The realization of carbon peak and carbon neutrality requires a comprehensive and profound transformation of economic and social systems. It is imperative not only for addressing climate change but also for the transformation and upgrading of various industries. This shift entails transitioning from high energy consumption and pollution to focusing on high-quality development, thereby facilitating the transition to a green and low-carbon economy and ultimately realizing sustainable economic and social progress. As modern agriculture evolves and agricultural modernization advances, there is substantial potential and demand for emission reduction in agricultural sectors and rural areas. Agricultural institutions of tertiary education undertake the crucial responsibility of cultivating talents capable of contributing to carbon neutrality efforts. Consequently, it is essential to integrate the concept of carbon neutrality into undergraduate education to meet the growing need for cultivating a workforce capable of achieving national carbon neutrality goals. In this study, a questionnaire is used to analyze the views and current situation of students majoring in animal production regarding the concept of carbon neutrality, and to explore how to conceptualize a knowledge system of carbon neutrality applicable to animal production programs in agricultural institutions.

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.

As artificial intelligence (AI) technology continues to advance and iterate, various industries have undergone intelligent reformation. China’s animal husbandry industry, given its importance for people’s livelihoods, is no exception to this transformation. Using AI technology in this field is becoming increasingly common since it not only improves production efficiency but also revolutionizes traditional business models. Animal science is a fundamental discipline that drives the progress of animal husbandry by studying the growth, breeding, nutritional needs, and feeding management of livestock and poultry. This discipline also explores advanced veterinary theories and technologies for epidemic prevention and control. The ultimate objective of this discipline is to ensure the production of high-quality and sufficient animal products to fulfill the demands of both production and daily life. It is predicted that the deep integration of AI technology into animal science will bring unprecedented opportunities to the animal husbandry industry. This study aims to explore the impact of artificial intelligence (AI) on students’ learning experiences and future educational directions. By situating the research within the context of current developments in educational technology, we hope to provide valuable insights for educators and policymakers and employ a questionnaire survey to explore the perceptions and attitudes of students majoring in animal science from various agricultural institutions in China toward this integration. The results of the study provide valuable and practical references for the cultivation and development of artificial intelligence talent in China’s livestock industry.

The shortage of interdisciplinary talent is a critical bottleneck constraining the development of smart agriculture. Taking Jiangsu Province as a case study, this research constructs and empirically validates an ecosystem model for cultivating interdisciplinary talent oriented toward smart agriculture. In the theoretical construction phase, an initial three-dimensional model covering “core actors,” “supportive environment,” and “resource elements” was proposed based on ecosystem theory and literature review. This model was subsequently refined through in-depth interviews (March–August 2024, 60–120 min each) and thematic analysis with 58 diverse stakeholders across 13 prefecture-level cities in Jiangsu Province, encompassing universities, agribusinesses, government agencies, research institutes, and frontline practitioners. In the empirical testing phase, structural equation modeling was employed to analyze 382 valid questionnaire responses covering six dimensions: policy environment, market environment, university–enterprise collaboration, curriculum resources, platform resources, and talent cultivation effectiveness (20 items in total). The findings indicate that: (1) the ecosystem model demonstrates good fit and strong explanatory power, with a pronounced “university–enterprise” dual-core driving effect; (2) government policy guidance and platform construction play pivotal supportive roles; (3) market demand and industrial policy constitute critical external driving forces; and (4) “industry–education integrated practice platforms” together with “modular interdisciplinary curricula” exert the most direct positive influence on cultivation outcomes. Based on these findings, this study offers systematic recommendations from three perspectives—mechanism coordination, policy optimization, and resource allocation—providing a theoretically grounded and practically referenced solution for cultivating interdisciplinary talent in smart agriculture.