Bootstrapping for penalized spline regression.

We describe and contrast several different bootstrapping procedures for penalized spline smoothers. The bootstrapping procedures considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered an estimation technique to find an unknown smooth function. The smooth function is represented in a high dimensional spline basis, with spline coefficients estimated in a penalized form. Under the second framework, the unknown function is treated as a realization of a set of random spline coefficients, which are then predicted in a linear mixed model. We describe how bootstrapping methods can be implemented under both frameworks, and we show in theory and through simulations and examples that bootstrapping provides valid inference in both cases. We compare the inference obtained under both frameworks, and conclude that the latter generally produces better results than the former. The bootstrapping ideas are extended to hypothesis testing, where parametric components in a model are tested against nonparametric alternatives.Methods; Framework; Regression; Linear mixed model; Mixed model; Model; Theory; Simulation; Hypothesis testing;

A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach.

The aim of the present study was to identify genomic region(s) associated with the length of the calving interval in primiparous (n = 6866) and multiparous (n = 5071) Holstein cows. The single nucleotide polymorphism (SNP) solutions were estimated using a weighted single-step genomic best linear unbiased prediction (WssGBLUP) approach and imputed high-density panel (777 k) genotypes. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. The results showed that the accuracies of GEBVs with WssGBLUP improved by +5.4 to +5.7, (primiparous cows) and +9.4 to +9.7 (multiparous cows) percent points over accuracies from the pedigree-based BLUP. The most accurate genomic evaluation was provided at the second iteration of WssGBLUP, which was used to identify associated genomic regions using a windows-based GWAS procedure. The proportion of additive genetic variance explained by windows of 50 consecutive SNPs (with an average of 165 Kb) was calculated and the region(s) that accounted for equal to or more than 0.20% of the total additive genetic variance were used to search for candidate genes. Three windows of 50 consecutive SNPs (BTA3, BTA6, and BTA7) were identified to be associated with the length of the calving interval in primi- and multiparous cows, while the window with the highest percentage of explained genetic variance was located on BTA3 position 49.42 to 49.52 Mb. There were five genes including , , , , and inside the windows associated with the length of the calving interval. The biological process terms including alanine transport, L-alanine transport, proline transport, and glycine transport were identified as the most important terms enriched by the genes inside the identified windows

Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms

This study aimed to evaluate the associations between transition cow conditions and diseases TD with fertility in Holstein cows, and to compare analytic methods for doing so. Kaplan-Meier, Cox proportional hazard, and decision tree models were used to analyze the associations of TD with the pregnancy risk at 120 and 210 DIM from a 1-year cohort with 1946 calvings from one farm. The association between TD and fertility was evaluated as follows: 1 cows with TD whether complicated with another TD or not TD-all, versus healthy cows, and 2 cows with uncomplicated TD TD-single, versus cows with multiple TD TD+; complicated cases, versus healthy cows. The occurrence of twins, milk fever, retained placenta, metritis, ketosis, displaced abomasum, and clinical mastitis were recorded. Using Kaplan-Meier models, in primiparous cows the 120 DIM pregnancy risk was 62% (95% CI: 57-67 %) for healthy animals. This was not significantly different for TD-single (58%; 95% CI: 51-66 %) but was reduced for TD+ (45%; 95% CI: 33-60 %). Among healthy primiparous cows, 80% (95% CI: 75-84 %) were pregnant by 210 DIM, but pregnancy risk at that time was reduced for primiparous cows with TD-single (72%; 95% CI: 65-79 %) and TD+ (62%; 95% CI: 49-75 %). In healthy multiparous cows, the 120 DIM pregnancy risk was 53% (95% CI: 49-56 %), which was reduced for TD-single (36%; 95% CI: 31-42 %) and TD+ (30%; 95% CI: 24-38 %). The 210 DIM pregnancy risk for healthy multiparous cows was 70% (95% CI: 67-72 %), being higher than the 210 DIM pregnancy risk for multiparous cows with TD-single (47%; 95% CI: 42-53 %) or TD+ (46%; 95% CI: 38-54 %). Cows with TD-all presented similar pregnancy risk estimates as for TD+. Cox proportional hazards regressions provided similar magnitudes of effects as the Kaplan-Meier estimates. Survival analysis and decision tree models identified parity as the most influential variable affecting fertility. Both modeling techniques concurred that TD + had a greater effect than TD-single on the probability of pregnancy at 120 and 210 DIM. Decision trees for individual TD identified that displaced abomasum affected fertility at 120 DIM in primiparous while metritis was the most influential TD at 120 and 210 DIM for multiparous cows. The data were too sparse to assess multiple interactions in multivariable Cox proportional hazard models for individual TD. Machine learning helped to explore interactions between individual TD to study their hierarchical effect on fertility, identifying conditional relationships that merit further investigation

'Thunder Measure Vet Device' : een praktische en objectieve methode om de lichaamsconditie van melkvee te schatten

The scoring of the body condition at specific time points during the lactation cycle has proven to be essential in the nutritional management of modern dairy herds. The 'Thunder Measure (TM) Vet Device' has recently been developed by Ingenera SA, Switzerland to accurately and objectively measure the body condition score (BCS) of dairy cows in the field. Based on a smartphone app linked to a laser device, the system makes an analysis of three dorsal view photographs taken, for example, when cows are lined up in the feed alley. In the present study, the correlation and repeatability of the system were examined in comparison with the conventional visual measurement of BCS and ultrasonographic measurement of the backfat thickness (BFT). The conventional measurement of the BCS was done by a veterinary surgeon experienced in body condition scoring and by less experienced veterinary undergraduate students. The results obtained via the TM Vet Device were only moderately correlated with the BFT measurements (r=0.38, P<0.001), but were highly correlated (r=0.82, P<0.001) and showed good agreement with the BCS results obtained by the experienced veterinary surgeon. The BCS results obtained by the undergraduate students were highly variable, leading to a highly variable correlation with the results gathered using the TM Vet Device (r=0.23 (P<0.05) to r=0.74 (P<0.001)). The repeatability of the results obtained by the device was very high (91%). Only the repeatability of the results obtained by the experienced veterinarian (93%) and the BFT measurement (96%) were higher. In lean animals, some overscoring by the device was noted in comparison with the scores given by the experienced veterinary surgeon, although this overscoring diminished as the body condition score assigned by the veterinary surgeon increased. The ease to use and the accuracy of the results obtained allow the TM Vet Device to be considered a useful tool in the nutritional management of a modern dairy herd

Serum biochemical profile in Holstein Friesian and Belgian blue calves in the first 48 hours of life

Specific age-related changes in blood variables of calves have previously been reported. The very first hours after birth are however not fully investigated, and results originating from different breeds are combined. The purpose of this study was to investigate the variation in biochemical variables during the first 48 hours after birth in Holstein-Friesian (HF) and Belgian Blue (BB) calves. Nineteen HF calves born vaginally and 23 BB calves delivered by caesarean section were sampled within 30 min after birth, and at 24 and 48 h of life. The concentration of albumin, chloride, sodium, potassium, calcium, phosphate, urea, creatinine, glucose, b-hydroxybutyrate, total protein, and activity of AST, cGT and glutamate dehydrogenase were evaluated. In both groups, significant decreases were recorded at 24 and/or 48 hours compared with 30 min for albumin, calcium, chloride and creatinine, while significant increases were found for AST, cGT, bilirubin, GLDH, glucose and total protein. Changes in analyte concentrations or activities, followed the same trend in both groups, thus suggesting typical features of the newborn calf maturation. The first 24 hours after birth seem to represent a temporal key point in the newborn calf\u2019s life for switching from maternal dependence to a self-sufficient and independent survival. This study confirms that age-specific values should be considered for precise interpretation of laboratory results of newborn calves

Prediction of first test day milk yield using historical records in dairy cows

The transition between two lactations remains one of the most critical periods during the productive life of dairy cows. In this study, we aimed to develop a model that predicts the milk yield of dairy cows from test day milk yield data collected in the previous lactation. In the past, data routinely collected in the context of herd improvement programmes on dairy farms have been used to provide insights in the health status of animals or for genetic evaluations. Typically, only data from the current lactation is used, comparing expected (i.e., unperturbed) with realised milk yields. This approach cannot be used to monitor the transition period due to the lack of unperturbed milk yields at the start of a lactation. For multiparous cows, an opportunity lies in the use of data from the previous lactation to predict the expected production of the next one. We developed a methodology to predict the first test day milk yield after calving using information from the previous lactation. To this end, three random forest models (nextMILKFULL, nextMILKPH, and nextMILKP) were trained with three different feature sets to forecast the milk yield on the first test day of the next lactation. To evaluate the added value of using a machine-learning approach against simple models based on contemporary animals or production in the previous lactation, we compared the nextMILK models with four benchmark models. The nextMILK models had an RMSE ranging from 6.08 to 6.24 kg of milk. In conclusion, the nextMILK models had a better prediction performance compared to the benchmark models. Application-wise, the proposed methodology could be part of a monitoring tool tailored towards the transition period. Future research should focus on validation of the developed methodology within such tool

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings

Between and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring