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High-yielding dairy cows encounter metabolic challenges in early lactation. Typically, BHB, measured at a specific time point, is employed to diagnose the metabolic status of cows based on a predetermined threshold. However, in early lactation, BHB is highly dynamic, with high interindividual variability in its time profile. This could limit the effectiveness of the single measurement and threshold-based diagnosis and could contribute to the disparities in reports linking metabolic status with productive and reproductive outcomes. This research examines the trajectories of BHB to unveil intercow variations and identify latent metabolic groups. We compiled a dataset from 2 observational studies involving a total of 195 lactations from multiparous Holstein Friesian cows. The dataset encompasses measurements of BHB, nonesterified fatty acids (NEFA), and insulin from blood samples collected at 3, 6, 9, and 21 DIM, along with weekly determinations of milk composition and fatty acids (FA) proportions in milk fat. In both experiments, milk yield (MY) and feed intake were recorded daily during the first month of lactation. We explored interindividual and intraindividual variations in metabolic responses using the trajectories of blood BHB and evaluated the presence of distinct metabolic groups based on such variations. For this purpose, we employed the growth mixture model, a trajectory clustering technique. Our findings unveil novel insights into the diverse metabolic responses among cows, encompassing both trajectory patterns and the magnitude of blood BHB concentrations. Specifically, we identified 3 latent metabolic groups: the quickly increasing BHB (QuiBHB) cluster (≈10%) exhibited a higher initial BHB concentration than other clusters, peaked on d 9 (average maximum BHB of 2.4 mM) and then declined by d 21; the slowly increasing BHB (SloBHB) cluster (≈23%) started with a lower BHB concentration, gradually increased until d 9, and reached the highest BHB concentration at d 21 (1.6 mM serum BHB at the end of the experimental period); and the low BHB (LoBHB) cluster (≈67%) began with the lowest serum BHB concentration (serum BHB <0.75 mM) and remained relatively stable throughout the sampling period. Notably, the 3 metabolic groups exhibited significant physiological disparities, which were evident in blood NEFA and insulin concentrations. The QuiBHB and SloBHB cows exhibited higher NEFA and lower insulin concentrations as compared with the LoBHB cows. Interestingly, these metabolic differences extended to MY and DMI during the first month of lactation. The elevated BHB concentrations observed in QuiBHB cows were linked with lower DMI and MY as compared with SloBHB and LoBHB cows. Accordingly, these animals were considered metabolically impaired. Conversely, SloBHB cows displayed higher MY along with increased DMI, and thus the elevated BHB might be indicative of an adaptive response for these cows. The QuiBHB cows also displayed higher proportions of UFA, MUFA, and total C18:1 FA in milk during the first week of lactation. Prediction of the QuiBHB cows using these FA and test day variables resulted in moderate predictive accuracy (area under the receiver operating characteristic curve >0.7). Given the limited sample size for the development of prediction models and the variation in DIM among samples in the same week, the result is indicative of the predictive potential of the model and room for model optimization. In summary, distinct metabolic groups of cows could be identified based on the trajectories of blood BHB in early lactation., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRT TD and MRT MM . When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRT TD and MRT MM , postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRT TD and MRT MM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRT TD and MRT MM , resulting in an adjusted R 2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRT TD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings., (© 2024, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of β-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R 2 ) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R 2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R 2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R 2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R 2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing., (© 2024, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Early lactation metabolic imbalance is an important physiological change affecting the health, production, and reproduction of dairy cows. The aims of this study were (1) to evaluate the potential of test-day (TD) variables with or without milk fatty acids (FA) content to classify metabolically imbalanced cows and (2) to evaluate the robustness of the metabolic classification with external data. A data set was compiled from 3 experiments containing plasma β-hydroxybutyrate, nonesterified FA, glucose, insulin-like growth factor-I, FA proportions in milk fat, and TD variables collected from 244 lactations in wk 2 after calving. Based on the plasma metabolites, 3 metabolic clusters were identified using fuzzy c-means clustering and the probabilistic membership value of each cow to the 3 clusters was determined. Comparing the mean concentration of the plasma metabolites, the clusters were differentiated into metabolically imbalanced, moderately impacted, and balanced. Following this, the 2 metabolic status groups identified were imbalanced cows (n = 42), which were separated from what we refer to as "others" (n = 202) based on the membership value of each cow for the imbalanced cluster using a threshold of 0.5. The following 2 FA data sets were composed: (1) FA (groups) having high prediction accuracy by Fourier-transform infrared spectroscopy and, thus, have practical significance, and (2) FA (groups) formerly identified as associated with metabolic changes in early lactation. Metabolic status prediction models were built using FA alone or combined with TD variables as predictors of metabolic groups. Comparison was made among models and external evaluations were performed using an independent data set of 115 lactations. The area under the receiver operating characteristics curve of the models was between 75 and 91%, indicating their moderate to high accuracy as a diagnostic test for metabolic imbalance. The addition of FA groups to the TD models enhanced the accuracy of the models. Models with FA and TD variables showed high sensitivities (80-88%). Specificities of these models (73-79%) were also moderate and acceptable. The accuracy of the FA models on the external data set was high (area under the receiver operating characteristics curve between 76 and 84). The persistently good performance of models with Fourier-transform infrared spectroscopy-quantifiable FA on the external data set showed their robustness and potential for routine screening of metabolically imbalanced cows in early lactation., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Data on metabolic profiles of blood sampled at d 3, 6, 9, and 21 in lactation from 117 lactations (99 cows) were used for unsupervised k-means clustering. Blood metabolic parameters included β-hydroxybutyrate (BHB), nonesterified fatty acids, glucose, insulin-like growth factor-1 (IGF-1) and insulin. Clustering relied on the average and range of the 5 blood parameters of all 4 sampling days. The clusters were labeled as imbalanced (n = 42) and balanced (n = 72) metabolic status based on the values of the blood parameters. Various random forest models were built to predict the metabolic cluster of cows during early lactation from the milk composition. All the models were evaluated using a leave-group-out cross-validation, meaning data from a single cow were always present in either train or test data to avoid any data leakage. Features were either milk fatty acids (MFA) determined by gas chromatography (MFA [GC]) or features that could be determined during a routine dairy herd improvement (DHI) analysis, such as concentration of fat, protein, lactose, fat/protein ratio, urea, and somatic cell count (determined and reported routinely in DHI registrations), either or not in combination with MFA and BHB determined by mid-infrared (MIR), denoted as MFA [MIR] and BHB [MIR], respectively, which are routinely analyzed but not routinely reported in DHI registrations yet. Models solely based on fat, protein, lactose, fat/protein ratio, urea and somatic cell count (i.e., DHI model) were characterized by the lowest predictive performance [area under the receiver operating characteristic curve (AUC ROC ) = 0.69]. The combination of the features of the DHI model with BHB [MIR] and MFA [MIR] powerfully increased the predictive performance (AUC ROC = 0.81). The model based on the detailed MFA profile determined by GC analysis did not outperform (AUC ROC = 0.81) the model using the DHI-features in combination with BHB [MIR] and MFA [MIR]. Predictions solely based on samples at d 3 were characterized by lower performance (AUC ROC DHI + BHB [MIR] + MFA [MIR] model at d 3: 0.75; AUC ROC MFA [GC] model at d 3: 0.73). High predictive performance was found using samples from d 9 and 21. To conclude, overall, the DHI + BHB [MIR] + MFA [MIR] model allowed to predict metabolic status during early lactation. Accordingly, these parameters show potential for routine prediction of metabolic status., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The measurement of pH in the reticulorumen in combination with a time-pH threshold has been widely applied in research to diagnose subacute ruminal acidosis. However, other pH metrics also have biological value. In this study, 44 animals were monitored during the transition period using reticuloruminal pH boluses. Traditional and more complex pH characteristics were calculated to characterize the reticuloruminal pH profile: time pH <6, slope of a logistic cumulative pH curve (β 0 ), and deviations [squared error (SqEr)] from pH predictions based on a harmonic static model. In this study, we aimed to examine the associations between those pH metrics and metabolic health parameters, feed intake, and activity. Finally, to describe the reticuloruminal pH dynamically, we also constructed a dynamic linear model. The results of this model were studied in relation to feed intake. All pH parameters were mutually correlated (particularly β 0 and SqEr; mean Pearson correlation of -0.52). pH patterns, rather than time pH <6, were associated with metabolic health and feed intake: high variation in daily pH (β 0 parameter) was reflected in higher blood concentrations of nonesterified fatty acids. Moreover, pH deviations of the harmonic model were negatively associated with feed intake and rumination behavior. This research confirms the biological importance of pH metrics focusing on pH variation and pH deviations and provides deeper insight into its associations with metabolic health status, feed intake, and activity during early lactation., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Colostrum is the first milk produced by a cow after she gives birth. Compared with mature milk, it has a high concentration of immunoglobulin G. Calves are born without circulating antibodies, thus ingestion of colostrum is necessary to protect the calf against pathogens in the first challenging weeks of life. In addition to the life-saving supply of antibodies, colostrum contains minerals, vitamins, growth factors, and immune cells. Recently, microRNAs (miRNAs) were added to that list. MicroRNAs are short, non-coding RNA molecules that can regulate gene expression at the post-transcriptional level. They are thought to act as key regulators of diverse biological and developmental processes. Colostrum contains higher amounts of miRNAs than mature milk; immune- and development-related miRNAs are prominent. Their expression pattern in milk is likely to be influenced by maternal nutrition and environment. The fat content of the maternal diet appears to have a major effect on expression of miRNAs in milk and in the neonate. The immunological state of the mammary gland seems to affect miRNA expression as well. In cows diagnosed with subclinical mastitis, alterations in the expression of miRNAs in milk have been observed. It is believed that miRNAs in colostrum and milk are signaling molecules passed from mother to newborn. They are packaged in extracellular vesicles, which makes them resistant to the harsh conditions in the gastrointestinal tract. Therefore, they can reach the small intestine, where they are absorbed and transferred into the bloodstream. MicroRNAs are important for the development of the intestines. For example, miRNAs stimulate cell viability, proliferation, and stem cell activity of the intestinal epithelium. Furthermore, miRNAs seem to act as key players in the development of the complete immune system. They can, among other things, regulate B- and T-cell differentiation and affect interleukin production of macrophages. The abundance of miRNAs in colostrum and milk and the possibility for their absorption in the intestines of the neonate supports the hypothesis that these tiny molecules are important for the development of the newborn. The probable relation of diet to the expression of miRNAs by the mother creates a possible avenue to optimize expression of miRNAs and improve neonatal maturation., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of the present research was to assess factors associated with first-lactation milk yield in dairy heifers, including maternal and environmental factors, factors related to the development of the heifer and factors related to its offspring such as gender of the calf. In addition, the potential underlying mechanism, in particular metabolic adaptations, was further explored. Data on body growth, reproduction and milk yield of 74 Holstein Friesian heifers on three herds in Flanders (Belgium) were collected. At birth, body measurements of the heifers were recorded and blood samples were taken (in order) to determine basal glucose and insulin concentrations. Body measurements were assessed every 3 months until first calving, and gender and weight of their first calf were recorded. Information on fertility and milk yield of the heifer and its dam were collected from the herd databases. Daily temperature and photoperiod were recorded from the database of the Belgian Royal Meteorological Institute. Linear mixed models were run with herd as a random factor, to account for differences in herd management. Heifers grew 867±80.7 g/day during their first year of life and were inseminated at 14.8±1.34 months. First calving took place at 24.5±1.93 months, at a weight of 642±61.5 kg and heifers produced 8506±1064 kg energy corrected milk during their first 305-day lactation. Regression models revealed that none of the maternal factors such as milk yield and parity, nor the growth of the heifer during the 1st year of life were associated with milk yield during first lactation. Age, and to a lesser extent BW at first parturition were positively associated with first-lactation milk yield. In addition, the season of birth, but not calving, had a significant influence on milk yield, with winter-born heifers producing less than heifers born in any other season. The lower yielding winter-born heifers had higher insulin concentrations at birth, whereas glucose concentrations were similar, the latter being suggestive for lower insulin sensitivity of the peripheral tissues. Furthermore, environmental temperature at the end of gestation was negatively correlated with neonatal insulin concentrations. In conclusion, results of the present study suggest heifers born during the hotter months are born with a higher peripheral insulin sensitivity, finally leading to a higher first-lactation milk yield.

The aim of this study was to evaluate the dose response on in vitro methane (CH) production of PUFA to which the inoculum donor animals had been exposed early in life. Sixteen Holstein calves (160 ± 3 and 365 ± 2 kg BW) at 6 and 12 mo of age were used as inoculum donors. Half of the calves were given increasing amounts of extruded linseed from birth (22 g/d) until 4 mo of age (578 g/d) first mixed with milk and then included in their concentrate. Linseed oil (LSO) was supplemented in vitro at 5 different doses (0, 0.6, 1.2, 2.4, and 4.8 mg/mL). Supplementation of LSO in the rumen inocula at both ages linearly decreased ( < 0.05) the in vitro CH production. Total in vitro VFA production was not affected by LSO supplementation. Inhibition of CH was smaller when using the rumen inoculum from calves that had received a similar treatment early in life ( < 0.05). Differences in response to in vitro supplementation of a type of fatty acids similar to those applied during early life suggest some "changes" in the functioning of the rumen microbial community.

The objective of the present retrospective cohort study was to evaluate potential associations between environmental factors and dam characteristics, including level of milk production during gestation, and insulin traits in newborn Holstein calves. Birth weight and gestational age of the calves at delivery were determined. On the next day, heart girth, wither height and diagonal length of both the calves and their dams were measured. Parity, body condition score and age at calving were recorded for all dams. For the cows, days open before last gestation, lactation length (LL), length of dry period (DP) and calving interval were also calculated. The magnitude and shape of the lactation curve both quantified using the MilkBot model based on monthly milk weights, were used to calculate the amount of milk produced during gestation. Using the same procedure, cumulative milk production from conception to drying off (MGEST) was calculated. A blood sample was collected from all calves (n=481; 169 born to heifers and 312 born to cows) at least 5 h after a milk meal on day 3 of life to measure basal glucose and insulin levels. In addition, an intravenous glucose-stimulated insulin secretion test was performed in a subset of the calves (n=316). After descriptive analysis, generalized linear mixed models were used to identify factors that were significantly associated with the major insulin traits (Insb, basal insulin level; QUICKI, quantitative insulin sensitivity check index; AIR, acute insulin response; DI, disposition index) of the newborn calves. The overall average birth weight of the calves was 42.7 ± 5.92 kg. The insulin traits were significantly associated with gender and season of birth when data of all calves were analyzed. In addition, the insulin traits in calves born to cows were significantly associated with MGEST, DP and LL. The Insb was estimated to be higher in calves born to the cows having passed a higher MGEST (P=0.076) and longer DP (P=0.034). The QUICKI was estimated to be lower in calves born to the cows having passed a higher MGEST (P=0.030) and longer DP (P=0.058). Moreover, the AIR (P=0.009) and DI (P=0.049) were estimated to be lower in male compared with female calves. Furthermore, the AIR (P=0.036) and DI (P=0.039) were estimated to be lower in calves born to cows having passed a longer LL. The decisive effects of MGEST, DP and LL in cows on the insulin traits of their calves may provide a basis for developing managerial interventions to improve metabolic health of the offspring.

For dairy cattle on pasture in temperate regions, it is largely unknown to what degree hot summer conditions impact energy metabolism, milk yield and milk composition and how effective shade is in reducing these negative effects. During the summer of 2012, a herd of Holstein cows was kept on pasture without access to shade (treatment NS). During the summers of 2011 and 2013, the herd was divided into a group with (treatment S) and a group without (treatment NS) access to shade. Shade was provided by young trees combined with shade cloths (80% reduction in solar radiation). A weather station registered the local climatic conditions on open pasture, from which we calculated daily average Heat Load Index (HLI) values. The effects of HLI and shade on rectal temperature (RT), blood plasma indicators of hyperventilation and metabolic changes due to heat stress, milk yield and milk composition were investigated. RT increased with increasing HLI, but was less for S cows than for NS cows (by 0.02°C and 0.03°C increase per unit increase of HLI, respectively). Hyperchloraemia (an increased blood plasma concentration of Cl-), a sign of hyperventilation, increased for NS cows but not for S cows. The plasma concentration of alkaline phosphatase, a regulator of energy metabolism in the liver, decreased with increasing HLI for NS cows only. Access to shade, thus, reduced the effect of HLI on RT, hyperchloraemia and the regulation of metabolism by the liver. As HLI increased, the plasma concentration of cholesterol decreased (indicating increased lipolysis) and the plasma concentration of creatinine increased (indicating increased protein catabolism). These effects did not differ between S and NS cows. For NS cows, after a lag-time of 2 days, the milk yield decreased with increasing HLI. For S cows, the milk yield was unaffected by HLI and its quadratic factor. The milk concentrations of lactose, protein and fat decreased as HLI increased, but only the effect on milk protein content was remediated by shade. In conclusion, access to shade tempered the negative effects of high HLI on RT, hyperchloraemia and a blood plasma indicator of changing energy metabolism (generally) as well as prevented the decrease in milk yield observed in cows without access to shade.

The objective of the present study was to evaluate environmental and dam factors associated with birth size of Holstein calves. Data of 1,594 births from dairy herds in Belgium and Germany were analyzed in a retrospective cross-sectional study. Immediately after birth, the birth weight of the calves was measured. On the next day, the heart girth (HG), withers height, and diagonal length of the dams and calves were measured. Parity, body condition score, gestation length (GL), and age at calving were recorded for all dams. For the cows, days open, lactation length, length of the dry period, and calving interval were also calculated. The magnitude and shape of the lactation that took place during gestation was quantified using the MilkBot model based on monthly milk weights. Using the same procedure, cumulative milk production from conception to drying off (MGEST) was calculated. After descriptive analyses, mixed models were used to identify factors that are significantly associated with the birth weight (most consistent measure of size at birth) of the calves born to both heifers and cows. Of the variables offered to the offspring birth weight model in heifers (n=540), calf sex, season of calving, GL, HG, withers height, diagonal length, and age at calving were significant. The mean birth weight of the calves born to heifers was estimated to be 41.3±1.01kg. In comparison to calves born to old (25.5 to 37.3mo; n=99) heifers, the birth weight was estimated to be 2.75, 3.29, and 2.35kg heavier when the calves were born to very young (20.3 to <22mo; n=98), young (22 to <23.5mo; n=145), and standard aged (23.5 to <25.5mo; n=198) heifers, respectively. Of the variables offered to the offspring birth weight model in cows (n=1,054), calf sex, season of calving, GL, parity, dry period, and MGEST were significant. The mean birth weight of the calves born to cows was estimated to be 44.1±0.99kg. For cows having an identical HG, the birth weight of the calves was estimated to be 0.97 and 1.11kg higher in cows with low (1,400 to <5,400kg) and high (6,500 to <7,200) MGEST, respectively, compared with cows with very high (7,200 to 11,600kg) MGEST. The decisive effects of age at calving in heifers and of high milk production levels during gestation in cows on the birth weight of their calves may provide a basis for developing managerial interventions to improve long-term health and productivity of the offspring., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)