Similar to all dairy systems internationally, pasture-based dairy systems are under increasing pressure to reduce their greenhouse gas (GHG) emissions. Ireland and New Zealand are 2 countries ...operating predominantly pasture-based dairy production systems where enteric CH4 contributes 23% and 36% of total national emissions, respectively. Ireland currently has a national commitment to reduce 51% of total GHG emissions by 2030 and 25% from agriculture by 2030, as well as striving to achieve climate neutrality by 2050. New Zealand's national commitment is to reduce 10% of methane emissions by 2030 and between 24% and 47% reduction in methane emissions by 2050. To achieve these reductions, factors that affect enteric methane (CH4) production in a pasture-based system need to be investigated. The objective of this study was to assess the relationship between enteric CH4 and other animal traits (feed intake, metabolic liveweight, energy corrected milk yield, milk urea concentration, and body condition score BCS) in a grazing dairy system. Enteric CH4 emissions were measured on 45 late lactation (213.8 ± 29 d after calving) grazing Holstein-Friesian and Holstein-Friesian × Jersey crossbred cows (lactation number 3.01 ± 1.65, 538.64 ± 59.37 kg live weight, and 3.14 ± 0.26 BCS) using GreenFeed monitoring equipment for 10 wk. There was a training period for the cows to use the GreenFeed of 3 wk before the 10-wk study period. The average enteric CH4 produced in the study was 352 g ± 45.7 g per day with an animal to animal coefficient of variation of 13%. Dry matter intake averaged 16.6 kg ± 2.23 kg per day, while milk solids (fat plus protein) averaged 1.62 kg ± 0.29 kg per day. A multiple linear regression model indicated that each one unit increase in energy corrected milk yield, metabolic liveweight and milk urea concentration, resulted in an increase in enteric CH4 production per day by 3.9, 1.74, and 1.38 g, respectively. Although each one unit increase in BCS resulted in a decrease in 39.03 g CH4 produced per day. When combined, these factors explained 47% of the variation in CH4 production, indicating that there is a large proportion of variation not included in the model. The repeatability of the CH4 measurements was 0.66 indicating that cows are relatively consistently exhibiting the same level of CH4 throughout the study. Therefore, enteric CH4 production is suitable for phenotyping.
Enteric methane (CH4) emissions of 3 genetic groups (GG) of dairy cows were recorded across the grazing season (early March to late October). The 3 GG were (1) high economic breeding index (EBI) ...Holstein-Friesian (HF) representative of the top 1% of dairy cows in Ireland at the time of the study (elite), (2) national average (NA) EBI, which were representative of the average HF dairy cow in Ireland, and (3) purebred Jersey (JE) cows. Enteric CH4 was recorded using GreenFeed technology. Seasonal variation in CH4 was observed, with the lowest daily CH4 emissions and CH4 expressed per unit of dry matter intake occurring in spring (253 g/d and 15.56 g/kg, respectively), intermediate in summer (303 g/d and 18.26 g/kg, respectively), and greatest in autumn (324 g/d and 19.80 g/kg, respectively). Seasonal variation was also observed in the proportion of gross energy intake converted to CH4 (Ym); in the spring the Ym was lowest at 0.046, increasing to 0.053 and 0.058 in the summer and autumn, respectively. There was no difference in daily CH4 between the elite and NA, whereas JE had lower CH4 emissions compared with the elite. When expressed per unit of milk solids (fat + protein yield; MS), the elite and JE produced 6.8% and 9.7% less CH4 per kilogram of MS, respectively, compared with NA. There was no difference between the GG for CH4 per unit of DMI or the Ym. This research emphasizes the variation in CH4 emissions across the grazing season and among cows of differing genetic merit for CH4 emission intensities but not for CH4 per unit of DMI or the Ym.
The objective of this study was to compare mid-infrared reflectance spectroscopy (MIRS) analysis of milk and near-infrared reflectance spectroscopy (NIRS) analysis of feces with regard to their ...ability to predict the dry matter intake (DMI) of lactating grazing dairy cows. A data set comprising 1,074 records of DMI from 457 cows was available for analysis. Linear regression and partial least squares regression were used to develop the equations using the following variables: (1) milk yield (MY), fat percentage, protein percentage, body weight (BW), stage of lactation (SOL), and parity (benchmark equation); (2) MIRS wavelengths; (3) MIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (4) NIRS wavelengths; (5) NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (6) MIRS and NIRS wavelengths; and (7) MIRS wavelengths, NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity. The equations were validated both within herd using animals from similar experiments and across herds using animals from independent experiments. The accuracy of equations was greater for within-herd validation compared with across-herds validation. Across-herds validation was deemed the more suitable method to assess equations for robustness and real-world application. The benchmark equation was more accurate coefficient of determination (R2) = 0.60; root mean squared error (RMSE) = 1.68 kg than MIRS alone (R2 = 0.30; RMSE = 2.23 kg) or NIRS alone (R2 = 0.16; RMSE = 2.43 kg). The combination of the benchmark equation with MIRS (R2 = 0.64; RMSE = 1.59 kg) resulted in slightly superior fitting statistics compared with the benchmark equation alone. The combination of the benchmark equation with NIRS (R2 = 0.58; RMSE = 1.71 kg) did not result in a more accurate prediction equation than the benchmark equation. The combination of MIRS and NIRS wavelengths resulted in superior fitting statistics compared with either method alone (R2 = 0.36; RMSE = 2.15 kg). The combination of the benchmark equation and MIRS and NIRS wavelengths resulted in the most accurate equation (R2 = 0.68; RMSE = 1.52 kg). A further analysis demonstrated that Holstein-Friesian cows could predict the DMI of Jersey × Holstein-Friesian crossbred cows using both MIRS and NIRS. Similarly, the Jersey × Holstein-Friesian animals could predict the DMI of Holstein-Friesian cows using both MIRS and NIRS. The equations developed in this study have the capacity to predict DMI of grazing dairy cows. From a practicality perspective, MIRS in combination with variables in the benchmark equation is the most suitable equation because MIRS is currently used on all milk-recorded milk samples from dairy cows.
Breeding values for feed intake and feed efficiency in beef cattle are generally derived indoors on high-concentrate (HC) diets. Within temperate regions of north-western Europe, however, the ...majority of a growing beef animal's lifetime dietary intake comes from grazed grass and grass silage. Using 97 growing beef cattle, the objective of the current study was to assess the repeatability of both feed intake and feed efficiency across 3 successive dietary test periods comprising grass silage plus concentrates (S+C), grazed grass (GRZ) and a HC diet. Individual DM intake (DMI), DMI/kg BW and feed efficiency-related parameters, residual feed intake (RFI) and gain to feed ratio (G : F) were assessed. There was a significant correlation for DMI between the S+C and GRZ periods (r = 0.32; P < 0.01) as well as between the S+C and HC periods (r = 0.41; P < 0.001), whereas there was no association for DMI between the GRZ and HC periods. There was a significant correlation for DMI/kg BW between the S+C and GRZ periods (r = 0.33; P < 0.01) and between the S+C and HC periods (r = 0.40; P < 0.001), but there was no association for the trait between the GRZ and HC periods. There was a significant correlation for RFI between the S+C and GRZ periods (r = 0.25; P < 0.05) as well as between S+C and HC periods (r = 0.25; P < 0.05), whereas there was no association for RFI between the GRZ and HC periods. Gain to feed ratio was not correlated between any of the test periods. A secondary aspect of the study demonstrated that traits recorded in the GRZ period relating to grazing bite rate, the number of daily grazing bouts and ruminating bouts were associated with DMI (r = 0.28 to 0.42; P < 0.05 - 0.001), DMI/kg BW (r = 0.36 to 0.45; P < 0.01 - 0.001) and RFI (r = 0.31 to 0.42; P < 0.05 - 0.001). Additionally, the number of ruminating boli produced per day and per ruminating bout were associated with G : F (r = 0.28 and 0.26, respectively; P < 0.05). Results from this study demonstrate that evaluating animals for both feed intake and feed efficiency indoors on HC diets may not reflect their phenotypic performance when consuming conserved forage-based diets indoors or when grazing pasture.
Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data ...generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.
Although 3-NOP has been proven to reduce enteric methane (CH4) by ∼30% in indoor systems of dairying when the additive is mixed throughout a total mixed ration (TMR), there has been very limited ...research to date in grazing systems in which the most convenient method of additive supplementation is at milking twice daily. To investigate the effect of twice daily 3-NOP supplementation on enteric CH4 emissions, a 12-week study was undertaken in which treatment cows (n = 26) were supplemented with 3-NOP (80 mg per kg dry matter intake; DMI) twice daily at morning and evening milking, while control cows (n = 26) received no additive supplementation. Enteric CH4, hydrogen (H2) and carbon dioxide (CO2) were measured using GreenFeed units, while milk production, body weight (BW), body condition score (BCS) and DMI were monitored to determine the effect of 3-NOP supplementation on productivity. There was no significant effect of 3-NOP supplementation on any of the aforementioned parameters with the exception of CH4 and H2 production, respectively. Cows supplemented with 3-NOP produced ∼36% more H2 (P < 0.001) across a 24-h period, with reductions in CH4 production of 28.5% recorded in the 3 h after additive consumption (P < 0.001), however, levels of CH4 production returned to that of the control group thereafter. When CH4 production was considered across the entire 24-h period, the cows offered 3-NOP produced ∼5% less CH4 than the control (P < 0.050). Future research should focus on methods to increase the efficacy of the additive throughout the day which would include the deployment of a slow-release form or an out of parlor feeding system that allows animals consume the product at additional time points.
Greenhouse gas (GHG) emissions and nitrogen (N) efficiencies were modeled for 2 genetic groups (GG) of Holstein-Friesian cows across 3 contrasting feeding treatments (FT). The 2 GG were (1) high ...economic breeding index (EBI) animals representative of the top 5% of cows nationally (elite) and (2) EBI representative of the national average (NA). The FT represented (1) generous feeding of pasture, (2) a slight restriction in pasture allowance, and (3) a high-concentrate feeding system with adequate pasture allowance. Greenhouse gas and N balance models were parameterized using outputs generated from the Moorepark Dairy Systems model, a stochastic budgetary simulation model, having integrated biological data pertaining to the 6 scenarios (2 GG × 3 FT) obtained from a 4-yr experiment conducted between 2013 and 2016. On a per hectare basis, total system GHG emissions were similar for both elite and NA across the 3 FT. Per unit of product, however, the elite group had 10% and 11% lower GHG emissions per kilogram of fat- and protein-corrected milk and per kilogram of milk solids (MSO; fat + protein kg), respectively, compared with the NA across the 3 FT. The FT incorporating high concentrate supplementation had greater absolute GHG emissions per hectare as well as GHG per kilogram of fat- and protein-corrected milk and MSO. The elite group had a slightly superior N use efficiency (N output/N input) and lower N surplus (N input – N output) compared with the NA group. The high concentrate FT had an inferior N use efficiency and a higher N surplus. The results of the current study demonstrate that breeding for increased EBI will lead to a general improvement in GHG emissions per unit of product as well as improved N efficiency. The results also illustrate that reducing concentrate supplementation will reduce GHG emissions, GHG emissions intensity, while improving N efficiency in the context of pasture-based dairy production.
The objective of this study was to develop and validate models to predict dry matter intake (DMI) of grazing dairy cows using animal energy sinks and status traits in combination with traits related ...to grazing behaviour, body measurements, thermal imaging, heart rate and blood pressure. The dataset used to develop the models comprised 33 measurements from 113 Holstein-Friesian dairy cows. Multivariable regression models were constructed incorporating each independent variable into a benchmark model incorporating the energy sinks (milk yield MY, fat %, protein % and body weight BW) and status traits (feeding treatment, parity and calving day of year). Of the 33 variables tested, 10 showed an association with DMI (P < 0.25). These variables were incorporated into a backward linear regression model. Variables were retained in this model if P < 0.05. Grazing bout duration and rumination mastication rate were retained in the final model. The inclusion of these variables in the model increased DMI prediction by 0.01 (coefficient of determination R² = 0.85) compared to the benchmark model alone (R² = 0.84). The models were applied to data recorded on an independent herd of 51 dairy cows. The R² upon validation was 0.80 for the benchmark model and 0.79 for the model incorporating rumination mastication rate and grazing bout duration in combination with the benchmark variables. The separation of grazing bout duration and rumination mastication rate to predict DMI revealed rumination mastication rate slightly increases predictive accuracy upon external validation (R² = 0.81), whereas grazing bout duration did not (R² = 0.78). This suggests that grazing bout duration is not a robust trait for DMI prediction. Results from this study suggest that rumination mastication rate can slightly increase the accuracy of DMI prediction surpassing known energy sinks and status traits.
Enteric methane (CH4) emissions of 3 genetic groups (GG) of dairy cows were recorded across the grazing season (early March to late October). The 3 GG were (1) high economic breeding index (EBI) ...Holstein-Friesian (HF) representative of the top 1% of dairy cows in Ireland at the time of the study (elite), (2) national average (NA) EBI, which were representative of the average HF dairy cow in Ireland, and (3) purebred Jersey (JE) cows. Enteric CH4 was recorded using GreenFeed technology. Seasonal variation in CH4 was observed, with the lowest daily CH4 emissions and CH4 expressed per unit of dry matter intake occurring in spring (253 g/d and 15.56 g/kg, respectively), intermediate in summer (303 g/d and 18.26 g/kg, respectively), and greatest in autumn (324 g/d and 19.80 g/kg, respectively). Seasonal variation was also observed in the proportion of gross energy intake converted to CH4 (Ym); in the spring the Ym was lowest at 0.046, increasing to 0.053 and 0.058 in the summer and autumn, respectively. There was no difference in daily CH4 between the elite and NA, whereas JE had lower CH4 emissions compared with the elite. When expressed per unit of milk solids (fat + protein yield; MS), the elite and JE produced 6.8% and 9.7% less CH4 per kilogram of MS, respectively, compared with NA. There was no difference between the GG for CH4 per unit of DMI or the Ym. This research emphasizes the variation in CH4 emissions across the grazing season and among cows of differing genetic merit for CH4 emission intensities but not for CH4 per unit of DMI or the Ym.
•In the absence of a weighing scales live-weight of Holstein-Friesian and Jersey heifers can be successfully predicted using body length, heart girth and withers height and their ...combinations.•Regression equations of live-weight on heart girth, body volume and a polynomial of length, girth, and height equations, respectively, were accurate predictors of live-weight for pasture-based dairy heifers (R² > 0.92 and RMSE < 19.1 kg).•Body volume of the heifer is recommended as the most suitable predictor of live-weight.
Monitoring the live-weight of dairy heifers and thus meeting weight-for-age targets is regarded as one of the most important aspects of a heifer rearing enterprise as it optimizes future production. This is particularly important in pasture-based heifer rearing systems where growth is non-linear due to seasonal variation in grass growth and quality. Data were collected throughout the rearing period to estimate the live-weight of pasture-based Holstein-Friesian (n = 130) and Jersey (n = 57) dairy heifers using linear body measurements. Live-weight was regressed on heart girth, body volume and a polynomial of body length, heart girth, and withers height; all equations were validated within-herd. All three equations were accurate predictors of live-weight for pasture-based dairy heifers (R² > 0.92 and RMSE < 19.1 kg), therefore, in the absence of weighing scales, live-weight can be successfully predicted using linear body measurements. The equation which utilizes body volume of the heifer is proposed as the most suitable predictor of live-weight.
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