Cuban Journal of Agricultural Science Vol. 57, january-december 2023, ISSN: 2079-3480
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CU-ID: https://cu-id.com/1996/v57e26
Animal Science

Estimated enteric methane production from cattle and small ruminants fed on diets with tropical legume forages

 

iDJ. M. Castro-Montoya1Graduate School, Faculty of Agricultural Sciences, University of El Salvador, San Salvador, El Salvador.*✉: joaquin.montoya@ues.edu.sv

iDMizeck Chagunda2Institute of Agricultural Sciences in the Tropics, Animal Breeding and Husbandry in the Tropics and Subtropics, University of Hohenheim, 70593 Stuttgart, Germany.


1Graduate School, Faculty of Agricultural Sciences, University of El Salvador, San Salvador, El Salvador.

2Institute of Agricultural Sciences in the Tropics, Animal Breeding and Husbandry in the Tropics and Subtropics, University of Hohenheim, 70593 Stuttgart, Germany.

 

*Email: joaquin.montoya@ues.edu.sv

With the aim of exploring the effects of tropical legumes on methane emissions, methane production from ruminants fed tropical legumes was estimated using predictive equations based on diet’s nutrient characteristics, dry matter intake (DMI), and digestibility from 258 in vivo studies (1355 treatments) from literature. The dataset was divided into adult and growing cattle, goat, and sheep. Additionally, subsets were created depending on the growth habit of the legume: herb, shrub and tree. Methane was expressed relative to metabolic body weight (MBW), DMI, digestible organic matter intake (DOMI), and milk yield or average daily gain (ADG). Estimated methane for each subset of data and for each unit of expression was regressed on the proportion of legume in the diet. Increasing proportion of legumes decreased methane relative to MBW, DMI and ADG -but not relative to milk yield and DOMI- in cattle. For small ruminants, increasing legume proportion decreased estimated methane relative to MBW, DMI and DOMI (by tendency), but no effects were observed on methane relative to ADG, although these effects were likely underestimated. Herb legumes consistently showed the greater decreases in estimated methane in both cattle and small ruminants, while shrubs showed the smaller effects on methane decrease. These analyses highlight the potential of tropical legumes to decrease methane emissions, with differences between types of legumes, with improved effects were found in combination with grasses and concentrate. Further evidence is needed to affirm undeniable positive effects of legumes on the decrease of emissions relative to final product.

Keywords: 
intensity emissions, herb legumes, ruminants, methane inhibition

Received: 22/11/2022; Accepted: 15/6/2023

Data availability, Funding & Conflict of Interest: The database used for this study is available upon request to the authors. No funding was utilized for the execution of this study. The authors declare no conflict of interest.

Author´s contribution: J.M. Castro-Montoya: Conceptualization, Methodology, Formal analysis, Data curation, Writing - original draft, Visualization. Mizeck Chagunda: Conceptualization, Writing - Review & Editing

CONTENT

Incorporating legume forages in ruminants feeding has several positive impacts on the sustainability of the agricultural system including biological N fixation, control of erosion, recycling of nutrients, and maintenance of biodiversity (Schultze-Kraft et al. 2018Schultze-Kraft, R., Rao, I.M., Peters, M., Clements, R.J., Bai, C. & Liu, G. 2018. "Tropical forage legumes for environmental benefits: An overview". Tropical Grasslands - Forrajes Tropicales, 6: 1-1, ISSN: 2346-3775. http://dx.doi.org/10.17138/tgft(6)1-14.) as well as increase productivity in cattle. Enteric methane is one of the most commonly discussed environmental impacts of ruminants, and feeding legumes has been proposed as a via to inhibit methanogenesis, mainly due to their contents of phytogenic compounds with the potential to modulate rumen fermentation. Moreover, if the premise of an increased productivity when feeding legumes is achieved, the intensity of emissions (the amount of methane per unit of final product) would likely also decrease.

A great number of studies have explored the in vitro effects of legumes on methane, generally showing their ability to decrease methane. However, in vitro fermentation cannot account for dry matter intake (DMI), a main determinant of methane production, neither for the adaptation of rumen microbes to a diet. Literature on in vivo trials is scarce and less conclusive, with several studies not providing estimates of DMI or the proportion of legumes consumed. At the time of the writing, ten studies were found reporting methane production accompanied by DMI (Possenti et al. 2008Possenti, R.A., Franzolin, R., Schammas, E.A., Demarchi, J.J., Frighetto, R.T.S. & Lima, M.A.D. 2008. "Efeitos de dietas contendo Leucaena leucocephala e Saccharomyces cerevisiae sobre a fermentação ruminal e a emissão de gás metano em bovinos". Revista Brasileira de Zootecnia, 37: 1509-1516, ISSN: 1806-9290. http://dx.doi.org/10.1590/S1516-35982008000800025. , Kennedy and Charmley 2012Kennedy, P.M. & Charmley, E. 2012. "Methane yields from Brahman cattle fed tropical grasses and legumes". Animal Production Science, 52: 225-239, ISSN: 1836-5787. http://dx.doi.org/10.1071/AN11103. , Delgado et al. 2013Delgado, D.C., Galindo, J., Cairo, J., Orta, I. & Dorta, N. 2013. "Supplementation with foliage of L. leucocephala. Its effect on the apparent digestibility of nutrients and methane production in sheep". Cuban Journal of Agricultural Science, 47(3): 267-271, ISSN: 2079-3480., Moreira et al. 2013Moreira, G.D., Lima, P.D., Borges, B.O., Primavesi, O., Longo, C., McManus, C., Abdalla, A. & Louvandini, H. 2013. "Tropical tanniniferous legumes used as an option to mitigate sheep enteric methane emission". Tropical Animal Health and Production, 45: 879-882, ISSN: 1573-7438. https://doi.org/10.1007/s11250-012-0284-0. , Archimède et al. 2016Archimède, H., Rira, M., Barde, D.J., Labirin, F., Marie‐Magdeleine, C., Calif, B., Périacarpin, F., Fleury, J., Rochette, Y., Morgavi, D.P. & Doreau, M. 2016. "Potential of tannin‐rich plants, Leucaena leucocephala, Glyricidia sepium and Manihot esculenta, to reduce enteric methane emissions in sheep". Journal of Animal Physiology and Animal Nutrition, 100: 1149-1158, ISSN: 1439-0396. https://doi.org/10.1111/jpn.12423. , Pineiro-Vásquez et al. 2018Pineiro-Vázquez, A.T., Canul-Solis, J.R., Jiménez-Ferrer, G.O., Alayón-Gamboa, J.A., Chay-Canul, A.J., Ayala-Burgos, A.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2018. "Effect of condensed tannins from Leucaena leucocephala on rumen fermentation, methane production and population of rumen protozoa in heifers fed low-quality forage". Asian-Australasian Journal of Animal Sciences, 31: 1738-1746, ISSN: 1976-5517. https://doi.org/10.5713/ajas.17.0192. , Washaya et al. 2018Washaya, S., Mupangwa, J. & Muchenje, V. 2018. "Chemical composition of Lablab purpureus and Vigna unguiculata and their subsequent effects on methane production in Xhosa lop-eared goats". South African Journal of Animal Science, 48: 445-458, ISSN: 2221-4062. http://dx.doi.org/10.4314/sajas.v48i3.5. , Lima et al. 2020Lima, P.D., Abdalla Filho, A.L., Issakowicz, J., Ieda, E.H., Corrêa, P.S., de Mattos, W.T., Gerdes, L., McManus, C., Abdalla, A.L. & Louvandini, H. 2020. "Methane emission, ruminal fermentation parameters and fatty acid profile of meat in Santa Inês lambs fed the legume macrotiloma". Animal Production Science, 60: 665-673, ISSN: 1836-5787. https://doi.org/10.1071/AN19127. , Montoya-Flores et al. 2020Montoya-Flores, M.D., Molina-Botero, I.C., Arango, J., Romano-Muñoz, J.L., Solorio-Sánchez, F.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2020. "Effect of dried leaves of Leucaena leucocephala on rumen fermentation, rumen microbial population, and enteric methane production in crossbred heifers". Animals, 10: 300, ISSN: 2076-2615. https://doi.org/10.3390/ani10020300. and Suybeng et al. 2020Suybeng, B., Charmley, E., Gardiner, C.P., Malau-Aduli, B.S. & Malau-Aduli, A.E. 2020. "Supplementing Northern Australian beef cattle with Desmanthus tropical legume reduces in-vivo methane emissions". Animals, 10: 2097, ISSN 2076-2615. https://doi.org/10.3390/ani10112097.). With such a small sample size it is difficult to conclude on the effects of tropical legumes on methane emissions, particularly knowing that in 6 of the 10 studies Leucaena leucocephala was the legume tested. The effects of leucaena cannot be ascribed to all tropical legumes, as tree, shrub and herb legumes differ in composition and degradability (Castro-Montoya and Dickhoefer 2020Castro-Montoya, J.M., Goetz, K. & Dickhoefer, U. 2020. "In vitro fermentation characteristics of tropical legumes and grasses of good and poor nutritional quality and the degradability of their neutral detergent fibre". Animal Production Science, 61: 645-654, ISSN: 1836-5787. https://doi.org/10.1071/AN20136. ).

The limited number of studies is a consequence of the complexity of the methane measurement techniques, not available for the majority of research groups in tropical regions. However, the potential of these forages can be explored using predictive equations to estimate methane based on dietary characteristics (Ellis et al. 2007Ellis, 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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. and Ramin and Huhtanen 2013Ramin, M. & Huhtanen, P. 2013. "Development of equations for predicting methane emissions from ruminants". Journal of Dairy Science, 96: 2476-2493, ISSN: 1525-3198. https://doi.org/10.3168/jds.2012-6095. ) including some who have been developed for tropical environments (Patra 2017Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. ). Even though, the accuracy and global applicability of such equations can be debated, they still allow for the screening of large number of dietary strategies with effects on DMI, organic matter digestibility (OMD) and, therefore, on methane production. Hence, the objective of this study was to estimate methane emissions from literature data in a large number of dietary treatments containing tropical legumes, attempting to assess their potential to mitigate methane emissions from ruminants accounting for their effects on DMI, digestibility and final product’s yields.

Materials and Methods

 

Database, parameters extracted and calculations

 

The data used for the current study derived from a database reported in Castro-Montoya and Dickhoefer (2020)Castro-Montoya, J.M. & Dickhoefer, U. 2020. "The nutritional value of tropical legume forages fed to ruminants as affected by their growth habit and fed form: A systematic review". Animal Feed Science and Technology, 269: 1-14, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2020.114641. . For the current analysis, the studies had to report legume species, their proportion in the diet, the nutritional composition of the diet [organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) concentration], animal body weight (BW), DMI, and OM -or dry matter (DM)- total tract digestibility. If available, milk yield (kg/d) and average daily gain (ADG) were recorded. A total of 258 studies fulfilled these criteria, resulting in 1355 dietary treatments. Further parameters were estimated. The feeding level (FL), presented as a multiple of maintenance energy requirements, was estimated by dividing the metabolizable energy (ME) intake by the maintenance ME requirement for the animals. The ME requirement for each animal was calculated based on the BW reported using the estimates from the meta analysis of Salah et al. (2014)Salah, N., Sauvant, D. & Archimède, H. 2014. "Nutritional requirements of sheep, goats and cattle in warm climates: A meta-analysis". Animal, 8: 1439-1447, ISSN: 1751-732X. https://doi.org/10.1017/S1751731114001153. for tropical small ruminants and cattle (542.64 and 631.26 kJ ME/kg BW0.75, respectively).

The majority of the studies did not provide information on the ME concentration of the diet, therefore this was estimated according to Minson (1984)Minson, D.J. 1984. "Digestibility and voluntary intake by sheep of five Digitaria species". Australian Journal of Experimental Agriculture, 24: 494-500, ISSN: 1446-5574. https://doi.org/10.1071/EA9840494. [Eq. (1) ME ( MJ kg ) =   0.157 DOM +   0.059 CP   1.073 ] with CP and digestible OM as predictors, two parameters that account for differences between legumes and grasses.

ME ( MJ kg ) =   0.157 DOM +   0.059 CP   1.073  Eq.(1)

Where

DOM = concentration of digestible organic matter in the diet (g/100 g DM);

CP = concentration of crude protein in the diet (g/100 g DM);

Digestible OM concentration was estimated from the concentration of OM in the diet and from the OMD (g/kg) reported in the studies. For those studies not reporting OMD but where DM digestibility was available, the former was estimated from DM digestibility (DMD (g/kg)) using regressions derived from the data used for this study [Eq. (2) OMD ( g kg ) =   100.2   +   0.876   × DMD ; R 2 = 0.72 ; for grasses only , (3) OMD ( g kg ) =   108.0   +   0.848   × DMD ;   R 2 = 0.65 ; for mixed legume grasses rations , (4) OMD ( g kg ) =   44.9   +   0.951   × DMD ; R 2 = 0.88 ;   for legumes only ].

OMD ( g kg ) =   100.2   +   0.876   × DMD ; R 2 = 0.72 ; for grasses only  Eq.(2)
OMD ( g kg ) =   108.0   +   0.848   × DMD ;   R 2 = 0.65 ; for mixed legume grasses rations  Eq.(3)
OMD ( g kg ) =   44.9   +   0.951   × DMD ; R 2 = 0.88 ;   for legumes only  Eq.(4)

Equations to estimate methane

 

For the estimation of methane production, equations considering DMI, NDF, ADF, and ME concentrations or intakes, as well as OMD were favored, parameters that capture the characteristics of legume forages, the differences within types of legume and between legumes and grasses, while also allowing for the estimation of methane from a significant amount of observations. In this regard, the predictive equations produced by Patra (2017)Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. and Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. were considered. From the equations produced by Patra (2017)Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. the Binomial 12 [Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 ], Binomial 13 [Eq. (6) CH 4   ( MJ d ) =   1.490 ± 0.745   +   0.418 ± 0.232   × DMI +   0.0415 ± 0.0118   × DMI 2    +   4.311 ± 0.718   × ADFI   0.977 ± 0.138   × ADFI 2 ], Linear 17 [Eq. (7) CH 4    ( MJ d ) =   0.910 ± 0.746   +   1.472 ± 0.154   × DMI   1.388 ± 0.451   × FL   0.669 ± 0.338   × ADFI ] and Linear 18 [Eq. (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm ] were selected based on their performance and the availability of the predictor variables within this dataset.

C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2  Eq.(5)
CH 4   ( MJ d ) =   1.490 ± 0.745   +   0.418 ± 0.232   × DMI +   0.0415 ± 0.0118   × DMI 2    +   4.311 ± 0.718   × ADFI   0.977 ± 0.138   × ADFI 2  Eq.(6)
CH 4    ( MJ d ) =   0.910 ± 0.746   +   1.472 ± 0.154   × DMI   1.388 ± 0.451   × FL   0.669 ± 0.338   × ADFI  Eq.(7)
CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm  Eq.(8)

Where

DMI = dry matter intake in kg/d;

NDFI = NDF intake in kg/d;

ADFI = ADF intake (kg/d);

FL = feeding level as multiple of the maintenance requirements.

OMDm = organic matter digestibility (g/kg) at a maintenance level of feeding (estimated based on OMD following the procedure outlined by Ramin and Huhtanen (2013)Ramin, M. & Huhtanen, P. 2013. "Development of equations for predicting methane emissions from ruminants". Journal of Dairy Science, 96: 2476-2493, ISSN: 1525-3198. https://doi.org/10.3168/jds.2012-6095. .

Where DMI = dry matter intake in kg/d; NDFI = NDF intake in kg/d; ADFI = ADF intake (kg/d); FL = feeding level as multiple of the maintenance requirements; OMDm = organic matter digestibility (g/kg) at a maintenance level of feeding (estimated based on OMD following the procedure outlined by Ramin and Huhtanen (2013)Ramin, M. & Huhtanen, P. 2013. "Development of equations for predicting methane emissions from ruminants". Journal of Dairy Science, 96: 2476-2493, ISSN: 1525-3198. https://doi.org/10.3168/jds.2012-6095. .

From the work of Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. predictive equations 6c [Eq. (9) CH 4   ( MJ d ) =   3.44 ±   0.937   +   0.502 ±   0.115   × DMI +   0.506 ±   0.211   × NDFI ], 7c [Eq. (10) CH 4   ( MJ d ) =   3.63 ±   0.921   +   0.0549 ±   0.00939   × MEI +   0.606 ±   0.306   × ADFI ], 8c [Eq. (11) CH 4   ( MJ d ) =   4.41 ±   1.13   +   0.0224 ±   0.0106   × MEI +   0.980 ±   0.241   × NDFI ], and 10c [Eq. (12) CH 4   ( MJ d ) =   3.41 ±   0.973   +   0.520 ±   0.120   × DMI 0.996 ±   0.447   × ADFI + 1.15 ±   0.321   × NDFI ] were used to estimate methane in this dataset:

CH 4   ( MJ d ) =   3.44 ±   0.937   +   0.502 ±   0.115   × DMI +   0.506 ±   0.211   × NDFI  Eq.(9)
CH 4   ( MJ d ) =   3.63 ±   0.921   +   0.0549 ±   0.00939   × MEI +   0.606 ±   0.306   × ADFI  Eq.(10)
CH 4   ( MJ d ) =   4.41 ±   1.13   +   0.0224 ±   0.0106   × MEI +   0.980 ±   0.241   × NDFI  Eq.(11)
CH 4   ( MJ d ) =   3.41 ±   0.973   +   0.520 ±   0.120   × DMI 0.996 ±   0.447   × ADFI + 1.15 ±   0.321   × NDFI  Eq.(12)

Where

DMI = dry matter intake in kg/d;

NDFI = NDF intake in kg/d;

MEI = ME intake in MJ/d;

ADFI = ADF intake in kg/d.

Subsetting of data

 

The dataset was divided into cattle and small ruminants, and subsets were created according to physiological stage (adult or growing), species (goat and sheep). and legume’s growth habit (herb, shrub and tree legumes). The latter classification was based on the PLANTS database (https://plants.usda.gov) of the Natural Conservation service of the United Sates as described by Castro-Montoya and Dickhoefer (2018)Castro‐Montoya, J.M. & Dickhoefer, U. 2018. "Effects of tropical legume silages on intake, digestibility and performance in large and small ruminants: A review". Grass and Forage Science, 73: 26-39, ISSN: 1365-2494. https://doi.org/10.1111/gfs.12324. . Descriptive statistics of the variables used in the study are presented in table 1. Another subset of data was created including only diets with grass forage with and without concentrate supplementation from small ruminants (table 1). This was not done for cattle because of the reduced number of observations available.

Table 1.  Descriptive statistics of parameters used for the prediction of methane emissions for the different datasets used.
Variable n Average Median Standard deviation Minimum Maximum
Adult cattle
Body weight (kg) 70 378.00 367.00 140.40 180.00 820.00
Dry matter intake (kg/d) 76 9.27 8.70 3.55 2.50 19.60
Organic matter digestibility (g/kg) 47 624.00 630.00 72.00 420.00 751.00
Digestible organic matter intake (kg/d) 38 5.45 4.84 2.97 1.51 12.60
Metabolizable energy (MJ/kg DM) 38 8.24 8.49 1.33 4.69 10.60
Metabolizable energy intake (MJ/d) 38 81.8 72.3 46.2 22.30 196.00
Feeding level 35 1.50 1.47 0.63 0.51 2.77
Neutral detergent fiber intake (kg/d) 69 5.21 5.31 1.71 1.34 8.64
Acid detergent fiber intake (kg/d) 57 3.27 3.00 1.15 0.85 5.96
Milk yield (kg/d) 23 7.68 6.30 4.50 3.75 22.00
Growing cattle
Body weight (kg) 79 195.00 181.00 94.80 36.00 411.00
Dry matter intake (kg/d) 79 4.62 4.30 2.01 0.34 9.40
Organic matter digestibility (g/kg) 44 573.00 584.00 92.9 129.00 724.00
Digestible organic matter intake (kg/d) 44 2.17 2.23 0.75 0.51 3.60
Metabolizable energy (MJ/kg DM) 44 7.78 7.86 1.27 1.66 9.38
Metabolizable energy intake (MJ/d) 44 32.30 33.20 11.00 7.21 53.00
Feeding level 44 1.08 1.03 0.37 0.23 2.20
Neutral detergent fiber intake (kg/d) 76 2.79 2.74 1.31 0.08 6.57
Acid detergent fiber intake (kg/d) 57 1.85 1.81 0.87 0.32 4.08
Average daily gain (g/d) 43 429.3 434.00 237.10 0.00 920.00
Adult goat
Body weight (kg) 98 31.10 29.00 11.90 14.20 67.00
Dry matter intake (kg/d) 100 0.88 0.68 0.51 0.11 2.61
Organic matter digestibility (g/kg) 84 594.00 622.00 120.4 293.00 759.00
Digestible organic matter intake (kg/d) 82 0.45 0.36 0.28 0.06 1.17
Metabolizable energy (MJ/kg DM) 82 8.17 8.63 1.84 3.59 11.3
Metabolizable energy intake (MJ/d) 82 6.80 5.44 4.25 0.86 18.0
Feeding level 82 0.94 0.91 0.40 0.21 1.84
Neutral detergent fiber intake (kg/d) 87 0.49 0.43 0.28 0.07 1.67
Acid detergent fiber intake (kg/d) 74 0.32 0.31 0.16 0.04 1.02
Growing goat
Body weight (kg) 232 15.10 14.90 4.06 5.43 25.80
Dry matter intake (kg/d) 235 0.51 0.50 0.15 0.11 0.96
Organic matter digestibility (g/kg) 145 631.00 632.00 84.00 455.00 819.00
Digestible organic matter intake (kg/d) 138 0.30 0.27 0.11 0.13 0.65
Metabolizable energy (MJ/kg DM) 138 8.76 8.84 1.33 5.60 11.80
Metabolizable energy intake (MJ/d) 138 4.54 4.11 1.62 1.94 9.90
Feeding level 135 1.09 1.07 0.34 0.48 2.36
Neutral detergent fiber intake (kg/d) 217 0.28 0.27 0.09 0.04 0.53
Acid detergent fiber intake (kg/d) 193 0.18 0.17 0.07 0.02 0.38
Average daily gain (g/d) 82 44.9 38.7 25.90 1.60 143.00
Adult sheep
Body weight (kg) 253 31.90 29.50 11.50 12.50 70.00
Dry matter intake (kg/d) 266 0.80 0.74 0.31 0.16 1.87
Organic matter digestibility (g/kg) 233 558 570 85.00 270.00 769.00
Digestible organic matter intake (kg/d) 211 0.40 0.38 0.19 0.05 1.04
Metabolizable energy (MJ/kg DM) 211 7.50 7.66 1.30 3.79 10.70
Metabolizable energy intake (MJ/d) 211 5.94 5.55 2.84 0.74 15.90
Feeding level 205 0.83 0.82 0.32 0.09 1.97
Neutral detergent fiber intake (kg/d) 242 0.48 0.48 0.19 0.06 1.01
Acid detergent fiber intake (kg/d) 205 0.31 0.30 0.13 0.04 0.61
Growing sheep
Body weight (kg) 357 21.60 19.50 15.00 4.10 209.00
Dry matter intake (kg/d) 357 0.70 0.68 0.20 0.21 1.28
Organic matter digestibility (g/kg) 207 592.00 578.00 83.70 408.00 840.00
Digestible organic matter intake (kg/d) 190 0.40 0.36 0.30 0.19 4.27
Metabolizable energy (MJ/kg DM) 190 8.53 7.95 6.82 5.22 101.00
Metabolizable energy intake (MJ/d) 190 5.92 5.35 4.80 2.72 66.9
Feeding level 190 1.12 0.96 1.32 0.23 18.7
Neutral detergent fiber intake (kg/d) 353 0.41 0.39 0.13 0.10 0.82
Acid detergent fiber intake (kg/d) 298 0.25 0.24 0.09 0.09 0.61
Average daily gain (g/d) 75 57.6 48.00 34.90 2.00 170.00
No legumes small ruminants
Body weight (kg) 264 24.10 21.20 11.40 5.43 70.00
Dry matter intake (kg/d) 271 0.65 0.59 0.32 0.17 2.89
Organic matter digestibility (g/kg) 200 586.00 592.00 106.90 227.00 840.00
Digestible organic matter intake (kg/d) 184 0.34 0.31 0.19 0.09 1.28
Metabolizable energy (MJ/kg DM) 184 7.79 7.90 1.67 2.56 11.7
Metabolizable energy intake (MJ/d) 184 5.08 4.55 2.88 1.14 19.7
Feeding level 179 0.85 0.78 0.38 0.17 2.21
Neutral detergent fiber intake (kg/d) 195 618.00 620.00 116.70 242.00 899.00
Acid detergent fiber intake (kg/d) 253 0.42 0.40 0.19 0.10 1.41
Average daily gain (g/d) 33 42.90 36.20 28.4 1.60 120

Using an energy density of 55.5 MJ/kg (Elert 2006Elert, G. 2020. The Physics Hypertextbook. Available: https://physics.info/energy-chemical/. June 4.), the estimated methane in MJ/d from Eq. (5 C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 - 12) CH 4   ( MJ d ) =   3.41 ±   0.973   +   0.520 ±   0.120   × DMI 0.996 ±   0.447   × ADFI + 1.15 ±   0.321   × NDFI were converted into g/d. Methane estimated from Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 for cattle and from Eq. (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm for small ruminants was further expressed as g/kg metabolic body weight (MBW), g/kg DMI, g/kg digestible organic matter intake (DOMI), g/kg milk, and g/kg ADG.

With the objective of comparing different forages without influence of other ingredients, a subset of data was created where small ruminants were fed exclusively on grasses, straw/stover, herb legumes, shrub legumes, or tree legumes. Boxplots were created with the forages categories for methane in g/kg DMI and in g/kg DOMI. Also, a subset was created to compare different forages when supplemented with concentrate feed in small ruminants. First, observations were selected where grasses or straw/stover were fed in proportions between 60 and 90 g/100 g DM, with the remainder of the DM coming from concentrate feeds. Second, observations were selected where legumes were fed mixed with grasses, where the legume inclusion ranged between 20 and 50 g/100 g DM, and where the total forage proportion (grass + legume) ranged between 60 and 90 g/100 g DM, with the remainder of the DM coming from concentrate feeds. Legumes were then divided into herbs, shrubs and trees. Boxplots were created with the forage categories for methane in g/kg DMI, in g/kg DOMI, and in g/kg ADG.

Statistical analysis

 

Outliers in the predicted methane from each equation were identified and removed based on the interquartile range. Pearson correlation was estimated within the methane predictions from all equations using the cor ( ) function of R program. The estimated methane production from Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 for cattle and from Eq. (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm for small ruminants was regressed on the legume proportion in the diet using the lm ( ) function of R. Regression analyses were conducted independently for the cattle and the small ruminant datasets. Within each of these datasets, further regression analyses were conducted for subsets based on the physiological stage (adult or growing), species (goat or sheep), and growth habit of legumes (herb, shrub, tree). For the dataset including only control treatments in small ruminants, predicted methane emissions were regressed on the proportion of concentrate in the diet following the procedure described above. Significances of the slopes were declared at P < 0.05, whereas tendencies were declared at 0.05 < P < 0.10.

Results

 

For the cattle dataset there was a high correlation between methane estimates from all equations, with those of Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 having the highest correlations with all other estimates (data not shown). Average methane estimated (g/d) for cattle was consistently higher when derived from the equations of Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. [Eq. (9 CH 4   ( MJ d ) =   3.44 ±   0.937   +   0.502 ±   0.115   × DMI +   0.506 ±   0.211   × NDFI - 12) CH 4   ( MJ d ) =   3.41 ±   0.973   +   0.520 ±   0.120   × DMI 0.996 ±   0.447   × ADFI + 1.15 ±   0.321   × NDFI ] than when estimated from the equations of Patra (2017)Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. [Eq. (5 C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 - 8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm ] (Table 2). For the dataset on small ruminants Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 , (6) CH 4   ( MJ d ) =   1.490 ± 0.745   +   0.418 ± 0.232   × DMI +   0.0415 ± 0.0118   × DMI 2    +   4.311 ± 0.718   × ADFI   0.977 ± 0.138   × ADFI 2 and (7) CH 4    ( MJ d ) =   0.910 ± 0.746   +   1.472 ± 0.154   × DMI   1.388 ± 0.451   × FL   0.669 ± 0.338   × ADFI (Patra 2017Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. ) produced a high number of negative estimations, and were not considered for further analyses (data not shown). Similarly, the estimates from the equations of Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. ranged between 71.6 and 313 g/d (data not shown) and were deemed unrealistic, thus were also disregarded for further analysis. Only the estimates of Eq. (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm (Patra 2017Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. ) appeared to be within realistic ranges of methane production for small ruminants (Table 2).

Table 2.  Summary statistics of estimated methane for cattle and small ruminants
Average Median Standard deviation Minimum Maximum
Cattle
Patra (2017)Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. (g/d) Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 128.0 131.2 50.6 7.4 249.1
Eq. (6) CH 4   ( MJ d ) =   1.490 ± 0.745   +   0.418 ± 0.232   × DMI +   0.0415 ± 0.0118   × DMI 2    +   4.311 ± 0.718   × ADFI   0.977 ± 0.138   × ADFI 2 128.2 137.4 49.6 5.3 261.2
Eq. (7) CH 4    ( MJ d ) =   0.910 ± 0.746   +   1.472 ± 0.154   × DMI   1.388 ± 0.451   × FL   0.669 ± 0.338   × ADFI 135.8 128.2 74.8 32.4 313.6
Eq. (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm 122.1 118.8 52.8 48.7 254.0
Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. (g/d) Eq. (9) CH 4   ( MJ d ) =   3.44 ±   0.937   +   0.502 ±   0.115   × DMI +   0.506 ±   0.211   × NDFI 158.4 154.5 48.1 65.7 301.8
Eq. (10) CH 4   ( MJ d ) =   3.63 ±   0.921   +   0.0549 ±   0.00939   × MEI +   0.606 ±   0.306   × ADFI 149.2 134.8 49.7 84.8 273.4
Eq. (11) CH 4   ( MJ d ) =   4.41 ±   1.13   +   0.0224 ±   0.0106   × MEI +   0.980 ±   0.241   × NDFI 169.8 154.9 49.6 96.8 287.6
Eq. (12) CH 4   ( MJ d ) =   3.41 ±   0.973   +   0.520 ±   0.120   × DMI 0.996 ±   0.447   × ADFI + 1.15 ±   0.321   × NDFI 165.2 163.2 49.2 74.9 296.6
Small ruminants
Patra (2017)Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. (g/d) Eq. (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm 25.9 26.2 22.4 2.12 62.7

The regressions presented in table 3 as well as scatterplots in figure 1and figure 2 correspond to Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 (Patra 2017Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. ). Overall for cattle, when expressed relative to MBW, DMI and ADG, increasing proportions of legume in the diet decreased methane emissions (P ≤ 0.04) (figure 1) (table 3, Reg. 1, Reg. 7, Reg. 23). When expressed relative to DOMI or milk yield, the proportion of legume in the diet did not affect methane production (P ≥ 0.48) even though negative slopes for legume proportion were observed in both cases (figure 1) (table 3, Reg. 13 and Reg. 19). For adult cattle, methane in any unit was not affected by legume proportion in the diet (table 3; P ≥ 0.27), even though a negative slope was observed for all regressions with the exception of methane in g/kg DOMI (table 3, Reg. 14). For growing cattle methane in all units of expression decreased with increasing legume proportion (P < 0.01) (Table 3, Reg. 3, Reg. 9, Reg. 15, Reg. 23). Methane in g/kg MBW was greater in adult compared with growing cattle (figure 1) but the opposite appeared when expressed in g/kg DMI (figure 1).

Table 3.  Regression equations of legume proportion in the diet on methane emissions for cattle ruminants.
Variable (n1)   Intercept SE Slope SE P slope
Methane relative to metabolic body weight (g/kg MBW)
Reg. 1 (134) All 2.23 0.085 -7.17 × 10-3 2.24 × 10-3 <0.01
Reg. 2 (61) Adult 2.24 0.157 -5.80 × 10-3 5.61 × 10-3 0.30
Reg. 3 (73) Growing 2.17 0.113 -6.73 × 10-3 2.56 × 10-3 0.01
Reg. 4 (65) Herb 2.49 0.150 -10.0 × 10-3 4.33 × 10-3 0.02
Reg. 5 (19) Shrub 2.07 0.148 -6.31 × 10-3 4.33 × 10-3 0.16
Reg. 6 (50) Tree 1.98 0.123 -5.36 × 10-3 2.90 × 10-3 0.07
Methane relative to dry matter intake (g/kg DMI)
Reg. 7 (137) All 21.2 0.359 -1.98 × 10-2 0.99 × 10-2 0.04
Reg. 8 (66) Adult 19.8 0.527 -1.13 × 10-2 1.90 × 10-2 0.55
Reg. 9 (71) Growing 23.2 0.411 -4.54 × 10-2 0.97 × 10-2 <0.01
Reg. 10 (70) Herb 19.9 0.585 2.01 × 10-2 1.76 × 10-2 0.26
Reg. 11 (19) Shrub 21.3 0.543 -0.37 × 10-2 1.59 × 10-2 0.82
Reg. 12 (48) Tree 22.1 0.625 -4.89 × 10-2 1.54 × 10-2 <0.01
Methane relative to digestible organic matter intake (g/kg DOMI)
Reg. 13 (75) All 38.9 1.41 -0.925 × 10-2 3.46 × 10-2 0.79
Reg. 14 (35) Adult 32.5 2.72 13.0 × 10-2 11.7 × 10-2 0.27
Reg. 15 (40) Growing 45.7 1.71 -9.81 × 10-2 3.32 × 10-2 0.01
Reg. 16 (24) Herb 35.2 2.77 13.6 × 10-2 8.30 × 10-2 0.12
Reg. 17 (13) Shrub 34.3 1.84 9.02 × 10-2 4.65 × 10-2 0.08
Reg. 18 (38) Tree 42.1 2.11 -8.77 × 10-2 4.67 × 10-2 0.07
Methane relative to milk yield (g/kg MY)
Reg. 19 (45) All 30.7 4.45 -1.27 × 10-1 1.81 × 10-1 0.48
Reg. 20 (30) Herb 28.7 5.62 -1.47 × 10-1 2.17 × 10-1 0.50
Reg. 21 (5) Shrub 28.4 15.03 1.15 × 10-1 7.64 × 10-1 0.89
Reg. 22 (10) Tree 31.6 5.82 1.31 × 10-1 2.52 × 10-1 0.62
Methane relative to average daily gain (g/kg ADG)
Reg. 23 (38) All 0.275 0.027 -1.73 × 10-3 0.621 × 10-3 0.01
Reg. 24 (16) Herb 0.322 0.054 -2.66 × 10-3 1.21 × 10-3 0.04
Reg. 25 (5) Shrub 0.304 0.101 -1.83 × 10-3 2.79 × 10-3 0.56
Reg. 26 (17) Tree 0.242 0.032 -1.24 × 10-3 0.719 × 10-3 0.11

1number of observations used in the regression

Figure 1.  Scatter plot of the regression of legume proportion in the diet on methane emissions expressed in different units for the complete cattle data (solid black line) and for data separated into growing and adult animals.
Figure 2.  Scatter plot of the regression of legume proportion in the diet on methane emissions of cattle expressed in different units for data separated into herb, shrub and tree legumes.

Regarding legume’s growth habit, when expressed as g/kg MBW herbs decreased methane production (P = 0.02), trees tended to decrease it (P = 0.07) and shrubs did not have an effect (P = 0.16) (table 3, Reg. 4-6) ). Herbs showed a greater methane production (g/kg MBW) than both trees and shrubs (figure 2). Relative to DMI, methane decreased with increasing proportions of tree legumes (P < 0.01), while no effects were observed for herbs and shrubs (P ≥ 0.26) (table 3, Reg. 10-12). Methane relative to DOMI (figure 2) tended to increase with increasing proportion of shrubs in the diet (P = 0.08) and tended to decrease with tree legumes (P = 0.07), while no effects of herbs was detected even though a positive slope was obvious (figure 2) and greater than that of shrubs (table 3, Reg. 16-18). Relative to milk no effects of legume type was significant (P ≥ 0.50), and only herbs had a negative slope (table 3, Reg. 20-23). Relative to ADG herbs showed a decrease in methane with increasing proportion of legume (P = 0.04), with no effects found for shrubs and trees (P ≥ 0.11) (table 3, Reg. 24-26).

For small ruminants overall, increasing proportions of legumes decreased methane emissions relative to MBW, DMI and DOMI (by tendency; P = 0.06) (table 4, Reg. 27, 35, 43), but did not have an effect on methane in g/kg ADG (P = 0.97; table 4, Reg. 51). When analyzing the data by species (goats and sheep) and physiological stage (i.e. adult and growing) no effect was observed for adult goats or growing sheep in any unit of methane expression (P > 0.10) (table 4, Reg. 28, 31, 36, 39, 44, 47, 53). Feeding increasing proportions of legumes decreased methane in growing goats when expressed as g/kg MBW, g/kg DMI, and g/kg DOMI (by tendency; P = 0.08) (table 4, Reg. 29, 37, 45). For adult sheep methane decreased when expressed relative to DMI (by tendency; P = 0.06) or DOMI (table 4, Reg. 38, 46). Moreover, growing goats appeared to produce more methane than growing sheep when expressed relative to MBW, DMI and DOMI, but differences between adult and growing animals for other units of expression of methane appeared less clear (figure 3).

Table 4.  Regression equations of legume proportion in the diet on methane emissions for small ruminants.
Variable (n1)   Intercept SE Slope SE P slope
Methane relative to metabolic body weight (g/kg MBW)
Reg. 27 (572) All 2.46 0.051 -2.62 × 10-3 1.14 × 10-3 0.02
Reg. 28 (75) Goat - Adult 2.03 0.149 3.92 × 10-3 3.11 × 10-3 0.21
Reg. 29 (124) Goat - Growing 3.10 0.116 -7.54 × 10-3 2.74 × 10-3 0.01
Reg. 30 (190) Sheep - Adult 2.19 0.065 -1.34 × 10-3 1.31 × 10-3 0.31
Reg. 31 (183) Sheep - Growing 2.37 0.090 -0.986 × 10-3 2.24 × 10-3 0.66
Reg. 32 (160) Herb 2.66 0.102 -5.12 × 10-3 2.03 × 10-3 0.01
Reg. 33 (58) Shrub 1.95 0.174 3.07 × 10-3 3.19 × 10-3 0.34
Reg. 34 (354) Tree 2.45 0.064 -2.65 × 10-3 1.57 × 10-3 0.09
Methane relative to dry matter intake (g/kg DMI)
Reg. 35 (576) All 40.4 1.15 -5.15 × 10-2 2.56 × 10-2 0.04
Reg. 36 (72) Goat - Adult 32.2 3.20 7.93 × 10-2 6.76 × 10-2 0.24
Reg. 37 (125) Goat - Growing 48.8 2.57 -13.0 × 10-2 6.14 × 10-2 0.04
Reg. 38 (192) Sheep - Adult 40.0 1.77 -6.71 × 10-2 3.52 × 10-2 0.06
Reg. 39 (187) Sheep - Growing 37.2 2.19 -0.51 × 10-2 5.50 × 10-2 0.93
Reg. 40 (160) Herb 45.0 2.41 -11.9 × 10-2 4.80 × 10-2 0.01
Reg. 41 (58) Shrub 34.1 4.53 5.65 × 10-2 8.42 × 10-2 0.50
Reg. 42 (358) Tree 39.9 1.37 -5.21 × 10-2 3.36 × 10-2 0.12
Methane relative to digestible organic matter intake (g/kg DOMI)
Reg. 43 (578) All 77.1 2.24 -0.953 × 10-1 4.98 × 10-2 0.06
Reg. 44 (75) Goat - Adult 62.3 6.72 2.27 × 10-1 13.9 × 10-2 0.11
Reg. 45 (125) Goat - Growing 87.3 5.21 -2.19 × 10-1 12.4 × 10-2 0.08
Reg. 46 (189) Sheep - Adult 82.2 3.58 -1.99 × 10-1 7.14 × 10-2 0.01
Reg. 47 (189) Sheep - Growing 70.8 3.94 -0.263 × 10-1 9.96 × 10-2 0.79
Reg. 48 (160) Herb 86.2 4.62 -2.58 × 10-1 9.22 × 10-2 0.01
Reg. 49 (59) Shrub 58.2 8.89 2.65 × 10-1 16.2 × 10-2 0.11
Reg. 50 (359) Tree 77.2 2.65 -1.16 × 10-1 6.54 × 10-2 0.08
Methane relative to average daily gain (g/kg ADG)
Reg. 51 (135) All 0.526 0.055 -0.071 × 10-3 1.63 × 10-3 0.97
Reg. 52 (64) Goat - Growing 0.665 0.075 -3.40 × 10-3 2.39 × 10-3 0.16
Reg. 53 (71) Sheep - Growing 0.413 0.079 2.23 × 10-3 2.20 × 10-3 0.32
Reg. 54 (43) Herb 0.644 0.110 -1.79 × 10-3 2.79 × 10-3 0.53
Reg. 55 (7) Shrub 0.817 0.437 0.143 × 10-3 0.10 × 10-3 0.99
Reg. 56 (85) Tree 0.515 0.061 -1.72 × 10-3 2.06 × 10-3 0.41

1number of observations used in the regression

Figure 3.  Scatter plot of the regression of legume proportion in the diet on methane emissions for small ruminants expressed in different units for the complete dataset (solid black line) and for data separated into adult and growing goat and sheep

Moreover, in small ruminants, herbs decreased methane relative to MBW, DMI and DOMI (P = 0.01; Table 4, Reg. 32, 40, 48) (figure 4). Tree legumes only tended to decrease methane relative to MBW and DOMI (table 4, Reg. 34, 50). Feeding increasing proportions of shrub legumes did not have any effect on methane (P ≥ 0.11), but it is important to note that it showed a positive slope in all cases (table 4, Reg. 33, 41, 49, 55) (figure 4).

Figure 4.  Scatter plot of the regression of legume proportion in the diet on methane emissions for small ruminants expressed in different units for data separated into herb, shrub and tree legumes.

The regression of methane emissions on the concentrate proportion in diets with grasses but no legumes showed that higher concentrate proportion in the diet decreased methane emissions relative to DMI, DOMI and ADG (P ≤ 0.03), but not when expressed in g/kg MBW (P = 0.41) (table 5, Reg. 57, 60, 63, 66) (figure 5). However, when the analysis was conducted separately for grasses and for crop residues (straw or stover) supplementing grasses with concentrates always decreased methane emissions (P ≤ 0.02; table 5, Reg. 58, 61, 64, 67), but when concentrates supplemented straw/stover methane tended to decrease only in g/kg DOMI (P = 0.05), while it tended to increase when expressed in g/kg MBW (P = 0.05), with no effects over methane in g/kg DMI or g/kg ADG (P ≥ 0.56) (table 5, Reg. 59, 62, 65, 68).

Table 5.  Regression of estimated methane on concentrate proportion in diets of small ruminants having grass or straw/stover as sole forage source in the diet.
Variable (n1)   Intercept SE Slope SE P slope
Methane relative to metabolic body weight (g/kg MBW)
Reg. 57 (151) All 2.45 0.06 -2.28 × 10-3 2.75 × 10-3 0.41
Reg. 58 (949) Grass 2.52 0.08 -7.96 × 10-3 3.33 × 10-3 0.02
Reg. 59 (57) Straw/Stover 2.33 0.10 9.09 × 10-3 4.54 × 10-3 0.05
Methane relative to dry matter intake (g/kg DMI)
Reg. 60 (152) All 46.5 1.53 -15.9 × 10-2 5.82 × 10-2 <0.01
Reg. 61 (93) Grass 45.0 1.94 -30.3 × 10-2 7.56 × 10-2 <0.01
Reg. 62 (59) Straw/Stover 49.6 2.05 2.72 × 10-2 7.51 × 10-2 0.72
Methane relative to digestible organic matter intake (g/kg DOMI)
Reg. 63 (152) All 95.3 2.96 -56.9 × 10-2 11.3 × 10-2 <0.01
Reg. 64 (97) Grass 89.9 3.59 -82.4 × 10-2 14.3 × 10-2 <0.01
Reg. 65 (55) Straw/Stover 106.0 3.75 -26.6 × 10-2 13.3 × 10-2 0.05
Methane relative to average daily gain (g/kg ADG)
Reg. 66 (27) All 0.861 0.139 -7.35 × 10-3 3.24 × 10-3 0.03
Reg. 67 (16) Grass 0.956 0.141 -15.2 × 10-3 4.21 × 10-3 <0.01
Reg. 68 (11) Straw/Stover 0.826 0.314 -3.54 × 10-3 5.84 × 10-3 0.56

1 number of observations used in the regression

Figure 5.  Scatter plot of the regression of concentrate proportion in the diet on methane emissions for small ruminants expressed in different units. Solid line represents the general regression line.

Discussion

 

For this discussion the reader must keep in mind that methane was estimated from predictive equations and not directly measured, which carries an uncertainty in the results obtained from these analyses. However, similar exercises have been made before (Chagunda et al. 2010Chagunda, M.G., Flockhart, J.F. & Roberts, D.J. 2010. "The effect of forage quality on predicted enteric methane production from dairy cows". International Journal of Agricultural Sustainability, 8: 250-256, ISSN: 1747-762X. https://doi.org/10.3763/ijas.2010.0490. ) with results in line with in vivo observations. Moreover, the trends observed in this study across the subsets of data are consistent throughout the study and in accordance with physiological principles. Another important aspect to consider is that legumes contain a number of bioactive compounds with the capability to alter rumen fermentation, including methane production (Archimède et al. 2011Archimède, H., Eugène, M., Magdeleine, C.M., Boval, M., Martin, C., Morgavi, D. P., Lecomte, P. & Doreau, M. 2011. "Comparison of methane production between C3 and C4 grasses and legumes". Animal Feed Science and Technology, 166: 59-64, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2011.04.003. ). Secondary compounds may have effects on DMI and OMD, both parameters strongly linked with methane production, thus indirectly, the equations used to estimate methane have accounted for some of the possible effects of secondary compounds. Even with these caveats, the robustness of this study lays in the volume of information that could be analyzed, where even small tendencies might be indicative of important and biologically relevant effects. Therefore, the results of the current analysis certainly contribute to the discussion on the ability of tropical legumes to decrease methanogenesis.

Predictive equations used

 

Equations to predict methane were selected based on the available parameters in our dataset. Therefore, equations including e.g. fat, non-structural carbohydrates, or gross energy concentration were not considered. Equations based exclusively on DMI were also disregarded, as these do not consider the differences in the nutritional composition of legumes and grasses. Hence, predictive equations considering DMI, OMD, NDF and ADF concentration or intake were purposely targeted. Recent studies have shown that, when accounting for the nutritional composition and at high inclusion levels, diets with increasing legume level tend to decrease DMI and OMD (da Silva et al. 2017da Silva, T., Pereira, O., Martins, R., Agarussi, M., da Silva, L., Rufino, L., Valadares, F. & Ribeiro, K. 2017. "Stylosanthes cv. Campo Grande silage and concentrate levels in diets for beef cattle". Animal Production Science, 58: 539-545, ISSN: 1836-5787. https://doi.org/10.1071/AN15781. and Montoya-Flores et al. 2020Montoya-Flores, M.D., Molina-Botero, I.C., Arango, J., Romano-Muñoz, J.L., Solorio-Sánchez, F.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2020. "Effect of dried leaves of Leucaena leucocephala on rumen fermentation, rumen microbial population, and enteric methane production in crossbred heifers". Animals, 10: 300, ISSN: 2076-2615. https://doi.org/10.3390/ani10020300. ) with clear differences between herb, shrub and tree legumes (Castro-Montoya and Dickhoefer 2018Castro‐Montoya, J.M. & Dickhoefer, U. 2018. "Effects of tropical legume silages on intake, digestibility and performance in large and small ruminants: A review". Grass and Forage Science, 73: 26-39, ISSN: 1365-2494. https://doi.org/10.1111/gfs.12324. ). Along this line, tropical legumes have a fairly high concentration of NDF and ADF, which reflects on a lower in vitro OMD than grasses, particularly for shrub legumes (Castro-Montoya and Dickhoefer 2020Castro-Montoya, J.M. & Dickhoefer, U. 2020. "The nutritional value of tropical legume forages fed to ruminants as affected by their growth habit and fed form: A systematic review". Animal Feed Science and Technology, 269: 1-14, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2020.114641. ), hence, this study aimed at capturing this variation into the estimation of methane.

For the cattle dataset, including both adult and growing animals, all the equations used yielded estimates of methane within expected values for large ruminants (table 3). For example, estimates of Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 to (7) CH 4    ( MJ d ) =   0.910 ± 0.746   +   1.472 ± 0.154   × DMI   1.388 ± 0.451   × FL   0.669 ± 0.338   × ADFI ranged between 0.76 and 1.58 MJ/kg DMI, while Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. and Yan et al. (2009)Yan, T., Porter, M.G. & Mayne, C.S. 2009. "Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters". Animal, 3: 1455-1462, ISSN: 1751-732X. https://doi.org/10.1017/S175173110900473X. , reported methane emissions ranging from 1.12 to 1.49 MJ/kg DMI. For the cattle dataset, there was a high correlation between all predictive equations (r ≥ 0.71; data not shown). The equations of Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. had consistently higher estimated methane than that of Patra (2017)Patra, A.K. 2017. "Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems". Mitigation and Adaptation Strategies for Global Change, 22: 629-650, ISSN: 1573-1596. https://doi.org/10.1007/s11027-015-9691-7. (table 2) likely due to the higher overall methane production of the temperate cattle used by Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. . Not only because Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 was developed from tropical cattle, but also because it was the equation that allowed for the maximum number of observations predicted, the estimates of Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 were used for the calculation of methane relative to MBW, DMI, DOMI, milk yield, and ADG, and the subsequent regression analyses.

For small ruminants Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 , (6) CH 4   ( MJ d ) =   1.490 ± 0.745   +   0.418 ± 0.232   × DMI +   0.0415 ± 0.0118   × DMI 2    +   4.311 ± 0.718   × ADFI   0.977 ± 0.138   × ADFI 2 and (7) CH 4    ( MJ d ) =   0.910 ± 0.746   +   1.472 ± 0.154   × DMI   1.388 ± 0.451   × FL   0.669 ± 0.338   × ADFI produced a high number of negative estimations, likely due to the quadratic nature of NDFI and ADFI in Eq. (5) C H 4   ( MJ d ) =   1.012 ± 0.709   +   0.308 ± 0.249   × DMI +   0.0404 ± 0.0119   × DMI 2   +   2.424 ± 0.415   × NDFI   0.290 ± 0.0409   × NDFI 2 and (6) CH 4   ( MJ d ) =   1.490 ± 0.745   +   0.418 ± 0.232   × DMI +   0.0415 ± 0.0118   × DMI 2    +   4.311 ± 0.718   × ADFI   0.977 ± 0.138   × ADFI 2 and due to the high ADF concentrations in diets in the tropics. Equation (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm , based on the OMD, FL and DMI, yielded methane estimates with mean, min and max (g/d) of 25.9, 2.12 and 62.7 (table 2). The estimates of Eq. (8) CH 4   ( MJ d ) =   1.559 ± 2.010   +   1.217 ± 0.164   × DMI   2.418 ± 0.724   × FL +   0.00714 ± 0.00316   × OMDm still appeared to be on the higher end of daily methane emissions for small ruminants (Pelchen and Peters 1998Pelchen, A. & Peters, K.J. 1998. "Methane emissions from sheep". Small Ruminant Research, 27: 137-150, ISSN: 1879-0941. https://doi.org/10.1016/S0921-4488(97)00031-X. ), but despite the possible overestimation, these estimates were used to calculate methane relative to MBW, DMI, DOMI, and ADG, and the subsequent regression analyses. The estimates from the equations of Ellis et al. (2007)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: 3456-3466, ISSN: 1525-3198. https://doi.org/10.3168/jds.2006-675. averaged methane emissions between 71.4 and 88.9 g/d and were deemed not plausible for tropical small ruminants (data not shown).

In vivo methane production when feeding tropical legumes

 

Methane synthesis is a function of intake, therefore, robust conclusions can only be obtained when DMI has been reported in a study, thus manuscripts with grazing animals are not included in the following discussion. Under those considerations, ten studies were found reporting methane emissions and DMI from ruminants fed on tropical legumes comparing them with a control diet. Six of those in vivo studies tested the effects of leucaena, with four of them finding decreases in methane emissions (Archimède et al. 2016Archimède, H., Rira, M., Barde, D.J., Labirin, F., Marie‐Magdeleine, C., Calif, B., Périacarpin, F., Fleury, J., Rochette, Y., Morgavi, D.P. & Doreau, M. 2016. "Potential of tannin‐rich plants, Leucaena leucocephala, Glyricidia sepium and Manihot esculenta, to reduce enteric methane emissions in sheep". Journal of Animal Physiology and Animal Nutrition, 100: 1149-1158, ISSN: 1439-0396. https://doi.org/10.1111/jpn.12423. , Montoya-Flores et al. 2020Montoya-Flores, M.D., Molina-Botero, I.C., Arango, J., Romano-Muñoz, J.L., Solorio-Sánchez, F.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2020. "Effect of dried leaves of Leucaena leucocephala on rumen fermentation, rumen microbial population, and enteric methane production in crossbred heifers". Animals, 10: 300, ISSN: 2076-2615. https://doi.org/10.3390/ani10020300. , Pineiro-Vásquez et al. 2018Pineiro-Vázquez, A.T., Canul-Solis, J.R., Jiménez-Ferrer, G.O., Alayón-Gamboa, J.A., Chay-Canul, A.J., Ayala-Burgos, A.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2018. "Effect of condensed tannins from Leucaena leucocephala on rumen fermentation, methane production and population of rumen protozoa in heifers fed low-quality forage". Asian-Australasian Journal of Animal Sciences, 31: 1738-1746, ISSN: 1976-5517. https://doi.org/10.5713/ajas.17.0192. and Possenti et al. 2008Possenti, R.A., Franzolin, R., Schammas, E.A., Demarchi, J.J., Frighetto, R.T.S. & Lima, M.A.D. 2008. "Efeitos de dietas contendo Leucaena leucocephala e Saccharomyces cerevisiae sobre a fermentação ruminal e a emissão de gás metano em bovinos". Revista Brasileira de Zootecnia, 37: 1509-1516, ISSN: 1806-9290. http://dx.doi.org/10.1590/S1516-35982008000800025. ) while two other finding no effects (Delgado et al. 2013Delgado, D.C., Galindo, J., Cairo, J., Orta, I. & Dorta, N. 2013. "Supplementation with foliage of L. leucocephala. Its effect on the apparent digestibility of nutrients and methane production in sheep". Cuban Journal of Agricultural Science, 47(3): 267-271, ISSN: 2079-3480. and Moreira et al. 2013Moreira, G.D., Lima, P.D., Borges, B.O., Primavesi, O., Longo, C., McManus, C., Abdalla, A. & Louvandini, H. 2013. "Tropical tanniniferous legumes used as an option to mitigate sheep enteric methane emission". Tropical Animal Health and Production, 45: 879-882, ISSN: 1573-7438. https://doi.org/10.1007/s11250-012-0284-0. ). Importantly, three of the studies reporting a decrease in methane when feeding leucaena also found a decrease in OMD (Archimède et al. 2016Archimède, H., Rira, M., Barde, D.J., Labirin, F., Marie‐Magdeleine, C., Calif, B., Périacarpin, F., Fleury, J., Rochette, Y., Morgavi, D.P. & Doreau, M. 2016. "Potential of tannin‐rich plants, Leucaena leucocephala, Glyricidia sepium and Manihot esculenta, to reduce enteric methane emissions in sheep". Journal of Animal Physiology and Animal Nutrition, 100: 1149-1158, ISSN: 1439-0396. https://doi.org/10.1111/jpn.12423. , Pineiro-Vásquez et al. 2018Pineiro-Vázquez, A.T., Canul-Solis, J.R., Jiménez-Ferrer, G.O., Alayón-Gamboa, J.A., Chay-Canul, A.J., Ayala-Burgos, A.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2018. "Effect of condensed tannins from Leucaena leucocephala on rumen fermentation, methane production and population of rumen protozoa in heifers fed low-quality forage". Asian-Australasian Journal of Animal Sciences, 31: 1738-1746, ISSN: 1976-5517. https://doi.org/10.5713/ajas.17.0192. and Montoya-Flores et al. 2020Montoya-Flores, M.D., Molina-Botero, I.C., Arango, J., Romano-Muñoz, J.L., Solorio-Sánchez, F.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2020. "Effect of dried leaves of Leucaena leucocephala on rumen fermentation, rumen microbial population, and enteric methane production in crossbred heifers". Animals, 10: 300, ISSN: 2076-2615. https://doi.org/10.3390/ani10020300. ). When testing other legume species, Suybeng et al. (2020)Suybeng, B., Charmley, E., Gardiner, C.P., Malau-Aduli, B.S. & Malau-Aduli, A.E. 2020. "Supplementing Northern Australian beef cattle with Desmanthus tropical legume reduces in-vivo methane emissions". Animals, 10: 2097, ISSN 2076-2615. https://doi.org/10.3390/ani10112097. found decreases in methane (g/kg DMI) with increasing levels of Descanthus spp. but ADG decreased at the highest inclusion level (31 g/100 g DM). Three additional studies did not find any effect of feeding Lablab purpureus, Vigna unguiculata, Styzolobium aterrimum, Mimosa caesalpiniaefolia and Macrotyloma axiliare on methane emissions in g/kg DMI (Moreira et al., 2013Moreira, G.D., Lima, P.D., Borges, B.O., Primavesi, O., Longo, C., McManus, C., Abdalla, A. & Louvandini, H. 2013. "Tropical tanniniferous legumes used as an option to mitigate sheep enteric methane emission". Tropical Animal Health and Production, 45: 879-882, ISSN: 1573-7438. https://doi.org/10.1007/s11250-012-0284-0. ; Washaya et al., 2018Washaya, S., Mupangwa, J. & Muchenje, V. 2018. "Chemical composition of Lablab purpureus and Vigna unguiculata and their subsequent effects on methane production in Xhosa lop-eared goats". South African Journal of Animal Science, 48: 445-458, ISSN: 2221-4062. http://dx.doi.org/10.4314/sajas.v48i3.5. and Lima et al., 2020Lima, P.D., Abdalla Filho, A.L., Issakowicz, J., Ieda, E.H., Corrêa, P.S., de Mattos, W.T., Gerdes, L., McManus, C., Abdalla, A.L. & Louvandini, H. 2020. "Methane emission, ruminal fermentation parameters and fatty acid profile of meat in Santa Inês lambs fed the legume macrotiloma". Animal Production Science, 60: 665-673, ISSN: 1836-5787. https://doi.org/10.1071/AN19127. ).

Moreover, Kennedy and Charmley (2012)Kennedy, P.M. & Charmley, E. 2012. "Methane yields from Brahman cattle fed tropical grasses and legumes". Animal Production Science, 52: 225-239, ISSN: 1836-5787. http://dx.doi.org/10.1071/AN11103. quantified the methane emission from several diets containing Lablab purpureus, Leucaena leucocephala, Stylosantes hamata, and Macroptilium bracteatum, but the study was not designed to compare those emissions to those of other diets. The authors found that when increasing the legume proportion in the diet from 20 to 40 g/100 g DM, the changes in methane production where not consistent and depended on the basal grass, the legume species, and the unit in which methane was expressed (Kennedy and Charmley 2012Kennedy, P.M. & Charmley, E. 2012. "Methane yields from Brahman cattle fed tropical grasses and legumes". Animal Production Science, 52: 225-239, ISSN: 1836-5787. http://dx.doi.org/10.1071/AN11103. ). The meta-analysis of Archimède et al. (2011)Archimède, H., Eugène, M., Magdeleine, C.M., Boval, M., Martin, C., Morgavi, D. P., Lecomte, P. & Doreau, M. 2011. "Comparison of methane production between C3 and C4 grasses and legumes". Animal Feed Science and Technology, 166: 59-64, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2011.04.003. concluded that legumes from warm environments produced less methane than their grasses counterparts, but the analysis was based on only 12 observations comparing legumes versus grasses. All of these studies point towards reductions in methane production when tropical legumes are fed, but the heterogeneity in legume species, animal characteristics, and diet’s nutritional composition make it venturous to draw a conclusion on the potential of these forages to reduce methane emissions.

Overall effects of legume forages on estimated methane production

 

Methane decreased with increasing legume proportion in the diet when expressed relative to MBW, however, emissions could decrease simply because of a lower DMI or degradability of the feed. Indeed, recent studies showed that high proportions of tropical legumes decrease intake, as well as OM and NDF degradability of the diet (Castro-Montoya and Dickhoefer 2018Castro‐Montoya, J.M. & Dickhoefer, U. 2018. "Effects of tropical legume silages on intake, digestibility and performance in large and small ruminants: A review". Grass and Forage Science, 73: 26-39, ISSN: 1365-2494. https://doi.org/10.1111/gfs.12324. and da Silva et al. 2017da Silva, T., Pereira, O., Martins, R., Agarussi, M., da Silva, L., Rufino, L., Valadares, F. & Ribeiro, K. 2017. "Stylosanthes cv. Campo Grande silage and concentrate levels in diets for beef cattle". Animal Production Science, 58: 539-545, ISSN: 1836-5787. https://doi.org/10.1071/AN15781. ). In this regard, when expressed relative to DMI, methane still decreased with increasing legume proportion in the diet. However, when expressed relative to DOMI tropical legumes caused a less obvious response in methane, with no effects when fed to cattle and only a tendency to decrease in small ruminants.

Several mechanisms by which legumes may exert this decrease in methane production are discussed. First, microbial degradation of cellulose and hemicellulose yields H2 that must be later removed from the rumen environment (Carroll and Hungate 1955Carroll, E.J. & Hungate, R.E. 1955. "Formate dissimilation and methane production in bovine rumen contents". Archives of Biochemistry and Biophysics, 56: 525-536, ISSN: 1096-0384. https://doi.org/10.1016/0003-9861(55)90272-1. ). A lower fiber degradability compared with grasses has been observed in tropical legumes, resulting in less substrate for methane formation. Second, diets conducive of higher propionate production sequester H2 away from methane (Marty and Demeyer 1973Marty, R.J. & Demeyer, D.I. 1973. "The effect of inhibitors of methane production of fermentation pattern and stoichiometry in vitro using rumen contents from sheep given molasses". British Journal of Nutrition, 30: 369-376, ISSN: 1475-2662. https://doi.org/10.1079/BJN19730041. ), however, studies have shown that tropical legumes have higher acetate to propionate ratios (e.g. Castro-Montoya et al. 2020Castro-Montoya, J.M., Goetz, K. & Dickhoefer, U. 2020. "In vitro fermentation characteristics of tropical legumes and grasses of good and poor nutritional quality and the degradability of their neutral detergent fibre". Animal Production Science, 61: 645-654, ISSN: 1836-5787. https://doi.org/10.1071/AN20136. and Montoya-Flores et al. 2020Montoya-Flores, M.D., Molina-Botero, I.C., Arango, J., Romano-Muñoz, J.L., Solorio-Sánchez, F.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2020. "Effect of dried leaves of Leucaena leucocephala on rumen fermentation, rumen microbial population, and enteric methane production in crossbred heifers". Animals, 10: 300, ISSN: 2076-2615. https://doi.org/10.3390/ani10020300. ), thus, a higher sequestration of methane from propionate seems unlikely.

Also, it has been argued that the presence of secondary compounds in legumes may play a role on activity and number of methanogens (Makkar et al. 2007Makkar, H.P.S., Francis, G. & Becker, K. 2007. "Bioactivity of phytochemicals in some lesser-known plants and their effects and potential applications in livestock and aquaculture production systems". Animal, 1: 1371-1391, ISSN: 1751-732X. https://doi.org/10.1017/S1751731107000298. ). The in vivo studies reporting microbial activity and methane are inconclusive. The study of Montoya-Flores et al. (2020)Montoya-Flores, M.D., Molina-Botero, I.C., Arango, J., Romano-Muñoz, J.L., Solorio-Sánchez, F.J., Aguilar-Pérez, C.F. & Ku-Vera, J.C. 2020. "Effect of dried leaves of Leucaena leucocephala on rumen fermentation, rumen microbial population, and enteric methane production in crossbred heifers". Animals, 10: 300, ISSN: 2076-2615. https://doi.org/10.3390/ani10020300. with leucaena did not find any effect of the legume on methanogens number, despite the high concentration of total and condensed tannins and a decreased methane production. Conversely, Lima et al. (2020)Lima, P.D., Abdalla Filho, A.L., Issakowicz, J., Ieda, E.H., Corrêa, P.S., de Mattos, W.T., Gerdes, L., McManus, C., Abdalla, A.L. & Louvandini, H. 2020. "Methane emission, ruminal fermentation parameters and fatty acid profile of meat in Santa Inês lambs fed the legume macrotiloma". Animal Production Science, 60: 665-673, ISSN: 1836-5787. https://doi.org/10.1071/AN19127. found no decrease in methane production when macrotyloma was fed to lambs but found a decrease in the abundance of methanogens (also an increase in Ruminococcus flavefaciesn and Fibrobacter succionogens). In this study it was not possible to prove the direct effects of secondary compounds on methane, nevertheless, the indirect effects of those compounds, if any, could reflect on OMD and DMI, variables considered in these estimations. Importantly, not all tropical legumes are rich in secondary compounds neither it is proven that their activity is biologically significant to exert an effect on methanogenesis, therefore, not all the effects of legumes on methane production can be ascribed to secondary compounds.

Decreased degradability and intake can have negative consequences if their extent affects the supply of nutrients to the animal, ultimately decreasing its performance. Therefore, the best indicator of the effectiveness of a strategy to reduce methane is the reduction of intensity of emissions. In this regard, methane relative to milk yield was not affected by legume proportion (even though a negative slope was observed), whereas methane relative to ADG declined in cattle fed legumes. For methane relative to milk yield, it is important to notice that in the current dataset 95 % of the yields recorded was below 16.7 kg/d (average + 2 standard deviations), therefore the current results represent methane emissions of cattle with low to medium production level. Much higher milk yields are commonly found across the tropics. Not only are diets of high yielding cows usually well balanced in terms of nutrients, but also marginal improvements tend to decrease with higher yields. It is therefore uncertain and worth researching how tropical legumes would influence the intensity of emissions at high milk yield levels. The scenario presented for growing cattle appears to cover a larger range of growth for tropical cattle, where the majority of the ADG’s recorded were below 903 g/d.

Contrary to the cattle dataset, no effect of legumes was observed on methane in g/kg ADG for small ruminants. For goats, decreases in methane relative to MBW, DMI and DOMI were observed, hence a decrease in methane in g/kg ADG would be expected provided that ADG is maintained or increased when feeding legumes. Indeed, median ADG for sheep and goats was 48.0 and 38.7 g/d, respectively, higher than median ADG of small ruminants fed no legumes (36.2 g/d) (table 1). A previous study suggested a quadratic relationship between legume level and ADG, with ADG being maximized around 400 g legume/kg DM (Castro-Montoya and Dickhoefer 2018Castro‐Montoya, J.M. & Dickhoefer, U. 2018. "Effects of tropical legume silages on intake, digestibility and performance in large and small ruminants: A review". Grass and Forage Science, 73: 26-39, ISSN: 1365-2494. https://doi.org/10.1111/gfs.12324. ). High levels of legume inclusion were more common in small ruminants than in cattle (figure 1, Figure 3), therefore, a quadratic effect of legume proportion in the diet on methane in g/kg ADG was tested, but was found not significant (data not shown). Maybe more relevant, for small ruminants, but not for cattle, a number of observations reported a negative ADG, that is when animals lost weight throughout the experimental period. These data were not used for this analysis, as such calculation would produce a negative methane emission which is biologically impossible. Therefore, 12, 9 and 2 observations were removed from the calculations of methane in g/kg ADG of small ruminants without legume, growing sheep, and growing goat, respectively. When considering those negative observations, the median ADG of diets without legumes (25.0 g/d) was much lower than the median observed for growing sheep and goats, (48.0 and 38.1 g/d, respectively; data not shown). This means that the effect of feeding legumes to small ruminants on methane relative to ADG are indeed underestimated in this study, and that a greater reduction in methane emissions, for example over the lifespan of an animal, is likely when improving their feeding via legumes.

Effects of legumes forages according to their growth habit on estimated methane emissions

 

Recent literature reviews on tropical legumes have shown strong differences in the nutritional characteristics between herb, shrub and tree legumes (Castro-Montoya and Dickhoefer 2018Castro‐Montoya, J.M. & Dickhoefer, U. 2018. "Effects of tropical legume silages on intake, digestibility and performance in large and small ruminants: A review". Grass and Forage Science, 73: 26-39, ISSN: 1365-2494. https://doi.org/10.1111/gfs.12324. and Castro-Montoya and Dickhoefer 2020Castro-Montoya, J.M. & Dickhoefer, U. 2020. "The nutritional value of tropical legume forages fed to ruminants as affected by their growth habit and fed form: A systematic review". Animal Feed Science and Technology, 269: 1-14, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2020.114641. ) with shrubs having the highest NDF concentrations, and herbs having the greatest in vivo and in vitro OMD. Herb, shrub and tree legumes also differ in terms of ME, secondary compounds, and CP, to mention some (Castro-Montoya and Dickhoefer 2020Castro-Montoya, J.M. & Dickhoefer, U. 2020. "The nutritional value of tropical legume forages fed to ruminants as affected by their growth habit and fed form: A systematic review". Animal Feed Science and Technology, 269: 1-14, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2020.114641. ). All of these parameters will influence intake level, fermentation patterns, total tract digestibility, and performance. It is therefore expectable that different types of legumes will elicit different responses on methane production. Remarkably, the differential effects of herb, shrub and tree legumes were consistent across cattle and small ruminants and reflected previously outlined differences in their nutritional composition. A greater degradability would lead to higher methane emissions, and indeed, higher methane production was observed for herbs when expressed relative to MBW. When expressed relative to DMI and DOMI, herb legumes have higher methane emissions at levels of inclusion below 500 g/kg DM, but because of the steeper slope, with greater inclusion levels herbs produce less methane than shrubs and trees. Shrubs clearly show the lesser potential to decrease methane emissions, even having a tendency for higher emissions when expressed relative to DOMI, likely related to their higher NDF content directly linked to methane production.

According to the current analysis, tree legumes appear to be intermediate in their potential to decrease methane. Tree legumes have a higher lignin concentration and a lower OMD than herbs (Castro-Montoya and Dickhoefer 2020Castro-Montoya, J.M., Goetz, K. & Dickhoefer, U. 2020. "In vitro fermentation characteristics of tropical legumes and grasses of good and poor nutritional quality and the degradability of their neutral detergent fibre". Animal Production Science, 61: 645-654, ISSN: 1836-5787. https://doi.org/10.1071/AN20136. ), as well as a decreased DMI and NDF digestibility (Castro-Montoya and Dickhoefer 2018Castro‐Montoya, J.M. & Dickhoefer, U. 2018. "Effects of tropical legume silages on intake, digestibility and performance in large and small ruminants: A review". Grass and Forage Science, 73: 26-39, ISSN: 1365-2494. https://doi.org/10.1111/gfs.12324. ). The lower digestibility an intake of tree legumes may reflect on a lower animal performance, and ultimately in their potential to decrease methane. Only the foliage of tree legumes is fed to ruminants, resulting in that, compared to herbs, tree legumes have a higher CP content, but also higher secondary compounds content. The presence of secondary compounds in trees may further influence their effects on methane, but the extent and direction of such effects are uncertain, as outlined before. For example, Tiemann et al. (2008)Tiemann, T., Lascano, C., Kreuzer, M. & Hess, H. 2008. "The ruminal degradability of fibre explains part of the low nutritional value and reduced methanogenesis in highly tanniniferous tropical legumes". Journal of the Science of Food and Agriculture, 88: 1794-1803, ISSN: 1097-0010. https://doi.org/10.1002/jsfa.3282. found no correlation between the concentration of condensed tannins and NDF degradability of tropical legumes in vitro. The unclear effects of secondary compounds on ruminants’ nutrition and methane are likely related to their diversity, and must not be neglected from research, this, however, escapes the scope of this study.

The higher degradability and Metabolizable energy of herbs (Castro-Montoya and Dickhoefer 2020Castro-Montoya, J.M. & Dickhoefer, U. 2020. "The nutritional value of tropical legume forages fed to ruminants as affected by their growth habit and fed form: A systematic review". Animal Feed Science and Technology, 269: 1-14, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2020.114641. ) is likely conducive of a greater positive effect on animal performance, whereas shrubs would have the lowest. Indeed, when expressed relative to ADG and milk yield, the slopes of the regression of legume herbs on methane were consistently negative and showed a greater potential to decrease intensity of emissions than trees and shrubs (figure 2).

Estimated methane emissions from diets of small ruminants without legumes

 

In general, the results of the current study evidence that legumes have potential to decrease methane emissions, particularly when expressed relative to DMI and DOMI. The decreases, however, where not comparable to those achieved when concentrate is supplemented to grasses or crop residues from cereals, as reflected in the greater negative slopes in table 5 compared with those of Table 4. Interestingly, the effects of feeding concentrate were contrasting when the basal forage was a grass or a crop residue. Concentrate feeding is regarded as a strategy to decrease methane emissions as less substrate for H2 producing is available, while steering the fermentation towards more propionate production (Carroll and Hungate, 1955Carroll, E.J. & Hungate, R.E. 1955. "Formate dissimilation and methane production in bovine rumen contents". Archives of Biochemistry and Biophysics, 56: 525-536, ISSN: 1096-0384. https://doi.org/10.1016/0003-9861(55)90272-1. ), while increasing animal productivity, thereby decreasing the intensity of emissions. In the current analysis, higher concentrate supplementation in the diet of small ruminants fed on grass constantly decreased methane emissions, including when expressed relative to ADG, an effect not always observed with the legumes. However, when concentrate was supplemented to straw, methane tended to increase when expressed relative to MBW and had no effect on methane relative to DMI. The supplementation of straw with concentrate feed certainly caused an increase in the overall diet digestibility, thereby producing more methane. Indeed, when methane was expressed relative to DOMI the supplementation of concentrate tended to decrease methane emissions. The effects of supplementing concentrate feeds to crop residues were not obvious when expressed relative to ADG. The reduction of methane when feeding a concentrate feed is remarkable considering that in most cases the portion referred as concentrate consisted of only a cereal meal, brans, cakes or byproducts from other industry, or mixtures of cereal and protein meals. This highlights, on the one hand, the low quality of basal forages found across the tropics, but on the other hand, the great potential that adding even a small proportion of readily digestible energy or protein sources may have in the utilization of that basal forage, performance, and emissions.

Importantly, the authors do not want to suggest that concentrates should be used in detriment of legume forages. Utilizing legume forages has several advantages for the agricultural production system. Moreover, enteric methane is not the only measurement of environmental impact of a given strategy, and other factors like carbon footprint, water footprint, or biodiversity should be considered for the legume forages vs. concentrates comparison.

Archimède et al. (2011)Archimède, H., Eugène, M., Magdeleine, C.M., Boval, M., Martin, C., Morgavi, D. P., Lecomte, P. & Doreau, M. 2011. "Comparison of methane production between C3 and C4 grasses and legumes". Animal Feed Science and Technology, 166: 59-64, ISSN: 0377-8401. https://doi.org/10.1016/j.anifeedsci.2011.04.003. found that sole legumes decrease methane emissions compared with sole grasses. In an attempt to do a similar evaluation boxplots were created comparing estimated methane for diets containing only grasses, only straw/stover, or only legumes (figure 6A-B). The boxplots clearly show higher methane emissions from both grasses and straw compared with only legumes for both methane in g/kg DMI and in g/kg DOMI, with herbs consistently having less methane than that of shrubs and trees. Unfortunately, not enough observations were available to compare sole forages on the basis of methane relative to ADG. Furthermore, when the grasses supplemented with concentrates were compared with mixed legume-grass rations supplemented with concentrates (figure 6C-D-E), relative to DMI and DOMI, median methane produced by grasses was lower than that of herbs and shrubs, and appeared similar to that of trees. However, when expressed relative to ADG the lowest median methane was found for diets where herbs were combined with grasses and supplemented with concentrate, slightly lower than those of only grass supplemented with concentrate. The synergism between legumes and grasses has been previously reported (Minson 1990Minson, D.J. 1990. Forage in ruminant nutrition. Academic Press Inc. CA, USA.), albeit not specific for methane emissions, but it could be related to a greater partitioning of energy away from metabolic losses.