Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources

Milk fatty acid (FA) profile has been previously used as a predictor of enteric CH₄output in dairy cows fed diets supplemented with plant oils, which can potentially impact ruminal fermentation. The objective of this study was to investigate the relationships between milk FA and enteric CH₄ emissions in lactating dairy cows fed different types of forages in the context of commonly fed diets. A total of 81 observations from three separate 3×3 Latin square design (32-day periods) experiments including a total of 27 lactating cows (96±27 days in milk; mean±SD) were used. Dietary forages were included at 60% of ration dry matter and were as follows: (1) 100% corn silage, (2) 100% alfalfa silage, (3) 100% barley silage, (4) 100% timothy silage, (5) 50 : 50 mix of corn and alfalfa silages, (6) 50 : 50 mix of barley and corn silages and (7) 50 : 50 mix of timothy and alfalfa silages. Enteric CH₄output was measured using respiration chambers during 3 consecutive days. Milk was sampled during the last 7 days of each period and analyzed for components and FA profile. Test variables included dry matter intake (DMI; kg/day), NDF (%), ether extract (%), milk yield (kg/day), milk components (%) and individual milk FA (% of total FA). Candidate multivariate models were obtained using the Least Absolute Shrinkage and Selection Operator and Least-Angle Regression methods based on the Schwarz Bayesian Criterion. Data were then fitted into a random regression using the MIXED procedure including the random effects of cow, period and study. A positive correlation was observed between CH₄ and DMI (r=0.59,P<0.001), whereas negative associations were observed between CH₄ and cis9-17:1 (r=−0.58, P<0.001), and trans8, cis13-18:2 (r=−0.51,P<0.001). Three different candidate models were selected and the best fit candidate model predicted CH₄ with a coefficient of determination of 0.84 after correction for cow, period and study effects and was: CH₄ (g/day)=319.7−57.4×15:0−13.8×cis9-17:1−39.5×trans10-18:1−59.9×cis11-18:1−253.1×trans8, cis12-18:2−642.7×trans8, cis13-18:2−195.7×trans11, cis15-18:2+16.5×DMI. Overall and linear prediction biases of all models were not significant (P>0.19). Milk FA profile and DMI can be used to predict CH₄emissions in dairy cows across a wide range of dietary forage sources     

Effects of nitrogen fertilisation rate and maturity of grass silage on methane emission by lactating dairy cows

Grass silage is typically fed to dairy cows in temperate regions. However, in vivo information on methane (CH₄) emission from grass silage of varying quality is limited. We evaluated the effect of two rates of nitrogen (N) fertilisation of grassland (low fertilisation (LF), 65 kg of N/ha; and high fertilisation (HF), 150 kg of N/ha) and of three stages of maturity of grass at cutting: early maturity (EM; 28 days of regrowth), mid maturity (MM; 41 days of regrowth) and late maturity (LM; 62 days of regrowth) on CH₄ production by lactating dairy cows. In a randomised block design, 54 lactating Holstein–Friesian dairy cows (168±11 days in milk; mean±standard error of mean) received grass silage (mainly ryegrass) and compound feed at 80 : 20 on dry matter basis. Cows were adapted to the diet for 12 days and CH₄ production was measured in climate respiration chambers for 5 days. Dry matter intake (DMI; 14.9±0.56 kg/day) decreased with increasing N fertilisation and grass maturity. Production of fat- and protein-corrected milk (FPCM; 24.0±1.57 kg/day) decreased with advancing grass maturity but was not affected by N fertilisation. Apparent total-tract feed digestibility decreased with advancing grass maturity but was unaffected by N fertilisation except for an increase and decrease in N and fat digestibility with increasing N fertilisation, respectively. Total CH₄ production per cow (347±13.6 g/day) decreased with increasing N fertilisation by 4% and grass maturity by 6%. The smaller CH₄ production with advancing grass maturity was offset by a smaller FPCM and lower feed digestibility. As a result, with advancing grass maturity CH₄ emission intensity increased per units of FPCM (15.0±1.00 g CH₄/kg) by 31% and digestible organic matter intake (33.1±0.78 g CH₄/kg) by 15%. In addition, emission intensity increased per units of DMI (23.5±0.43 g CH₄/kg) by 7% and gross energy intake (7.0±0.14% CH₄) by 9%, implying an increased loss of dietary energy with advancing grass maturity. Rate of N fertilisation had no effect on CH₄ emissions per units of FPCM, DMI and gross energy intake. These results suggest that despite a lower absolute daily CH₄ production with a higher N fertilisation rate, CH₄ emission intensity remains unchanged. A significant reduction of CH₄ emission intensity can be achieved by feeding dairy cows silage of grass harvested at an earlier stage of maturity.     

Evaluation of on-farm methods for testing the human–animal relationship in dairy herds with cubicle loose housing systems—test–retest and inter-observer reliability and consistency to familiarity of test person

The human–animal relationship is an important factor when considering animal welfare at herd level. In the present study, two behavioural tests for the on-farm assessment of the human–animal relationship at herd level of dairy cows housed in loose housing cubicle systems were evaluated with respect to inter-observer reliability, test–retest reliability, effect of familiarity of test person as well as inter-correlation of the two tests. In a voluntary animal approach (VAA) test, the number of cows and the latencies to approach and touch a stationary test person was measured. In an avoidance (AV) test, the cows’ avoidance reactions to an approaching test person were categorised. A first study was carried out in 12 commercial Danish and Austrian dairy farms. On each farm, both behaviour tests were carried out on the same day and repeated within 4–5 days. For each test, cows were tested by both an unfamiliar and a familiar test person (the stock-person) and two observers simultaneously registered the animals’ test responses. The inter-observer reliability of both behavioural tests was found to be high (VAA: 2.5-m approach r=0.98 (P<0.001) and touch r=0.97 (P<0.001); AV: Kappa coefficient_weighted=0.886 (prevalence index for flight distance≥2 m is 0.636)). The cows at herd level showed shortest latency for touching an unfamiliar test person on the first test day (P=0.006). Further, the AV test had a high test–retest reliability (Kappa coefficient_weighted=0.503 (prevalence index for flight distance≥2 m is −0.660)) and results indicated no effect of familiarity of test person (Kappa coefficient_weighted=0.463 (prevalence index for flight distance≥2 m is −0.677)). In a second study, the correlation between the two behavioural tests (similar measures) was evaluated. On each of 10 commercial Danish dairy farms with loose housing cubicle systems at four repeated sessions, both behaviour tests were carried out on the same day. For each test cows were tested by the stock-person. The VAA and AV tests at herd level were highly correlated (r_s=−0.84; P=0.002).The results suggest that the AV test is valid and applicable for on-farm assessment of the human–animal relationship at herd level. This accounts only partly for the VAA test, which seem to be more unclear regarding motivation for the animals’ approach behaviour.     

Variation in enteric methane emissions among cows on commercial dairy farms

Methane (CH₄) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH₄ emissions can be substantial. The extent of variation in CH₄ emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH₄ emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH₄ during milking on commercial dairy farms. Enteric CH₄ emissions from 1964 individual cows across 21 farms were measured for at least 7 days/cow using CH₄ analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH₄ emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH₄ emissions varied among farms. Considerable variation in CH₄ emissions was observed among cows after adjusting for fixed and random effects, with the CV ranging from 22% to 67% within farms. This study confirms that enteric CH₄ emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.     

Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows

This study investigates the feasibility to predict individual methane (CH₄) emissions from dairy cows using milk mid-infrared (MIR) spectra. To have a large variability of milk composition, two experiments were conducted on 11 lactating Holstein cows (two primiparous and nine multiparous). The first experiment aimed to induce a large variation in CH₄ emission by feeding two different diets: the first one was mainly composed of fresh grass and sugar beet pulp and the second one of maize silage and hay. The second experiment consisted of grass and corn silage with cracked corn, soybean meal and dried pulp. For each milking period, the milk yields were recorded twice daily and a milk sample of 50 ml was collected from each cow and analyzed by MIR spectrometry. Individual CH₄ emissions were measured daily using the sulfur hexafluoride method during a 7-day period. CH₄ daily emissions ranged from 10.2 to 47.1 g CH₄/kg of milk. The spectral data were transformed to represent an average daily milk spectrum (AMS), which was related to the recorded daily CH₄ data. By assuming a delay before the production of fermentation products in the rumen and their use to produce milk components, five different calculations were used: AMS at days 0, 0.5, 1, 1.5 and 2 compared with the CH₄ measurement. The equations were built using Partial Least Squares regression. From the calculated R²_cv, it appears that the accuracy of CH₄ prediction by MIR changed in function of the milking days. In our experimental conditions, the AMS at day 1.5 compared with the measure of CH₄ emissions gave the best results. The R² and s.e. of the cross-validation were equal to 0.79 and 5.14 g of CH₄/kg of milk. The multiple correlation analysis performed in this study showed the existence of a close relationship between milk fatty acid (FA) profile and CH₄ emission at day 1.5. The lower R² (R² = 0.76) obtained between FA profile and CH₄ emission compared with the one corresponding to the obtained calibration (R²_c = 0.87) shows the interest to apply directly the developed CH₄ equation instead of the use of correlations between FA and CH₄. In conclusion, our preliminary results suggest the feasibility of direct CH₄ prediction from milk MIR spectra. Additional research has the potential to improve the calibrations even further. This alternative method could be useful to predict the individual CH₄ emissions at farm level or at the regional scale and it also could be used to identify low-CH₄-emitting cows.     

Water footprint and economic water productivity assessment of eight dairy cattle farms based on field measurement

Water scarcity prevailing in the drylands is threatening the sustainability of livestock production systems. The water footprint (WF) indicator was proposed as a metric of water use. This study aimed to determine the WF and the economic water productivity (EWP) of 1 kg of fat and protein-corrected milk (FPCM) in eight dairy farms (n = 8; animals = 117 ± 62; area = 198 ± 127; 95% confidence level) in northern Tunisia. Then, to assess the effects of three simulation scenarios targeting the reduction of the WF of milk production (scenario A: using triticale silage to replace, on DM basis, the silage of maize, sorghum or ray-grass; scenario B: reducing by 56% the wastage of water devoted to milking, cooling, cleaning and servicing; scenario C: using concentrate feeds imported from Brazil and Argentina instead of that imported from France). A year-round monitoring of on-farm practices was performed using water-meters and recording equipment installed in key locations in the target dairy farms: (i) water used for feed production, (ii) cow watering, (iii) servicing water, (v) crop and forage production and (iv) economic and production performance were controlled by water source (green and blue). Over the eight farms evaluated, milk production consumed on average 1.36 ± 0.41 m³/kg FPCM, of which 0.93 ± 0.40 m³/kg FPCM was green water and 0.42 ± 0.30 m³/kg FPCM was blue water. However, virtual water of 1 kg FPCM averaged 43% ± 14.3%. Water used for feed production for lactating cows represents approximately 87% ± 6% of the total WF of milk production. However, drinking and servicing water contributed by 3.75% ± 2% and 9% ± 5% to the total WF of milk, respectively. The EWP assessment revealed that the selected dairy farms had a relatively small gross margin per m³ of water averaging US$ 0.05 ± 0.04. The variation in WF of milk was mainly associated with diets’ ingredients, which affected milk productivity and water consumption. Scenario analysis indicated that using feed with less water requirements or importing feeds from countries where its water consumption is low could reduce consumptive water use for milk production by up to 16%. The efficient use of servicing water could reduce blue WF of milk by up to 4%. The implementation of these measures would lead to potential total water savings in the Tunisian dairy sector of 646 million m³ per year (30%).     

Relationships between methane emission of Holstein Friesian dairy cows and fatty acids,volatile metabolites and non-volatile metabolites in milk

This study investigated the relationships between methane (CH₄) emission and fatty acids, volatile metabolites (V) and non-volatile metabolites (NV) in milk of dairy cows. Data from an experiment with 32 multiparous dairy cows and four diets were used. All diets had a roughage : concentrate ratio of 80 : 20 based on dry matter (DM). Roughage consisted of either 1000 g/kg DM grass silage (GS), 1000 g/kg DM maize silage (MS), or a mixture of both silages (667 g/kg DM GS and 333 g/kg DM MS; 333 g/kg DM GS and 677 g/kg DM MS). Methane emission was measured in climate respiration chambers and expressed as production (g/day), yield (g/kg dry matter intake; DMI) and intensity (g/kg fat- and protein-corrected milk; FPCM). Milk was sampled during the same days and analysed for fatty acids by gas chromatography, for V by gas chromatography–mass spectrometry, and for NV by nuclear magnetic resonance. Several models were obtained using a stepwise selection of (1) milk fatty acids (MFA), V or NV alone, and (2) the combination of MFA, V and NV, based on the minimum Akaike’s information criterion statistic. Dry matter intake was 16.8±1.23 kg/day, FPCM yield was 25.0±3.14 kg/day, CH₄ production was 406±37.0 g/day, CH₄ yield was 24.1±1.87 g/kg DMI and CH₄ intensity was 16.4±1.91 g/kg FPCM. The observed CH₄ emissions were compared with the CH₄ emissions predicted by the obtained models, based on concordance correlation coefficient (CCC) analysis. The best models with MFA alone predicted CH₄ production, yield and intensity with a CCC of 0.80, 0.71 and 0.69, respectively. The best models combining the three types of metabolites included MFA and NV for CH₄ production and CH₄ yield, whereas for CH₄ intensity MFA, NV and V were all included. These models predicted CH₄ production, yield and intensity better with a higher CCC of 0.92, 0.78 and 0.93, respectively, and with increased accuracy (C_b) and precision (r). The results indicate that MFA alone have moderate to good potential to estimate CH₄ emission, and furthermore that including V (CH₄ intensity only) and NV increases the CH₄ emission prediction potential. This holds particularly for the prediction model for CH₄ intensity.     

Is there a relationship between genetic merit and enteric methane emission rate of lactating Holstein-Friesian dairy cows?

The present study was undertaken to examine the effect of cow genetic merit on enteric methane (CH₄) emission rate. The study used a data set from 32 respiration calorimeter studies undertaken at this Institute between 1992 and 2010, with all studies involving lactating Holstein-Friesian dairy cows. Cow genetic merit was defined as either profit index (PIN) or profitable lifetime index (PLI), with these two United Kingdom genetic indexes expressing the expected improvement in profit associated with an individual cow, compared with the population average. While PIN is based solely on milk production, PLI includes milk production and a number of other functional traits including health, fertility and longevity. The data set had a large range in PIN (n=736 records, −£30 to +£63) and PLI (n=548 records, −£131 to +£184), days in milk (18 to 354), energy corrected milk yield (16.0 to 45.6 kg/day) and CH₄ emission (138 to 598 g/day). The effect of cow genetic merit (PIN or PLI) was evaluated using ANOVA and linear mixed modelling techniques after removing the effects of a number of animal and diet factors. The ANOVA was undertaken by dividing each data set of PIN and PLI into three sub-groups (PIN:<£3, £3 to £15 and >£15, PLI:<£23, £23 to £67 and >£67) with these being categorised as low, medium and high genetic merit. Within the PIN and PLI data sets there was no significant differences among the three sub-groups in terms of CH₄ emission per kg feed intake or per kg energy corrected milk yield, or CH₄ energy (CH₄-E) output as a proportion of energy intake. Linear regression using the whole PIN and PLI data sets also demonstrated that there was no significant relationship between either PIN or PLI, and CH₄ emission per kg of feed intake or CH₄-E output as a proportion of energy intake. These results indicate that cow genetic merit (PIN or PLI) has little effect on enteric CH₄ emissions as a proportion of feed intake. Instead enteric CH₄ production may mainly relate to total feed intake and dietary nutrient composition.     

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle

In response to the increased concern over agriculture’s contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH₄) per cow per year after accounting for replacement costs, ranging 8.9–323 kg CH₄/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.     

Production,partial cash flows and greenhouse gas emissions of simulated dairy herds with extended lactations

The transition period is the most critical period in the lactation cycle of dairy cows. Extended lactations reduce the frequency of transition periods, the number of calves and the related labour for farmers. This study aimed to assess the impact of 2 and 4 months extended lactations on milk yield and net partial cash flow (NPCF) at herd level, and on greenhouse gas (GHG) emissions per unit of fat- and protein-corrected milk (FPCM), using a stochastic simulation model. The model simulated individual lactations for 100 herds of 100 cows with a baseline lactation length (BL), and for 100 herds with lactations extended by 2 or 4 months for all cows (All+2 and All+4), or for heifers only (H+2 and H+4). Baseline lactation length herds produced 887 t (SD: 13) milk/year. The NPCF, based on revenues for milk, surplus calves and culled cows, and costs for feed, artificial insemination, calving management and rearing of youngstock, was k€174 (SD: 4)/BL herd per year. Extended lactations reduced milk yield of the herd by 4.1% for All+2, 6.9% for All+4, 1.1% for H+2 and 2.2% for H+4, and reduced the NPCF per herd per year by k€7 for All+2, k€12 for All+4, k€2 for H+2 and k€4 for H+4 compared with BL herds. Extended lactations increased GHG emissions in CO₂-equivalents per t FPCM by 1.0% for All+2, by 1.7% for All+4, by 0.2% for H+2 and by 0.4% for H+4, but this could be compensated by an increase in lifespan of dairy cows. Subsequently, production level and lactation persistency were increased to assess the importance of these aspects for the impact of extended lactations. The increase in production level and lactation persistency increased milk production of BL herds by 30%. Moreover, reductions in milk yield for All+2 and All+4 compared with BL herds were only 0.7% and 1.1% per year, and milk yield in H+2 and H+4 herds was similar to BL herds. The resulting NPCF was equal to BL for All+2 and All+4 and increased by k€1 for H+2 and H+4 due to lower costs for insemination and calving management. Moreover, GHG emissions per t FPCM were equal to BL herds or reduced (0% to −0.3%) when lactations were extended. We concluded that, depending on lactation persistency, extending lactations of dairy cows can have a positive or negative impact on the NPCF and GHG emissions of milk production.     

Unexpected Decrease in Milk Production after Fenbendazole Treatment of Dairy Cows during Early Grazing Season

Gastrointestinal nematodes (GIN) infection can impair milk production (MP) in dairy cows. To investigate whether MP would be optimized by spring targeted-selective anthelmintic treatment in grazing cows, we assessed (1) the effect on MP of an anthelmintic treatment applied 1.5 to 2 months after turn-out, and (2) herd and individual indicators associated with the post-treatment MP response. A randomized controlled clinical trial was conducted in 13 dairy farms (578 cows) in western France in spring 2012. In each herd, lactating cows of the treatment group received fenbendazole orally, control cows remained untreated. Daily cow MP was recorded from 2 weeks before until 15 weeks after treatment. Individual serum pepsinogen and anti-Ostertagia antibody levels (expressed as ODR), faecal egg count and bulk tank milk (BTM) Ostertagia ODR were measured at treatment time. Anthelmintic treatment applied during the previous housing period was recorded for each cow. In each herd, information regarding heifers’ grazing and anthelmintic treatment history was collected to assess the Time of Effective Contact (TEC, in months) with GIN infective larvae before the first calving. The effect of treatment on weekly MP averages and its relationships with herd and individual indicators were studied using linear mixed models with two nested random effects (cow within herd). Unexpectedly, spring treatment had a significant detrimental effect on MP (-0.92 kg/cow/day on average). This negative MP response was particularly marked in high producing cows, in cows not treated during the previous housing period or with high pepsinogen levels, and in cows from herds with a high TEC or a high BTM ODR. This post-treatment decrease in MP may be associated with immuno-inflammatory mechanisms. Until further studies can assess whether this unexpected result can be generalized, non-persistent treatment of immunized adult dairy cows against GIN should not be recommended in early grazing season.     

An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology

Purpose

Life cycle assessment (LCA) studies of carbon footprint (CF) of milk from grass-based farms are usually limited to small numbers of farms (<30) and rarely certified to international standards, e.g. British Standards Institute publicly available specification 2050 (PAS 2050). The goals of this study were to quantify CF of milk from a large sample of grass-based farms using an accredited PAS 2050 method and to assess the relationships between farm characteristics and CF of milk.

Materials and methods

Data was collected annually using on-farm surveys, milk processor records and national livestock databases for 171 grass-based Irish dairy farms with information successfully obtained electronically from 124 farms and fed into a cradle to farm-gate LCA model. Greenhouse gas (GHG) emissions were estimated with the LCA model in CO₂ equivalents (CO₂-eq) and allocated economically between dairy farm products, except exported crops. Carbon footprint of milk was estimated by expressing GHG emissions attributed to milk per kilogram of fat and protein-corrected milk (FPCM). The Carbon Trust tested the LCA model for non-conformities with PAS 2050. PAS 2050 certification was achieved when non-conformities were fixed or where the effect of all unresolved non-conformities on CF of milk was?<?±5 %.

Results and discussion

The combined effect of LCA model non-conformities with PAS 2050 on CF of milk was <1 %. Consequently, PAS 2050 accreditation was granted. The mean certified CF of milk from grass-based farms was 1.11 kg of CO₂-eq/kg of FPCM, but varied from 0.87 to 1.72 kg of CO₂-eq/kg of FPCM. Although some farm attributes had stronger relationships with CF of milk than the others, no attribute accounted for the majority of variation between farms. However, CF of milk could be reasonably predicted using N efficiency, the length of the grazing season, milk yield/cow and annual replacement rate (R ²?=?0.75). Management changes can be applied simultaneously to improve each of these traits. Thus, grass-based farmers can potentially significantly reduce CF of milk.

Conclusions

The certification of an LCA model to PAS 2050 standards for grass-based dairy farms provides a verifiable approach to quantify CF of milk at a farm or national level. The application of the certified model highlighted a wide range between the CF of milk of commercial farms. However, differences between farms’ CF of milk were explained by variation in various aspects of farm performance. This implies that improving farm efficiency can mitigate CF of milk.     

Phosphorus feeding practices,barriers to and motivators for minimising phosphorus feeding to dairy cows in diverse dairy farming systems

Minimising phosphorus (P) feeding to dairy cows can reduce feed costs and minimise water pollution without impairing animal performance. This study aimed to determine current P feeding practices and identify the barriers to and motivators for minimising P feeding on dairy farms, using Great Britain (GB) dairy farming as an example of diverse systems. Farmers (n = 139) and feed advisers (n = 31) were involved simultaneously in independent questionnaire surveys on P feeding in dairy farms. Data on the herd size, milk yield and concentrate fed were analysed using ANOVA to investigate the effect of farm classification, region, and feed professional advice. Chi-square tests were used to investigate associations between farm characteristics and implemented P feeding and management practices. Most farmers (72%) did not know the P concentration in their lactating cow’s diet and did not commonly adopt precision P feeding practices, indicating that cows might have been offered dietary P in excess of recommended P requirement. Farmers’ tendency to feed P in excess of recommendations increased with herd size, but so did their awareness of P pollution issues and likeliness of testing manure P. However, 68% of farmers did not analyse manure P, indicating that mineral P fertiliser application rates were not adjusted accordingly, highlighting the risk of P being applied beyond crops’ requirement. Almost all farmers (96%) were willing to lower dietary P concentration but the uncertainty of P availability in feed ingredients (30%) and concerns over reduced cow fertility (22%) were primary barriers. The willingness to reduce dietary P concentrations was driven by the prospect of reducing environmental damage (28%) and feed costs (27%) and advice from their feed professionals (25%). Most farmers (70%) relied on a feed professional, and these farmers had a higher tendency to analyse their forage P. However, farmers of pasture-based systems relied less on feed professionals. Both farmers (73%) and feed advisers (68%) were unsatisfied with the amount of training on P management available. Therefore, the training on P management needs to be more available and the influence that feed professionals have over P feeding should be better utilised. Study findings demonstrate the importance of considering type of dairy farming systems when developing precision P feeding strategies and highlight the increasing importance of feed professionals in minimising P feeding.     

Genetic and non-genetic factors associated with milking order in lactating dairy cows

The ability to rapidly identify temporal deviations of an animal from its norm will be important in the management of individual cows in large herds. Furthermore, predictors of genetic merit for especially health traits are useful to augment the accuracy of selection, and thus genetic gain, in breeding programs. The objective of this study was to estimate the repeatability of milking order and to quantify the contribution of differences in additive genetic variation to phenotypic differences (i.e., heritability). The data used in this study included 9813 herd milk recording test-day records with time of milking from 85,532 cows in 1143 herds across an 8-year period. Milking order was available for both morning and evening milking for each cow with, on average, 3.33 milk test-day records (i.e., 6.66 milking events) per lactation, and on average 1.62 lactations per cow. Variance components for milking order were estimated using animal linear mixed models; covariance components between milking order and milk yield, milk composition and somatic cell score (i.e., logarithm₁₀ somatic cell count) were estimated also using animal linear mixed models. The heritability of milking order was 0.20 indicating partial genetic control of milking order. The repeatability of milking order within test-day, within lactation, and across lactations was 0.63, 0.51, and 0.47, respectively. Milking order was positively (P < 0.001), but weakly, phenotypically correlated with milk yield (r = 0.04), and milk fat concentration (r = 0.01) and negatively (P < 0.001), but weakly, correlated with milk protein concentration (r = −0.02) and somatic cell score (r = −0.05). Milking order was positively (P < 0.05), although weakly, genetically correlated with milk yield (r = 0.07) and negatively (P < 0.05), but also weakly, genetically correlated with somatic cell score (r = −0.08). This study is the first to show a contribution of additive genetics to milking order in dairy cattle but the genetic correlation between milking order and somatic cell score was weak.     

Low degradable protein supply to increase nitrogen efficiency in lactating dairy cows and reduce environmental impacts at barn level

Generally, <30% of dairy cattle’s nitrogen intake is retained in milk. Large amounts of nitrogen are excreted in manure, especially in urine, with damaging impacts on the environment. This study explores the effect of lowering dietary degradable nitrogen supplies – while maintaining metabolisable protein – on dairy cows’ performance, nitrogen use efficiency and gas emissions (NH₃, N₂O, CH₄) at barn level with tied animals. Two dietary N concentrations (CP: 12% DM for LowN; 18% DM for HighN) were offered to two groups of three lactating dairy cows in a split-plot design over four periods of 2 weeks. Diets were formulated to provide similar metabolisable protein supply, with degradable N either in deficit or in excess (PDIN of 84 and 114 g/kg DM for LowN and HighN, respectively). Cows ingested 0.8 kg DM/day less on the LowN diet, which was also 2.5% less digestible. Milk yield and composition were not significantly affected. N exported in milk was 5% lower (LowN: 129 g N/day; HighN: 136 g N/day; P<0.001) but milk protein yield was not significantly affected (LowN: 801 g/day; HighN: 823 g/day; P=0.10). Cows logically ingested less nitrogen on the LowN diet (LowN: 415 g N/day; HighN: 626 g N/day; P<0.001) resulting in a higher N use efficiency (N milk/N intake; LowN: 0.31; HighN: 0.22; P<0.001). N excreted in urine was almost four times lower on the LowN diet (LowN: 65 g N/day; HighN: 243 g N/day; P<0.001) while urinary urea N concentration was eightfold lower (LowN: 4.6 g/l; HighN: 22.9 g/l; P<0.001). Ammonia emission (expressed in g/h in order to remove periods of the day with potential interferences with volatile molecules from feed) was also lower on the LowN diet (LowN: 1.03 g/h per cow; HighN: 1.25 g/h per cow; P<0.05). Greenhouse gas emissions (N₂O and CH₄) at barn level were not significantly affected by the amount of dietary N. Offering low amounts of degradable protein with suitable metabolisable protein amounts to cattle improved nitrogen use efficiency and lowered ammonia emissions at barn level. This strategy would, however, need to be validated for longer periods, other housing systems (free stall barns) and at farm level including all stages of manure management.     

Individual variation and repeatability of methane production from dairy cows estimated by the CO2 method in automatic milking system

The objectives of this study were to investigate the individual variation, repeatability and correlation of methane (CH₄) production from dairy cows measured during 2 different years. A total of 21 dairy cows with an average BW of 619±14.2 kg and average milk production of 29.1±6.5 kg/day (mean±s.d.) were used in the 1^st year. During the 2^nd year, the same cows were used with an average BW of 640±8.0 kg and average milk production of 33.4±6.0 kg/day (mean±s.d.). The cows were housed in a loose housing system fitted with an automatic milking system (AMS). A total mixed ration was fed to the cows ad libitum in both years. In addition, they were offered concentrate in the AMS based on their daily milk yield. The CH₄ and CO₂ production levels of the cows were analysed using a Gasmet DX-4030. The estimated dry matter intake (EDMI) was 19.8±0.96 and 23.1±0.78 (mean±s.d.), and the energy-corrected milk (ECM) production was 30.8±8.03 and 33.7±5.25 kg/day (mean±s.d.) during the 1^st and 2^nd year, respectively. The EDMI and ECM had a significant influence (P<0.001) on the CH₄ (l/day) yield during both years. The daily CH₄ (l/day) production was significantly higher (P<0.05) during the 2^nd year compared with the 1^st year. The EDMI (described by the ECM) appeared to be the key factor in the variation of CH₄ release. A correlation (r=0.54) of CH₄ production was observed between the years. The CH₄ (l/day) production was strongly correlated (r=0.70) between the 2 years with an adjusted ECM production (30 kg/day). The diurnal variation of CH₄ (l/h) production showed significantly lower (P<0.05) emission during the night (0000 to 0800 h). The between-cows variation of CH₄ (l/day, l/kg EDMI and l/kg ECM) was lower compared with the within-cow variation for the 1^st and 2^nd years. The repeatability of CH₄ production (l/day) was 0.51 between 2 years. In conclusion, a higher EDMI (kg/day) followed by a higher ECM (kg/day) showed a higher CH₄ production (l/day) in the 2^nd year. The variations of CH₄ (l/day) among the cows were lower than the within-cow variations. The CH₄ (l/day) production was highly repeatable and, with an adjusted ECM production, was correlated between the years.     

Are dairy cows with a more reactive temperament less efficient in energetic metabolism and do they produce more enteric methane?

It remains unknown whether dairy cows with more reactive temperament produce more enteric methane (CH₄) and are less bioenergetically efficient than the calmer ones. The objectives of this study were (a) to evaluate the relationship between cattle temperament assessed by traditionally used tests with energetic metabolism and enteric CH₄ emissions by crossbred dairy cows; (b) to assess how cows’ restlessness in respiration chambers affects energetic metabolism and enteric CH₄ emissions. Temperament indicators were evaluated for 28 primiparous F1 Holstein-Gyr cows tested singly in the handling corral (entrance time, crush score, flight speed, and flight distance) and during milking (steps, kicks, defecation, rumination, and kick the milking cluster off). Cows’ behaviors within respiration chambers were also recorded for each individual kept singly. Digestibility and calorimetry trials were performed to obtain energy partitioning and CH₄ measures. Cows with more reactive temperament in milking (the ones that kicked the milking cluster off more frequently) spent 25.24% less net energy on lactation (P = 0.04) and emitted 36.77% more enteric CH₄/kg of milk (P = 0.03). Furthermore, cows that showed a higher frequency of rumination at milking parlor allocated 57.93% more net energy for milk production (P < 0.01), spent 50.00% more metabolizable energy for milk production (P < 0.01) and 37.10% less CH₄/kg of milk (P = 0.04). Regarding the handling temperament, most reactive cows according to flight speed, lost 29.16% less energy as urine (P = 0.05) and tended to have 14.30% more enteric CH₄ production (P = 0.08), as well as cows with a lower entrance time (most reactive) that also lost 13.29% more energy as enteric CH₄ (P = 0.04). Temperament and restless behavior of Holstein-Gyr cows were related to metabolic efficiency and enteric CH₄ emissions. Cows’ reactivity and rumination in the milking parlor, in addition to flight speed and entrance time in the squeeze chute during handling in the corral, could be useful measures to predict animals more prone to metabolic inefficiency, which could negatively affect the sustainability of dairy systems.     

Relationship of three measures of behavior to milk production

Nine commerical herds were observed on three separate occasion for flight distance, i.e., how close a human can come before a stationary cow moves away, and approach distance, i.e., how close a cow will come to a stationary human. Herdsmen from each herd scored their cows on parlor behavior using a scoring system that ranged from 1 (the most docile) to 5 (the most aggressive). These three behavior factors were compared with milk production. Both mean flight distance and mean approach distance were 1.6 m. Mean parlor score was 2.2 and mean production was 9153 kg. These were significant inter-herd correlations between flight distance and approach distance (r=0.30), flight distance and parlor score (r=0.20), and parlor score and milk production (r=0.08); however, only flight distance and approach distance (r=0.18), and flight distance and parlor score (r=0.12) were significantly correlated within herds. There was no significant intraherd correlation between milk production and any of the behavioral factors considered. These findings indicate that while parlor score is associated with milk production across herds, flight distance and approach distance are not useful indicators of milk production.     

Prediction of enteric methane production,yield, and intensity in dairy cattle using an intercontinental database

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.     

Reducing methane on-farm by feeding diets high in fat may not always reduce life cycle greenhouse gas emissions

Purpose

To consider whether feed supplements that reduce methane emissions from dairy cows result in a net reduction in greenhouse gas (GHG) intensity when productivity changes and emissions associated with extra manufacturing and management are included.

Methods

A life cycle assessment was undertaken using a model farm based on dairy farms in Victoria, Australia. The system boundary included the creation of farm inputs and on-farm activities up to the farm gate where the functional unit was 1 L of fat and protein corrected milk (FPCM). Electricity and diesel (scaled per cow), and fertiliser inputs (scaled on farm size) to the model farm were based on average data from a survey of farms. Fertiliser applied to crops was calculated per area of crop. Animal characteristics were based on available data from farms and literature. Three methane-reducing diets (containing brewers grain, hominy or whole cotton seed) and a control diet (cereal grain) were modelled as being fed during summer, with the control diet being fed for the remainder of the year in all cases.

Results and discussion

Greenhouse gas intensity (kg CO₂-eq/L FPCM) was lower than the control diet when the hominy (97 % compared with control) and brewers grain (98 %) diets were used but increased when the whole cottonseed diet was used (104 %). On-farm GHG emissions (kg CO₂-eq) were lower than the control diet when any of the methane-reducing diets were used (98 to 99.5 % of emissions when control diet fed). Diesel use in production and transport of feed supplements accounted for a large portion (63 to 93 %) of their GHG intensity (kg CO₂-eq/t dry matter). Adjusting fertiliser application, changing transport method, changing transport fuel, and using nitrification inhibitors all had little effect on GHG emissions or GHG intensity.

Conclusions

Although feeding strategies that reduce methane emissions from dairy cows can lower the GHG emissions up to the farm gate, they may not result in lower GHG intensities (g CO₂-eq/L FPCM) when pre-farm emissions are included. Both transport distance and the effect of the feed on milk production have important impacts on the outcomes.