Using fatty acid contents in milk to improve fertility of dairy cows?

Improving dairy cow fertility by means of genetic selection has become increasingly important over the last years in order to overcome the declining cow fertility. This study investigated whether the fatty acids profile in milk could be used as an early predictor of genetic merit for fertility. Genetic covariances among 17 fatty acid contents in milk and the number of days from calving to conception were estimated from 29,792 first-parity Holstein cows. Results substantiated the unfavorable relationship among fertility and body fat mobilization in early lactation. Also, about 75% of the genetic variability of fertility was explained by the variability in milk fatty acids profile over the lactation indicating that these traits could be used to supplement genetic evaluations for fertility

Deployment of models predicting compressed sward height on Wallonia: results and feedback

peer reviewedThere is currently high interest in integrating data linked to remote sensing and methods from the machine-learning domain to develop tools to support pasture management. In this context, over the past two years, we have published models predicting the available compressed sward height (CSH) in pastures using Sentinel-1, Sentinel-2, and meteorological data. These scalable models could provide the basis of a decision support system (DSS) available for Walloon farmers. A platform performing the CSH prediction was developed and this paper aims to provide some insights in its prediction capabilities and tackle the challenge of using data acquired at different moments in time. Predictions were made from the beginning of January until the end of October 2021 using our most promising published models. After data cleaning, the coefficient of variation of CSH predictions, calculated for each studied date (n=35) and parcel (n=192,862), ranged from 0 to 986. This extreme variation suggests some prediction imperfections. Before the integration of the platform in a DSS, the main task to solve is the issue of missing or non-operational S1 or S2 data. Indeed, even if a gap filling method was applied, only 62% of potentially exploitable dates were usable.ROADSTE

Overview of possibilities and challenges of the use of infrared spectrometry in cattle breeding

peer reviewedNear or mid-infrared (NIR or MIR) spectrometry is a versatile and cost-efficient technology used in cattle production to trace the chemical composition of gases, liquids and solid matters. Recent research showed the potential of MIR spectrometry in milk to predict many different milk components but also status and well-being of the cows, quality of their products, their efficiency and their environmental impact. Under changing socio-economic circumstances, novels traits could help to select for enlarged breeding objectives. But the following challenges need to be overcome: (1) access to and harmonization of MIR data; (2) availability of reference values representing the variability to be described, also highlighting the importance of international collaborations; (3) difficulties to obtain, but also to transfer prediction equations between instruments; (4) modeling of the massive longitudinal data generated; (5) estimation of parameters to assess phenotypic and genetic variability and links with other traits leading to the; (6) assessment of the position of novel traits in breeding objectives. Recent research reported how to address these issues for traits close to routine use including fatty acids and methane. Expected future developments include direct use of MIR data and multivariate modeling of novel traits. Similarly, genomic prediction for novel traits, which are limited by the availability of phenotyped reference populations, will also benefit from the use of correlated, MIR predicted, traits. Currently, MIR instruments can only be used in the frame of milk recording and not on-farm. But recent research showed that NIR is closing the gap thereby allowing advances in precise on-farm phenotyping and giving new opportunities for breeding, but also management. Possibilities for the use of infrared technologies for other trait groups such as meat composition and quality should allow cross-fostering of developments

Linear and curvilinear effects of inbreeding on production traits for walloon Holstein cows

The nonlinear effects of inbreeding were studied by comparing linear and curvilinear regression models of phenotypic performances on inbreeding coefficients for production traits (milk, fat, and protein yields) of Holstein cows in their first lactation. Three different regression models (linear, quadratic, and cubic) were introduced separately into a single-trait, single-lactation, random regression test-day model. The significance of the different regression coefficients was studied based on a t-test after estimation of error variances and covariances associated with the different regression coefficients. All of the tested regression coefficients were significantly different from 0. The traditional regression coefficients of milk, fat, and protein yields on inbreeding were, respectively, -22.10, -1.10, and -0.72 kg for Holstein cows in their first lactation. However, the estimates of 305-d production losses for various classes of animals based on inbreeding coefficients showed that the effect of inbreeding was not a linear function of the percentage of inbreeding. The 305-d milk yield loss profiles attributable to inbreeding, obtained by the various regression models, were different. However, for inbreeding coefficients between 0 and 10%, these differences were small

Genome-wide association study for mid-infrared methane predictions in Walloon dairy cows

peer reviewedThis study aimed to identify genomic regions associated with two mid infrared-based CH4 traits [predicted daily CH4 emission (PME, g/d), and log-transformed predicted CH4 intensity (LMI)] in Walloon dairy cows. The data consisted of 1,529,282 test-day records from 229,465 cows distributed in 1,530 herds collected from 2006 to 2021. Random regression test-day models were used to estimate variance components. The proportion of genetic variance explained by windows of 50 consecutive SNPs was calculated and regions accounting for at least 1.0% of the total genetic variance were identified. Mean (SD) daily h2 estimated for PME and LMI were 0.14 (0.05) and 0.24 (0.05), respectively. Two regions on BTA14 (positions 1.86 to 2.12, and 1.48 to 1.68 Mb) were associated with both PME and LMI. A region between 144.38 to 144.46 Mb on BTA1 was associated with PME; and the region between 2.68 and 2.94 Mb on BTA14 was associated with LMI. Results showed potential for genome-enhanced advisory systems to reduce methane emissions

Variability of major fatty acid contents in Luxembourg dairy cattle

Common human health concerns and imminent needs for more sustainable nutrition patterns require from dairy industry and farmers a. o. a closer look at milk fatty acid (FA) profile. Therefore up to date calibration equations using mid-infrared (MIR) spectrometry were developed permitting the estimation of FA contents in bovine milk. The aim of this study was to estimate the variability of the major FA from data collected during the Luxembourg routine milk recording. A total of 148,296 milk samples with MIR-spectra were collected from October 2007 to January 2009 on 36,522 cows belonging to 5 breeds in 718 herds and scanned by Foss MilkoScan FT6000. The contents of saturated FA, monounsaturated FA, omega-9, short chain FA, medium chain FA, and long chain FA were obtained using Belgian MIR calibration equations. Analyzes were done by a multi-trait multi-lactation animal mixed models. Fixed effects were herd*test date, lactation stage lactation number, age*lactation number, and breed effect. Random effects were herd*year of calving, permanent environment within and across lactation, animal effect, and residual effect. Breed differences as well as lactation effects were observed. Our results showed moderate heritability values suggesting the existence of a FA genetic variability. The variability of the first Luxembourg breeding values was large enough to develop selection tools for improving the nutritional quality of bovine milk fat.Peer reviewe

Estimation of Myostatin gene effects on production traits and fatty acid contents in bovine milk

peer reviewedThe aim of this study was to estimate the genetic parameters of milk, fat, and protein yields, saturated (SFA) and monounsaturated fatty acid (MUFA) contents in bovine milk and to estimate the Myostatin (mh) gene effect on these traits. For this purpose, 51,614 test-day records (24,124, 16,145, and 11,345 for first, second and third lactation, respectively) of 3,098 dual purpose Belgian Blue cows in 38 herds from the Walloon Region of Belgium were used. Because only 2,301 animals, including 1,082 cows with test-day records, were genotyped for mh, the gene content of non-genotyped animals was predicted from animals with a known genotype using the relationships with these animals. Variance components were estimated using Restricted Maximum Likelihood. A 3-lactations, 5-traits random regression test-day mixed model, based on the official Walloon genetic evaluation model for production traits, was used with an additional fixed regression on mh gene content to estimate allele substitution effects. Daily heritability estimates (average of 3 lactations) were 0.34 for SFA and 0.16 for MUFA and were higher than those for production traits (0.11, 0.10, and 0.09 for milk, fat, and protein yields, respectively). Allele substitution effects approximate standard-errors) for mh through the three lactations were-0.628 (+0.343),-0.024 (0.014) and -0.021 (+0.009) kg per day for milk, fat, and protein yields, respectively. Concerning SFA and MUFA contents in milk, the average allele substitution effects were -0.001 (+0.027) and 0.029 (+0.023) g/dl of milk. To conclude, results from this study showed that milk performance traits and milk fatty acid profile are influenced by mh genotypes