Prediction of Daily and Lactation Yields of Milk, Fat, and Protein Using an Autoregressive Repeatability Test Day Model

Association of Mycobacterium avium paratuberculosis serostatus with age at first calving, calving interval, and milk production in dairy cows

Mycobacterium avium ssp. paratuberculosis (MAP) is the causative agent of bovine paratuberculosis, also known as Johne's disease. This infection is responsible for negative effects, ranging from reduction of milk production to reproductive compromise and increased susceptibility to other diseases such as mastitis. Contradictory information on the association between this infection and reproductive performance has been reported in dairy cows. The aim of this work was to investigate associations between individual cow MAP seropositivity and lifetime reproduction and production performance. The MAP serum ELISA (IDEXX MAP Ac) results from all the 13,071 adult cows present on 191 farms and corresponding birth- and calving-date records obtained from the National Association for Genetic Improvement of Dairy Cattle were used. Cows and farms were classified as positive or negative, based on ELISA results. Outcomes assessed were age at first calving (AFC), intercalving intervals (ICI) from first to fourth interval, and average milk production per day of productive cycle (Milk-305/ICI, a ratio between 305-d corrected milk production and the number of days of the respective calving interval). Multilevel mixed models were used to investigate the association of cow MAP status with AFC, ICI, and Milk-305/ICI. Three levels were considered in the models: "measurement occasion," the first level, was nested within cows and cows were nested within farms. The "measurement occasion" is the time point to which all the observed measures (between 2 successive parturitions, such as milk production and somatic cell count) were referred. Our results indicate that MAP-positive cows have a significantly lower 14-d mean AFC than MAP-negative cows. The overall average ICI in our study was 432.5 d (standard deviation: 94.6). The average ICI, from first to fourth, was not significantly affected by MAP seropositivity. No significant effect of MAP positivity was found on the overall ICI. In relation to Milk-305/ICI, MAP-positive cows did not produce significantly less milk than negative cows across their productive lifetime. We observed higher but nonsignificant Milk-305/ICI (kg/d) in MAP-positive cows. In our study, the proportion of MAP-positive cows within lactations remained similar across all lactations, suggesting that seropositivity did not increased drop-off rate.

Comparison and estimation of different linear and nonlinear lactation curve submodels in random regression analyses on dairy cattle

In the random regression model (RRM) for milk yield, by replacing empirical lactation curves with the five-order Legendre polynomial to fit fixed groups, the RRM can be transformed to a hierarchical model that consisted of a RRM in the first hierarchy with Legendre polynomials as individuals’ lactation curves resolved by restricted maximum likelihood (REML) software, and a multivariate animal model for phenotypic regression coefficients in the second hierarchy resolved by DMU software. Some empirical lactation functions can be embedded into the RRM at the first hierarchy to well fit phenotypic lactation curve of the average observations across all animals. The functional relationship between each parameter and time can be described by a Legendre polynomial or an empirical curve usually called submodel, and according to three commonly used criteria, the optimal submodels were picked from linear and nonlinear submodels except for polynomials. The so-called hierarchical estimation for the RRMs in dairy cattle indicated that more biologically meaningful models were available to fit the lactation curves; moreover, with the same number of parameters, the empirical lactation curves (MIL1, MIL5, and MK1 for 3, 4, and 5 parameters, respectively) performed higher goodness of fit than Legendre polynomial when modelling individuals’ phenotypic lactation curves.

Modelos para avaliação genética da produção de leite no dia do controle nas três primeiras lactações

RESUMO Objetivou-se verificar se a utilização do modelo autorregressivo (MAR) é adequada para obtenção de parâmetros genéticos para produção de leite no dia do controle (PLDC) de bovinos leiteiros da raça Gir. Foram analisados 125.191 registros de produções diárias, nas três primeiras lactações, por meio dos modelos de repetibilidade (MREP) e MAR. No MREP, foi considerado o efeito de ambiente de curto prazo; no MAR, foi considerado, também, o efeito de ambiente de longo prazo. Os modelos foram comparados por meio do logaritmo da função de máxima verossimilhança ( − 2 log L ). A herdabilidade estimada pelo MREP foi 0,18; no caso do MAR, as estimativas para primeira, segunda e terceira lactações foram 0,32, 0,28 e 0,26, respectivamente. A estimativa de autocorrelação dos componentes de variância de longo prazo foi próxima de zero, e as de curto prazo foram de alta magnitude para primeira (0,79), segunda (0,79) e terceira (0,81) lactações. Logo, a influência do ambiente de curto prazo dentro de cada lactação não é a mesma. O valor de − 2 log L mais próximo de zero foi obtido para o MAR (-294.884,7778) em relação ao MREP (-329.266,4810). Assim, o MAR é adequado para obtenção de estimativas de parâmetros genéticos para PLDC nas três primeiras lactações de bovinos leiteiros.

Association of paratuberculosis sero-status with milk production and somatic cell counts across 5 lactations, using multilevel mixed models, in dairy cows

The aim of this work was to investigate associations between individual cow Mycobacterium avium ssp. paratuberculosis (MAP) seropositivity, 305-d corrected milk production, and somatic cell count during 5 lactations lifespan in Portuguese dairy herds using multilevel mixed models. We used MAP serum ELISA (Idexx MAP Ac, Idexx Laboratories Inc., Westbrook, ME) results (n = 23,960) from all the 20,221 adult cows present in 329 farms and corresponding 47,586 lactation records from the National Dairy Improvement Association. Cows and farms were classified as positive or negative. Multilevel mixed models were used to investigate the association of cow MAP status with variation in milk production and somatic cell count. Cow MAP status, farm status, and lactation number were considered as independent variables. A quadratic function of lactation number was used to mimic the effect of lactation order on milk production. The models considered 3 levels: measurement occasion (level 1) within cow (level 2) and cow within farm (level 3). Four final models were produced, including all herds and cows, to address the effect of farm status (models 1 and 2) or the effect of cow status (models 3 and 4) on the outcome variables. Our results show that MAP status affects milk production. Losses are detectable from third lactation onward. During the first 5 lactations, positive cows accumulated an average loss of 1,284.8 kg of milk when compared with the negative cows. We also observed that somatic cell counts were higher in positive cows and a positive interaction occurs between cow status and lactation number, suggesting a positive association between MAP infection and increased somatic cell counts. Our results are in line with previous studies, suggesting a possible positive relation between cow milk production and susceptibility to MAP infection.

Comparison of modelling techniques for milk-production forecasting

The objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤ 12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%)=8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%)=12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%)=10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions.

Sustainable use and genetic improvement

Sustainable use of animal genetic resources for agriculture and food production is proposed as the best strategy for maintaining their diversity. Achievement of sustainable use would continue to support livelihoods and minimize the long-term risk for survival of animal populations. The concept of sustainable use has economic, environmental and socio-cultural dimensions. Sustainable use of animal genetic resources also contributes to food security, rural development, increasing employment opportunities and improving standards of living of keepers of breeds. Supporting the rearing of breeds through better infrastructure, services, animal health care, marketing opportunities and other interventions would make a significant contribution to the sustainable use of animal genetic resources.

Sustainable use envisages the use and improvement of breeds that possess high levels of adaptive fitness to the prevailing environment. It also encompasses the deployment of sound genetic principles for sustainable development of the breeds and the sustainable intensification of the production systems themselves. Sustainable use and genetic improvement rely on access to a wide pool of genetic resources.

Genetic improvement programmes need to be considered in terms of national agriculture and livestock development objectives, suitability to local conditions and livelihood security as well as environmental sustainability. Genetic improvement can involve choice of appropriate breeds, choice of a suitable pure breeding or crossbreeding system and application of within-breed genetic improvement. The choice of appropriate breeds and crossbreeding systems in developed countries has been a major contributor to the large increases in productivity, and has benefited greatly from the fact that developed country animal genetic resources are well characterized and relatively freely exchanged. Where proper steps have been followed by careful assessment of demand, execution, delivery, impact and cost-benefit analyses, successful within-breed improvement has been realized within indigenous populations in developing countries. Breeding objectives and programmes for subsistence oriented and pastoralist systems are likely to be entirely different from conventional programmes. Crossbreeding has been most successful where it is followed by a rigorous selection programme involving livestock owners' participation and substantial public sector investment in the form of technical support. In any genetic improvement programme, inbreeding needs to be monitored and controlled.

Within-breed genetic improvement is normal practice in the developed world, and has become a highly technical enterprise, involving a range of reproduction, recording, computing and genomic technologies. Emerging genomic technologies promise the ability to identify better, use and improve developing world animal genetic resources in the foreseeable future. Useful systems can, however, be established without the need for application of advanced technology or processes.

Natural antibodies in bovine milk and blood plasma: variability among cows, repeatability within cows, and relation between milk and plasma titers

Innate immunity plays an important role in preventing (barrier function) or combating infection (effector function). An important humoral component of innate immunity is formed by natural antibodies (NAb). The objectives of this study were to determine presence, variation among cows and repeatability within cows over time of total NAb titers directed to the pathogen-associated molecular patterns lipopolysaccharide, lipoteichoic acid (LTA) and peptidoglycan, and titers of NAb directed to the glycoprotein keyhole limpet hemocyanin in milk and plasma of individual cows. Furthermore in milk the antibody isotypes IgG1, IgG2, IgM and IgA binding LTA were analyzed. Ten milk and blood samples were obtained from each of 20 clinically healthy dairy cows from first to seventh parity during a period of 3 weeks. Total NAb binding lipopolysaccharide, LTA, peptidoglycan, and keyhole limpet hemocyanin were detected in milk and plasma, with titers considerably higher in plasma than in milk. Total NAb titers showed significant variation among cows, and repeatability within cows over time (ranging from 0.60 to 0.93). Titers of NAb in milk and plasma were positively correlated (correlation ranging from 0.69 to 0.91). Natural antibodies in milk binding LTA were of all 4 isotypes tested, although IgG2 was on average only present at low titers. All 4 isotypes in milk binding LTA also showed variation among cows, and repeatability within cows over time (ranging from 0.84 to 0.92). We conclude that NAb can be measured in a consistent and repeatable manner in bovine milk and blood plasma.

Effects of repeated gram-positive and gram-negative clinical mastitis episodes on milk yield loss in Holstein dairy cows

The objective of this study was to estimate the effects of recurrent episodes of gram-positive and gram-negative cases of clinical mastitis (CM) on milk production in Holstein dairy cows. We were interested in the severity of repeated cases in general, but also in the severity of the host response as judged by milk production loss when a previous case was caused by a similar or different microorganism. The results were based on data from 7,721 primiparous lactations and 13,566 multiparous lactations in 7 large dairy herds in New York State. The distribution of organisms in the CM cases showed 28.5% gram-positive cases, 31.8% gram-negative cases, 15.0% others, and 24.8% with no organism identified. Mixed models, with a random herd effect and an autoregressive covariance structure to account for repeated measurements, were used to quantify the effect of repeated CM and several other control variables (parity, week of lactation, other diseases) on milk yield. Our data indicated that repeated CM cases showed a very similar milk loss compared with the first case. No reduction of severity was present with increasing count of the CM case. Gram-negative cases had more severe milk loss compared with gram-positive and other cases irrespective of the count of the case in lactation. Milk loss in multipara (primipara) due to gram-negative CM was approximately 304 kg (228 kg) in the 50 d following CM. This loss was approximately 128 kg (133 kg) for gram-positive cases and 92 kg (112 kg) for other cases. The severity of a second case of gram-negative CM was not reduced by previous cases of gram-negative CM in multipara and only slightly less severe in a similar scenario in primipara cows. Similarly, a previous gram-positive case did not reduce severity of a second or third gram-positive case. Hence, our data do not support that immunological memory of previous exposure to an organism in the same generic class provides protection for a next case of CM with an organism in the same class.

Evaluation of Autoregressive Covariance Structures for Test-Day Records of Holstein Cows: Estimates of Parameters

The objective of this study was to compare test-day (TD) models with autoregressive covariance structures for the estimation of genetic and environmental components of variance for milk, fat and protein yields, and somatic cell score (SCS) in Holstein cows. Four models were compared: model I (CS model) was a simple TD repeatability animal model with compound symmetry covariance structure for environmental effects, model II (ARpe model) and model III (ARe model) had first-order autoregressive covariance structures for TD permanent or residual environmental effects, respectively, and model IV (305-d model) was a simple animal model using 305-d records. Data were 106,472 first-lactation TD records of 12,071 Holstein cows calving from 1996 through 2001. Likelihood ratio tests indicated that ARpe and ARe models fit the data significantly better than the CS model. The ARe model resulted in slightly smaller estimates of genetic variance and heritability than did the CS model. Estimates of residual variance were always smaller with the CS model than with the ARe model with the autoregressive covariance structure among TD residual effects. Estimates of heritability with different TD models were in the range of 0.06 to 0.11. The 305-d model resulted in estimates of heritability in the range of 0.11 to 0.36. The autoregressive covariance structure among TD residual effects may help to prevent bias in heritability estimates for milk, fat and protein yields, and SCS.