As a core species in the global livestock industry, cattle play an irreplaceable role in human food security and economic development. Beef cattle and dairy cattle meet the dietary needs of billions of people around the world by providing high-quality protein and dairy products, respectively. With the growth in population and the intensification of the pressure of climate change, traditional breeding techniques may be unsuitable to meet the increasingly growing demands for sustainable and highly adaptable processes. In recent years, the rapid development of genomics, bioinformatics, and gene-editing technologies has provided unprecedented tools and perspectives for the genetic improvement of cattle, driving the precise design and efficient development of new cattle breeds. However, the development of new cattle breeds still faces multiple bottlenecks pertaining to scientific, ethical, and industrialization aspects, which can be addressed through interdisciplinary collaboration. In this review, we will systematically assess the technological progress in the genetic breeding of beef cattle and dairy cattle, analyze the integration path of traditional breeding and modern biotechnology, and explore the future directions of cattle breeding research under the sustainable development goals, with the aim of providing theoretical support for cattle breeding.

The nutritional value of goat milk is determined by the composition of its fatty acids, with particular importance placed on the role of unsaturated fatty acids in promoting human health. CircRNAs have been known to affect fatty acid metabolism through different pathways. In this study, high-throughput sequencing was employed to construct expression profiles of mammary tissue harvested during the dry period and peak lactation stages of dairy goats. Differentially expressed circRNAs and mRNAs were screened, revealing significantly higher expression levels of circRNA-08436 and ELOVL6 during the peak lactation period compared with the dry period. Thus, circRNA-08436 and ELOVL6 were chosen for subsequent studies. The findings demonstrated that circRNA-08436 not only promotes the synthesis of triglyceride (TAG) and cholesterol in goat mammary epithelial cells (GMECs), but also increases the concentrations of saturated fatty acids in the cells. Through the utilization of software prediction, the dual luciferase reporter system, and qRT-PCR, it was observed that circRNA-08436 binds to miR-195, with its overexpression reducing the expression levels of miR-195 and inhibiting TAG synthesis. In addition, circRNA-08436 upregulated the expression levels of the miR-195 target gene ELOVL6. The data also revealed that YTHDC1 facilitated the transport of circRNA-08436 from the nucleus to the cytoplasm, while YTHDC2 in the cytoplasm functioned as a “reader” to identify and degrade circRNA-08436. Taken together, these findings contribute to a better understanding of the molecular regulation of fatty acid metabolism in the mammary glands of dairy goats, thus offering a sound theoretical basis for the production of high-quality goat milk.

As AI becomes increasingly integrated into higher education, its contribution to sustainable agricultural education requires assessment beyond technology use alone. This is particularly important in higher agricultural education, where future professionals require disciplinary, digital, and sustainability-related competencies. However, multidimensional evidence linking students AI Literacy, perceived educational value, institutional support, and perceived educational effects remains limited. This study assessed students’ perceptions of AI-driven educational development among 417 undergraduate students from five universities in Jiangsu Province, China. Measurement quality was evaluated using reliability analysis and WLSMV confirmatory factor analysis, followed by multidimensional statistical analyses. Educational Value of AI and Intention to Use It received the highest mean score (3.52), whereas Institutional Support for AI Education was lowest (2.74). Perceived effects differed across domains, with Development of Talent for Sustainable Agriculture rated highest (3.54) and Educational Resources and Equity lowest (3.26; p < 0.001). Educational Value of AI and Intention to Use It (ρ = 0.578) and AI Literacy (ρ = 0.538) showed the strongest associations with perceived effects. Students with prior AI-related educational exposure scored higher across all major constructs. These findings suggest that sustainable AI integration in higher agricultural education is associated with students’ AI Literacy, institutional support, equitable access to digital educational resources, and the integration of AI into authentic agricultural learning. The study provides evidence for aligning AI-enabled education with educational quality, equity, and the development of competencies required for sustainable agriculture.