中国乳业 ›› 2026, Vol. 0 ›› Issue (6): 30-39.doi: 10.12377/1671-4393.26.06.04

• 反刍动物营养与健康养殖专题 • 上一篇    下一篇

反刍动物营养-健康-减排协同调控技术研究进展

吴泽鹏, 吴炜俊*, 李颂群, 李冬梅, 范存斐, 孙克龙, 陈绮萍, 张婉莹, 刘帆, 林思怡   

  1. 广州风行乳业股份有限公司,广东广州 510610
  • 出版日期:2026-06-25 发布日期:2026-07-22
  • 通讯作者: *吴炜俊(1995-),男,广东江门人,硕士,工程师,研究方向为食品加工与安全。
  • 作者简介:吴泽鹏(1991-),男,广东揭阳人,本科,助理工程师,研究方向为食品质量安全;李颂群(1977-),女,广东梅州人,本科,工程师,研究方向为乳品质量管控;李冬梅(1985-),女,福建漳州人,硕士,高级工程师,研究方向为乳制品质量管理和技术改进;范存斐(1986-),女,山西大同人,硕士,助理工程师,研究方向为乳品检测分析;孙克龙(1983-),男,黑龙江肇东人,本科,助理工程师,研究方向为食品质量安全;陈绮萍(1992-),女,广东广州人,本科,助理工程师,研究方向为食品质量安全;张婉莹(1992-),女,广东云浮人,本科,助理工程师,研究方向为食品质量安全;刘 帆(1987-),男,湖南常德人,本科,助理工程师,研究方向为乳品生产质量管理;林思怡(1994-),女,广东肇庆人,本科,助理工程师,研究方向为食品质量安全。

Research Progress on Synergistic Regulation Technology of Nutrition, Health and Emission Reduction in Ruminants

WU Zepeng, WU Weijun*, LI Songqun, LI Dongmei, FAN Cunfei, SUN Kelong, CHEN Qiping, ZHANG Wanying, LIU Fan, LIN Siyi   

  1. Guangzhou Fengxing Dairy Co.,Ltd.,Guangzhou Guangdong 510610
  • Online:2026-06-25 Published:2026-07-22

摘要: 在全球气候变暖及国内奶业振兴战略的双重背景下,兼顾高产高效与绿色低碳的反刍动物养殖模式,已成为行业可持续发展的核心研究课题。本文以智能营养调控与绿色减排协同发展为研究核心,从营养代谢、微生态互作、应激调控三个层面,深入阐释反刍动物营养、机体健康与污染物减排的协同作用机制。文章系统总结了物联网传感、大数据机器学习、智能饲喂设备等智慧技术在精准营养管控中的应用现状,剖析了相关技术闭环管理的优势与现存短板,并重点综述低碳养殖理念下的技术创新成果,涵盖新型饲料资源开发、环保添加剂施用、低排放日粮优化设计等内容,同时梳理了各类技术的产业落地应用成效。研究明确了当前领域存在四大核心问题:营养-健康-减排协同机理研究较为零散;智慧养殖技术规模化适配性不足;减排技术经济效益与长期稳定性欠佳;行业统一的一体化评价标准尚未完善。针对以上瓶颈,本文从多组学技术深化应用、低成本智能设备研发、差异化规模养殖技术体系构建、综合减排评价机制搭建等方面展望未来研究与产业化方向,旨在为我国反刍动物养殖业智能化、健康化、绿色化转型升级提供科学依据与理论支撑。

关键词: 反刍动物, 智能营养调控, 绿色减排, 协同增效机制, 健康养殖

Abstract: Against the dual backdrop of global warming and China’s strategy for revitalizing the dairy industry, ruminant production systems that balance high productivity, high efficiency and low carbon footprint have emerged as a core research topic for the sustainable development of the sector. Centered on the synergistic advancement of intelligent nutritional regulation and green emission reduction, this paper elaborates on the synergistic mechanisms linking ruminant nutrition, animal health and pollutant mitigation from three dimensions: nutritional metabolism, microecological interaction and stress regulation. It systematically reviews the current applications of smart technologies—including Internet of Things (IoT) sensors, big data and machine learning, and intelligent feeding equipment—in precision nutritional management, and analyzes the merits and inherent limitations of closed-loop management enabled by these technologies. This work further highlights technological innovations under the low-carbon breeding framework, such as the exploitation of novel feed resources, application of eco-friendly additives and formulation of low-emission diets, alongside their practical industrial performance. Four pivotal challenges prevailing in this field are identified: fragmented research on the synergistic nutrition-health-emission reduction mechanisms; poor scalability of smart breeding technologies; unsatisfactory economic viability and long-term stability of emission abatement techniques; and the absence of unified integrated evaluation criteria for the industry. To address the above bottlenecks, future research priorities and industrialization pathways are prospected, covering in-depth deployment of multi-omics technologies, development of low-cost intelligent facilities, establishment of differentiated technical frameworks for farms of varying scales, and construction of comprehensive emission assessment systems. This study intends to provide scientific evidence and theoretical support for the intelligent, healthy and green transformation of China’s ruminant livestock industry.

Key words: ruminant, intelligent nutritional regulation, green emission reduction, synergistic enhancement mechanism, healthy farming

[1] Castillo-Lopez E,Petri R M,Ricci S,et al.Dynamic changes in salivation,salivary composition,and rumen fermentation associated with duration of high-grain feeding in cows[J].Journal of Dairy Science,2021,104(4):4875-4892.
[2] Narain D.Animal agriculture's potential financial risks[J].Science(New York,N.Y.),2023,379(6630):341-342.
[3] Calsamiglia S,Ferret A,Reynolds C K,et al.Strategies for optimizing nitrogen use by ruminants[J].Animal,2010,4(7):1184-1196.
[4] Tapio I,Snelling T J,Strozzi F,et al.The ruminal microbiome associated with methane emissions from ruminant livestock[J].Journal of Animal Science and Biotechnology,2017,8(2):289-299.
[5] Patra A,Park T,Kim M,et al.Rumen methanogens and mitigation of methane emission by anti-methanogenic compounds and substances[J].Journal of Animal Science and Biotechnology,2017,8(2):271-288.
[6] Wallace R J,Snelling T J,McCartney C A,et al.Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism[J].Genetics Selection Evolution,2017,49(1):9.
[7] Zeleke A W,Dimonaco N J,Lawther K,et al.Reducing crude protein content in the diet of lactating dairy cows improved nitrogen-use-efficiency and reduced N excretion in urine,whilst having no obvious effects on the rumen microbiome[J].Journal of Animal Science and Biotechnology,2025,16(1):113.
[8] Wallace R J,Rooke J A,McKain N,et al.The rumen microbial metagenome associated with high methane production in cattle[J].BMC Genomics,2015,16(1):839.
[9] Yang Z,Zheng Y,Liu S,et al.Rumen metagenome reveals the mechanism of mitigation methane emissions by unsaturated fatty acid while maintaining the performance of dairy cows[J].Animal Nutrition,2024,18(3):296-308.
[10] Zhu Y,Bu D,Ma L.Integration of multiplied omics,a step forward in systematic dairy research[J].Metabolites,2022,12(3):225.
[11] Zhang J,Gaowa N,Wang Y,et al.Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period[J].Journal of Dairy Science,2023,106(3):2071-2088.
[12] Patra A K.Enteric methane mitigation technologies for ruminant livestock:A synthesis of current research and future directions[J].Environmental Monitoring and Assessment,2012,184(4):1929-1952.
[13] Koch F,Thom U,Albrecht E,et al.Heat stress directly impairs gut integrity and recruits distinct immune cell populations into the bovine intestine[J].Proceedings of the National Academy of Sciences of the United States of America,2019,116(21):10333-10338.
[14] Rajaraman B,Selvaraj A,Bae E K,et al.Ruminal methane emissions,metabolic,and microbial profile of Holstein steers fed forage and concentrate,separately or as a total mixed ration[J].PloS ONE,2018,13(8):e0202446.
[15] Belanche A,de la Fuente O G,Newbold C Jamie.Effect of progressive inoculation of fauna-free sheep with holotrich protozoa and total-fauna on rumen fermentation,microbial diversity and methane emissions[J].Fems Microbiology Ecology,2014,91(3):26.
[16] Li H,La S K,Zhang L Y,et al.Metabolomics and amino acid profiling of plasma reveals the metabolite profiles associated with nitrogen utilisation efficiency in primiparous dairy cows[J].Animal:An International Journal of Animal Bioscience,2024,18(10):101202.
[17] Tedeschi L O,Greenwood P L,Ilan H.Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming[J].Journal of Animal Science,2021,99(2):1-11.
[18] Rial C,Laplacette A,Caixeta L,et al.Metabolic-digestive clinical disorders of lactating dairy cows were associated with alterations of rumination,physical activity,and lying behavior monitored by an ear-attached sensor[J].Journal of Dairy Science,2023,106(12):9323-9344.
[19] Maman L G,Palizban F,Atanaki F F,et al.Co-abundance analysis reveals hidden players associated with high methane yield phenotype in sheep rumen microbiome[J].Scientific Reports,2020,10(1):4995.
[20] Krizova L,Richter M,Trinacty J.Continuous monitoring of ruminal pH and redox-potential in dry cows using a novel wireless ruminal probe[J].Advances in Animal Biosciences,2010,1(1):252.
[21] Li Q S,Wang R,Ma Z Y,et al.Dietary selection of metabolically distinct microorganisms drives hydrogen metabolism in ruminants[J].The ISME Journal,2022,16(11):2535-2546.
[22] He X,Zeng Z,Liu Y,et al.An internet of things-based cluster system for monitoring lactating sows’feed and water intake[J].Agriculture(Basel),2024,14(6):848.
[23] Kaniyamattam K,Tedeschi L O.Asas-Nanp symposium:Mathematical modeling in animal nutrition:agent-based modeling for livestock systems:the mechanics of development and application[J].Journal of Animal Science,2023(101):1-11.
[24] Wang H,Liu J,Dong Z,et al.Artificial intelligence-based metabolic energy prediction model for animal feed proportioning optimization[J].Italian Journal of Animal Science,2023,22(1):942-952.
[25] Chowdhury M R,Wilkinson R G,Sinclair L A.Reducing dietary protein and supplementation with starch or rumen-protected methionine and its effect on performance and nitrogen efficiency in dairy cows fed a red clover and grass silage-based diet[J].Journal of Dairy Science,2024,107(6):3543-3557.
[26] Nadeem G,Anis M I.Investigation of bovine disease and events through Machine Learning Models[J].Pakistan Journal of Agricultural Research,2024,37(2):102-114.
[27] Tangorra F M,Calcante A.Energy consumption and technical-economic analysis of an automatic feeding system for dairy farms:Results from a field test[J].Journal of Agricultural Engineering,2017,49(4):228-232.
[28] Neethirajan S.Transforming the adaptation physiology of farm animals through sensors[J].Animals,2020,10(9):1512.
[29] Liu N,Qi J,An X,et al.A review on information technologies applicable to precision dairy farming:Focus on behavior,health monitoring,and the precise feeding of dairy cows[J].Agriculture,2023,13(10):1858.
[30] Neupane R,Aryal A,Haeussermann A,et al.Evaluating machine learning algorithms to predict lameness in dairy cattle[J].PLoS ONE (v.1;2006),2024,19(7):17.
[31] Durand M,Largouet C,Beaufort L B D,et al.Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms[J].Journal of Animal Science,2023,101:1-11.
[32] Zhou R,Wu J,Lang X,et al.Effects of oregano essential oil on in vitro ruminal fermentation,methane production,and ruminal microbial community[J].Journal of Dairy Science,2020,103(3):2303-2314.
[33] Bach A,Elcoso G,Miguel E,et al.Modulation of milking performance,methane emissions,and rumen microbiome on dairy cows by dietary supplementation of a blend of essential oils[J].Animal:An International Journal of Animal Bioscience,2023,17(6):100825.
[34] Doyle N,Mbandlwa P,Kelly W J,et al.Use of Lactic Acid Bacteria to reduce methane production in ruminants,a critical review[J].Frontiers in Microbiology,2019,10:2207.
[35] Wang J,Liu T,Xu J,et al.Mannan oligosaccharides reduce the carbon footprint by decreasing methane emission and nitrogen excretion in Xiangdong black goats[J].Animal Feed Science and Technology,2025(328):116464.
[36] Jeong J,Yu C,Kang R,et al.Application of propionate-producing bacterial consortium in ruminal methanogenesis inhibited environment with bromoethanesulfonate as a methanogen direct inhibitor[J].Frontiers in Veterinary Science,2024(11):1422474.
[37] Kashenye B N,Zhang J.Gastrointestinal modification based on probiotic feed additive enviro-alleviators to reduce enteric methane production in ruminant and non-ruminant livestock[J].Resources,Environment and Sustainability,2025(22):100276.
[38] Ren Y,Yue C,Wang W,et al.Metabolomics combined with 16S rDNA revealed the effects of low protein diet on rumen microbiome structure,nitrogen utilization rate and differential metabolites in feces and urine of dairy cows[J].Research in Veterinary Science,2025(196):105901.
[39] Mitsunaga M T,Garcia N L B,Pereira R B L,et al.Current trends in Artificial Intelligence and bovine mastitis research:A bibliometric review approach[J].Animals,2024,14(14):2023.
[40] Beauregard A,Dallaire M,Gervais R,et al.Lactational performance of cows fed extruded flaxseed in commercial dairy herds[J].Animal-Open Space,2023(2):100043.
[41] Li S,Zhang M,Hou L,et al.A framework for cost-effectiveness analysis of greenhouse gas mitigation measures in dairy industry with an application to dairy farms in China[J].Journal of Environmental Management,2024(370):122521.
[42] 王乃健,于春凤,管乐胜,等.数智化精准饲喂集成技术在奶牛养殖上的应用——以寒亭优然牧业有限责任公司为例[J].中国乳业,2024(11):29-34.
[43] Hu T,Zhang J,Zhang X,et al.The development of Smart Dairy Farm System and its application in nutritional grouping and Mastitis Prediction[J].Animals,2023,13(5):804.
[44] Tedeschi L O,Menendez H M,Aline R.Asas-Nanp symposium:Mathematical Modeling in animal nutrition:Training the future generation in data and predictive analytics for sustainable development.A summary of the 2021 and 2022 Symposia[J].Journal of Animal Science,2023,101:1-3.
[45] 齐凯. 基于惯性传感器的奶牛行为识别及控制电路中DAC的设计与实现[D].泰安:山东农业大学,2022.
[46] 刘琪,孙威,韩吉雨,等.伊起牛智慧牧业生态系统的开发与应用[J].中国奶牛,2024(5):63-68.
[47] 陈继国. 智慧管理系统在牧场的应用[J].中国乳业,2020(8):57-59.
[48] Weigele H C,Gygax L,Steiner A,et al.Moderate lameness leads to marked behavioral changes in dairy cows[J].Journal of Dairy Science,2017,101(3):2370-2382.
[49] 席瑞谦,王娟,李正义,等.奶牛智能饲喂关键技术研究[J].中国农机化学报,2021,42(2):190-196.
[50] Upton J,Murphy M,Boer I J M D,et al.Investment appraisal of technology innovations on dairy farm electricity consumption[J].Journal of Dairy Science,2015,98(2):898-909.
[51] Prado A D,Vibart R E,Bilotto F M,et al.Feed additives for methane mitigation:Assessment of feed additives as a strategy to mitigate enteric methane from ruminants-Accounting;How to quantify the mitigating potential of using antimethanogenic feed additives[J].Journal of Dairy Science,2025,108(1):411-429.
[52] Murphy M D.Over 20 years of machine learning applications on dairy farms:A comprehensive mapping study[J].Sensors,2021,22(1):52.
[53] Yu Z,Yan M,Wang J.Rumen microbiome nutriomics:Harnessing omics technologies for enhanced understanding of rumen microbiome functions and ruminant nutrition[J].Animal Nutriomics,2024,1(1):10.
[54] Udaya S,Kazi K,Jayawardhane K N,et al.The potential of Novel Gene Editing-Based Approaches in Forages and Rumen Archaea for reducing livestock methane emissions[J].Agriculture,2022,12(11):1780.
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