Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis.

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.

Nitrogen isotopic discrimination as a biomarker of between-cow variation in the efficiency of nitrogen utilization for milk production: A meta-analysis.

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.

Red clover-rich grassland increases equol concentration in eggs from free-range laying hens.

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.

Oxygen as a key parameter in in vitro dynamic and multi-compartment models to improve microbiome studies of the small intestine?

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.

Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation.

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.

Effects of hydrolysable tannin-treated grass silage on milk yield and composition, nitrogen partitioning and nitrogen isotopic discrimination in lactating dairy cows.

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.

Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach.

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 ß-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 effect of oak tannin (Quercus robur) and hops (Humulus lupulus) on dietary nitrogen efficiency, methane emission, and milk fatty acid composition of dairy cows fed a low-protein diet including linseed.

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 (CH4) 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 CH4 production of cows. Cows fed diet C0 produced 17% more enteric CH4 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, CH4 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.

Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers.

Evaluation and mitigation of enteric methane (CH4) emissions from ruminant livestock, in particular from dairy cows, have acquired global importance for sustainable, climate-smart cattle production. Based on CH4 reference measurements obtained with the SF6 tracer technique to determine ruminal CH4 production, a current equation permits evaluation of individual daily CH4 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 SF6 to measure CH4 production from cattle. This study aimed to develop an equation that allows estimating CH4 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 SF6 reference measurements regarding the potential to estimate daily CH4 emissions of dairy cows from milk FT-MIR spectra. A total of 584 RC reference CH4 measurements (mean ± standard deviation of 400 ± 72 g of CH4/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 SF6 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 SF6-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 SF6 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 CH4 production, which is 23 g/d lower than the RMSE for the SF6-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 CH4/d was estimated, which is consistent with the range of the values measured using both RC and SF6 techniques. This study confirmed that milk FT-MIR spectra could be used as a potential proxy to estimate daily CH4 emissions from dairy cows provided that the variability to predict is covered by the model.

Genetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle.

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 CH4 emission. This study aimed mainly to estimate (1) the genetic parameters of 2 milk mid-infrared-based CH4 proxies [predicted daily CH4 emission (PME, g/d), and log-transformed predicted CH4 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 CH4 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 CH4 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 CH4 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 CH4 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 effect of concentrate allocation on traffic and milk production of pasture-based cows milked by an automatic milking system.

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.

Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra.

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.

Capitalizing on fine milk composition for breeding and management of dairy cows.

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.

Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra.

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.

Short communication: Novel method to predict body weight of primiparous dairy cows throughout the lactation.

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.

Association between age at first calving, year and season of first calving and milk production in Holstein cows.

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.

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

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.

Effectiveness of extruded rapeseed associated with an alfalfa protein concentrate in enhancing the bovine milk fatty acid composition.

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.

Nutritional properties of potato protein concentrate compared with soybean meal as the main protein source in feed for the double-muscled Belgian Blue bull.

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.

Influence of an increase in diet structure on milk conjugated linoleic acid content of cows fed extruded linseed.

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.