Genetic parameter estimation to milk yield and fat and protein yield deviated from 3% of concentration in milk, in dairy herds of southern Chile

Uribe, H.; González, V Humberto; Gatica, Carlos Patricio Alvarez

doi:10.4067/s0719-81322017000200071

Cited by 5 publications

(10 citation statements)

References 3 publications

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“…This is very similar to that reported by García et al (2001) (0.24 ± 0.001), who used a subset of the data included in this study. This estimate (0.26 ± 0.011) is higher than that reported in two studies on Chilean dairy cows conducted by Uribe et al (2017) and Montaldo et al (2015), who reported values of 0.16 ± 0.004 and 0.19 ± 0.006, respectively. Uribe and Smulders (2004) found that heritability estimate for milk yield in Chilean Overo Colorado cattle was 0.25, which is similar to that found in this study (Table 3).…”

Section: Heritabilitycontrasting

confidence: 60%

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(Co)variances Between Cattle Postpartum Body Weight and Dairy Traits in a Pastoral System in Chile

Uribe

González

2018

Chil. j. agric. anim. sci.

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In Chile, the genetic parameters of dairy cow body weight have not been estimated due to lack of data. The objective of this study was to determine variance components of milk yield traits and postpartum body weight. Data were obtained from a dairy research farm in southern Chile. A multitrait animal mixed repeatability model was used and genetic parameters were estimated using the VCE software. Heritability estimates for yields of milk, milk fat and protein, and postpartum body weight were 0.26 ± 0.011, 0.30 ± 0.011, 0.27± 0.012 and 0.43 ± 0.047, respectively. Genetic correlations for postpartum body weight and yields of milk, milk fat and protein were 0.34, 0.40 and 0.38, respectively. Phenotypic correlations were also positive and generally 10% lower than genetic correlations. It is concluded that the correlations between postpartum body weight and milk yield traits are positive and not very high. The genetic selection to favour yield would increase cow's body weight.

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Section: Heritabilitycontrasting

confidence: 60%

“…The milk yield mean was (4,432 ± 1,224 (Table1), and it is much lower than that reported by Elzo et al (2004) and Uribe et al (2017). They reported values of 7,502 ± 1,829 and 7,606 ± 1,670, respectively, adjusted to a 305-days mature equivalent.…”

Section: Phenotypic Parametersmentioning

confidence: 66%

(Co)variances Between Cattle Postpartum Body Weight and Dairy Traits in a Pastoral System in Chile

Uribe

González

2018

Chil. j. agric. anim. sci.

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“…The fieldwork has been conducted from November 2018 till January 2019 in three regions of Chile: Araucanía, Los Ríos, and Los Lagos, where 76% of all dairy milk in Chile is produced (Gonzalez & Katial, 2016;Uribe et al, 2017). The city of Valdivia is a central point of work, and it is 850 km to the south of Santiago, the capital of Chile.…”

Section: The Sector Of Studymentioning

confidence: 99%

“…In 2016, Chilean milk production reached 1.99 billion liters. Moreover, the economic success of a farmer in Chile is continuously more focused on milk solids output per unit of land rather than milk yield per cow (Uribe et al, 2017).…”

Section: The Sector Of Studymentioning

confidence: 99%

Unpacking the Precision Technologies for Adaptation of the Chilean Dairy Sector. A Structural-functional Innovation System Analysis

Klerkx

Hahn

Boisier

et al. 2021

Journal of Technology Management & Innovation

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Precision technologies and smart farming practices are spreading globally. However, there are still limited insights into how these technologies develop in newly adopted countries and adapt to different local systems, especially in emergent economies. This article aims to diagnose the technological innovation system of precision technologies within Chile's dairy sector, focusing on the possibilities of development and co-evolution. A structural-functional innovation system analysis is performed, based upon 41 semi-structured interviews. The main results indicate that precision technologies are in the implementation phase in the Chilean dairy sector, with scarce experience and undeveloped knowledge of their benefits. Moreover, there are insufficient interactions between different actors with limited information sharing due to the lack of trust as a cultural issue, affecting the development of smart farming practices. Some strategies to develop and co-evolve the technological systems are discussed, as is the need to increase trust and cooperation between the actors of the dairy sector.

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“…Some attempts have been made to estimate the genetic parameters of the Chilean Holstein population but none of them has used RRMs (Elzo et al 2004, Montaldo et al 2017, Uribe et al 2017. The objective of this study was to estimate genetic parameters such as heritability (h 2 ) and repeatability (rep) for daily milk yield, using orthogonal polynomials in a random regression test-day model in cows of Los Ríos Region, Chile.…”

Section: Introductionmentioning

confidence: 99%

Estimation of genetic parameters for milk yield using a random regression test-day model in first parity dairy cows under pasture-based systems of Los Ríos region in Chile

Uribe

Lembeye²

2020

Austral j. vet. sci.

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In dairy cattle, genetic selection for milk yield was generally based on 305 days lactation records that were calculated from available monthly test-day milk yield records. A test-day milk yield record, multiplied by the number of days between the current and following test-day, was the monthly milk yield and summed to all other monthly milk yields represented a 305 days lactation yield. Cows that for any reason did not complete their lactation got a 305 days yield via correction factors assuming a common lactation curve. Random regression models allow individual deviation from a common curve. The objective of this study was to estimate genetic parameters for daily milk yield using a random regression model (RRM) in Chilean dairy cows. A data set containing 97,683 monthly test-day records of 10,528 cows from 15 commercial dairy herds of Los Ríos Region in southern Chile was used. Days in milk (DIM) were modelled using the fourth-order Legendre polynomials and the model also included, as fixed effects, contemporary group and cow age at test-day as a covariate. The average daily milk yield was 17.83 ± 5.25 kg. Average estimated heritability and repeatability from five to 305 DIM was 0.26 ± 0.02 and 0.61 ± 0.04, respectively. The heritability estimate varied from 0.23 to 0.31. Both parameters did not vary dramatically except after 270 DIM when repeatability increased while heritability decreased. Although the estimated genetic parameters did not seriously depart from the most recent results available in the Chilean literature, they are mathematically more precise for estimating the true parameters than those calculated using adjustment factors, suggesting that the model used could be the starting point to develop a genetic evaluation system for dairy cattle in Chile.

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Genetic parameter estimation to milk yield and fat and protein yield deviated from 3% of concentration in milk, in dairy herds of southern Chile

Cited by 5 publications

References 3 publications

(Co)variances Between Cattle Postpartum Body Weight and Dairy Traits in a Pastoral System in Chile

(Co)variances Between Cattle Postpartum Body Weight and Dairy Traits in a Pastoral System in Chile

Unpacking the Precision Technologies for Adaptation of the Chilean Dairy Sector. A Structural-functional Innovation System Analysis

Estimation of genetic parameters for milk yield using a random regression test-day model in first parity dairy cows under pasture-based systems of Los Ríos region in Chile

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