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Genomic selection improves the possibility of applying multiple breeding programs in different environments
Slagboom, M. ; Kargo, M. ; Sørensen, A.C. ; Thomasen, J.R. ; Mulder, H.A. - \ 2019
Journal of Dairy Science 102 (2019)9. - ISSN 0022-0302 - p. 8197 - 8209.
breeding strategy - dairy cow - genetic gain - genotype by environment interaction
One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (rg). The break-even correlation (rb) refers to the specific rg where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if rg is higher than rb, whereas 2 environment-specific BP have higher genetic gain if rg is lower than rb. Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find rb when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on rb and genetic gain; and (4) to find rb from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The rb was 0.65 and not different from rb with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on rb was not large. A different size in 2 environments decreased rb by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high rg. When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when rg between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP.
The economic impact of drying off cows with a dry-off facilitator (cabergoline) compared with 2 methods of gradual cessation of lactation for European dairy farms
Steeneveld, W. ; Prado-Taranilla, A. De; Krogh, K. ; Hogeveen, H. - \ 2019
Journal of Dairy Science 102 (2019)8. - ISSN 0022-0302 - p. 7483 - 7493.
cabergoline - dairy cow - drying off - economics - intramammary infection
An abrupt method to dry off cows has disadvantages and is considered inappropriate for current dairy cows due to welfare issues and risks for intramammary infections (IMI). A gradual cessation of lactation (by feeding or milking frequency reduction) has been the generally recommended method for drying off cows to prevent these adverse effects. However, a new alternative to the gradual approach is to abruptly stop milking at the same time as using cabergoline (CAB), a prolactin inhibitor. The aim of the study was to compare the net costs of 3 different methods of drying off cows [gradual reduction in feed (gradual-feeding), gradual reduction in milking frequency (gradual-milking), and abrupt cessation of milking with CAB (abrupt-CAB)]. A stochastic Monte Carlo simulation model, at cow level, was developed to calculate the net costs of applying these methods. All inputs for the model were based on literature information, authors' expertise, and expert knowledge. The net costs were determined by only including costs and benefits, which varied between the 3 methods. The model simulated a cow from 7 d before the day of drying off until the end of the next lactation. The likelihood of whether a cow was leaking milk early in the dry period was determined. Subsequently, it was determined whether or not the cow will get an IMI during the dry period, where the probability of getting an IMI was higher for cows leaking milk than for cows not leaking milk. If the IMI was not cured during the dry period, the cow had an IMI at calving. Also, milk production and feed requirements were modeled, and labor for applying the drying off method was included. For all methods, the net costs were calculated as the sum of costs for feed during the gradual feed reduction period, costs for applying the gradual-milking method, and the IMI costs during the dry period and lactation, minus the milk revenues during the transition from lactation to the dry period. Under default conditions, the average net cost of abrupt-CAB was €49.6/cow. The data showed that 90% of the net costs ranged from −€13.7 to €307.8/cow. The average net costs for gradual-feed and gradual-milking were €99.1 and €71.5/cow, respectively. In conclusion, abrupt-CAB saved €49.5 and €21.9/cow on average compared with gradual-feeding and gradual-milking, respectively. This difference was mainly due to more milk returns and lower labor and IMI costs during lactation.
Intramammary antimicrobial treatment of subclinical mastitis and cow performance later in lactation
Borne, Bart H.P. van den; Schaik, Gerdien van; Lam, Theo J.G.M. ; Nielen, Mirjam ; Frankena, Klaas - \ 2019
Journal of Dairy Science 102 (2019)5. - ISSN 0022-0302 - p. 4441 - 4451.
antimicrobial - clinical mastitis - dairy cow - milk yield - somatic cell count
The aim of this study was to evaluate long-term therapeutic effects of antimicrobial treatment of recently acquired subclinical mastitis (RASCM) during lactation. Quarter-level clinical mastitis (CM) follow-up, composite somatic cell counts (SCC), and cow-level milk yield later in lactation were evaluated using follow-up data from 2 previously published linked randomized field trials. The first trial randomly assigned antimicrobial treatment with any intramammary product or negative control to culture-positive quarters of cows having a first elevated composite SCC after 2 consecutive low composite SCC measurements. Untreated cows that had a second elevated composite SCC at the next measurement and were staphylococci-positive (i.e., Staphylococcus aureus or non-aureus staphylococci) were randomly assigned to treatment or control. Quarter-level CM cases were reported by the participating herd personnel, and milk yield and composite SCC data were obtained from the regular test-day recording. Frailty survival models were used to evaluate the long-term therapeutic effects of antimicrobial treatment of RASCM on quarter-level CM follow-up. Mixed linear regression models were applied to quantify the effect on milk yield and composite SCC. Data of 638 quarters from 486 cows in 38 herds were available for statistical analyses, of which 229 quarters of 175 cows received antimicrobial treatment for RASCM. Antimicrobial treatment culminated in reduced composite SCC levels later in lactation but did not result in different milk yield levels or CM follow-up compared with control cows. Antimicrobial treatment of cows with RASCM should therefore only be considered in exceptional situations given the current focus on antimicrobial usage reduction in animal husbandry.
Genetic analysis of udder conformation traits derived from automatic milking system recording in dairy cows
Poppe, M. ; Mulder, H.A. ; Ducro, B.J. ; Jong, G. de - \ 2019
Journal of Dairy Science 102 (2019)2. - ISSN 0022-0302 - p. 1386 - 1396.
automatic milking system - conformation - dairy cow - udder
Cartesian teat coordinates measured by automatic milking systems (AMS) provide new opportunities to record udder conformation traits and to study changes in udder conformation genetically and phenotypically within and between parities. The objective of this study was to estimate heritabilities and repeatabilities of AMS-based udder conformation traits within parities, to estimate genetic correlations between parities for AMS-based udder conformation traits, and to estimate genetic correlations between AMS-based udder conformation traits and classifier-based udder conformation traits, longevity, and udder health. Data from 70 herds, including 12,663 first-parity cows, 10,206 second-parity cows, and 7,627 third-parity cows, were analyzed using univariate and bivariate mixed animal models. Heritabilities of the AMS udder conformation traits were large (0.37–0.67) and genetic correlations between the AMS udder conformation traits and classifier-based traits were strong (>0.91). Repeatabilities within parities were large as well (0.89–0.97), indicating that a single record on udder conformation per lactation reflects udder conformation well. Genetic correlations of AMS udder conformation traits between parities were strong (0.88–1.00) and were stronger than the permanent environmental correlations. This shows that udder conformation changes over parities, but this change is mostly due to nongenetic factors. Based on these results, the current herd classification system, where cows are scored on udder conformation once in first parity, is sufficient. The AMS udder conformation traits as defined in this study have limited value as replacement for classifier-based udder conformation traits because they have smaller genetic correlations with functional traits than classifier-based traits. In summary, udder conformation hardly changes genetically between parities and is highly repeatable within parities. Udder conformation traits based on AMS need fine-tuning before they can replace classifier-based traits, and AMS teat coordinates probably contain additional information about udder health that is yet to be explored.
Production, partial cash flows and greenhouse gas emissions of simulated dairy herds with extended lactations
Kok, A. ; Lehmann, J.O. ; Kemp, B. ; Hogeveen, H. ; Middelaar, C.E. van; Boer, I.J.M. de; Knegsel, A.T.M. van - \ 2019
Animal 13 (2019)5. - ISSN 1751-7311 - p. 1074 - 1083.
dairy cow - lactation length - lactation persistency - milk yield - simulation model
The transition period is the most critical period in the lactation cycle of dairy cows. Extended lactations reduce the frequency of transition periods, the number of calves and the related labour for farmers. This study aimed to assess the impact of 2 and 4 months extended lactations on milk yield and net partial cash flow (NPCF) at herd level, and on greenhouse gas (GHG) emissions per unit of fat- and protein-corrected milk (FPCM), using a stochastic simulation model. The model simulated individual lactations for 100 herds of 100 cows with a baseline lactation length (BL), and for 100 herds with lactations extended by 2 or 4 months for all cows (All+2 and All+4), or for heifers only (H+2 and H+4). Baseline lactation length herds produced 887 t (SD: 13) milk/year. The NPCF, based on revenues for milk, surplus calves and culled cows, and costs for feed, artificial insemination, calving management and rearing of youngstock, was k€174 (SD: 4)/BL herd per year. Extended lactations reduced milk yield of the herd by 4.1% for All+2, 6.9% for All+4, 1.1% for H+2 and 2.2% for H+4, and reduced the NPCF per herd per year by k€7 for All+2, k€12 for All+4, k€2 for H+2 and k€4 for H+4 compared with BL herds. Extended lactations increased GHG emissions in CO2-equivalents per t FPCM by 1.0% for All+2, by 1.7% for All+4, by 0.2% for H+2 and by 0.4% for H+4, but this could be compensated by an increase in lifespan of dairy cows. Subsequently, production level and lactation persistency were increased to assess the importance of these aspects for the impact of extended lactations. The increase in production level and lactation persistency increased milk production of BL herds by 30%. Moreover, reductions in milk yield for All+2 and All+4 compared with BL herds were only 0.7% and 1.1% per year, and milk yield in H+2 and H+4 herds was similar to BL herds. The resulting NPCF was equal to BL for All+2 and All+4 and increased by k€1 for H+2 and H+4 due to lower costs for insemination and calving management. Moreover, GHG emissions per t FPCM were equal to BL herds or reduced (0% to -0.3%) when lactations were extended. We concluded that, depending on lactation persistency, extending lactations of dairy cows can have a positive or negative impact on the NPCF and GHG emissions of milk production.
Is rumination time an indicator of methane production in dairy cows?
Zetouni, L. ; Difford, G.F. ; Lassen, J. ; Byskov, M.V. ; Norberg, E. ; Løvendahl, P. - \ 2018
Journal of Dairy Science 101 (2018)12. - ISSN 0022-0302 - p. 11074 - 11085.
dairy cow - dry matter intake - methane - rumination time
As long as large-scale recording of expensive-to-measure and labor-consuming traits, such as dry matter intake (DMI) and CH4 production (CH4P), continues to be challenging in practical conditions, alternative traits that are already routinely recorded in dairy herds should be investigated. An ideal indicator trait must, in addition to expressing genetic variation, have a strong correlation with the trait of interest. Our aim was to estimate individual level and phenotypic correlations between rumination time (RT), CH4P, and DMI to determine if RT could be used as an indicator trait for CH4P and DMI. Data from 343 Danish Holstein cows were collected at the Danish Cattle Research Centre for a period of approximately 3 yr. The data set consisted of 14,890 records for DMI, 15,835 for RT, and 6,693 for CH4P. Data were divided in primiparous cows only (PC) and all cows (MC), and then divided in lactation stage (early, mid, late, and whole lactation) to analyze the changes over lactation. Linear mixed models, including an animal effect but no pedigree, were used to estimate the correlations among traits. Phenotypic and individual level correlations between RT and both CH4P and DMI were close to zero, regardless of lactation stage and data set (PC or MC). However, CH4P and DMI were highly correlated, both across lactation stages and data sets. In conclusion, RT is unsuitable to be used as an indicator trait for either CH4P or DMI. Our study failed to validate RT as a useful indicator trait for both CH4P and DMI, but more studies with novel phenotypes can offer different approaches to select and incorporate important yet difficult to record traits into breeding goals and selection indexes.
Indicators of resilience during the transition period in dairy cows : A case study
Dixhoorn, I.D.E. van; Mol, R.M. de; Werf, J.T.N. van der; Mourik, S. van; Reenen, C.G. van - \ 2018
Journal of Dairy Science 101 (2018)11. - ISSN 0022-0302 - p. 10271 - 10282.
behavior - dairy cow - dynamic indicator - resilience - transition period
The transition period is a demanding phase in the life of dairy cows. Metabolic and infectious disorders frequently occur in the first weeks after calving. To identify cows that are less able to cope with the transition period, physiologic or behavioral signals acquired with sensors might be useful. However, it is not yet clear which signals or combination of signals and which signal properties are most informative with respect to disease severity after calving. Sensor data on activity and behavior measurements as well as rumen and ear temperature data from 22 dairy cows were collected during a period starting 2 wk before expected parturition until 6 wk after parturition. During this period, the health status of each cow was clinically scored daily. A total deficit score (TDS) was calculated based on the clinical assessment, summarizing disease length and intensity for each cow. Different sensor data properties recorded during the period before calving as well as the period after calving were tested as a predictor for TDS using univariate analysis of covariance. To select the model with the best combination of signals and signal properties, we quantified the prediction accuracy for TDS in a multivariate model. Prediction accuracy for TDS increased when sensors were combined, using static and dynamic signal properties. Statistically, the most optimal linear combination of predictors consisted of average eating time, variance of daily ear temperature, and regularity of daily behavior patterns in the dry period. Our research indicates that a combination of static and dynamic sensor data properties could be used as indicators of cow resilience.
Value of the Dutch Holstein Friesian germplasm collection to increase genetic variability and improve genetic merit
Doekes, H.P. ; Veerkamp, R.F. ; Bijma, P. ; Hiemstra, S.J. ; Windig, J. - \ 2018
Journal of Dairy Science 101 (2018)11. - ISSN 0022-0302 - p. 10022 - 10033.
conservation - dairy cow - gene bank collection - genetic diversity - genetic improvement
National gene bank collections for Holstein Friesian (HF) dairy cattle were set up in the 1990s. In this study, we assessed the value of bulls from the Dutch HF germplasm collection, also known as cryobank bulls, to increase genetic variability and improve genetic merit in the current bull population (bulls born in 2010–2015). Genetic variability was defined as 1 minus the mean genomic similarity (SIMSNP) or as 1 minus the mean pedigree-based kinship (fPED). Genetic merit was defined as the mean estimated breeding value for the total merit index or for 1 of 3 subindices (yield, fertility, and udder health). Using optimal contribution selection, we minimized relatedness (maximized variability) or maximized genetic merit at restricted levels of relatedness. We compared breeding schemes with only bulls from 2010 to 2015 with schemes in which cryobank bulls were also included. When we minimized relatedness, inclusion of genotyped cryobank bulls decreased mean SIMSNP by 0.7% and inclusion of both genotyped and nongenotyped cryobank bulls decreased mean fPED by 2.6% (in absolute terms). When we maximized merit at restricted levels of relatedness, inclusion of cryobank bulls provided additional merit at any level of mean SIMSNP or mean fPED except for the total merit index at high levels of mean SIMSNP. Additional merit from cryobank bulls depended on (1) the relative emphasis on genetic variability and (2) the selection criterion. Additional merit was higher when more emphasis was put on genetic variability. For fertility, for example, it was 1.74 SD at a mean SIMSNP restriction of 64.5% and 0.37 SD at a mean SIMSNP restriction of 67.5%. Additional merit was low to nonexistent for the total merit index and higher for the subindices, especially for fertility. At a mean SIMSNP of 64.5%, for example, it was 0.60 SD for the total merit index and 1.74 SD for fertility. In conclusion, Dutch HF cryobank bulls can be used to increase genetic variability and improve genetic merit in the current population, although their value is very limited when selecting for the current total merit index. Anticipating changes in the breeding goal in the future, the germplasm collection is a valuable resource for commercial breeding populations.
Dynamic forecasting of individual cow milk yield in automatic milking systems
Jensen, Dan B. ; Voort, Mariska van der; Hogeveen, Henk - \ 2018
Journal of Dairy Science 101 (2018)11. - ISSN 0022-0302 - p. 10428 - 10439.
dairy cow - dynamic linear model - milk yield - somatic cell count
Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.
Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography–based milk fatty acid profiles
Gastelen, S. van; Mollenhorst, H. ; Antunes-Fernandes, E.C. ; Hettinga, K.A. ; Burgsteden, G.G. van; Dijkstra, J. ; Rademaker, J.L.W. - \ 2018
Journal of Dairy Science 101 (2018)6. - ISSN 0022-0302 - p. 5582 - 5598.
dairy cow - enteric methane production - milk fatty acid concentration - milk Fourier-transform infrared spectroscopy
The objective of the present study was to compare the prediction potential of milk Fourier-transform infrared spectroscopy (FTIR) for CH4 emissions of dairy cows with that of gas chromatography (GC)–based milk fatty acids (MFA). Data from 9 experiments with lactating Holstein-Friesian cows, with a total of 30 dietary treatments and 218 observations, were used. Methane emissions were measured for 3 consecutive days in climate respiration chambers and expressed as production (g/d), yield (g/kg of dry matter intake; DMI), and intensity (g/kg of fat- and protein-corrected milk; FPCM). Dry matter intake was 16.3 ± 2.18 kg/d (mean ± standard deviation), FPCM yield was 25.9 ± 5.06 kg/d, CH4 production was 366 ± 53.9 g/d, CH4 yield was 22.5 ± 2.10 g/kg of DMI, and CH4 intensity was 14.4 ± 2.58 g/kg of FPCM. Milk was sampled during the same days and analyzed by GC and by FTIR. Multivariate GC-determined MFA–based and FTIR-based CH4 prediction models were developed, and subsequently, the final CH4 prediction models were evaluated with root mean squared error of prediction and concordance correlation coefficient analysis. Further, we performed a random 10-fold cross validation to calculate the performance parameters of the models (e.g., the coefficient of determination of cross validation). The final GC-determined MFA–based CH4 prediction models estimate CH4 production, yield, and intensity with a root mean squared error of prediction of 35.7 g/d, 1.6 g/kg of DMI, and 1.6 g/kg of FPCM and with a concordance correlation coefficient of 0.72, 0.59, and 0.77, respectively. The final FTIR-based CH4 prediction models estimate CH4 production, yield, and intensity with a root mean squared error of prediction of 43.2 g/d, 1.9 g/kg of DMI, and 1.7 g/kg of FPCM and with a concordance correlation coefficient of 0.52, 0.40, and 0.72, respectively. The GC-determined MFA–based prediction models described a greater part of the observed variation in CH4 emission than did the FTIR-based models. The cross validation results indicate that all CH4 prediction models (both GC-determined MFA–based and FTIR-based models) are robust; the difference between the coefficient of determination and the coefficient of determination of cross validation ranged from 0.01 to 0.07. The results indicate that GC-determined MFA have a greater potential than FTIR spectra to estimate CH4 production, yield, and intensity. Both techniques hold potential but may not yet be ready to predict CH4 emission of dairy cows in practice. Additional CH4 measurements are needed to improve the accuracy and robustness of GC-determined MFA and FTIR spectra for CH4 prediction.
Effects of reduced intramammary antimicrobial use during the dry period on udder health in Dutch dairy herds
Vanhoudt, A. ; Hees-Huijps, K. van; Knegsel, A.T.M. van; Sampimon, O.C. ; Vernooij, J.C.M. ; Nielen, M. ; Werven, T. van - \ 2018
Journal of Dairy Science 101 (2018)4. - ISSN 0022-0302 - p. 3248 - 3260.
antimicrobial - dairy cow - dry period - selective dry cow therapy - udder health
Dry cow therapy (DCT) in the Netherlands changedfrom mainly blanket to selective antimicrobial DCT.This transition was supported by a national guideline,with the individual somatic cell count (SCC) at thelast milk recording before dry-off as the main selectioncriterion for antimicrobial DCT. The aim of this retrospectiveobservational study is to evaluate the SCCdynamics during the dry period at the herd and individualdry period level following the national transitionfrom mainly blanket to selective antimicrobial DCT.At the herd level, we used 2 data sets to evaluate theSCC dynamics during the dry period: (1) a nationaldata set containing 3,493 herds with data availablefrom 2011 through 2015 and (2) a veterinary practicedata set containing 280 herds with data available from2013 through 2015. The herd level analysis was carriedout using key performance indicators provided via milkrecording (CRV, Arnhem, the Netherlands): the percentageof cows that developed a new intramammaryinfection (IMI) during the dry period and the percentageof cows cured of an IMI during the dry period.The effect of DCT at individual dry period level wasanalyzed with a mixed-effects logistic regression modelbased on 4,404 dry periods from 2,638 cows in 20 herdswithin the veterinary practice data set. For these 20herds, individual SCC data from milk recordings andindividual cow DCT were available from 2013 through2015. No significant changes were observed to the SCCdynamics during the dry period at the herd level. Thepercentage of cows that developed a new IMI duringthe dry period ranged between 16 and 18%, and thepercentage of cows cured from an IMI during the dryperiod ranged between 74 and 76%. At the individual dry period level, a low SCC at the first milk recordingfollowing a dry period was associated with the use ofintramammary antimicrobial DCT with or without theconcurrent use of an intramammary teat sealer [oddsratio (OR) = 2.16 and OR = 2.07, respectively], the useof DCT with an intramammary teat sealer only (OR =1.35), and a low SCC at the last milk recording beforedry-off (OR = 1.78). This study demonstrates that theselection of cows for DCT without antimicrobials basedon SCC thresholds at the last milk recording is possiblewithout significant changes to udder health andreduced the use of antimicrobials.
The relationship between milk metabolome and methane emission of Holstein Friesian dairy cows: Metabolic interpretation and prediction potential
Gastelen, S. van; Antunes Fernandes, E.C. ; Hettinga, K.A. ; Dijkstra, J. - \ 2018
Journal of Dairy Science 101 (2018)3. - ISSN 0022-0302 - p. 2110 - 2126.
dairy cow - enteric methane production - Milk metabolome
This study aimed to quantify the relationship between CH4 emission and fatty acids, volatile metabolites, and nonvolatile metabolites in milk of dairy cows fed forage-based diets. Data from 6 studies were used, including 27 dietary treatments and 123 individual observations from lactating Holstein-Friesian cows. These dietary treatments covered a large range of forage-based diets, with different qualities and proportions of grass silage and corn silage. Methane emission was measured in climate respiration chambers and expressed as production (g per day), yield (g per kg of dry matter intake; DMI), and intensity (g per kg of fat- and protein-corrected milk; FPCM). Milk samples were analyzed for fatty acids by gas chromatography, for volatile metabolites by gas chromatography-mass spectrometry, and for nonvolatile metabolites by nuclear magnetic resonance. Dry matter intake was 15.9 ± 1.90 kg/d (mean ± SD), FPCM yield was 25.2 ± 4.57 kg/d, CH4 production was 359 ± 51.1 g/d, CH4 yield was 22.6 ± 2.31 g/kg of DMI, and CH4 intensity was 14.5 ± 2.59 g/kg of FPCM. The results show that changes in individual milk metabolite concentrations can be related to the ruminal CH4 production pathways. Several of these relationships were diet driven, whereas some were partly dependent on FPCM yield. Next, prediction models were developed and subsequently evaluated based on root mean square error of prediction (RMSEP), concordance correlation coefficient (CCC) analysis, and random 10-fold cross-validation. The best models with milk fatty acids (in g/100 g of fatty acids; MFA) alone predicted CH4 production, yield, and intensity with a RMSEP of 34 g/d, 2.0 g/kg of DMI, and 1.7 g/kg of FPCM, and with a CCC of 0.67, 0.44, and 0.75, respectively. The CH4 prediction potential of both volatile metabolites alone and nonvolatile metabolites alone was low, regardless of the unit of CH4 emission, as evidenced by the low CCC values (<0.35). The best models combining the 3 types of metabolites as selection variables resulted in the inclusion of only MFA for CH4 production and CH4 yield. For CH4 intensity, MFA, volatile metabolites, and nonvolatile metabolites were included in the prediction model. This resulted in a small improvement in prediction potential (CCC of 0.80; RMSEP of 1.5 g/kg of FPCM) relative to MFA alone. These results indicate that volatile and nonvolatile metabolites in milk contain some information to increase our understanding of enteric CH4 production of dairy cows, but that it is not worthwhile to determine the volatile and nonvolatile metabolites in milk to estimate CH4 emission of dairy cows. We conclude that MFA have moderate potential to predict CH4 emission of dairy cattle fed forage-based diets, and that the models can aid in the effort to understand and mitigate CH4 emissions of dairy cows.
Estimating the economic impact of subclinical ketosis in dairy cattle using a dynamic stochastic simulation model
Mostert, P.F. ; Bokkers, E.A.M. ; Middelaar, C.E. van; Hogeveen, H. ; Boer, I.J.M. de - \ 2018
Animal 12 (2018)1. - ISSN 1751-7311 - p. 145 - 154.
cost - dairy cow - disease - modelling - parity
The objective of this study was to estimate the economic impact of subclinical ketosis (SCK) in dairy cows. This metabolic disorder occurs in the period around calving and is associated with an increased risk of other diseases. Therefore, SCK affects farm productivity and profitability. Estimating the economic impact of SCK may make farmers more aware of this problem, and can improve their decision-making regarding interventions to reduce SCK. We developed a dynamic stochastic simulation model that enables estimating the economic impact of SCK and related diseases (i.e. mastitis, metritis, displaced abomasum, lameness and clinical ketosis) occurring during the first 30 days after calving. This model, which was applied to a typical Dutch dairy herd, groups cows according to their parity (1 to 5+), and simulates the dynamics of SCK and related diseases, and milk production per cow during one lactation. The economic impact of SCK and related diseases resulted from a reduced milk production, discarded milk, treatment costs, costs from a prolonged calving interval and removal (culling or dying) of cows. The total costs of SCK were €130 per case per year, with a range between €39 and €348 (5 to 95 percentiles). The total costs of SCK per case per year, moreover, increased from €83 per year in parity 1 to €175 in parity 3. Most cows with SCK, however, had SCK only (61%), and costs were €58 per case per year. Total costs of SCK per case per year resulted for 36% from a prolonged calving interval, 24% from reduced milk production, 19% from treatment, 14% from discarded milk and 6% from removal. Results of the sensitivity analysis showed that the disease incidence, removal risk, relations of SCK with other diseases and prices of milk resulted in a high variation of costs of SCK. The costs of SCK, therefore, might differ per farm because of farm-specific circumstances. Improving data collection on the incidence of SCK and related diseases, and on consequences of diseases can further improve economic estimations.
Relationships between methane emission of Holstein Friesian dairy cows and fatty acids, volatile metabolites and non-volatile metabolites in milk
Gastelen, S. van; Antunes-Fernandes, E.C. ; Hettinga, K.A. ; Dijkstra, Jan - \ 2017
Animal 11 (2017)9. - ISSN 1751-7311 - p. 1539 - 1548.
dairy cow - methane emission - milk fatty acid - milk non-volatile metabolite - milk volatile metabolite
This study investigated the relationships between methane (CH4) emission and fatty acids, volatile metabolites (V) and non-volatile metabolites (NV) in milk of dairy cows. Data from an experiment with 32 multiparous dairy cows and four diets were used. All diets had a roughage : concentrate ratio of 80 : 20 based on dry matter (DM). Roughage consisted of either 1000 g/kg DM grass silage (GS), 1000 g/kg DM maize silage (MS), or a mixture of both silages (667 g/kg DM GS and 333 g/kg DM MS; 333 g/kg DM GS and 677 g/kg DM MS). Methane emission was measured in climate respiration chambers and expressed as production (g/day), yield (g/kg dry matter intake; DMI) and intensity (g/kg fat- and protein-corrected milk; FPCM). Milk was sampled during the same days and analysed for fatty acids by gas chromatography, for V by gas chromatography–mass spectrometry, and for NV by nuclear magnetic resonance. Several models were obtained using a stepwise selection of (1) milk fatty acids (MFA), V or NV alone, and (2) the combination of MFA, V and NV, based on the minimum Akaike’s information criterion statistic. Dry matter intake was 16.8±1.23 kg/day, FPCM yield was 25.0±3.14 kg/day, CH4 production was 406±37.0 g/day, CH4 yield was 24.1±1.87 g/kg DMI and CH4 intensity was 16.4±1.91 g/kg FPCM. The observed CH4 emissions were compared with the CH4 emissions predicted by the obtained models, based on concordance correlation coefficient (CCC) analysis. The best models with MFA alone predicted CH4 production, yield and intensity with a CCC of 0.80, 0.71 and 0.69, respectively. The best models combining the three types of metabolites included MFA and NV for CH4 production and CH4 yield, whereas for CH4 intensity MFA, NV and V were all included. These models predicted CH4 production, yield and intensity better with a higher CCC of 0.92, 0.78 and 0.93, respectively, and with increased accuracy (C b) and precision (r). The results indicate that MFA alone have moderate to good potential to estimate CH4 emission, and furthermore that including V (CH4 intensity only) and NV increases the CH4 emission prediction potential. This holds particularly for the prediction model for CH4 intensity.
Lameness detection in dairy cattle : single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing
Hertem, T. Van; Bahr, C. ; Schlageter Tello, A. ; Viazzi, S. ; Steensels, M. ; Romanini, C.E.B. ; Lokhorst, C. ; Maltz, E. ; Halachmi, I. ; Berckmans, D. - \ 2016
Animal 10 (2016)9. - ISSN 1751-7311 - p. 1525 - 1532.
dairy cow - individual history - lameness detection - multi-sensing - sensor technology
The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.
Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow
Brask, M. ; Weisbjerg, M.R. ; Hellwing, A.L.F. ; Bannink, A. ; Lund, P. - \ 2015
Animal 9 (2015)11. - ISSN 1751-7311 - p. 1795 - 1806.
carbon - dairy cow - enteric fermentation - modelling methane - VFA
Many feeding trials have been conducted to quantify enteric methane (CH4) production in ruminants. Although a relationship between diet composition, rumen fermentation and CH4 production is generally accepted, the efforts to quantify this relationship within the same experiment remain scarce. In the present study, a data set was compiled from the results of three intensive respiration chamber trials with lactating rumen and intestinal fistulated Holstein cows, including measurements of rumen and intestinal digestion, rumen fermentation parameters and CH4 production. Two approaches were used to calculate CH4 from observations: (1) a rumen organic matter (OM) balance was derived from OM intake and duodenal organic matter flow (DOM) distinguishing various nutrients and (2) a rumen carbon balance was derived from carbon intake and duodenal carbon flow (DCARB). Duodenal flow was corrected for endogenous matter, and contribution of fermentation in the large intestine was accounted for. Hydrogen (H2) arising from fermentation was calculated using the fermentation pattern measured in rumen fluid. CH4 was calculated from H2 production corrected for H2 use with biohydrogenation of fatty acids. The DOM model overestimated CH4/kg dry matter intake (DMI) by 6.1% (R 2=0.36) and the DCARB model underestimated CH4/kg DMI by 0.4% (R 2=0.43). A stepwise regression of the difference between measured and calculated daily CH4 production was conducted to examine explanations for the deviance. Dietary carbohydrate composition and rumen carbohydrate digestion were the main sources of inaccuracies for both models. Furthermore, differences were related to rumen ammonia concentration with the DOM model and to rumen pH and dietary fat with the DCARB model. Adding these parameters to the models and performing a multiple regression against observed daily CH4 production resulted in R 2 of 0.66 and 0.72 for DOM and DCARB models, respectively. The diurnal pattern of CH4 production followed that of rumen volatile fatty acid (VFA) concentration and the CH4 to CO2 production ratio, but was inverse to rumen pH and the rumen hydrogen balance calculated from 4×(acetate+butyrate)/2×(propionate+valerate). In conclusion, the amount of feed fermented was the most important factor determining variations in CH4 production between animals, diets and during the day. Interactions between feed components, VFA absorption rates and variation between animals seemed to be factors that were complicating the accurate prediction of CH4. Using a ruminal carbon balance appeared to predict CH4 production just as well as calculations based on rumen digestion of individual nutrients.