Persistence of differences between dairy cows categorized as low or high methane emitters, as estimated from milk mid-infrared spectra and measured by GreenFeed

Longitudinal modeling of residual carbon dioxide and residual feed intake in the Nordic Red dairy cattle

Feed utilization efficiency is an important trait in dairy production playing a significant role in reducing feed costs and lowering methane emission. One of the metrics used to measure feed efficiency in dairy cows is residual feed intake (RFI). This metric requires routine measurement of feed intake. Since there is a positive high correlation between heat production and carbon dioxide (CO2) production on the one hand and heat production and efficiency on the other hand, residual carbon dioxide (RCO2) might be a useful metric to improve feed efficiency. The objectives of this study were to model the trajectories of RCO2 and RFI as well as to estimate their repeatabilities and correlations at different stages of lactation. Daily CO2 output and feed intake were recorded from 46 primiparous Nordic Red dairy cows using two Greenfeed Emissions Monitoring™ systems from 2 to 305 days in milk (DIM). Edited data comprised 5 995 daily averages. To calculate predicted values of CO2 and DM intake (DMI), prediction models were developed by fitting multiple regression models to observations. Subsequently, RCO2 and RFI were calculated by subtracting predicted values of CO2 and DMI from their corresponding actual observations. A random regression bivariate model was fitted to estimate repeatabilities and animal correlations within lactation at different DIMs between RCO2 and RFI traits. The model fitted included fixed effects of year-month of recording, lactation month, fixed regressions as well as random regressions for the animal effect. The residual variance was considered to be heterogeneous. Repeatabilities and animal correlations of RCO2 and RFI between selected DIM (for every 30 DIM i.e., 6, 36,…, 246 and 276) were calculated. Repeatability of RCO2 was high at the beginning of lactation (0.72 at DIM 6) and decreased around the peak of milk production (0.27 at DIM 96) and again increased gradually toward the end of lactation. Similarly, RFI also had high repeatability at the beginning (0.86 at DIM 6); however, it decreased in mid-lactation (0.37 at DIM 156) and then increased toward the end of lactation. Animal correlations between RCO2 and RFI were moderate to high on the same DIM and ranged from 0.37 to 0.88. Overall, we found that animals with higher CO2 production than expected also consume more DMI than expected, but the moderate correlation between RCO2 and RFI found in this study calls for more research to assess the potential of RCO2 to become a new feed efficiency metric.

Effects of Acacia mearnsii added to silages differing in nutrient composition and condensed tannins on ruminal and manure-derived methane emissions of dairy cows

This study investigated the effects of acacia (extract of Acacia mearnsii) and sainfoin (Onobrychis viciifolia) as condensed tannin (CT)-rich sources on ruminal and manure methane (CH4) emissions in comparison with non-CT silages characterized by different contents of the cell wall and water-soluble carbohydrates. In a 3 × 6 incomplete Latin square design, 30 Holstein cows (63 ± 23 d in milk; mean ± SD; 33.8 ± 7.6 kg of milk per day, body weight 642 ± 81 kg) were provided with ad libitum access to 1 of 6 total mixed rations comprising 790 g of silage and 210 g of concentrate per kilogram of dry matter (DM). The silages were either rich in sainfoin [neutral detergent fiber (NDF): 349 g/kg of DM], perennial ryegrass (NDF: 420 g/kg of DM), or red clover (NDF: 357 g/kg of DM). Each silage was supplemented with 20 g/kg (of total diet DM) of acacia or straw meal. Feed intake and milk yield were recorded daily. Milk composition and ruminal fluid characteristics and microbiota were analyzed. The individual ruminal CH4 production was determined using the GreenFeed system, and CH4 emissions from the manure of cows fed the same diets were measured in a parallel experiment over 30 d at 25°C using a dynamic flux chamber. The CT sources did not reduce CH4 yield or emission intensity. Acacia reduced milk production (from 26.3 to 23.2 kg/d) and DM intake (from 19.7 to 16.7 kg/d) when supplemented with ryegrass, and both CT sources reduced the milk protein content and yield. Acacia supplementation and ryegrass silage reduced the ruminal acetate:propionate ratio. Furthermore, during acacia treatment, the abundance of Methanobrevibacter archaea tended to be lower and that of Thermoplasmata was higher. Acacia reduced the CH4 emissions from manure for the ryegrass group by 17% but not for the sainfoin and clover groups. Feeding sainfoin silage resulted in the lowest manure-derived CH4 emissions (-47% compared with ryegrass). In conclusion, acacia reduced ruminal CH4 production by 10%, but not emission intensity, and the mitigation effect of sainfoin depended on the silage to which it was compared. Because mitigation was partially associated with animal productivity losses, careful evaluation is required before the implementation of tanniferous feeds in farm practice.

Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows

To reduce methane (CH₄) emissions of dairy cows by animal breeding, CH₄ measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH₄, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH₄ and carbon dioxide production recorded by GreenFeed (GF) and for CH₄ and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH₄ recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH₄ production (grams/day) by GF units and of CH₄ concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH₄ records were averaged per week. For daily and weekly mean GF CH₄ the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH₄ the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH₄ production and sniffer CH₄ concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH₄ concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH₄ production (grams/day) as recorded with GF.

The Importance of Cow-Individual Effects and Diet, Ambient Temperature, and Horn Status on Delayed Luminescence of Milk from Brown Swiss Dairy Cows

To investigate the importance of cow-individual effects and the importance of horn status (horned vs. disbudded), of diet (hay with and without concentrates), and of ambient temperature (10 °C vs. 25 °C) on delayed luminescence (DL) parameters of milk samples, fluorescence excitation spectroscopic (FES) measurements were performed on a total of n = 152 milk samples from 20 cows of a cross-over experiment. Cow-individual variation was investigated in relation to the horn status, diet effects were evaluated by cow in relation to sampling effects, and regression analysis was used to evaluate the importance of the experimental factors on the variation of emission parameters. Variation of short-term emission after yellow excitation (530 to 800 nm) was predominantly related to the individual cow (disbudded cows tended to higher values), and was partly affected by feeding, with higher emission for concentrate-added diets. Short-term emission after white excitation (260 to 850 nm) was most related to ambient temperature, with higher values at warm temperature. Higher emission was observed also in aged (stored) samples or after delayed cooling. The emission after yellow showed to be more robust to handling and ageing of the milk than the emission after white; possible relations to digestive processes of the cow (including the microbiome) are warranted.

The Effect of Rumination Time on Milk Performance and Methane Emission of Dairy Cows Fed Partial Mixed Ration Based on Maize Silage

Simple Summary

Greenhouse gas emission has attracted considerable public attention in recent years, driving the search for genetic, nutritional, and management strategies to reduce methane emissions and increase the sustainability of milk production. Rumination activity has an important function in feed particle size reduction, condition of feeding behavior, and feed intake as well as in stabilizing rumen fluid pH through saliva production. A total of 365 high-yielding Polish Holstein -Friesian multiparous dairy cows were included in the study covering 24 to 304 days of lactation. Next, the data from the cows were assigned to three groups based on daily rumination time: low rumination up to 412 min/day (up to 25th rumination percentile), medium rumination from 412 to 527 min/day (between the 25th and 75th percentile), and high rumination above 527 min/day (from the 75th percentile). We showed that a longer rumination time leads to a lower methane emission level. Therefore, strategies that increase chewing activity may be used to reduce the environmental impact of dairy cows production.

Abstract

The objective of this study was to determine the effect of the rumination time on milk yield and composition as well as methane emission during lactation in high-yielding dairy cows fed a partial mixed ration based on maize silage without pasture access. A total of 365 high-yielding Polish Holstein-Friesian multiparous dairy cows were included in the study covering 24 to 304 days of lactation. Methane emission, rumination time, and milk production traits were observed for the period of 12 months. Next, the data from the cows were assigned to three groups based on daily rumination time: low rumination up to 412 min/day (up to 25th rumination percentile), medium rumination from 412 to 527 min/day (between the 25th and 75th percentile), and high rumination above 527 min/day (from the 75th percentile). Rumination time had no effect on milk yield, energy-corrected milk yield, or fat and protein-corrected milk yield. High rumination time had an effect on lower fat concentration in milk compared with the medium and low rumination groups. The highest daily CH₄ production was noted in low rumination cows, which emitted 1.8% more CH₄ than medium rumination cows and 4.2% more than high rumination cows. Rumination time affected daily methane production per kg of milk. Cows from the high rumination group produced 2.9% less CH₄ per milk unit compared to medium rumination cows and 4.6% in comparison to low rumination cows. Similar observations were noted for daily CH₄ production per ECM unit. In conclusion, a longer rumination time is connected with lower methane emission as well as lower methane production per milk unit in high-yielding dairy cows fed a maize silage-based partial mixed ration without pasture access.

In Vitro and In Vivo Evaluation of the Effects of a Compound Based on Plants, Yeast and Trace Elements on the Ruminal Function of Dairy Cows

The high production levels reached by the dairy sector need adjustment in nutritional inputs and efficient feed conversion. In this context, we evaluated a compound (QY—Qualix Yellow) combining optimized inputs in trace elements and 20% MIX 3.0. In a first step, the effects of MIX 3.0 on ruminal function were assessed in vitro by incubating ruminal fluid with the mixture at a ratio of 20:1. The results obtained encouraged us to test QY in vivo, on a herd of dairy cows. The herd was divided into one group of 19 dairy cows receiving the compound and a control group of 20 animals conducted in the same conditions, but which did not received the compound; the production performance and feed efficiency of the two groups were compared. In vitro experiments showed improved digestion of acid and neutral detergent fibres by 10%. The propionate production was enhanced by 14.5% after 6 h incubation with MIX 3.0. The plant mixture decreased the production of methane and ammonia by 37% and 52%, respectively, and reduced the number of protozoa by 50%. An increase in milk yield by 2.4 kg/cow/d (p < 0.1), combined with a decrease in concentrate consumption of 0.27 kg DM/cow/d (p < 0.001), was observed in vivo after consumption of the compound. Sixty-six days after the beginning of the trial, methane emissions per kg of milk were significantly lower in the group receiving QY. In conclusion, MIX 3.0 induced change in ruminal function in vitro and, when it entered into the composition of the QY, it appeared to improve feed efficiency and production performance in vivo.

Response to Climate Change: Evaluation of Methane Emissions in Northern Australian Beef Cattle on a High Quality Diet Supplemented with Desmanthus Using Open-Circuit Respiration Chambers and GreenFeed Emission Monitoring Systems

Simple Summary

The beef industry in Northern Australia is characterized by an extensive grazing system in dry tropical rangelands defined by climate change indices of very low rainfall, a prolonged dry season and feeds of low nutritive value. In response, beef cattle need to be more efficient in converting the available drought-tolerant feeds to muscle, in an attempt to minimize greenhouse gas emissions. This study addressed the problem of reducing methane emissions from tropical beef cattle with the goal of decreasing the impact of climate change and greenhouse gas emissions in Northern Australia. The primary objective was to compare the effect of supplementing tropical beef cattle with both good quality lucerne and poor quality hay with increasing levels of different Desmanthus cultivars on in vivo methane emission. The results showed that in tropical beef cattle on high-quality diets, irrespective of cultivar and emission evaluation method, Desmanthus does not reduce methane emissions.

Abstract

The main objective of this study was to compare the effect of supplementing beef cattle with Desmanthus virgatus cv. JCU2, D. bicornutus cv. JCU4, D. leptophyllus cv. JCU7 and lucerne on in vivo methane (CH₄) emissions measured by open-circuit respiration chambers (OC) or the GreenFeed emission monitoring (GEM) system. Experiment 1 employed OC and utilized sixteen yearling Brangus steers fed a basal diet of Rhodes grass (Chloris gayana) hay in four treatments—the three Desmanthus cultivars and lucerne (Medicago sativa) at 30% dry matter intake (DMI). Polyethylene glycol (PEG) was added to the diets to neutralize tannin binding and explore the effect on CH₄ emissions. Experiment 2 employed GEM and utilized forty-eight animals allocated to four treatments including a basal diet of Rhodes grass hay plus the three Desmanthus cultivars in equal proportions at 0%, 15%, 30% and 45% DMI. Lucerne was added to equilibrate crude protein content in all treatments. Experiment 1 showed no difference in CH₄ emissions between the Desmanthus cultivars, between Desmanthus and lucerne or between Desmanthus and the basal diet. Experiment 2 showed an increase in CH₄ emissions in the three levels containing Desmanthus. It is concluded that on high-quality diets, Desmanthus does not reduce CH₄ emissions.

Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions

The aims of this work were to study on dairy farm conditions: i) the repeatability of long-term enteric CH₄ emissions measurement from lactating dairy cows using GreenFeed (GF); ii) the ranking of dairy cows according to their CH₄ emissions across diets. Forty-five Holstein lactating dairy cows were randomly assigned to 3 equivalent groups at the beginning of their lactation. The experiment was composed of 3 successive periods: i) pre-experimental period (weeks 1 to 5) in which all cows received a common diet; ii) a dietary treatment transition period (weeks 6 to 10); and iii) an experimental period (weeks 11 to 26) in which each group was fed a different diet. Experimental diets were formulated to generate more or less CH₄ production: i) a diet based on ryegrass silage and concentrates, low in starch and lipid, designed to induce high CH₄ emissions (CH4+); ii) a diet based on maize silage and concentrates, rich in starch, designed to induce intermediate CH₄ emissions (CH4int); iii) a diet based on maize silage and concentrates, rich in starch and lipid, designed to induce low CH₄ emissions (CH4-). Gas emissions were individually measured using GF systems. Repeatability of gas emissions, dry matter intake (DMI) and dairy performances measurements was calculated from data averaged over 1, 2, 4, and 8 weeks for each animal. Hierarchical cluster analysis was performed to rank individual animals according to their CH₄ emissions. No significant differences were observed for daily CH₄ emissions (g/day) among diets, because of lower DMI of CH4+ cows. When CH₄ emissions were referred to units of DMI or milk, the differences among diets emerged as significant and persistent over the observed period of lactation. Repeatability values of gas emissions measurements were higher than 0.7 averaged over 8 weeks of measurement, but still higher than 0.6 for CH₄ g/day, CO₂ g/day, CH₄ g/kg milk, and CH₄/CO₂ even averaging only 2 weeks of measurement. The repeatability of CH₄ emissions measurement was systematically lower than those of DMI or dairy performance parameters, like milk and FPCM yield, irrespective of the averaged measurement period. The dairy cow ranking was not stable over time between all individuals or within any of the diets. In our experimental conditions, the GF performance in the long term can be considered reliable in differentiating dairy herds by their CH₄ emissions according to diets with different methanogenic potential, but did not allow the ranking of individual dairy cows within a same diet. Our data highlight the importance of phenotyping animals across environment in which they will be expected to perform.

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH₄/DM: 0.232-0.317) and methane intensity per kg of corrected milk (CH₄/CM: 0.177-0.279), smaller per kg cheese solids (CH₄/SO: 0.093-0.165), but greater per kg fresh cheese (CH₄/CU: 0.203-0.267) and for methane production (dCH₄: 0.195-0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH₄/DM (0.45-0.60), and very variable for dCH₄ according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH₄/CM, CH₄/CU and dCH₄, could be considered in breeding programs aimed at the improvement of milk ecological footprint.

The use of milk Fourier transform mid-infrared spectra and milk yield to estimate heat production as a measure of efficiency of dairy cows

Background

Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions, fermentation gases and heat. Heat production may differ among dairy cows despite comparable milk yield and body weight. Therefore, heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers. The latter is an accurate but time-consuming technique. In contrast, milk Fourier transform mid-infrared (FTIR) spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows. Thus, this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers, milk FTIR spectra and milk yield measurements from dairy cows.

Methods

Heat production was computed based on the animal’s consumed oxygen, and produced carbon dioxide and methane in respiration chambers. Heat production data included 168 24-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows. Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers. Milk yield was determined to predict heat production using a linear regression. Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000. The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum. A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares (PLS) regression.

Results

Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW^0.75. The coefficient of determination for the linear regression between milk yield and heat production was 0.46, whereas it was 0.23 for the FTIR spectra-based PLS model. The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57% and a root mean square error of prediction of 86.5 kJ/kg BW^0.75. The ratio of performance to deviation (RPD) was 1.56.

Conclusion

The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.

Symposium review: Genomic selection for reducing environmental impact and adapting to climate change

The world has been warming as greenhouse gases accumulate. Worldwide from 1880 to 2012, the average surface temperature has increased by about 0.85°C and by 0.12°C per decade since 1951. The world's cattle population is a contributor to atmospheric methane, a potent greenhouse gas, in addition to suffering from high temperatures combined with humidity. This makes research into reducing the global footprint of dairy cows of importance on a long-term horizon, while improving tolerance to heat could alleviate the effects of rising temperatures. In December 2017, genomic estimated breeding values for heat tolerance in dairy cattle were released for the first time in Australia. Currently, heat tolerance is not included in the Balanced Performance Index (Australia's national selection index), and the correlation between heat tolerance breeding values and Balanced Performance Index is -0.20, so over time, heat tolerance has worsened due to lack of selection pressure. However, in contrast, sizable reductions in greenhouse gas emissions have been achieved as a favorable response to selecting for increased productivity, longevity, and efficiency, with opportunities for even greater gains through selecting for cow emissions directly. Internationally considerable research effort has been made to develop breeding values focused on reducing methane emissions using individual cow phenotypes. This requires (1) definition of breeding objectives and selection criteria and (2) assembling a sufficiently large data set for genomic prediction. Selecting for heat tolerance and reduced emissions directly may improve resilience to changing environments while reducing environmental impact.

Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows

Since heritability of CH₄ emissions in ruminants was demonstrated, various attempts to generate large individual animal CH₄ data sets have been initiated. Predicting individual CH₄ emissions based on equations using milk mid-infrared (MIR) spectra is currently considered promising as a low-cost proxy. However, the CH₄ emission predicted by MIR in individuals still has to be confirmed by measurements. In addition, it remains unclear how low CH₄ emitting cows differ in intake, digestion, and efficiency from high CH₄ emitters. In the current study, putatively low and putatively high CH₄ emitting Brown Swiss cows were selected from the entire Swiss herdbook population (176,611 cows), using an MIR-based prediction equation. Eventually, 15 low and 15 high CH₄ emitters from 29 different farms were chosen for a respiration chamber (RC) experiment in which all cows were fed the same forage-based diet. Several traits related to intake, digestion, and efficiency were quantified over 8 d, and CH₄ emission was measured in 4 open circuit RC. Daily CH₄ emissions were also estimated using data from 2 laser CH₄ detectors (LMD). The MIR-predicted CH₄ production (g/d) was quite constant in low and high emission categories, in individuals across sites (home farm, experimental station), and within equations (first available and refined versions). The variation of the MIR-predicted values was substantially lower using the refined equation. However, the predicted low and high emitting cows (n = 28) did not differ on average in daily CH₄ emissions measured either with RC or estimated using LMD, and no correlation was found between CH₄ predictions (MIR) and CH₄ emissions measured in RC. When individuals were recategorized based on CH₄ yield measured in RC, differences between categories of 10 low and 10 high CH₄ emitters were about 20%. Low CH₄ emitting cows had a higher feed intake, milk yield, and residual feed intake, but they differed only weakly in eating pattern and digesta mean retention times. Low CH₄ emitters were characterized by lower acetate and higher propionate proportions of total ruminal volatile fatty acids. We concluded that the current MIR-based CH₄ predictions are not accurate enough to be implemented in breeding programs for cows fed forage-based diets. In addition, low CH₄ emitting cows have to be characterized in more detail using mechanistic studies to clarify in more detail the properties that explain the functional differences found in comparison with other cows.