Individual Cow Level Research Articles

Variation in the carbon footprint of milk production on smallholder dairy farms in central Kenya

Milk production by smallholders in Africa has a high carbon footprint (CF) and is predicted to increase significantly in the coming decades. This study, based on data from a sample of 382 farms in central Kenya, is the first assessment of the CF of milk production in Sub-Saharan Africa based on a large dataset of actual farm management practices. The aims of the study were (1) to determine whether there are significant differences in the CF of farms with different feeding systems (i.e., zero-grazing, grazing and mixed systems), and (2) to identify factors associated with variability in CF between farms. This analysis is used to identify options for mitigating GHG emissions from Kenya’s growing dairy production. Average CF ranged between 2.19 and 3.13 kg CO2e/kg fat and protein corrected milk (FPCM), depending on the GWPs and allocation method used. Analysis based on variability in farm management showed that CF was similar between farms with zero-grazing and mixed feeding systems, and significantly higher on farms with grazing only feeding systems, but no difference was detected when input parameter uncertainty was considered. At individual cow level, variation in milk yields explained more than 70% of the variation in GHG intensity. At farm level, milk yield explained less than half of variation in CF. CF was correlated with feed characteristics, manure management practices and herd size and composition. In particular, the level of concentrate use was positively correlated with CF, and was the most important factor explaining variation in CF not attributable to variation in milk yield. Our findings suggest that promoting balanced feed rations and feeding concentrate according to cows’ needs across the lactation cycle could provide opportunities to both increase milk production and reduce the CF of milk production on smallholder farms in central Kenya. Supporting smallholder farmers to implement these mitigation options will require interventions at several levels in feed supply chains in the dairy sector.

Opportunities and limitations of milk mid-infrared spectra-based estimation of acetone and β-hydroxybutyrate for the prediction of metabolic stress and ketosis in dairy cows

Subclinical (SCK) and clinical (CK) ketosis are metabolic disorders responsible for big losses in dairy production. Although Fourier-transform mid-infrared spectrometry (FTIR) to predict ketosis in cows exposed to great metabolic stress was studied extensively, little is known about its suitability in predicting hyperketonemia using individual samples, e.g. in small dairy herds or when only few animals are at risk of ketosis. The objective of the present research was to determine the applicability of milk metabolites predicted by FTIR spectrometry in the individual screening for ketosis. In experiment 1, blood and milk samples were taken every two weeks after calving from Holstein (n = 80), Brown Swiss (n = 72) and Swiss Fleckvieh (n = 58) cows. In experiment 2, cows diagnosed with CK (n = 474) and 420 samples with blood β-hydroxybutyrate [BHB] <1.0 mmol/l were used to investigate if CK could be detected by FTIR-predicted BHB and acetone from a preceding milk control. In experiment 3, correlations between data from an in farm automatic milk analyser and FTIR-predicted BHB and acetone from the monthly milk controls were evaluated. Hyperketonemia occurred in majority during the first eight weeks of lactation. Correlations between blood BHB and FTIR-predicted BHB and acetone were low (r = 0.37 and 0.12, respectively, P < 0.0001), as well as the percentage of true positive values (11.9 and 16.6%, respectively). No association of FTIR predicted ketone bodies with the interval of milk sampling relative to CK diagnosis was found. Data obtained from the automatic milk analyser were moderately correlated with the same day FTIR-predicted BHB analysis (r = 0.61). In conclusion, the low correlations with blood BHB and the small number of true positive samples discourage the use of milk mid-infrared spectrometry analyses as the only method to predict hyperketonemia at the individual cow level.

Accuracy of estimates of milk production per lactation from limited test-day and recall data collected at smallholder dairy farms

Milk production per lactation (MPL) is a key metric of dairy farms. Accurate estimation of MPL requires regular recording, which is laborious and costly. In smallholder systems in the tropics, therefore, generally very few records are available to estimate MPL. Cross-sectional studies collect only one single record per lactation, and even longitudinal studies usually yield only a limited number of records per lactation. Such data recording methods, therefore, are sometimes extended with records recalled by farmers. The accuracy of MPL-estimates based on such limited and imperfect data, however, is unknown. The aim of the present study was to assess the accuracy of MPL-estimates from a single record and a limited number of records per lactation, obtained from smallholder dairy farms in Nakuru County, Kenya. Test-day records from a milk recording scheme for 114 smallholders were used to prepare three datasets with: i) a complete number of test-days (CTD, 5803 records), ii) a limited number of test-days (LTD, 1583 records), and iii) a single test-day (STD, 471 records). In addition, farmers’ recall data (i.e. farmers retrieve information from the past, through memory) from a survey of 29 farms with 56 lactations were used to prepare two datasets with: i) a limited number of recall moments per lactation (LRM, 200 records), and ii) a single recall moment per lactation (SRM, 56 records). These five datasets were used to derive MPL-estimates, at individual cow level or at herd level. The latter was done to mimic a situation without individual cow data, but only herd data (i.e. yield and size). MPL-estimates for CTD were set as a benchmark to quantify the accuracies, based on the relative mean absolute error (RMAE) and root mean square error (RMSE), of MPL-estimates for LTD and STD. As a benchmark dataset was absent for recall data, we computed a virtual benchmark to quantify the accuracies of MPL-estimates for LRM and SRM. At cow level, accuracy of MPL-estimates was highest for LTD (RMAE 15%), and lowest for SRM (RMAE 28%), while accuracies for STD and LRM were intermediate (RMAEs ~ 20%). At herd level, accuracy was higher for STD (RMAE 13%) than for SRM (RMAE 25%). We also showed that to detect a difference of, for example, 100 kg in MPL we need 3002 cows for CTD, and between 3620 and 5003 cows when using alternative data collection methods. Hence, depending on the study objective, alternative data recording methods provide labor-saving and cost-effective ways to estimate MPL in data-scarce smallholder dairy systems.

Relative effect of milk constituents on fertility performance of milk-recorded, spring-calving dairy cows in Ireland

Several studies have reported associations between milk composition data and fertility performance. However, no work to date has estimated the effect of milk constituents on fertility performance in cows with low milk constituent concentrations. The objective of this study was to assess the association between milk constituents, animal characteristics, and time from mating start date (MSD) to conception using survival analysis. Furthermore, we aimed to investigate the relative effect of each variable by predicting median times to conception for animals with different combinations of characteristics and milk compositions. The final data set consisted of 87,227 cow lactation records from 64,519 cows in 2,049 herds with calving dates from 2010 to 2013. Milk recording data from each lactation were used, including test day recordings at 0 to 30, 31 to 60, and 61 to 90 d in milk (DIM). The analysis was limited to spring-calving cows (i.e., animals calving from January to May inclusive). Mating start date was determined for each unique herd in each year. A cow-specific MSD (MSDcow) was defined taking into consideration the MSD for each herd and the calving date and a minimum calving to insemination interval of each herd year. The conception date for each cow was estimated using the subsequent calving date. Cows with no subsequent calving date were assumed not to have conceived. Time from MSDcow to approximate conception date was analyzed using survival analysis. Cox proportional hazard models were constructed for each of the 3 recording windows: 0 to 30, 31 to 60, and 61 to 90 DIM. A fourth model was used to assess the dynamics of milk composition over the 3 windows. To investigate the effect of these variables, model outputs were used to create parametric accelerated failure time models to predict median survival times for animals at the 10th and 90th percentiles of the variable of interest but otherwise identical across the rest of the variables. Results demonstrated that fertility breeding subindex had the largest effect on time from MSDcow to conception, with an additional 62 d open for those in the 10th percentile versus those in the 90th percentile. Of the milk constituents, milk lactose concentration had the greatest effect on MSD to conception, particularly when measured from 0 to 30 DIM. An additional 10 d open resulted from comparing those in the 10th and 90th percentiles. Milk protein concentration, although statistically significant, had a lower effect on fertility outcome when comparing cows in the 10th and 90th percentiles for this exposure variable. The greatest effect was found in the 61 to 90 DIM recording window, where cows in the 10th percentile had an additional 9 d open at the subsequent breeding season compared with those in the 90th percentile. Overall, our study shows that although the associations between milk constituents and fertility are statistically significant, their overall influence in determining MSD to conception in this study population is relatively modest, particularly compared with fertility breeding subindex, when comparing cows at the 10th and 90th percentiles. Of the milk constituents measured, milk lactose concentration measured at 0 to 30 DIM had the greatest effect in determining fertility outcome when comparing cows at the 10th and 90th percentiles. The predictive value of early-lactation test day milk composition data on hazard of pregnancy during the following breeding period, within a spring-calving context, appears to be relatively modest at the individual-cow level. Further work is required to test the usefulness of these associations at the herd level.

Cohort-level disease prediction by extrapolation of individual-level predictions in transition dairy cattle

Dairy cattle experience metabolic stress during the transition from late gestation to early lactation resulting in higher risk for several economically important diseases (e.g. mastitis, metritis, and ketosis). Metabolic stress is described as a physiological state composed of 3 processes: nutrient metabolism, oxidative stress, and inflammation. Current strategies for monitoring transition cow nutrient metabolism include assessment of plasma non-esterified fatty acids and beta-hydroxybutyrate concentrations around the time of calving. Although this method is effective at identifying cows with higher disease risk, there is often not enough time to implement intervention strategies to prevent health disorders from occurring around the time of calving. Previously, we published predictive models for early lactation diseases at the individual cow level at dry-off. However, it is unknown if predictive probabilities from individual-level models can be aggregated to the cohort level to predict cohort-level incidence. Therefore, our objective was to test different data aggregation methods using previously published models that represented the 3 components of metabolic stress (nutrient metabolism, oxidative stress, and inflammation). We included 277 cows from five Michigan dairy herds for this prospective cohort study. On each farm, two to four calving cohorts were formed, totaling 18 cohorts. We measured biomarker data at dry-off and followed the cows until 30 days post-parturition for cohort disease incidence, which was defined as the number of cows: 1) having one or more clinical transition disease outcome, and/or 2) having an adverse health event (abortion or death of calf or cow) within each cohort. We tested three different aggregation methods that we refer to as the p-central, p-dispersion, and p-count methods. For the p-central method, we calculated the averaged predicted probability within each cohort. For the p-dispersion method, we calculated the standard deviation of the predicted probabilities within a cohort. For the p-count method, we counted the number of cows above a specified threshold of predicted probability within each cohort. We built four sets of models: one for each aggregation method and one that included all three aggregation methods (p-combined method). We found that the p-dispersion method was the only method that produced viable predictive models. However, these models tended to overestimate incidence in cohorts with low observed counts and underestimate risk in cohorts with high observed counts.

Prediction of blood β-hydroxybutyrate content and occurrence of hyperketonemia in early-lactation, pasture-grazed dairy cows using milk infrared spectra

The objective of this study was to evaluate the ability of milk infrared spectra to predict blood β-hydroxybutyrate (BHB) concentration for use as a management tool for cow metabolic health on pasture-grazed dairy farms and for large-scale phenotyping for genetic evaluation purposes. The study involved 542 cows (Holstein-Friesian and Holstein-Friesian × Jersey crossbreds), from 2 farms located in the Waikato and Taranaki regions of New Zealand that operated under a seasonal-calving, pasture-based dairy system. Milk infrared spectra were collected once a week during the first 5 wk of lactation. A blood "prick" sample was taken from the ventral labial vein of each cow 3 times a week for the first 5 wk of lactation. The content of BHB in blood was measured immediately using a handheld device. After outlier elimination, 1,910 spectra records and corresponding BHB measures were used for prediction model development. Partial least square regression and partial least squares discriminant analysis were used to develop prediction models for quantitative determination of blood BHB content and for identifying cows with hyperketonemia (HYK). Both quantitative and discriminant predictions were developed using the phenotypes and infrared spectra from two-thirds of the cows (randomly assigned to the calibration set) and tested using the remaining one-third (validation set). A moderate accuracy was obtained for prediction of blood BHB. The coefficient of determination (R2) of the prediction model in calibration was 0.56, with a root mean squared error of prediction of 0.28 mmol/L and a ratio of performance to deviation, calculated as the ratio of the standard deviation of the partial least squares model calibration set to the standard error of prediction, of 1.50. In the validation set, the R2 was 0.50, with root mean squared error of prediction values of 0.32 mmol/L, which resulted in a ratio of performance to deviation of 1.39. When the reference test for HYK was defined as blood concentration of BHB ≥1.2 mmol/L, discriminant models indicated that milk infrared spectra correctly classified 76% of the HYK-positive cows and 82% of the HYK-negative cows. The quantitative models were not able to provide accurate estimates, but they could differentiate between high and low BHB concentrations. Furthermore, the discriminant models allowed the classification of cows with reasonable accuracy. This study indicates that the prediction of blood BHB content or occurrence of HYK from milk spectra is possible with moderate accuracy in pasture-grazed cows and could be used during routine milk testing. Applicability of infrared spectroscopy is not likely suited for obtaining accurate BHB measurements at an individual cow level, but discriminant models might be used in the future as herd-level management tools for classification of cows that are at risk of HYK, whereas quantitative models might provide large-scale phenotypes to be used as an indicator trait for breeding cows with improved metabolic health.

Patent infections with Fasciola hepatica and paramphistomes (Calicophoron daubneyi) in dairy cows and association of fasciolosis with individual milk production and fertility parameters

Infections with the liver fluke Fasciola hepatica may result in considerable economic losses in the dairy livestock industry worldwide. Infections have been associated with detrimental impacts on milk production and milk quality as well as reduced fertility. However, most related data rely on examinations on herd level and the rather few studies on individual cow level are based solely on antibodies as measure for F. hepatica infections. This entails the risk of including false-positives as anti-F. hepatica antibodies persist for months even if the infection is cleared. Therefore, the presented study aimed to overcome this issue by assessing the association between F. hepatica infections measured via faecal egg counts (FEC) and milk production as well as fertility parameters in individual dairy cows. In total, 2006 faecal samples from 1166 Black and White dairy cows from 17 small and medium-sized German grassland farms were examined in July and September 2015. The relationship between patent F. hepatica infections and the milk production parameters milk yield, milk protein content, milk fat content and somatic cell score (SCS) was assessed in a linear mixed model using test-day records of individual cows. Patent F. hepatica infections were found on 35.3% (7/17) of farms with an individual cow prevalence of 10.1% (97/963) in July and 9.1% (95/1036) in September. Patent rumen fluke infections were detected on 17.6% (3/17) farms with an individual cow prevalence of 0.4% (4/963) in July and 0.7% (9/1036) in September. No significant association was found between F. hepatica infection status and either SCS as an indicator of udder health or milk production parameters, despite 0.06 and 0.10% lower values for milk protein and fat content in patently infected cows. Linear mixed models and generalized linear mixed models were established to estimate the impact of fasciolosis on the fertility parameters calving to first service (CTFS), calving interval (CI), success in first insemination (SFI) and 56-day nonreturn rate (NRR56). A significantly higher average CTFS of 4.69 days was detected in F. hepatica infected cows (P = 0.025), but no significant relationship was found for the other fertility parameters.

The informative value of an overview on antibiotic consumption, treatment efficacy and cost of clinical mastitis at farm level

This paper addresses whether it is worthwhile investing time in a more comprehensive documentation and analysis of farm specific data for mastitis treatment. Whilst most farmers keep some records, many refrain from investing more effort in making them more detailed. Therefore, information on farm-specific antibiotic consumption, effectiveness of treatments and the costs of mastitis are lacking; as well as the ability to identify and realise possibilities for improvement. An observational study was conducted on 30 dairy farms, to obtain an overview of farming practice, recording detailed data (using herd management software) on: milk records, diagnostics, preventive and therapeutic treatments and cost of mastitis, on an individual cow level. Out of a total of 30,633 cows, 67% received medication for the treatment or prevention of mastitis over a year. Antibiotics were chosen for 96% of udder treatments; including those for dry cows. Over 32% of the antibiotics used during lactation belonged to the ‘highest priority critically important antimicrobial’ category. Success of therapeutic treatment (assessed by individual somatic cell count (SCC)) ranged from 18% to 59% and total costs of mastitis per cow and year from € 158 to € 483. The high variations in antibiotic consumption, treatment outcomes and cost of mastitis between farms, showed that average or incomplete figures risk giving the wrong impression of a farm.It is thus worthwhile to invest in documentation and analysis of data, so that it is clear where action is required and which investments can be expected to be financially feasible. Benefits emerge from knowledge of antibiotic consumption allowing monitoring and reduction in antibiotic use (as demanded by consumers and legislation), improvements in animal health and welfare thanks to regular checks of how effective a treatment is, and economic benefits due to knowledge of the costs caused by mastitis.

Effect of parity weighting on milk production forecast models

The objectives of this study were to compare the prediction accuracy of two milk prediction models at the individual cow level and to develop, compare and evaluate six input data preprocessing treatments designed to factor parity information into the milk prediction model configuration process. The two models were a nonlinear auto-regressive model with exogenous input and a polynomial curve fitting model. These were tested using six different parity data input treatments. Different combinations of static parity weight, dynamic parity weight and removal of the first lactation data were selected as input treatments. Lactation data from 39 individual cows were extracted from a sample herd of pasture-based Holstein-Friesian cattle located in the south of Ireland and situated in close proximity. The models were trained using three years of historical milk production data and were employed for the prediction of the total daily milk yield of the fourth lactation for each individual cow using a 305-day forecast horizon. The nonlinear auto-regressive model with exogenous input was found to provide higher prediction accuracy than the polynomial curve fitting model for individual cows using each input treatment. An improvement in forecast accuracy was observed in 62% of test cows (24 of 39). However, on average across the entire population, only part of the treatments delivered an increase in accuracy and the success rate varied between test groups. Prediction performance was strongly influenced by the cows' historical milk production relative to parity and also the prediction year. These results highlighted the importance of examining the accuracy of milk prediction models and model training strategies across multiple time horizons. Removal of the first lactation and applying static parity weight were shown to be the two most successful input treatments. The results showed that historical parity weighting trends had a substantial effect on the success rate of the treatments for both milk production forecast models.

Epidemiology of subclinical hypocalcemia in early-lactation Holstein dairy cows: The temporal associations of plasma calcium concentration in the first 4 days in milk with disease and milk production

The objectives of this study were to characterize the epidemiology of subclinical hypocalcemia (SCH) in Holstein dairy cows by assessing the temporal associations of plasma Ca concentrations in the first 4 d in milk (DIM) with the risk of cows being diagnosed with metritis or displaced abomasum (or both), and milk production across the first 15 wk of lactation. A prospective cohort study was conducted in 2 dairy herds in New York State, in which cows had a blood sample collected daily for the first 4 DIM. A total of 396 Holstein cows (137 primiparous and 259 multiparous) were enrolled. Multivariable Poisson regression models were built to evaluate the associations of plasma Ca concentration at each of the 4 d following parturition with the risk of primiparous cows being diagnosed with metritis and multiparous cows being diagnosed with metritis, displaced abomasum, or both. Similarly, generalized linear mixed models were built to evaluate the associations of plasma Ca concentration with milk production across the first 15 wk of lactation. Plasma Ca concentration was assessed on a continuous scale in all models; dichotomization and SCH classification only occurred in the final models if the Ca concentration variable was meaningful by creating an optimized threshold based on receiver operating characteristic curve analysis. Plasma Ca concentration assessed at 1 DIM was not associated with the risk of metritis in primiparous cows, but an association was observed at 2, 3, and 4 DIM (critical thresholds were plasma Ca concentration ≤2.15, 2.10, and 2.15 mmol/L, respectively). Plasma Ca concentration was associated with the risk of metritis or displaced abomasum diagnosis (or both) for 2nd parity animals at 2 DIM (threshold ≤1.97 mmol/L), and at 4 DIM for 3rd and greater lactations (threshold ≤2.20 mmol/L). Reduced plasma Ca concentration was associated with higher milk production when assessed at 1 DIM in primiparous and multiparous cows, and lower milk production when assessed at 4 DIM in multiparous cows only. For primiparous cows, plasma Ca concentration was not associated with lower milk production at any of the DIM assessed. In conclusion, assessments of SCH at the individual cow level must take into account the DIM of Ca concentration measurement and parity of the cow, as the epidemiology of the disorder was demonstrated to be highly dependent on these variables. This study advances the knowledge of the epidemiology of SCH and better establishes thresholds for optimizing SCH diagnosis.

The effect of patent Dictyocaulus viviparus (re)infections on individual milk yield and milk quality in pastured dairy cows and correlation with clinical signs

BackgroundInfections with the bovine lungworm Dictyocaulus viviparus might lead to reduced milk production and detrimental impacts on milk quality resulting in considerable economic losses in dairy farming.MethodsIn the presented field study, 1988 faecal samples were collected from 1166 Black and White dairy cows allocated in 17 small and medium-sized German grassland farms. Faecal samples were collected in summer and autumn 2015 to assess D. viviparus larvae excretion. Test-day records were used to estimate the association between patent D. viviparus infections in individual cows and the milk production parameters milk yield, milk protein and milk fat content by using linear mixed models. Bulk tank milk (BTM) samples from each farm and individual milk samples from those cows which were excreting larvae in summer were collected in autumn. In addition, occurrence of the clinical symptom “coughing” was noted in individual cows during autumn sampling to determine its association with patent lungworm infections.ResultsPatent D. viviparus infections were found on 23.5% (4/17) of farms with a prevalence at the individual cow level of 0.9% (9/960) in summer and 3.4% (35/1028) in autumn. No BTM sample exceeded the BTM ELISA cut-off value of 0.410 optical density ratio (ODR), the mean value was 0.168 ODR. Only one individual milk sample exceeded the individual milk ELISA cut-off value of 0.573 ODR (mean value of 0.302 ODR). A patent D. viviparus infection status was associated with a lower average daily milk yield of 1.62 kg/cow/day (P = 0.0406). No significant association was found with milk protein or fat content representing milk quality parameters. Coughing was observed in 5.9% (61/1028) of cows. Of the coughing cows, only 4.9% (3/61) had a patent lungworm infection. Fisher’s exact test showed no significant difference between infected and non-infected coughing cows.ConclusionsFarmers and veterinarians should be aware that patent lungworm (re)infections in dairy cows reduce milk yield, despite the absence of clinical signs. Furthermore, if dairy cows present with coughing, other differential diagnoses need to be considered in addition to dictyocaulosis.

Breed of cow and herd productivity affect milk nutrient recovery in curd, and cheese yield, efficiency and daily production

Little is known about cheese-making efficiency at the individual cow level, so our objective was to study the effects of herd productivity, individual herd within productivity class and breed of cow within herd by producing, then analyzing, 508 model cheeses from the milk of 508 cows of six different breeds reared in 41 multi-breed herds classified into two productivity classes (high v. low). For each cow we obtained six milk composition traits; four milk nutrient (fat, protein, solids and energy) recovery traits (REC) in curd; three actual % cheese yield traits (%CY); two theoretical %CYs (fresh cheese and cheese solids) calculated from milk composition; two overall cheese-making efficiencies (% ratio of actual to theoretical %CYs); daily milk yield (dMY); and three actual daily cheese yield traits (dCY). The aforementioned phenotypes were analyzed using a mixed model which included the fixed effects of herd productivity, parity, days in milk (DIM) and breed; the random effects were the water bath, vat, herd and residual. Cows reared in high-productivity herds yielded more milk with higher nutrient contents and more cheese per day, had greater theoretical %CY, and lower cheese-making efficiency than low-productivity herds, but there were no differences between them in terms of REC traits. Individual herd within productivity class was an intermediate source of total variation in REC, %CY and efficiency traits (10.0% to 17.2%), and a major source of variation in milk yield and dCY traits (43.1% to 46.3%). Parity of cows was an important source of variation for productivity traits, whereas DIM affected almost all traits. Breed within herd greatly affected all traits. Holsteins produced more milk, but Brown Swiss cows produced milk with higher actual and theoretical %CYs and cheese-making efficiency, so that the two large-framed breeds had the same dCY. Compared with the two large-framed breeds, the small Jersey cows produced much less milk, but with greater actual and theoretical %CYs, similar efficiencies and a slightly lower dCY. Compared with the average of the specialized dairy breeds, the three dual-purpose breeds (Simmental and the local Rendena and Alpine Grey) had, on average, similar dMY, lower actual and theoretical %CY, similar fat and protein REC, and slightly greater cheese-making efficiency.

Variation of milk coagulation properties, cheese yield, and nutrients recovery in curd of cows of different breeds before, during and after transhumance to highland summer pastures.

This paper aimed at evaluating the effect of summer transhumance to mountain pastures of dairy cows of different breeds on cheese-making ability of milk. Data were from 649 dairy cows of specialized (Holstein Friesian and Brown Swiss) dual purpose (Simmental) and local (mostly Rendena and Alpine Grey) breeds. The Fourier-Transform Infra-Red Spectra (FTIRS) of their milk samples were collected before and after transhumance in 109 permanent dairy farms, and during transhumance in 14 summer farms (with multi-breeds herds) of the Trento Province, north-eastern Italy. A variety of 18 traits describing milk coagulation, curd firming, cheese yield and nutrients recovery in curd/loss in whey were predicted on the basis of FTIRS collected at the individual cow level. Moving the cows to summer farms improved curd firming traits but reduced cheese yields because of an increase of water and fat lost in the whey. During summer grazing, most of cheese-making traits improved, often non-linearly. The milk from summer farms supplementing cows with more concentrates showed better curd firming and cheese yield, because of lower fat lost in the whey. The breed of cows affected almost all the traits with a worst cheese-making ability for milk samples of Holsteins through all the trial, and interacted with concentrate supplementation because increasing compound feed tended to improve cheese-making traits for all breed, with the exception of local breeds for coagulation time and of Brown Swiss for curd firming time. In general, summer transhumance caused a favourable effect on cheese-making aptitude of milk, even though with some difference according to parity, initial days in milk, breed and concentrate supplementation of cows.

Assessing cow-calf welfare. Part 2: Risk factors for beef cow health and behavior and stockperson handling.

Epidemiological studies can be used to identify risk factors for livestock welfare concerns but have not been conducted in the cow-calf sector for this purpose. The objectives of this study were to investigate the relationships of 1) herd-level management, facilities, and producer perspectives with cattle health and behavior and stockperson handling and 2) stockperson handling on cattle behavior at the individual cow level. Cow ( = 3,065) health and behavior and stockperson handling during a routine procedure (e.g., pregnancy checks) were observed on 30 California ranches. Management and producer perspectives were evaluated using an interview, and handling facility features were recorded at the chute. After predictors were screened for univariable associations, multivariable models were built for cattle health (i.e., thin body condition, lameness, abrasions, hairless patches, swelling, blind eyes, and dirtiness) and behavior (i.e., balking, vocalizing, stumbling and falling in the chute and while exiting the restraint, and running out of the restraint) and stockperson handling (i.e., electric prod use, moving aid use, tail twisting, and mis-catching cattle). When producers empathized more toward an animal's pain experience, there was a lower risk of swelling (odds ratio [OR] = 0.7) but a higher risk of lameness (OR = 1.3), which may indicate a lack of awareness of the latter. Training stockpersons using the Beef Quality Assurance program had a protective effect on cow cleanliness and mis-catching in the restraint (OR = 0.2 and OR = 0.5, respectively). Hydraulic chutes increased the risk of vocalizations (OR = 2.7), possibly because these systems can apply greater pressure to the sides of the animal than manual restraints. When a moving aid was used to move an individual cow, it increased the risk of her balking, but when hands, in particular, were used, the risk of balking decreased across the herd (OR = 34.1 and OR = 0.3, respectively). Likewise, individual cows were at a greater risk of balking, vocalizing, stumbling and falling in the chute, and stumbling and running at exit when they were touched with an electric prod (OR = 11.0, OR = 3.3, OR = 1.9, OR = 2.3, OR = 1.8, and OR = 1.7, respectively). Although the implications of using moving aids are unclear, reducing the use of electric prods could improve cattle handling. In conclusion, cattle handling was influenced by a number of facility and stockperson factors: personnel training, facility design, and electric prod use are key areas for future improvements.

The nonlinear effect of somatic cell count on milk composition, coagulation properties, curd firmness modeling, cheese yield, and curd nutrient recovery

The aim of this study was to investigate the relationships between somatic cell count (SCC) in milk and several milk technological traits at the individual cow level. In particular, we determined the effects of very low to very high SCC on traits related to (1) milk yield and composition; (2) coagulation properties, including the traditional milk coagulation properties (MCP) and the new curd firming model parameters; and (3) cheese yield and recovery of milk nutrients in the curd (or loss in the whey). Milk samples from 1,271 Brown Swiss cows from 85 herds were used. Nine coagulation traits were measured: 3 traditional MCP [rennet coagulation time (RCT, min), curd firming rate (k20, min), and curd firmness after 30 min (a30, mm)] and 6 new curd firming and syneresis traits [potential asymptotic curd firmness at infinite time (CFP, mm), curd firming instant rate constant (kCF, % × min−1), syneresis instant rate constant (kSR, % × min−1), rennet coagulation time estimated using the equation (RCTeq, min), maximum curd firmness achieved within 45 min (CFmax, mm), and time at achievement of CFmax (tmax, min)]. The observed cheese-making traits included 3 cheese yield traits (%CYCURD, %CYSOLIDS, and %CYWATER, which represented the weights of curd, total solids, and water, respectively, as a percentage of the weight of the processed milk) and 4 nutrient recoveries in the curd (RECFAT, RECPROTEIN, RECSOLIDS, and RECENERGY, which each represented the percentage ratio between the nutrient in the curd and milk). Data were analyzed using a linear mixed model with the fixed effects of days in milk, parity, and somatic cell score (SCS), and the random effect of herd-date. Somatic cell score had strong influences on casein number and lactose, and also affected pH; these were traits characterized by a quadratic pattern of the data. The results also showed a negative linear relationship between SCS and milk yield. Somatic cell score influenced almost all of the tested coagulation traits (both traditional and modeled), with the exceptions of k20, CFP, and kSR. Gelation was delayed when the SCS decreased (slightly) and when it increased (strongly) with respect to a value of 2, as confirmed by the quadratic patterns observed for both RCT and RCTeq. The SCS effect on a30 showed a quadratic pattern almost opposite to that observed for RCT. With respect to the CFt parameters, kCF decreased linearly as SCS increased, resulting in a linear decrease of CFmax and a quadratic pattern for tmax. Milk SCS attained significance for %CYCURD, %CYWATER, and RECPROTEIN. As the SCS increased beyond 3, we observed a progressive quadratic decrease of the water retained in the curd (%CYWATER), which caused a parallel decrease in %CYCURD. With respect to RECPROTEIN, the negative effect of SCS was almost linear. Recovery of fat and (consequently) RECENERGY was characterized by a more evident quadratic trend, with the most favorable values associated with an intermediate SCS. Together, our results confirmed that high SCS has a negative effect on milk composition and technological traits, highlighting the nonlinear trends of some traits across the different classes of SCS. Moreover, we report that a very low SCS has a negative effect on some technological traits of milk.

The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows

Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in dairy cows was 0.88 (20.0MJ), 0.78 (18.6MJ), and 0.63 (22.0MJ), respectively, based on cross-validation. These studies, however, are limited to results from one research group based on data from 2 contrasting production systems in the United Kingdom and Ireland and would need to be replicated, especially in a range of production systems because the prediction equations are not accurate when the variability used in validation is not represented in the calibration data set. Heritable genetic variation exists for all predicted traits. Phenotypic differences in energy intake also exists among animals stratified based on genetic merit for energy intake predicted from milk FTIRS, substantiating the usefulness of such FTIR-predicted phenotypes not only for day-to-day herd management, but also as part of a breeding strategy to improve cow performance.

Analysis of Johne's disease ELISA status and associated performance parameters in Irish dairy cows.

BackgroundInfection with Mycobacterium avium subspecies paratuberculosis (MAP) has been associated with reductions in milk production in dairy cows and sub optimal fertility. The aim of this study was to highlight the production losses associated with testing MAP ELISA positive in Irish dairy cows. Secondary objectives included investigation of risk factors associated with testing MAP ELISA positive. A survey of management practices on study farms was also conducted, with examination of associations between management practices and herd MAP status.Blood samples were collected from 4188 breeding animals on 22 farms. Samples were ELISA tested using the ID Screen Paratuberculosis Indirect Screening Test. Production parameters examined included milk yield, milk fat, milk protein, somatic cell count, and calving interval. The association between MAP ELISA status and production data was investigated using multi-level mixed models. Logistic regression was used to identify risk factors for testing JD blood ELISA positive at individual cow level and to identify associations between farm management practices and herd MAP status.ResultsData were available for 3528 cows. The apparent prevalence recorded was 7.4 %. Mixed model analysis revealed no statistically significant association between testing MAP ELISA positive and dairy cow production parameters. Risk factors associated with testing positive included larger sized herds being over twice more likely to test positive than smaller herds (OR 2.4 P = <0.001). Friesians were less likely to test positive relative to other breeds. A number of study farmers were engaged in management practices that have previously been identified as high risk for MAP transmission e.g., 73.1 % pooled colostrum and 84.6 % of study farmers used the calving area to house sick animals throughout the year. No significant associations however, were identified between farm management practices and herd MAP status.ConclusionNo production losses were identified; however an apparent prevalence of 7.4 % was recorded. With the abolition of EU milk quotas herd size in Ireland is expanding, as herds included in this study were larger than the national average, results may be indicative of future JD levels if no JD control programmes are implemented to minimise transmission.

Feeding behavior improves prediction of dairy cow voluntary feed intake but cannot serve as the sole indicator

Low-cost feeding-behavior sensors will soon be available for commercial use in dairy farms. The aim of this study was to develop a feed intake model for the individual dairy cow that includes feeding behavior. In a research farm, the individual cows’ voluntary feed intake and feeding behavior were monitored at every meal. A feed intake model was developed based on data that exist in commercial modern farms: ‘BW,’ ‘milk yield’ and ‘days in milking’ parameters were applied in this study. At the individual cow level, eating velocity seemed to be correlated with feed intake (R2=0.93 to 0.94). The eating velocity coefficient varied among individuals, ranging from 150 to 230 g/min per cow. The contribution of feeding behavior (0.28) to the dry matter intake (DMI) model was higher than the contribution of BW (0.20), similar to the contribution of fat-corrected milk (FCM)/BW (0.29) and not as large as the contribution of FCM (0.49). Incorporating feeding behavior into the DMI model improved its accuracy by 1.3 (38%) kg/cow per day. The model is ready to be implemented in commercial farms as soon as companies introduce low-cost feeding-behavior sensors on commercial level.

Benchmarking dairy herd health status using routinely recorded herd summary data

Genetic improvement of dairy cattle health through the use of producer-recorded data has been determined to be feasible. Low estimated heritabilities indicate that genetic progress will be slow. Variation observed in lowly heritable traits can largely be attributed to nongenetic factors, such as the environment. More rapid improvement of dairy cattle health may be attainable if herd health programs incorporate environmental and managerial aspects. More than 1,100 herd characteristics are regularly recorded on farm test-days. We combined these data with producer-recorded health event data, and parametric and nonparametric models were used to benchmark herd and cow health status. Health events were grouped into 3 categories for analyses: mastitis, reproductive, and metabolic. Both herd incidence and individual incidence were used as dependent variables. Models implemented included stepwise logistic regression, support vector machines, and random forests. At both the herd and individual levels, random forest models attained the highest accuracy for predicting health status in all health event categories when evaluated with 10-fold cross-validation. Accuracy (SD) ranged from 0.61 (0.04) to 0.63 (0.04) when using random forest models at the herd level. Accuracy of prediction (SD) at the individual cow level ranged from 0.87 (0.06) to 0.93 (0.001) with random forest models. Highly significant variables and key words from logistic regression and random forest models were also investigated. All models identified several of the same key factors for each health event category, including movement out of the herd, size of the herd, and weather-related variables. We concluded that benchmarking health status using routinely collected herd data is feasible. Nonparametric models were better suited to handle this complex data with numerous variables. These data mining techniques were able to perform prediction of health status and could add evidence to personal experience in herd management.

Thin sole in cattle

Lameness has long been one of the three major economic disorders, particularly in dairy cattle. Thin soles, excessive wear of the soles, are only one reason for lameness. Their occurrence varies from farm to farm. Outbreaks may occur on intensively managed dairy farms, particularly on pasture-based systems. However, the disorder has been recognised in housed dairy and feed-lotted beef cows. Typically, thin soles are seen in dairy cows in early lactation exposed to wet conditions at claw level and long walking distances. Abrasive material/s on the walking surfaces and feeding platforms usually speed up the process of excessive wear. Excessive sole wear may be expected in new or re-concreted milking parlours and yards. The new concrete is very abrasive. In such situations, more than half of the herd may develop lameness within a few weeks from exposure to the new/re-concreted area (popularly referred to as ‘new concrete disease’). Early signs of thin soles include a slow, painful gait. The walking style is often described as ‘walking on egg shells’. Milk production is lowered at individual cow and group level. On clinical examination the dorsal wall of the claw is short and the sole is flexible on thumb pressure. Haemorrhages in all claw zones may be present. The incidence of sole ulcers, white line disease, toe ulcers, subsolar abscess and osteitis of the third phalanx is often increased. Control should aim to prevent excessive wear, early detection of the affected cows and improve the claw horn quality. This article discusses the thin sole disorder not addressing that caused by over trimming.