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Special Issue:
动物营养合辑Animal Nutrition
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| Quantification and prediction of enteric methane emissions from Chinese lactating Holstein dairy cows fed diets with different dietary neutral detergent fiber/non-fibrous carbohydrate (NDF/NFC) ratios |
| DONG Li-feng1*, JIA Peng1,2*, LI Bin-chang1, WANG Bei1, YANG Chun-lei3, LIU Zhi-hao1, DIAO Qi-yu1 |
1 Feed Research Institute, Chinese Academy of Agricultural Sciences/Sino-US Joint Lab on Nutrition and Metabolism of Ruminant/CAAS-CIAT Joint Laboratory in Advanced Technologies for Sustainable Agriculture, Beijing 100081, P.R.China
2 Institute of ruminant research, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, P.R.China
3 College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, 310014, P.R.China |
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摘要
本研究旨在研究不同中性洗涤纤维/非纤维碳水化合物（NDF/NFC）比例的日粮对不同泌乳阶段荷斯坦奶牛生产性能、养分消化率和CH4排放的影响，建立日粮结构和产奶量与CH4的模型，并将该模型与其他已发表的预测模型进行了对比分析。试验将36头泌乳奶牛分为三个处理组，即NDF/NFC=1.19（低NDF/NFC组）、NDF/NFC=1.54（中NDF/NFC处理组）和NDF/NFC=1.68（高NDF/NFC处理组）。使用六氟化硫示踪法测定瘤胃CH4排放量，酸不溶性灰分法测定营养物质消化率。结果表明，泌乳早期、中期和晚期奶牛随着日粮NDF/NFC比例的增加，干物质采食量（DMI）均显著降低（P<0.01），分别从20.9 降至15.4 kg d-1，从15.3降至11.6 kg d-1，从16.4降至15.0 kg d-1。三种处理中，泌乳中期和后期奶牛的DM和总能(GE)消化率最高(P<0.05)。随着日粮中NDF/NFC比例的增加，泌乳早期、中期和晚期奶牛CH4排放量呈现线性增加(P<0.05)，分别从325.2到391.9 kg d-1，261.0到399.8 kg d-1，241.8到390.6 kg d-1。每单位代谢体重、DM摄入量、NDF摄入量或脂肪校正产奶量下的CH4排放量随着日粮中NDF/NFC比例的增加而增加。此外，泌乳早期、中期和晚期奶牛随着日粮NDF/NFC比例的增加，CH4能占摄入GE的比例显著增加（P<0.05），分别从4.87%到8.12%，5.16%到9.25%，5.06%到8.17%。建立的模型结果表明，使用DM摄入量作为单一变量的方程比使用其他饲粮或产奶量变量的方程产生更大的R2值。将每个泌乳阶段获得的数据合并后，与任何其他预测变量相比，DM摄入量仍然是更好的CH4排放量预测指标（R2=0.786，P=0.026）。与本文开发的预测方程相比，先前公布的方程具有更高水平的均方根预测误差，反映它们无法准确预测中国荷斯坦奶牛的CH4排放水平。中国饲养模式下的泌乳奶牛CH4产量的量化以及相关预测方程的建立，将有助于建立区域或国家的CH4排放清单和改进乳制品生产过程中的CH4减排方法。
Abstract Methane (CH4) emissions from ruminant production are a significant source of anthropogenic greenhouse gas production, but few studies have examined the enteric CH4 emissions of lactating dairy cows under different feeding regimes in China. This study aimed to investigate the influence of different dietary neutral detergent fiber/non-fibrous carbohydrate (NDF/NFC) ratios on production performance, nutrient digestibility, and CH4 emissions for Holstein dairy cows at various stages of lactation. It evaluated the performance of CH4 prediction equations developed using local dietary and milk production variables compared to previously published prediction equations developed in other production regimes. For this purpose, 36 lactating cows were assigned to one of three treatments with differing dietary NDF/NFC ratios: low (NDF/NFC=1.19), medium (NDF/NFC=1.54), and high (NDF/NFC=1.68). A modified acid-insoluble ash method was used to determine nutrient digestibility, while the sulfur hexafluoride technique was used to measure enteric CH4 emissions. The results showed that the dry matter (DM) intake of cows at the early, middle, and late stages of lactation decreased significantly (P<0.01) from 20.9 to 15.4 kg d–1, 15.3 to 11.6 kg d–1, and 16.4 to 15.0 kg d–1, respectively, as dietary NDF/NFC ratios increased. Across all three treatments, DM and gross energy (GE) digestibility values were the highest (P<0.05) for cows at the middle and late lactation stages. Daily CH4 emissions increased linearly (P<0.05), from 325.2 to 391.9 kg d–1, 261.0 to 399.8 kg d–1, and 241.8 to 390.6 kg d–1, respectively, as dietary NDF/NFC ratios increased during the early, middle, and late stages of lactation. CH4 emissions expressed per unit of metabolic body weight, DM intake, NDF intake, or fat-corrected milk yield increased with increasing dietary NDF/NFC ratios. In addition, CH4 emissions expressed per unit of GE intake increased significantly (P<0.05), from 4.87 to 8.12%, 5.16 to 9.25%, and 5.06 to 8.17% respectively, as dietary NDF/NFC ratios increased during the early, middle, and late lactation stages. The modelling results showed that the equation using DM intake as the single variable yielded a greater R2 than equations using other dietary or milk production variables. When data obtained from each lactation stage were combined, DM intake remained a better predictor of CH4 emissions (R2=0.786, P=0.026) than any other variables tested. Compared to the prediction equations developed herein, previously published equations had a greater root mean square prediction error, reflecting their inability to predict CH4 emissions for Chinese Holstein dairy cows accurately. The quantification of CH4 production by lactating dairy cows under Chinese production systems and the development of associated prediction equations will help establish regional or national CH4 inventories and improve mitigation approaches to dairy production.
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Received: 19 August 2020
Accepted: 30 August 2021
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Fund:
This study was supported by the Key Program for International S&T Cooperation Projects of China (2016YFE0109000), the National Natural Science Foundation of China (31802085 and 31702133), and the Central Public-interest Scientific Institution Basal Research Fund of Chinese Academy of Agricultural Sciences (Y2021GH18-2).
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| About author: DONG Li-feng, Tel: +86-10-82106094, E-mail: donglifeng@caas.cn; JIA Peng, Tel: +86-10-82106094, E-mail: jiapeng123123@163.com; Correspondence DIAO Qi-yu, Tel: +86-10-82106055, Fax: +86-10-62169105, E-mail: diaoqiyu@caas.cn
* These authors contributed equally to this study. |
Cite this article:
DONG Li-feng, JIA Peng, LI Bin-chang, WANG Bei, YANG Chun-lei, LIU Zhi-hao, DIAO Qi-yu.
2022.
Quantification and prediction of enteric methane emissions from Chinese lactating Holstein dairy cows fed diets with different dietary neutral detergent fiber/non-fibrous carbohydrate (NDF/NFC) ratios. Journal of Integrative Agriculture, 21(3): 797-811.
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Agle M, Hristov A N, Zaman S, Schneider C, Ndegwa P M, Vaddella V K. 2010. Effect of dietary concentrate on rumen fermentation digestibility and nitrogen losses in dairy cows. Journal of Dairy Science, 93, 4211–4222.
Aguerre M J, Wattiaux M A, Powell J M. 2012. Emissions of ammonia, nitrous oxide, methane, and carbon dioxide during storage of dairy cow manure as affected by dietary forage-to-concentrate ratio. Journal of Dairy Science, 95, 1–8.
Agurre M J, Wattiaux M A, Powell J M, Broderick G A, Arndt C. 2011. Effect of forage-to-concentrate ratio in dairy cow diets on emission of methane, carbon dioxide, and ammonia, lactation performance, and manure excretion. Journal of Dairy Science, 94, 3081–3093.
Allen M S. 2000. Effects of diet on short-term regulation of feed intake by lactating dairy cattle. Journal of Dairy Science, 83, 1598–1624.
AOAC (Association of Official Analytical Chemists). 2016. International Official Methods of Analysis. 20th ed. Association of Official Analytical Chemists International, Rockville, MD, USA.
Appuhamy J A D R N, France J, Kebreab E. 2016. Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand. Global Change Biology, 22, 3039–3056.
Beukes P C, Gregorini P, Romera A J, Levy G, Waghorn G C. 2010. Improving production efficiency as a strategy to mitigate greenhouse gas emissions on pastoral dairy farms in New Zealand. Agriculture, Ecosystems and Environment, 136, 358–365.
Charmley E, Williams S R O, Moate P J, Hegarty R S, Herd R M, Oddy V H, Reyenga P, Staunton K M, Anderson A, Hannah M C. 2016. A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science, 56, 169–180.
Dong L F, Ferris C P, McDowell D A, Yan T. 2015. Effects of diet forage proportion on maintenance energy requirement and the efficiency of metabolizable energy use for lactation by lactating dairy cows. Journal of Dairy Science, 98, 1–10.
Dong L F, Li B C, Diao Q Y. 2019. Effects of dietary forage proportion on feed intake, growth performance, nutrient digestibility, and enteric methane emissions of Holstein heifers. Animals, 9, 725.
Ellis J L, Kebreab E, Odongo N E, McBride B W, Okine E K, France J. 2007. Prediction of methane production from dairy and beef cattle. Journal of Dairy Science, 90, 3456–3467.
Engelke S W, Daş G, Derno M, Tuchscherer A, Wimmers K, Rychlik M, Kienberger H, Berg W, Kuhla B, Metges C C. 2019. Methane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed. Journal of Dairy Science, 102, 1788–1802.
van Gastelen S, Dijkstra J, Bannink A. 2019. Are dietary strategies to mitigate enteric methane emission equally effective across dairy cattle, beef cattle, and sheep. Journal of Dairy Science, 102, 6109–6130.
Hassanat F, Gervais R, Benchaar C. 2017. Methane production, ruminal fermentation characteristics, nutrient digestibility, nitrogen excretion, and milk production of dairy cows fed conventional or brown midrib corn silage. Journal of Dairy Science, 100, 2625–2636.
Höglund-Isaksson L. 2012. Global anthropogenic methane emissions 2005–2030: Technical mitigation potentials and costs. Atmospheric Chemistry and Physics, 12, 9079–9096.
Hristov A N, Kebreab E, Niu M, Oh J, Bannink A, Bayat A R, Boland T B, Brito A F, Casper D P, Crompton L A, Dijkstra J, Eugene M, Garnsworthy P C, Haque N, Hellwing A L F, Huhtanen P, Kreuzer M, Kuhla B, Lund P, Madsen J, et al. 2018. Uncertainties in enteric methane inventories, measurement techniques, and prediction models. Journal of Dairy Science, 101, 1–20.
Hristov A N, Ott T, Tricarico J, Rotz A, Waghorn G, Adesogan A, Dijkstra J, Montes F, Oh J, Kebreab E, Oosting S J, Gerber P J, Henderson B, Makkar H P S, Firkins J L. 2013. Mitigation of methane and nitrous oxide emissions from animal operations: III. A review of animal management mitigation options. Journal of Animal Science, 91, 5095–5113.
Huhtanen P, Ramin M, Hristov A N. 2019. Enteric methane emission can be reliably measured by the GreenFeed monitoring unit. Livestock Science, 222, 21–40.
IPCC (Intergovernmental Panel on Climate Change). 2006. Intergovernmental Panel on Climate Change guidelines for National Greenhouse Gas Inventories. Vol. 4. Agricultural, Forestry and Other Land Use. Chapter 10: Emissions from Livestock and Manure Management. IGES, Kanagawa, Japan.
Jiao H P, Yan T H, Wills D A, Carson A F, McDowell D A. 2014. Development of prediction models for quantification of total methane emission from enteric fermentation of young Holstein cattle at various ages. Agriculture, Ecosystems and Environment, 183, 160–166.
Johnson K A, Johnson D E. 1995. Methane emissions from cattle. Journal of Animal Science, 73, 2483–2492.
Kebreab E, Johnson K A, Archibeque S L, Pape D, Wirth Y. 2008. Model for estimating enteric methane emissions from United States dairy and feedlot cattle. Journal of Animal Science, 86, 2738–2748.
van Keulen J, Young B A. 1977. Evaluation of acid-insoluble ash as a natural marker in ruminant digestibility studies. Journal of Animal Science, 44, 282–287.
Krizsan S J, Ahvenjärvi S, Huhtanen O. 2010. A meta-analysis of passage rate estimated by rumen evacuation with cattle and evaluation of passage rate prediction models. Journal of Dairy Science, 93, 5890–5901.
Lage C F A, Raisnen S E, Stefenoni H, Melgar A, Chen X, Oh J, Fetter M E, Kniffen M, Fabin R A, Hristov A N. 2021. Lactational performance, enteric gas emissions, and plasma amino acid profile of dairy cows fed diets with soybean or canola meals included on an equal protein basis. Journal of Dairy Science, 104, 3052–3066.
Lan W, Yang C L. 2019. Ruminal methane production: Associated microorganisms and the potential of applying hydrogen-utilizing bacteria for mitigation. Science of the Total Environment, 654, 1270–1283.
Kamilaris C, Dewhurst R J, Sykes A J, Alexander P. 2020. Modelling alternative management scenarios of economic and environmental sustainability of beef finishing systems. Journal of Cleaner Production, 253, 119888.
Mills J A N, Kebreab E, Yates C M, Crompton L A, Cammell S B, Dhanoa M S, Agnew R E, France J. 2003. Alternative approaches to predicting methane emissions from dairy cows. Journal of Animal Science, 81, 3141–3150.
Moate P J, Williams S R O, Jacobs J L, Hannah M C, Beauchemin K A, Eckard R J, Wales W J. 2016. Wheat is more potent than corn or barley for dietary mitigation of enteric methane emissions from dairy cows. Journal of Dairy Science, 100, 1–15.
Moraes L E, Strathe A B, Fadel J G, Casper D P, Kebreab E. 2014. Prediction of enteric methane emissions from cattle. Global Change Biology, 20, 2140–2148.
Niu M, Kebreab E, Hristov A N, Oh J, Arndt C, Bannink A, Bayat A R, Brito A F, Boland T, Casper D, Crompton L A, Dijkstra J, Eugene M A, Garnsworthy P C, Haque M N, Hellwing A L F, Huhtanen P, Kreuzer M, Kuhla B, Lund P, et al. 2018. Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology, 24, 3368–3389.
NY/T 34–2004. 2004. Feeding Standard of Dairy Cattle. Ministry of Agriculture of the People’s Republic of China, Beijing, China. (in Chinese)
Oberson J L, Probst S, Schlegel P. 2019. Magnesium absorption as influenced by the rumen passage kinetics in lactating dairy cows fed modified levels of fibre and protein. Animal, 13, 1412–1420.
Patra A K. 2014. Prediction of enteric methane emission from buffaloes using statistical models. Agriculture, Ecosystems and Environment, 195, 139–148.
Patra A K, Lalhriatpuii M. 2016. Development of statistical models for prediction of enteric methane emission from goats using nutrient composition and intake variables. Agriculture, Ecosystems and Environment, 215, 89–99.
Payne R W, Harding S A, Murray D A, Soutar D M, Baird D B, Glaser A I, Channing I C, Welham S J, Gilmour A R, Thompson R, Webster R. 2013. The Guide to GenStat Release 16, Part 2: Statistics. VSN International, Hemel Hempstead, UK.
van Soest P J, Robertson J B, Lewis B A. 1991. Symposium: Carbohydrate methodology, metabolism and nutritional implications in dairy cattle methods for dietary fiber, neutral detergent fiber and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science, 74, 4377–4384.
Steinfeld H, Gerber P, Wassenaar T, Castel V, Rosales M, de Haan C. 2006. Livestock’s Long Shadow. Food and Agriculture Organization, Rome.
St-Pierre N R. 2003. Reassessment of biases in predicted nitrogen flows to the duodenum by NRC 2001. Journal of Dairy Science, 86, 344–350.
Tangjitwattanachai N, Phaowphaisal I, Otsuka M, Ommart K. 2015. Enteric methane emission, energetic efficiency and energy requirements for the maintenance of beef cattle in the tropics. Japan Agricultural Research Quarterly, 9, 399–407.
Trotta R J, Klotz J L, Harmon D L. 2018. Effects of source and level of dietary energy supplementation on in vitro digestibility and methane production from tall fescue-based diets. Animal Feed Science and Technology, 242, 41–47.
Velarde-Guillén J, Pellerin D, Benchaar C, Wattiaux M A, Charbonneau É. 2019. Development of an equation to estimate the enteric methane emissions from Holstein dairy cows in Canada. Canadian Journal of Animal Science, 99, 792–803.
Wattiaux PAS M A, Uddin M E, Letelier P, Jackson R D, Larson R A. 2019. Emission and mitigation of greenhouse gases from dairy farms: The cow, the manure, and the field. Applied Animal Science, 35, 238–254.
van Wyngaard J D V, Meeske R, Erasmus L J. 2018. Effect of concentrate level on enteric methane emissions, production performance, and rumen fermentation of Jersey cows grazing kikuyu-dominant pasture during summer. Journal of Dairy Science, 101, 1–13.
Yan T, Agnew R E, Gordon F J, Porter M G. 2000. Prediction of methane energy output in dairy and beef cattle offered grass silage-based diets. Livestock Production Science, 64, 253–263.
Yang Y, Zhang J J, Wang C. 2018. Forecasting China’s carbon intensity: Is China on track to comply with its Copenhagen commitment? The Energy Journal, 39, 63–68.
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