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ABSTRACT: Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

ABSTRACT: Estimating the efficiency of N utilization for milk production (MNE) of individual cows at a large scale is difficult, particularly because of the cost of measuring feed intake. Nitrogen isotopic discrimination (Δ15N) between the animal (milk, plasma, or tissues) and its diet has been proposed as a biomarker of the efficiency of N utilization in a range of production systems and ruminant species. The aim of this study was to assess the ability of Δ15N to predict the between-animal variability in MNE in dairy cows using an extensive database. For this, 20 independent experiments conducted as either changeover (n = 14) or continuous (n = 6) trials were available and comprised an initial data set of 1,300 observations. Between-animal variability was defined as the variation observed among cows sharing the same contemporary group (CG; individuals from the same experimental site, sampling period, and dietary treatment). Milk N efficiency was calculated as the ratio between mean milk N (grams of N in milk per day) and mean N intake (grams of N intake per day) obtained from each sampling period, which lasted 9.0 ± 9.9 d (mean ± SD). Samples of milk (n = 604) or plasma (n = 696) and feeds (74 dietary treatments) were analyzed for natural 15N abundance (δ15N), and then the N isotopic discrimination between the animal and the dietary treatment was calculated (Δ15n = δ15Nanimal − δ15Ndiet). Data were analyzed through mixed-effect regression models considering the experiment, sampling period, and dietary treatment as random effects. In addition, repeatability estimates were calculated for each experiment to test the hypothesis of improved predictions when MNE and Δ15N measurements errors were lower. The considerable protein mobilization in early lactation artificially increased both MNE and Δ15N, leading to a positive rather than negative relationship, and this limited the implementation of this biomarker in early lactating cows. When the experimental errors of Δ15N and MNE decreased in a particular experiment (i.e., higher repeatability values), we observed a greater ability of Δ15N to predict MNE at the individual level. The predominant negative and significant correlation between Δ15N and MNE in mid- and late lactation demonstrated that on average Δ15N reflects MNE variations both across dietary treatments and between animals. The root mean squared prediction error as a percentage of average observed value was 6.8%, indicating that the model only allowed differentiation between 2 cows in terms of MNE within a CG if they differed by at least 0.112 g/g of MNE (95% confidence level), and this could represent a limitation in predicting MNE at the individual level. However, the one-way ANOVA performed to test the ability of Δ15N to differentiate within-CG the top 25% from the lowest 25% individuals in terms of MNE was significant, indicating that it is possible to distinguish extreme animals in terms of MNE from their N isotopic signature, which could be useful to group animals for precision feeding.

The objective of this study was to evaluate the effects of oak tannin extract (OTE) added in forage before ensiling on dairy cows fed at 92% of their digestible protein requirements. Six multiparous lactating Holstein cows were used in a crossover design (two treatments × two periods). The control treatment (CON) was based on a diet including 50% of grass silage, whereas the experimental treatment (TAN) included grass silage sprayed with OTE (26 g/kg DM) just before baling. Milk yield (on average 24 kg fat protein corrected milk per day) was not affected, but both milk and rumen fatty acids profiles were impacted by OTE. Nitrogen intake (415 g N per cow per day) and nitrogen use efficiency (NUE; 0.25 on average) were not affected, but a shift from urine (−8% of N intake relatively to control, P = 0.06) to faecal N (+5%; P = 0.004) was observed with the TAN diet (P ≤ 0.05). Nitrogen apparent digestibility was thus reduced for TAN (−3%; P ≤ 0.05). The effect of OTE on ruminal and milk FA profiles suggests an impact on rumen microbiota. Nitrogen isotopic discrimination between animal proteins and diet (Δ15N) was evaluated as a proxy for NUE. While no differences in NUE were observed across diets, a lower Δ15N of plasma proteins was found when comparing TAN v. CON diets. This finding supports the concept that Δ15N would mainly sign the N partitioning at the metabolic level rather than the overall NUE, with the latter also being impacted by digestive processes. Our results agree with a N shift from urine to faeces, and this strategy can thus be adopted to decrease the environmental impact of ruminant protein feeding.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

Increased economic, societal and environmental challenges facing agriculture are leading to a greater focus on effective way to combine grazing and automatic milking systems (AMS). One of the fundamental aspects of robotic milking is cows’ traffic to the AMS. Numerous studies have identified feed provided, either as fresh grass or concentrate supplement, as the main incentive for cows to return to the robot. The aim of this study was to determine the effect of concentrate allocation on voluntary cow traffic from pasture to the robot during the grazing period, to highlight the interactions between grazed pasture and concentrate allocation in terms of substitution rate and the subsequent effect on average milk yield and composition. Thus, 29 grazing cows, milked by a mobile robot, were monitored for the grazing period (4 months). They were assigned to two groups: a low concentrate (LC) group (15 cows) and a high concentrate (HC) group (14 cows) receiving 2 and 4 kg concentrate/cow per day, respectively; two allocations per day of fresh pasture were provided at 0700 and 1600 h. The cows had to go through the AMS to receive the fresh pasture allocation. The effect of concentrate level on robot visitation was calculated by summing milkings, refusals and failed milkings/cow per day. The impact on average daily milk yield and composition was also determined. The interaction between lactation number and month was used as an indicator of pasture availability. Concentrate allocation increased significantly robot visitations in HC (3.60±0.07 visitations/cow per day in HC and 3.10±0.07 visitations/cow per day in LC; P

The effects of first calving (FC) in Holstein heifers on their first lactation, second lactation and lifetime milk production were examined from an initial database of 459 743 animals that first calved between 1 January 1990 and 31 July 2010 in Wallonia, Belgium. The FC age class (18 to 22, 22 to 26, 26 to 30, 30 to 34, 34 to 38 and 38 to 42 months), the FC season and FC year class (1990 to 1994, 1995 to 1999, 2000 to 2004 and 2005 to 2010) were considered when analysing the first and second lactation data. Lifetime data were similarly analysed, but did not include animals that calved after 2005 because many of them were still lactating. Only 24% of animals had their FC before 26 months of age. Animals that first calved between 22 and 26 months of age had more lactations and productive days during their life. They also had higher first and second lactation milk production and lifetime milk production. Summer or autumn FC improved first lactation, second lactation and lifetime milk production, as well as production per day of lactation, compared with winter or spring FC. Compared with animals that calved for the first time in 1990 to 1994, animals with a FC in 2000 to 2004 had a longer calving interval (0.5 months), fewer lactations per animal (−0.6) and fewer days in their lifetime lactation (a reduction of 144 days). As a result, the animals’ lifetime production did not increase between 1990 to 1994 and 2000 to 2004, although milk production per day of lactation (22.85v. 20.49 l/day) and per day of life (11.49v. 10.78 l/day) improved. Milk fat content was lower in 2000 to 2004 than in 1990 to 1994, but protein content remained relatively constant, probably because of the cows’ higher production level and increased dietary concentrate supplementation.

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 R2cv, 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 (R2c = 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.

The objective of this experiment was to compare the nutritional properties of potato protein concentrate, a by-product of the starch industry produced entirely in Europe, with that of soybean meal (SBM), for growing cattle. The experiment was conducted on double-muscled Belgian Blue bulls, fitted with rumen, duodenal and ileal cannulas, according to a 4 × 4 Latin square design. They were fed three different iso-N and iso-net energy diets formulated according to the Dutch feed evaluation system, differing in the nature of the main protein source, which was either SBM (‘SBM’ treatment), potato protein concentrate (PPC, ‘PPC’ treatment) or an iso-N mixture of these two protein sources (‘mixed’ treatment). A fourth treatment consisted of ‘PPC’ supplemented by 9.5% digestible proteins supplied by duodenal perfusion of sodium caseinate (CAS, ‘PPC + CAS’ treatment). No significant difference was observed in the ruminal fluid pH, whereas both ‘PPC’ and ‘PPC + CAS’ had the effect of reducing the ruminal ammonia nitrogen (N-NH3) concentration. No significant difference was observed in the apparent intestinal digestibility of the dry matter (DM), organic matter (OM) or N. Outflows of non-NH3-N, microbial proteins and dietary proteins from the rumen were similar for ‘PPC’, ‘SBM’ and ‘mixed’, and increased with CAS infusion by 20%, 17% and 27%, respectively. On the basis of in vivo observations, the degradability of SBM and PPC proteins was estimated at 0.60 and 0.43, respectively, corresponding to the values quoted in the literature. The supply of digestible essential amino acids (EAA) was significantly greater with ‘PPC + CAS’ and did not differ among ‘SBM’, ‘mixed’ and ‘PPC’. This illustrates the difficulty of altering the amino acid (AA) pattern of digestible protein by the nature of the protein of dietary origin when an animal is fed a high nutritional value diet. N retention was not affected by replacing SBM with PPC, but increased by 10% with CAS infusion. On the basis of the plasma AA pattern, the supply of digestible Met was probably limiting with ‘SBM’, ‘mixed’ and ‘PPC’. The CAS perfusion supplemented all AA, including Met, leading to increased N retention. This improvement was limited, however, and N utilisation remained unchanged between treatments. In conclusion, despite a more favourable EAA pattern, PPC offered no advantage compared with SBM for growing bulls when diets were formulated according to the Dutch feed evaluation system.

This experiment studied the effect of a modest difference in diet structure value (SV) on milk conjugated linoleic acid (CLA) contents of cows fed diets supplemented with extruded linseed, in situations where the diets provided enough SV and therefore did not induce milk fat depression. Six lactating Holstein cows were used in a crossover design with two treatments (‘SV 1.50’ and ‘SV 1.73’) and two periods of 21 days. The ‘SV 1.50’ diet contained 59% maize silage, 13% soya bean meal, 13% sugar beet pulp and 14% Nutex Compact (containing 56% extruded linseed) (dry matter (DM) basis) and was offered as a restricted total mixed ration. For the ‘SV 1.73’ diet, 8% wheat straw (DM basis) was added to the ‘SV 1.50’ diet as an additional structure source. The two diets had a forage-to-concentrate ratio of 59 : 41 and 62 : 38. The inclusion of straw in the diet resulted in an additional intake of NDF (+1110 g/day), which accounted for 90% of the additional intake of OM, whereas additional intakes of the other nutrients were minor. Milk yield and composition did not differ among treatments. The inclusion of straw in the diet did not affect the milk levels of t10–18:1, 18:2n-6, c9-16:1, c9-18:1, c11-18:1, 6:0, 8:0, 20:4 and 20:5. It decreased the milk levels of c9,t11-CLA (2.13% v. 3.03% of fatty acids (FA) reported, P < 0.001), t11-18:1 (4.99% v. 7.10% of FA reported, P < 0.001), 18:3n-3, t9-16:1 and t9-18:1, while it increased the milk levels of 6:0–14:0 (20.90% v. 19.69% of FA reported, P < 0.01), 16:0 (26.55% v. 25.25% of FA reported, P < 0.01), 18:0 (13.54% v. 12.59% of FA reported, P < 0.001), 17:0, 20:0 and 22:5. Regarding the ratio between FA, the inclusion of straw increased the 18:0/total C18 FA ratio (37.74% v. 32.07%, P < 0.001), whereas it decreased the total trans-C18 FA/total C18 FA ratio (15.46% v. 20.34%, P < 0.001), the t11-18:1/total C18 FA ratio (13.70% v. 17.95%, P < 0.01) and the c9,t11-CLA/total C18 FA ratio (5.82% v. 7.64%, P < 0.001). We conclude from this experiment that even a modest increase in SV to a diet supplemented with extruded linseed, yet already providing enough SV, alters the rumen lipid metabolism and, hence, CLA levels in milk fat.

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Estimating the efficiency of N utilization for milk production (MNE) of individual cows at a large scale is difficult, particularly because of the cost of measuring feed intake. Nitrogen isotopic discrimination (Δ 15 N) between the animal (milk, plasma, or tissues) and its diet has been proposed as a biomarker of the efficiency of N utilization in a range of production systems and ruminant species. The aim of this study was to assess the ability of Δ 15 N to predict the between-animal variability in MNE in dairy cows using an extensive database. For this, 20 independent experiments conducted as either changeover (n = 14) or continuous (n = 6) trials were available and comprised an initial data set of 1,300 observations. Between-animal variability was defined as the variation observed among cows sharing the same contemporary group (CG; individuals from the same experimental site, sampling period, and dietary treatment). Milk N efficiency was calculated as the ratio between mean milk N (grams of N in milk per day) and mean N intake (grams of N intake per day) obtained from each sampling period, which lasted 9.0 ± 9.9 d (mean ± SD). Samples of milk (n = 604) or plasma (n = 696) and feeds (74 dietary treatments) were analyzed for natural 15 N abundance (δ 15 N), and then the N isotopic discrimination between the animal and the dietary treatment was calculated (Δ 15 n = δ 15 N animal - δ 15 N diet ). Data were analyzed through mixed-effect regression models considering the experiment, sampling period, and dietary treatment as random effects. In addition, repeatability estimates were calculated for each experiment to test the hypothesis of improved predictions when MNE and Δ 15 N measurements errors were lower. The considerable protein mobilization in early lactation artificially increased both MNE and Δ 15 N, leading to a positive rather than negative relationship, and this limited the implementation of this biomarker in early lactating cows. When the experimental errors of Δ 15 N and MNE decreased in a particular experiment (i.e., higher repeatability values), we observed a greater ability of Δ 15 N to predict MNE at the individual level. The predominant negative and significant correlation between Δ 15 N and MNE in mid- and late lactation demonstrated that on average Δ 15 N reflects MNE variations both across dietary treatments and between animals. The root mean squared prediction error as a percentage of average observed value was 6.8%, indicating that the model only allowed differentiation between 2 cows in terms of MNE within a CG if they differed by at least 0.112 g/g of MNE (95% confidence level), and this could represent a limitation in predicting MNE at the individual level. However, the one-way ANOVA performed to test the ability of Δ 15 N to differentiate within-CG the top 25% from the lowest 25% individuals in terms of MNE was significant, indicating that it is possible to distinguish extreme animals in terms of MNE from their N isotopic signature, which could be useful to group animals for precision feeding., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

1. The aim of this study was to evaluate the production of equol (4',7-isoflavandiol; a bacterial polyphenol metabolite which is an isoflavandiol oestrogen metabolised from daidzein from plants) enriched eggs from free-range hens fed different pasture species. Four species were tested: red clover, white clover, ryegrass and chicory.2. The study was conducted from June to September 2017 on eight free range, outdoor areas, each containing fifteen laying hens and sown with a single pasture species3. Precursors of equol (daidzein, formononetin) were analysed every fortnight from the fresh pasture cover in each area, as well as equol and daidzein levels in eggs.4. Daidzein and formononetin concentrations in the fresh pasture samples differed significantly according to species (P < 0.001), whereby red clover had the highest concentrations of daidzein and formononetin (85 and 996 µg/g DM, respectively).5. Equol concentration in eggs differed according to pasture species (P < 0.001). Equol concentrations reached about 1,200 ng/g DM in eggs from hens with access to red clover. These eggs can represent a valuable source of equol in the human diet.

Background: A robust proxy for estimating methane (CH 4 ) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH 4 emissions from milk Fourier transform mid-infrared (FT-MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH 4 were developed using a combined dataset including daily CH 4 measurements (n = 1089; g d -1 ) collected using the SF 6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables., Results: Models developed based on a combined RC and SF 6 dataset predicted the expected pattern in CH 4 values (in g d -1 ) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R 2 = 0.68 and standard error = 57 g CH 4 d -1 )., Conclusions: The models developed accounted for more of the observed variability in CH 4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry., (© 2020 Society of Chemical Industry.)

We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSE CV ) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models' performance fairly. We found RMSE CIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models' performance with those achieved by the National Research Council's equation.

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

Apple pomace (AP) is known to be rich in biomolecules beneficial for health and it may advantageously be used to overcome the critical step of piglets' weaning. The study aimed to determine the effect of two levels of incorporation of AP on the performance, intestinal morphology, and microbiota of weaned piglets and investigate this feed ingredient as a weaning strategy. An experiment was performed with 42 piglets from weaning (28 days old) over a five-week period, including three iso-energetic and iso-nitrogenous diets (0%, 2%, and 4% dried AP diets) with seven pen-repetitions per diet (two pigs per pen). AP diets were beneficial for the average daily gain calculated on week 3 ( p = 0.038) and some parameters of the intestinal architecture on the 35 post-weaning day. The 4% AP diet was beneficial for the feed conversion ratio ( p = 0.002) and the energetic feed efficiency ( p = 0.004) on the 35 post-weaning day. AP tended to influence the consistency of feces (softer to liquid, p = 0.096) and increased the counts of excreted pathogens ( p = 0.072). Four percent AP influenced the richness of the microbiota and the bacteria profile as observed for the phylum Bacteroidetes or the class Clostridia. The 4% AP diet appeared as an interesting weaning strategy that should be evaluated in a large cohort.

Dairy farming systems are evolving. This study presents dairy producers' perceptions of their ideal future farm (IFF) to ensure revenue, and attempts to determine the reasons for this choice, the environmental aspects related to this choice, the proximity between the current farm and the IFF and the requirements for reaching this IFF. Just before the end of the European milk quota, a total of 245 Walloon dairy producers answered a survey about the characteristics of their IFF and other socio-environmental-economic information. A multiple correspondence analysis (MCA) was carried out using seven characteristics of the IFF (intensive vs. extensive, specialised vs. diversified, strongly vs. weakly based on new technologies, managed by a group of managers vs. an independent farmer, employed vs. familial workforce, local vs. global market, standard vs. quality-differentiated production) to observe the relationships between them. Based on the main contributors to the second dimension of the MCA, this axis was defined as an IFF gradient between the local-based extensive (LBE) producers (26%) and the global-based intensive (GBI) producers (46%). The differences of IFF gradient between modalities of categorical variables were estimated using generalised linear models. Pearson correlations were calculated between the scores on the IFF gradient and quantitative variables. Finally, frequencies of IFF characteristics and the corresponding characteristic for the current situation were calculated to determine the percentages of "unhappy" producers. Some reasons for the choice of IFF by the producers have been highlighted in this study. Environmental initiatives were more valued by LBE than GBI producers. Low similarity was observed between the current farm situation of the respondents and their IFF choice. LBE and GBI producers differed significantly regarding domains of formation (technical and bureaucratic vs. transformation and diversification respectively) and paths of formation (non-market vs. market respectively). Two kinds of farming systems were considered by dairy producers and some socioeconomic and environmental components differed between them., Competing Interests: The authors have declared that no competing interests exist.

Tannins are secondary plant compounds which have been extensively studied in order to improve the nitrogen use efficiency (NUE) of ruminants. A meta-analysis was performed of 58 in vivo experiments comparing milk yield, composition and nitrogen metabolism of lactating dairy cows fed diets with or without tannins. The meta-analysis shows that tannins have no impact on corrected milk yield, fat and protein content or NUE (p > .05). However, tannins reduce ruminal ammoniacal nitrogen (N) production by 16% (from 10.95 to 8.47 mg/dl on average), milk urea by 9% (from 15.82 to 14.03 mg/dl) and urinary N excretion (-11%; p < .05). This is compensated for by a lower apparent N digestibility (61.51% with dietary tannins compared to 66.17% without). The effect of tannin on N metabolism parameters increases with tannin dose (p < .05). The shift from urinary to faecal N may be beneficial for environment preservation, as urinary N induces more harmful emissions than faecal N. From a farmer's perspective, tannins seem unable to increase fat- and protein-corrected milk yield or reduce feed protein requirements and thus have no direct economic benefit. Potentially less costly than tannin extracts, forage or by-products naturally rich in tannins could still be useful to reduce the environmental impact of ruminant protein feeding., (© 2020 Blackwell Verlag GmbH.)

In vitro digestion and fermentation models are frequently used for human and animal research purposes. Different dynamic and multi-compartment models exist, but none have been validated with representative microbiota in the distal parts of the small intestine. We recently developed a dynamic and multi-compartment piglet model introducing microbiota in an ileum bioreactor. However, it presented discrepancies compared to in vivo data. Recommendations are available to standardize studies in this field. They target the digestion model but include elements of a fermentation model. But no recommendation is given concerning control of the atmosphere. The gastrointestinal tract is generally associated with anaerobiosis to conduct a good fermentation process. In this study, we attempted to improve the ileal microbiota of the piglet model by testing inoculation: real intestinal content vs feces; the latter being generally used for ethical and economical aspects. Results showed a positive effect of using real intestinal content. Fusobacteriia were less abundant in the model, Bacteroidia were better maintained in the colon. But for the ileum, results showed that anoxic conditions in the ileum bioreactor conditioned the microbial profile probably more than the type of inoculum itself, leading to the general conclusion that in vitro dynamic and multi-compartment models probably have to get oxygenated to improve microbiome studies of the small intestine., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Phenotypes related to feed efficiency were predicted from records easily acquired by breeding organizations. A total of 461,036 and 354,148 records were collected from the first and second parity Holstein cows. Equations were applied to the milk mid-infrared spectra to predict the main milk components and coupled with animal characteristics to predict the body weight (pBW). Dry matter intake (pDMI) was predicted from pBW using the National Research Council (NRC) equation. The consumption index (pIC) was estimated from pDMI and fat, and protein corrected milk. All traits were modeled using single trait test-day models. Descriptive statistics were within the expected range. Milk yield, pDMI, and pBW were phenotypically positively related (r ranged from 0.08 to 0.64). As expected, pIC was phenotypically negatively correlated with milk yield (-0.77 and -0.80 for the first and second lactation) and slightly positively correlated with pBW (0.16 and 0.07 for the first and second lactation). Later, parity cows seemed to have a better feed efficiency as they had a lower pIC. Although the prediction accuracy was moderate, the observed behaviors of studied traits by year, stage of lactation, and parity were in agreement with the literature. Moreover, as a genetic component was highlighted (heritability around 0.18), it would be interesting to realize a genetic evaluation of these traits and compare the obtained breeding values with the ones estimated for sires having daughters with reference feed efficiency records.

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The objective of this study was to evaluate the effects of oak tannin extract (OTE) added in forage before ensiling on dairy cows fed at 92% of their digestible protein requirements. Six multiparous lactating Holstein cows were used in a crossover design (two treatments × two periods). The control treatment (CON) was based on a diet including 50% of grass silage, whereas the experimental treatment (TAN) included grass silage sprayed with OTE (26 g/kg DM) just before baling. Milk yield (on average 24 kg fat protein corrected milk per day) was not affected, but both milk and rumen fatty acids profiles were impacted by OTE. Nitrogen intake (415 g N per cow per day) and nitrogen use efficiency (NUE; 0.25 on average) were not affected, but a shift from urine (-8% of N intake relatively to control, P = 0.06) to faecal N (+5%; P = 0.004) was observed with the TAN diet (P ≤ 0.05). Nitrogen apparent digestibility was thus reduced for TAN (-3%; P ≤ 0.05). The effect of OTE on ruminal and milk FA profiles suggests an impact on rumen microbiota. Nitrogen isotopic discrimination between animal proteins and diet (Δ15N) was evaluated as a proxy for NUE. While no differences in NUE were observed across diets, a lower Δ15N of plasma proteins was found when comparing TAN v. CON diets. This finding supports the concept that Δ15N would mainly sign the N partitioning at the metabolic level rather than the overall NUE, with the latter also being impacted by digestive processes. Our results agree with a N shift from urine to faeces, and this strategy can thus be adopted to decrease the environmental impact of ruminant protein feeding.

The study aimed to adapt the SHIME® model, developed to simulate human digestion and fermentation, to a baby-SPIME (baby Simulator of Pig Intestinal Microbial Ecosystem). What constitutes a unique feature of this model is its twofold objective of introducing an ileal microbial community and mimicking a dietary weaning transition. This model should then be ideally suited to test the dietary weaning strategies of piglets in vitro. Regarding the microbiota, the main phyla making up the model were Firmicutes, Bacteroidetes and Proteobacteria although Bacteroidetes decreased after inoculation (p = 0.043 in ileum, p = 0.021 in colon) and Delta-Proteobacteria were favoured (p = 0.083 in ileum, p = 0.043 in colon) compared to Gamma-Proteobacteria. The designed model led to a low representation of Bacilli - especially Lactobacillus sp. in the ileum and a weak representation of Bacteroidia in the proximal colon. However, Mitsuokella and Prevotella were part of the major genera of the model along with Bifidobacterium, Fusobacterium, Megasphaera and Bacteroides. As a result of weaning, two major changes - normally occurring in vivo - were detected in the system: firstly, Firmicutes diminished when Bacteroidetes increased particularly in the proximal colon; secondly, Bacteroides decreased and Prevotella increased (mean value for four runs). In terms of metabolite production, while a ratio acetate: propionate: butyrate of 60:26:14 was obtained in post-weaned colon, the expected inversion of the ratio propionate: butyrate in the post-weaned ileum was unfortunately not observed. To conclude, the so-called baby-SPIME model meets expectations regarding the resident microbiota of the proximal colon bioreactor and the metabolites produced thereof. In terms of the evolution of major groups of bacteria, the in vitro weaning process appeared to be successful. However, higher concentration of butyric acid would have been expected in ileum part of newly weaned piglets, as observed in vivo. The microbiota in the ileum bioreactor seemed in fact to act like a pre-colon. This suggests that microbial profile in ileum bioreactor had to be improved., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R 2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R 2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

The objective of this study was to test the effects of inclusion of hop pellets (HP) and oak tannin extracts (OT) alone or in combination on N efficiency, methane (CH 4 ) emission, and milk production and composition in 2 experiments with dairy cows fed low-N rations supplemented with linseed. In both experiments, 6 lactating Holstein cows were assigned to 3 dietary treatments in a 3 × 3 duplicated Latin square design (21-d periods). Cows were fed a total mixed ration at a restricted level to meet their nutrient requirements. In experiment 1, 169 g dry matter (DM) of OT or 56 g DM of HP was included separately in the control diet (C1). In experiment 2, the additives were included together (OT-HP) in the control diet (C2) similar to C1. Diet C2 was compared with a control without linseed (C0). In experiment 1, the supplementation of the control diet with OT decreased urinary N excretion by 12%. In experiment 2, the combination of OT and HP decreased urinary N by 7%. Oak tannin extracts and HP alone or in combination did not influence the daily enteric CH 4 production of cows. Cows fed diet C0 produced 17% more enteric CH 4 daily than those fed diet C2. Intake of diet C2, which contained 6.7% extruded linseed on a DM basis (experiment 2), decreased the sum of 6:0 to 14:0 fatty acids (-16%) and palmitic acid (-26%) and increased the stearic acid (+50%), oleic acid (+36%), vaccenic acid (trans-11 18:1; +285%), rumenic acid (cis-9,trans-11 18:2; +235%), and α-linolenic acid (+100%) in milk fat. The supplementation of diet C2 with the OT-HP mixture further improved the milk's fatty acid composition. Intake of the OT alone increased α-linolenic acid by 17.7% (experiment 1). The results of this study show that at the economically acceptable dose we tested, hops had no effect on urinary N excretion, CH 4 emission, milk production, and milk composition. By contrast, supplementation of diets with oak tannin extract can be considered for reducing urinary N excretion. The combination of oak tannin and hops had no more effect than oak tannin alone except on the milk fatty acid profile, which was favorably influenced from a nutritional point of view., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Evaluation and mitigation of enteric methane (CH 4 ) emissions from ruminant livestock, in particular from dairy cows, have acquired global importance for sustainable, climate-smart cattle production. Based on CH 4 reference measurements obtained with the SF 6 tracer technique to determine ruminal CH 4 production, a current equation permits evaluation of individual daily CH 4 emissions of dairy cows based on milk Fourier transform mid-infrared (FT-MIR) spectra. However, the respiration chamber (RC) technique is considered to be more accurate than SF 6 to measure CH 4 production from cattle. This study aimed to develop an equation that allows estimating CH 4 emissions of lactating cows recorded in an RC from corresponding milk FT-MIR spectra and to challenge its robustness and relevance through validation processes and its application on a milk spectral database. This would permit confirming the conclusions drawn with the existing equation based on SF 6 reference measurements regarding the potential to estimate daily CH 4 emissions of dairy cows from milk FT-MIR spectra. A total of 584 RC reference CH 4 measurements (mean ± standard deviation of 400 ± 72 g of CH 4 /d) and corresponding standardized milk mid-infrared spectra were obtained from 148 individual lactating cows between 7 and 321 d in milk in 5 European countries (Germany, Switzerland, Denmark, France, and Northern Ireland). The developed equation based on RC measurements showed calibration and cross-validation coefficients of determination of 0.65 and 0.57, respectively, which is lower than those obtained earlier by the equation based on 532 SF 6 measurements (0.74 and 0.70, respectively). This means that the RC-based model is unable to explain the variability observed in the corresponding reference data as well as the SF 6 -based model. The standard errors of calibration and cross-validation were lower for the RC model (43 and 47 g/d vs. 66 and 70 g/d for the SF 6 version, respectively), indicating that the model based on RC data was closer to actual values. The root mean squared error (RMSE) of calibration of 42 g/d represents only 10% of the overall daily CH 4 production, which is 23 g/d lower than the RMSE for the SF 6 -based equation. During the external validation step an RMSE of 62 g/d was observed. When the RC equation was applied to a standardized spectral database of milk recordings collected in the Walloon region of Belgium between January 2012 and December 2017 (1,515,137 spectra from 132,658 lactating cows in 1,176 different herds), an average ± standard deviation of 446 ± 51 g of CH 4 /d was estimated, which is consistent with the range of the values measured using both RC and SF 6 techniques. This study confirmed that milk FT-MIR spectra could be used as a potential proxy to estimate daily CH 4 emissions from dairy cows provided that the variability to predict is covered by the model., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

Increased economic, societal and environmental challenges facing agriculture are leading to a greater focus on effective way to combine grazing and automatic milking systems (AMS). One of the fundamental aspects of robotic milking is cows' traffic to the AMS. Numerous studies have identified feed provided, either as fresh grass or concentrate supplement, as the main incentive for cows to return to the robot. The aim of this study was to determine the effect of concentrate allocation on voluntary cow traffic from pasture to the robot during the grazing period, to highlight the interactions between grazed pasture and concentrate allocation in terms of substitution rate and the subsequent effect on average milk yield and composition. Thus, 29 grazing cows, milked by a mobile robot, were monitored for the grazing period (4 months). They were assigned to two groups: a low concentrate (LC) group (15 cows) and a high concentrate (HC) group (14 cows) receiving 2 and 4 kg concentrate/cow per day, respectively; two allocations per day of fresh pasture were provided at 0700 and 1600 h. The cows had to go through the AMS to receive the fresh pasture allocation. The effect of concentrate level on robot visitation was calculated by summing milkings, refusals and failed milkings/cow per day. The impact on average daily milk yield and composition was also determined. The interaction between lactation number and month was used as an indicator of pasture availability. Concentrate allocation increased significantly robot visitations in HC (3.60±0.07 visitations/cow per day in HC and 3.10±0.07 visitations/cow per day in LC; P<0.001) while milkings/cow per day were similar in both groups (LC: 2.37±0.02/day and HC: 2.39±0.02/day; Ns). The average daily milk yield over the grazing period was enhanced in HC (22.39±0.22 kg/cow per day in HC and 21.33±0.22 kg/cow per day in LC; P<0.001). However the gain in milk due to higher concentrate supply was limited with regards to the amount of provided concentrates. Milking frequency in HC primiparous compared with LC was increased. In the context of this study, considering high concentrate levels as an incentive for robot visitation might be questioned, as it had no impact on milking frequency and limited impact on average milk yield and composition. By contrast, increased concentrate supply could be targeted specifically to primiparous cows.

Many countries have pledged to reduce greenhouse gases. In this context, the dairy sector is one of the identified sectors to adapt production circumstances to address socio-environmental constraints due to its large carbon footprint related to CH 4 emission. This study aimed mainly to estimate (1) the genetic parameters of 2 milk mid-infrared-based CH 4 proxies [predicted daily CH 4 emission (PME, g/d), and log-transformed predicted CH 4 intensity (LMI)] and (2) their genetic correlations with milk production traits [milk (MY), fat (FY), and protein (PY) yields] from first- and second-parity Holstein cows. A total of 336,126 and 231,400 mid-infrared CH 4 phenotypes were collected from 56,957 and 34,992 first- and second-parity cows, respectively. The PME increased from the first to the second lactation (433 vs. 453 g/d) and the LMI decreased (2.93 vs. 2.86). We used 20 bivariate random regression test-day models to estimate the variance components. Moderate heritability values were observed for both CH 4 traits, and those values decreased slightly from the first to the second lactation (0.25 ± 0.01 and 0.22 ± 0.01 for PME; 0.18 ± 0.01 and 0.17 ± 0.02 for LMI). Lactation phenotypic and genetic correlations were negative between PME and MY in both first and second lactations (-0.07 vs. -0.07 and -0.19 vs. -0.24, respectively). More close scrutiny revealed that relative increase of PME was lower with high MY levels even reverting to decrease, and therefore explaining the negative correlations, indicating that higher producing cows could be a mitigation option for CH 4 emission. The PME phenotypic correlations were almost equal to 0 with FY and PY for both lactations. However, the genetic correlations between PME and FY were slightly positive (0.11 and 0.12), whereas with PY the correlations were slightly negative (-0.05 and -0.04). Both phenotypic and genetic correlations between LMI and MY or PY or FY were always relatively highly negative (from -0.21 to -0.88). As the genetic correlations between PME and LMI were strong (0.71 and 0.72 in first and second lactation), the selection of one trait would also strongly influence the other trait. However, in animal breeding context, PME, as a direct quantity CH 4 proxy, would be preferred to LMI, which is a ratio trait of PME with a trait already in the index. The range of PME sire estimated breeding values were 22.1 and 29.41 kg per lactation in first and second parity, respectively. Further studies must be conducted to evaluate the effect of the introduction of PME in a selection index on the other traits already included in this index, such as, for instance, fertility or longevity., (The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

The aim of this study was to estimate phenotypic and genetic correlations between methane production (Mp) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation. Calibration equations predicting daily Mp (g/d) and milk fatty acid contents (g/100 dL of milk) were applied on milk mid-infrared spectra related to Walloon milk recording. A total of 241,236 predictions of Mp and milk fatty acids were used. These data were collected between 5 and 305 d in milk in 33,555 first-parity Holstein cows from 626 herds. Pedigree data included 109,975 animals. Bivariate (i.e., Mp and a fatty acid trait) random regression test-day models were developed to estimate phenotypic and genetic parameters of Mp and milk fatty acids. Individual short-chain fatty acids (SCFA) and groups of saturated fatty acids, SCFA, and medium-chain fatty acids showed positive phenotypic and genetic correlations with Mp (from 0.10 to 0.16 and from 0.23 to 0.30 for phenotypic and genetic correlations, respectively), whereas individual long-chain fatty acids (LCFA), and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed null to positive phenotypic and genetic correlations with Mp (from -0.03 to 0.13 and from -0.02 to 0.32 for phenotypic and genetic correlations, respectively). However, these correlations changed throughout lactation. First, de novo individual and group fatty acids (i.e., C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, SCFA group) showed low phenotypic or genetic correlations (or both) in early lactation and higher at the end of lactation. In contrast, phenotypic and genetic correlations between Mp and C16:0, which could be de novo synthetized or derived from blood lipids, were more stable during lactation. This fatty acid is the most abundant fatty acid of the saturated fatty acid and medium-chain fatty acid groups of which correlations with Mp showed the same pattern across lactation. Phenotypic and genetic correlations between Mp and C17:0 and C18:0 were low in early lactation and increased afterward. Phenotypic and genetic correlations between Mp and C18:1 cis-9 originating from the blood lipids were negative in early lactation and increased afterward to become null from 18 wk until the end of lactation. Correlations between Mp and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed a similar or intermediate pattern across lactation compared with fatty acids that compose them. Finally, these results indicate that correlations between Mp and milk fatty acids vary following lactation stage of the cow, a fact still often ignored when trying to predict Mp from milk fatty acid profile., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The main goal of this study was to develop, apply, and validate a new method to predict an indicator for CH4 eructed by dairy cows using milk mid-infrared (MIR) spectra. A novel feature of this model was the consideration of lactation stage to reflect changes in the metabolic status of the cow. A total of 446 daily CH4 measurements were obtained using the SF6 method on 142 Jersey, Holstein, and Holstein-Jersey cows. The corresponding milk samples were collected during these CH4 measurements and were analyzed using MIR spectroscopy. A first derivative was applied to the milk MIR spectra. To validate the novel calibration equation incorporating days in milk (DIM), 2 calibration processes were developed: the first was based only on CH4 measurements and milk MIR spectra (independent of lactation stage; ILS); the second included milk MIR spectra and DIM information (dependent on lactation stage; DLS) by using linear and quadratic modified Legendre polynomials. The coefficients of determination of ILS and DLS equations were 0.77 and 0.75, respectively, with standard error of calibration of 63g/d of CH4 for both calibration equations. These equations were applied to 1,674,763 milk MIR spectra from Holstein cows in the first 3 parities and between 5 and 365 DIM. The average CH4 indicators were 428, 444, and 448g/d by ILS and 444, 467, and 471g/d by DLS for cows in first, second, and third lactation, respectively. Behavior of the DLS indicator throughout the lactations was in agreement with the literature with values increasing between 0 and 100 DIM and decreasing thereafter. Conversely, the ILS indicator of CH4 emission decreased at the beginning of the lactation and increased until the end of the lactation, which differs from the literature. Therefore, the DLS indicator seems to better reflect biological processes that drive CH4 emissions than the ILS indicator. The ILS and DLS equations were applied to an independent data set, which included 59 respiration chamber measurements of CH4 obtained from animals of a different breed across a different production system. Results indicated that the DLS equation was much more robust than the ILS equation allowing development of indicators of CH4 emissions by dairy cows. Integration of DIM information into the prediction equation was found to be a good strategy to obtain biologically meaningful CH4 values from lactating cows by accounting for biological changes that occur throughout the lactation., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Body weight (BW) of dairy cows can be estimated using linear conformation traits (calculated BW; CBW), which are generally recorded only once during a lactation. However, predicted BW (PBW) throughout the lactation would be useful, e.g., at milk-recording dates allowing feed-intake prediction for advisory purposes. Therefore, a 2-step approach was developed to obtain PBW for each milk-recording date. In the first step, a random-regression test-day model was used with CBW as observations to predict PBW. The second step consisted in changing means and (co)variances of prior distributions for the additive genetic random effects of the test-day model by using priors derived from results of the first step to predict again PBW. A total of 25,061 CBW from 24,919 primiparous Holstein cows were computed using equations from literature. Using CBW as observations, PBW was then predicted over the whole lactation for 232,436 dates corresponding to 207,375milk-recording dates and 25,061 classification dates. Results showed that using both steps (the 2-step approach) provided more accurate predictions than using only the first step (the one-step approach). Based on the results of this preliminary study, BW of dairy cows could be predicted throughout the lactation using this procedure. These predictions could be useful in milk-recording systems to compute traits of interest (e.g., feed-intake prediction). The developed novel method is also flexible because actual direct measurements of BW can also be used together with CBW, the prediction model being able to accommodate different levels of accuracies of used BW phenotypes., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The effects of first calving (FC) in Holstein heifers on their first lactation, second lactation and lifetime milk production were examined from an initial database of 459 743 animals that first calved between 1 January 1990 and 31 July 2010 in Wallonia, Belgium. The FC age class (18 to 22, 22 to 26, 26 to 30, 30 to 34, 34 to 38 and 38 to 42 months), the FC season and FC year class (1990 to 1994, 1995 to 1999, 2000 to 2004 and 2005 to 2010) were considered when analysing the first and second lactation data. Lifetime data were similarly analysed, but did not include animals that calved after 2005 because many of them were still lactating. Only 24% of animals had their FC before 26 months of age. Animals that first calved between 22 and 26 months of age had more lactations and productive days during their life. They also had higher first and second lactation milk production and lifetime milk production. Summer or autumn FC improved first lactation, second lactation and lifetime milk production, as well as production per day of lactation, compared with winter or spring FC. Compared with animals that calved for the first time in 1990 to 1994, animals with a FC in 2000 to 2004 had a longer calving interval (0.5 months), fewer lactations per animal (-0.6) and fewer days in their lifetime lactation (a reduction of 144 days). As a result, the animals' lifetime production did not increase between 1990 to 1994 and 2000 to 2004, although milk production per day of lactation (22.85 v. 20.49 l/day) and per day of life (11.49 v. 10.78 l/day) improved. Milk fat content was lower in 2000 to 2004 than in 1990 to 1994, but protein content remained relatively constant, probably because of the cows' higher production level and increased dietary concentrate supplementation.

Effective assessment of sulphur (S) status in cattle is important for optimal health, yet remains difficult. Rumen fluid S concentrations are preferred, but difficult to sample under practical conditions. This study aimed to evaluate salivary S concentration as estimator of S status in cattle. Saliva and rumen fluid samples were collected from dairy cows (n = 16) as well as samples of different feedstuffs offered to the animals. The N and S concentrations were determined using the Dumas technique. The average dietary N and S content were calculated as well as N:S ratio of saliva, rumen fluid and diet. Salivary S concentrations could not predict rumen fluid or dietary S concentrations (p > 0.05). The log transformed salivary N:S ratio (x) could predict the rumen fluid N:S ratio (y) with a linear equation of y = 9.83 (±4.59) x + 0.39 (±4.56) (r = 0.497, p = 0.05), but left too much residual variation to serve as reliable predictor. Further research should investigate this relationship in the search for an S status estimator.

This study investigates the feasibility to predict individual methane (CH(4)) 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 CH(4) 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 CH(4) emissions were measured daily using the sulfur hexafluoride method during a 7-day period. CH(4) daily emissions ranged from 10.2 to 47.1 g CH(4)/kg of milk. The spectral data were transformed to represent an average daily milk spectrum (AMS), which was related to the recorded daily CH(4) 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 CH(4) measurement. The equations were built using Partial Least Squares regression. From the calculated R(2)(cv), it appears that the accuracy of CH(4) 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 CH(4) emissions gave the best results. The R(2) and s.e. of the cross-validation were equal to 0.79 and 5.14 g of CH(4)/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 CH(4) emission at day 1.5. The lower R(2) (R(2) = 0.76) obtained between FA profile and CH(4) emission compared with the one corresponding to the obtained calibration (R(2)(c) = 0.87) shows the interest to apply directly the developed CH(4) equation instead of the use of correlations between FA and CH(4). In conclusion, our preliminary results suggest the feasibility of direct CH(4) 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 CH(4) emissions at farm level or at the regional scale and it also could be used to identify low-CH(4)-emitting cows.

Linseed and rapeseed, good sources of 18:3 n-3 and cis9-18:1, respectively, have been shown to improve the bovine milk fatty acid (FA) profile. However, rapeseed, unlike linseed, has little effect on the concentration of 18:3 n-3 in milk fat. Alfalfa protein concentrate (APC), besides being a valuable protein source for milk production, contains lipids rich in 18:3 n-3. Therefore, this experiment aimed at (1) evaluating the transfer efficiency of unsaturated FA (UFA), especially 18:3 n-3, of APC to bovine milk fat, and (2) evaluating whether extruded rapeseed (ER) associated with APC is as effective as extruded linseed (EL) in enhancing the bovine milk fat composition. Six lactating Holstein cows were used in a replicated 2 × 2 Latin square design with 2 iso-energy, iso-nitrogen and iso-FA corn silage-based diets (EL and ER-APC) and two 21-d periods. Extruded linseed, as main UFA source, was included in the first diet, whereas ER, as main UFA source, and APC, as supplemental 18:3 n-3, were included in the second diet. Diets were distributed as a restricted total mixed ration. Compared with the EL diet, the ER-APC diet, where ER was associated with APC, increased milk concentration of 18:3 n-3 (1.18 vs. 1.31% of FA) and cis9-18:1 (18.35 vs. 20.01% of FA). The apparent transfer efficiency of 18:3 n-3 from diet to milk was almost twice as much for the ER-APC diet than for the EL diet (7.4 vs. 3.8% of intake). Extruded linseed accounted for 84% of 18:3 n-3 provided in the EL diet, whereas ER and APC accounted for 33 and 38% of 18:3 n-3 provided in the ER-APC diet, respectively. Because both EL and ER underwent extrusion in similar conditions, these results suggest that 18:3 n-3 of EL in the EL diet and ER in the ER-APC diet were subjected to more extensive ruminal biohydrogenation than 18:3 n-3 of APC in the ER-APC diet. This experiment shows that corn silage-based diets supplemented with ER as the main UFA source, associated with APC as supplemental 18:3 n-3, are as effective as corn silage-based diets supplemented with EL as the main UFA source, in increasing bovine milk UFA and 18:3 n-3 contents. Furthermore, at similar levels of dietary incorporation, this experiment shows that the ruminal bypass of 18:3 n-3 is higher for APC compared with EL., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this experiment was to compare the nutritional properties of potato protein concentrate, a by-product of the starch industry produced entirely in Europe, with that of soybean meal (SBM), for growing cattle. The experiment was conducted on double-muscled Belgian Blue bulls, fitted with rumen, duodenal and ileal cannulas, according to a 4 × 4 Latin square design. They were fed three different iso-N and iso-net energy diets formulated according to the Dutch feed evaluation system, differing in the nature of the main protein source, which was either SBM ('SBM' treatment), potato protein concentrate (PPC, 'PPC' treatment) or an iso-N mixture of these two protein sources ('mixed' treatment). A fourth treatment consisted of 'PPC' supplemented by 9.5% digestible proteins supplied by duodenal perfusion of sodium caseinate (CAS, 'PPC + CAS' treatment). No significant difference was observed in the ruminal fluid pH, whereas both 'PPC' and 'PPC + CAS' had the effect of reducing the ruminal ammonia nitrogen (N-NH3) concentration. No significant difference was observed in the apparent intestinal digestibility of the dry matter (DM), organic matter (OM) or N. Outflows of non-NH3-N, microbial proteins and dietary proteins from the rumen were similar for 'PPC', 'SBM' and 'mixed', and increased with CAS infusion by 20%, 17% and 27%, respectively. On the basis of in vivo observations, the degradability of SBM and PPC proteins was estimated at 0.60 and 0.43, respectively, corresponding to the values quoted in the literature. The supply of digestible essential amino acids (EAA) was significantly greater with 'PPC + CAS' and did not differ among 'SBM', 'mixed' and 'PPC'. This illustrates the difficulty of altering the amino acid (AA) pattern of digestible protein by the nature of the protein of dietary origin when an animal is fed a high nutritional value diet. N retention was not affected by replacing SBM with PPC, but increased by 10% with CAS infusion. On the basis of the plasma AA pattern, the supply of digestible Met was probably limiting with 'SBM', 'mixed' and 'PPC'. The CAS perfusion supplemented all AA, including Met, leading to increased N retention. This improvement was limited, however, and N utilisation remained unchanged between treatments. In conclusion, despite a more favourable EAA pattern, PPC offered no advantage compared with SBM for growing bulls when diets were formulated according to the Dutch feed evaluation system.

This experiment studied the effect of a modest difference in diet structure value (SV) on milk conjugated linoleic acid (CLA) contents of cows fed diets supplemented with extruded linseed, in situations where the diets provided enough SV and therefore did not induce milk fat depression. Six lactating Holstein cows were used in a crossover design with two treatments ('SV 1.50' and 'SV 1.73') and two periods of 21 days. The 'SV 1.50' diet contained 59% maize silage, 13% soya bean meal, 13% sugar beet pulp and 14% Nutex Compact (containing 56% extruded linseed) (dry matter (DM) basis) and was offered as a restricted total mixed ration. For the 'SV 1.73' diet, 8% wheat straw (DM basis) was added to the 'SV 1.50' diet as an additional structure source. The two diets had a forage-to-concentrate ratio of 59 : 41 and 62 : 38. The inclusion of straw in the diet resulted in an additional intake of NDF (+1110 g/day), which accounted for 90% of the additional intake of OM, whereas additional intakes of the other nutrients were minor. Milk yield and composition did not differ among treatments. The inclusion of straw in the diet did not affect the milk levels of t10-18:1, 18:2n-6, c9-16:1, c9-18:1, c11-18:1, 6:0, 8:0, 20:4 and 20:5. It decreased the milk levels of c9,t11-CLA (2.13% v. 3.03% of fatty acids (FA) reported, P < 0.001), t11-18:1 (4.99% v. 7.10% of FA reported, P < 0.001), 18:3n-3, t9-16:1 and t9-18:1, while it increased the milk levels of 6:0-14:0 (20.90% v. 19.69% of FA reported, P < 0.01), 16:0 (26.55% v. 25.25% of FA reported, P < 0.01), 18:0 (13.54% v. 12.59% of FA reported, P < 0.001), 17:0, 20:0 and 22:5. Regarding the ratio between FA, the inclusion of straw increased the 18:0/total C18 FA ratio (37.74% v. 32.07%, P < 0.001), whereas it decreased the total trans-C18 FA/total C18 FA ratio (15.46% v. 20.34%, P < 0.001), the t11-18:1/total C18 FA ratio (13.70% v. 17.95%, P < 0.01) and the c9,t11-CLA/total C18 FA ratio (5.82% v. 7.64%, P < 0.001). We conclude from this experiment that even a modest increase in SV to a diet supplemented with extruded linseed, yet already providing enough SV, alters the rumen lipid metabolism and, hence, CLA levels in milk fat.

Six lactating Holstein cows were assigned to a replicated Latin square design to test the effect of dietary vitamin E on milk fat depression and on the increased production of milk trans-10 C18:1 classically observed when feeding high doses of unsaturated fatty acids with low-fiber diets. Two diets (linseed diet and linseed diet + 12,000 IU of vitamin E/d) were compared during 2 periods of 21 d. The linseed diet presented a forage-to-concentrate ratio of 50:50 and contained extruded linseed (1.86 kg/d) and linseed oil (190 g/d). It was conceived to favor the "trans-11 to trans-10 shift" (low structural value and high level of unsaturated fatty acids). Milk yield and protein content were not affected by the diets. Milk of cows fed the linseed diet presented the typical symptoms of milk fat depression associated with a shift in biohydrogenation pathways: low fat content and high level of trans-10 C18:1. However, the high dose of dietary vitamin E provided significantly increased milk fat content (by 17.93%) and yield (by 15.56%) and decreased trans-10 C18:1 content (by 47.06%). In addition, it managed to significantly increase the daily yields of vaccenic (by 102.56%) and rumenic acids (by 56.67%). However, the sequence of administration of vitamin E influenced its effect, as vitamin E seemed to be more active in limiting the "trans-11 to trans-10 shift" when it was incorporated in the diet simultaneously with the fat. Once the shift had occurred, the subsequent addition of vitamin E was no longer able to completely counteract this process.

In many dietary conditions, methionine (Met) and lysine (Lys) are the most limiting amino acids (AA) for ruminants. The AA protected from ruminal fermentation are not commercially available, with the exception of Met which is not always economical, especially for meat production. This study measured ruminal escape of free Met and Lys supplemented intraruminally to fast growing bulls. Six double-muscled Belgian Blue bulls, fed a high concentrate diet and fitted with a rumen cannula, received free Met (40 g x d(-1)) and free Lys (60 g x d(-1)), individually or simultaneously, in a duplicated Latin square design. The mean ruminal escape of Met and Lys reached 37 and 45% respectively, and did not differ if administered separately or together. Plasma Lys and Met concentrations were increased by 504 and 126%, respectively. Substantial proportions of free AA escaped ruminal fermentation and were available for absorption from the small intestine when they were administered at physiologically high levels.

The three-step technique was used to determine the requirements of total amino acids (TAA) and the first-limiting amino acid (AA) in growing double-muscled Belgian Blue bulls (BBb). In Exp. 1, three double-muscled BBb weighing initially 306 +/- 28 kg received a basal diet consisting of 30% meadow hay and 70% concentrate that was poor in digestible protein but had adequate NE because of continuous infusion of dextrose into the duodenum. The intestinal apparent digestibility of essential AA (EAA) was defined according to their duodenal and ileal flows. It averaged 72% but varied between 60% for Met and 79% for Arg. In Exp. 2, five double-muscled BBb (334 +/- 22 kg) received the same diet supplemented with duodenal infusions of dextrose and four doses of Na-caseinate (28, 56, 84, and 112% of intestinal digestible dietary AA) in a 4 x 4 Latin square design with one additional animal. Nitrogen retention for the basal diet alone and the four increasing supplements of Na-caseinate reached 49, 61, 70, 80, and 86 g/d, respectively. Nitrogen utilization improved from 34.3% without Na-caseinate supplementation to a maximum of 40.6%, with the third dose supplying 788 g/d of apparently digestible AA. Based on patterns of plasma concentrations, Met, Phe, and Arg were probably the limiting AA when animals optimized N utilization. In Exp. 3, six double-muscled BBb (315 +/- 25 kg) fed the basal diet received duodenal infusions of dextrose and AA, equivalent to the third dose in Exp. 2, except for digestible Met (9.3, 14.4, 18.4, 22.4, 26.4, and 30.4 g/d) in a 6 x 6 Latin square design. The Met requirement was close to 26.4 g/d on the basis of N retention.