Phenotypic analysis of milk composition, milk urea nitrogen and somatic cell score of Italian Jersey cattle breed

The present study aimed to assess the phenotypic variation of milk yield (MY) and quality traits in Italian Jersey (IJ) breed. Sources of variation were investigated through a linear mixed model, including the fixed effects of days in milk (DIM), parity, calving season, milking frequency, recording type, the interaction between DIM and parity, and the random effects of herd-test-day (HTD), cow and the residual. Results highlighted the high contents of milk fat (5.18%), protein (4.08%) and casein (3.16%) of IJ cows. Somatic cell score, averaging 3.35 units, should be lowered through specific managerial actions. Phenotypic variances of MY and milk quality traits were mainly due to cow effect, whereas phenotypic variance of milk urea nitrogen (MUN) content was mainly due to HTD effect, meaning that managerial conditions, especially feeding, are very important to explain the variation of MUN. In conclusion, the present study allowed to characterise milk of IJ cows at population level and to identify environmental factors associated with variation of MY and quality traits, which will be useful to adjust phenotypic records in genetic evaluation of Jersey breed.Highlights Factors affecting milk quality traits of Italian Jersey cows have been investigated. Phenotypic variance of milk composition and SCS was mainly due to cow effect, and that of MUN was mainly driven by herd-test-day effect. Significant environmental factors identified in the present study will be used to adjust phenotypic records in genetic analysis

Genetic aspects of heifer fertility in Italian Holstein population

Fertility is fundamental to enhance the production efficiency of the dairy herd and thus it is a contributor to annual farm profitability. Cow fertility has been included in Italian Holstein breeding objectives since 2009. Heifer fertility is another key trait that deserves attention as it has direct connection with overall efficiency. In general, the main goal is to improve conception and daughter pregnancy rates, favour shorter calving interval in lactating cows, and reduce failure of conceiving in heifers. The advantages of heifer over cow fertility traits are the early availability in life and, overall, the moderate to strong genetic correlations with fertility of lactating cows. The aims of the present study are to assess genetic parameters of Italian Holstein heifers and develop an aggregate selection index to improve heifer fertility. Data (ANAFIBJ, Cremona, Italy) included information on insemination, calving, and pregnancy diagnosis dates of Italian Holstein heifers. The investigated traits (mean ± standard deviation) were age at first insemination (AFI, mo; 17.25 ± 2.89), nonreturn rate at 56 d from the first insemination (NRR56, binary; 0.78 ± 0.41), conception rate at first insemination (CR, binary; 0.61 ± 0.49), and interval from first to last insemination (IFL, d; 26.09 ± 51.85). Genetic parameters were estimated using a 4-trait animal model that included the fixed effects of herd-year of birth and month of birth for AFI, and herd-year-season of birth and month-year of insemination for IFL, NRR56, and CR. The animal additive genetic effect was included as random term. An aggregate index was developed from the estimated additive genetic (co)variance matrix by considering CR as the breeding goal and AFI, NRR56, and IFL as selection criteria. Heritability ranged from 0.012 (CR) to 0.015 (IFL), except for AFI (0.071). Conception rate at first insemination was strongly correlated with both IFL (−0.730) and NRR56 (0.668), and weakly to AFI (−0.065). The relative emphasis placed on each selection criteria in the aggregate index was 10%, 47%, and 43% for AFI, IFL, and NRR56, respectively. Results of the present study suggest that heifer fertility should be considered as an additional trait in the breeding objectives of Italian Holstein

The effect of selection on casein genetic polymorphisms and haplotypes in Italian Holstein cattle

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Genetic and nongenetic variation of heifer fertility in Italian Holstein cattle

Excellent fertility performance is important to maximize farmers’ profit and to reduce the number of culled animals. Although female fertility of adult cows has been included in Italian Holstein breeding objectives since 2009, little has been done to quantify genetic variation of heifer fertility characteristics so far. The aim of the present study was to estimate genetic parameters of 4 fertility traits in nulliparous Italian Holstein heifers and to develop an aggregate selection index to improve heifer fertility. Data were retrieved from the national fertility database and included information on insemination, calving, and pregnancy diagnosis dates. The investigated phenotypes (mean ± standard deviation) were age at first insemination (AFI, mo; 17.25 ± 2.89), nonreturn rate at 56 d from the first insemination (NRR56, binary; 0.78 ± 0.41), conception rate at first insemination (CR, binary; 0.61 ± 0.49), and interval from first to last insemination (IFL, d; 26.09 ± 51.85). Genetic parameters were estimated using a 4-trait animal model that included the following fixed effects: herd-year of birth and month of birth for AFI, and herd-year-season of birth and month-year of insemination for IFL, NRR56, and CR; the animal additive genetic effect (fitted to the pedigree-based relationship matrix) was considered as a random term. An aggregate index was developed from the estimated additive genetic (co)variance matrix by considering CR as the breeding goal and AFI, NRR56, and IFL as selection criteria. Heritability estimates from average covariance matrices ranged from 0.012 (CR) to 0.015 (IFL), with the exception of AFI (0.071). Conception rate at first insemination was strongly correlated with both IFL (−0.730) and NRR56 (0.668), and weakly to AFI (−0.065), and the relative emphasis placed on each selection criteria in the aggregate index was 10%, 47%, and 43% for AFI, IFL, and NRR56, respectively. The results of the present study suggest that heifer fertility should be considered as an additional trait in the breeding objectives of Italian Holstein

Predicted Feed Efficiency index applied to Italian Holstein Friesian cattle population

Submitted 2020-08-02 | Accepted 2020-09-21 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.326-330Feed efficiency has a major influence on farm profitability and environmental stewardship in the dairy industry. The aim of this study was to describe a new selection index adopted by the Italian Holstein and Jersey Association (ANAFIJ, Cremona, Italy) to improve feed efficiency using data recorded by the official dairy recording system. Predicted dry matter intake (pDMI) was derived from milk yield, fat content, and estimated cow body weight. Fat-protein corrected milk (FPCM) was derived from milk yield corrected for fat, protein, and a fixed coefficient for lactose content (4.80%). Therefore, the predicted feed efficiency (pFE) was estimated as ratio between FPCM and pDMI. Average pFE was 1.27±0.18 (kg.d-1) with heritability of 0.32. Predicted Feed Efficiency index (pFEi), traditional and genomic, has been implemented in the Italian Holstein Friesian evaluation system. Results suggest that pFEi may be a new breeding objective for Italian Friesians. The official selection index (PFT), in use since 2002, is positively correlated with pFEi. However, the introduction of pFEi will improve the positive feed efficiency trend. This approach will permit the Italian Holstein Friesian breeders to improve feed efficiency, without increasing costs of recording system. However, to avoid the risk of selecting animals with an excessive negative energy balance after calving, it would be useful to include in the pFE a correction for body condition score and reproductive performances. Meanwhile, in order to increase the accuracy of the predicted phenotype, an Italian consortium is creating a consistent phenotypic critical mass of individual data for dry matter intake in cows, heifers and young bulls.Keywords: feed efficiency, cattle breeding, dry matter intake, breeding value estimationReferencesCassandro, M. (2020). Animal breeding and climate change, mitigation and adaptation. Journal of Animal Breeding and Genetics, 137(2), 121-122. https://doi.org/10.1111/jbg.12469Cassandro, M. et al. (2010). Genetic parameters of predicted methane production in Holstein Friesian cows. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany.Cassandro, M., Mele, M. and Stefanon, B. (2013). Genetic aspects of enteric methane emission in livestock ruminants. Italian Journal of Animal Science, 12(3), 450-458.De Haas, Y. et al. (2012). Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. Journal of Dairy Science, 95(10), 6103-6112.De Vries, M. and Veerkamp, R. (2000). Energy balance of dairy cattle in relation to milk production variables and fertility. Journal of Dairy Science, 83(1), 62-69.Finocchiaro, R. et al. (2017). Body weight prediction in Italian Holstein cows. ICAR Technical Series, 22, 95-98.Hegarty, R. et al. (2007). Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science, 85(6), 1479-1486.Hurley, A. M. et al. (2018). Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection. Journal of Dairy Science, 101(2), 1267-1280. https://doi.org/https://doi.org/10.3168/jds.2017-12841Meuwissen, T. H., Hayes, B. J. and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829.National Research Council. (2001). Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001. The National Academies Press. http://www.nap.edu/catalog.php?record_id=9825Pryce, J. et al. (2014). Genomic selection for feed efficiency in dairy cattle. Animal, 8(1), 1-10.Sjaunja, L. et al. (1990). A Nordic proposal for an energy corrected milk formula. Proceedings of the 2nd Session of Committee for Recording and Productivity of Milk Animal, Paris, p. 156.Veerkamp, R. et al. (2000). Genetic correlation between days until start of luteal activity and milk yield, energy balance, and live weights. Journal of Dairy Science, 83(3), 577-583.Verbyla, K. et al. (2010). Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science, 93(6), 2757-2764.Wall, E., Coffey, M. and Brotherstone, S. (2007). The relationship between body energy traits and production and fitness traits in first-lactation dairy cows. Journal of Dairy Science, 90(3), 1527-1537.Wallace, R. J. et al. (2019). A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Science Advances, 5(7), eaav8391

Genetics of alternative somatic cell count traits in Italian Holsteins

Mastitis is one of the most costly diseases in dairy herds. Alternative somatic cell count (SCC, cells mL-1) traits have been proposed to improve udder health. The aim of this study was to estimate genetic parameters of alternative SCC traits in first-parity Italian Holstein cows. The original dataset was edited to include records between 5 and 305 days in milk (DIM). Contemporary groups were defined as cows calving in the same herd-year-season (HYS) and HYS with less than 5 animals were removed. After edits, 21,671 cows from 152 herds were available for statistical analysis. Alternative SCC traits were: mean and standard deviation of somatic cell score [(SCS, 3 + log2(SCC/100,000)] within lactation (SCSt and SD_SCSt, respectively), between 5 and 150 DIM (SCS150 and SD_SCS150, respectively), and between 151 and 305 DIM (SCS305 and SD_SCS305, respectively); infection, a dichotomous trait indicating that at least one test-day record had SCC greater than 100,000 cells mL-1 within lactation; severity, the ratio between the number of test-days with SCC greater than 100,000 cells mL-1 and the total number of test-days; severity2, the ratio between the number of test-days with SCC greater than 400,000 cells mL-1 and the total number of test-days; and subclinical mastitis (SCM), identified as two consecutive test-days with SCC below 100,000 cells mL-1 followed by a test-day greater than 400,000 cells mL-1. Means of SCSt, SCS150, SCS305, infection, severity and severity2 were 3.48, 3.12, 3.44, 0.83, 0.40 and 0.14, respectively. Heritabilities of and genetic correlations between the aforementioned traits were estimated using univariate and bivariate animal models, respectively, considering HYS, age of the cow at calving, and number of lactation test-days as fixed effects, and additive genetic animal and residual as random factors. The pedigree included 73,009 animals (6 generations). Heritability estimates were 0.11 for severity, 0.08 for SCSt, 0.06 for SCS150 and SCS305, and 0.02 for SD_SCSt, SD_SCS150, SD_SCS305, infection and SCM. The strongest genetic correlations (0.94 to 0.99) were between SCS traits (SCSt, SCS150 and SCS305) and infection, severity and severity2. Although heritability estimates were generally low, exploitable genetic variation exists for SCC traits. Combination of such traits into an udder health index with appropriate emphasis may enhance genetic gain in resistance to mastitis of Italian Holstein population

Prediction of gross feed efficiency in Italian Holstein Friesian bulls

The aim of this study was to predict gross feed efficiency of Italian Holstein Friesian bulls selected for production, functional and type traits. A total of 12,238 bulls, from April 2015 genetic evaluation, were used. Predicted daily gross feed efficiency (pFE) was obtained as ratio between milk yield (MY) and predicted dry matter intake (pDMI). Phenotypic trend for MY, predicted body weight (pBW) and pFE were calculated by the bull birth year. The results suggest that pFE can be successfully selected to increase profitability of dairy cattle using the current milk recording system. Direct measurements on DMI should be considered to confirm results of pFE obtained in the present study

Development of a selection index for resistance to subclinical ketosis in Holstein Friesian dairy cows

At the onset of lactation, high-yielding dairy cows could often experience a period of negative energy balance. This is reflected in a loss of body condition, due to body fat mobilization, and an increase of circulating ketone bodies, particularly \u3b2-hydroxybutyrate (BHB). This condition, known as hyperketonaemia, can result in (sub)clinical ketosis with negative implications on cow productivity and functionality, including health and fertility. The objective of the present study was to develop a genetic evaluation of resistance to subclinical ketosis for Holstein dairy cattle using data routinely available from the national milk recording system and linear classification. Milk BHB and fat-to-protein ratio (FPR) was available on more than 2.2 million test-days records belonging to Holstein cows in the first 90 days-in-milk from first up to the third lactation. These records were subsequently matched to the closest linear classification date when body condition score (BCS) was measured by an expert evaluator. The pedigree of cows has traced back up to 6 generations. (Co)variance components were estimated using trivariate linear mixed models; in particular, for BHB and FPR the fixed effects of herd-test-day, the two-way interaction between the week of lactation and parity, and the three-way interaction between classes of age at calving, parity and year of calving were considered. The linear model for BCS included the fixed effects of herd-year-round of classification, year of calving and the two-way interaction between age at calving and stage of lactation. The additive genetic effect and, only for BHB and FPR, the permanent environment were the two random terms. Due to computational constraints, (co)variance components were estimated on ten different subsets including 400 herds each, and subsequently averaged. Milk BHB and FPR and BCS averaged 0.056, 1.152 and 2.99, respectively. Heritability estimates were 0.093, 0.090 and 0.157 for BHB, PFR and BCS, while repeatability estimates were 0.179 (BHB) and 0.209 (FPR). The genetic (phenotypic in parenthesis) correlations were 0.159 (0.279; BHB vs. FRP), 120.161 ( 120.038; BHB vs. BCS) and 120.140 ( 120.049; FPR vs. BCS). The present study suggests that an exploitable additive genetic variation exists for milk BHB, and it could be used to set up breeding strategies aiming at improving resistance to subclinical ketosis through genetic selection

Implementation of Ketosis breeding value in Italian Holstein

An increase of circulating ketone bodies is associated, particularly at the onset of the lactation, with (sub)clinical ketosis, which may reduce cows\u2019 health, production and increase culling rate. The aim of the current research was to develop a genetic evaluation for subclinical ketosis for Holstein dairy cattle using data routinely available from the national milk recording system and linear type classification. For this breeding value three traits were considered: 1) \u3b2-hydroxybutyrate (BHB), 2) fat-to-protein ratio (FPR), both measured during routine milk recording, and 3) linear body condition score (BCS) measured by a classifier. Both FPR and BCS were used as indicator traits for sub-clinical ketosis. Currently milk BHB and FPR were available on more than 2.2 million test-days records belonging to Holstein cows in the first 90 days-in-milk from first, second and third lactation. These records were subsequently matched to the closest linear classification date when body condition score (BCS) was scored. The pedigree of phenotyped cows was traced back up to 4 generations. (Co)variance components were estimated using trivariate linear mixed models; in particular, for BHB and FPR the fixed effects of herd-test-day, the two-way interaction between week of lactation and parity, and the three-way interaction between classes of age at calving, parity and year of calving were considered. The additive genetic effect and, only for BHB and FPR, the permanent environment were the random effects. Heritability estimates were 0.093, 0.090 and 0.157 for BHB, FPR and BCS, respectively, while repeatability estimates were 0.179 (BHB) and 0.209 (FPR). Phenotypically, milk BHB was positively correlated with FPR (0.279) and weakly with BCS (-0.038), similarly to the correlation estimated between FPR and BCS (-0.049). Milk BHB was genetically correlated with FPR (0.159) and BCS (-0.161), while the genetic correlation between FPR and BCS was -0.14. The results from the present study demonstrated the presence of exploitable genetic variation for breeding purposes resulting in EBVs

Sviluppo di un indice per la resistenza alla mastite

Durante l\u2019ultimo convegno della Federazione Mondiale della razza Frisona (WHFF), svoltosi in Argentina dal 31 marzo al 3 aprile 2016, l\u2019ANAFI ha presentato il suo lavoro in corso per la messa a punto di un nuovo indice per la resistenza alla mastite. Per la costituzione di questo indice viene utilizzata la conta delle cellule somatiche (CCS, cellule/mL di latte), carattere rilevato routinariamente nel corso dei controlli funzionali e, quindi, gi\ue0 disponibile nel sistema di raccolta dati nazionale. L\u2019obiettivo \ue8 quello di sfruttare le informazioni gi\ue0 presenti ma gestirle in maniera diversa. L\u2019attuale indice per le cellule somatiche non verr\ue0 sostituito perch\ue9 importante per la qualit\ue0 del latte e fondamentale in quanto fa parte dell\u2019indice composto per la trasformazione casearia (ITC), ufficiale dal dicembre 2013