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Development of enteric methane emission factors for Holstein-Friesian and Norwegian first lactation cows
Abstract
The objectives were to accurately quantify enteric methane (CH4) emissions for first lactation dairy cows and to use these data to develop CH4 prediction equations. Calorimeter measurements and production data were used to calculate CH4 emissions for Holstein-Friesian (HF, n = 32) and Norwegian (n = 32) first lactation cows during a 305-d lactation period. Methane outputs were similar between HF and Norwegian (123 vs. 126 kg/305 d) when offered high-concentrate diets, but HF produced more CH4 (P < 0.05) than Norwegian (105 vs. 98 kg/305 d) when given low-concentrate diets. The HF offered high-concentrate diets had a lower (P <.05) CH4 emission per energy-corrected milk yield (16.3 g/kg) than the other three treatments (19.7–20.4 g/kg). These data were then used to develop CH4 prediction equations, which provide an alternative approach to estimate enteric CH4 emissions for HF and Norwegian first lactation dairy cows.
... Milk samples were taken at each milking, bulked in proportion to yield for days 1-3 and days 4-6, and subsequently analysed for GE and N concentrations. Energy corrected milk yield (ECM) was determined as described by Chen and Yan (2015). ...
The European livestock sector has a significant deficit of high-quality protein feed ingredients. Consequently there is interest in using locally grown protein grain crops to partially or completely replace imported protein feeds in dairy cow rations. Field bean (FB; Vicia faba) has been identified as a locally grown crop with significant potential. The current study was designed to examine the effects of FB on cow performance and nutrient utilization in the diet of early-lactation dairy cows, including high levels of FB (up to 8.4 kg/cow per day). The experiment used 72 dairy cows in a 3-treatment continuous design (from calving until wk 20 of lactation). All cows were given ad libitum access to a mixed ration comprising grass silage and concentrates [45:55 on a dry matter (DM) basis]. Concentrates offered contained either 0, 349, or 698 g of FB/kg of concentrate (treatments FB0, FB-Low, and FB-High, respectively), with FB completely replacing soybean meal, rapeseed meal, maize gluten, and wheat in the concentrate for the FB-High treatment. Following completion of the 20-wk experiment, ration digestibility, nutrient utilization, and methane (CH4) production were measured using 4 cows from each treatment. Neither silage DM intake, total DM intake, nor milk yield were affected by treatment. Cows on FB0 had a higher milk fat content than those on FB-High, and cows on FB0 and FB-Low had higher milk protein contents than did those on FB-High. Field bean inclusion increased the degree of saturation of milk fat produced. Milk fat yield, milk protein yield, and milk fat plus protein yield were higher with FB0 than with either FB-Low or FB-High. Treatment had no effect on the digestibility of DM, organic matter, nitrogen (N), gross energy, or neutral detergent fiber, whereas digestibility of acid detergent fiber was higher with FB0 than with FB-High. Neither the efficiency of gross energy or N utilization, nor any of the CH4 production parameters examined, were affected by treatment. Similarly, none of the fertility or health parameters examined were affected by treatment. The reduction in milk fat observed may have been due to the higher starch content of the FB-High diet, and the reduction in milk protein may have been due to a deficit of methionine in the diet. It is likely that these issues could be overcome by changes in ration formulation, thus allowing FB to be included at the higher range without loss in performance.
Within Europe field bean (Vicia Faba) (FB) has been identified as a crop with potential to partially replace imported protein feeds in dairy cow rations. The current study was designed to examine the impact of FB inclusion level in dairy cow diets on performance and nutrient utilisation. Sixty mid-lactation dairy cows were used in a four treatment continuous design (ten weeks duration) experiment. All cows were given ad libitum access to grass silage, and were supplemented with 10.0 kg concentrate/cow/day. Concentrates offered contained 0, 166, 333 or 473 g FB/kg concentrate (treatments FB0, FB1.7, FB3.3 and FB4.7, respectively), with the FB partially replacing soya bean meal and rapeseed meal. On completion of the 10 week experiment, ration digestibility was measured using four cows from each treatment. While there was a trend for milk protein content (linear: P = 0.081) to decrease with increasing FB inclusion levels, FB inclusion had no effect on silage dry matter (DM) intake, total DM intake, milk yield, milk fat content, milk fat yield, milk protein yield, milk fat + protein yield, live-weight change and body condition score change. Similarly, FB inclusion had no effect on DM, organic matter and gross energy digestibility coefficients, although there was a linear increase in nitrogen (N) digestibility (P < 0.041) with increasing FB inclusion levels. Faecal N/N intake (P < 0.042) and milk N/N intake (P < 0.047) decreased with FB inclusion, while energy utilisation was unaffected. Field bean inclusion had no effect on any of the methane production parameters measured. The results of this study demonstrate that FB can be included in dairy cow diets at levels up to 4.7 kg/day with few negative effects on cow performance, and as such may have potential to replace imported protein feeds in dairy cow diets. However, total diet crude protein levels were relatively high in this study, and different responses might have been observed if diet crude protein levels had been lower.
The objectives were to develop a national CH4 emission inventory for domestic livestock in Saudi Arabia and to identify possible mitigation to reduce these emissions. The inventory was developed using data derived from farm surveys and national statistical records in Saudi Arabia from 2003 to 2007, as well as Tier 1 and 2 emission factors for enteric fermentation and manure management as proposed by the Intergovernmental Panel on Climate Change. The national CH4 emission inventory, developed using Tier 1 default factors, increased from 87,069t in 2003 to 100,971t in 2007, with a CO2 equivalent of 2,176,735 and 2,524,287t, respectively. The contribution to the inventory varied considerably among provinces, ranging from 0.1% (Northern) to 40.6% (Riyadh) in 2007 when horse data were not included. Among animal species, dairy cattle and sheep were the biggest CH4 producers (37.7% and 35.5%, respectively), followed by camels (13.4%), goats (11.4%), chickens (1.6%) and horses (0.4%). National CH4 emissions consisted of 0.87 from enteric fermentation and 0.13 from manure storage. Tier 2 CH4 emission factors were developed for dairy cattle and sheep, with each species divided into two groups (i.e., specialised high genetic merit and traditional low genetic merit), and each group of cattle and sheep were further divided into subgroups according to their physiological states. These factors for enteric fermentation and manure management were all different from Tier 1 default factors, and resulted in a decrease of 2088t in CH4 emission inventories (CO2 equivalent of 52,200t) from cattle and sheep when estimated using Tier 2, rather than Tier 1, factors. In comparison with other countries, the national CH4 emission inventory from domestic animals in Saudi Arabia is relatively small in terms of CH4 emissions per unit of land area or human population. These results can be used, with data from other agricultural sectors, to develop a national greenhouse gas inventory for the entire agricultural sector of Saudi Arabia.This paper is part of the special issue entitled: Greenhouse Gases in Animal Agriculture – Finding a Balance between Food and Emissions, Guest Edited by T.A. McAllister, Section Guest Editors: K.A. Beauchemin, X. Hao, S. McGinn and Editor for Animal Feed Science and Technology, P.H. Robinson.
Ruminants contribute to global warming by releasing methane (CH4) gas by enteric fermentation. This has increased interest among animal scientists to develop and improve equations predicting CH4 production. The objectives of the current study were to collect a data set from respiration studies and to evaluate the effects of dietary and animal factors on CH4 production from diets that can safely be fed to dairy cows, using a mixed model regression analysis. Therefore, diets containing more than 75% concentrate on a dry matter (DM) basis were excluded from the analysis. The final data set included a total of 298 treatment means from 52 published papers with 207 cattle and 91 sheep diets. Dry matter intake per kilogram of body weight (DMIBW), organic matter digestibility estimated at the maintenance level of feeding (OMDm), and dietary concentrations of neutral detergent fiber (NDF), nonfiber carbohydrates (NFC), and ether extract (EE) were the variables of the best-fit equation predicting CH4 energy (CH4-E) as a proportion of gross energy intake (GE): CH4-E/GE (kJ/MJ) = −0.6 (±12.76) − 0.70 (±0.072) × DMIBW (g/kg) + 0.076 (±0.0118) × OMDm (g/kg) − 0.13 (±0.020) × EE (g/kg of DM) + 0.046 (±0.0097) × NDF (g/kg of DM) + 0.044 (±0.0094) × NFC (g/kg of DM), resulting in the lowest root mean square error adjusted for random study effect (adj. RMSE = 3.26 kJ/MJ). Total CH4 production (L/d) in the cattle data set was closely related to DM intake. However, further inclusion of other variables improved the model: CH4 (L/d) = −64.0 (±35.0) + 26.0 (±1.02) × DM intake (kg/d) − 0.61 (±0.132) × DMI2(centered) + 0.25 (±0.051) × OMDm (g/kg) − 66.4 (±8.22) × EE intake (kg of DM/d) − 45.0 (±23.50) × NFC/(NDF + NFC), with adj. RMSE of 21.1 L/d. Cross-validation of the CH4-E/GE equation [observed CH4-E/GE = 0.96 (±0.103) × predicted CH4-E/GE + 2.3 (±7.05); R2 = 0.85, adj. RMSE = 3.38 kJ/MJ] indicated that differences in CH4 production between the diets could be predicted accurately. We conclude that feed intake is the main determinant of total CH4 production and that CH4-E/GE is negatively related to feeding level and dietary fat concentration and positively to diet digestibility, whereas dietary carbohydrate composition has only minor effects.
The relationship among diet composi- tion, intake, and methane production was investigated with data during 404 total energy balance trials with Holstein cows. Methane production in all trials was measured in open circuit respiration chambers. The most useful predictor of total methane production was amounts of soluble residue, hemicellulose, and cellu- lose that apparently were digested. The regression was methane (Meal) = .439 + .273 + .015 kg digestible soluble residue + .512 -+ .078 kg digestible hemicellulose + 1.393 + .097 kg digestible cellulose.
The precision of a number of new models for predicting silage intake by beef cattle was investigated with independent data using the mean-square prediction error and compared with two previously published models (Agricultural Research Council, 1980; Lewis, 1981). The new models generally performed well relative to the previous models.
The new models included a number constructed using the technique of ridge regression which were shown to be consistently better predictors than the models obtained from the same estimation data by stepwise least-squares regression. Better prediction was also obtained by reducing the number of variables in the least-squares models below that required to maximize R ² in the estimation data. The poor performance of the least-squares models with the best R ² may be attributed to collinearity between the independent variates in the estimation data.
Most of the models considered overpredicted relative to observed intakes. This may have been the result of differences in breed type and management of the animals between the test data and the estimation data used to construct the models, that is the use of the models with the test data involved a degree of extrapolation.
It is concluded that ridge regression and deletion of variables offer a positive step forward in intake prediction compared with models based on maximizing R ² in the estimation data. However, further work is needed to clarify the effect of factors such as breed and rearing system on intake and to clarify the usefulness of various fibre measures in intake prediction. A number of new models are proposed which utilize a range of input variables thus allowing flexibility in their use in practical situations.
Records on animal performance form a basic part of management. Without records it is not possible to measure the effect of changes in management nor assess the individual performance of an animal. Condition scoring provides a means of recording body condition and may assist in monitoring changes in body reserves. However, it is of limited use if other, more significant, records are not kept. It provides an enhancement to a recording scheme in which reproduction, production health and feeding/grazing records are kept. These records should be simple. Some data are required on calvings and inseminations, milk yield and/or rate of growth, preventative and curative health measures and on the probable feed available to an animal and how this feed is utilized. Body condition scoring will provide some data for this final item.
Since 1992 a number of lactating dairy cows (n=247) and beef steers (n=75) were offered grass silage-based diets in a range of feeding experiments and subjected to gaseous exchange measurements in calorimetric chambers at the Agricultural Research Institute of Northern Ireland. The objective of the present study was therefore to use the energy metabolism data from these studies to evaluate the relationship between methane energy output (CH4-E) and a number of animal and dietary factors. There were no significant differences between dairy and beef cattle in terms of silage dry matter (DM) intake as a proportion of total DM intake (SDMI/TDMI), total acid detergent fibre (ADF) intake as a proportion of TDMI (TADFI/TDMI) or silage ADF intake as a proportion of TADFI (SADFI/TADFI). Animal type also had no significant effect on CH4-E as a proportion of gross energy (GE) intake (CH4-E/GEI) or digestible energy (DE) intake (CH4-E/DEI). The data from both dairy and beef cattle were thus pooled to predict CH4-E. CH4-E/GEI and CH4-E/DEI were each significantly related to feeding level or dietary factors (P<0.001). These two ratios (CH4-E/GEI and CH4-E/DEI) were reduced by proportionately 0.0078 and 0.0123, respectively, as feed intake increased one level above maintenance. However an increase of 0.10 in SDMI/TDMI, TADFI/TDMI or SADFI/TADFI would increase CH4-E/GEI by 0.0025, 0.0069 or 0.0048; or CH4-E/DEI by 0.0035, 0.0107 or 0.0067. CH4-E (MJ/day) was significantly related to GE or DE intake (P<0.001) with a coefficient of 0.055 or 0.071 and a constant of 3.23 or 3.32. The prediction of CH4-E was thus examined using various combinations of intake (GE or DE) with feeding level above maintenance or/and dietary factor (SDMI/TDMI, TADFI/TDMI or SADFI/TADFI). The relationships were all highly significant (P<0.001) and the R2 values ranged from 0.851 to 0.888. The equations relating CH4-E to DE intake, feeding level above maintenance (FL-1) and SADFI/TADFI or SDMI/TDMI had the highest R2 value, and these two equations also gave the most accurate prediction when using published results. These two equations are
The data set used in the present study was obtained from 20 energy metabolism studies involving 579 lactating dairy cows (511 Holstein-Friesian, 36 Norwegian Red, and 32 Jersey-Holstein crossbreds) varying in genetic merit, lactation number, stage of lactation, and live weight. These cows were offered diets based on grass silage (n=550) or fresh grass (n=29), and their energy intake and outputs, including methane energy (CH(4)-E), were measured in indirect open-circuit respiration calorimeter chambers. The objective was to use these data to evaluate relationships between CH(4)-E output and a range of factors in animal production and energetic efficiency in lactating dairy cows under normal feeding regimens. The CH(4)-E as a proportion of milk energy output (E(l)), E(l) adjusted to zero energy balance (E(l(0))), or intakes of gross energy (GE), digestible energy (DE), or metabolizable energy (ME) was significantly related to a wide range of variables associated with milk production (E(l) and E(l(0))) and energy parameters (energy intake, metabolizability, partitioning, and utilization efficiencies). Three sets of linear relationships were developed with experimental effects removed. The CH(4)-E/GE intake (r(2)=0.50-0.62) and CH(4)-E/E(l) (r(2)=0.41-0.68) were reduced with increasing feeding level, E(l)/metabolic body weight (MBW; kg(0.75)), E(l(0))/MBW, GE intake/MBW, DE intake/MBW, and ME intake/MBW. Increasing dietary ME/DE decreased CH(4)-E/E(l) (r(2)=0.46) and CH(4)-E/GE intake (r(2)=0.72). Dietary ME concentration and ME/GE were also negatively related to CH(4)-E/GE intake (r(2)=0.47). However, increasing heat production/ME intake increased CH(4)-E as a proportion of E(l) (r(2)=0.41), E(l(0)) (r(2)=0.67) and energy intake (GE, DE, and ME; r(2)=0.62 and 0.70). These proportional CH(4)-E variables were reduced with increasing ratios of E(l)/ME intake and E(l(0))/ME intake and efficiency of ME use for lactation (r(2)=0.49-0.70). Fitting CH(4)-E/E(l) or CH(4)-E/E(l(0)) against these energetic efficiencies in quadratic rather than linear relationships significantly increased r(2) values (0.49-0.67 vs. 0.59-0.87). In conclusion, CH(4)-E as a proportion of energy intake (GE, DE, and ME) and milk production (E(l) and E(l(0))) can be reduced by increasing milk yield and energetic efficiency of milk production or by reducing energy expenditure for maintenance. The selection of dairy cows with high energy utilization efficiencies and milk productivity offers an effective approach to reducing enteric CH(4) emission rates.
The quantification of lactation response requires that various yields of milk of various chemical compositions he equated to a common term. Although the gross energy value is the most common characteristic to which all organic substances can be reduced, its direct measurement is expensive. As a consequence, a study was made of the relationships among the energy value and the concentrations of total solids, solids-not-fat (SNF), fat, protein, lactose, and ash in 600 two- and seven-day composite samples of milk from 42 Holstein cows and ranging from 1.6 to 6.4% fat. An analysis of 21 different combinations of milk components showed that the most practical equation for the accurate prediction (C.V. = 1.3%) of the energy value of milks having a wide range of chemical composition is:Energy (kcal/lb) = 41.84 (% fat) + 22.29 (% SNF) − 25.58. From this, the following equation was derived to compute the solids-corrected milk (SCM) yield containing 340 kcal of energy per pound: SCM (lb) = 12.3 (F) + 6.56 (SNF) − 0.0752 (M); where F, SNF, and M are expressed as pounds of fat, solids-not-fat, and milk, respectively. This equation predicted the milk energy output of cows producing low-fat milk effected by dietary means as accurately as that of normal milk. The Gaines 4% FCM equation underestimated by as much as 15% the energy output in milk containing less than 3% of fat.
The data used in the present study were derived from a 2 (genotype) x 2 (plane of nutrition) factorial design production study using Holstein-Friesian (n = 32) and Norwegian (n = 32) first-lactation dairy cattle offered grass silage-based diets from 1 to 44 wk of lactation. The high nutrition diet had concentrate inclusions (g/kg of dry matter) of 600, 500, and 400 for lactation days of < 101, 101 to 200, and > 200, respectively, and the low nutrition diet included concentrates at 300, 200, and 100 for the same periods. Dietary metabolizable energy (ME) concentrations were measured in calorimetric chambers at lactation d 80, 160, and 240 respectively, and then applied to production data to calculate ME intake. From wk 1 to 44 of lactation, Holstein-Friesian cows had a consistently lower accumulated live weight gain and body condition score, and a consistently higher ME intake and milk energy output than Norwegian cows, irrespective of the plane of nutrition. Compared with Norwegian cows using mean data derived from the 2 planes of nutrition, Holstein-Friesian cows produced a significantly higher proportion of milk energy output over ME intake in early and mid lactation, although this increase was not significant in late lactation. In contrast, Holstein-Friesian cows partitioned a significantly lower proportion of ME intake into body tissue than Norwegian cows in early lactation, although the differences were not significant in mid or late lactation. When ME intake and energy used for maintenance, milk, and body tissue were taken into account, the efficiency of ME use for lactation was similar between the 2 genotypes offered the high or low concentrate diet during the whole lactation. It is concluded that Holstein-Friesian cows can produce more milk energy than Norwegian cows, mainly as a result of higher ME intake and because of a greater ability to partition more energy into milk and less into body tissue. The effect on energy partitioning mainly occurs in early and midlactation and is particularly evident with high concentrate diets.
Technical committee on responses to nutrients, report number 5, nutritive requirements of ruminant animals: Energy
- Food Research Agricultural
- Council
The development of an online calculator for estimating greenhouse gas emissions from northern Ireland dairy farms, and an examination of emissions from a number of farms
- A Aubry
- T Yan
- S J Morrison
- C P Ferris
Average milk yield, available at: http://www.dairyco.org.uk/market-information/farming-data/milk-yield/average-milk-yield
- Dairyco