Hasil untuk "Dairy processing. Dairy products"

A. K. Anal, Harjinder Singh

P. Walstra, R. Jenness

S. Oliver, B. Jayarao, R. Almeida

S.N. Sanchez-Sierra, Matias Bermann, Natascha Vukasinovic et al.

The single-step genomic best linear unbiased predictor (ssGBLUP) along with the algorithm for proven and young (APY) are used to compute GEBV in livestock populations with extensive genomic data. Calculating GEBV reliabilities is computationally expensive, particularly with many genotyped animals, because it requires inverting the left-hand side of the mixed model equations. However, reliabilities in ssGBLUP models can be approximated by leveraging the sparse structure of the APY. The primary computational bottleneck of the algorithm lies in a matrix multiplication step, which scales quadratically with the size of the core set. This study aimed to decrease the computing time for approximating GEBV reliabilities in ssGBLUP by reducing the size of the core set in APY without compromising the precision of the reliability approximations. Reliabilities were approximated for a single-trait model for calf respiratory disease in Holsteins (h2 = 0.042). A dataset comprising 4,563,070 animals in the pedigree, 1,629,592 genotypes, and 1,585,306 records was used for the study. Core sets of varying sizes (25k, 20k, 15k, 10k, and 5k) were evaluated. Approximated reliabilities obtained with a core set size of 25k were used as a comparison benchmark. Correlations between approximated reliabilities obtained with different core sizes and the benchmark ranged from 0.94 to 1.00, whereas the intercept and slope of the regression of the benchmark reliabilities on the smaller core reliabilities ranged from −0.16 to 0.38 and from 0.64 to 1.15, respectively. Computing times varied significantly, with the fastest approximation (55.02 min) achieved using a 5k core, compared with 171.27 min for the 25k core benchmark. This represents a 3.1-fold reduction in computing time and a 2.1-fold reduction in memory usage when comparing the 25k core size with the 5k core size. Additionally, more substantial savings can be obtained as the number of traits increases. Having fewer genotyped animals in the APY core is a reasonable approach to accelerate GEBV reliability calculations; however, changes in the approximated reliabilities occur, underscoring the trade-off between computational efficiency and the accuracy of the approximations.

A. Castaneda, N. Indugu, K. Lenker et al.

Identifying cows with low CH4-emitting potential can greatly contribute to CH4 abatement in dairy herds. However, this process has been cumbersome and labor-intensive. Ear tags and collar-based accelerometers measure rumination and chewing behaviors, potentially identifying novel phenotypes in cows. This study aimed to determine whether rumination and eating time are linked to enteric CH4 emissions and serve as proxies to identify CH4 yield phenotype in lactating dairy cows. We applied the dynamic time warping model to rumination and eating time datasets to classify cows differing in these phenotypes. We calculated the distances between cows differing in rumination and eating times and depicted them in a principal component analysis plot. From 49 cows in early to mid lactation, 10 low-rumination, and 10 high-rumination cows were selected to test the relationship between rumination and eating time with CH4 yield phenotype over 7 wk. Enteric CH4 emissions were measured using the GreenFeed System. The dynamic time warping model identified cows with distinct rumination and eating patterns. High-rumination cows had higher DMI and milk yield, and lower enteric CH4 emissions than low-rumination cows. High-rumination cows also had lower CH4 intensity and higher production efficiency than low-rumination cows. Overall, rumination and eating time can be suitable proxies for identifying CH4 yield phenotype in dairy cows. Further studies, including larger dairy herds, different dietary regimens, and stages of lactation in dairy cattle and other ruminant species to validate rumination and eating time as proxies for identifying CH4 yield phenotype, are required.

K.R. Johnston, D.C. Reyes, K.N. Klobucher et al.

ABSTRACT: We aimed to evaluate the effects of prepartum supplementation of different I sources (Ascophyllum nodosum [ASCO] meal and ethylenediamine dihydroiodide [EDDI]) on colostrum yield, blood concentrations of glucose, BHB, and thyroid hormones, and growth of dairy calves. Forty multiparous Holstein cows were blocked by lactation number and expected calving date and assigned to 1 of 4 treatments 28 d before parturition: (1) EDDI supplemented (11 mg/d) to a basal diet to meet the NRC (2001) I concentration of 0.5 mg of I/kg of DMI (control = CON [0 g/d of ASCO meal]; actual I concentration = 0.68 mg/kg of DMI); (2) CON plus 57 g/d of ASCO meal (low seaweed supplementation = LSW); (3) CON plus 113 g/d of ASCO meal (high seaweed supplementation = HSW); or (4) CON plus 151.3 mg/d of EDDI formulated to match the amount of I provided by HSW (high EDDI = HEDDI). Forty-one calves were blocked based on their dams' treatments and received 300 g of IgG via colostrum replacer immediately after birth. At 24 h after calving, calves were offered (DM basis) 676 g of milk replacer (25.3% CP, 16.5% fat) until d 49 and 338 g until weaning. Free-choice texturized starter (28.2% CP) and water were offered ad libitum from 24 h to 8 wk of life. Blood samples were collected for analyses of IgG (0 h and 24 h of age), thyroid hormones (d 14, 28, and 56 of age), and BHB (weekly). On d 5 of life, a xylose challenge was conducted by supplementing 0.5 g of d-xylose/kg of BW, with blood samples taken over a 12-h period. Weekly skeletal and BW measurements were also recorded. The concentration of colostral fat was greater in HSW than HEDDI cows, and it tended to increase linearly with ASCO meal supplementation. Although I intake increased linearly with feeding incremental amounts of ASCO meal to close-up cows, the transfer of I from feed to colostrum decreased quadratically. Neither I intake nor colostral I transfer efficiency changed when feeding HSW versus HEDDI. Calves born to HSW dams had a greater initial BW and final hip height, as well as tendencies for greater weekly hip height and final withers height compared with HEDDI calves. Calf BW gain tended to decrease linearly with ASCO meal supplementation. The 24-h apparent efficiency of absorption of IgG tended to increase in HSW versus HEDDI calves. Plasma glucose concentration on d 5 of life decreased linearly in response to incremental levels of ASCO meal. Furthermore, the plasma concentration of biweekly total thyroxin and whole-blood concentrations of weekly BHB and final BHB responded quadratically to ASCO meal, with the lowest values observed for LSW calves. In summary, prepartum supplementation with incremental amounts of ASCO meal to close-up cows did not change colostrum composition. However, feeding HSW versus HEDDI increased colostral fat concentration and initial BW in calves.

Yanan Xia, Weigang Miao, Jianjun Zhao et al.

ABSTRACT: The flavor development of koumiss is intricately linked to its starter culture. This research aimed to delve into the microbial diversity of mare milk and koumiss, isolate the dominant lactic acid bacteria (LAB) and yeast, and assess the effects of composite strains on the fermentation characteristics and flavor quality of koumiss. In mare milk, the dominant microbial genera were Enterobacter and Rhodotorula. As fermentation progressed, the microbial diversity of mare milk gradually declined, and Lactobacillus and Dekkera became the dominant bacterial genera. A total of 42 LAB types and 24 yeast types were isolated. A 1:1 combination of Lactobacillus helveticus 3-4 and Kazachstania unispora A-3 led to a high viable bacterial count and rapid acid production in koumiss. Twenty-one flavor substances were detected, endowing the koumiss with intense umami and sour profiles. This study offers a theoretical foundation for the development and industrial application of koumiss starters.

Enhong Liu, Haiyu Yang, Miel Hostens

Large Language Models (LLM) hold potential to support dairy scholars and farmers by supporting decision-making and broadening access to knowledge for stakeholders with limited technical expertise. However, the substantial computational demand restricts access to LLM almost exclusively through cloud-based service, which makes LLM-based decision support tools impractical for dairy farming. To address this gap, lightweight alternatives capable of running locally on farm hardware are required. In this work, we benchmarked 20 open-source Small Language Models (SLM) available on HuggingFace under farm-realistic computing constraints. Building on our prior work, we developed an agentic AI system that integrates five task-specific agents: literature search, web search, SQL database interaction, NoSQL database interaction, and graph generation following predictive models. Evaluation was conducted in two phases. In the first phase, five test questions were used for the initial screening to identify models capable of following basic dairy-related instructions and performing reliably in a compute-constrained environment. Models that passed this preliminary stage were then evaluated using 30 questions (five per task category mentioned above, plus one category addressing integrity and misconduct) in phase two. In results, Qwen-4B achieved superior performance across most of task categories, although showed unstable effectiveness in NoSQL database interactions through PySpark. To our knowledge, this is the first work explicitly evaluating the feasibility of SLM as engines for dairy farming decision-making, with central emphases on privacy and computational efficiency. While results highlight the promise of SLM-assisted tools for practical deployment in dairy farming, challenges remain, and fine-tuning is still needed to refine SLM performance in dairy-specific questions.

E.M. Sitko, A. Laplacette, D. Duhatschek et al.

ABSTRACT: The objectives of this prospective cohort study were to characterize associations among genomic merit for fertility with ovarian and endocrine function and the estrous behavior of dairy cows during an entire nonhormonally manipulated estrous cycle. Lactating Holstein cows entering their first (n = 82) or second (n = 37) lactation had ear-notch tissue samples collected for genotyping using a commercial genomic test. Based on genomic predicted transmitting ability values for daughter pregnancy rate (gDPR), cows were classified into high (Hi-Fert; gDPR > 0.6, n = 36), medium (Med-Fert; gDPR −1.3 to 0.6, n = 45), and low fertility (Lo-Fert; gDPR < −1.3, n = 38) groups. At 33 to 39 DIM, cohorts of cows were enrolled in the Presynch-Ovsynch protocol for synchronization of ovulation and initiation of a new estrous cycle. Thereafter, the ovarian function and endocrine dynamics were monitored daily until the next ovulation by transrectal ultrasonography and concentrations of progesterone (P4), estradiol, and FSH. Estrous behavior was monitored with an ear-attached automated estrus detection system that recorded physical activity and rumination time. Overall, we observed an association between fertility group and the ovarian and hormonal phenotype of dairy cows during the estrous cycle. Cows in the Hi-Fert group had greater circulating concentrations of P4 than cows in the Lo-Fert group from d 4 to 13 after induction of ovulation and from day −3 to −1 before the onset of luteolysis. The frequency of atypical estrous cycles was 3-fold greater for cows in the Lo-Fert than the Hi-Fert group. We also observed other modest associations between genomic merit for fertility with the follicular dynamics and estrous behavior. We found several associations between milk yield and parity with ovarian, endocrine, and estrous behavior phenotypes as cows with greater milk yield and in the second lactation were more likely to have unfavorable phenotypes. These results demonstrate that differences in reproductive performance between cows of different genomic merit for fertility classified based on gDPR may be partially associated with circulating concentrations of P4, the incidence of atypical phenotypes during the estrous cycles, and, to a lesser extent, the follicular wave dynamics. The observed physiological and endocrine phenotypes might help explain part of the differences in reproductive performance between cows of superior and inferior genomic merit for fertility.

S. Heirbaut, X.P. Jing, B. Stefańska et al.

ABSTRACT: Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of β-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R2) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing.

Tony C. Bruinjé, Stephen J. LeBlanc

ABSTRACT: Negative associations of health disorders with reproductive performance, often measured with pregnancy per AI or the risk of pregnancy loss, have been demonstrated extensively. Most studies investigated common clinical diseases but did not include subclinical disorders comprehensively. They often evaluated cows subjected to hormonal synchronization protocols for timed AI, limiting the ability to understand how disease may affect spontaneous reproductive function, which is essential for targeted management programs with selective hormonal intervention. It is plausible that metabolic and inflammatory disorders have short- and long-term detrimental effects on different features of reproductive function that result in or contribute to reduced fertility. These may include: (1) re-establishment of endocrine function to promote follicular growth and first ovulation postpartum, (2) corpus luteum (CL) function, (3) estrus expression, and (4) uterine environment, fertilization, and embryonic development. In this narrative literature review, we discuss insights and knowledge gaps linking health disorders with these processes of reproductive function. A growing set of observational studies with adequate internal validity suggest that these outcomes may be affected by metabolic and inflammatory disorders that are common in the early postpartum period. A better characterization of these risk factors in multisite studies with greater external validity is warranted to develop decision-support tools to identify subgroups of cows that are more or less likely to be successful in targeted reproductive management programs.

C. Fiol, M. Moratorio, M. Carriquiry et al.

The objectives of this study were to determine the social behavior response to repeated social regrouping (SR), and if social rank and the changes of social rank affect the growth rate and some metabolites and hormones linked to energy status, in “resident” replacement dairy heifers subjected to SR for 205 d. Fourteen dairy heifers (153.3 ± 16.1 kg; 9 to 11 mo old), maintained under grazing conditions, were regrouped every 21 d with 5 new animals (total = 10 SR). Two days after each SR, heifers were weighed and their withers height was measured, and blood samples for nonesterified fatty acids, glucose, and IGF-1 were taken. Concentrations of nonesterified fatty acids, glucose, and IGF-1 were taken. Social behavior was registered by continuous sampling and social status was calculated by the ETlog software, considering the total number of agonistic interactions on 2 d before and 7 d after each SR. A dominance scale was constructed in each SR, and heifers were categorized as high-ranked (HRA), medium-ranked (MRA), and low-ranked (LRA). In addition, the social rank in the first and the last 5 SR were compared, resulting in 3 categories: heifers that remained in the same social rank (Mai), and heifers that lowered (Low) and raised (Rai) their social ranks. Variables were analyzed by repeated measures using a generalized lineal mixed model, considering the social rank in each SR or the change in social rank, the number of SR and their interaction as main effects, and the farm of origin and animal as random effects. Heifers performed more agonistic (2.6 vs. 1.8 ± 0.6, day of SR and 7 d later, respectively) and total (3.8 ± 1.2 vs. 2.5 ± 0.7, day of SR and 7 d later, respectively) interactions on the day of each SR than 7 d after, whereas the number of interactions decreased after the first 2 SR. Medium-ranked heifers presented greater IGF-1 concentrations than HRA heifers at SR1, SR4, and SR9, and than LRA heifers at SR3 and SR4, whereas IGF-1 concentrations were greater in HRA heifers than MRA heifers at SR3 and SR7. In addition, LRA heifers had greater IGF-1 concentrations than MRA ones at SR3 and compared with HRA heifers at SR9. Heifers that lowered their social had greater IGF-1 concentrations than Mai heifers along the SR (132.5 ± 17.1 vs. 97.8 ± 11.2 ng/mL, Low and Mai heifers, respectively) and at SR1, SR3, and SR9. In conclusion, although regrouping animals according to their characteristics can facilitate farm handling, it should be considered that endocrine profile may be affected according to heifers' position in the social hierarchy.

Abhijin Adiga, Ayush Chopra, Mandy L. Wilson et al.

One Health issues, such as the spread of highly pathogenic avian influenza~(HPAI), present significant challenges at the human-animal-environmental interface. Recent H5N1 outbreaks underscore the need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a synthetic spatiotemporal gridded dataset of livestock production and processing, human population, and wild birds for the contiguous United States, called a \emph{digital similar}. This representation is a result of fusing diverse datasets using statistical and optimization techniques, followed by extensive verification and validation. The livestock component includes farm-level representations of four major livestock types -- cattle, poultry, swine, and sheep -- including further categorization into subtypes such as dairy cows, beef cows, chickens, turkeys, ducks, etc. Weekly abundance data for wild bird species identified in the transmission of avian influenza are included. Gridded distributions of the human population, along with demographic and occupational features, capture the placement of agricultural workers and the general population. We demonstrate how the digital similar can be applied to evaluate spillover risk to dairy cows and poultry from wild bird population, then validate these results using historical H5N1 incidences. The resulting subtype-specific spatiotemporal risk maps identify hotspots of high risk from H5N1 infected wild bird population to dairy cattle and poultry operations, thus guiding surveillance efforts.

Luca Barbieri, Bernardo Camajori Tedeschini, Mattia Brambilla et al.

Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigation and imaging sensors via Vehicle-to-Everything (V2X) communications for joint positioning and environmental perception. In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors. Vehicles process their LiDAR point clouds using a Deep Neural Network (DNN), namely a 3D object detector, to identify and localize possible static objects present in the driving environment. Data are then aggregated by a centralized infrastructure that performs Data Association (DA) using a Message Passing Neural Network (MPNN) and runs the Implicit Cooperative Positioning (ICP) algorithm. The proposed approach is evaluated using two realistic driving scenarios generated by a high-fidelity automated driving simulator. The results show that CLS-MPNN outperforms a conventional non-cooperative localization algorithm based on Global Navigation Satellite System (GNSS) and a state-of-the-art cooperative Simultaneous Localization and Mapping (SLAM) method while approaching the performances of an oracle system with ideal sensing and perfect association.

Lynn A. Olthof, Joseph J. Domecq, Barry J. Bradford

With over 9 million cows in the United States, Holstein is the dominant breed in the US dairy population; however, the US Jersey population is growing. The objective of this study was to determine the profitability of Holstein and Jersey cows managed similarly on the same farms. Holstein and Jersey economic performance was compared within 3 north central US dairies, each milking more than 500 cows. The herds' average distribution was 21% Jersey (27 ± 0.67 kg/d milk, 4.92% ± 0.24 fat, 3.72% ± 0.03 protein) and 79% Holstein (37 ± 1.98 kg/d milk, 3.85% ± 0.21 fat, 3.17% ± 0.17 protein). A comparative budget approach was used to assess economic factors that differed between the breeds on a per cow annual basis, based on the assumption that an existing farm would be constrained by stalls and parlor to an equal number of Jersey and Holstein cows. Data from 2020 were gathered from farm management software, on-farm evaluations, and producer interviews. Sensitivity analysis was performed to determine which conditions would lead to different conclusions. Factors considered in the analysis included milk and component production, milk bonuses, ration prices, and dry matter (DM) intake. In a 2021 price scenario, Holstein cows ranged from $345 to $601 more profitable than Jersey cows on a per cow annual basis. Although Jersey cows had an advantage in component concentration, Holstein cows produced 13 ± 4.7% more fat and 22 ± 6.6% more protein annually due to greater milk yield. This accounted for most of the profitability advantage for Holsteins; 78% of the revenue advantage for Holstein cows came from increased component production. Few health and reproductive differences were found. The sensitivity analysis revealed, if all other factors remained the same, Jersey profitability would equal that of Holstein if any of the following changes occurred (assuming no change in Holstein metrics): mean Jersey milk production increased to 31 kg/d; milk price adjustments decreased from −$0.008 to −$0.11 per kg fluid milk; lactating cow ration price increased from $0.27 per kg DM to $0.53 per kg DM; or Jersey DM intake decreased from 20 to 15 kg/d. The study did not consider crossbred profitability or new infrastructure investments. In conclusion, Holstein cows were more profitable than Jersey cows on these 3 north central US dairies.

Jing Lu, Tong Zhu, Ying Dai et al.

ABSTRACT: Protein lactosylation is a significant modification that occurs during the heat treatment of dairy products, causing changes in proteins' physical-chemical and nutritional properties. Knowledge of the detailed lactosylation information on milk proteins under various heat treatments is important for selecting appropriate thermo-processing and identifying markers to monitor heat load in dairy products. In the present study, we used proteomics techniques to investigate lactosylated proteins under different heating temperatures. We observed a total of 123 lactosylated lysines in 65 proteins, with lactosylation even occurring in raw milk. The number of lactosylated lysines and proteins increased moderately at 75°C to 130°C, but dramatically at 140°C. We found that 6 out of 10, 9 out of 16, 6 out of 12, and 5 out of 15 lysine residues in κ-casein, β-lactoglobulin, α-lactalbumin, and αS1-casein, respectively, were lactosylated under the applied heating treatment. Moreover, different lactosylation states of individual lysines and proteins can indicate the intensity of heating processes. Lactosylation of K14 in β-lactoglobulin could distinguish pasteurized and UHT milk, while lactosylation of lactotransferrin can reflect moderate heat treatment of products.

K. Persson Waller, M. Myrenås, S. Börjesson et al.

ABSTRACT: Staphylococcus chromogenes and Staphylococcus simulans are commonly found in intramammary infections (IMI) associated with bovine subclinical mastitis, but little is known about genotypic variation and relatedness within species. This includes knowledge about genes encoding antimicrobial resistance (AMR) and potential virulence factors (pVF). The aim of this study was therefore to investigate these aspects by whole-genome sequencing of milk isolates from Swedish dairy cows with subclinical mastitis in an observational study. We also wanted to study if specific genotypes were associated with persistent IMI and the inflammatory response at udder quarter level. In total, 105 and 118 isolates of S. chromogenes and S. simulans, respectively, were included. Isolates were characterized using a 7-locus multilocus sequence typing (7-MLST), core genome analysis and in-silico analysis of AMR and pVF genes. Forty-seven sequence types (ST) and 7 core genome clusters of S. chromogenes were identified, and the most common ST were ST-6 and ST-109, both belonging to cluster VII. A 7-locus MLST scheme for S. simulans was not available, but 3 core genome clusters and 5 subclusters were described. Overall, substantial variation in ST and clusters among cows and herds were found in both species. Some ST of S. chromogenes were found in several herds, indicating spread between herds. Moreover, within-herd spread of the same genotype was observed for both species. Only a few AMR genes [blaZ, strpS194, vga(A)] were detected in a limited number of isolates, with the exception of blaZ coding for β-lactamase, which was identified in 22% of the isolates of S. chromogenes with ST-19, ST-102, and ST-103 more commonly carrying this gene compared with other ST. However, the blaZ gene was not identified in S. simulans. The average total number of pVF detected per isolate was similar in S. chromogenes (n = 30) and S. simulans (n = 33), but some variation in total numbers and presence of specific pVF or functional groups of pVF, was shown between ST/clusters within species. Differences in inflammatory response and potentially in persistent IMI at udder quarter level were found between S. chromogenes subtypes but not between S. simulans subtypes. In conclusion, the results from the present study generates new insight into the epidemiology of bovine S. chromogenes and S. simulans IMI, which can have implications for future prevention and antimicrobial treatment of infections related to these species.

Matthew C. Lucy

J.P. Edwards, N. McMillan, R.H. Bryant et al.

ABSTRACT: Milking 3 times in 2 d (3-in-2) could enhance the attractiveness of the dairy workplace relative to twice-a-day milking (TAD) by reducing labor requirements for milking and increasing workforce flexibility. The objective of this study was to quantify the farm system interactions associated with milking 3-in-2 at 3 stages of lactation, with the aim of providing guidance to pasture-based dairy farmers and advisors on the likely consequences of adopting 3-in-2 milking on farm productivity and business performance. Seventy-nine multiparous and 37 primiparous cows were randomly allocated to 4 experimental farms stocked at 3.5 cows/ha. One herd was milked TAD for the whole lactation (August 2019 to May 2020), with the remaining 3 milked 3-in-2 for either the whole lactation, after December 1 when cows were an average of 101 d in milk, or after March 1 when days in milk averaged 189 d. Milking intervals over 48 h were 10-14-10-14 h for TAD and 12-18-18 h for 3-in-2. Animal, pasture, and farm system data were analyzed by linear regression, with the dependent variable being the annualized value of the performance metric of interest, and the number of days in the lactation milked 3-in-2 as the independent variable. For the proportion of the season milked 3-in-2, there was a significant effect on milk (−11%), protein (−8%), and lactose (−12%) yield per cow per year, but no effect of fat. Additionally, there was a positive effect (+6%) on body condition score before dry-off and the energy required for liveweight change (+26%), and a negative effect on the energy required for walking (−30%). There were no differences in estimated feed eaten, or pasture herbage accumulation, composition, or quality. Therefore, pasture management and feed allocation under 3-in-2 should be similar to TAD. On commercial farms, the degree to which reduced milk income can be offset by lower costs will be highly farm-specific, but opportunities for savings were identified in the results. The short walking distances on the research farm and potential to improve farm management using the time saved from fewer milkings suggests better production may be achieved with 3-in-2 milking on a commercial farm.

H Mohammadi , A H Khaltabadi Farahani , M H Moradi and I Hajkhodadadi

Introduction: Understanding the genetic control of growth traits is essential for effective poultry breeding poultry. One way to identify new loci and confirm existing QTL is through genome-wide association analysis (GWAA) (Wang et al., 2011). In addition, identifying loci with large effects on economically important traits, has been one of the important goal to poultry breeding. QTL assisted selection and genomic regions affecting the production traits have been considered to increase the efficiency of selection and improve production performance (Seabury et al., 2017). Genome wide association studies typically focus on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence offer a limited understanding of the trait under study. A solution to tackle the aforementioned problems, and expand understanding of the genetic background of complex traits, is to move up the analysis from the SNP to the gene and gene-set levels (Peñagaricano et al., 2013). In a gene-set analysis, a group of related genes that harbor significant SNP previously identified in GWAS, is tested for over-representation in a specific pathway. Material and methods: The aim of the this study was to genome wide association studies (GWAS) based on Gene set enrichment analysis for identifying the loci associated with related to body weight and shank length and diameter traits in advanced intercross line (AIL) using the high-confidence SNPs that enable us to study 161376 SNP markers simultaneously. For this purpose, the 599 advanced intercross line and 161376 markers were performed with mixed linear model (MLM) approach was used for the GWAS of the F9 generation, as implemented in the GCTA package (v1.92) (Yang et al., 2011) and no any correction to adjust the error rate. The gene set analysis consisted of three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and (3) the association analysis between each functional category and the phenotype of interest. In brief, for each trait, nominal P-values < 0.005 from the GWAS analyses were used to identify significant SNP. Using the biomaRt R package, the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene, to include SNP located in regulatory regions. For the assignment of the genes to functional categories, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were used. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into 3 components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge on molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene-set. The gene enrichment analysis was performed with the goseq R package. In the next step, a bioinformatics analysis was implemented to identify the biological pathways performed in BioMart, Panther, DAVID and GeneCards databases. Results and discussion: Gene set enrichment analysis has proven to be a great complement of genome-wide association analysis (Abdalla et al., 2016). Among available gene set databases, GO is probably the most popular, whereas KEGG is a relatively new tool that is gaining ground in livestock genomics (Morota et al., 2015, 2016). We had hypothesized that the use of gene set information could improve prediction. However, neither of the gene set SNP classes outperformed the standard whole-genome approach. Gene sets have been primarily developed using data from model organisms, such as mice and flies, so it is possible that some of the genes included in these terms are irrelevant for meat production. It is likely that a better understanding of the biology underlying meat production specifically, plus an advance in the annotation of the chicken genome, can provide new opportunities for predicting production using gene set information. Eleven SNP markers on chromosomes 1, 2, 4, 5, 7, 8, 10, 11, and 27 located in MSTN, CAPN3, PNPLA3, ANXA2, IGF1, LDB2, LEPR, FN1, ¬TMEM135, MC4R, EDN1, and ADAMTS18 genes were identified. Some of the genes found to be consistent with some previous studies. Those seem to be involved in biological pathways related to muscle skeletal growth, energy metabolism and bone growth and development. According to pathway analysis, 19 pathways from gene ontology and KEGG pathway were associated with the body weight, shank length and diameter trait (P˂0.05). Among those pathways, the regulation of muscle organ development, regulation of cell growth and anatomical structure homeostasis biological pathway have important roles in the growth and skeletal muscle development. Also, the anatomical structure formation involved in morphogenesis, positive regulation of ossification and calcium signaling pathway presumably has significant association with body weight and shank length as well as diameter traits. Some of these regulatory regions, such as enhancers, are located far from the genes. Therefore, although the gene might be part of the analysis, the relevant variant would probably not be included in the gene set SNP class. Finally, linkage disequilibrium interferes with the use of biological information in prediction because irrelevant regions (regions without any biological role) capture part of the information encoded in relevant regions, causing both regions to exhibit similar predictive abilities. The use of very high density SNP data or even whole genome sequence data could alleviate some of these issues. Finally, it is worth noting that our gene-set enrichment analysis was conducted using a panel of SNP obtained from a single marker regression GWAS, which relies on a simplified theory of the genomic background of traits, without considering for instance the joint effect of SNP. Hence, other approaches (e.g., GWAS exploring SNP by SNP interactions) might provide a better basis for biological pathway analysis. Conclusion: Our observations agreed with the previous results from GWAS of body weight, shank length and diameter traits. Moreover, additional regions in the chicken genome associated with economically important traits were revealed. Our findings would contribute to a better understanding of the genetic control of growth traits in broiler chicken accelerating the genetic progress in poultry breeding programs.