Body and milk traits as indicators of dairy cow energy status in early lactation

The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (Delta BW), change in body condition score (Delta BCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 +/- 0.30, 0.43 +/- 0.22, and 0.13 +/- 0.06 mmol/L, respectively; all means +/- standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, Delta BW, Delta BCS, FPR x Delta BW, and days in milk. The model resulted in a cross-validation coefficient of determination (R(2)cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R(2)cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 x C14:0, and days in milk (R(2)cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R(2)cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.Peer reviewe

Metafounder approach for single-step genomic evaluations of Red Dairy cattle

Peer reviewe

Genetic parameters for endocrine and traditional fertility traits, hyperketonemia and milk yield in dairy cattle

High-yielding cows may suffer from negative energy balance during early lactation, which can lead to ketosis and delayed ability of returning to cyclicity after calving. Fast recovery after calving is essential when breeding for improved fertility. Traditionally used fertility traits, such as the interval from calving to first insemination (CFI), have low heritabilities and are highly influenced by management decisions. Herd Navigator (TM) management program samples and analyses milk progesterone and beta-hydroxybutyrate (BHB) automatically during milking. In this study, the genetic parameters of endocrine fertility traits (measured from milk progesterone) and hyperketonemia (measured from milk BHB) in early lactation were evaluated and compared with traditional fertility traits (CFI, interval from calving to the last insemination and interval from first to last insemination) and the milk yield in red dairy cattle herds in Finland. Data included observations from 14 farms from 2014 to 2017. Data were analyzed with linear animal models using DMU software and analyses were done for first parity cows. Heritability estimates for traditional fertility traits were low and varied between 0.03 and 0.07. Estimated heritabilities for endocrine fertility traits (interval from calving to the first heat (CFH) and commencement of luteal activity (C-LA)) were higher than for traditional fertility traits (0.19 to 0.33). Five slightly different hyperketonemia traits divided into two or three classes were studied. Linear model heritability estimates for hyperketonemia traits were low, however, when the threshold model was used for binary traits the estimates became slightly higher (0.07 to 0.15). Genetic correlation between CFH and C-LA for first parity cows was high (0.97) as expected since traits are quite similar. Moderate genetic correlations (0.47 to 0.52) were found between the endocrine fertility traits and early lactation milk yield. Results suggest that the data on endocrine fertility traits measured by automatic systems is a promising tool for improving fertility, specifically when more data is available. For hyperketonemia traits, dividing values into three classes instead of two seemed to work better. Based on the current study and previous studies, where higher heritabilities have been found for milk BHB traits than for clinical ketosis, milk BHB traits are a promising indicator trait for resistance to ketosis and should be studied more. It is important that this kind of data from automatic devices is made available to recording and breeding organizations in the future.Peer reviewe

Principal component and factor analytic models in international sire evaluation

Background: Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured. Methods: Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries. Results: In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared. Conclusions: Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries