Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot(®) lactation model

Assessing Molecular Localization of Symbiont Microalgae in Coral Branches Through Mass Spectrometry Imaging

Reef-building corals are a fundamental pillar of coral reef ecosystems in tropical and subtropical shallow environments. Corals harbor symbiotic dinoflagellates belonging to the family Symbiodiniaceae, commonly known as zooxanthellae. Extensive research has been conducted on this symbiotic relationship, yet the fundamental information about the distribution and localization of Symbiodiniaceae cells in corals is still limited. This information is crucial to understanding the mechanism underlying the metabolite exchange between corals and their algal symbionts, as well as the metabolic flow within holobionts. To examine the distribution of Symbiodiniaceae cells within corals, in this study, we used fluorescence imaging and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MS-Imaging) on branches of the Acropora tenuis coral. We successfully prepared frozen sections of the coral for molecular imaging without fixing or decalcifying the coral branches. By combining the results of MS-Imaging with that of the fluorescence imaging, we determined that the algal Symbiodiniaceae symbionts were not only localized in the tentacle and surface region of the coral branches but also inhabited the in inner parts. Therefore, the molecular imaging technique used in this study could be valuable to further investigate the molecular dynamics between corals and their symbionts.

The use of machine learning to predict somatic cell count status in dairy cows post-calving

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Prediction of persistency for day 305 of lactation at the moment of the insemination decision

When deciding on the voluntary waiting period of an individual cow, it might be useful to have insight into the persistency for the remainder of that lactation at the moment of the insemination decision, especially for farmers who consider persistency in their reproduction management. Currently, breeding values for persistency are calculated for dairy cows but, to our knowledge, prediction models to accurately predict persistency at different moments of insemination are lacking. This study aimed to predict lactation persistency for DIM 305 at different insemination moments (DIM 50, 75, 100, and 125). Available cow and herd level data from 2005 to 2022 were collected for a total of 20,508 cows from 85 herds located in the Netherlands and Belgium. Lactation curve characteristics were estimated for every daily record using the data up to and including that day. Persistency was defined as the number of days it takes for the milk production to decrease by half during the declining stage of lactation, and calculated from the estimated lactation curve characteristic 'decay'. Four linear regression models for each of the selected insemination moment were built separately to predict decay at DIM 305 (decay-305). Independent variables included the lactation curve characteristics at the selected insemination moment, daily milk yield, age, calving season, parity group and other herd variables. The average decay-305 of primiparous cows was lower than that of multiparous cows (1.55 *10-3 vs. 2.41*10-3, equivalent to a persistency of 447 vs. 288 days, respectively). Results showed that our models had limitations in accurately predicting persistency, although predictions improved slightly at later insemination moments, with R2 values ranging between 0.27 and 0.41. It can thus be concluded that, based only on cow and herd milk production information, accurate prediction of persistency for DIM 305 is not feasible.

Effect of reproductive management programs that prioritized artificial insemination at detected estrus or timed artificial insemination on the economic performance of primiparous Holstein cows of different genetic merit for fertility

The objective of this randomized controlled experiment was to evaluate the effect of reproductive management programs that prioritized artificial insemination (AI) at detected estrus (AIE) or timed AI (TAI) during the first lactation on the economic performance of dairy cows of different genomically enhanced predicted transmitting ability for fertility. Lactating primiparous Holstein cows from 6 commercial farms were stratified into high, medium, and low fertility groups based on a reproduction index value calculated from multiple genomically enhanced predicted transmitting abilities to predict the number of days to achieve pregnancy. Within herd and fertility group, cows were randomly assigned either to a program that prioritized AIE (P-AIE; n = 1,416) and used TAI for cows not AIE for all AI services or another that prioritized TAI and had an extended voluntary waiting period for first service and prioritized TAI for second and greater AI services (P-TAI; n = 1,338). Cash flow (CF) per cow accumulated for the experimental (first) and second calving interval (CIN) and cash flow per slot per 28 mo after calving in the experimental lactation were calculated. Market and rearing heifer cost values were used for estimating CF. For cows in the high fertility group, a positive effect of delayed pregnancy on milk income during the first lactation was observed (+$248 for P-TAI) but was insufficient to generate significant differences in CF between treatments mainly because of milk income compensation in the second lactation (+$125 for P-AIE) and minor reductions in reproductive cost and gains in calf value for the P-AIE treatment. In this regard, CF for 2 CIN was greater for the P-TAI treatment by $61 and $86 for market and rearing replacement heifer cost, respectively. Similarly, CF per slot was favorable to the P-TAI treatment but only by $13 and $47 for market and rearing replacement heifer cost, respectively. For cows in the low fertility group, CF was numerically in favor of the P-AIE treatment due to a pregnancy and herd exit dynamics that resulted in gains in milk income over feed cost during the first ($29) and second ($113) lactation. Differences in CF for the 2 CIN were $58 and $47 for market or rearing heifer value, respectively, and $77 and $19 for market and rearing heifer values, respectively for the slot analysis. Differences in CF between cows of different genetic merit for fertility were consistent across treatment and estimation method. Of note, cows in the low fertility group had greater CF than cows in the high fertility group in all comparisons, ranging from $198 per cow for 2 CIN to as much as $427 per slot. For the low fertility group, greater milk production contributed directly (milk income over feed cost) and indirectly (reduced culling) to increased CF. We concluded that genetic merit for fertility and CF are associated because cows of inferior genetic potential for fertility had greater CF than cows of superior genetic for fertility despite some increased costs and reduced revenues. Also, the magnitude of the CF differences observed for cows of different genetic merit for fertility managed with the P-AIE or P-TAI program may be valuable to commercial dairy farms but did not allow to conclusively support the choice of a type of reproductive management strategy for cows of different genetic merit for fertility.

Herd level economic comparison between the shape of the lactation curve and 305 d milk production

Herd milk production performance is generally evaluated using the herd's average 305-day milk production (HM305). Economic comparisons between herds are also often made using HM305. Comparing herds is thus based on summarized milk production, and not on the form of the lactation curves of the cows within the herd. Cow lactation curve characteristics can be aggregated on a calendar year basis to herd lactation curve characteristics (HLCC) (herd magnitude, herd time to peak yield and herd persistency). Thus far, no literature has evaluated whether the shape of the lactation curve (described by HLCC) is better able to explain the economic variation of herds than summarized milk production such as HM305 does. This study aims to determine whether HM305 or HLCC is better able to explain the variation in economic performance between herds. To do so, we evaluated 8 years of Dutch longitudinal data on milk production and the financial accounts of 1,664 herds. Cow lactation curve characteristics were calculated through lactation curve modeling and aggregated to HLCC on a calendar year basis for two parity groups (primiparous cows and multiparous cows). Using income over feed cost per cow (IOFC-cow) or per 100 kg milk (IOFC-milk) as the dependent variable separately, we developed four linear mixed models. Two models were used to analyse the association between herd economic performance and HLCC; the other two models were used to analyse the association between herd economic performance and HM305. A Cox test and J test were used to compare two non-nested models to investigate whether HM305 or HLCC better explain IOFC. The average IOFC-cow was €2,305 (SD = 408) per year, while the average IOFC-milk was €32.1 (SD = 4.6). Results showed that HLCC and HM305 explain the same amount of variance of IOFC-cow or IOFC-milk. IOFC-cow was associated with HM305 and HLCC (except herd time to peak yield for primiparous cows). Herd magnitude was most strongly associated with IOFC-cow, followed by herd persistency and herd time to peak yield of multiparous cows. IOFC-milk was not associated with HM305 or HLCC (except for a weak negative association with herd persistency for primiparous cows). IOFC-cow and IOFC-milk were driven most by time effects. In conclusion, HLCC and HM305 explain the same amount of variance in IOFC-cow or IOFC-milk. HLCC is more computationally expensive, while HM305 is more readily available.

Investigating the effect of temporal, geographic, and management factors on US Holstein lactation curve parameters

We fit the Wood's lactation model to an extensive database of test-day milk production records of US Holstein cows to obtain lactation-specific parameter estimates and investigated the effects of temporal, spatial, and management factors on lactation curve parameters and 305-d milk yield. Our approach included 2 steps as follows: (1) individual animal-parity parameter estimation with nonlinear least-squares optimization of the Wood's lactation curve parameters, and (2) mixed-effects model analysis of 8,595,413 sets of parameter estimates from individual lactation curves. Further, we conducted an analysis that included all parities and a separate analysis for first lactation heifers. Results showed that parity had the most significant effect on the scale (parameter a), the rate of decay (parameter c), and the 305-d milk yield. The month of calving had the largest effect on the rate of increase (parameter b) for models fit with data from all lactations. The calving month had the most significant effect on all lactation curve parameters for first lactation models. However, age at first calving, year, and milking frequency accounted for a higher proportion of the variance than month for first lactation 305-d milk yield. All parameter estimates and 305-d milk yield increased as parity increased; parameter a and 305-d milk yield rose, and parameters b and c decreased as year and milking frequency increased. Calving month estimates parameters a, b, c, and 305-d milk yield were the lowest values for September, May, June, and July, respectively. The results also indicated the random effects of herd and cow improved model fit. Lactation curve parameter estimates from the mixed-model analysis of individual lactation curve fits describe well US Holstein lactation curves according to temporal, spatial, and management factors.

A randomised controlled trial to evaluate the impact of indoor living space on dairy cow production, reproduction and behaviour

As a global society, we have a duty to provide suitable care and conditions for farmed livestock to protect animal welfare and ensure the sustainability of our food supply. The suitability and biological impacts of housing conditions for intensively farmed animals is a complex and emotive subject, yet poorly researched, meaning quantitative evidence to inform policy and legislation is lacking. Most dairy cows globally are housed for some duration during the year, largely when climatic conditions are unfavourable. However, the impact on biology, productivity and welfare of even the most basic housing requirement, the quantity of living space, remains unknown. We conducted a long-term (1-year), randomised controlled trial (CONSORT 10 guidelines) to investigate the impact of increased living space (6.5 m2 vs 3 m2 per animal) on critical aspects of cow biology, behaviour and productivity. Adult Holstein dairy cows (n = 150) were continuously and randomly allocated to a high or control living space group with all other aspects of housing remaining identical between groups. Compared to cows in the control living space group, cows with increased space produced more milk per 305d lactation (primiparous: 12,235 L vs 11,592 L, P < 0.01; multiparous: 14,746 L vs 14,644 L, P < 0.01) but took longer to become pregnant after calving (primiparous: 155 d vs 83 d, P = 0.025; multiparous: 133 d vs 109 d). In terms of behaviour, cows with more living space spent significantly more time in lying areas (65 min/d difference; high space group: 12.43 h/day, 95% CI = 11.70-13.29; control space group: 11.42 h/day, 95% CI = 10.73-12.12) and significantly less time in passageways (64 min/d), suggesting enhanced welfare when more space was provided. A key physiological difference between groups was that cows with more space spent longer ruminating each day. This is the first long term study in dairy cows to demonstrate that increased living space results in meaningful benefits in terms of productivity and behaviour and suggests that the interplay between farmed animals and their housed environment plays an important role in the concepts of welfare and sustainability of dairy farming.

Effects of replacement of corn silage and distillers' grain diets with hay during prepartum on the health and production performance of dairy heifers postpartum

In this study, we aimed to comprehensively evaluate the effects of prepartum replacement of corn silage and distillers' grain diets with hay on the postpartum health and production performance in dairy heifers. Pregnant Holstein dairy heifers were randomly assigned to two groups and fed different diets with the same net energy and protein concentrations. The low corn silage plus hay (CH) group diet mainly contained oat hay (28.5%), corn silage (25%) and folium ginkgo (23%). The high corn silage plus distillers' grains (CD) group diet contained corn silage (53.5%), distillers' grains (5%) and folium ginkgo (18%). Dairy heifers were fed from the beginning of pregnancy to the end of the transition period. Calf birthweight, postpartum morbidity, milk production and reproductive performance were determined. Compared with the cows in the CH group, those in the CD group presented increased calf birthweight and a high risk of some postpartum disorders (p < 0.05), particularly ketosis (37.63% vs. 24.54%) and retained placenta (9.41% vs. 3.92%). However, there was no difference between the groups in reproductive performance, including first timed artificial insemination (TAI) rate, date of TAI and conception rate of TAI. The lactation curves showed no difference in milk yield during the peak lactation period of primiparous cows between the CH and CD groups; the total milk production was 33.39 kg and 33.19 kg respectively. Notably, the time to peak milk production was delayed in the CD group. It can be concluded that replacing corn silage and distillers' grains with hay can reduce postpartum morbidity in dairy heifers and shorten the time to peak milk production.

Influence of environmental factors and parity on milk yield dynamics in barn-housed dairy cattle

We investigated the effects of environmental factors on average daily milk yield and day-to-day variation in milk yield of barn-housed Scottish dairy cows milked with an automated milking system. An incomplete Wood gamma function was fitted to derive parameters describing the milk yield curve including initial milk yield, inclining slope, declining slope, peak milk yield, time of peak, persistency (time in which the cow maintains high yield beyond the peak), and predicted total lactation milk yield (PTLMY). Lactation curves were fitted using generalized linear mixed models incorporating the above parameters (initial milk yield, inclining and declining slopes) and both the indoor and outdoor weather variables (temperature, humidity, and temperature-humidity index) as fixed effects. There was a higher initial milk yield and PTLMY in multiparous cows, but the incline slope parameter and persistency were greatest in primiparous cows. Primiparous cows took 54 d longer to attain a peak yield (mean ± standard error) of 34.25 ± 0.58 kg than multiparous (47.3 ± 0.45 kg); however, multiparous cows yielded 2,209 kg more PTLMY. The best models incorporated 2-d lagged minimum temperature. However, effect of temperature was minimal (primiparous decreased milk yield by 0.006 kg/d and multiparous by 0.001 kg/d for each degree increase in temperature). Both primiparous and multiparous cows significantly decreased in day-to-day variation in milk yield as temperature increased (primiparous cows decreased 0.05 kg/d for every degree increase in 2-d lagged minimum temperature indoors, which was greater than the effect in multiparous cows of 0.008 kg/d). Though the model estimates for both indoor and outdoor were different, a similar pattern of the average daily milk yield and day-to-day variation in milk yield and milk yield's dependence on environmental factors was observed for both primiparous and multiparous cows. In Scotland, primiparous cows were more greatly affected by the 2-d lagged minimum temperature compared with multiparous cows. After peak lactation had been reached, primiparous and multiparous cows decreased milk yield as indoor and outdoor minimum temperature increased.

Effect of reproductive management programs for first service on replacement dairy heifer economics

Our objective was to evaluate cash flow for dairy heifers managed for first service with programs that relied primarily on insemination at detected estrus (AIE), timed AI (TAI), or a combination of both. Holstein heifers from 2 commercial farms were randomized to receive first service with sexed semen after the beginning of the AI period (AIP) at 12 mo of age with 1 of 3 treatments: (1) PGF+AIE (n = 317): AIE after PGF_2α injections every 14 d (up to 3) starting at the beginning of the AIP; heifers not AIE 9 d after the third PGF_2α were enrolled in the 5d-Cosynch (5dCP) protocol; (2) ALL-TAI (n = 315): TAI after ovulation synchronization with the 5dCP protocol; and (3) PGF+TAI (n = 334): AIE after 2 PGF_2α injections 14 d apart (second PGF_2α at beginning of AIP). If not AIE 9 d after the second PGF_2α, the 5dCP protocol was used for TAI. After first service heifers were AIE or received TAI after the 5dCP with conventional semen. Individual heifer cash flow (CF) for up to a 15-mo period (d 0 = beginning of AIP) was calculated using reproductive cost (rearing only), feed cost (rearing only), income over feed cost (lactation only), calf value, operating cost, and with or without replacement cost. A stochastic analysis with Monte Carlo simulation was used to estimate differences in CF for a range of market values for inputs and outputs. Time to pregnancy for up to 100 d after the beginning of the AIP was analyzed by Cox's proportional regression, binary data with logistic regression, and continuous outcomes by ANOVA. Time to pregnancy (hazard ratio and 95% CI) was reduced for the ALL-TAI versus the PGF+AIE treatment (1.20; 1.02-1.42), but it was similar for ALL-TAI and PGF+TAI (1.13; 0.95-1.33) and the PGF+AIE and PGF+TAI treatments (1.07; 0.91-1.25). The proportion of heifers not pregnant by 100 d did not differ (PGF+AIE = 7.0%; PGF+TAI = 6.5%; ALL-TAI = 6.8%). When including replacement cost, CF ($/slot per 15 mo) differences were $51 and $42 in favor of the PGF+TAI and ALL-TAI compared with the PGF+AIE treatment, and $9 in favor of the PGF+TAI compared with the ALL-TAI treatment but did not differ statistically. Excluding heifers that were replaced to evaluate the effect of timing of pregnancy differences only, the difference in CF between the PGF+AIE with the PGF+TAI and ALL-TAI treatment was the same (i.e., $15) and favored the programs that used more TAI, but also did not differ statistically. Stochastic simulation results were in line with those of the deterministic analysis confirming the benefit of the programs that used more TAI. We concluded that submission of heifers for first service with TAI only or TAI in combination with AIE generated numerical differences in CF of potential value to commercial dairy farms. Reduced rearing cost and increased revenue during lactation increased CF under fixed (not statistically significant) or simulated variable market conditions.

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

Simple Summary

The high costs of genotyping normally compel researchers to work with reduced sample sizes. Contextually, population observations may no longer compensate for the lack of sufficient data to fit lactation curves, hindering model efficiency, explicative ability, and predictive potential. Individualized lactation curve analyses may save these drawbacks, but may be time-demanding, which may be prevented through computational automatization. An SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the curve described by the milk components of the milk of 159 Murciano-Granadina does selected for genotyping analyses. Protein, fat, dry matter, lactose, and somatic cell counts curves were evaluated and modelled, while peak and persistence were estimated to maximize the ability to understand and anticipate productive responses in Murciano-Granadina goats, which may translate into improved profitability of goat milk as a product.

Abstract

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R²) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R² and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R² SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry.

Exploring the impact of oral health-related quality of life on the child’s family structure

Aim: The aim of this study was to explore the impact of oral health-related quality of life (OHRQoL) on the child’s family structure. Methods: A representative sample of 613 children from public preschools, in a city in southeastern Brazil, were enrolled in this cross-sectional study. The sample was determined through probabilistic sampling in two stages (preschools and children). The outcome variable (Early Childhood Oral Health Impact Scale [ECOHIS]) was multi-categorized in children and family. Independent individual variables were sex, race, family income, parents’/caregivers’ level of education, family income, dental caries, and malocclusion. Initially, individual analyses were performed, relating the study variables to the outcome variables, estimating the raw odds ratio with the respective confidence intervals of 95%. The variables with p < 0.20 in the individual analyses were tested in the multiple logistic regression models, and those with p < 0.10 remained in the model. Results: Impact on OHRQoL was reported by 40.9% of the children and 17% of their families. Children with low family income and caries experience had, respectively, 1.53 (95% CI: 1.00-2.32) (p = 0.0465) and 2.96 (95% CI: 1.81-4.84) (p < 0.0001) more chance of presenting negative impact on OHRQoL. Conclusions: The aspects that most affected the OHRQoL of child’s family structure were low income and dental caries experience.

Profitability of dairy cows submitted to the first service with the Presynch-Ovsynch or Double-Ovsynch protocol and different duration of the voluntary waiting period

The objective of this study was to compare cash flow and parameters of economic performance for dairy cows submitted to the first service using a combination of insemination at detected estrus and timed AI (TAI) in cows synchronized with the Presynch-Ovsynch (PSOv) protocol versus all TAI after synchronization of ovulation with the Double-Ovsynch (DO) protocol with different durations of the voluntary waiting period. A secondary objective was to calculate the variation in cash flow under different input pricing scenarios through stochastic Monte Carlo simulation. Lactating Holstein cows from a commercial dairy farm were randomly assigned to 3 first-service management programs. Cows in the PSOv treatment (n = 450) received first service through a combination of insemination at detected estrus after a voluntary waiting period of 50 d in milk (DIM; i.e., after second PGF_2α treatment of the protocol) and TAI at 72 ± 3 DIM. Cows in the DO60 (n = 458) and DO88 (n = 462) treatments received first service by TAI at 60 ± 3 and 88 ± 3 DIM, respectively. Individual cow cash flow was calculated for the calving interval after enrollment and for an 18-mo period after calving. Cash flow was the aggregation of daily income over feed cost, replacement cost, calf value, recombinant bovine somatotropin treatment cost, reproductive cost, and other operating expenses. All analyses were conducted separately for primiparous and multiparous cows. Continuous, binomial, and time to event outcomes were analyzed using ANOVA, logistic regression, and Cox's proportional hazard regression in SAS (SAS Institute Inc., Cary, NC). Treatments affected the dynamics of pregnancy creation, which affected time to pregnancy during lactation. As a result, we observed differences in the proportion of nonpregnant cows at the end of lactation, herd exit dynamics, lactation length, calving interval, and proportion of cows that calved again. Some of these effects varied by parity, affecting the direction and magnitude of treatment differences within parity group. For primiparous cows, maximum cash flow differences per slot for the 18-mo period were in the range of $26 (PSOv > DO60) to $29 (DO88 > DO60) but did not differ statistically. For multiparous cows, maximum cash flow differences per slot for the 18-mo period were in the range of $122 (PSOv > DO88) to $155 (DO60 > DO88) but did not differ statistically. Despite the substantial differences in cash flow (in particular for multiparous cows) caused by the effect of treatments on reproductive performance, herd exit dynamics, and calving interval, large variability in overall cash flow among individual cows and compensation between multiple outcomes resulted in lack of statistical differences in cash flow. Outcomes from the stochastic analysis indicated that similar trends for differences between treatments would be observed under varying scenarios for economic input values. In conclusion, we did not detect statistically significant cash flow differences between the PSOv, DO60, and DO88 treatments, but numerical trends and stochastic simulation indicated that the DO88 and PSOv treatments were more economically favorable than the DO60 treatment for primiparous cows. For multiparous cows, the DO60 and PSOv treatments were more economically favorable than the DO88 treatment.

A multi-country study to assess the effect of a treatment with moxidectin pour-on during the dry period on milk production in dairy cows

A randomized clinical study was conducted in a total of 45 commercial dairy farms in France (14 farms), Germany (28 farms) and the UK (3 farms) to evaluate the effect of an anthelmintic treatment on milk yield in the subsequent lactation. A total of 1287 animals with suspected exposure to Ostertagia ostertagi were included in the study. Animals were treated during the dry period (7-77days before parturition) with moxidectin pour-on (Cydectin^® 0.5% Pour-On, Zoetis; 638 animals) or left untreated (649 animals) according to a randomized block design. Animals were either heifers (n=296) or multiparous cows (n=991). The milk production was monitored at regular intervals after treatment up to 335days after lactation, and analysed using a general linear mixed model with the milk production as outcome variable and several random effects. The effect on milk yield after anthelmintic treatment over the whole subsequent lactation varied from no effect (-0.43kg/day; P=0.35) to an increase of milk yield with 2.35kg/day (P=0.01), depending on the study region and parity of the cows. Lactation curve analysis suggested that the treatment effect was mainly caused by a slower decay of the milk production in the treated animals compared to untreated animals. The present study highlights the beneficial effect of a topical treatment with moxidectin before parturition on milk yield in the subsequent lactation, as well as the importance of a careful evaluation of nematode exposure risk based on local grazing management practices to guide and target production-based anthelmintic treatment decisions.

Fitting milk production curves through nonlinear mixed models

The aim of this work was to fit and compare three non-linear models (Wood, Milkbot and diphasic) to model lactation curves from two approaches: with and without cow random effect. Knowing the behaviour of lactation curves is critical for decision-making in a dairy farm. Knowledge of the model of milk production progress along each lactation is necessary not only at the mean population level (dairy farm), but also at individual level (cow-lactation). The fits were made in a group of high production and reproduction dairy farms; in first and third lactations in cool seasons. A total of 2167 complete lactations were involved, of which 984 were first-lactations and the remaining ones, third lactations (19 382 milk yield tests). PROC NLMIXED in SAS was used to make the fits and estimate the model parameters. The diphasic model resulted to be computationally complex and barely practical. Regarding the classical Wood and MilkBot models, although the information criteria suggest the selection of MilkBot, the differences in the estimation of production indicators did not show a significant improvement. The Wood model was found to be a good option for fitting the expected value of lactation curves. Furthermore, the three models fitted better when the subject (cow) random effect was considered, which is related to magnitude of production. The random effect improved the predictive potential of the models, but it did not have a significant effect on the production indicators derived from the lactation curves, such as milk yield and days in milk to peak.

Effect of Calf Gender on Milk Yield and Fatty Acid Content in Holstein Dairy Cows

The scale of sexed semen use to avoid the birth of unwanted bull calves in the UK dairy industry depends on several economic factors. It has been suggested in other studies that calf gender may affect milk yield in Holsteins- something that would affect the economics of sexed semen use. The present study used a large milk recording data set to evaluate the effect of calf gender (both calf born and calf in utero) on both milk yield and saturated fat content. Linear regression was used to model data for first lactation and second lactation separately. Results showed that giving birth to a heifer calf conferred a 1% milk yield advantage in first lactation heifers, whilst giving birth to a bull calf conferred a 0.5% advantage in second lactation. Heifer calves were also associated with a 0.66kg reduction in saturated fatty acid content of milk in first lactation, but there was no significant difference between the genders in second lactation. No relationship was found between calf gender and milk mono- or polyunsaturated fatty acid content. The observed effects of calf gender on both yield and saturated fatty acid content was considered minor when compared to nutritional and genetic influences.