Genetic Component of Heat Stress in Dairy Cattle, Development of Heat Index Function

Production data obtained from AIPL USDA included 119,337 first-parity, test-day records of 15,012 Holsteins from 134 Georgia farms collected in 1990 to 1997. Weather information was obtained from the Georgia Automated Environmental Monitoring Network and included daily minimum, average, and maximum temperatures and humidity for 21 stations throughout the state. Each test-day record was augmented with weather information from the closest weather station. Analyses were based on models that included effects of herd-year-season, age, test day, milking frequency, and several types of heat and humidity. The best model used a temperature-humidity index. With this model, the average test-day yield for milk was about 26.3 kg for a temperature-humidity index <72 and decreased at about 0.2 kg per unit increase in the temperature-humidity index for a temperature-humidity index > or =72. For fat and protein, the test yield was 0.92 and 0.85 kg at a temperature-humidity index <72, respectively, and declined at a rate of 0.012 and 0.009 kg per degree of the temperature-humidity index, respectively. The temperature-humidity index calculated with the available weather information can be used to account for the effect of heat stress on production.     

Effect of heat stress on production of Mediterranean dairy sheep

A study on heat stress in Mediterranean dairy sheep was undertaken with the objective to examine the relationship between milk production and heat stress, to estimate the additive genetic variances of milk production traits and heat tolerance, and to investigate the possibility of future selection for increased heat tolerance. Production data included 59,661 test-day records belonging to 6624 lactations of 4428 lactating ewes from 17 flocks collected from 1994 through 2003. The traits investigated were daily milk yield, fat and protein percentage, and daily yield of fat-plus-protein. The pedigree file consisted of 5306 animals; in addition to the 4428 animals with records, 188 male and 690 female ancestors were included. Heat stress was modeled by using data from a weather station. Apart from the effects of the weather conditions of the milk recording test-day, the effects of the preceding 1, 2, and 3 d were determined. Because longer periods of heat stress might have a more severe effect than shorter periods, 2-, 3-, and 4-d periods were also considered, by averaging the weather data measurements. Fixed regression analyses were based on models that included effects of flock nested within year of test-day, DIM (days in milk) class x parity class, and several types of weather indicators. The preferred model using the temperature-humidity index (THI) gave a smoother pattern than did the model with temperature x humidity interaction. Both daily milk and fat-plus-protein yield appeared to decrease at THI > or = 23, in all periods considered. Based on the 4-d period, yield decreased for each unit increase of THI above 23 [-62.8 g/unit (-4.2%) for daily milk yield and -8.9 g/unit (-4.9%) for daily fat-plus-protein yield]. Fat and protein percentages appeared to be unaffected by heat stress. A test-day repeatability model was applied for estimation of genetic parameters. The genetic correlations between the general additive effect and the additive effect of heat tolerance were negative (approximately -0.8) for both daily milk and fat-plus-protein yields in all periods considered. Therefore, milk yield is antagonistic with heat tolerance, and selection only for increased milk production will reduce heat tolerance.     

Exploration of relationships between claw disorders and milk yield in Holstein cows via recursive linear and threshold models

Relationships between claw disorders and test-day milk yield recorded in 2005 on 5,360 Holstein cows, kept on 11 large-scale dairy farms in eastern Germany, were analyzed in a Bayesian framework with standard linear and threshold models and recursive linear and threshold models. Four different claw disorders, digital dermatitis (DD), sole ulcer (SU), wall disorder (WD), and interdigital hyperplasia (IH), were scored as binary traits within 200 d after calving and analyzed separately. Incidences of disorders were 13.7% for DD, 16.5% for SU, 9.8% for WD, and 6.7% for IH. Heritabilities of disorders were greater when applying threshold or recursive threshold models than with linear or linear recursive models. Posterior means of genetic correlations between test-day milk production and claw disorders ranged from 0.17 to 0.44, suggesting that breeding strategies focusing on increased milk yield will increase incidences of disorders as a correlated response. A progressive path of lagged relationships was postulated for recursive models describing first the influence of test-day milk yield (MY1) on claw disorders and second, the effect of the disorder on milk production level at the following test day (MY2). In recursive models, structural coefficients describe recursive relationships at the phenotypic level. The structural coefficient λ₂₁ was the gradient of disease (trait 2) with respect to MY1 (trait 1) for a model with a recursive effect of trait 1 on trait 2. The increase of disease incidence of the 4 different disorders per 1-kg increase of MY1 ranged from λ₂₁=0.006 to λ₂₁= 0.024 on the visible scale when applying recursive linear models, and from λ₂₁= 0.003 to λ₂₁= 0.016 on the underlying liability scale for recursive threshold models. The rate of change in MY2 (trait 3) with respect to the previous claw disorder is given by λ₃₂ for a model with a recursive effect from trait 2 to trait 3. Structural coefficients λ₃₂ ranged from −0.12 to −0.68 predicting that a 1-unit increase in the incidence of any disorder reduces milk yield at the following test day by up to 0.67 kg. Rank correlations between sire posterior means for the same claw disorders among different models were >0.84, but some changes in rank of sires in distinct top-10 lists were observed. Structural equation models are of increasing importance in genetic evaluations, and this study showed the possible application of recursive systems, even for categorical data.     

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024–0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from −0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from −0.42 to −0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from −0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.     

Influence of breed,milk production,season, and ambient temperature on dairy cow reticulorumen temperature

Automatic monitoring of core body temperature in dairy cattle could be useful for identification of illness, heat stress, general physiological stress, and estrus. The SmartBolus (TenXSys Inc., Eagle, ID) system used a reticulorumen bolus to automatically record and transmit dairy cow temperatures. The objective of this research was to characterize the influence of milk yield (MY), time of day, breed, ambient temperature (AT), and season on reticulorumen temperatures (RT) in lactating dairy cows. Continuous RT and AT were collected by SmartBolus transponders every 15 min (96 records per d) from 93 cows (65 Holstein, 18 crossbred, and 10 Jersey) for 615 d. Mean (±SD) daily RT, AT, and MY were 40.14 ± 0.32°C, 12.20 ± 10.61°C, and 33.85 ± 8.67 kg, respectively. The maximum and minimum RT were recorded at 2330 and 1000 h, respectively. Ambient temperature increased RT. Summer RT was significantly greater than spring, fall, or winter RT. The effect of MY on RT varied by breed, season, and AT. Crossbred RT was significantly lower than Holstein RT after adjusting for MY. Crossbred RT responded less to increasing AT than did Holstein RT, potentially indicating improved heat tolerance among these crossbred dairy cows. Reticulorumen temperature increased more dramatically for cows with greater milk yield as AT increased, demonstrating that high-producing cows are more susceptible to heat stress than low-producing cows. These results could be useful in interpretation of automatic temperature system data, heat stress management, and genetic selection of heat-tolerant cows.     

The effect of day-only versus day-plus-night cooling of dairy cows

The aim of this study was to assess effects on milk yield (MY), rumen temperature, and panting score when lactating dairy cows were cooled during the day only or during the day and night. The study was conducted over 106 d during using 120 multiparous Holstein-Friesian cows assigned to 2 treatments (60 cows/treatment; 2 pens/treatment): (1) day cooling (DC): overhead sprinklers (large droplet) and fans while in the dairy holding yard only, shade and fans at the feedpad, and a shaded loafing area; and (2) enhanced day+night cooling (EDN): overhead sprinklers (large droplet) and fans in dairy holding yard, ducted air blowing onto cows during milking, plus thorough wetting (shower array) on exit from dairy; shade and fans at feedpad (turned off at night); and shaded loafing area + ducted fan-forced air blowing onto cows at night. The ducted air at night was manually activated at 2030 h when the maximum daily temperature-humidity index exceeded 75 and remained on until 0430 h the next day. The cows were fed a total mixed ration ad libitum, and feed intake was determined on a pen basis. Rumen temperature and cow activity were obtained from each cow at 10-min intervals via rumen boluses. Panting scores were obtained by direct observation 4 times a day at approximately 0430, 0930, 1530, and 2030 h. Cows were milked twice daily: 0500 to 0600 h and 1600 to 1700 h. Individual MY were obtained at each milking and combined to give individual daily totals. The EDN cows had greater daily MY (+2.05 kg/cow per day) over the duration of the study compared with DC cows. Rumen temperature during the third heat wave was lower for EDN (39.51 ± 0.01°C) than for DC (39.66 ± 0.01°C) cows. During the most severe heat wave (heat wave 3), MY for the 2 groups was similar, but over the 6 d following the heat wave, EDN cows had greater daily MY (+3.61 kg/cow per day). Rumen temperature was lower for EDN (39.58 ± 0.01°C) than for DC (40.10 ± 0.01°C) cows.     

Modeling the effects of heat stress in animal performance and enteric methane emissions in lactating dairy cows

Heat stress (HS) negatively affects dry matter intake (DMI), milk yield (MY), feed efficiency (FE), and free water intake (FWI) in dairy cows, with detrimental consequences to animal welfare, health, and profitability of dairy farms. Absolute enteric methane (CH₄) emission, yield (CH₄/DMI), and intensity (CH₄/MY) may also be affected. Therefore, the goal of this study was to model the changes in dairy cow productivity, water intake, and absolute CH₄ emissions, yield, and intensity with the progression (days of exposure) of a cyclical HS period in lactating dairy cows. Heat stress was induced by increasing the average temperature by 15°C (from 19°C in the thermoneutral period to 34°C) while keeping relative humidity constant at 20% (temperature-humidity index peaks of approximately 83) in climate-controlled chambers for up to 20 d. A database composed of individual records (n = 1,675) of DMI and MY from 82 heat-stressed lactating dairy cows housed in environmental chambers from 6 studies was used. Free water intake was also estimated based on DMI, dry matter, crude protein, sodium, and potassium content of the diets, and ambient temperature. Absolute CH₄ emissions was estimated based on DMI, fatty acids, and dietary digestible neutral detergent fiber content of the diets. Generalized additive mixed-effects models were used to describe the relationships of DMI, MY, FE, and absolute CH₄ emissions, yield, and intensity with HS. Dry matter intake and absolute CH₄ emissions and yield reduced with the progression of HS up to 9 d, when it started to increase again up to 20 d. Milk yield and FE reduced with the progression of HS up to 20 d. Free water intake (kg/d) decreased during the exposure to HS mainly because of a reduction in DMI; however, when expressed in kg/kg of DMI it increased modestly. Methane intensity also reduced initially up to d 5 during HS exposure but then started to increase again following the DMI and MY pattern up to d 20. However, the reductions in CH₄ emissions (absolute, yield, and intensity) occurred at the expense of decreases in DMI, MY, and FE, which are not desirable. This study provides quantitative predictions of the changes in animal performance (DMI, MY, FE, FWI) and CH₄ emissions (absolute, yield, and intensity) with the progression of HS in lactating dairy cows. The models developed in this study could be used as a tool to help dairy nutritionists to decide when and how to adopt strategies to mitigate the negative effects of HS on animal health and performance and related environmental costs. Thus, more precise and accurate on-farm management decisions could be taken with the use of these models. However, application of the developed models outside of the ranges of temperature-humidity index and period of HS exposure included in this study is not recommended. Also, validation of predictive capacity of the models to predict CH₄ emissions and FWI using data from in vivo studies where these variables are measured in heat-stressed lactating dairy cows is required before these models can be used.     

Temperature-humidity indices as indicators of milk production losses due to heat stress

Meteorological data (1993 to 2004) from 2 public weather stations in Phoenix, Arizona, and Athens, Georgia, were analyzed with test day milk yield data from herds near weather stations to identify the most appropriate temperature-humidity index (THI) to measure losses in milk production due to heat stress in the semiarid climate of Arizona and the humid climate of Georgia. Seven THI with different weightings of dry bulb temperature and humidity were compared. Test-day data were analyzed using 2 models to determine threshold of heat stress and rate of decline of milk production associated with a specific THI. Differences in thresholds of heat stress were found among indices and between regions. Indices with higher weights on humidity were best in the humid climate, whereas indices with larger weights on temperature were the best indicators of heat stress in the semiarid climate. Humidity was the limiting factor of heat stress in humid climates, whereas dry bulb temperature was the limiting factor of heat stress in dry climates.     

How heat stress conditions affect milk yield,composition, and price in Italian Holstein herds

An edited data set of 700 bulk and 46,338 test-day records collected between 2019 and 2021 in 42 Holstein-dominated farms in the Veneto Region (North of Italy) was available for the present study. Information on protein, fat and lactose content, somatic cell count, and somatic cell score was available in bulk milk as well as individual test-day records, whereas urea concentration (mg/dL), differential somatic cell count (%), and milk yield (kg/d) were available for test-day records only. Milk features were merged with meteorological data retrieved from 8 weather stations located maximum 10 km from the farms. The daily and weekly temperature-humidity index (THI; wTHI) and maximum daily (MTHI) and weekly temperature-humidity index were associated with each record to evaluate the effect of heat stress conditions on milk-related traits through linear mixed models. Least squares means were estimated to evaluate the effect of THI and, separately, of MTHI on milk characteristics correcting for conventional systematic factors. Overall, heat stress conditions lowered the quality of both bulk milk and test-day records, with fat and protein content being greatly reduced, and somatic cell score and differential somatic cell count augmented. Milk yield was not affected by either THI or MTHI in this data set, but the effect of elevated THI and MTHI was in general stronger on test-day records than on bulk milk. Farm-level economic losses of reduced milk quality rather than reduced yield as consequence of elevated THI or MTHI was estimated to be between $23.57 and $43.98 per farmer per day, which is of comparable magnitude to losses resulting from reduced production. Furthermore, MTHI was found to be a more accurate indicator of heat stress experienced by a cow, explaining more variability of traits compared with THI. The negative effect of heat stress conditions on quality traits commences at lower THI/MTHI values compared with milk yield. Thus, a progressive farmers' income loss due to climatic changes is already a reality and it is mainly due to deterioration of milk quality rather than quantity in the studied area.     

Effects of wildfire smoke exposure on innate immunity,metabolism, and milk production in lactating dairy cows

Wildfires are particularly prevalent in the Western United States, home to more than 2 million dairy cows that produce more than 25% of the nation's milk. Wildfires emit fine particulate matter (PM_2.5) in smoke, which is a known air toxin and is thought to contribute to morbidity in humans by inducing inflammation. The physiological responses of dairy cows to wildfire PM_2.5 are unknown. Herein we assessed the immune, metabolic, and production responses of lactating Holstein cows to wildfire PM_2.5 inhalation. Cows (primiparous, n = 7; multiparous, n = 6) were monitored across the wildfire season from July to September 2020. Cows were housed in freestall pens and thus were exposed to ambient air quality. Air temperature, relative humidity, and PM_2.5 were obtained from a monitoring station 5.7 km from the farm. Animals were considered to be exposed to wildfire PM_2.5 if daily average PM_2.5 exceeded 35 µg/m³ and wildfire and wind trajectory mapping showed that the PM_2.5 derived from active wildfires. Based on these conditions, cows were exposed to wildfire PM_2.5 for 7 consecutive days in mid-September. Milk yield was recorded daily and milk components analysis conducted before, during, and after exposure. Blood was taken from the jugular vein before, during, and after exposure and assayed for hematology, blood chemistry, and blood metabolites. Statistical analysis was conducted using mixed models including PM_2.5, temperature-humidity index (THI), parity (primiparous or multiparous), and their interactions as fixed effects and cow as a random effect. Separate models included lags up to 7 d to identify delayed and persistent effects from wildfire PM_2.5 exposure. Exposure to elevated PM_2.5 from wildfire smoke resulted in lower milk yield during exposure and for 7 d after last exposure and higher blood CO₂ concentration, which persisted for 1 d following exposure. We observed a positive PM_2.5 by THI interaction for eosinophil and basophil count and a negative PM_2.5 by THI interaction for red blood cell count and hemoglobin concentration after a 3-d lag. Neutrophil count was also lower with a combination of higher THI and PM_2.5. We found no discernable effect of PM_2.5 on haptoglobin concentration. Effects of PM_2.5 and THI on metabolism were contingent on day of exposure. On lag d 0, blood urea nitrogen (BUN) was reduced with higher combined THI and PM_2.5, but on subsequent lag days, THI and PM_2.5 had a positive interaction on BUN. Conversely, THI and PM_2.5 had a positive interacting effect on nonesterified fatty acids (NEFA) on lag d 0 but subsequently caused a reduction in circulating NEFA concentration. Our results suggest that exposure to high wildfire-derived PM_2.5, alone or in concert with elevated THI, alters systemic metabolism, milk production, and the innate immune system.     

Methodological guidelines: Cow milk mid-infrared spectra to predict reference enteric methane data collected by an automated head-chamber system

Various methodological protocols were tested on milk samples from cows fed diets affecting both methanogenesis and milk synthesis to identify the best approach for the prediction of GreenFeed system (GF) measured methane (CH₄) emissions by milk mid-infrared (MIR) spectroscopy. The models developed were also tested on a data set from cows fed chemical inhibitors of CH₄ emission [3-nitrooxypropanol (3NOP)] that just marginally affect milk composition. A total of 129 primiparous and multiparous Holstein cows fed diets with different methanogenic potential were considered. Individual milk yield (MY) and dry matter intake were recorded daily, whereas fat- and protein-corrected milk (FPCM) was recorded twice a week. The MIR spectra from 2 consecutive milkings were collected twice a week. Twenty CH₄ spot measurements with GF were taken as the basic measurement unit (BMU) of CH₄. The equations were built using partial least squares regression by splitting the database into calibration and validation data sets (excluding 3NOP samples). Models were developed for milk MIR spectra by milking and on day spectra obtained by averaging spectra from 2 consecutive milkings. Models based on day spectra were calibrated by using CH₄ reference data for a measurement duration of 1, 2, 3, or 4 BMU. Models built from the average of the day spectra collected during the corresponding CH₄ measurement periods were developed. Corrections of spectra by days in milk (DIM) and the inclusion of parity, MY, and FPCM as explanatory variables were tested as tools to improve model performance. Models built on day milk MIR spectra gave slightly better performances that those developed using spectra from a single milking. Long duration of CH₄ measurement by GF performed better than short duration: the coefficient of determination of validation (R²V) for CH₄ emissions expressed in grams per day were 0.60 vs. 0.52 for 4 and 1 BMU, respectively. When CH₄ emissions were expressed as grams per kilogram of dry of matter intake, grams per kilogram of MY, or grams per kilogram of FPCM, performance with a long duration also improved. Coupling GF reference data with the average of milk MIR spectra collected throughout the corresponding CH₄ measurement period gave better predictions than using day spectra (R²V = 0.70 vs. 0.60 for CH₄ as g/d on 4 BMU). Correcting the day spectra by DIM improved R²V compared with the equivalent DIM-uncorrected models (R²V = 0.67 vs. 0.60 for CH₄ as g/d on 4 BMU). Adding other phenotypic information as explanatory variables did not further improve the performance of models built on single day DIM-corrected spectra, whereas including MY (or FPCM) improved the performance of models built on the average of spectra (uncorrected by DIM) recorded during the CH₄ measurement period (R²V = 0.73 vs. 0.70 for CH₄ as g/d on 4 BMU). When validating the models on the 3NOP data set, predictions were poor without (R²V = 0.13 for CH₄ as g/d on 1 BMU) or with (R²V = 0.31 for CH₄ as g/d on 1 BMU) integration of 3NOP data in the models. Thus, specific models would be required for CH₄ prediction when cows receive chemical inhibitors of CH₄ emissions not affecting milk composition.     

Physiological responses and lactational performances of late-lactation dairy goats under heat stress conditions

Eight Murciano-Granadina dairy goats in late lactation were exposed to different ambient conditions, using metabolic cages in a climatic chamber. The experimental design was a crossover (2 periods of 35 d and 4 goats each) and conditions were (1) thermal neutral (TN; 15 to 20°C day-night) and (2) heat stress (HS; 12-h day at 37°C and 12-h night at 30.5°C). Humidity was maintained at 40% and light-dark was constant (12–12 h). The forage:concentrate ratio was adjusted daily for maintaining similar value in TN and HS goats (70:30). Water was freely available at ambient temperature. Rectal temperature and respiratory rate (0800, 1200 and 1700 h) and milk yield were recorded daily, whereas milk composition, nonesterified fatty acids and haptoglobin in blood were analyzed weekly. At d 25, additional blood samples were taken for analysis of metabolites and indicators of the acid-base balance. Digestibility coefficients and N balance were determined (d 31 to 35) and body weight was recorded (d 35). Compared with TN goats, HS goats experienced greater rectal temperature (+0.58°C), respiratory rate (+48 breaths/min), water intake (+77%) and water evaporation (+207%). Intake of HS goats rapidly declined until d 7 (−40%), partially recovered from d 7 to 19, and steadied thereafter (−14%). No changes in digestibility or N balance were detected. Blood nonesterified fatty acids and haptoglobin peaked at d 7 in HS goats but did not vary thereafter. Although milk yield did not vary by treatment, milk of HS goats contained −12.5% protein and −11.5% casein than TN goats. Panting reduced concentration and pressure of CO₂ in the blood of HS goats, but they were able to maintain their blood pH similar to the TN group by lowering HCO₃⁻ and increasing Cl⁻ concentrations in their blood. In conclusion, HS dairy goats showed dramatic physiological changes during the first week of treatment and partially recovered thereafter. They were able to maintain milk yield by losing body mass, but milk protein content and protein yield were depressed. Further research is needed to assess the response of dairy goats to HS at earlier stages of lactation.     

Genetic determination of the onset of heat stress on daily milk production in the US Holstein cattle

Existence of individual variation in the onset of heat stress for daily milk yield of dairy cows was assessed. Data included 353,376 test-day records of 38,383 first-parity Holsteins from a random sample of US herds. Three hierarchical models were investigated. Model 1 inferred the value of a temperature-humidity index (THI) at which mean yield began to decline as well as the extent of that decline. Model 2 assumed individual variation in yield decline beyond a common THI threshold. Model 3 additionally assumed individual variation for the onset of heat stress. Deviance information criteria indicated the superiority of model 3 over model 2. For model 2, genetic correlation between milk yield in the absence of heat stress and the THI threshold for heat stress was −0.4 (0.11) [marginal posterior mean (marginal posterior standard deviation)]. For model 3, genetic correlations were −0.53 (0.05) between milk yield and THI threshold and −0.62 (0.08) between milk yield and yield decay beyond the THI threshold. Total standard deviation (sum of additive genetic and permanent environmental standard deviations) for the THI threshold was 3.95 (0.06), and more than half of that variation had an additive genetic origin [56% (5%)]. Because of the high genetic correlation [0.95 (0.03)] between yield decay and THI threshold with model 3, using only one of them as a selection criterion for heat tolerance would modify the other in the desired direction.     

The effects of heat stress in Italian Holstein dairy cattle

The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from −0.24 to −0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives.     

A novel method of analyzing daily milk production and electrical conductivity to predict disease onset

This study evaluates the changes in milk production (yield; MY) and milk electrical conductivity (MEC) before and after disease diagnosis and proposes a cow health monitoring scheme based on observing individual daily MY and MEC. All reproductive and health events were recorded on occurrence, and MY and MEC were collected at each milking from January 2004 through November 2006 for 587 cows. The first 24 mo (January 2004 until December 2005) were used to investigate the effects of disease on MY and MEC, model MY and MEC of healthy animals, and develop a health monitoring scheme to detect disease based on changes in a cow's MY or MEC. The remaining 11 mo of data (January to November 2006) were used to compare the performance of the health monitoring schemes developed in this study to the disease detection system currently used on the farm. Mixed model was used to examine the effect of diseases on MY and MEC. Days in milk (DIM), DIM × DIM, and ambient temperature were entered as quantitative variables and number of calves, parity, calving difficulty, day relative to breeding, day of somatotropin treatment, and 25 health event categories were entered as categorical variables. Significant changes in MY and MEC were observed as early as 10 and 9 d before diagnosis. Greatest cumulative effect on MY over the 59-d evaluation period was estimated for miscellaneous digestive disorders (mainly diarrhea) and udder scald, at −304.42 and −304.17 kg, respectively. The greatest average daily effect was estimated for milk fever with a 10.36-kg decrease in MY and 8.3% increase in MEC. Milk yield and MEC was modeled by an autoregressive model using a subset of healthy cow records. Six different self-starting cumulative sum and Shewhart charting schemes were designed using 3 different specificities (98, 99, and 99.5%) and based on MY alone or MY and MEC. Monitoring schemes developed in this study issue alerts earlier relative to the day of diagnosis of udder, reproductive, or metabolic problems, are more sensitive, and give fewer false-positive alerts than the disease detection system currently used on the farm.     

Short communication: Increased somatic cell count is associated with milk loss and reduced feed efficiency in lactating dairy cows

The objective was to evaluate the relationship of somatic cell count (SCC; cells/mL) with milk yield, energy-corrected milk yield (ECM; kg/d), dry matter intake (DMI; kg/d), feed efficiency for milk (FE_MY; kg of milk/kg of DMI), and feed efficiency for ECM (FE_ECM; kg of ECM/kg of DMI) in lactating dairy cows. We analyzed an SCC database consisting of 7 experiments, which were conducted at The Pennsylvania State University's Dairy Teaching and Research Center between 2009 and 2015. The experiments included in the SCC database were randomized block designs and investigated dietary effects on cow performance over 6 to 11 wk. Each experiment took repeated measurements of SCC, milk yield, milk composition, and DMI. After exclusion of records from cows without lactation number, days in milk, and only 1 measurement, the database comprised 1,094 observations of 254 cows for estimating the effect of SCC on milk yield, DMI, and FE_MY and 1,079 observations of 250 cows for estimating the effect of SCC on ECM and FE_ECM. Data were analyzed in R using a linear mixed model with natural logarithm of SCC, lactation number (1, 2, and ≥3), days in milk, and the interactions of the linear predictors as fixed effects and cow within block and experiment as random effect. Natural logarithm of SCC was negatively correlated with milk yield, ECM, DMI, FE_MY, and FE_ECM. Our results suggest that a cow with relatively high SCC (250,000 cells/mL) compared with a cow with a relatively low SCC (50,000 cells/mL) produces, on average, 1.6 kg/d less milk, consumes 0.3 kg/d less DMI, produces 0.04 kg less milk per kg of DMI, and produces 0.03 less ECM per kg of DMI. The observed decrease of feed efficiency with increased SCC adds to previously known economic losses and environmental impacts associated with mastitis, which should provide a further incentive to control mastitis in dairy cows.     

Genetic effects of heat stress on milk yield of Thai Holstein crossbreds

The threshold for heat stress on milk yield of Holstein crossbreds under climatic conditions in Thailand was investigated, and genetic effects of heat stress on milk yield were estimated. Data included 400,738 test-day milk yield records for the first 3 parities from 25,609 Thai crossbred Holsteins between 1990 and 2008. Mean test-day milk yield ranged from 12.6 kg for cows with <87.5% Holstein genetics to 14.4 kg for cows with ≥93.7% Holstein genetics. Daily temperature and humidity data from 26 provincial weather stations were used to calculate a temperature-humidity index (THI). Test-day milk yield varied little with THI for first parity except above a THI of 82 for cows with ≥93.7% Holstein genetics. For third parity, test-day milk yield started to decline after a THI of 74 for cows with ≥87.5% Holstein genetics and declined more rapidly after a THI of 82. A repeatability test-day model with parities as correlated traits was used to estimate heat stress parameters; fixed effects included herd-test month-test year and breed groups, days in milk, calving age, and parity; random effects included 2 additive genetic effects, regular and heat stress, and 2 permanent environment, regular and heat stress. The threshold for effect of heat stress on test-day milk yield was set to a THI of 80. All variance component estimates increased with parity; the largest increases were found for effects associated with heat stress. In particular, genetic variance associated with heat stress quadrupled from first to third parity, whereas permanent environmental variance only doubled. However, permanent environmental variance for heat stress was at least 10 times larger than genetic variance. Genetic correlations among parities for additive effects without heat stress considered ranged from 0.88 to 0.96. Genetic correlations among parities for additive effects of heat stress ranged from 0.08 to 0.22, and genetic correlations between effects regular and heat stress effects ranged from −0.21 to −0.33 for individual parities. Effect of heat stress on Thai Holstein crossbreds increased greatly with parity and was especially large after a THI of 80 for cows with a high percentage of Holstein genetics (≥93.7%). Individual sensitivity to heat stress was more environmental than genetic for Thai Holstein crossbreds.     

Relationship of thermal status to productivity in heat-stressed dairy cows given recombinant bovine somatotropin

The responses of lactating Holstein cows to daily administration of bovine somatotropin (bST) were measured at thermoneutrality (Tn) and under both constant and cycled heat-stress conditions to determine the relationship between thermal status and bST-induced shifts in milk production. All tests included a 5-d acclimation period at Tn (18°C), followed by a 2-d increase in ambient temperature to 28.5°C. After d 3, ambient temperature was cycled between 28.5 (day) and 25.5°C (night) for 4 d. Daily injections with either 31 mg of bST or saline began on d 1 of the experiment. Milk production, feed intake, and respiratory rate (RR) were measured daily. Intraperitoneal, telemetric temperature transmitters were used for a continuous measure of core body temperature (T_core). Blood samples were collected during each phase to evaluate the changes in serum chemistry in response to bST and heat stress. Following a 15-d recovery, cows were switched across injection treatments and the study was repeated. Milk production decreased by ∼18.4% below the initial yield at Tn by the end of 7 d of heat challenge. Although a reduction in milk production occurred during heat stress in both groups, milk production was higher in bST-treated cows compared with control cows during periods of constant and cyclic heat. Likewise, bST treatment during the entire period increased the milk-to-feed ratio over the control level by ∼11.3%. Plasma insulin-like growth factor 1 and serum nonesterified fatty acids accompanied the increased growth hormone level with bST treatment (∼122.0 and 88.8%, respectively), whereas plasma urea nitrogen was reduced by ∼13.3% to reflect the shift to lipid metabolism. There was no difference in T_core of the treatment and control groups at Tn. Both bST and control cows increased RR and T_core above the Tn level by ∼94.8 and 2.9%, respectively, during constant heat, with a greater increase in T_core of bST-treated compared with control cows (∼0.6%). The increase in RR during heat stress preceded T_core by 1 d for both groups. During cyclic heat, T_core decreased by ∼0.4% compared with constant heat in both the control and bST-treated groups. Bovine somatotropin treatment increased milk production similarly during the Tn and heat-stress periods, ∼8.3% over the control; however, the bST-induced increase in milk-to-feed ratio was greatest during the continuous and cyclic heat-stress phases, ∼16.2%. This increase occurred together with the elevation in T_core.     

Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows

Automatic milking systems record an enormous amount of data on milk yield and the cow itself. These type of big data are expected to contain indicators for health and resilience of cows. In this study, the aim was to define and estimate heritabilities for traits related with fluctuations in daily milk yield and to estimate genetic correlations with existing functional traits, such as udder health, fertility, claw health, ketosis, and longevity. We used daily milk yield records from automatic milking systems of 67,025 lactations in the first parity from 498 herds in the Netherlands. We defined 3 traits related to the number of drops in milk yield using Student t-tests based on either a rolling average (drop rolling average) or a regression (drop regression) and the natural logarithm of the within-cow variance of milk yield (LnVar). Average milk yield was added to investigate the relationships between milk yield and these new traits. ASReml was used to estimate heritabilities, breeding values (EBV), and genetic correlations among these new traits and average milk yield. Approximate genetic correlations were calculated using correlations between EBV of the new traits and existing EBV for health and functional traits correcting for nonunity reliabilities using the Calo method. Partial genetic correlations controlling for persistency and average milk yield and relative contributions to reliability were calculated to investigate whether the new traits add new information to predict fertility, health, and longevity. Heritabilities were 0.08 for drop rolling average, 0.06 for drop regression, and 0.10 for LnVar. Approximate genetic correlations between the new traits and the existing health traits differed quite a bit, with the strongest correlations (?0.29 to ?0.52) between LnVar and udder health, ketosis, persistency, and longevity. This study shows that fluctuations in daily milk yield are heritable and that the variance of milk production is best among the 3 fluctuations traits tested to predict udder health, ketosis, and longevity. Using the residual variance of milk production instead of the raw variance is expected to further improve the trait to breed healthy, resilient, and long-lasting dairy cows.     

Effect of heat stress during the dry period on mammary gland development

Heat stress during the dry period negatively affects hepatic metabolism and cellular immune function during the transition period, and milk production in the subsequent lactation. However, the cellular mechanisms involved in the depressed mammary gland function remain unknown. The objective of the present study was to determine the effect of heat stress during the dry period on various indices of mammary gland development of multiparous cows. Cows were dried off approximately 46 d before expected calving and randomly assigned to 2 treatments, heat stress (HT, n = 15) or cooling (CL, n = 14), based on mature equivalent milk production. Cows in the CL treatment were provided with sprinklers and fans that came on when ambient temperatures reached 21.1°C, whereas HT cows were housed in the same barn without fans and sprinklers. After parturition, all cows were housed in a freestall barn with cooling. Rectal temperatures were measured twice daily (0730 and 1430 h) and respiration rates recorded at 1500 h on a Monday-Wednesday-Friday schedule from dry off to calving. Milk yield and composition were recorded daily up to 280 d in milk. Daily dry matter intake was measured from dry off to 42 d relative to calving. Mammary biopsies were collected at dry off, −20, 2, and 20 d relative to calving from a subset of cows (HT, n = 7; CL, n = 7). Labeling with Ki67 antigen and terminal deoxynucleotidyl transferase deoxyuridine triphosphate nick-end labeling were used to evaluate mammary cell proliferation and apoptosis, respectively. The average temperature-humidity index during the dry period was 76.6 and not different between treatments. Heat-stressed cows had higher rectal temperatures in the morning (38.8 vs. 38.6°C) and afternoon (39.4 vs. 39.0°C), greater respiration rates (78.4 vs. 45.6 breath/min), and decreased dry matter intake (8.9 vs. 10.6 kg/d) when dry compared with CL cows. Relative to HT cows, CL cows had greater milk production (28.9 vs. 33.9 kg/d), lower milk protein concentration (3.01 vs. 2.87%), and tended to have lower somatic cell score (3.35 vs. 2.94) through 280 d in milk. Heat stress during the dry period decreased mammary cell proliferation rate (1.0 vs. 3.3%) at −20 d relative to calving compared with CL cows. Mammary cell apoptosis was not affected by prepartum heat stress. We conclude that heat stress during the dry period compromises mammary gland development before parturition, which decreases milk yield in the next lactation.