Does the Acknowledgement of αS1-Casein Genotype Affect the Estimation of Genetic Parameters and Prediction of Breeding Values for Milk Yield and Composition Quality-Related Traits in Murciano-Granadina?

A total of 2090 lactation records for 710 Murciano-Granadina goats were collected during the years 2005–2016 and analyzed to investigate the influence of the αS1-CN genotype on milk yield and components (protein, fat, and dry matter). Goats were genetically evaluated, including and excluding the αS1-CN genotype, in order to assess its repercussion on the efficiency of breeding models. Despite no significant differences being found for milk yield, fat and dry matter heritabilities, protein production heritability considerably increased after aS1-CN genotype was included in the breeding model (+0.23). Standard errors suggest that the consideration of genotype may improve the model’s efficiency, translating into more accurate genetic parameters and breeding values (PBV). Genetic correlations ranged from −0.15 to −0.01 between protein/dry matter and milk yield/protein and fat content, while phenotypic correlations were −0.02 for milk/protein and −0.01 for milk/fat or protein content. For males, the broadest range for reliability (RAP) (0.45–0.71) was similar to that of females (0.37–0.86) when the genotype was included. PBV ranges broadened while the maximum remained similar (0.61–0.77) for males and females (0.62–0.81) when the genotype was excluded, respectively. Including the αS1-CN genotype can increase production efficiency, milk profitability, milk yield, fat, protein and dry matter contents in Murciano-Granadina dairy breeding programs.

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Random regression models to estimate genetic parameters for test-day milk yield and composition in Iranian buffaloes

Archives Animal Breeding ◽

10.7482/0003-9438-56-027 ◽

2013 ◽

Vol 56 (1) ◽

pp. 276-284 ◽

Cited By ~ 1

Author(s):

M. Madad ◽

N. Ghavi Hossein-Zadeh ◽

A. A. Shadparvar ◽

D. Kianzad

Keyword(s):

Milk Yield ◽

Milk Fat ◽

Milk Protein ◽

Genetic Parameters ◽

Genetic Correlations ◽

Random Regression ◽

Production Traits ◽

Environmental Correlations ◽

Protein Percentage ◽

Heritability Estimates

Abstract. The objective of this study was to estimate genetic parameters for milk yield and milk percentages of fat and protein in Iranian buffaloes. A total of 9,278 test-day production records obtained from 1,501 first lactation buffaloes on 414 herds in Iran between 1993 and 2009 were used for the analysis. Genetic parameters for productive traits were estimated using random regression test-day models. Regression curves were modeled using Legendre polynomials (LPs). Heritability estimates were low to moderate for milk production traits and ranged from 0.09 to 0.33 for milk yield, 0.01 to 0.27 for milk protein percentage and 0.03 to 0.24 for milk fat percentage, respectively. Genetic correlations ranged from −0.24 to 1 for milk yield between different days in milk over the lactation. Genetic correlations of milk yield at different days in milk were often higher than permanent environmental correlations. Genetic correlations for milk protein percentage ranged from −0.89 to 1 between different days in milk. Also, genetic correlations for milk percentage of fat ranged from −0.60 to 1 between different days in milk. The highest estimates of genetic and permanent environmental correlations for milk traits were observed at adjacent test-days. Ignoring heritability estimates for milk yield and milk protein percentage in the first and final days of lactation, these estimates were higher in the 120 days of lactation. Test-day milk yield heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in Iranian milking buffaloes.

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Schätzung genetischer Parameter für Testtagsergebnisse von Milchleistungsmerkmalen bei Bunten Deutschen Edelziegen

Archives Animal Breeding ◽

10.5194/aab-47-193-2004 ◽

2004 ◽

Vol 47 (2) ◽

pp. 193-202

Author(s):

D. Bömkes ◽

H. Hamann ◽

O. Distl

Keyword(s):

Protein Content ◽

Milk Yield ◽

Milk Fat ◽

Genetic Parameters ◽

Genetic Correlations ◽

Protein Yield ◽

Lower Saxony ◽

Genetic Progress ◽

Performance Traits ◽

Milk Performance

Abstract. Title of the paper: Estimation of genetic parameters for test day records of milk performance traits in German Improved Fawn The objectives of this study were to estimate genetic parameters for milk performance traits of German Improved Fawn by using univariate und multivariate animal models. The analysis was based on 27,778 test day records of 1,848 German Improved Fawn with 3,574 lactation records distributed over 229 flocks in Lower Saxony, Saxony and Baden-Wuerttemberg. Milk records were sampled between 1988 and 2002. The animals in our analysis were the progeny of 455 sires and 1.148 does. Heritabilities estimated with a multivariate test day model with fixed regression were h2 = 0.19, 0.16 and 0.15 for milk, fat and protein yield. For fat and protein content and Somatic Cell Score (SCS) heritabilities were h2 = 0.17, 0.14 and 0.16, respectively. The additive genetic correlations between milk yield and fat as well as protein yield of German Improved Fawn were very high and positive (rg = 0.84 and rg = 0.77). Milk yield and milk contents were genetically negative correlated with rg = −0.28 for fat and rg = −0.22 for protein content. A moderate additive genetic correlation (rg = 0.48) between fat and protein content was estimated. There were no considerable additive genetic correlations between fat yield and protein content as well as between fat content and protein yield (rg = 0.05 and rg = 0.09). Additive genetic correlations between milk, fat or protein yield and SCS were high and negative, whereas additive genetic correlations between fat or protein content and SCS were low and positive. The genetic parameters estimated from field test records allow to achieve genetic progress in milk performance traits of German Improved Fawn.

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Genetic relationship between milk dry matter and other milk traits in extended lactations of Polish Holstein cows

Czech Journal of Animal Science ◽

10.17221/49/2009-cjas ◽

2010 ◽

Vol 55 (No. 3) ◽

pp. 91-104 ◽

Cited By ~ 6

Author(s):

K. Yazgan ◽

J. Makulska ◽

A. Węglarz ◽

E. Ptak ◽

M. Gierdziewicz

Keyword(s):

Milk Yield ◽

Milk Fat ◽

Fixed Effects ◽

Dry Matter ◽

Genetic Correlations ◽

Random Regression ◽

Holstein Cows ◽

Milk Traits ◽

Data Set ◽

The Third

The objective of this research was to examine heritabilities and genetic, phenotypic and permanent environmental relationships between milk dry matter (DM) and milk traits such as milk, fat, protein and lactose yields, milk urea nitrogen (MUN) and somatic cell score (SCS) in extended (to 395 days) lactations of Holstein cows from a big farm in Poland. The data set consisted of 78 059 test day records from the first, second and third lactations of 3 792 cows, daughters of 210 sires and 1 677 dams. Single- or two-trait random regression models were used with fixed effects of calving year, calving month, dry period and calving interval and random additive genetic and permanent environmental effects. The last two fixed effects were not included in the analysis of first lactation data. The highest values of heritabilities for all traits, except DM, were observed in the second lactation. First lactation heritabilities for all traits – except milk yield and SCS – were smaller than those in the third lactation. Lactose yield was highly heritable, with average h<SUP>2</SUP> equal to 0.25, 0.29 and 0.28 in lactations 1, 2 and 3, respectively. Heritability for DM was slightly lower than that for lactose (0.22, 0.26 and 0.28 for lactations 1, 2 and 3, respectively). In all lactations heritabilities for SCS were below 0.1. Genetic correlations between DM and milk yield (0.64–0.74) were lower than those between MUN and milk yield (0.67–0.79) as well as between lactose and milk yield (0.72–0.82). In general, DM was much more closely correlated with fat or protein yield (0.55–0.79) than with MUN or lactose (0.38–0.76). Only in the third lactation the correlation between DM and protein (0.72) was lower than between lactose and protein (0.76). For all lactations there were very high genetic correlations between DM and lactose (0.96–0.98) and high correlations between DM and MUN (0.63–0.83) and between lactose and MUN (0.70–0.85). The results suggest that further research is needed, focused on DM and its relationship with other traits in larger populations.  

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Genetic Parameters of Bovine Milk Fatty Acid Profile, Yield, Composition, Total and Differential Somatic Cell Count

Animals ◽

10.3390/ani10122406 ◽

2020 ◽

Vol 10 (12) ◽

pp. 2406

Author(s):

Tania Bobbo ◽

Mauro Penasa ◽

Martino Cassandro

Keyword(s):

Animal Models ◽

Somatic Cell ◽

Cell Count ◽

Milk Yield ◽

Milk Fat ◽

Fixed Effects ◽

Somatic Cell Count ◽

Genetic Parameters ◽

Genetic Correlations ◽

Bovine Milk

The growing interest of consumers for milk and dairy products of high nutritional value has pushed researchers to evaluate the feasibility of including fatty acids (FA) in selection programs to modify milk fat profile and improve its nutritional quality. Therefore, the aim of this study was to estimate genetic parameters of FA profile predicted by mid-infrared spectroscopy, milk yield, composition, and total and differential somatic cell count. Edited data included 35,331 test-day records of 25,407 Italian Holstein cows from 652 herds. Variance components and heritability were estimated using single-trait repeatability animal models, whereas bivariate repeatability animal models were used to estimate genetic and phenotypic correlations between traits, including the fixed effects of stage of lactation, parity, and herd-test-date, and the random effects of additive genetic animal, cow permanent environment and the residual. Heritabilities and genetic correlations obtained in the present study reflected both the origins of FA (extracted from the blood or synthesized de novo by the mammary gland) and their grouping according to saturation or chain length. In addition, correlations among FA groups were in line with correlation among individual FA. Moderate negative genetic correlations between FA and milk yield and moderate to strong positive correlations with fat, protein, and casein percentages suggest that actual selection programs are currently affecting all FA groups, not only the desired ones (e.g., polyunsaturated FA). The absence of association with differential somatic cell count and the weak association with somatic cell score indicate that selection on FA profile would not affect selection on resistance to mastitis and vice versa. In conclusion, our findings suggest that genetic selection on FA content is feasible, as FA are variable and moderately heritable. Nevertheless, in the light of correlations with other milk traits estimated in this study, a clear breeding goal should first be established.

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Genetic relationships between internal parasite resistance and production traits in Merino sheep

Animal Production Science ◽

10.1071/an15469 ◽

2017 ◽

Vol 57 (2) ◽

pp. 209 ◽

Cited By ~ 14

Author(s):

D. J. Brown ◽

N. M. Fogarty

Keyword(s):

Genetic Parameters ◽

Genetic Correlations ◽

Production Traits ◽

Parasite Resistance ◽

Breeding Programs ◽

Sheep Breeding ◽

Breeding Values ◽

Merino Sheep ◽

Internal Parasite ◽

Australian Sheep

Breeding Merino sheep that are resistant to internal parasites alleviates the high costs associated with treatment of worm infestation and loss of production, as well as mitigating the development of anthelmintic resistance among the major worm species. Faecal worm egg count ((cube root transformation), wec) can be used in sheep as a measure of internal parasite resistance. Accurate estimates of genetic parameters for wec are required for calculation of Australian Sheep Breeding Values and inclusion of worm resistance in sheep breeding programs. This study provides updated estimates of heritability for wec and its genetic correlations with production traits. Data were analysed from a wide range of Australian and New Zealand Merino sheep in the MERINOSELECT database, which included 141 flocks with 801 flock years and up to 217 137 animals with wec recorded in at least one of four ages (W = weaning, P = post weaning, Y = yearling, H = hogget). The heritability estimates ranged from 0.16 ± 0.01 for Ywec to 0.29 ± 0.01 for Wwec, with generally high genetic correlations between the ages. Bivariate analyses estimated genetic correlations between wec at the various ages and growth, carcass quality, reproduction and wool production traits at various ages. These genetic correlations were generally small or close to zero, albeit with some significantly different from zero. The moderate heritability for wec (0.2–0.3) and its high phenotypic variation (coefficient of variation >30%) shows that relatively rapid selection response for worm resistance could be achieved. Inclusion of wec in sheep breeding programs to increase worm resistance would be expected to have little if any impact on other important production traits. These genetic parameters have been incorporated into MERINOSELECT by Sheep Genetics to provide Australian Sheep Breeding Values for wec and appropriate indices for wool and meat production. There is evidence that genotype × environment interactions may be important in some environments by reducing the accuracy of Australian Sheep Breeding Values for wec. Hence it may be prudent for breeders to implement strategies that manage the risk of any impact of genotype × environment on their breeding program.

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Estimation of genetic parameters and trends for energy-corrected 305-d milk yield in Iranian Holsteins

Archives Animal Breeding ◽

10.5194/aab-55-420-2012 ◽

2012 ◽

Vol 55 (5) ◽

pp. 420-426

Author(s):

N. G. Hossein-Zadeh

Keyword(s):

Animal Models ◽

Milk Yield ◽

Genetic Parameters ◽

Genetic Correlations ◽

Physiological Mechanism ◽

Animal Breeding ◽

Dairy Herds ◽

Breeding Values ◽

Estimation Of Genetic Parameters ◽

Genetic Trends

Abstract. Calving records from the Animal Breeding Centre of Iran collected from January 1995 to December 2007 and comprising 217973 calving events of Holsteins from 704 dairy herds were analysed using univariate and bivariate linear animal models to estimate heritabilities and genetic correlations for energy-corrected 305-d milk yield (ECM) in the first three lactations of Holstein cows. Genetic trends were obtained by regressing yearly mean estimates of breeding values on calving year. Average ECM increased from parity 1 through parity 3. Estimates of heritabilities were from 0.14 to 0.21 for ECM and decreased over the parities. The greatest genetic correlations were between ECM2 and ECM3 (0.96), and the greatest phenotypic correlations were between ECM1 and ECM2 (0.57) and ECM2 and ECM3 (0.57). The high and positive genetic correlations between ECM traits at different lactations are evidence for common genetic and physiological mechanism controlling these traits. There were positive and increasing phenotypic and genetic trends for ECM over the years (P<0.001). Higher heritability of the ECM in the first parity along with the high genetic correlations between first-lactation ECM with these traits in other lactations shows that higher potential exists for selecting animals for ECM based on their first parity records.

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Genetic parameters of test day records of British Holstein-Friesian heifers

Animal Science ◽

10.1017/s0003356100037211 ◽

1992 ◽

Vol 55 (1) ◽

pp. 11-21 ◽

Cited By ~ 64

Author(s):

B. L. Pander ◽

W. G. Hill ◽

R. Thompson

Keyword(s):

Maximum Likelihood ◽

Milk Yield ◽

Milk Fat ◽

Genetic Parameters ◽

Genetic Correlations ◽

Lactation Performance ◽

Likelihood Methods ◽

Holstein Friesian ◽

Maximum Likelihood Methods ◽

Heritability Estimates

AbstractEstimates of genetic parameters for test day records of yields of milk, fat and protein and concentrations of fat and protein were obtained on 47 736 British Holstein-Friesian heifers in 7973 herds, progeny of 40 proven (to improve connectedness) and 707 young sires (comprising about one-fifth of the progeny), using multivariate restricted maximum likelihood methods with a sire model.Heritability estimates for lactation yields of milk, fat and protein and concentrations of fat and protein were 0·49, 0·39, 0·43, 0·63 and 0·47, respectively. Estimates for individual test day records of these traits ranged from 0·27 to 0·43, 0·16 to 0·34, 0·22 to 0·33, 0·11 to 0·48 and 0·21 to 0·43, respectively. Generally, heritability estimates for test day records were lowest at start and highest in mid lactation.Estimates of genetic correlations among yields of a trait on different test days ranged from 0·57 to 0·99, and for fat and protein concentrations from 0·34 to 0·99, the correlations being highest for adjacent tests. Phenotypic correlations were lower than genetic correlations. Genetic correlations of test day records with corresponding lactation traits were high (0·76 to 0·99), being highest in mid lactation.Genetic correlations of test day milk yield with test day yields and concentrations of fat and protein throughout the lactation were similar to those for complete lactation.The high heritabilities of test day yields and their high genetic correlations with complete lactation, except for the first 1 or 2 test days, suggest that lactation performance may be predicted from test days in early and mid lactation.

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Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing

Sensors ◽

10.3390/s21123971 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3971

Author(s):

Gabriel Silva de Oliveira ◽

José Marcato Junior ◽

Caio Polidoro ◽

Lucas Prado Osco ◽

Henrique Siqueira ◽

...

Keyword(s):

Remote Sensing ◽

Dry Matter ◽

Pearson Correlation ◽

Genetic Correlations ◽

Remote Sensing Data ◽

Breeding Program ◽

Yield Traits ◽

Dry Matter Yield ◽

Breeding Programs ◽

Ground Truth Data

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha−1 for LDMY and from 413.07 to 506.56 kg·ha−1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.

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Effects of protected vegetable fats on rumen metabolism and productivity dairy cows

Veterinariya, Zootekhniya i Biotekhnologiya ◽

10.36871/vet.zoo.bio.202109009 ◽

2021 ◽

Vol 1 (9) ◽

pp. 68-74

Author(s):

A. V. Golovin ◽

Keyword(s):

Dairy Cows ◽

Milk Yield ◽

Milk Fat ◽

Dry Matter ◽

Volatile Fatty Acids ◽

Metabolic Energy ◽

Black And White ◽

Rumen Metabolism ◽

Economic Experiment ◽

Feed Costs

In a scientific economic experiment conducted in the experimental farm «Klenovo-Chegodaevo» (Moscow) on three groups of Holsteinized black-and-white cows with a milk yield of 7000 kg of milk per lactation, 10 heads in each, it was found that the inclusion in the diet of cows of the experimental groups tested protected fats (hydrogenated and fractionated) in the amount of 300 g per head per day, contributed to the tendency for more intensive metabolic processes in the rumen due to a slight increase in the concentration of volatile fatty acids by 5,6–7,4% and an increase in the mass of microorganisms in the contents of the rumen by 5,4–14,4% (P≥0,05). At the same time, an increase in the concentration of metabolic energy in the dry matter of the cows ration from 10,7 to 11,0 MJ / kg in the period from 21 to 120 days of lactation, due to the inclusion of protected fats in the diet of cows from the experimental groups, contributed to an increase in milk yield 4% fat content for 100 days of the experiment by 9,7% and 11,0% (P≤0,05), compared with the control, as well as the production of milk fat and protein, respectively by 9,6–11,0% (P≤0,05 in the second case) and 7,4–8,3%, feed costs expressed in ME decreased by 4,9–5,2%.

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Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

Archives Animal Breeding ◽

10.5194/aab-59-243-2016 ◽

2016 ◽

Vol 59 (2) ◽

pp. 243-248 ◽

Cited By ~ 3

Author(s):

Hafedh Ben Zaabza ◽

Abderrahmen Ben Gara ◽

Hedi Hammami ◽

Mohamed Amine Ferchichi ◽

Boulbaba Rekik

Keyword(s):

Dairy Cattle ◽

Bayesian Methods ◽

Milk Fat ◽

Variance Components ◽

Genetic Parameters ◽

Genetic Correlations ◽

Bayesian Procedure ◽

Milk Traits ◽

Residual Variances ◽

Estimation Of Variance

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

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