RESEARCH ARTICLE

The effects of dietary supplementation with 3-nitrooxypropanol on enteric methane emissions, rumen fermentation, and production performance in ruminants: a meta-analysis

Hanbeen Kim1,#https://orcid.org/0000-0003-2803-7318, Hyo Gun Lee1,#http://orcid.org/0000-0002-3286-7368, Youl-Chang Baek2https://orcid.org/0000-0003-4454-5339, Seyoung Lee3https://orcid.org/0000-0001-7991-0565, Jakyeom Seo1,*https://orcid.org/0000-0002-9176-5206
Author Information & Copyright
1Department of Animal Science, Life and Industry Convergence Research Institute, Pusan National University, Miryang 50463, Korea
2National Institute of Animal Science, Rural Development Administration, Wanju 55365, Korea
3Division of Animal Husbandry, Yonam College, Cheonan 31005, Korea
*Corresponding author: Jakyeom Seo, Department of Animal Science, Life and Industry Convergence Research Institute, Pusan National University, Miryang 50463, Korea. Tel: +82-55-350-5513 E-mail: jseo81@pusan.ac.kr

#These authors contributed equally to this work.

© Copyright 2020 Korean Society of Animal Science and Technology. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Oct 31, 2019 ; Revised: Nov 13, 2019 ; Accepted: Dec 2, 2019

Published Online: Jan 31, 2020

Abstract

The aim of this study was to investigate the effects of 3-nitrooxypropanol (NOP) on gas production, rumen fermentation, and animal performances depending on animal type using a meta-analysis approach. A database consisted of data from 14 studies, 18 experiments and 55 treatments. The supplementation of NOP linearly decreased methane (CH4) emissions [g/kg dry matter intake (DMI)] regardless of animal type and length of experimental period (beef, p < 0.0001, R2 = 0.797; dairy, p = 0.0003, R2 = 0.916; and long term, p < 0.0001, R2 = 0.910). The total volatile fatty acids (VFA) concentration and the proportion of acetate, based on beef cattle database, were significantly decreased with increasing NOP supplementation (p = 0.0015, R2 = 0.804 and p = 0.0003, R2 = 0.918), whereas other individual VFAs was increased. Based on the dairy database, increasing levels of NOP supplementation linearly decreased proportion of acetate (p = 0.0284, R2 = 0.769) and increased that of valerate (p = 0.0340, R2 = 0.522), regardless of significant change on other individual VFAs. In animal performances, the DMI, from beef cattle database, tended to decrease when the levels of NOP supplementation increased (p = 0.0574, R2 = 0.170), whereas there was no significant change on DMI from dairy cattle database. The NOP supplementation tended to decrease milk yield (p = 0.0606, R2 = 0.381) and increase milk fat and milk protein (p = 0.0861, R2 = 0.321, p = 0.0838, R2 = 0.322). NOP is a viable candidate as a feed additive because of its CH4 mitigation effects, regardless of animal type and experiment period, without adverse effects on animal performances.

Keywords: Animal performance; Feed additive; Methane mitigation; 3-Nitrooxypropanol; Rumen fermentation

INTRODUCTION

Reducing methane (CH4) emissions in rumen is a critical challenge to ruminant nutritionists. This is because CH4 is a substantial anthropogenic greenhouse gas, possessing a global warming potential 28–34 times greater than carbon dioxide (CO2) [1], and makes up 2%–12% of the loss of dietary gross energy (GE) intake to the ruminants [2]. Thus, there have been numerous global efforts to mitigate ruminal CH4 emissions, using various feed additives such as tannin [3,4], dietary fats containing polyunsaturated fatty acids [5], plant essential oils [6,7], and phytochemicals [8,9].

3-Nitrooxypropanol (NOP) is a chemical compound, designed by Duval and Kindermann [10], which reduces CH4 emissions produced by the rumen from microbial fermentation. The NOP is a structural analogue of methyl coenzyme-M, which inhibits the activity of methyl coenzyme-M reductase related to the final step of methanogenesis [11]. Until now, total 14 in vivo studies using NOP supplementation were performed on various domestic ruminants, including sheep [12], beef cattle [1319], and dairy cattle [2025]. According to the results of previous studies using NOP in vivo, the CH4 emissions and proportion of acetate (% total volatile fatty acids, VFA) clearly decreased, whilst the proportion of propionate (% total VFA) significantly increased, but any adverse effects were not detected.

In a recent meta-analysis, Jayanegara et al. [26] observed that increasing NOP supplementation linearly decreased CH4 emissions regardless of type of CH4 unit, when a meta-analysis was investigated on 10 in vivo studies [12,1624]. Dijkstra et al. [27] revealed that NOP supplementation has stronger CH4 mitigation effects in dairy cattle than in beef cattle, and those effects were decreased in increasing dietary fiber content, when a meta-analysis was conducted using 9 in vivo studies [1624]. With our knowledge, there is no meta-analysis study investigating the effects of supplementation of NOP on CH4 reduction in a long term experiment, and the changes of rumen fermentation by NOP supplementation in a related with ruminant types was not analyzed as well.

In the present meta-analysis, therefore, we hypothesized that NOP supplementation might be affected differently on rumen fermentation characteristics depending upon animal type adding recent in vivo studies which was not included in previous meta-analysis studies [1315,25]. The aim of this study was to investigate the effects of NOP on enteric gas production, rumen fermentation, and animal performances depending on animal type using a meta-analysis approach.

MATERIALS AND METHODS

Development of database

All studies used in the meta-analysis were collected from the Google Scholar database using NOP, CH4, and ruminants as keywords. In total, variables from 14 studies, 18 experiments and 55 treatments were integrated into the database, as described in Table 1. The investigated factors were gas production (CH4, H2, and CO2), rumen fermentation parameters [pH, total VFA production, acetate, propionate, butyrate, iso-butyrate, valerate, iso-valerate, acetate to propionate ratio (A:P ratio), ammonia nitrogen (NH3-N), bacteria, protozoa, and methanogen], and production performances [dry matter intake (DMI), dry matter digestibility (DMD), organic matter digestibility (OMD), neutral detergent fiber digestibility (NDFD), milk yield (MY), milk fat (MF), milk protein (MP), milk lactose (ML), and fat-corrected milk (FCM)]. Since all variables were not available across all experiments in the database, therefore, the number of observations used for regression analyses varied between independent and response variables. Units for NOP supplementation were expressed as NOP mg/kg of DMI. There were several differences on experimental animal, administration method of NOP, forage ratio in the diet, neutral detergent fiber (NDF) composition, and measurements methods of CH4 emissions among all used studies [1225] (Table 1). In short, the administration methods of NOP were direct administration via cannula, top dressed, and mixed with diet. forage ratio in dairy cattle showed narrow range (37.9% to 65.8%), although forage ratio in beef cattle were wide range (8% to 100%). Measurements of CH4 emissions were conducted using a respiratory chamber system equipped with infrared CH4 detectors, GreenFeed System (C-Lock Inc., Rapid City, SD, USA), and the sulfur hexafluoride (SF6) tracer gas method.

Table 1. Summary of the studies used for the meta-analysis
Study no. References Animals NOP level (mg/kg DMI) Methods of administration Forage ratio (in TMR, %) NDF(%DM) CH4 measurement
1 Martínez-Fernández et al. [9] Sheep 0 and 111.2 Direct administration via cannula 54.4 41.5 RCS
2 Romero-Perez et al. [15] Beef 0, 47.4, 143.6, and 304.9 Top dressed 60.0 37.6 RCS
3 Romero-Perez et al. [16] Beef 0 and 280.1 Mixed with diet 60.0 38.6 RCS
4 Vyas et al. [13] Beef 0, 100, and 200 Mixed with diet 8 and 70.0 19.2 and 36.4 RCS
5 Vyas et al. [14] Beef 0, 50, 75, 100, 150, and 200 Mixed with diet 8.0 and 65.0 27.0 and 41.7 RCS
6 Vyas et al. [12] Beef 0, 125 and 200 Mixed with diet 8.0 and 65.0 18.4 and 40.5 RCS
7 Martinez-Fernandez et al. [11] Beef 0 and 337.8 Mixed with roughage 100.0 66.1 RCS
8 Kim et al. [10] Beef 0 and 100 Mixed with diet and direct administration via cannula 9.8 and 64.4 14.6 and 28.3 GFS
9 Haisan et al. [17] Dairy 0 and 129.5 Mixed with diet 37.9 26.5 SF6
10 Reynolds et al. [21] Dairy 0, 26.6 and 135.1 Direct administration via cannula 51.2 39.8 RCS
11 Hristov et al. [19] Dairy 0, 40.0, 60.0, and 80.0 Mixed with diet 60.7 27.6 GFS and SF6
12 Lopes et al. [20] Dairy 0 and 60.0 Mixed with diet 55.5 30.9 GFS
13 Haisan et al. [18] Dairy 0, 68.3 and 132.3 Mixed with diet 60.0 33.8 SF6
14 Van Wesemael et al. [22] Dairy 0, 71.7 and 75.1 Mixed with roughage and mixed in pellet 65.8 34.6 GFS

NOP, 3-nitrooxypropanol; DMI, dry matter intake; TMR, total mixed ration; NDF, neutral detergent fiber; CH4, methane; RCS, respiratory chamber system; SF6, sulfur hexafluoride tracer; GFS, GreenFeed System; VFA, volatile fatty acids; MP, microbial population; MC, milk component.

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Statistical analysis

All statistical analyses were carried out using the PROC UNIVARIATE, PROC MIXED and PROC REG procedures of the SAS ver. 9.4 (SAS Institute Inc., Cary, NC, USA). Outliers in the dataset were screened using an absolute studentized residual value (> 2) before conducting the statistical analysis. The dataset was analyzed statistically using PROC MIXED of SAS (2008), according to St-Pierre [28]. The model was as follows:

Y i j = B 0 + B 1 X i j + s i + b i X i j + e i j

Where Yij is the dependent variable, B0 is the overall intercept across all experiments (fixed effect), B1 is the slope of Y on X (fixed effect), Xij is the value j of the continuous predictor variable X in experiment i (the concentration of dietary NOP supplementation), si is the random effect of experiment i, bi is the random effect of the slope in experiment i, and eij is the unexplained residual error. The variable experiment was declared in the CLASS statement. The slops and intercepts by experiment were included as random effects, and an unstructured variance-covariance matrix (type = un) was performed at the random part of the model [28]. When random covariance between slope and intercept was not significant, a variance-covariance matrix (type = vc) was performed [28]. Individual observed values of the dependent variables were corrected with corresponding residual errors and regressed on the X variable (the concentration of dietary NOP supplementation). The relationship between the dependent variables (CH4 production, total VFA production, the proportion of each VFA, and DMI) and NOP supplementation was expressed in three types of linear regression under different animal databases (e.g., total, beef, and dairy). Along with the model statistics from the regression equations, the p-value of each intercept and slope, root mean square error (RMSE), and coefficient of determination (R2) are also presented.

RESULTS

Description of the database

The description of all variables included in database listed in Table 2 and variables on beef and dairy cattle described in Table 3. The CH4 emissions expressed in terms of g/kg DMI, were 17.29 ± 5.481 g/kg DMI (Table 2). The emission of H2 and CO2 and rumen fermentation parameters varied widely in different studies suggesting that relatively a wide range of data were included in the database. The mean value of CH4 emission (g/kg DMI) on beef database and dairy database ranged from 3.10 to 28.20 and 7.18 to 23.50 g/kg DMI, respectively. The mean and standard deviation of total VFA concentration were higher on beef cattle studies than on dairy cattle studies (112.61 ± 25.142 and 99.22 ± 8.424 mM, respectively), although each VFA proportion of beef database was similar with those of dairy database (Table 3). There was a big difference on mean of DMI between beef database and dairy database (beef DMI, 9.09 ± 1.751; dairy DMI, 22.22 ± 3.732 kg/d).

Table 2. Description of gas emission, rumen fermentation characteristics, and production performances in ruminant database
Parameter Parameter estimates
n Mean SD Median MIN MAX
Total data
 CH4 (g/kg DMI) 55 17.29 5.481 17.80 3.10 28.20
 H2 (g/d) 26 1.95 3.091 0.89 0.00 12.43
 CO2 (g/d) 25 10,674.44 3,005.371 10,500.00 6,240.00 14,905.00
 pH 22 6.43 0.210 6.43 6.13 6.96
 Total VFA (mM) 30 108.26 20.857 103.45 74.50 160.50
 Acetate (%) 30 58.74 7.291 58.70 44.10 74.40
 Propionate (%) 30 22.66 5.976 21.25 14.30 42.60
 Butyrate (%) 30 12.85 2.927 13.40 5.00 17.80
 Iso-butyrate (%) 30 1.12 0.337 1.08 0.57 2.10
 Valerate (%) 30 1.94 0.472 1.85 1.20 3.19
 Iso-valerate (%) 30 1.92 0.642 1.97 0.66 3.18
 A:P ratio 30 2.88 0.919 2.77 1.06 4.90
 Ammonia (mg/dL) 28 12.75 12.368 7.90 2.72 51.00
 Bacteria 15 8.18 9.223 7.13 0.00 34.50
 Methanogen 15 4.24 3.919 2.78 0.01 15.46
 Protozoa 13 2.90 1.552 2.57 1.35 5.56
 DMI (kg/d) 55 12.85 6.967 10.30 0.84 28.00
 DMD (%) 14 68.10 4.863 68.70 58.40 75.30
 OMD (%) 14 70.26 4.291 70.45 62.00 77.40
 NDFD (%) 14 49.29 9.970 50.50 30.70 64.40

1) 1010/g of rumen digesta.

2) 108/g of rumen digesta.

3) 105/g of rumen digesta.

SD, standard deviation; MIN, minimum value in database; MAX, maximum value in database; CH4, methane; DMI, dry matter intake; H2, hydrogen; CO2, carbon dioxide; VFA, volatile fatty acids; A:P ratio, acetate to propionate ratio; DMD, dry matter digestibility; OMD, organic matter digestibility; NDFD, neutral detergent fiber digestibility.

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Table 3. Description of gas emission, rumen fermentation characteristics, and performances in beef and dairy cattle database
Parameter Parameter estimates
n Mean SD Median MIN MAX
Beef
 CH4 (g/kg DMI) 36 17.52 6.031 17.85 3.10 28.20
 pH 14 6.46 0.249 6.45 6.13 6.96
 Total VFA (mM) 18 112.61 25.142 104.65 74.50 160.50
 Acetate (%) 18 58.41 8.498 59.65 44.10 74.40
 Propionate (%) 18 23.49 7.216 21.00 15.90 42.60
 Butyrate (%) 18 12.28 3.475 12.95 5.00 17.80
 Iso-butyrate (%) 18 1.11 0.180 1.07 0.88 1.52
 Valerate (%) 18 1.92 0.552 1.84 1.20 3.19
 Iso-valerate (%) 18 2.08 0.558 1.97 1.20 3.18
 A:P ratio 18 2.85 0.998 2.89 1.06 4.70
 DMI (kg/d) 36 9.09 1.751 9.13 6.05 12.10
Dairy
 CH4 (g/kg DMI) 17 16.81 4.560 17.80 7.18 23.50
 pH 8 6.37 0.106 6.38 6.20 6.50
 Total VFA (mM) 10 99.22 8.424 99.95 85.80 109.00
 Acetate (%) 10 57.70 4.195 57.94 52.10 65.70
 Propionate (%) 10 22.53 2.144 22.39 19.30 26.40
 Butyrate (%) 10 14.13 1.380 14.21 11.10 15.90
 Iso-butyrate (%) 10 0.94 0.238 1.00 0.57 1.19
 Valerate (%) 10 2.03 0.331 2.09 1.57 2.57
 Iso-valerate (%) 10 1.63 0.768 1.93 0.66 2.58
 A:P ratio 10 2.65 0.455 2.64 2.02 3.51
 DMI (kg/d) 17 22.22 3.732 21.30 18.30 28.00
 Milk yield (kg/d) 17 32.69 7.803 28.20 25.80 46.40
 Milk fat (%) 17 3.93 0.332 4.02 3.31 4.35
 Milk protein (%) 17 3.28 0.198 3.19 3.06 3.61
 Milk lactose (%) 14 4.61 0.195 4.65 4.26 4.81
 FCM (kg/d) 17 32.25 8.143 29.00 23.90 46.41

SD, standard deviation; MIN, minimum value in database; MAX, maximum value in database; CH4, methane; DMI, dry matter intake; H2, hydrogen; CO2, carbon dioxide; VFA, volatile fatty acids; A:P ratio, acetate to propionate ratio; FCM, fat corrected milk.

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Gas emissions

3-NOP supplementation linearly decreased CH4 production (g/kg DMI) of total ruminant (p < .0001, R2 = 0.744). The CH4 emissions in both beef and dairy cattle significantly decreased with increasing NOP supplementation (p < 0.0001, R2 = 0.797 and p = 0.0003, R2 = 0.916, respectively), however, the slope value in the linear regression for dairy is smaller than that for beef. The significant linear decrease in CH4, with increasing levels of NOP supplementation, was also observed in the long-term in vivo studies (p < 0.0001, R2 = 0.910). The H2 emissions increased with increasing levels of NOP (p = 0.0234, R2 = 0.361), the increasing of NOP did not affect CO2 emission (p = 0.5286, R2 = 0.065).

Ruminal parameters and animal performances

The linear regressions of ruminal fermentation parameters, with increasing levels of NOP supplementation, from the all in vivo studies are shown in Table 5.

Table 4. Equations for linear regression of gas parameters on 3-nitrooxypropanol levels (mg/kg of DMI)
Parameter Parameter estimates Model statistics
n intercept SE intercept p-value Slope SE slope p-value RMSE R 2
CH4 (g/kg DMI, total) 54 20.636 1.0186 < 0.0001 −0.041 0.0047 < 0.0001 1.793 0.744
CH4 (g/kg DMI, beef) 35 21.365 1.4766 < 0.0001 −0.037 0.0043 < 0.0001 1.678 0.797
CH4 (g/kg DMI, dairy) 16 20.068 1.1647 < 0.0001 −0.073 0.0084 0.0003 1.010 0.916
CH4 (g/kg DMI, long term) 19 21.379 2.1144 < 0.0001 −0.053 0.0055 < 0.0001 1.482 0.910
H2 (g/d) 24 −0.105 0.2949 0.7304 0.024 0.0079 0.0234 2.391 0.361
CO2 (g/d) 24 10,622.0 1,028.20 < 0.0001 −0.785 1.1842 0.5286 268.34 0.065

DMI, dry matter intake; CH4, methane; H2, hydrogen, CO2, carbon dioxide; SE, standard error; RMSE, residual mean square error; R2, coefficient of determination.

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Table 5. Equations for linear regression of ruminal fermentation parameters on 3-nitrooxypropanol levels (mg/kg of DMI) from ruminant database
Parameter Parameter estimates Model statistics
n intercept SE intercept p-value Slope SE slope p-value RMSE R 2
Ruminal fermentation parameters
 pH 21 6.368 0.0643 < 0.0001 0.0007 0.00018 0.0071 0.048 0.678
 Total VFA (mM) 27 107.230 3.8455 < 0.0001 −0.0366 0.01090 0.0073 4.897 0.388
 Acetate (%) 29 61.303 2.0731 < 0.0001 −0.0310 0.00303 < 0.0001 0.875 0.898
 Propionate (%) 28 20.000 1.1388 < 0.0001 0.0128 0.00329 0.0031 1.250 0.448
 Butyrate (%) 28 12.364 0.6298 < 0.0001 0.0108 0.00333 0.0087 0.899 0.404
 Iso-butyrate (%) 28 1.004 0.0618 < 0.0001 0.0005 0.00019 0.0301 0.068 0.366
 Valerate (%) 28 1.719 0.1083 < 0.0001 0.0018 0.00038 0.0007 0.117 0.581
 Iso-valerate (%) 29 1.737 0.1595 < 0.0001 0.0021 0.00071 0.0135 0.217 0.381
 A:P ratio 28 3.153 0.2602 < 0.0001 −0.0034 0.00037 < 0.0001 0.110 0.884
 Ammonia (mg/dL) 26 10.606 1.9932 0.0005 −0.0112 0.00408 0.0228 1.658 0.001
 Bacteria 14 7.535 2.8407 0.0453 0.0068 0.00748 0.4157 1.211 0.052
 Methanogen 14 4.114 1.3232 0.0266 −0.0076 0.00288 0.0574 0.641 0.610
 Protozoa 12 2.426 0.4245 0.0046 0.0003 0.00270 0.9243 0.550 0.457
Animal performances
 DMI (g/kg) 50 12.074 1.5541 < 0.0001 −0.0017 0.00072 0.0304 0.329 0.170
 DMD (%) 13 68.022 2.6669 0.0001 0.0024 0.00559 0.6979 1.119 0.055
 OMD (%) 14 69.818 2.9233 0.0002 0.0047 0.00899 0.6359 1.273 0.086
 NDFD (%) 14 48.338 6.0701 0.0041 0.0111 0.01125 0.3952 1.944 0.123

1) 1010/g of rumen digesta.

2) 108/g of rumen digesta.

3) 105/g of rumen digesta.

VFA, volatile fatty acids; A:P ratio, acetate to propionate ratio; DMI, dry matter intake; DMD, dry matter digestibility; OMD, organic matter digestibility; NDFD, neutral detergent fiber digestibility; SE, standard error; RMSE, residual mean square error; R2, coefficient of determination.

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Total VFA concentrations, based on the database from whole studies, appeared to have a significant linear reduction with increasing NOP supplementation (p = 0.0073, R2 = 0.388; Table 5). The NOP supplementation linearly decreased the proportion of acetate (p < 0.0001, R2 = 0.898), whereas the proportion of propionate was linearly increased with increasing levels of NOP supplementation (p = 0.0031, R2 = 0.448). This led to a linear reduction of the A:P ratio (p < 0.0001, R2 = 0.884; Table 5). There was linear increase on the proportion of butyrate, iso-butyrate, valerate, and iso-valerate with increasing levels of NOP supplementation (Table 5). The pH was slightly increased (p = 0.0071, R2 = 0.678) with increasing levels of NOP supplementation (Table 5). In the microbial population, methanogen counts were tended to decrease with increasing levels of NOP supplementation (p = 0.0574, R2 = 0.610), although there was no significant change on the counts of total bacteria and protozoa (p = 0.4157, R2 = 0.052 and p = 0.9243, R2 = 0.457, respectively). In animal performances, based on the database from total in vivo studies, DMI was slightly decreased when NOP supplementation was increased (p = 0.0304, R2 = 0.170), although increase of NOP supplementation did not affect digestibility of DM, OM, and NDF (Table 5).

The total VFA concentration and the proportion of acetate, based on beef cattle database, were significantly decreased with increasing NOP supplementation (p = 0.0015, R2 = 0.804 and p = 0.0003, R2 = 0.918; Table 6). The increase of NOP significantly increased the proportion of propionate, butyrate, iso-butyrate, and valerate when analyzed using beef cattle database (Table 6). The response of NOP supplementation on A:P ratio (Slop = –0.0036, p = 0.0002, and R2 = 0.924) and DMI (Slop = –0.0016, p = 0.0574, and R2 = 0.170) in beef was similar with those from total database.

Table 6. Equations for linear regression of ruminal fermentation parameters on 3-nitrooxypropanol levels (mg/kg of DMI) from beef database
Parameter Parameter estimates Model statistics
n intercept SE intercept p-value Slope SE slope p-value RMSE R 2
Total VFA (mM) 16 113.370 8.5296 < 0.0001 −0.0622 0.01131 0.0015 2.886 0.804
Acetate (%) 15 61.209 3.3136 < 0.0001 −0.0298 0.00336 0.0003 0.902 0.918
Propionate (%) 17 22.215 2.9321 0.0003 0.0112 0.00429 0.048 1.514 0.425
Butyrate (%) 17 11.298 1.2231 < 0.0001 0.0087 0.00332 0.0473 0.975 0.452
Iso-butyrate (%) 17 1.072 0.0571 < 0.0001 0.0005 0.00019 0.0396 0.078 0.426
Valerate (%) 17 1.775 0.2273 0.0002 0.0015 0.00042 0.0158 0.126 0.636
Iso-valerate (%) 18 1.843 0.1876 < 0.0001 0.0020 0.00092 0.0733 0.240 0.349
A:P ratio 17 3.187 0.3969 0.0002 −0.0036 0.00037 0.0002 0.107 0.924
DMI (g/kg) 36 9.103 0.5638 < 0.0001 −0.0016 0.00075 0.0574 0.331 0.170

VFA, volatile fatty acids; A:P ratio, acetate to propionate ratio; DMI, dry matter intake; SE, standard error; RMSE, residual mean square error; R2, coefficient of determination.

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When analyzed using dairy database, similarly for total and beef cattle database, the proportion of acetate (p = 0.0284, R2 = 0.769) and A:P ratio (p = 0.0628, R2 = 0.552) were decreased, whereas that of valerate (p = 0.0340, R2 = 0.522) was linearly increased with increasing NOP supplementation (Table 7). However, there was no significant change on the proportion of propionate (p = 0.1591), butyrate (p = 0.3667), iso-butyrate (p = 0.3832), and iso-valerate (p = 0.2395). In the dairy production performances, the NOP supplementation had no significant linear relationship with DMI (p = 0.1760), FCM (p = 0.5718), and milk lactose percentage (p = 0.2263). The percentage of milk fat (p = 0.0861, R2 = 0.321) and protein (p = 0.0838, R2 = 0.322) tended to increase, although the milk yield (p = 0.0606, R2 = 0.381) tended to decrease with increasing levels of NOP addition (Table 7).

Table 7. Equations for linear regression of ruminal fermentation parameters on 3-nitrooxypropanol levels (mg/kg of DMI) from dairy database
Parameter Parameter estimates Model statistics
N intercept SE intercept p-value Slope SE slope p-value RMSE R 2
Total VFA (mM) 10 99.762 4.0520 0.0001 −0.0240 0.02516 0.4102 3.404 0.051
Acetate (%) 10 59.653 2.0003 < 0.0001 −0.0339 0.00851 0.0284 1.070 0.769
Propionate (%) 10 21.831 0.9936 0.0002 0.0124 0.00662 0.1591 0.851 0.466
Butyrate (%) 9 13.855 0.2088 < 0.0001 0.0068 0.00588 0.3667 0.680 0.528
Iso-butyrate (%) 10 0.922 0.1383 0.0069 0.0002 0.00021 0.3832 0.051 0.000
Valerate (%) 10 1.952 0.1569 0.0011 0.0025 0.00068 0.0340 0.115 0.522
Iso-valerate (%) 10 1.456 0.3296 0.0215 0.0049 0.00335 0.2395 0.304 0.160
A:P ratio 10 2.811 0.2202 0.001 −0.0028 0.00098 0.0628 0.157 0.552
DMI (g/kg) 17 22.051 1.4831 < 0.0001 −0.0032 0.00202 0.1760 0.316 0.211
Milk yield (kg/d) 17 32.557 2.9927 0.0001 −0.0122 0.00507 0.0606 0.791 0.381
Milk fat (%) 17 3.845 0.1514 < 0.0001 0.0012 0.00058 0.0861 0.092 0.321
Milk protein (%) 17 3.275 0.0873 < 0.0001 0.0005 0.00023 0.0838 0.033 0.322
Milk lactose (%) 14 4.597 0.0897 < 0.0001 0.0001 0.00010 0.2263 0.015 0.214
FCM (kg/d) 14 31.771 3.7451 0.0011 0.0072 0.01169 0.5718 1.109 0.008

VFA, volatile fatty acids; A:P ratio, acetate to propionate ratio; DMI, dry matter intake; FCM, 4% fat corrected milk; SE, standard error; RMSE, residual mean square error; R2, coefficient of determination.

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DISCUSSION

Methane mitigation

The present study conducted a meta-analysis using total 14 in vivo studies published from 2014 to 2019, and the meta-analysis showed that supplementation of NOP was effective to a significant linear decrease in CH4 yield (g/kg DMI), regardless of animal type compared with those fed a diet without NOP. It is similar with a result from Jayanegara et al. [26] who reported NOP supplementation had an effect of CH4 mitigation regardless of type of CH4 unit (CH4 g per BW, DMI, milk, DMI, and digested OM). Dijkstra et al. [27] revealed that NOP supplementation has stronger CH4 mitigation effects in dairy cattle than in beef cattle, when a meta-analysis was analyzed using 9 in vivo studies from 2014 to 2018 [1624]. In present study including the latest articles(reference addition), we also observed that the effects of CH4 mitigation by increasing levels of NOP supplementation in dairy cattle were more critical than those in beef cattle, indicating that the appropriate level of NOP to reduce CH4 emissions may vary depending upon the animal type.

The most important factor in investigating an effective CH4 mitigation strategy in rumen is persistent efficacy. With our knowledge, total 5 in vivo studies were conducted to investigate the effects of NOP supplementation on sustained mitigation of CH4 emission [15,16,19,22,25]. Romero-Perez et al. [19], conducted a long-term study where eight ruminally cannulated heifers were fed a TMR, with a 60% forage ratio, supplemented with NOP (2 g/d of NOP) for about 146 d. Methane emissions were reduced up to 59% for both the g/d and g/kg of DMI in the NOP supplemented groups. Hristov et al. [22], reported that dairy cows that were fed diets containing NOP (40, 60, and 80 mg/kg of feed DM) produced up to 30% less enteric CH4 throughout 12 weeks. In addition, two studies conducted a long-term experiment (238 d) including a backgrounding phase (105 d) and finishing phase (105 d) using beef cattle as the experimental animal [15,16]. In the backgrounding phase, Vyas et al. [16] reported that a significant linear reduction of CH4 (g/d) was observed with increasing levels of NOP supplementation. Whereas in the finishing phase, the significant CH4 (g/d) reduction was only observed when a high dose of NOP supplementation was applied (84% decrease compared to control). Vyas et al. [15] observed that NOP could decrease the CH4 production (g/kg DMI) by 42% with improving gain-to-feed ratio (G:F) by 5% when NOP was added by 200 mg/kg DM with backgrounding diet, and they also stated 37% reduction of CH4 production (g/kg DMI) with increasing G:F by 3% by supplementation of 125 mg/kg DM of NOP in the finishing period. More recently, Van Wesemael et al. [25] reported NOP can reduce CH4 emissions (g/kg DMI) about 20% regardless of type of NOP supplementation (NOP incorporated into a concentrate pellet vs. NOP mixed with basal roughage), when dairy cattle fed 1.6 g/d of NOP throughout 10 weeks. With consistent previous results, this meta-analysis revealed that there was the significant linear decrease in CH4 production (g/kg DMI) by supplementation of NOP on long-term in vivo studies, indicating that NOP might be an effective feed additive to mitigate CH4 emissions sustainably.

Ruminal parameters

In the present study, a meta-analysis, based on the database including all experiments, revealed that NOP supplementation linearly decreased total VFA concentration and proportion of acetate, on the other hand linearly increased proportion of other individual VFAs, which was similar with a previous meta-analysis study [26]. On the other hand, NOP supplementation had different an effect intensity on total VFA and individual VFA proportion depending on animal type, although CH4 emissions (g/kg DMI) were decreased with increasing levels of NOP regardless of animal type.

Methanogenesis is a main part of removing metabolic hydrogen in the rumen, and accumulated H2 resulting from methanogenesis inhibition may be incorporated into propionate producing pathway and reductive acetogenesis [29]. Accumulated H2 were also involved in the reduction of rumen fermentation through the inhibition of the re-oxidation of cofactors [30], therefore, it is consistent with present study based on beef database showing the reduction of total VFA concentration when NOP supplementation was increased (Table 6). Inconsistent with beef cattle, based on dairy cattle, it was revealed that increasing NOP supplementation only had linear relationship on proportion of acetate and valerate. Lopes et al. [23], who studied the effect of a dietary NOP addition on rumen microbial diversity, observed a decrease of Ruminococcus spp. known as acetate producing fibrolytic bacteria (p < 0.01), an increase of Selenomonadales including propionate producing bacteria (p < 0.05), and an increase of Butyrivibrio spp. known as butyrate producing bacteria. This indicated that changes of microbial compositions by NOP supplementation, might affect the concentration of each VFA, although NOP is not a material that directly manipulate the growth of rumen microbes. Generally, starch amount in feed ration could affect especially proportion of propionate in VFAs. When we investigated starch content in feed ration, starch (%DM) was 21.3 ± 4.98 (data not shown, dairy cattle database) [20,22,24,25], 39.3 ± 13.11 (data not shown, beef cattle database) [15,16,18,19]. Considering the lower starch content in the dairy cattle, increased metabolic hydrogen generated in methane reduction may be diverted to different hydrogen sink than the propionate producing pathway. Bleicher and Winter [31] revealed that formate was increased by Methanobacterium formicicum, when methanogenesis was inhibited by bromoethanesulphonic acid, suggesting that increased H2 was utilized to produce formate. Ungerfeld [29] also stated alone both hydrogen sink (propionate and reductive acetogenesis) could not explain all incorporation of hydrogen generated by inhibition of methane production, thus, Ungerfeld [32] reported considering other hydrogen sinks, such as other fermentation products (formate, valerate, caproate, ethanol, and lactate) and microbial protein or fatty acid synthesis is important in studying about inhibition of methanogenesis. Several studies revealed that NOP supplementation might increase proportion of caproate when high dose of NOP supplemented [21,24], although other studies showed no significant change [15,20] or significant decrease [14]. Reynolds et al. [24] reported NOP significantly increased ethanol production when was supplemented to 2,500 mg/d, and Kim et al. [13] observed significant increase of lactic acid when NOP was ruminally infused in high grain diet. A few studies showed increase of several fermentation products as hydrogen sink, more evidence will be needed to understand mechanism of metabolic hydrogen produced from CH4 reduction by NOP supplementation in rumen.

Animal performances

Animal performances in response to NOP supplementation were presented in Table 4. The DMI, from beef cattle database, was tended to decrease when the levels of NOP supplementation increased, whereas there was no significant change on DMI from dairy cattle database. This is consistent with all previous studies using dairy cattle, which reported that the use of NOP did not change the DMI significantly [2024]. Allen [33] stated that DMI might be decreased by increased starch digestion in reticulo-rumen and absorbed propionate might affects satiety and ingestion patterns. It is speculated that the higher starch content in beef cattle than dairy cattle might affect not only rumen fermentation, but also DMI, although the results should be interpreted with caution because various conditions can affect DMI, such as chemical composition (NDF and starch), particle size, silage fermentation products [33].

In the present study, the MY tended to decrease with increasing NOP supplementation, although all previous studies from dairy cattle, consistently observed no significant difference between the control and NOP groups [2025]. These differences might be caused by numerical decreases in MY from most studies [2022,24,25]. In the present study, results of the meta-analysis showed a tendency of increasing MF and MP, affecting milk price importantly, suggesting the use of NOP did not negatively affect the milk proportion.

Romero-Perez et al. [18], reported that NOP supplementation had quadratic effects on the DMD (p = 0.05) and OMD (p = 0.06). Hristov et al. [22], reported quadratic effects on the DMD (p = 0.006) and OMD (p = 0.06) with increasing levels of NOP, except for the NDFD. Haisan et al. [21] observed an increase in the DMD and OMD with NOP supplementation. Reynolds et al. [24] observed a tendency of DMD (p = 0.08) and OMD (p = 0.06) to decrease, when comparing doses between the control group and 2,500 mg/d of NOP. Thus, inconsistent results of nutrient digestibility would have affected the results of the present meta-analysis. Many studies postulated that CH4 mitigation might affect the increase of available dietary GE. However, in this study, available dietary GE from reduced CH4 emissions might not be totally utilized for animal production.

In conclusion, NOP is a viable candidate as a feed additive because of its strong CH4 mitigation effects, regardless of animal type and experiment period, without adverse effects on animal performances. The magnitude of NOP supplementation effect was varied in relation to animal types. Thus, further research will be needed to identify the relationship between NOP supplementation and dietary content (starch, non-fiber carbohydrate, and NDF).

Competing interests

No potential conflict of interest relevant to this article was reported.

Funding sources

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1056904).

Acknowledgements

Not applicable.

Availability of data and material

Upon reasonable request, the datasets of this study can be available from the corresponding author.

Authors’ contributions

Conceptualization: Kim HB, Lee HG, Seo JK.

Data curation: Kim HB, Lee HG, Baek YC, Seo JK.

Formal analysis: Kim HB.

Methodology: Kim HB, Lee HG, Seo JK.

Software: Kim HB, Lee HG.

Validation: Lee SY, Baek YC.

Investigation: Kim HB, Lee HG.

Writing - original draft: Kim HB, Lee HG, Seo JK.

Writing - review & editing: Lee SY, Baek YC.

Ethics approval and consent to participate

This article does not require IRB/IACUC approval because there are no human and animal participants.

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