Short communication: relationship between body growth and mammary development in dairy heifers

Our objective was to determine if prepubertal rate of body weight (BW) gain, independent of diet, was related to mammary development of dairy heifers. Data from two studies recently conducted at Michigan State University were used to identify factors, within a dietary treatment group, that would account for variation in first lactation milk production or amount of mammary parenchymal DNA at the time of puberty. Factors analyzed for variation in milk production during first lactation were: postpartum BW, prepubertal BW gain, gestational BW gain, postpartum BW gain, body condition score (BCS) at breeding, and BCS at calving. Factors analyzed for variation in mammary parenchymal DNA at puberty were: BW at slaughter, age at puberty, prepubertal BW gain and body protein and body fat content at slaughter. For both analyses, prepubertal BW gain did not account for any of the variation in mammary development. The only significant covariate for the milk production model (r2 = 0.44) was BCS at breeding. Similarly, the only significant covariate in the parenchymal DNA model (r2 = 0.22) was body fat content at slaughter. These results suggest that, within a dietary treatment, heifers that grow faster do not have impaired mammary development, and increased body fatness may be a better predictor of impaired mammary development than BW gain

Short communication: relationship between body growth and mammary development in dairy heifers

Our objective was to determine if prepubertal rate of body weight (BW) gain, independent of diet, was related to mammary development of dairy heifers. Data from two studies recently conducted at Michigan State University were used to identify factors, within a dietary treatment group, that would account for variation in first lactation milk production or amount of mammary parenchymal DNA at the time of puberty. Factors analyzed for variation in milk production during first lactation were: postpartum BW, prepubertal BW gain, gestational BW gain, postpartum BW gain, body condition score (BCS) at breeding, and BCS at calving. Factors analyzed for variation in mammary parenchymal DNA at puberty were: BW at slaughter, age at puberty, prepubertal BW gain and body protein and body fat content at slaughter. For both analyses, prepubertal BW gain did not account for any of the variation in mammary development. The only significant covariate for the milk production model (r2 = 0.44) was BCS at breeding. Similarly, the only significant covariate in the parenchymal DNA model (r2 = 0.22) was body fat content at slaughter. These results suggest that, within a dietary treatment, heifers that grow faster do not have impaired mammary development, and increased body fatness may be a better predictor of impaired mammary development than BW gain

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirementsseries is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and United States of America), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs

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 GenTORE and 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

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirementsseries is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition

Guidelines to measure individual feed intake of dairy cows for genomic and genetic evaluations

The widespread use of genomic information in dairy cattle breeding programs haspresented the opportunity to select for feed intake and feed efficiency. This is becauseanimals from research herds can be used as a reference population to calibrate a genomicprediction equation, which is then used to predict the breeding value for selectioncandidates based on their own genotype. To implement genomic prediction and performgenetic analysis for feed intake, several partners have brought together their expertiseand existing feed intake records. Based on this experience we aim to provide someguidelines on the recording and handling of feed intake records. The consortium used amixture of standardised experimental data coming from larger genetic experiments orseveral smaller nutritional studies. The latter has provided some statistical challenges.Also, data was combined across countries, experimental herds and feeding systems. Despitethe perceived roughness of such data, it has proven to be very successful for genomicprediction, with proper statistical modelling. Ideally the whole lifetime of all cows shouldbe measured, but this is unrealistic. Often, animals are recorded for part of one (or more)lactation(s) only. Guidelines on the proper statistical modelling and usefulness of existingdata are needed. Selection index theory can help to establish the optimal recording periodacross and within lactation. It is also critical to identify how many records are requiredand what are the most informative animals for measuring feed intake. Geneticrelationships with the selection candidates are an important criterion. Finally, since(residual) feed intake is only part of the breeding goal, it is important to consider recordingof other traits as well, and the genetic parameters are needed to define the breeding goalsproperly