutions explicitly define competency indicators related to “agricultural carbon neutrality” or a “Digital Village” in their training programs, which may leave graduates underprepared to address real-world challenges, particularly in terms of climate change and intelligent decision-making.

3.3.2. Outdated Curricula Lack Relevance for Emerging Technologies and Interdisciplinary Integration

The frequency of curriculum updates is insufficient compared to rapid technological innovations, with significant deficiencies in both relevance to emerging technologies and interdisciplinary integration. Courses such as Agricultural Big Data tend to emphasize legacy technologies like Hadoop, with less attention paid to emerging domains such as edge computing and “agricultural metaverse”. The credit allocated to integrated interdisciplinary courses (e.g., “Agriculture + Artificial Intelligence”, “Ecology + Economics”) remains below 8% compared to the average level of 25% in European and American agricultural institutions.

3.3.3. Deficient Practical Training Constrains the Development of Students’ Innovation Capability

The questionnaire data identify two prominent problems, namely, the improper allocation of training resources and insufficient incentives for participation in innovation. Specifically, questionnaire surveys indicate that 60% of students have “scarce opportunities to operate intelligent equipment”, while 45% of employers point out that graduates “cannot independently deploy agricultural IoT systems”. Furthermore, the participation rate in national agricultural innovation competitions (e.g., Smart Agriculture Challenge) is merely 35%, reflecting systemic gaps in mechanisms to encourage involvement in innovation.

3.3.4. Irrational Faculty Structure with Deficient Interdisciplinary Expertise and Practical Experience

A structural mismatch exists between the capabilities of faculties and industry demands. This study shows that intelligent agriculture courses in some institutions are taught by faculty members from the Computer Science Department. They lack understanding of agricultural application scenarios, consequently overemphasizing technical principles while neglecting agricultural implementation. Simultaneously, channels through which engineers from agricultural enterprises can engage with teaching remain underdeveloped. Only 5% of institutions appoint them as practical mentors, severely hindering the integration of industrial experience into pedagogy.

3.4. Synthesis of Key Challenges and Proposed Solutions

To systematically address the multifaceted challenges identified in Section 3.1, Section 3.2 and Section 3.3, this study proposes targeted solutions as well as a critical examination of their potential limitations. This synthesis ensures a holistic understanding of the reform landscape, bridging diagnostic analysis between current problems and the proposal of a new cultivation model in Section 4.

Table 1. Key challenges in agricultural talent cultivation and corresponding evidence-based countermeasures.

Key Challenge	Proposed Countermeasures	Supporting References
Misaligned Educational Objectives	Redefine competency dimensions to include sustainability and intelligence metrics. Align objectives with UN SDGs and Agriculture 4.0 framework.	[11,17,21]
Outdated Curriculum System	Introduce innovative courses (e.g., Agri-Big Data) and implement a Dynamic Adjustment Mechanism (DCAM). Develop interdisciplinary clusters and cross-institutional credit systems.	[8,15,23]
Deficient Practical Training	Co-build industry–academia platforms (e.g., innovation centers). Drive learning with real-world R&D projects and innovation competition.	[21,24,25]
Irrational Faculty Structure	Adopt dual-appointment systems for interdisciplinary and industry experts. Mandate periodic industry secondments and international training.	[20,26]

consolidates the core misalignments between the current talent cultivation system and the demands of sustainable and intelligent agriculture. It maps each key challenge with a set of evidence-based countermeasures, providing a concise roadmap for reform.

However, the effective implementation of these countermeasures is subject to contextual constraints. A proactive analysis of these potential limitations, presented in

Table 2. Potential limitations and contextual constraints of the proposed countermeasures.

Proposed Countermeasures	Implementation Challenges	Contextual Constraints
Interdisciplinary Courses and DCAM	Administrative silos and conflicts with resource allocation. High costs and faculty workload with frequent updates.	Bureaucratic university structures; limited institutional funding.
Industry–Academia Integration	Lack of long-term incentives for deep enterprise engagement. Scarcity of advanced real-world training scenarios.	Corporate profit-driven motives; overall immaturity of the smart agriculture sector.
Faculty Structure Optimization	Scarcity of qualified interdisciplinary talents. Insufficient incentives from traditional academic appraisal systems.	Fierce global competition for talent; slow reform of faculty evaluation systems.
Policy and Funding Dependence	Risk of widening regional and institutional disparities. Unsustainability of reforms upon termination of special funding.	Uneven regional development; lack of self-sustaining mechanisms.

, is crucial for designing robust and adaptable reform pathways.

4. Construction of a Talent Cultivation Model Oriented to Sustainable and Intelligent Agriculture

The diagnostic analysis presented in the preceding chapter identifies critical misalignments between the current talent cultivation framework and the evolving demands of sustainable and intelligent agriculture. To bridge these gaps, we propose a four-dimensional collaborative model integrating educational objectives, curricula, practical training, and faculty development (the OCPF model), as depicted in Figure 3. Grounded in empirical findings from our case studies and questionnaire surveys, this model incorporates a Dynamic Curriculum Adjustment Mechanism (DCAM) that can systematically address any deficiencies identified.

4.1. Proposed Educational Objectives for the OCPF Model

In alignment with the national Rural Vitalization Strategy, we aim to cultivate talented individuals in interdisciplinary agriculture who are equipped with sustainable philosophies, are proficient in intelligent agricultural technologies, and possess an integrated, innovative mindset along with practical capabilities. The core dimensions needed for agricultural competency are the following:

• Sustainable Philosophy: Understanding the core principles of agricultural ecosystem balance, resource recycling, and low-carbon production, coupled with the ability to formulate agricultural solutions that align with the UN’s Sustainable Development Goals (SDGs) [23].
• Intelligent Technology Application: Proficiency in applying IoT, big data analytics, artificial intelligence (AI), and blockchain technology in agriculture to optimize the management of whole-chain agricultural production [27].
• Cross-domain Innovation Capability: Competence in integrating multidisciplinary knowledge (e.g., agronomy, information science, and environmental engineering) to effectively address complex agricultural challenges such as climate change and precision resource management.
• Global–Local Integration: The ability to stay abreast of international frontiers in agricultural technology while adapting to the specific context of China, thereby promoting contextualized applications of technologies.

4.2. Proposed Curricular System for the OCPF Model

4.2.1. Introducing Developmental Courses to Strengthen Technology-Driven Learning

Core modules incorporate new offerings such as Sustainable Agricultural System Design, Intelligent Agricultural Equipment and Robotics, and Agricultural Big Data Analysis and Decision-Making. These new modules cover cutting-edge fields like agricultural carbon neutrality and digital farming operations. Meanwhile, technical tool courses strengthen students’ practical orientation through modules such as Python for Agricultural Data Analysis, Agricultural Drone Operation, and Blockchain-Based Agricultural Product Traceability, ensuring that students master the application of industry-standard tools. Furthermore, to broaden students’ international perspectives, globally benchmarked electives can integrate emerging fields (e.g., vertical farming and agricultural metaverse) by referencing international Agriculture 4.0 curricula.

4.2.2. Promoting Interdisciplinary Integration and Reconstruction of Knowledge Frameworks

Clusters of cross-disciplinary courses can be developed, comprising modules such as “Agriculture + AI” and “Ecology + Economics”, in the form of Agricultural Ecological Economics and Intelligent Breeding and Genomics. Subsequently, project-based learning (PBL) can be adopted to support cross-disciplinary students in tackling real-world problems (e.g., “Digital Village Construction” and “Smart Farm Planning”), thereby cultivating systematic thinking and the ability to solve complex problems. This approach of breaking down disciplinary barriers and adopting a problem-oriented curriculum design has become a consensus in international agricultural education reform. For instance, the core of sustainable food system programs at several leading North American universities is to train students to tackle “wicked problems” in the food domain through systems thinking training and multidisciplinary knowledge integration (Valley et al., 2017) [28]. This validates the effectiveness of interdisciplinary course clusters in cultivating talents from a practical perspective. Finally, a cross-institutional credit transfer system is established, enabling students to participate in external courses (e.g., computer science, environmental policy) at comprehensive universities and research institutes.

4.2.3. Optimizing Theory–Practice Balance to Enhance Application Capabilities

Structurally, the proportion of practical courses has increased from 20% to 40%, with half utilizing modern facilities (e.g., intelligent greenhouses and agricultural robots). Pedagogically, the “industry–academia co-teaching” course design engages industry experts in curriculum development, ensuring the deep integration of theory with industrial scenarios. For instance, the course Agricultural IoT Technology is co-taught by university faculty members and engineers from enterprises using AI and IoT teams. In addition, the DCAM updates 30% of the course content biannually based on industrial technology trends.

4.3. Proposed Practical Training in the OCPF Model

4.3.1. Deepening Industry–Academia Integration to Build Integrated Ecosystems

To cultivate students’ practical and innovative capacities in smart agriculture, deepening industry–academia integration is crucial to construct a cohesive university–enterprise ecosystem. Research, such as the study on India’s National Agricultural Higher Education Project, underscores that robust academia–industry integration is vital—through shared high-tech labs, structured internships, and co-designed curricula—to enhancing graduate employability and innovation commercialization [24]. In practice, this entails establishing industry–academia integration bases with companies like DJI Agriculture and Alibaba Digital Agriculture, and creating Smart Agriculture Innovation Centers equipped with platforms such as digital farm sandboxes and intelligent machinery systems. A demand-driven cultivation model is implemented across the talent development cycle to align training with enterprise needs [29]. This is exemplified by the targeted “Agricultural E-commerce and Blockchain Traceability” program launched in conjunction with Pinduoduo. Furthermore, student innovation funds are provided to support technology incubation and accelerate growth.

4.3.2. Research Project-Driven Innovation Capability Cultivation

First, undergraduates are encouraged to participate in national key R&D initiatives, including Intelligent Agricultural Machinery projects through sub-tasks like agricultural robotics path optimization and remote sensing data modeling. Then, interdisciplinary innovation groups are established under the guidance of cross-domain advisors, focusing on innovative research, including the design of agricultural carbon neutrality pathways and the development of agricultural metaverse scenarios. The outcomes of this research are integrated into credit assessment systems. Next, global joint practices are enhanced via partnerships with international organizations (e.g., CGIAR), providing students with exchange opportunities in Belt and Road smart agriculture demonstration projects to encourage cross-cultural collaboration.

4.3.3. Competition-Enhanced Learning to Stimulate Practical Competence

Significantly, it is necessary to establish national competition systems (e.g., National College Smart Agriculture Innovation Contest and Agricultural Robotics Challenge), so that performance affects graduate admissions and employment referrals. In addition, industry-related problem-solving contests can be held, which are co-designed with agri-tech firms to solve real technical challenges; the winning solutions receive industrialization support. Finally, international academic exchange platforms should be developed to fund students’ participation in conferences (e.g., IAED Annual Meeting), thereby displaying their innovations and expanding global networks.

4.4. Faculty Development

4.4.1. Optimizing Faculty Structure by Introducing Interdisciplinary Talents

Universities should implement a “dual-appointment system” to attract global scholars with backgrounds in agriculture and information (e.g., agricultural AI experts from UC Davis) through Smart Agriculture Chair Professor positions. In addition, an Industry Mentors-in-Residence Program can be launched, appointing CTOs or senior engineers from enterprises as adjunct professors and mandating ≥32 annual standard teaching hours. Furthermore, international mobility should be established in faculties through the Belt and Road Agricultural Education Alliance, enabling the sharing of resources with institutions like Wageningen University and Cornell University.

4.4.2. Enhancing Faculty Capacity Building to Break Down Knowledge Barriers

Universities should conduct “technology–pedagogy dual-track training” (e.g., Ministry of Agriculture’s Digital Agriculture Advanced Training Program) on emerging technologies like edge computing and agricultural digital twins, including them into teaching modules. In addition, interdisciplinary teaching–research communities (e.g., Sustainable Agricultural Education Research Center) can be built to co-develop cross-disciplinary curricula. In addition, a faculty–industry secondment system should be implemented. This system would require junior faculty to complete six months of enterprise practice per three-year cycle, for instance, Pinduoduo’s agricultural supply chain optimization. This practice would count towards professional title assessments.

4.4.3. Establishing Faculty Incentives and Evaluation Systems

Universities should adopt an outcome-oriented evaluation system that incorporates student competition awards (with a 30% weighting) and research commercialization into performance metrics. A Teaching Innovation Award Fund should be created to recognize teams that excel in curricular reforms. Additionally, support should be provided for faculty members to obtain international teaching certifications (e.g., from the European Association of Agricultural Engineers) to enhance their global pedagogical perspectives.

5. Case Analysis: An In-Depth Study of Yangzhou University

The analysis in the preceding chapters identifies systemic flaws in China’s agricultural higher education, situated within the global shift toward sustainability and intelligent systems. While contextualized by national conditions, these challenges—including disciplinary silos, outdated curricula, and theory–practice gaps—reflect a universal paradigm shift in agricultural education.

This chapter explores the proposed OCPF model in practice through a case study of Yangzhou University. We examine how the institution has operationalized this model’s core dimensions, translating its principles into practice with measurable outcomes. This case study serves as an empirical testbed, illustrating a viable pathway from identifying systemic issues to implementing a functional cultivation model.

5.1. Educational Objectives

Aiming to advance high-quality modern agricultural development in the Yangtze River Delta, Yangzhou University constructed a four-dimensional objective system, integrating composite competencies for sustainable and intelligent agriculture:

• Sustainability Literacy: The university embedded principles of “efficient agricultural resource utilization” and “low-carbon circular agriculture” into professional standards. As such, competency is certified not through written exams but via practical demonstrations of proficiency conducted at the university’s Smart Agriculture Training Base, ensuring that skills are industry-ready.
• Intelligent Technology Application: A “Smart Agriculture” micro-credential program was established, making proficiency in at least two core technologies a mandatory requirement (e.g., agricultural drone operation and remote sensing data analysis). Competency is certified not through written exams but via practical demonstrations conducted at the university’s Smart Agriculture Training Base, ensuring skills are industry-ready.
• Industry Alignment: Co-developed with the Jiangsu Provincial Department of Agriculture and Rural Affairs, students’ attainment of these competencies is evaluated through their performance in authentic, industry-sponsored projects, creating a direct feedback loop between market needs and educational outcomes.
• Innovation–Entrepreneurship Orientation: The university implemented “innovation credits”, allowing participation in competitions or entrepreneurship to substitute traditional course credits. Here, performance in national events like the Smart Agriculture Innovation Contest becomes a direct and high-stakes metric for evaluating innovative capabilities, directly linking extracurricular achievement to academic credit.
• SDGs Integration Plan: The UN’s SDGs are embedded in the practice modules of traditional majors. Courses such as Rice Cultivation and Carbon Neutrality assess students’ knowledge of sustainability by requiring them to design solutions for specific targets—such as modeling a 30% cut in carbon emissions—merging theoretical knowledge with practical results-driven design.

5.2. Curricula

Yangzhou University has constructed a “Foundation, Frontier, and Cross-boundary” curriculum framework:

• Innovative Technology Course Clusters: New courses, including Agricultural Big Data Mining, Intelligent Agricultural Machinery Systems Design and Blockchain and Agricultural Product Traceability, comprise 25% of total professional credits. The pioneering course, Agricultural Metaverse and Virtual Farms, employs VR and AR technologies to simulate smart agriculture scenarios.
• Interdisciplinary Course Packages: Cross-disciplinary courses, such as Bioinformatics and AI and Agricultural Economics and Digital Marketing, allow students to earn 30% of their credits from the School of Information Science (e.g., agronomy majors taking Python Applications in Agriculture).
• Dynamic Update Mechanism: A curriculum committee involving industry experts from DJI Agriculture evaluates the technological relevance of 30% of the course content annually. In 2023, Generative AI Tools (e.g., ChatGPT, OpenAI, version as of 2023) in Agricultural Consulting was added as an elective that explicitly addresses technical principles and agricultural applicability.
• According to the 2022 Curriculum Evaluation Report, students’ satisfaction with new smart agriculture courses reached 89%, which is 23% higher than satisfaction rates for traditional courses.

5.3. Practical Training

Yangzhou University enhanced students’ innovation capabilities through a “Three-Tiered Progressive” practice system:

• Foundational Training Tier: The campus features a 33-acre (200 mu) Smart Agriculture Training Base equipped with unmanned agricultural aircraft, multispectral drones, and intelligent greenhouse systems, with 5000 h of annual equipment operation.
• Industrial Application Tier: Twelve field laboratories co-built with the Jiangsu Agricultural Reclamation Group and Longping High-Tech company have implemented authentic problem-solving projects. In 2022, students majoring in agronomy developed a Digital Rice–Wheat Rotation Management System and deployed it at Yancheng farms, reducing the use of fertilizer by 18%.
• Innovation Tier: The Qinghe Maker Center funded 17 student entrepreneurship projects from 2021 to 2023 with RMB 5 million provided for seed funding, including AI Crab Pond Water Quality Monitoring and Straw-Based Biodegradable Mulch Film; three projects won the national innovation gold awards.
• Dual-Supervision System: Under the dual guidance of enterprise engineers and academic mentors, Ph.D. candidates at the Yangzhou University–Syngenta Joint Lab have developed the “Wheat Fusarium Head Blight Intelligent Early-Warning Model”. This model has been promoted nationwide by the Ministry of Agriculture.

5.4. Faculty Expertise

Yangzhou University optimized its faculty structure through a three-dimensional strategy known as “Introduction–Cultivation–Deployment”:

• High-End Talent Recruitment: Six professors, each possessing over three years of dual expertise in agriculture and information technology, were introduced, including one chief scientist of the national smart agriculture R&D program. In addition, interdisciplinary teams (e.g., from Agricultural AI and Digital Rural Planning) were established.
• Faculty Capacity Development: A Dual-Qualified Teacher Certification Program was implemented, requiring six months of cumulative enterprise practice or technical training every five years. In addition, 15 faculty members were dispatched abroad in 2023 to study vertical farming technologies.
• Industry Expert Engagement: Enterprise specialists, including Digital Agriculture Directors and Agri-Product Supply Chain Experts, were appointed as adjunct professors, teaching courses such as Intelligent Breeding Industrialization and Agri-product E-commerce Practice. The industry-expert instruction rate reached 20%. By 2023, the number of faculty members with interdisciplinary expertise increased from 12% (2018) to 35%, and over RMB 30 million of industry-sponsored research projects on smart agriculture was secured.

5.5. Successful Experiences and Scaling Impact

Yangzhou University has developed a collaborative model involving governments, universities, and enterprises, forming a demand-driven closed-loop cultivation mechanism by integrating policy guidance, knowledge feedback from enterprises, and institutional resources. Building on this foundation, the curriculum adopts a “technology–scenario–competency mapping” approach, deconstructing intelligent agriculture technologies into specific application scenarios, such as drone-based plant protection and blockchain traceability. These scenarios are translated into modular course units for targeted learning. Concurrently, innovative incentive mechanisms, like the “Innovation Credit Bank” and “Micro-Credential + Major” programs, have effectively stimulated self-directed learning and interdisciplinary practice. A case in point is the low-cost digital transformation achieved by agronomy students through stacking the “Smart Agriculture Micro-Credential”. Regarding faculty optimization, a dual-appointment system recruits interdisciplinary leaders, and a “Technology Passport” certification framework accelerates the use of smart agriculture toolchains by traditional agriculture faculty members through standardized training.

The outcomes of these reforms have demonstrated significant progress (Figure 4):

• Curriculum Enhancement: The proportion of emerging interdisciplinary courses increased from 8% to 25%, while practical courses rose from 20% to 40%. Additionally, satisfaction with new courses reached 89%.
• Stakeholder Synergy: The involvement of industry experts in teaching grew from 5% to 20%, and the percentage of faculty members with interdisciplinary backgrounds saw a substantial increase from 12% to 35%. Furthermore, students’ participation in authentic industry projects surged from 15% to 48%.
• Student Capability Leap: The rates of research commercialization climbed from 12% to 40%, and intelligent technology proficiency increased from 40% to 82%. Notably, the 2022 cohort averaged 1.2 practical achievement certificates per graduate.

This model’s four-dimensional pathway, including objective precision, curricular contextualization, practical industrialization, and faculty interdisciplinarity, systematically resolved core issues such as the obsolete nature of curriculum, superficial practice, and homogeneity among faculty members. It offers a quantifiable and replicable benchmark for regional agricultural universities to achieve breakthroughs in talent cultivation under resource constraints.

6. Discussion

This study identifies the core challenges faced by agricultural universities in China, adapting to transitions towards sustainable and intelligent agriculture. Our findings reveal that traditional talent cultivation systems exhibit significant misalignments with industry needs, including educational objectives, curricula, practical training, and faculty structure. For instance, our analysis indicates that only a minority of institutions incorporate training on “agricultural carbon neutrality” nto their programs. This gap leaves graduates unprepared for the demands of Agriculture 4.0, which requires the integration of technical application and ecological governance. Second, the proposed “Objectives–Curriculum–Practice–Faculty” (OCPF) framework demonstrates efficacy for improving talent–industry alignment. Its strength lies in the synergistic integration of interdisciplinary course clusters, industry–academia platforms, and the Dynamic Curriculum Adjustment Mechanism (DCAM), as validated by the case study of Yangzhou University. Furthermore, the findings highlight that technological innovation poses a challenge to educational models, thereby validating the necessity of the embedded DCAM. Rapid advancements in agricultural technologies include the agricultural metaverse and generative AI [30]. For instance, the agricultural metaverse creates immersive virtual farms, enabling students to conduct risk-free, repeatable simulations of complex scenarios—such as managing a drought-stricken smart greenhouse or optimizing a fully automated vertical farm—thereby bridging the gap between theoretical knowledge and high-stakes field practice. Concurrently, generative AI supports personalized learning by generating customized case studies and assisting educators in evaluating complex project reports [31].

This study clarifies that the transformation of agricultural education is driven by both the global sustainability agenda and China’s internal demographic and strategic realities. Embedding sustainability principles and intelligent technology application as core competencies can bridge a critical gap in existing programs, which often neglect the essential integration of ecological governance and technical skills required for national strategies like the “Dual-Carbon Goals” and “Digital Village” initiative. These findings align with international calls for interdisciplinary, practice-oriented agricultural education, while the OCPF model offers a concrete, localized framework for its implementation.

However, the successful implementation of this model hinges on transcending institutional boundaries. The case study demonstrates that deep industry–academia integration is not merely about signing agreements but about co-building ecosystems for talent cultivation and innovation incubation. The role of enterprises must evolve from passive resource providers to active co-builders of the educational ecosystem. This necessitates innovations in policy and institutions. For governments, strengthening strategic leadership by establishing special funds (e.g., a Smart Agriculture Education Special Fund) is crucial to support interdisciplinary laboratories and regional demonstration bases. Concurrently, refining policies and regulations on industry–academia integration is essential, including the development of standardized collaboration contracts, clear guidelines for joint intellectual property management, and tax-incentive mechanisms to recognize corporate contributions. Advancing the internationalization of agricultural education through initiatives like the Belt and Road Agricultural Education Alliance is also a key enabler.

The successful implementation of the OCPF model depends on synergistic collaboration between universities and enterprises, coupled with strategic adaptation to regional contexts. For universities, this entails establishing a robust Technology Early Warning–Curriculum Response Mechanism, institutionalized by incorporating industry representatives into academic committees to ensure curricula dynamically align with technological trends. Complementing this, a Faculty Capacity Revitalization Plan is crucial, mandating periodic industrial practice and international training as a criterion to bridge the theory–practice gap. Concurrently, enterprises must transition from passive resource providers to active ecosystem co-builders. This can be achieved by collaborating to establish Industry–Academia Colleges, jointly developing certified skill-specific courses, and providing authentic operational data and problem scenarios that form the basis for project-based learning. Furthermore, the universal OCPF framework must be tailored to local conditions. Given China’s vast territory and significant regional diversity in agricultural production conditions [25,26], it is essential to consider tailored training programs that align with the distinct agricultural characteristics of each region. This indicates that the universal application of any model must be adapted to local contexts.

A limitation of the current study is the scope of its impact assessment. Building a longitudinal database to track talent cultivation outcomes would be a robust methodology to analyze the long-term effects of new models on students’ careers and upgrades made to the agricultural industry [32]. Such an approach would also validate data-driven dynamic optimization of the proposed cultivation model. In conclusion, this study positions the OCPF model as a structured framework within the broader context of systemic educational reform. The model’s significance derives not merely from its capacity to address four-dimensional misalignments identified but also from its inherent dynamism, which ensures its continued relevance amidst ongoing technological and policy shifts. While challenges regarding implementation are acknowledged, the model effectively bridges the gap between diagnostic analysis and practical intervention. It thereby provides a viable pathway for aligning Chinese agricultural higher education with the dual imperatives of sustainability and intelligence, establishing a foundation for both practical application and future scholarly investigation.

7. Conclusions

This study systematically addresses the critical misalignments between the existing talent cultivation paradigm in Chinese agricultural universities and the emerging demands of sustainable and intelligent agriculture. It establishes the imperative for a fundamental pedagogical transformation and, in response, conceptualizes and empirically validates the innovative “Objectives–Curriculum–Practice–Faculty” (OCPF) framework. The model’s efficacy, as demonstrated through the case study of Yangzhou University, is rooted in its integrated architecture and dynamic responsiveness, which synergizes interdisciplinary curricula, robust industry–academia symbiosis, a diversified faculty ecosystem, and a Dynamic Curriculum Adjustment Mechanism (DCAM) to facilitate a shift from reactive adaptation to innovation to proactive leadership. Ultimately, this research demonstrates that cultivating a new generation of agricultural pioneers necessitates transcending institutional boundaries and fostering a tripartite coalition between the government, industry, and academia.

Looking ahead, the transformation of agricultural higher education presents several pivotal developments for research and policy. Future studies should prioritize the development of sophisticated, multidimensional metrics to quantitatively assess the long-term impact of new cultivation models on graduate career trajectories, technological adoption rates, and broader agricultural sustainability indicators. Concurrently, interdisciplinary research is needed to explore the convergence of agricultural science, data ethics, and environmental psychology and navigate the societal implications of smart farming technologies. Furthermore, investigating scalable and equitable policy mechanisms—particularly financial models and incentive structures for industry–academia collaboration in less-developed regions—will be crucial for ensuring the widespread adoption of educational reforms. By advancing these research agendas, the academic community can provide the evidence-based insights necessary to refine cultivation models and guide the strategic evolution of China’s agricultural education system in the era of intelligence.