Review: Improving residual feed intake modelling in the context of nutritional- and genetic studies for dairy cattle

The residual feed intake (RFI) model has recently gained popularity for ranking dairy cows for feed efficiency. The RFI model ranks the cows based on their expected feed intake compared to the observed feed intake, where a negative phenotype (eating less than expected) is favourable. Yet interpreting the biological implications of the regression coefficients derived from RFI models has proven challenging. In addition, multitrait modelling of RFI has been proposed as an alternative to the least square RFI in nutrition and genetic studies. To solve the challenge with the biological interpretation of RFI regression coefficients and suggest ways to improve the modelling of RFI, an interdisciplinary effort was required between nutritionists and geneticists. Therefore, this paper aimed to explore the challenges with the traditional least square RFI model and propose solutions to improve the modelling of RFI. In the traditional least square RFI model, one set of fixed effects is used to solve systematic effects (e.g., seasonal effects and age at calving) for traits with different means and variances. Thereby, measurement and model fitting errors can accumulate in the phenotype, resulting in undesirable effects. A multivariate RFI model will likely reduce this problem, as trait-specific fixed effects are used. In addition, regression coefficients for DM intake on milk energy tend to have more biologically meaningful estimates in multitrait RFI models, which indicates a confounding effect between the fixed effects and regression coefficients in the least square RFI model. However, defining precise expectations for regression coefficients from RFI models or sourcing for accurate feed norm coefficients seems difficult, especially if the coefficients are applied to a wide cattle population with varying diets or management systems, for example. To improve multitrait modelling of RFI, we suggest improving the modelling of changes in energy status. Furthermore, a novel method to derive the energy density of the diet and individual digestive efficiency is proposed. Digestive efficiency is defined as the part of the efficiency associated with digestive processes, which primarily reflects the conversion from gross energy to metabolisable energy. We show the model was insensitive to prior values of energy density in feed and that there was individual variation in digestive efficiency. The proposed method needs further development and validation. In summary, using multitrait RFI can improve the accuracy of the ranking of dairy cows' feed efficiency, consequently improving economic and environmental sustainability on dairy farms.

Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

Association between change in body weight during early lactation and milk production in automatic milking system herds

The objective of this observational study was to investigate the association between percent body weight (BW) change in early lactation and the 90-d cumulative milk yield of dairy cows in automatic milking system (AMS) herds. Retrospective daily cow data were collected from the Lely T4C (Lely Industries, Maassluis, the Netherlands) software on 34 farms. Cows were categorized by parity into parity 1 (P1), parity 2 (P2), or parity 3 and greater (P3+). The BW change over the first 21 d of lactation was calculated as the percentage difference between the cow's average BW across d 20 through 22 and the average BW across d 2 through 4 (initial BW) postpartum. The 90-d cumulative milk yield was the outcome variable in a mixed linear regression model, with BW change, parity, their interaction, and season of calving as explanatory variables. Farm and cow nested within farm (n = 4,695) were random effects in the model. On average, cows in all 3 parity groups lost BW during the first 21 d in milk. The 21-d BW change had a negative quadratic relationship with 90-d cumulative milk yield for all parity groups; P1, P2, and P3+ cows with a 21-d BW change of -7.42%, -5.02%, and -4.52%, respectively, were more productive over 90 d in milk (P1 = 3,123 ± 52.6 kg, P2 = 4,271 ± 52.8 kg, and P3+ = 4,548 ± 52.2 kg). The findings of this study highlight the benefits of monitoring BW change in early lactation and may contribute to future research aimed to develop or improve predictive models for milk production in herds using AMS.

Possibility to Estimate Same Day Energy Status of Dairy Cows during First Half of Lactation by Non-Invasive Markers with Emphasis to Milk Fatty Acids

Postpartum negative energy balance (NEB) is detrimental to cows and decreases profitability in dairy farming. The two origins of milk fatty acids (FA), de novo synthesized in the mammary gland and plasma lipids initially originating from feed, rumen microbes and the animal's adipose tissue, make milk FA candidates as possible NEB biomarkers. The aim of this study was to assess the possibility to predict EB in cows in the first 150 days of lactation with BCS, milk traits and selected individual milk FA and the ratios of blood-derived and de novo synthesized FA. The daily EB of Estonian Holstein cows (N = 30) was calculated based on body weights and BCS values. Milk FA were analyzed with gas chromatography. The variance partitioning analysis revealed that milk production traits, BCS at calving, FA ratios and days in milk accounted for 67.1% of the EB variance. Random forest analysis indicated the highest impact of the ratios C18:1cis9/C12:0+C14:0, C18:1cis9+C18:0/C12:0+C14:0, C18:1cis9/C14:0, C18:1cis9+C18:0/C14:0, C18:1cis9/sum C5:0 to C14:0, C18:1cis9+C18:0/sum C5:0 to C14:0 or C18:1cis9/C15:0. FA and their ratios alone explained 63.6% of the EB variance, indicating the possibility to use milk FA and their ratios as sole predictors for the energy status in dairy cows.

Automatic measurement and prediction of Chinese Grown Pigs weight using multilayer perceptron neural networks

The knowledge of body size/weight is necessary for the general growth enhancement of swine as well as for making informed decisions that concern their health, productivity, and yield. Therefore, this work aims to automate the collection of pigs' body parameters using images from Kinect V2 cameras, and the development of Multilayer Perceptron Neural Network (MLP NN) models to predict their weight. The dataset obtained using 3D light depth cameras contains 9980 pigs across the S21 and S23 breeds, and then grouped into 70:15:15 training, testing, and validation sets, respectively. Initially, two MLP models were built and evaluations revealed that model 1 outperformed model 2 in predicting pig weights, with root mean squared error (RMSE) values of 5.5 and 6.0 respectively. Moreover, employing a normalized dataset, two new models (3 and 4) were developed and trained. Subsequently, models 2, 3, and 4 performed significantly better with a RMSE value of 5.29 compared to model 1, which has a RMSE value of 6.95. Model 3 produced an intriguing discovery i.e. accurate forecasting of pig weights using just two characteristics, age and abdominal circumference, and other error values show corresponding results.

Genomic and transcriptomic analyses enable the identification of important genes associated with subcutaneous fat deposition in Holstein cows

Subcutaneous fat deposition has many important roles in dairy cattle, including immunological defense and mechanical protection. The main objectives of this study are to identify key candidate genes regulating subcutaneous fat deposition in high-producing dairy cows by integrating genomic and transcriptomic datasets. A total of 1,654 genotyped Holstein cows are used to perform a genome-wide association study (GWAS) aiming to identify genes associated with subcutaneous fat deposition. Subsequently, weighted gene co-expression network analyses (WGCNA) are conducted based on RNA-sequencing data of 34 cows and de-regressed estimated breeding values of subcutaneous fat deposition. Lastly, differentially expressed (DE) mRNA, lncRNA, and differentially alternative splicing genes are obtained for 12 Holstein cows with extreme and divergent phenotypes for subcutaneous fat deposition. Forty-six protein-coding genes are identified as candidate genes regulating subcutaneous fat deposition in Holstein cattle based on the GWAS. Eleven overlapping genes are identified based on the analyses of DE genes and WGCNA. Furthermore, the candidate genes identified based on the GWAS, WGCNA, and analyses of DE genes are significantly enriched for pathways involved in metabolism, oxidative phosphorylation, thermogenesis, fatty acid degradation, and glycolysis/gluconeogenesis pathways. Integrating all findings, the NID2, STARD3, UFC1, DEDD, PPP1R1B, and USP21 genes are considered to be the most important candidate genes influencing subcutaneous fat deposition traits in Holstein cows. This study provides novel insights into the regulation mechanism underlying fat deposition in high-producing dairy cows, which will be useful when designing management and breeding strategies.

Balancing Trade-Offs in Milk Production by Making Use of Animal Individual Energy Balancing

Traditionally, the energy supply of dairy cows is based on the average performance of the herd. Because this contradicts the great variation in requirements between individual animals, the objective of the present study was to quantify both the extent and consequences of variation in the relevant sub-variables used to calculate the energy balance (EB) on an individual animal basis. Total energy supply (TES) and requirements (TER) of 28 multiparous German Holstein dairy cows fed TMR with 7.0 MJ NEL were studied between the 2nd and 15th week after calving. TES, mainly influenced by DMI, increased from 100.1 (week 2) to 152.1 MJ NEL/d (week 15; p < 0.01). Weekly coefficients of variation (CV) ranged between 0.10 and 0.16 and were similar to the CV of DMI (0.09 to 0.17). TER, as the sum of energy requirement for maintenance (body weight) and production (milk yield), decreased from 174.8 (week 2) to 164.5 MJ NEL/d (week 15; p < 0.01) and CV varied between 0.16 (week 2) and 0.07 (week 11). EB increased from −74.8 (week 2) to −12.4 MJ NEL/d (week 15; p < 0.01) and CV varied from 0.32 (week 3) to 1.01 (week 10). The results indicate that calculating EB on an individual animal basis is a prerequisite to identify animals with an increased risk of failing to cope with their energy situation, which cause failure costs that drain the profit of affected cows.

Supplementing Ruminally Protected Lysine, Methionine, or Combination Improved Milk Production in Transition Dairy Cows

The objectives of this study were to evaluate the effects of dietary supplementation of ruminally protected lysine (RPL), or methionine (RPM), and their combination (RPML) on the production efficiency of transition cows. A total of 120 pre-partum multiparous Holstein cows were assigned to four treatments based on previous lactation milk production, days (d) of pregnancy, lactation, and body condition score (BCS). Cows were fed a basal diet [pre-calving: 1.53 Mcal/kg dry matter (DM) and post-calving: 1.70 Mcal/kg DM] with or without supplemental ruminally protected amino acids (RPAA). Treatments were the basal diets without supplemental amino acids (CONTROL, n = 30), with supplemental methionine (RPM, pre-calving at 0.16% of DM and post-calving at 0.12% of DM, n = 30), with supplemental lysine (RPL, pre-calving at 0.33% of DM and post-calving at 0.24% DM, n = 30), and the combination (RPML, pre-calving at 0.16% RPM + 0.33% RPL of DM and post-calving at 0.12% RPM + 0.24 % RPL DM, n = 30). The dietary content of lysine was balanced to be within 6.157.2% metabolizable protein (MP)-lysine and that of methionine was balanced within 2.1-2.35% MP-methionine. Dry matter intake (DMI) was measured daily. Milk samples were taken on d 7, 14, and 21 days relative to calving (DRC), and milk yields were measured daily. Blood samples were taken on d -21, -14, -7 before expected calving and d 0, 7, 14, and 21 DRC. Data were analyzed using SAS software. There were significant Trt × time interactions (P < 0.01) for DMI pre- and post-calving period. The CON cows had lower DMI than RPM, RPL, and RPML, both pre-calving (P < 0.01) and post-calving periods (P < 0.01). Energy-corrected milk (P < 0.01), milk fat (P < 0.01), protein (P = 0.02), and lactose (P < 0.01) percentage levels were greater for RPM, RPL, and RPML cows compared to CON. Supplementing RPAA assisted in maintaining BCS post-calving than CON (P < 0.01). Blood concentrations of β-hydroxybutyrate decreased with RPM or RPL or the combination pre-calving (P < 0.01) and tended to decrease post-calving (P = 0.10). These results demonstrated that feeding RPL and RPM improved DMI and milk production efficiency, maintained BCS, and reduced β-hydroxybutyrate concentrations of transition cows.

Study on Body Size Measurement Method of Goat and Cattle under Different Background Based on Deep Learning

The feasibility of using depth sensors to measure the body size of livestock has been extensively tested. Most existing methods are only capable of measuring the body size of specific livestock in a specific background. In this study, we proposed a unique method of livestock body size measurement using deep learning. By training the data of cattle and goat with same feature points, different animal sizes can be measured under different backgrounds. First, a novel penalty function and an autoregressive model were introduced to reconstruct the depth image with super-resolution, and the effect of distance and illumination on the depth image was reduced. Second, under the U-Net neural network, the characteristics exhibited by the attention module and the DropBlock were adopted to improve the robustness of the background and trunk segmentation. Lastly, this study initially exploited the idea of human joint point location to accurately locate the livestock body feature points, and the livestock was accurately measured. According to the results, the average accuracy of this method was 93.59%. The correct key points for detecting the points of withers, shoulder points, shallowest part of the chest, highest point of the hip bones and ischia tuberosity had the percentages of 96.7%, 89.3%, 95.6%, 90.5% and 94.5%, respectively. In addition, the mean relative errors of withers height, hip height, body length and chest depth were only 1.86%, 2.07%, 2.42% and 2.72%, respectively.

Revisiting the Relationships between Fat-to-Protein Ratio in Milk and Energy Balance in Dairy Cows of Different Parities, and at Different Stages of Lactation

Simple Summary

Data from 840 Holstein-Friesian cows (1321 lactations) were used to evaluate trends in fat-to-protein ratios in milk (FPR), and the use of FPR as an indicator of energy balance (EB). The fat-to-protein ratio was negatively related to EB, and this relationship became more negative with increased parity. Regression slopes describing linear relationships between FPR and EB differed over time, although trends were inconsistent. Similarly, ‘High’ FPR scores in milk (≥1.5) were consistently associated with a greater negative energy balance, milk yields, body weight loss, and plasma non-esterified fatty acid concentrations; however, their relationships with dry matter intake did not follow a clear trend. Although FPR can provide an indication of EB at a herd level, this analysis suggests that FPR cannot accurately predict the EB of individual cows.

Abstract

A statistical re-assessment of aggregated individual cow data was conducted to examine trends in fat-to-protein ratio in milk (FPR), and relationships between FPR and energy balance (EB, MJ of ME/day) in Holstein-Friesian dairy cows of different parities, and at different stages of lactation. The data were collected from 27 long-term production trials conducted between 1996 and 2016 at the Agri-Food and Biosciences Institute (AFBI) in Hillsborough, Northern Ireland. In total, 1321 lactations (1 to 20 weeks in milk; WIM), derived from 840 individual cows fed mainly grass silage-based diets, were included in the analysis. The energy balance was calculated daily and then averaged weekly for statistical analyses. Data were further split in 4 wk. intervals, namely, 1–4, 5–8, 9–12, 13–16, and 17–20 WIM, and both partial correlations and linear regressions (mixed models) established between the mean FPR and EB during these periods. Three FPR score categories (‘Low’ FPR, <1.0; ‘Normal’ FPR, 1.0–1.5; ‘High’ FPR, >1.5) were adopted and the performance and EB indicators within each category were compared. As expected, multiparous cows experienced a greater negative EB compared to primiparous cows, due to their higher milk production relative to DMI. Relatively minor differences in milk fat and protein content resulted in large differences in FPR curves. Second lactation cows displayed the lowest weekly FPR, and this trend was aligned with smaller BW losses and lower concentrations of non-esterified fatty acids (NEFA) until at least 8 WIM. Partial correlations between FPR and EB were negative, and ‘greatest’ in early lactation (1–4 WIM; r = −0.38 on average), and gradually decreased as lactation progressed across all parities (17–20 WIM; r = −0.14 on average). With increasing parity, daily EB values tended to become more negative per unit of FPR. In primiparous cows, regression slopes between FPR and EB differed between 1–4 and 5–8 WIM (−54.6 vs. −47.5 MJ of ME/day), while differences in second lactation cows tended towards significance (−57.2 vs. −64.4 MJ of ME/day). Irrespective of the lactation number, after 9–12 WIM, there was a consistent trend for the slope of the linear relationships between FPR and EB to decrease as lactation progressed, with this likely reflecting the decreasing milk nutrient demands of the growing calf. The incidence of ‘High’ FPR scores was greatest during 1–4 WIM, and decreased as lactation progressed. ‘High’ FPR scores were associated with increased energy-corrected milk (ECM) yields across all parities and stages of lactation, and with smaller BW gains and increasing concentrations (log transformed) of blood metabolites (non-esterified fatty acid, NEFA; beta-hydroxybutyrate, BHB) until 8 WIM. Results from the present study highlight the strong relationships between FPR in milk, physiological changes, and EB profiles during early lactation. However, while FPR can provide an indication of EB at a herd level, the large cow-to-cow variation indicates that FPR cannot be used as a robust indicator of EB at an individual cow level.

Milk metabolites and fatty acids as noninvasive biomarkers of metabolic status and energy balance in early-lactation cows

The objective was to study the effects of week of lactation (WOL) and experimental nutrient restriction on concentrations of selected milk metabolites and fatty acids (FA), and assess their potential as biomarkers of energy status in early-lactation cows. To study WOL effects, 17 multiparous Holstein cows were phenotyped from calving until 7 WOL while allowed ad libitum intake of a lactation diet. Further, to study the effects of nutrient restriction, 8 of these cows received a diet containing 48% straw (high-straw) for 4 d starting at 24 ± 3 days in milk (mean ± SD), and 8 cows maintained on the lactation diet were sampled to serve as controls. Blood and milk samples were collected weekly for the WOL data set, and daily from d -1 to 3 of nutrient restriction (or control) for the nutritional challenge data set. Milk β-hydroxybutyrate (BHB), isocitrate, glucose, glucose-6-phosphate (glucose-6P), galactose, glutamate, creatinine, uric acid, and N-acetyl-β-d-glucosaminidase activity (NAGase) were analyzed in p.m. and a.m. samples, and milk FA were analyzed in pooled p.m. and a.m. samples. Average energy balance (EB) per day ranged from -27 MJ/d to neutral when cows received the lactation total mixed ration, and from -109 to -87 ± 7 MJ/d for high-straw (least squares means ± standard error of the mean). Plasma nonesterified FA concentration was 1.67 ± 0.13 mM and BHB was 2.96 ± 0.39 mM on the d 3 of high-straw (least squares means ± standard error of the mean). Milk concentrations of BHB, glucose, glucose-6P, glutamate, and uric acid differed significantly between p.m. and a.m. milkings. Milk isocitrate, glucose-6P, creatinine, and NAGase decreased, whereas milk glucose and galactose increased with WOL. Changes in milk BHB, isocitrate, glucose, glucose-6P, and creatinine were concordant during early lactation and in response to nutrient restriction. Milk galactose and NAGase were modulated by WOL only, whereas glutamate and uric acid concentrations responded to nutrient restriction only. The high-straw increased milk concentrations of FA potentially mobilized from adipose tissue (e.g., C18:0 and cis-9 C18:1 and sum of odd- and branched-chain FA (OBCFA) with carbon chain greater than 16; ∑ OBCFA >C16), and decreased concentrations of FA synthesized de novo by the mammary gland (e.g., sum of FA with 6 to 15 carbons; ∑ C6:0 to C15:0). Similar observations were made during early lactation. Plasma nonesterified FA concentrations had the best single linear regression with EB (R² = 0.62). Milk isocitrate, Σ C6:0 to C15:0. and cis-9 C18:1 had the best single linear regressions with EB (R² ≥ 0.44). Milk BHB, isocitrate, galactose, glutamate, and creatinine explained up to 64% of the EB variation observed in the current study using multiple linear regression. Milk concentrations of ∑ C6:0 to C15:0, C18:0, cis-9 C18:1, and ∑ OBCFA >C16 presented some of the best correlations and regressions with other indicators of metabolic status, lipomobilization, and EB, and their responses were concordant during early lactation and during experimental nutrient restriction. Metabolites and FA secreted in milk may serve as noninvasive indicators of metabolic status and EB of early-lactation cows.

Milk fatty acids as indicators of negative energy balance of dairy cows in early lactation

Most dairy cows experience negative energy balance (NEB) in early lactation because energy demand for milk synthesis is not met by energy intake. Excessive NEB may lead to metabolic disorders and impaired fertility. To optimize herd management, it is useful to detect cows in NEB in early lactation, but direct calculation of NEB is not feasible in commercial herds. Alternative methods rely on fat-to-protein ratio in milk or on concentrations of non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) in blood. Here, we considered methods to assess energy balance (EB) of dairy cows based on the fatty acid (FA) composition in milk. Short- and medium-chain FAs (primarily, C14:0) are typically synthesized de novo in the mammary gland and their proportions in milk fat decrease during NEB. Long-chain FAs C18:0 and C18:1 cis-9 are typically released from body fat depots during NEB, and their proportions increase. In this study, these FAs were routinely determined by Fourier-transform infrared spectroscopy (FTIR) of individual milk samples. We performed an experiment on 85 dairy cows in early lactation, fed the same concentrate ration of up to 5 kg per day and forage ad libitum. Daily milk yield and feed intake were automatically recorded. During lactation weeks 2, 4, and 6 after calving, two milk samples were collected for FTIR spectroscopy, Tuesday evening and Wednesday morning, blood plasma samples were collected Thursday morning. Net energy content in feed and net energy required for maintenance and lactation were estimated to derive EB, which was used to compare alternative indicators of severe NEB. Linear univariate models for EB based on NEFA concentration (deviance explained = 0.13) and other metabolites in blood plasma were outperformed by models based on concentrations of metabolites in milk: fat (0.27), fat-to-protein ratio (0.18), BHB (0.20), and especially C18:0 (0.28) and C18:1 cis-9 (0.39). Analysis of generalized additive models (GAM) revealed that models based on milk variables performed better than those based on blood plasma (deviance explained 0.46 vs. 0.21). C18:0 and C18:1 cis-9 also performed better in severe NEB prediction for EB cut-off values ranging from -50 to 0 MJ NEL/d. Overall, concentrations of C18:0 and C18:1 cis-9 in milk, milk fat, and milk BHB were the best variables for early detection of cows in severe NEB. Thus, milk FA concentrations in whole milk can be useful to identify NEB in early-lactation cows.

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

Short-term liveweight changes of dairy cows measured by stationary and walk-over weighing scales

Monitoring and detecting individual cows' liveweight (LW) and liveweight change (LWC) are important for estimation of nutritional requirements and health management, and could be useful to measure short-term feed intake, water consumption, defecation, and urination. Walk-over weighing (WOW) systems can facilitate measurements of LW for these purposes, providing automated LW recorded at different times of the day. We conducted a field study to (1) quantify the contribution of feed and water intake, as well as urine and feces excretions, to short-term LWC and (2) determine the feasibility of stationary and WOW scales to detect subtle changes in LW as a result of feed and water intake, urination, and defecation. In this experiment, 10 cows walked through a WOW system and then stood individually on a stationary scale collecting weights at 10 and 3.3 Hz, respectively. Cows were offered 4 kg of feed and 10 kg of water on the stationary scale. For each animal, LW before and after eating and drinking was then calculated using different approaches. Liveweight change was calculated as the difference between the initial and final LW before and after eating and drinking for each statistical measure. The weights of feed intake, water consumption, urination, and defecation were measured and used as predictors of LWC. Urine and feces were collected from individual cows while the cow was on the scale, using a container, and weighed separately. The agreement between LWC measured using either stationary or WOW scales was assessed to determine the sensitivity of the scales to detect subtle changes in LW using the coefficient of determination (R²), Lin's concordance correlation coefficient (CCC), and mean bias. The prediction model showed that most of the regression coefficients were not significantly different from +1.0 for feed and water, or -1.0 for urine and feces. The R² and CCC values demonstrated a satisfactory agreement between calculated and stationary LWC and values ranged from 0.60 to 0.92 and 0.71 to 0.94, respectively. A moderate agreement was achieved between calculated and automated LWC with R² and Lin's CCC values of 0.45 to 0.63 and 0.60 to 0.74, respectively. Therefore, results demonstrated that new algorithms and data processing methods need to be continuously explored and improved to obtain accurate measurements of LW to measure changes in LW, especially from WOW scales.

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies

Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations.

Whole-lactation feed intake, milk yield, and energy balance of Holstein and Swedish Red dairy cows fed grass-clover silage and 2 levels of byproduct-based concentrate

Ruminants can produce meat and milk from fibrous feed and byproducts not suitable for human consumption. However, high-yielding dairy cows are generally fed a high proportion of cereal grain and pulses, which could be consumed directly by humans. If high production of dairy cows could be maintained with ingredients of low human interest, the sustainability of dairy production would improve. In the present study, 37 multiparous [Holstein (n = 13) and Swedish Red (n = 24)] dairy cows were followed over a whole lactation. A low-concentrate diet of up to 6 kg concentrate per day (6kgConc) was fed to 27 cows, whereas 10 cows were fed a high-concentrate diet of up to 12 kg concentrate per day (12kgConc). The concentrate was mainly based on byproducts (sugar beet pulp, wheat bran, rapeseed meal, distiller's grain). Grass-clover silage of high digestibility was offered ad libitum. Over the whole lactation, cows on the 6kgConc diet had lower dry matter intake and higher forage intake than cows on the 12kgConc diet. Milk yield and energy balance were not influenced by dietary treatment. However, the cows on the 6kgConc diet numerically produced 2.4 kg less energy-corrected milk than cows on 12kgConc diet. The study lacked the statistical power to identify treatment effects on daily yield below 2.8 kg of milk due to low number of animals per treatment. Feed efficiency (as energy-corrected milk yield/dry matter intake or residual feed intake), body weight change, body condition change, milk fatty acid concentration in total milk fatty acids, plasma nonesterified fatty acids, glucose, β-hydroxybutyrate, and fertility measurements were not affected by diet, supporting the energy balance results. However, higher plasma concentrations of insulin-like growth factor-1 and insulin were observed in cows fed the 12kgConc diet. These findings show that cows can adapt to a high-forage diet virtually without human-grade ingredients, without compromising feed efficiency or energy balance, thereby contributing to sustainable food production.

Estimation of dairy goat body composition: A direct calibration and comparison of eight methods

The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D₂OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R² = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R² = 0.92, rSD = 0.76 kg), iii) D₂OS (R² = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R² = 0.87, rSD = 1.09 kg). The D₂OS combined with BW provided the best equation for EB protein mass (R² = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R² = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R² = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R² = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R² ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness.

Body condition and insulin resistance interactions with periparturient gene expression in adipose tissue and lipid metabolism in dairy cows

Adipose tissue plays an important role in a cow's ability to adapt to the metabolic demands of lactation, because of its central involvement in energy metabolism and immunity. High adiposity and adipose tissue resistance to insulin are associated with excessive lipid mobilization. We hypothesized that the response to a glucose challenge differs between cows of different body condition 21 d before and after calving and that the responses are explainable by gene expression in subcutaneous adipose tissue (SAT). In addition, we aimed to investigate insulin resistance with gene expression in SAT and lipid mobilization around parturition. Multiparous Holstein cows were grouped according to body conditions score (BCS) 4 wk before calving, as follows: BCS ≤ 3.0 = thin (T, n = 14); BCS 3.25 to 3.5 = optimal (O, n = 14); BCS ≥ 3.75 = over-conditioned (OC, n = 14). We collected SAT on d -21 and d 21 relative to calving. A reverse-transcriptase quantitative (RT-q)PCR was used to measure gene expression related to lipid metabolism. One hour after the collection of adipose tissue, an intravenous glucose tolerance test was carried out, with administration of 0.15 g of glucose per kg of body weight (with a 40% glucose solution). Once weekly from the first week before calving to the third week after calving, a blood sample was taken. The transition to lactation was associated with intensified release of energy stored in adipose tissue, a decrease in the lipogenic genes lipoprotein lipase (LPL) and diacylglycerol O-acyltransferase 2 (DGAT2), and an increase in the lipolytic gene hormone-sensitive lipase (LIPE). On d -21, compared with T cows, OC cows had lower mRNA abundance of LPL and DGAT2, and the latency of fatty acid response after glucose infusion was also longer (8.5 vs. 23.3 min) in OC cows. Cows with higher insulin area under the curve on d -21 had concurrently lower LPL and DGAT2 gene expression and greater concentration of fatty acids on d -7, d 7, and d 14. In conclusion, high adiposity prepartum lowers the whole-body lipid metabolism response to insulin and causes reduced expression of lipogenic genes in SAT 3 weeks before calving. In addition, more pronounced insulin release after glucose infusion on d -21 is related to higher lipid mobilization around calving, indicating an insulin-resistant state, and is associated with lower expression of lipogenic genes in SAT.

Model-based exploration of the impact of glucose metabolism on the estrous cycle dynamics in dairy cows

BACKGROUND

Nutrition plays a crucial role in regulating reproductive hormones and follicular development in cattle. This is visible particularly during the time of negative energy balance at the onset of milk production after calving. Here, elongated periods of anovulation have been observed, resulting from alterations in luteinizing hormone concentrations, likely caused by lower glucose and insulin concentrations in the blood. The mechanisms that result in a reduced fertility are not completely understood, although a close relationship to the glucose-insulin metabolism is widely supported.

RESULTS

Following this idea, we developed a mathematical model of the hormonal network combining reproductive hormones and hormones that are coupled to the glucose compartments within the body of the cow. The model is built on ordinary differential equations and relies on previously introduced models on the bovine estrous cycle and the glucose-insulin dynamics. Necessary modifications and coupling mechanisms are thoroughly discussed. Depending on the composition and the amount of feed, in particular the glucose content in the dry matter, the model quantifies reproductive hormones and follicular development over time. Simulation results for different nutritional regimes in lactating and non-lactating dairy cows are examined and compared with experimental studies. The simulations describe realistically the effects of nutritional glucose supply on the ovulatory cycle of dairy cattle.

CONCLUSIONS

The mathematical model enables the user to explore the relationship between nutrition and reproduction by running simulations and performing parameter studies. Regarding its applicability, this work is an early attempt towards developing in silico feeding strategies and may eventually help to refine and reduce animal experiments.

REVIEWERS

This article was reviewed by John McNamara and Tin Pang (nominated by Martin Lercher).

Predicting milk fatty acids and energy balance of dairy cows in Australia using milk mid-infrared spectroscopy

Mid-infrared spectroscopy (MIRS) is traditionally used for analysing milk fat, protein and lactose concentrations in dairy production, but there is growing interest in using it to predict difficult, or expensive-to-measure, phenotypes on a large scale. The resulting prediction equations can be applied to MIRS data from commercial herd-testing, to facilitate management and feeding decisions, or for genomic selection purposes. We investigated the ability of MIRS of milk samples to predict milk fatty acids (FAs) and energy balance (EB) of dairy cows in Australia. Data from 240 Holstein lactating cows that were part of two 32-day experiments, were used. Milk FAs were measured twice during the experimental period. Prediction models were developed using partial least-square regression with a 10-fold cross-validation. Measures of prediction accuracy included the coefficient of determination (R2cv) and root mean-square error. Milk FAs with a chain length of ≤16 were accurately predicted (0.89 ≤ R2cv ≤ 0.95), while prediction accuracy for FAs with a chain length of ≥17 was slightly lower (0.72 ≤ R2cv ≤ 0.82). The accuracy of the model prediction was moderate for EB, with the value of R2cv of 0.48. In conclusion, the ability of MIRS to predict milk FAs was high, while EB was moderately predicted. A larger dataset is needed to improve the accuracy and the robustness of the prediction models.

An Improved Single Shot Multibox Detector Method Applied in Body Condition Score for Dairy Cows

Simple Summary

Body condition score (BCS) is an important work for feeding management and cow welfare on the farm. The aim of our study is to assess the BCS automatically and replace the traditional manual method. In this study, we firstly built a non-contact and no-stress platform with a network camera, which can monitor the BCS of dairy cow remotely, and the back-view images of the cows were collected and the data set labeled by veterinary experts was built. Secondly, the improved Sing Shot multi-box Detector (SSD) algorithm was introduced to assess the BCS of each image. Finally, the experiments were carried out and the results showed the improved SSD had advantages of higher detecting speed and smaller model size compared with the original SSD.

Abstract

Body condition scores (BCS) is an important parameter, which is in high correlation with the health status of a dairy cow, metabolic disorder and milk composition during the production period. To evaluate BCS, the traditional methods rely on veterinary experts or skilled staff to look at a cow and touch it. These methods have low efficiency especially on large-scale farms. Computer vision methods are widely used but there are some improvements to increase BCS accuracy. In this study, a low cost BCS evaluation method based on deep learning and machine vision is proposed. Firstly, the back-view images of the cows are captured by network cameras, resulting in 8972 images that constituted the sample data set. The camera is a common 2D camera, which is cheaper and easier to install compared with 3D cameras. Secondly, the key body parts such as tails, pins and rump in the images were labeled manually, the Sing Shot multi-box Detector (SSD) method was used to detect the tail and evaluate the BCS. Inspired by DenseNet and Inception-v4, a new SSD was introduced by changing the network connection method of the original SSD. Finally, the experiments show that the improved SSD method can achieve 98.46% classification accuracy and 89.63% location accuracy, and it has: (1) faster detection speed with 115 fps; (2) smaller model size with 23.1 MB compared to original SSD and YOLO-v3, these are significant advantages for reducing hardware costs.

Body and milk traits as indicators of dairy cow energy status in early lactation

The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (ΔBW), change in body condition score (ΔBCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 ± 0.30, 0.43 ± 0.22, and 0.13 ± 0.06 mmol/L, respectively; all means ± standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, ΔBW, ΔBCS, FPR × ΔBW, and days in milk. The model resulted in a cross-validation coefficient of determination (R²cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R²cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 × C14:0, and days in milk (R²cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R²cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.

Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions

Machine vision technology has been used in automated body condition score (BCS) classification of dairy cows. The current vision-based classifications use information acquired from a limited number of body regions of the cow. Our study aimed to improve automated BCS classification by including multiple body condition-related features extracted from 3 viewpoints in 8 body regions. The data set of this study included 44 lactating cows with their BCS evenly distributed over the scale of BCS from 1.5 to 4.5 units. The body images of these cows were recorded over 2 consecutive days using 3-dimensional cameras positioned to view the cow from the top, right side, and rear. Each image was automatically processed to identify anatomical landmarks on the body surface. Around these anatomical landmarks, the bony prominences and body surface depressions were quantified to describe 8 body condition-related features. A manual BCS of each cow was independently assigned by 2 trained assessors using the same predefined protocol. With the extracted features as inputs and manual BCS as the reference, we built a nearest-neighbor classification model to classify BCS and obtained an overall classification sensitivity of 0.72 using a 10-fold cross-validation. We conclude that the sensitivity of automated BCS classification has been improved by expanding the selection of body condition-related features extracted from multiple body regions.

Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and β-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids and BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-β-d-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.

Intra-flock variability in the body reserve dynamics of meat sheep by analyzing BW and body condition score variations over multiple production cycles

Breeding for resilience requires a better understanding of intra-flock variability and the related mechanisms responsible for robustness traits. Among such traits, the animals' ability to cope with feed fluctuations by mobilizing or restoring body reserves (BR) is a key mechanism in ruminants. The objective of this work was to characterize individual variability in BR dynamics in productive Romane ewes reared in extensive conditions. The BR dynamics profiles were characterized by combining individual longitudinal measurements of BW and body condition scores (BCS) over several production cycles. Historical data, including up to 2628 records per trait distributed in 1146 ewes, underwent cluster analysis. Two to four trajectories were observed for BW depending on the cycle, while three trajectories were found for BCS, whatever the cycle. Most trajectories suggested that BR dynamics were similar but the level of BR may differ between ewes. Nevertheless, some trajectories suggested that both BR dynamics and levels were different for a proportion of ewes. Clustering on BW and BCS profiles adjusted for individual level trends, resulted in differences only in the level of BW or BCS, rather than differences in trajectories. Thus, the overall shape of trajectories was not changed considering or not the individual level trend across cycles. In addition to individual variability, the ewe's age at first lambing and litter size contributed to the distribution of the ewes between the trajectories. Regarding the entire productive life, three trajectories were observed for BW and BCS changes over three productive cycles. Increase in BW at each cycle suggested that ewes kept growing up until 3 to 4 years old in our conditions. Similar alternation of BCS gains and losses across cycles suggested BR dynamics might be repeatable. Many individual trajectories remained the same throughout a ewe's life, whatever the age at first lambing, parity or litter size. Our results demonstrate the relevance of using BW and BCS changes for characterizing the diversity of BR mobilization-accretion profiles in sheep in a long timespan perspective.

Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time

Animal robustness may be defined as a complex trait characterizing the ability of an individual to be adapted, productive and healthy under contrasted and fluctuating environmental situations. Such a trait is now considered an essential criterion in order to meet the more ambitious goals of farming sustainability. In ruminants, one of the key mechanisms via which robustness is expressed is the capacity to mobilize or restore body reserves (BR) to cope with the challenges of negative energy balances. The objectives of this work were as follows: 1) to estimate the genetic parameters related to BR dynamics in ewes over successive production cycles and 2) to investigate BR management relationships between different physiological stages. For this, historical individual BW and BCS data from 2,920 phenotyped ewes were used for genetic analysis. The changes in BW (∆BW) and BCS (∆BCS) over time were analyzed. Eight physiological stages were considered to investigate these changes over time: mating, early pregnancy, mid-pregnancy, lambing, early suckling, mid-suckling, weaning, and postweaning. The estimated heritability were low for both ∆BW (h2 = 0.13 to 0.18) and ∆BCS (h2 = 0.04 to 0.16). Moderate to high positive genetic correlations (from 0.48 to 0.91) were obtained between BR mobilization phases and between BR accretion phases. Similarly, moderate to high negative genetic correlations (from -0.36 to -0.75) were estimated between the BR mobilization and accretion periods, suggesting that mechanisms driving BR mobilization and accretion processes were genetically correlated. This is the first study in ruminants that demonstrate that the extent and temporal changes in profiles of BR mobilization and accretion are heritable and genetically linked, indicating that such traits could be considered in genetic programs aimed at improving robustness. Nevertheless, further research is needed for a more comprehensive understanding of BR dynamics, notably by including other physiological parameters (i.e., metabolites and hormones) and additional information on the productive and reproductive life of the ewe.

A dynamic model as a tool to describe the variability of lifetime body weight trajectories in livestock females

Until now, the development of precision livestock farming has been largely based on data acquisition automation. The future challenge is to develop interpretative tools to capitalize on high-throughput raw data and to generate benchmarks for phenotypic traits. We developed a dynamic model of female BW that converts BW time series into a vector of biologically meaningful parameters. The model is based on a first submodel that split a female's weight into elementary mass changes related to biological functions: growth (G component), reserves balance (R component), uterine load (U component), and maternal investment (M component). These elementary weight components are linked to the second submodel, which represents the litter developmental stages (oocyte, fetus, neonate, and juvenile) that drive elementary components of dam weight over each reproductive cycle. The so-called GRUM model is based on ordinary differential equations and laws of mass action. Input data are BW measures, age, and litter weight at birth for each parturition. Outputs of the fitting procedure are a vector of parameters related to each GRUM component and indexed by reproductive cycle. We illustrated the potential application of the model with a case study including growth and successive lactations ( = 202) from 45 dairy goats from the Alpine ( = 27) and Saanen ( = 18) breeds. The fitting procedure converged for all individuals, including goats that went through extended lactations. We analyzed the fitted parameters to quantify breed and parity effects over 4 reproductive cycles. We found significant differences between breeds regarding gestation components (fetal growth and reserves balance). We also found significant differences among reproductive cycles for reserves balance. Although these findings are based on a small sample, they illustrate how use the model can be to adapt herd management and implement grouping strategies to account for individual variability.

Adipose tissue insulin receptor and glucose transporter 4 expression, and blood glucose and insulin responses during glucose tolerance tests in transition Holstein cows with different body condition

Glucose uptake in tissues is mediated by insulin receptor (INSR) and glucose transporter 4 (GLUT4). The aim of this study was to examine the effect of body condition during the dry period on adipose tissue mRNA and protein expression of INSR and GLUT4, and on the dynamics of glucose and insulin following the i.v. glucose tolerance test in Holstein cows 21 d before (d -21) and after (d 21) calving. Cows were grouped as body condition score (BCS) ≤3.0 (thin, T; n = 14), BCS = 3.25 to 3.5 (optimal, O; n = 14), and BCS ≥3.75 (overconditioned, OC; n = 14). Blood was analyzed for glucose, insulin, fatty acids, and β-hydroxybutyrate concentrations. Adipose tissue was analyzed for INSR and GLUT4 mRNA and protein concentrations. During the glucose tolerance test 0.15 g/kg of body weight glucose was infused; blood was collected at -5, 5, 10, 20, 30, 40, 50, and 60 min, and analyzed for glucose and insulin. On d -21 the area under the curve (AUC) of glucose was smallest in group T (1,512 ± 33.9 mg/dL × min) and largest in group OC (1,783 ± 33.9 mg/dL × min), and different between all groups. Basal insulin on d -21 was lowest in group T (13.9 ± 2.32 µU/mL), which was different from group OC (24.9 ± 2.32 µU/mL. On d -21 the smallest AUC 5-60 of insulin in group T (5,308 ± 1,214 µU/mL × min) differed from the largest AUC in group OC (10,867 ± 1,215 µU/mL × min). Time to reach basal concentration of insulin in group OC (113 ± 14.1 min) was longer compared with group T (45 ± 14.1). The INSR mRNA abundance on d 21 was higher compared with d -21 in groups T (d -21: 3.3 ± 0.44; d 21: 5.9 ± 0.44) and O (d -21: 3.7 ± 0.45; d 21: 4.7 ± 0.45). The extent of INSR protein expression on d -21 was highest in group T (7.3 ± 0.74 ng/mL), differing from group O (4.6 ± 0.73 ng/mL), which had the lowest expression. The amount of GLUT4 protein on d -21 was lowest in group OC (1.2 ± 0.14 ng/mL), different from group O (1.8 ± 0.14 ng/mL), which had the highest amount, and from group T (1.5 ± 0.14 ng/mL). From d -21 to 21, a decrease occurred in the GLUT4 protein levels in both groups T (d -21: 1.5 ± 0.14 ng/mL; d 21: 0.8 ± 0.14 ng/mL) and O (d -21: 1.8 ± 0.14 ng/mL; d 21: 0.8 ± 0.14 ng/mL). These results demonstrate that in obese cows adipose tissue insulin resistance develops prepartum and is related to reduced GLUT4 protein synthesis. Regarding glucose metabolism, body condition did not affect adipose tissue insulin resistance postpartum.

Invited review: Opportunities for genetic improvement of metabolic diseases

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.

Milk Production and Fertility in Cattle

Evolutionary biology provides reasons for why the intensive selection for milk production reduces reproductive success rates. There is considerable exploitable genetic variation in reproductive performance in both dairy and beef cattle, and examination of national genetic trends demonstrates that genetic gain for both reproductive performance and milk production is possible in a well-structured breeding program. Reproductive failure is often postulated to be a consequence of the greater negative energy balance associated with the genetic selection for increased milk production. However, experimental results indicate that the majority of the decline in reproductive performance cannot be attributed to early lactation energy balance, per se; reproductive success will, therefore, not be greatly improved by nutritional interventions aimed at reducing the extent of negative energy balance. Modeling can aid in better pinpointing the key physiological components governing reproductive success and, also, the impact of individual improvements on overall fertility, helping to prioritize variables for inclusion in breeding programs.

Genetic associations of test-day fat:protein ratio with milk yield, fertility, and udder health traits in Nordic Red cattle

Interest is growing in finding indicator traits for the evaluation of nutritional or tissue energy status of animals directly at the individual animal level. The development and subsequent use of such traits in practice demands a clear understanding of the genetic and phenotypic associations with the various production and functional traits. In this study, the relationships during lactation between milk fat:protein ratio (FPR) and production and functional traits were estimated for Nordic Red cattle, in which published information is scarce. The objectives of this study were to estimate genetic associations of FPR with milk yield (MY), fertility, and udder health traits during different stages of lactation. Traits included in the analyses were MY, 4 fertility traits-days from calving to insemination (DFI), days open (DO), number of inseminations (NI), and nonreturn rate to 56 d (NRR)-and 2 udder health traits-test-day somatic cell score (SCS) and clinical mastitis (CM). Data were from a total of 22,422 first-lactation cows. Random regression models were used to estimate genetic parameters and associations between traits. The mean FPR in first-lactation cows was 1.28 and ranged from 1.25 to 1.45. During first lactation, the heritability of FPR ranged from 0.14 to 0.25. Genetic correlations between FPR and MY in early lactation (until 50 d in milk) were positive and ranged from 0.05 to 0.22; later in lactation, they were close to zero or negative, indicating that cows may have come out of the negative state of energy balance. The strength of genetic associations between FPR and fertility traits varied during lactation. In early lactation, correlations between FPR and the interval fertility traits DFI and DO were positive and ranged from 0.14 to 0.28. Genetic correlations between FPR and the udder health traits SCS and CM in early lactation ranged from 0.09 to 0.20. Milk fat:protein ratio is a heritable trait and easily available from routine milk-recording schemes. It can be used as a low-cost monitoring tool of poor health and fertility in the most critical phases of lactation and as an important indicator trait to improve robustness in dairy cows through selection.

Integrative control of energy balance and reproduction in females

There is a strong association between nutrition and reproduction. Chronic dietary energy deficits as well as energy surpluses can impair reproductive capacity. Metabolic status impacts reproductive function at systemic level, modulating the hypothalamic GnRH neuronal network and/or the pituitary gonadotropin secretion through several hormones and neuropeptides, and at the ovarian level, acting through the regulation of follicle growth and steroidogenesis by means of the growth hormone-IGF-insulin system and local ovarian mediators. In the past years, several hormones and neuropeptides have been emerging as important mediators between energy balance and reproduction. The present review goes over the main sites implicated in the control of energy balance linked to reproductive success and summarizes the most important metabolic and neuroendocrine signals that participate in reproductive events with special emphasis on the role of recently discovered neuroendocrine peptides. Also, a little overview about the effects of maternal nutrition, affecting offspring reproduction, has been presented.