Modeling heat stress effects on dairy cattle milk production in a tropical environment using test-day records and random regression models

In tropical environments, dairy cattle production is constrained by several factors, including climate. The seasonal loss of milk due to heat stress is a recurring challenge for many dairy producers. The objective of this study was to detect heat stress thresholds, milk yield loss and individual animal variations using random regression models for dairy cattle from test-day milk records. Data were obtained from the Kenya Livestock Breeders Organization for the years 2000–2017 and merged with weather data. The weather parameters were grid-interpolated solar and meteorological data obtained from the National Aeronautics and Space Administration/Prediction Of Worldwide Energy Resources (NASA/POWER). After editing, the records comprised 49 993, 45 251 and 36 136 test-day records for first, second, and third lactations, respectively, for the four main dairy breeds: Friesian (68.0%), Ayrshire (21.1%), Jersey (7.6%) and Guernsey (3.3%). Variance components were estimated using Restricted Maximum Likelihood in ASReml software. Random regression models with third-order Legendre polynomials were fitted to the average and individual lactation curves and the reaction norms. An extended factor analytic variance structure for the random cow effects was used to estimate (co)variances between days in milk and thermal load. The daily average temperature (TA) and temperature humidity index (THI) were identified as the most suitable thermal load indicators for assessing milk yield losses. Considering a one day lag, the estimated heat stress thresholds were about 22 °C and 69 index units for TA and THI, respectively. Almost no differences were observed for estimated residual variances between the thermal load indicators, indicating there was no better model fit by TA or THI. The heat stress thresholds and milk loss patterns are important for management of dairy production systems in the tropics with climatic conditions similar to this study. Data recording should be improved as a tool to monitor the expected impacts of climate change and mitigation measures.     

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.     

Statistical analysis of test-day milk yields using random regression models for the comparison of feeding groups during the lactation period

Random regression models are widely used in the field of animal breeding for the genetic evaluation of daily milk yields from different test days. These models are capable of handling different environmental effects on the respective test day, and they describe the characteristics of the course of the lactation period by using suitable covariates with fixed and random regression coefficients. As the numerically expensive estimation of parameters is already part of advanced computer software, modifications of random regression models will considerably grow in importance for statistical evaluations of nutrition and behaviour experiments with animals. Random regression models belong to the large class of linear mixed models. Thus, when choosing a model, or more precisely, when selecting a suitable covariance structure of the random effects, the information criteria of Akaike and Schwarz can be used. In this study, the fitting of random regression models for a statistical analysis of a feeding experiment with dairy cows is illustrated under application of the program package SAS. For each of the feeding groups, lactation curves modelled by covariates with fixed regression coefficients are estimated simultaneously. With the help of the fixed regression coefficients, differences between the groups are estimated and then tested for significance. The covariance structure of the random and subject-specific effects and the serial correlation matrix are selected by using information criteria and by estimating correlations between repeated measurements. For the verification of the selected model and the alternative models, mean values and standard deviations estimated with ordinary least square residuals are used.     

Statistical analysis of test-day milk yields using random regression models for the comparison of feeding groups during the lactation period

Abstract

Random regression models are widely used in the field of animal breeding for the genetic evaluation of daily milk yields from different test days. These models are capable of handling different environmental effects on the respective test day, and they describe the characteristics of the course of the lactation period by using suitable covariates with fixed and random regression coefficients. As the numerically expensive estimation of parameters is already part of advanced computer software, modifications of random regression models will considerably grow in importance for statistical evaluations of nutrition and behaviour experiments with animals. Random regression models belong to the large class of linear mixed models. Thus, when choosing a model, or more precisely, when selecting a suitable covariance structure of the random effects, the information criteria of Akaike and Schwarz can be used. In this study, the fitting of random regression models for a statistical analysis of a feeding experiment with dairy cows is illustrated under application of the program package SAS. For each of the feeding groups, lactation curves modelled by covariates with fixed regression coefficients are estimated simultaneously. With the help of the fixed regression coefficients, differences between the groups are estimated and then tested for significance. The covariance structure of the random and subject-specific effects and the serial correlation matrix are selected by using information criteria and by estimating correlations between repeated measurements. For the verification of the selected model and the alternative models, mean values and standard deviations estimated with ordinary least square residuals are used.     

Alteration in the in vitro activity of milk leukocytes during different parity in high yielding cross-bred cows

In vitro activity of milk leukocytes (viz. neutrophils, lymphocytes and macrophages) was evaluated in forty-eight (48) clinically healthy high-yielding cross-bred cows of mid-lactation stage (100–200 days of lactation), divided into four groups namely 1st parity (n = 12), 2nd parity (n = 12), 3rd parity (n = 12) and 4th and above parity (n = 12). Milk samples were taken (250 ml/cow) were taken. Milk somatic cell counts (SCC) and differential leukocyte counts (DLC) were performed microscopically. In vitro phagocytic index (PI) of milk neutrophils and macrophages was evaluated by colorimetric nitro blue tetrazolium reductive assay. Mitogen-induced milk lymphocyte blastogenic response was measured by colorimetric MTT (tetrazolium) assay after isolation of the milk leukocytes by density gradient centrifugation. Milk SCC differed significantly (p < 0.01) between different parity. Cows of 4 and above parity showed significantly (p < 0.01) higher milk SCC compared to primiparous cows. There was no significant difference in milk DLC during different parities in high-yielding cross-bred cows. There was a significant (p < 0.01) variation in lymphocyte blastogenesis amongst parity. The highest value of lymphocyte blastogenesis was seen at 3rd parity, whereas lowest value was obtained in the cows of both 1st and 4th or above parity. PI of milk neutrophils did not differ significantly between parity. PI of milk macrophages was significantly (p < 0.01) higher in 3rd parity and lower (p < 0.01) in 1st and 4th parities. The study indicated that depressed activity of milk lymphocytes and macropages was lower and SCC was higher in the cows of 4th and above parity indicating more mammary stress and hence susceptible to udder infection and mastitis. Therefore, better care and managemental interventions should be taken around these periods.     

Postpartum reproductive function: association with energy,metabolic and endocrine status in high yielding dairy cows

This study evaluated the effect of metabolic, endocrine and energy status on onset of ovarian cycle, days open (DO), and conception at first service in 90 multiparous Holstein cows, housed at a research farm. Dry matter intake, milk yield and body weight were measured daily from Week 2 antepartum (a.p.) to Week 20 postpartum (p.p.). Milk composition was determined four times per week and milk acetone was measured weekly. Blood samples for the determination of glucose, non-esterified fatty acids, cholesterol, creatinine, albumin, urea, beta-hydroxybutyrate, leptin, insulin, insulin-like growth factor-1, growth hormone, 3,5,3'-triiodothyronine (T(3)), and thyroxine (T(4)) were taken 2 weeks a.p., in Weeks 1-16, and 20 p.p. between 7:30 and 9:00 h. The onset of ovarian cycle was specified by weekly gynecological examination and by skim milk progesterone determination by radioimmunoassay (twice per week). Energy balance (EB) traits were calculated and expressed as accumulated negative EB from calving to EB equilibrium, EB nadir (EBN), rate of EB recovery after EBN (EBR), and time from calving to EBN and to EB equilibrium, respectively. The onset of ovarian cycle p.p. was not related to EB. However, a low degree of EBN and a fast EBR were associated with fewer DO, and EB at first service was positively related to conception. High plasma levels of T(3) and T(4) p.p. were associated with an early start of ovarian cycle, and high concentrations of glucose and cholesterol with a short calving to conception interval. Conception at first service was positively related to EB at first service and progesterone concentration 10-13 days after first service. In conclusion, thyroid hormones may play an important role in resumption of ovarian cyclicity p.p., and a good energy status enhances the chance of conception at first service and shortens DO.     

Seasonal changes in milk production and fertility in high yielding dairy cows in a desert climate

The lactational performance and fertility of high yielding Friesan dairy cows in a hot desert climate were analysed for summer and winter differences. The herd is situated in the Negev desert of Israel.Contrary to all previous reports,milk yield was significantly higher in summer than in winter; furthermore fertility was not lower in the hot months. The daily range in summer temperature was from 22° to 40°C and in winter from 8° to 22°C.The results are discussed in terms of the management and feeding practice of the farm. It was suggested that the cows had been acclimatized and that their thermoneutral zone has been raised.

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Zusammenfassung Die Milchleistung und Fertilität von Hochleistungs Friesischen Milchkühen im heissen Wüstenklima wurde auf Sommer- und Winterunterschiede untersucht. Die Herde wird in der Negev Wüste in Israel gehalten. Im Gegensatz zu allen bisherigen Berichten war die Milchleistung im Sommer signifikant höher als im Winter. Die Fertilität war in den heissen Monaten nicht niedriger als in den kühlen.Der tägliche Bereich der Temperaturen war im Sommer von 22°–40°C und im Winter von 8°–22°C. Die Ergebnisse werden unter Berücksichtigung der Haltung und Fütterung diskutiert. Es wird angenommen, dass die Kühe akklimatisiert sind und die thermoneutrale Zone erhöht ist.

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Resume On a examiné les différences pouvant exister entre l'hiver et l'été dans la production laitière et la fécondité de vaches laitières sélectionnées de race frisonne placées dans un climat désertique chaud.Le troupeau se trouvait en Israël,dans le désert du Néguev. Contrairement à tous les rapports antérieurs, la production laitière fut ici plus importante en été qu'en hiver et cela de façon significative, statistiquement parlant. La fécondité ne fut pas inférieure durant la saison chaude par rapport à la saison froide. La température journalière a varié entre 22° et 40°C en été et entre 8° et 22°C en hiver. Ces résultats seront discutés en tenant compte des méthodes de stabulation et de nutrition. On admet que le bétail est acclimaté et que sa zone thermo-neutre en a été relevée.

     

Insulin resistance: the link between metabolic disorders and cystic ovarian disease in high yielding dairy cows?

A preliminary investigation was performed to examine whether insulin resistance is a factor in the pathogenesis of cystic ovarian disease (COD) in high-yielding dairy cows. In total 30 cows, of which 15 were diagnosed as suffering from COD based on the anamnesis and clinical examination, and the other 15 served as matched controls, were subjected to an intravenous glucose tolerance test (IVGTT). The aim of the study was to investigate whether insulin activity was altered in COD cows. Differences in glucose clearance between the COD cows and their controls were analyzed comparing the fractional turnover rate (k), the glucose half-time (T1/2), and the area under the curve (AUC) 60 and 120 min after infusion. Differences in insulin response were analyzed comparing the insulin increment, the insulin peak concentration, and the AUC 60 and 120 min after glucose infusion. Although insulin resistance, attended by a secondary hyperinsulinemia, is stated to directly contribute to the ovarian abnormalities that characterize the polycystic ovary syndrome (PCOS) in human medicine, this was not observed in COD cows. On the contrary, COD cows appeared to have a low insulin response following an intravenous glucose load as compared with their matched controls. This was illustrated by significantly lower insulin increments (P = 0.04) and lower insulin peak concentrations (P = 0.04). As COD cows had a significantly lower insulin response to a standard glucose load, it was concluded that insulin could be a factor in the pathogenesis of COD in dairy cows.     

Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models

The aim of this study was to estimate genetic parameters for body weights of individually fed beef bulls measured at centralized testing stations in South Africa using random regression models. Weekly body weights of Bonsmara bulls (N = 2919) tested between 1999 and 2003 were available for the analyses. The model included a fixed regression of the body weights on fourth-order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84) for starting age and contemporary group effects. Random regressions on fourth-order orthogonal Legendre polynomials of the actual days on test were included for additive genetic effects and additional uncorrelated random effects of the weaning-herd-year and the permanent environment of the animal. Residual effects were assumed to be independently distributed with heterogeneous variance for each test day. Variance ratios for additive genetic, permanent environment and weaning-herd-year for weekly body weights at different test days ranged from 0.26 to 0.29, 0.37 to 0.44 and 0.26 to 0.34, respectively. The weaning-herd-year was found to have a significant effect on the variation of body weights of bulls despite a 28-day adjustment period. Genetic correlations amongst body weights at different test days were high, ranging from 0.89 to 1.00. Heritability estimates were comparable to literature using multivariate models. Therefore, random regression model could be applied in the genetic evaluation of body weight of individually fed beef bulls in South Africa.     

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.     

Influence of parity on follicular dynamics and resumption of ovarian cycle in postpartum dairy cows

The present study examined ovarian changes preceding the resumption of the ovarian cycle in postpartum dairy cows with different parities under similar body nutritional conditions. In postpartum primi- (n=6), bi- (n=4), and multiparous (n=6) Holstein dairy cows, ovarian ultrasonographic observations starting at 7 days after calving were performed every other day and then daily after the confirmation of clinical signs of oestrus for the detection of postpartum first ovulation. Blood samples were collected at the same time as ultrasonography and analyzed for oestradiol and progesterone to monitor ovarian activity. To evaluate the nutritional condition of the cows, body weight and body condition score (BCS, 1=emaciated to 5=obese) were measured weekly and blood samples for the analysis of glucose, insulin, and non-esterified fatty acid (NEFA) were collected at the same time until postpartum second ovulation. Dominant follicles (>8mm in diameter) of the first follicular wave were detected at 7 days after calving in all cows. The first wave follicle ovulated in five of six multiparous cows, whereas no first wave follicle ovulated in any of the primiparous cows. The days to first ovulation after calving in primiparous cows (31.8+/-8.3 days) were significantly greater (p<0.05) than those in multiparous cows (17.3+/-6.3 days), but were not significantly different from biparous cows (28.8+/-8.6 days). There was a significant relationship between parity and days to first ovulation after calving (p<0.05). BCS was maintained at a level of more than 2.5 during the postpartum period in all cows and there was no influence of parity on postpartum changes in BCS, glucose, insulin, or NEFA throughout the experiment. The present study demonstrated a negative relationship between parity and number of days from calving to first ovulation in dairy cows under similar body nutritional conditions. It is possible that the influence of parity on the resumption of ovarian cycle is modulated by the factors different from the nutrition-related changes during the postpartum period in dairy cows.     

Analysis of feed intake and energy balance of high-yielding first lactating Holstein cows with fixed and random regression models

At the dairy research farm Karkendamm, the individual roughage intake was measured since 1 September 2005 using a computerised scale system to estimate daily energy balances as the difference between energy intake and calculated energy requirements for lactation and maintenance. Data of 289 heifers with observations between the 11th and 180th day of lactation over a period of 487 days were analysed. Average energy-corrected milk yield, feed intake, live weight and energy balance were 31.8kg, 20.6kg, 584 kg and 13.6 MJ NEL (net energy lactation), respectively, per day. Fixed and random regression models were used to estimate repeatabilities, correlations between cow effects and genetic parameters. The resulting genetic correlations in different lactation stages demonstrate that feed intake and energy balance at the beginning and the middle of lactation are genetically different traits. Heritability of feed intake is low with h2=0.06 during the first days after parturition and increases in the middle of lactation, whereas the energy balance shows the highest heritability with h2=0.34 in the first 30 days of lactation. Genetic correlations between energy balance and feed intake and milk yield, respectively, illustrate that energy balance depends more on feed intake than on milk yield. Genetic correlation between body condition score and energy balance decreases rapidly within the first 100 days of lactation. Hence, to avoid negative effects on health and reproduction as consequences of strong energy deficits at the beginning of lactation, the energy balance itself should be measured and used as a selection criterion in this lactation stage. Since the number of animals is rather small for a genetic analysis, the genetic parameters have to be evaluated on a more comprehensive dataset.     

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images’ high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows’ or persons’ surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.     

Effect of grain and forage fractions of corn silage on milk production and composition in dairy cows

Corn silage (CS) is associated with a reduction in milk fat content. The fact that CS is constituted of a grain and a forage fraction could explain this effect. This experiment evaluated the effect of grain fraction of CS on rumen fermentation, production performance and milk composition. Earless CS (ECS) was harvested after manually removing corn ears from the plant. Whole CS (WCS) was harvested from the same field on the same day. Eight (four ruminally fistulated) multiparous Holstein cows (84 days in milk) were utilized in a double 4 × 4 Latin square with 21-day periods. Treatments were (dry matter (DM) basis) (1) 23.0% WCS; (2) 12.4% ECS plus 10.6% high moisture corn (HMC) to obtain reconstituted CS (RCS); (3) 23.0% ECS; and (4) 23.0% timothy silage (TS). Diets were formulated to be isonitrogenous and were fed as total mixed ration once a day. DM intake (DMI), milk yield, 4.0% fat-corrected milk (FCM), as well as protein concentration and yield were higher for WCS than ECS. Compared with WCS, cows tended to eat less with RCS, and produced less milk and milk protein. However, yield of FCM was similar between WCS and RCS. Milk fat concentration and yield, as well as the specific ratio of t11 18:1 to t10 18:1 in milk fat did not differ among diets. Milk urea-N tended to be higher for ECS than WCS and TS, whereas ruminal NH₃-N was higher with ECS than TS. Rumen pH decreased linearly with time after feeding but was not different between treatments. Higher acetate and lower propionate concentration resulted in greater acetate to propionate ratio with ECS compared with WCS. In conclusion, removing grain fraction from CS decreased milk production and modified rumen fermentation without affecting milk fat concentration and yield. Moreover, despite some differences in DMI and total ruminal volatile fatty acid concentration between WCS and RCS, the restoration of FCM yield, using HMC in RCS diets, to a level of production similar to WCS highlights the importance of energy and nutrients supplied by the grain fraction of CS to support milk yield.     

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle

We quantified the potential increase in accuracy of expected breeding value for weights of Nelore cattle, from birth to mature age, using multi-trait and random regression models on Legendre polynomials and B-spline functions. A total of 87,712 weight records from 8144 females were used, recorded every three months from birth to mature age from the Nelore Brazil Program. For random regression analyses, all female weight records from birth to eight years of age (data set I) were considered. From this general data set, a subset was created (data set II), which included only nine weight records: at birth, weaning, 365 and 550 days of age, and 2, 3, 4, 5, and 6 years of age. Data set II was analyzed using random regression and multi-trait models. The model of analysis included the contemporary group as fixed effects and age of dam as a linear and quadratic covariable. In the random regression analyses, average growth trends were modeled using a cubic regression on orthogonal polynomials of age. Residual variances were modeled by a step function with five classes. Legendre polynomials of fourth and sixth order were utilized to model the direct genetic and animal permanent environmental effects, respectively, while third-order Legendre polynomials were considered for maternal genetic and maternal permanent environmental effects. Quadratic polynomials were applied to model all random effects in random regression models on B-spline functions. Direct genetic and animal permanent environmental effects were modeled using three segments or five coefficients, and genetic maternal and maternal permanent environmental effects were modeled with one segment or three coefficients in the random regression models on B-spline functions. For both data sets (I and II), animals ranked differently according to expected breeding value obtained by random regression or multi-trait models. With random regression models, the highest gains in accuracy were obtained at ages with a low number of weight records. The results indicate that random regression models provide more accurate expected breeding values than the traditionally finite multi-trait models. Thus, higher genetic responses are expected for beef cattle growth traits by replacing a multi-trait model with random regression models for genetic evaluation. B-spline functions could be applied as an alternative to Legendre polynomials to model covariance functions for weights from birth to mature age.     

Ontogenetic patterns in heritable variation for body size: using random regression models in a wild ungulate population

Body size is an important determinant of fitness in many organisms. While size will typically change over the lifetime of an individual, heritable components of phenotypic variance may also show ontogenetic variation. We estimated genetic (additive and maternal) and environmental covariance structures for a size trait (June weight) measured over the first 5 years of life in a natural population of bighorn sheep Ovis canadensis. We also assessed the utility of random regression models for estimating these structures. Additive genetic variance was found for June weight, with heritability increasing over ontogeny because of declining environmental variance. This pattern, mirrored at the phenotypic level, likely reflects viability selection acting on early size traits. Maternal genetic effects were significant at ages 0 and 1, having important evolutionary implications for early weight, but declined with age being negligible by age 2. Strong positive genetic correlations between age-specific traits suggest that selection on June weight at any age will likely induce positively correlated responses across ontogeny. Random regression modeling yielded similar results to traditional methods. However, by facilitating more efficient data use where phenotypic sampling is incomplete, random regression should allow better estimation of genetic (co)variances for size and growth traits in natural populations.     

Variability,stability, and resilience of fecal microbiota in dairy cows fed whole crop corn silage

The microbiota of whole crop corn silage and feces of silage-fed dairy cows were examined. A total of 18 dairy cow feces were collected from six farms in Japan and China, and high-throughput Illumina sequencing of the V4 hypervariable region of 16S rRNA genes was performed. Lactobacillaceae were dominant in all silages, followed by Acetobacteraceae, Bacillaceae, and Enterobacteriaceae. In feces, the predominant families were Ruminococcaceae, Bacteroidaceae, Clostridiaceae, Lachnospiraceae, Rikenellaceae, and Paraprevotellaceae. Therefore, Lactobacillaceae of corn silage appeared to be eliminated in the gastrointestinal tract. Although fecal microbiota composition was similar in most samples, relative abundances of several families, such as Ruminococcaceae, Christensenellaceae, Turicibacteraceae, and Succinivibrionaceae, varied between farms and countries. In addition to the geographical location, differences in feeding management between total mixed ration feeding and separate feeding appeared to be involved in the variations. Moreover, a cow-to-cow variation for concentrate-associated families was demonstrated at the same farm; two cows showed high abundance of Succinivibrionaceae and Prevotellaceae, whereas another had a high abundance of Porphyromonadaceae. There was a negative correlation between forage-associated Ruminococcaceae and concentrate-associated Succinivibrionaceae and Prevotellaceae in 18 feces samples. Succinivibrionaceae, Prevotellaceae, p-2534-18B5, and Spirochaetaceae were regarded as highly variable taxa in this study. These findings help to improve our understanding of variation and similarity of the fecal microbiota of dairy cows with regard to individuals, farms, and countries. Microbiota of naturally fermented corn silage had no influence on the fecal microbiota of dairy cows.     

Evaluation of the effect of GnRH on follicular ovarian cysts in dairy cows using trans-rectal ultrasonography

The objectives of this study were to evaluate ovarian changes in cows with follicular ovarian cysts following treatment with either GnRH or saline. The parameters determined were the intervals from treatment to observation of a CL and from treatment to disappearance of the cyst, and the association between serum concentrations of LH, FSH and the LH/FSH ratio, before and after treatment, with the test intervals. Thirty-nine cows were identified as having follicular cysts. The GnRH treatment induced a significant increase in LH and the LH/FSH ratio. The gonadotropin response was not associated with the intervals from treatment to CL detection or to disappearance of the cyst. Survival curves for the intervals from treatment to CL detection and cyst disappearance indicate that treatment with GnRH or saline did not yield significantly different results for either parameter. The results question the efficacy of treating cystic ovarian disease with GnRH.