Characterization of the lactation curve in Murciano-Granadina dairy goats

León, Juan Manuel Bello; Macciotta, N.P.P.; Gama, L. T.; Barba, C; Delgado, Juan Vicente

doi:10.1016/j.smallrumres.2012.05.012

Cited by 49 publications

(47 citation statements)

References 35 publications

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“…Arnal et al (2018) demonstrated that the shape of the goat lactation curve is tied to the duration of the photoperiod and that month of kidding is strongly linked to mid-lactation production. Moreover, multiple studies have reported the influence of month of kidding (season of the year, more specifically) on peak yield, lactation persistence (ability to maintain production at a higher level after peak yield), and CMY of dairy goats (Montaldo et al, 1997;León et al, 2012). The findings presented here agree with previous studies that have shown that although goats kidding in winter have lower production at the beginning of lactation, they maintain peak production for longer than goats kidding during the spring.…”

Section: Milk Yieldsupporting

confidence: 90%

“…In the present study, differences in productivity varied as goats advanced into lactation, gradually decreasing between goats in first and third parity and increasing between goats in third and fourth or greater parity. Our results are supported by others who reported increased initial and peak milk yields with increasing parity (until about third or fourth parity), later peak with greater persistence in first-parity goats, and decreased persistence with increasing parity (Gipson and Grossman, 1990;León et al, 2012;Arnal et al, 2018). Figure 2 shows the proportion of goats being removed from the milking herd (dried off or culled) as lactation progresses.…”

Section: Milk Yieldsupporting

confidence: 89%

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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: 90%

Section: Milk Yieldsupporting

confidence: 89%

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

View full text Add to dashboard Cite

show abstract

“…These results are in contrast with the general knowledge about milk production curves during lactation. However, León et al (2012) in a recent study on lactation curves of Murciano-Granadina goats found that factors such as region, lactation number, and seasonal aspects affect both the scale and the shape of lactation curves. In particular, they found that goats in their first lactation have a lower initial production level, as well as a lower peak yield later on.…”

Section: Experimental Data Evaluationmentioning

confidence: 97%

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Zaninelli

Rossi

Tangorra

et al. 2014

Italian Journal of Animal Science

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Intramammary infection affects the quality and quantity of dairy goat milk. Health status (HS) and milk quality can be monitored by electrical conductivity (EC). The aim of the study was to determine the detection potential of EC when measured on-line on a daily basis and compared with readings from previous milkings. Milk yields (MYs) were investigated with the same approach. To evaluate these relative traits, a multivariate model based on fuzzy logic technology -which provided interesting results in cows -was used. Two foremilk samples from 8 healthy Saanen goats were measured daily over the course of six months. Bacteriological tests and somatic cells counts were used to define the HS. On-line EC measurements for each gland and MYs were also considered. Predicted deviations of EC and MY were calculated using a moving-average model and entered in the fuzzy logic model. The reported accuracy has a sensitivity of 81% and a specificity of 69%. Conclusions show that fuzzy logic is an interesting approach for dairy goats, since it offered better accuracy than other methods previously published. Nevertheless, specificity was lower than in dairy cows, probably due to the lack of a significant decrease of MY in diseased glands. Still, results show that the detection of the HS characteristics with EC is improved, when measured on-line, daily and compared with the readings from previous milkings.

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“…The main aims of these models are to describe and predict growth and make inferences based on an interpretation of growth parameters (Ratkowsky, 1983). Choosing a suitable mathematical function is crucial in efficiently describing growth evolution during the animal's commercial and biological life; an appropriate choice should, therefore, be based on data from different populations and herds (León et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Characterization of commercial and biological growth curves in the Segureña sheep breed

Lupi

Nogales

León³

et al. 2015

Animal

Self Cite

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Non-linear models were analysed to describe both the biological and commercial growth curves of the Segureña sheep, one of the most important Spanish breeds. We evaluated Brody, von Bertalanffy, Verhulst, logistic and Gompertz models, using historical data from the National Association of Segureña Sheep Breeders (ANCOS). These records were collected between 2000 and 2013, from a total of 129 610 weight observations ranging from birth to adulthood. The aim of this research was to establish the mathematical behaviour of body development throughout this breed's commercial life (birth to slaughter) and biological life (birth to adulthood); comparison between both slopes gives important information regarding the best time for slaughter, informs dietary advice according to animals' needs, permits economical predictions of productions and, by using the curve parameters as selection criteria, enables improvements in growth characteristics of the breed. Models were fitted according to the non-linear regression procedure of statistical package SPSS version19. Model parameters were estimated using the Levenberg-Marquardt algorithm. Candidate models were compared using the determinative coefficient, mean square error, number of iterations, Akaike information coefficient and biological coherence of the estimated parameters. The von Bertalanffy and logistic models were found to be best suited to the biological and commercial growth curves, respectively, for both sexes. The Brody equation was found to be unsuitable for studying the commercial growth curve. Differences between the parameters in both sexes indicate a strong impact of sexual dimorphism on growth. This can emphasize the value of the highest growth rate for females, indicating that they reach maturity earlier.Keywords: growth, models, sex, sheep, Zootechny ImplicationsNon-linear models can be an option to establish the mathematical behaviour of body development throughout the life of the Segureña sheep breed. These models require lower computational and faster convergence than other methods. Moreover, in genetic evaluation programmes with large data sets, non-linear models are more advantageous. The results showed a strong impact of sexual dimorphism on growth.

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Characterization of the lactation curve in Murciano-Granadina dairy goats

Cited by 49 publications

References 35 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

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Characterization of commercial and biological growth curves in the Segureña sheep breed

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