Local cattle breeds represent a national strategic animal genetic resource, endowed with irreplaceable value in terms of their distinctive meat quality and flavor, stress resistance, and environmental adaptability. In recent years, the field of digital and intelligent technologies has advanced by leaps and bounds. A new-generation integrated biotechnological breeding system, characterized by artificial intelligence, gene chips, and smart breeding platforms, is reshaping the landscape of beef cattle germplasm innovation. This article provides a comprehensive review of the research and development advancements in domestic intelligent breeding platforms and high-throughput gene chips specifically designed for beef cattle. It places particular emphasis on comparing the core features, parameter configurations, technical strengths, application scenarios, and strategic implications of three types of domestically developed chips tailored for local cattle breeding. Furthermore, it offers a detailed introduction to the establishment and operational status of several pivotal domestic intelligent breeding platforms dedicated to local beef cattle, including the National Beef Cattle Genetic Evaluation Center and Whole Genome Selection Platform, the Huaxi Cattle Intelligent Breeding Management System, the Inner Mongolia BeefNet Precision Breeding and Breeding System Cloud Platform, as well as the Ningxia Beef Cattle Breeding Information Management and Breeding Analysis System. The application of digital intelligence in empowering germplasm innovation technologies for local cattle breeds has yielded significant results. Meanwhile, it further analyzes the practical applications of machine learning and deep learning in key scenarios amid the rapid development of artificial intelligence, including intelligent phenotypic identification of beef cattle, non-destructive meat quality evaluation, optimization of genomic selection models, and full industry chain traceability. Finally, the paper points out the issues of scarcity of high-quality annotated breeding datasets, weak model interpretability, insufficient promotion and application, and difficulty in popularization among small and medium-sized ranches. It suggests solutions such as establishing unified data standards, algorithm interfaces, and computing power centers, developing small sample and federated learning, promoting open source sharing of core algorithms and models, and proposes the vision of building a unified and open intelligent local cattle breeding cloud ecosystem in the future, thereby providing a reference for the high-quality and sustainable development of China's local cattle breeding industry.

Objective Livestock-related QA is terminology-heavy and often involves indicators, parameters, and trade-offs in breeding and production plans. This study evaluates LLM professional usability using subjective responses and explicit scoring rubrics. Methods We built a Chinese vertical benchmark, the Genetics, Breeding, Nutrition, and Production Benchmark (GBNP-2026), with 510 subjective items (363 short-answer and 147 essay) spanning the same four domains, each with a reference answer, checkable scoring points, and domain tags. Nine open- and closed-source models were tested under a unified zero-shot, context-free protocol, scored by coverage and quality, followed by error attribution on low-score cases. Results Mean scores ranged from 64.03 to 83.99 and coverage from 70.6% to 91.9%. Essay items scored higher than short-answer items on average; breeding was the weakest domain for most models. Among 628 low-score samples, missing knowledge (37.3%) and hallucinations (25.0%) dominated. Missing-knowledge rates differed across domains (χ²=8.42, P = 0.038), highest in breeding (47.1%). Conclusion Compared with multiple-choice accuracy alone, coverage-plus-quality scoring helps separate capability gaps in this setting; strictly numerical or proof-style tasks still need a dedicated sub-benchmark. GBNP-2026 and the pipeline support reproducible comparison, error diagnosis, and data planning for SFT/RAG.

With the rapid development of modern seed industry and intelligent livestock farming, the phenotyping of important traits in livestock and poultry is transitioning from traditional manual measurement towards automated, intelligent, and high-throughput approaches. This review focuses on livestock such as pigs and poultry, systematically summarizing research progress in intelligent phenotyping technologies for key traits including growth, body measurements, physiological parameters, and behavior. It discusses the principles and application status of core technologies such as computer vision, laser radar, wearable and implantable sensors, analyzes AI-based data processing methods and their value in genetic evaluation, compares the development levels of intelligent phenotyping technology for livestock and poultry domestically and internationally, examines challenges in technology application, and prospects future directions such as multi-omics integration and intelligent breeding, this initiative systematically integrates multidisciplinary technological achievements, clarifies technical application pain points and breakthrough pathways, with a focus on core technological advantages like non-contact measurement and dynamic physiological monitoring, while balancing theoretical depth and industrial applicability. It aims to accelerate the transformation of China's livestock industry toward efficiency, green practices, and intelligence, ensuring the supply security of livestock and poultry products, and strengthening the technical foundation for enhancing the core competitiveness of the seed industry.