Relationships between milk mid-IR predicted gastro-enteric methane production and the technical and financial performance of commercial dairy herds.

Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4) production predicted from milk mid-IR (MIR) spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk MIR spectra (MIR-CH4 (g/day)) of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1024 herd and year records from 2007 to 2014. Pearson correlation coefficients (r) were used to assess significant relationships (P<0.05). Low HYMIR-CH4 was significantly associated with, amongst others, lower fat and protein corrected milk (FPCM) yield (r=0.18), lower milk fat and protein content (r=0.38 and 0.33, respectively), lower quantity of milk produced from forages (r=0.12) and suboptimal reproduction and health performance (e.g. longer calving interval (r=-0.21) and higher culling rate (r=-0.15)). Concerning economic results, low HYMIR-CH4 was significantly associated with lower gross margin per cow (r=0.19) and per litre FPCM (r=0.09). To conclude, this study suggested that low lactating dairy cow gastro-enteric CH4 production tended to be associated with more extensive or suboptimal management practices, which could lead to lower profitability. The observed low correlations suggest complex interactions between variables due to the use of on-farm data with large variability in technical and management practices.

The feeding system impacts relationships between calving interval and economic results of dairy farms.

The calving interval (CI) can potentially impact the economic results of dairy farms. This study highlighted the most profitable CI and innovated by describing this optimum as a function of the feeding system of the farm. On-farm data were used to represent real farm conditions. A total of 1832 accounts of farms recorded from 2007 to 2014 provided economic, technical and feeding information per herd and per year. A multiple correspondence analysis created four feeding groups: extensive, low intensive, intensive and very intensive herds. The gross margin and some of its components were corrected to account for the effect of factors external to the farm, such as the market, biological status, etc. Then the corrected gross margin (cGMc) and its components were modelled by CI parameters in each feeding system by use of GLM. The relationship between cGMc and the proportion of cows with CI<380 days in each feeding group showed that keeping most of the cows in the herd with CI near to 1 year was not profitable for most farms (for the very intensive farms there was no effect of the proportion). Moreover, a low proportion of cows (0% to 20%) with a near-to-1-year CI was not profitable for the extensive and low intensive farms. Extending the proportion of cows with CI beyond 459 days until 635 days (i.e. data limitation) caused no significant economic loss for the extensive and low intensive farms, but was not profitable for the intensive and very intensive farms. Variations of the milk and feeding components explained mainly these significant differences of gross margin. A link between the feeding system and persistency, perceptible in the milk production and CI shown by the herd, could explain the different relationships observed between the extent of CI and the economic results in the feeding groups. This herd-level study tended to show different economic optima of CI as a function of the feeding system. A cow-level study would specify these tendencies to give CI objectives to dairy breeders as a function of their farm characteristics.

Mediation analysis to estimate direct and indirect milk losses associated with bacterial load in bovine subclinical mammary infections.

Milk losses associated with mastitis can be attributed to either effects of pathogens per se (i.e. direct losses) or to effects of the immune response triggered by the presence of mammary pathogens (i.e. indirect losses). Test-day milk somatic cell counts (SCC) and number of bacterial colony forming units (CFU) found in milk samples are putative measures of the level of immune response and of the bacterial load, respectively. Mediation models, in which one independent variable affects a second variable which, in turn, affects a third one, are conceivable models to estimate direct and indirect losses. Here, we evaluated the feasibility of a mediation model in which test-day SCC and milk were regressed toward bacterial CFU measured at three selected sampling dates, 1 week apart. We applied this method on cows free of clinical signs and with records on up to 3 test-days before and after the date of the first bacteriological samples. Most bacteriological cultures were negative (52.38%), others contained either staphylococci (23.08%), streptococci (9.16%), mixed bacteria (8.79%) or were contaminated (6.59%). Only losses mediated by an increase in SCC were significantly different from null. In cows with three consecutive bacteriological positive results, we estimated a decreased milk yield of 0.28 kg per day for each unit increase in log2-transformed CFU that elicited one unit increase in log2-transformed SCC. In cows with one or two bacteriological positive results, indirect milk loss was not significantly different from null although test-day milk decreased by 0.74 kg per day for each unit increase of log2-transformed SCC. These results highlight the importance of milk losses that are mediated by an increase in SCC during mammary infection and the feasibility of decomposing total milk loss into its direct and indirect components.

Evaluating somatic cell scores with a Bayesian Gaussian linear state-space model.

Because accurate characterization of health state is important for managing dairy herds, we propose to validate the use of a linear state-space model (LSSM) for evaluating monthly somatic cell scores (SCSs). To do so, we retrieved SCS from a dairy database and collected reports on clinical mastitis collected in 20 farms, during the period from January 2008 to December 2011 in the Walloon region of Belgium. The dependent variable was the SCS, and the independent variables were the number of days from calving, year of calving and parity. The LSSM also incorporated an error-free underlying variable that described the trend across time as a function of previous clinical and subclinical status. We computed the mean sum of squared differences between observed SCS and median values of the posterior SCS distribution and constructed the receiver operating characteristic (ROC) curve for SCS thresholds going from 0 to 6. Our results show SCS estimates are close to observed SCS and area under the ROC curve is higher than 90%. We discuss the meaning of the parameters in light of our current knowledge of the disease and propose methods to incorporate, in LSSM, this knowledge often expressed in the form of ordinary differential equations.

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis.

Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n = 110), Ireland (n = 1658) and Scotland (n = 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS × LTF effects were significant (P < 0.001 and P = 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r = 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin - Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events - external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.