Genetic and environmental factors that influence production and quality of milk of Alpine and Saanen goats

Brito, Luiz F.; Silva, F.G.; Melo, Ana Lúcia Puerro de; Caetano, G.C.; Torres, R.A.; Rodrigues, Marcelo Teixeira; Menezes, G.R.O.

doi:10.4238/2011.december.14.9

Cited by 28 publications

(23 citation statements)

References 20 publications

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“…As a result, goats delivering multiple kids had greater CMY (+35 L) than goats delivering single kids (Table 3). Our findings agree with those of several authors who found a higher milk production in multiple deliveries in commercially milked goats (Browning et al, 1995;Crepaldi et al, 1999;Brito et al, 2011). Litter size is positively associated with antepartum levels of mammogenic hormones such as placental lactogen, progesterone, and prolactin (Lérias et al, 2014).…”

Section: Milk Yieldsupporting

confidence: 92%

Effects of month of kidding, parity number, and litter size on milk yield of commercial dairy goats in Australia

Zamuner

DiGiacomo

Cameron³

et al. 2020

Journal of Dairy Science

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The aim of this observational study was to identify the influence of key nongenetic factors such as month of kidding, parity, and litter size on milk yield and composition of Australian dairy goats throughout lactation. The study was conducted over 4 consecutive kidding seasons from June 2016 to March 2017. Data from 940 lactations of Saanen goats from a commercial herd were used to observe the effects of month of kidding, parity number, and litter size on total milk yield (L/goat) in early lactation (kidding to 90 d in milk; DIM), mid lactation (91-180 DIM), and late lactation (181-270 DIM), cumulative milk yield (from kidding to 270 DIM; CMY), average lactation length, proportion (%) of does reaching their target lactation length (270 DIM), somatic cell count (SCC), and percentages of milk fat and protein in early lactation. The mean herd responses throughout the entire study were as follows: CMY = 519 L/goat; lactation length = 233 d, with 70% of does reaching 270 DIM; milk fat = 4.2%; milk protein = 2.9%; and SCC = 6.2 × 10 5 cells/mL. Average milk production peaked in February and was lowest in June (2.4 vs. 1.8 L/goat per day, respectively). Milk yield was affected by month of kidding, parity number, and litter size in all phases of lactation. November kidders had the greatest CMY, and March kidders had the lowest CMY. March kidders had the shortest lactation length and the lowest proportion of does reaching 270 DIM. June kidders had the longest lactation length, whereas September kidders had the highest proportion of does reaching 270 DIM. Maximum milk yield was attained in third parity. Goats in fourth or greater parity had the shortest lactation length, the lowest proportion of does reaching 270 DIM, and the highest SCC. Goats delivering single kids had lower CMY, lower SCC, and higher percentages of fat and protein than does deliv-ering multiple kids. Our findings indicate that milk yield was primarily influenced by month of kidding, and the effects of month of kidding on milk yield were accentuated during mid lactation. However, the effects of month of kidding on milk yield varied significantly among parities.

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Section: Milk Yieldsupporting

confidence: 92%

Effects of month of kidding, parity number, and litter size on milk yield of commercial dairy goats in Australia

Zamuner

DiGiacomo

Cameron³

et al. 2020

Journal of Dairy Science

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“…Genetic correlations between SCS and fat, protein or lactose contents obtained by Lindmark-Månsson et al (2006) for dairy cattle were different from those obtained in our study (-0.050, 0.393 and -0.774, respectively), but correlation between logSCC and protein content in dairy sheep was closer to our result (0.37 vs 0.54; Baro et al, 1994). The genetic correlation between milk yield and lactose content estimated by Brito et al (2011) in the Brazilian population is different from our result (0.15 vs -0.46).…”

Section: Resultscontrasting

confidence: 52%

“…Similar heritability (0.35) was presented by Morris et al (2011) but for lactose yield. However, the lactose content found by Brito et al (2011) was much lower (0.17) than that presented in our and Andonov et al (2007) studies. In the literature different results for lactose content in cow's milk are presented -from 0.17 to 0.36, depending of the stage of lactation and parity with the lowest at the beginning of the first lactation (Ptak et al, 2012) up to an average heritability of 0.48 in the first, and 0.51 in the second and later parities (Miglior et al, 2007).…”

Section: Resultscontrasting

confidence: 50%

“…Heritability for lactose content was estimated only in the Norway dairy goat population using different methods and software (Andonov et al, 2007) and in the Brazilian population of Alpine and Saanen goats (Brito et al, 2011). The estimations in a Norwegian population ranged between 0.29 and 0.35, and were slightly higher than those obtained in the presented study.…”

Section: Resultsmentioning

confidence: 55%

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Genetic parameters of somatic cell score and lactose content in goat’s milk

Bagnicka¹,

Łukaszewicz²,

Ådnøy³

2016

J. Anim. Feed Sci.

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“…RRMs have the advantage of not only being able to deal with a greater volume of data, but also result in a greater detailing of the phenotype, which allows for more accurate estimates of the genetic values. Brito et al (2011) and Silva et al (2013a) assessed fixed effects that affect CMY and TDMY, respectively. The former demonstrated that fixed effects should be included in singletrait, multi-trait, and repeatability models, whereas the latter demonstrated that fixed effects should be included in RRMs.…”

Section: Introductionmentioning

confidence: 99%

Genetic evaluation of milk yield in Alpine goats for the first four lactations using random regression models

et al. 2014

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ABSTRACT. Random regression models have been used in evaluating test-day milk yield, providing accurate estimates of genetic values in animals. However, herd evaluation with only information from the first lactation may not be the best option from an economic perspective. Other factors should be taken into account, particularly other lactations. Our objective in this study was to analyze the genetic divergence between the first four lactations of Alpine goats. The RENPED software was used to perform descriptive statistics, check for errors in pedigree, recode the data, and for Pearson's and Spearman's correlations. The WOMBAT software was used to estimate the variance components and predict the breeding values. The CALC software was adopted to calculate the percentage of coincidence between the ranking of the animals and the animals kept in common at each lactation evaluation. The results show that selection using only the first lactation in small herds with a low degree of technology can be employed as a palliative measure, in view of the difficulty in evaluating all lactations. However, the selection of breeding goats and the production of catalogues should not be based only on the first lactation, because the results demonstrate inversions in the classification of the best breeders when other lactations are analyzed.

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Genetic and environmental factors that influence production and quality of milk of Alpine and Saanen goats

Cited by 28 publications

References 20 publications

Effects of month of kidding, parity number, and litter size on milk yield of commercial dairy goats in Australia

Effects of month of kidding, parity number, and litter size on milk yield of commercial dairy goats in Australia

Genetic parameters of somatic cell score and lactose content in goat’s milk

Genetic evaluation of milk yield in Alpine goats for the first four lactations using random regression models

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