Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (10): 1797-1806.doi: 10.3864/j.issn.0578-1752.2019.10.012

• ANIMAL SCIENCE·VETERINARY SCIENCE·RESOURCE INSECT • Previous Articles     Next Articles

Development of Models of Methane Emissions from Growing Sheep

ZHOU Yan1,3,DONG LiFeng1,DENG KaiDong2,XU GuiShan3,DIAO QiYu1()   

  1. 1 Key Laboratory of Feed Biotechnology of Ministry of Agriculture/Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081
    2 College of Animal Science , Jinling Institute of Technology, Nanjing 210038
    3 College of Animal Science and Technology, Tarim University, Alar 843300, Xinjiang
  • Received:2018-05-28 Accepted:2019-03-15 Online:2019-05-16 Published:2019-05-23
  • Contact: QiYu DIAO E-mail:diaoqiyu@caas.cn

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Abstract:

【Objective】 This study was aimed to investigate the methane (CH4) emission and develop CH4 prediction models of growing sheep. 【Method】 The average daily weight gain of the treatment of NFC/NDF = 0.78, 1.03 and 2.17 was used as the reference for the restricting level of feed for the other three treatments, respectively, and the digestibility of nutrients and methane production of meat sheep were measured on this basis. Furthermore, the regression relationships were established between CH4 production and dietary nutrient content, nutrient intake, digestible nutrients intake, and apparent digestibility of nutrients. 【Result】When the sheep grew from 25 to 35 kg BW, the most accurate single-variable and multivariate regression model were shown below: CH4（L/d）= -26.58 × NFC/NDF + 92.70（R 2 = 0.772, P <0.001）; CH4 (L/d) = 2.71 × NDFD - 2.45 × DMD - 0.97 × CPD + 124.46 (R 2 = 0.846, P = 0.001). When the sheep grew from 48 to 55 kg BW, the most accurate single-variable and multivariate regression model were shown below: CH4 (L/d) = -57.00 × GE (MJ·kg -1) + 1076.01 (R 2 = 0.581, P = 0.002); CH4/BW 0.75(L·kg -1) = - 0.01 × NDFI (g·d -1) - 0.13 × CPI (g·d -1) + 0.02 × DMI (g·d -1) + 0.84 (R 2 = 0.652, P = 0.019). The most accurate single-variable and multivariate regression model in the overall growing period of sheep were shown below: CH4(L/d)= -26.94 × NFC/NDF + 90.71（R 2= 0.655, P <0.001）; CH4/BW 0.75(L·kg -1) = 0.005 × DNDFI (g·d -1) + 0.011 × DDMI (g·d -1) - 0.097 × DCPI (g·d -1) - 4.78 (R 2 = 0.722, P <0.001). 【Conclusion】 The regression relationships were established for the respective growth periods (25-35 kg and 48-55 kg BW) and the overall growth period (25-55 kg BW). The studies showed that the optimal methane prediction factors for meat sheep at different weight were various, and methane production was greatly affected by dietary NFC/NDF, which could be used as a theoretical basis for the evaluation of methane production under the breeding model in China and a reference for the diets of meat sheep.

Key words: sheep, methane, prediction models, performance production, apparent digestibility

Table 1

Composition and nutrient levels of the experimental diets (%, air dry basis)"

项目
Items		 非纤维性碳水化合物/中性洗涤纤维（NFC/NDF）
0.78 1.03 2.17
原料Ingredients
羊草Chinese wildrye hay 65.00 50.00 35.00
玉米Corn 19.10 32.91 46.69
豆粕Soybean meal 13.00 14.05 15.10
磷酸氢钙CaHPO4 0.60 0.55 0.53
石粉Limestone 0.80 0.99 1.18
食盐NaCl 0.50 0.50 0.50
预混料Premix1） 1.00 1.00 1.00
合计Total 100.00 100.00 100.00
营养水平Nutrient levels
干物质DM 90.68 90.07 87.58
粗蛋白CP 8.08 8.60 9.46
代谢能ME/（MJ·kg-1）2） 8.03 8.02 8.04
粗脂肪EE 2.28 2.68 2.74
中性洗涤纤维NDF 45.59 39.65 36.59
钙Ca 1.14 1.17 1.15
磷P 0.33 0.39 0.41
非纤维性碳水化合物（NFC）3） 35.72 40.66 79.33
精粗比 Forage: Concentrate 35:65 50:50 65:35

Table 2

Correlation coefficient of methane emissions and chemical composition in feeds"

项目Items GE NDF NFC/NDF NDF/OM
25-35 kg -0.876** 0.832** -0.879** 0.858**
48-55 kg -0.762** 0.448 -0.760** 0.440
25-55 kg -0.794** 0.614** -0.809** 0.608**

Table 3

The methane models regressed to nutrient composition of the diets"

回归模型 Regression models R2 P值
CH4 (L·d-1) = 4.25 × NDF% - 134.17 0.691 <0.001
= -56.79 × GE (MJ·kg-1) + 1077.19 0.768 <0.001
= -26.59 × NFC/NDF + 92.70 0.772 <0.001
=207.15 × NDF/OM% - 34.17 × GE (MJ·kg-1) + 567.82 0.823 <0.001
48-55 kg
CH4(L·d-1) = 2.92 × NDF (%) - 80.07 0.201 0.124
= -57.00 × GE (MJ·kg-1) + 1076.01 0.581 0.002
= -26.56 × NFC/NDF + 87.73 0.577 0.003
= -2.31 × NDF/OM (%) - 79.12 × GE (MJ·kg-1) + 1587.73 0.638 0.006
25-55 kg
CH4(L·d-1) = 3.704× NDF (%) - 112.25 0.395 0.001
= -57.33 × GE (MJ·kg-1) + 1084.12 0.655 <0.001
= -26.94 × NFC/NDF + 90.72 0.655 <0.001
= -0.082 × NDF/OM (%) - 58.14 × GE (MJ·kg-1) + 1102.89 0.655 <0.001

Table 4

Correlation coefficient between the methane and nutrients intake, digestive nutrients intake, apparent digestibility of nutrients"

项目Items DMI CPI NDFI DDM DCP DNDF DMD CPD NDFD
25-35 kg
CH4 0.703** 0.171 0.796** 0.215 -0.515 0.641* -0.547 -0.563* -0.049
CH4/BW0.75 0.678* 0.141 0.778** 0.183 -0.521 0.616* -0.546 -0.556* -0.037
48-55 kg
CH4 0.518 0.117 0.672** 0.220 -0.356 0.443 -0.560* -0.585* -0.249
CH4/BW0.75 0.520 0.118 0.674* 0.220 -0.352 0.443 -0.562* -0.578* -0.240
25-55 kg
CH4 0.603** 0.219 0.708** 0.280 -0.401* 0.547** -0.549** -0.592** -0.144
CH4/BW0.75 0.714** 0.371 0.795** 0.392* -0.334 0.603** -0.556** -0.619** -0.172

Table 5

Regression relationship between the daily methane and nutrients intake, digestive nutrients intake, apparent digestibility of nutrients"

回归模型 Regression models R2 P值
25-35 kg
CH4(L·d-1) = 0.12 × NDFI (g) - 18.50 0.634 0.001
= 0.15 × DNDF (g) + 10.68 0.411 0.008
= -1.12 × CPD+ 113.60 0.317 0.045
= 0.22 × NDFI (g) - 0.072 × DMI (g) + 21.79 0.675 0.004
= 0.31 × DNDF (g) - 0.19 × DOM (g) + 106.45 0.661 0.004
= 2.66 × NDFD - 3.56 × DMD + 147.08 0.805 <0.001
= -0.016 × NDFI (g) - 1.49 × CPI (g) + 0.16 × DMI (g) + 45.83 0.826 0.001
= 0.17 × DNDF (g) + 0.001 × DDM (g) - 0.95 × DCP (g) + 66.88 0.819 0.001
= 2.71 × NDFD - 2.45 × DMD - 0.97 × CPD + 124.46 0.846 0.001
48-55 kg
CH4(L·d-1) = 0.18 × NDFI(g) - 47.63 0.452 0.012
= 0.20 × DNDF(g) + 7.06 0.188 0.139
= -1.47 × CPD + 131.61 0.343 0.036
= 0.26 × NDFI(g) - 0.043 × DMI(g) - 36.53 0.476 0.04
= 0.19 × DNDF(g) + 0.019 × DOM(g) - 16.92 0.193 0.342
= 0.46 × NDFD - 2.97 × DMD + 215.98 0.329 0.136
= -0.24 × NDFI(g) + 0.38 × DMI(g)-2.43 × CPI(g) + 15.77 0.647 0.02
= 0.048 × DNDF(g) - 1.72 × DCP(g)+0.21 × DDM(g) - 12.93 0.638 0.022
= 0.40 × NDFD - 1.04 × DMD - 1.12 × CPD + 157.89 0.356 0.245
25-55 kg
CH4(L·d-1) = 0.12 × NDFI(g) - 19.49 0.502 <0.001
= 0.17 × DNDF(g) + 4.54 0.300 0.004
= -1.29 × CPD + 122.17 0.351 0.001
= 0.19 × NDFI(g) - 0.044 × DMI(g) - 2.55 0.526 <0.001
= 0.19 × DNDF(g) - 0.02 × DDM(g) + 15.24 0.306 0.015
= 1.27 × NDFD - 2.93 × DMD + 174.29 0.414 0.002
= -0.14 × NDFI(g) + 0.27 × DMI(g) - 1.91 × CPI(g) + 19.89 0.708 <0.001
= 0.072 × DNDF(g) + 0.13 × DDM(g) - 1.31 × DCP(g) + 17.86 0.648 <0.001
= 1.30 × NDFD - 1.21 × CPD - 1.29 × DMD + 132.99 0.471 0.003

Table 6

Regression relationship between the methane per metabolic body weight and nutrients intake, digestive nutrients intake, apparent digestibility of nutrients"

回归模型 Regression models R2 P值
25-35 kg
CH4/BW0.75(L·kg-0.75) = 0.008 × NDFI(g) - 1.36 0.605 0.002
= 0.01 × DNDF(g) + 0.81 0.380 0.025
= 0.017 × NDFI(g) - 0.006 × DMI(g) + 2.03 0.658 0.005
= 0.023 × DNDF(g) - 0.014 × DOM(g) + 8.25 0.656 0.005
= 0.19 × NDFD - 0.26 × DMD + 10.76 0.777 0.001
= 0.011 × DMI(g) - 0.11 × CPI(g) + 3.85 0.810 <0.001
= 0.013 × DNDF(g) - 0.001 × DDM(g) - 0.068 × DCP(g) + 5.38 0.792 0.002
= 0.19 × NDFD - 0.18 × DMD - 0.066 × CPD + 9.20 0.812 0.001
48-55 kg
CH4/BW0.75(L·kg-0.75) = 0.010 × NDFI(g) - 2.62 0.454 0.012
= 0.011 × DNDF(g) - 0.48 0.196 0.13
= -0.079 × CPD + 7.05 0.335 0.038
= 0.014 × NDFI(g) - 0.002 × DMI(g) - 2.11 0.477 0.039
= 0.011 × DNDF(g) + 0.001 × DOM(g) - 0.98 0.201 0.326
= 0.028 × NDFD - 0.16 × DMD + 11.72 0.335 0.13
= -0.013 × NDFI(g) - 0.133 × CPI(g) + 0.021 × DMI(g) + 0.84 0.652 0.019
= 0.003 × DNDF(g) - 0.091 × DCP(g) + 0.011 × DDM(g) - 0.77 0.629 0.025
= 0.025 × NDFD - 0.051 × CPD - 0.075 × DMD + 9.06 0.354 0.248
25-55 kg
CH4/BW0.75(L·kg-0.75) = 0.010 × NDFI(g) - 2.53 0.632 <0.001
= 0.013 × DNDF(g) - 0.51 0.364 0.001
= -0.098 × CPD + 8.56 0.383 0.001
= 0.012 × NDFI(g) - 0.001 × DMI(g) - 1.95 0.637 <0.001
= 0.013 × DNDF(g) + 0.000 × DDM(g) - 0.67 0.364 0.005
= 0.083 × NDFD - 0.21 × DMD + 12.17 0.401 0.003
= 0.014 × DMI(g) - 0.004 × NDFI(g) - 0.094 × CPI(g) - 0.86 0.721 <0.001
= 0.005 × DNDF(g) + 0.011 × DDM(g) - 0.097 × DCP(g) - 4.78 0.722 <0.001
= 0.086 × NDFD - 0.10 × CPD - 0.064 × DMD + 8.62 0.481 0.002
[1]
[2] 丁丽娜 . 中国羊肉市场供求现状及未来趋势研究[D]. 北京:中国农业大学, 2014.
DING L N . Research on the current situation and future trend of mutton supply and demand in china[D]. Beijing: China Agricultural University, 2014.( in Chinese)
[3] 闵继胜, 胡浩 . 中国农业生产温室气体排放量的测算. 中国人口资源与环境, 2012,22(07):21-27.doi: 1002-2104(2012)07-0021-07.
MING J S, HU H . Calculation of greenhouse gases emission from agricultural production in China. China Population Resources and Environment, 2012,22(07):21-27. doi: 1002-2104 (2012) 07-0021-07. ( in Chinese)
[4] 许贵善, 刁其玉, 纪守坤, 邓凯东, 姜成钢, 屠焰, 刘洁, 赵一广, 马涛, 楼灿 . 20-35 kg杜寒杂交公羔羊能量需要参数. 中国农业科学, 2012,45(24):5082-5090.
doi: 10.3864/j.issn.0578-1752.2012.24.014
XU G S, DIAO Q Y, JI S K, DENG K D, JIANG C G, TU Y, LIU J, ZHAO Y G, MA T, LOU C . Energy requirement parameters of 20-35 kg Dorper and Thin-Tailed Han sheep crossbred male lambs. Scientia Agricultura Sinica, 2012,45(24):5082-5090.( in Chinese)
doi: 10.3864/j.issn.0578-1752.2012.24.014
[5] DENG K D, DIAO Q Y, JIANG C G, TU Y, ZHANG N F, LIU J, MA T, ZHAO Y G, XU G S . Energy requirements for maintenance and growth of Dorper crossbred ram lambs. Livestock Science, 2012,150(1-3):102-110.
doi: 10.1016/j.livsci.2012.08.006
[6] 赵一广 . 肉用绵羊甲烷排放的测定与估测模型的建立[D]. 北京:中国农业科学院, 2012.
ZHAO Y G . Measurement and prediction models of methane emissions from sheep[D]. Beijing: Chinese Academy of Agricultural Sciences, 2012.( in Chinese)
[7] 肖怡 . 三种益生菌对肉羊甲烷排放、物质代谢和瘤胃发酵的影响[D]. 阿拉尔:塔里木大学, 2016.
XIAO Y . Effects of three probiotics on methane emission, nutrient metabolism and rumen fermentation in mutton sheep[D]. Alar: Tarim University, 2016.( in Chinese)
[8] 丁静美, 成述儒, 邓凯东, 张明娇, 刁其玉, 屠焰 . 不同中性洗涤纤维与非纤维性碳水化合物比值饲粮对肉用绵羊甲烷排放的影响. 动物营养学报, 2017(3):806-813.doi: 10.3969/j.issn.1006-267x.2017. 03.010.
DING J M, CHENG S R, DENG K D, ZHANG M J, DIAO Q Y, TU Y . Effects of different neutral detergent fiber/non-fiberous carbohydrate diets on methane emission of meat sheep. Chinese Journal of Animal Nutrition, 2017(3):806-813. doi: 10.3969/j.issn.1006-267x.2017.03.010. ( in Chinese)
[9] 张丽英 . 饲料分析及饲料质量检测技术[M]. 中国农业大学出版社, 2010.
ZHANG L Y. Feed Analysis And Feed Quality Testing Technology [M]. China Agriculture Press, 2010.( in Chinese)
[10] KRISS M . Quantitative relations of the dry matter of the food consumed, the heat production, the gaseous outgo, and the insensible loss in body weight of cattle. Journal of Agricultural Research, 1930,40(3):283-295.
[11] AXELSSON J . The amount of produced methane energy in the European metabolic experiments with adult cattle. Kungliga Lantbrukshogskolans Annaler, 1949: 404-419.
[12] 韩继福, 冯仰廉, 张晓明, 莫放, 赵广永, 杨雅芳 . 阉牛不同日粮的纤维消化、瘤胃内VFA对甲烷产生量的影响. 中国兽医学报, 1997(3):278-280. doi: 10.16303/j.cnki.1005-4545.1997.03.023.
HAN J F, FENG Y L, ZHANG X M, MO F, ZHAO G Y, YANG Y F . Effects of fiber digestion and VFA in the rumen on the methane production in steers of different type of diets.Chinese Journal of Veterinary Science, 1997(3):278-280. doi: 10.16303/j.cnki.1005-4545.1997.03.023. (in Chinese)
[13] 韩继福, 冯仰廉, 张晓明, 莫放, 赵广永, 杨雅芳 . 日粮类型和羊草细度对肉牛瘤胃挥发性脂肪酸比例及能量转化效率的影响. 畜牧兽医学报, 1998,28(2):97-104.
HAN J F, FENG Y L, ZHANG X Y, MO F, ZHAO G Y, YANG Y F . Effects of different diets and different particle sizes of hay on proportion of acetic, propionic and butyric acids in the rumen and on energy conversion in steers feeding. Acta Veterinaria et Zootechnica Sinica, 1998,28(2):97-104.( in Chinese)
[14] PELCHEN A, PETERS K J . Methane emissions from sheep. Small Ruminant Research, 1998,27(2):137-150.
doi: 10.1016/S0921-4488(97)00031-X
[15] BLAXTER K L, CLAPPERTON J L . Prediction of the amount of methane produced by ruminants. British Journal of Nutrition, 1965, 19(4):511-522. doi: 10.1079/BJN19650046.
[16] JIAO H, YAN T, WILLS D A, CARSON A F, MCDOWELL D A . Development of prediction models for quantification of total methane emission from enteric fermentation of young Holstein cattle at various ages. Agriculture Ecosystems and Environment, 2014,183(183):160-166.doi: 10.1016/j.agee.2013.11.004.
doi: 10.1016/j.agee.2013.11.004
[17] 毛宏祥, 高凤仙, 王敏, 蒋载阳, 张秀敏, 龙栋磊, 王荣, 谭支良 . 肉牛胃肠道甲烷排放模型估算精度的评估分析. 畜牧兽医学报, 2017,48(4):690-698. doi: 10.11843/j.issn.0366-6964.2017.04.012.
MAO H X, GAO F X, WANG M, JIANG Z Y, ZHANG X M, LONG D L, WANG R, TAN Z L . Evaluating the accuracy of models to predict enteric methane emissions in beef cattle. Acta Veterinaria et Zootechnica Sinica, 2017,48(4):690-698. doi: 10.11843/j.issn.0366- 6964.2017.04.012. ( in Chinese)
[18] 张子仪 . 从科学发展观谈我国动物营养科研工作的跨越与回归. 中国畜牧杂志, 2005,41(8):3-5.
ZHANG Z Y . A review of the scientific development of modern animal nutrition research in China. Chinese Journal of Animal Science, 2005,41(8):3-5.( in Chinese)
[19] 罗维, 李季, 江永红 . 畜禽集约化养殖对生态环境的影响. 中国畜牧杂志, 2001,37(5):55-56.
LUO W, LI J, JIANG Y J . Effects of intensive culture of livestock and poultry on the ecological environment. Chinese Journal of Animal Science, 2001,37(5):55-56.( in Chinese)
[20] BEAUCHEMIN K A, MCGINN S M . Methane emissions from feedlot cattle fed barley or corn diets. Journal of Animal Science, 2005,83(3):653-661. doi: 10.2527/2005.833653x.
doi: 10.2527/2005.833653x
[21] 赵一广, 刁其玉, 刘洁, 姜成钢, 邓凯东, 屠焰 . 肉羊甲烷排放测定与模型估测. 中国农业科学, 2012,45(13):2718-2727.doi: 10.3864/j.issn.0578-1752.2012.13.016.
doi: 10.3864/j.issn.0578-1752.2012.13.016
ZHAO Y G, DIAO Q Y, LIU J, JIANG C G, DENG K D, TU Y . Estimation and regression models of methane emissions from sheep. Scientia Agricultura Sinica, 2012,45(13):2718-2727.(in Chinese) doi: 10.3864/j.issn.0578-1752.2012.13.016.
doi: 10.3864/j.issn.0578-1752.2012.13.016
[22] JENTSCH W, SCHWEIGEL M, WEISSBACH F, SCHOLZE H, PITROFF W, DERNO M . Methane production in cattle calculated by the nutrient composition of the diet. Archives of Animal Nutrition, 2007,61(1):10-19.doi: 10.1080/17450390601106580.
doi: 10.1080/17450390601106580
[23] VAN SOEST P J, MCCAMMONFELDMAN B, CANNAS A. The feeding and nutrition of small ruminants: application of the Cornell discount system to the feeding of dairy goats and sheep.[C] // Cornell Nutrition Conference for Feed Manufacturers. 1994.
[24] PRUSTY S, SONTAKKE U, KUNDU S S . Review: Methane and nitrous oxide emission from livestock manure. African Journal of Biotechnology, 2015,13(13):4200-4207.
[25] 刘洁, 刁其玉, 赵一广, 姜成钢, 李艳玲, 屠焰 . 饲粮不同NFC/NDF对肉用绵羊瘤胃pH、氨态氮和挥发性脂肪酸的影响. 动物营养学报, 2012,24(6):1069-1077.doi: 1006-267X(2012)06-1069-09.
doi: 10.3969/j.issn.1006-267x.2012.06.011
LIU J, DIAO Q Y, ZHAO Y G, JIANG C G, LI Y L, TU Y . Effects of dietary NFC/NDF ratios on rumen pH, NH3-N and VFA of meat sheep. Chinese Journal of Animal Nutrition, 2012,24(6):1069-1077. doi: 1006-267X(2012)06-1069-09. (in Chinese)
doi: 10.3969/j.issn.1006-267x.2012.06.011
[26] JOHNSON K A, JOHNSON D E . Methane emissions from cattle. Journal of Animal Science, 1995,73(8):2483-2492.doi: 10.2527/1995. 7382483x.
doi: 10.2527/1995.7382483x
[27] RAMIN M, HUHTANEN P . Development of equations for predicting methane emissions from ruminants. Journal of Dairy Science, 2013,96(4):2476-2493. doi: 10.3168/jds.2012-6095.
doi: 10.3168/jds.2012-6095
[28] HRISTOV A N, KEBREAB E, NIU M, OH J, BANNINK A, BAYAT A R, BOLAND T M, BRITO A F, CASPER D P, CROMPTON L A, DIJKSTRA J, EUGENE M, CARNSWORTHY P C, HAQUE N, HELLWING A L F, HUHTANEN P, KREUZER M, KUHLA B, YU Z . Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models. Journal of Dairy Science, 2018,101(7):6655. doi: 10.3168/jds. 2017-13536.
doi: 10.3168/jds.2017-13536
[29] APPUHAMY J A D R N, FRANCE J, KEBREAB E . Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand. Global Change Biology, 2016,22(9):3039-3056. doi: 10.1111/gcb.13339.
doi: 10.1111/gcb.2016.22.issue-9
[30] 赵敏孟 . 杜泊羊生长期能量代谢规律及需要量研究[D]. 泰安: 山东农业大学, 2013.
ZHAO M M . Study on energy metabolic pattern and energy requirement of growing Dorper sheep[D]. Taian: Shandong Agriculture University, 2013.(in Chinese)
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