The economics of fertility in the dairy herd

113

125

The main objective of this study was to estimate genetic relationships between lactation persistency and reproductive performance in first lactation. Relationships with day in milk at peak milk yield and estimated 305-d milk yield were also investigated. The data set contained 33,312 first-lactation Canadian Holsteins with first-parity reproductive, persistency, and productive information. Reproductive performance traits included age at first insemination, nonreturn rate at 56 d after first insemination as a virgin heifer and as a first-lactation cow, calving difficulty at first calving and calving interval between first and second calving. Lactation persistency was defined as the Wilmink b parameter for milk yield and was calculated by fitting lactation curves to test day records using a multiple-trait prediction procedure. An 8-trait genetic analysis was performed using the Variance Component Estimation package (VCE 5) via Gibbs sampling to estimate genetic parameters for all traits. Heritabilities of persistency, day in milk at peak milk yield and estimated 305-d milk yield were 0.18, 0.09 and 0.45, respectively. Heritabilities of reproduction were low and ranged from 0.03 to 0.19. The highest heritability was for age at first insemination. Heifer reproductive traits were lowly genetically correlated, whereas cow reproductive traits were moderately correlated. Heifers younger than average when first inseminated and/or conceived successfully at first insemination tended to have a more persistent first lactation. First lactation was more persistent if heifers had difficulty calving (r(g) = 0.43), or conceived successfully at first insemination in first lactation (r(g) = 0.32) or had a longer interval between first and second calving (r(g) = 0.17). Estimates of genetic correlations of reproductive performance with estimated 305-d milk yield were different in magnitude, but similar in sign to those with persistency (0.02 to 0.51).

Section: Genetic Correlations Between Reproduction and Productionmentioning

confidence: 99%

Genetic Relationships Between Persistency and Reproductive Performance in First-Lactation Canadian Holsteins

Muir¹,

Fatehi

Schaeffer

2004

113

125

“…To model dairy systems within the same herd and farming unit on a comparative period, while incorporating the cyclical nature of individual systems and the implications of changes to these systems, a Markov chain (Agrawal and Heady, 1972) can be used. A Markov chain approach has been used to model the effects of production factors such as disease (Allore and Erb, 1999) and fertility (Stott et al, 1999;Garnsworthy, 2004) on a herd. Stott et al (1999) used a Markov chain to establish the economic optimum total cost of fertility in a dairy herd.…”

Section: Introductionmentioning

confidence: 99%

“…A Markov chain approach has been used to model the effects of production factors such as disease (Allore and Erb, 1999) and fertility (Stott et al, 1999;Garnsworthy, 2004) on a herd. Stott et al (1999) used a Markov chain to establish the economic optimum total cost of fertility in a dairy herd. Santarossa et al (2004) incorporated this approach in an assessment of the sustainability of dairying.…”

Section: Introductionmentioning

confidence: 99%

The effect of improving cow productivity, fertility, and longevity on the global warming potential of dairy systems

Bell

Wall

Russell

et al. 2011

106

This study compared the environmental impact of a range of dairy production systems in terms of their global warming potential (GWP, expressed as carbon dioxide equivalents, CO(2)-eq.) and associated land use, and explored the efficacy of reducing said impact. Models were developed using the unique data generated from a long-term genetic line × feeding system experiment. Holstein-Friesian cows were selected to represent the UK average for milk fat plus protein production (control line) or were selected for increased milk fat plus protein production (select line). In addition, cows received a low forage diet (50% forage) with no grazing or were on a high forage (75% forage) diet with summer grazing. A Markov chain approach was used to describe the herd structure and help estimate the GWP per year and land required per cow for the 4 alternative systems and the herd average using a partial life cycle assessment. The CO(2)-eq. emissions were expressed per kilogram of energy-corrected milk (ECM) and per hectare of land use, as well as land required per kilogram of ECM. The effects of a phenotypic and genetic standard deviation unit improvement on herd feed utilization efficiency, ECM yield, calving interval length, and incidence of involuntary culling were assessed. The low forage (nongrazing) feeding system with select cows produced the lowest CO(2)-eq. emissions of 1.1 kg/kg of ECM and land use of 0.65 m(2)/kg of ECM but the highest CO(2)-eq. emissions of 16.1t/ha of the production systems studied. Within the herd, an improvement of 1 standard deviation in feed utilization efficiency was the only trait of those studied that would significantly reduce the reliance of the farming system on bought-in synthetic fertilizer and concentrate feed, as well as reduce the average CO(2)-eq. emissions and land use of the herd (both by about 6.5%, of which about 4% would be achievable through selective breeding). Within production systems, reductions in CO(2)-eq. emissions per kilogram of ECM and CO(2)-eq. emissions per hectare were also achievable by an improvement in feed utilization. This study allowed development of models that harness the biological trait variation in the animal to improve the environmental impact of the farming system. Genetic selection for efficient feed use for milk production according to feeding system can bring about reductions in system nutrient requirements, CO(2)-eq. emissions, and land use per unit product.

“…Improved reproduction makes cows more functional, reduces breeding and veterinary costs, and avoids involuntary disposal (Boichard, 1990;Stott et al, 1999;Vargas et al, 2002). In recent years, improving reproductive functionality has been one of the most important goals in dairy cattle breeding.…”

Section: Introductionmentioning

confidence: 99%

Selection for Female Fertility Using Censored Fertility Traits and Investigation of the Relationship with Milk Production

González-Recio

Alenda

Chang

et al. 2006

Bivariate models (censored linear-linear and censored threshold-linear) were used to estimate genetic parameters for production and fertility traits in the Spanish Holstein population. Records on 71,217 lactations from 41,515 cows were used: 30 and 36% of lactations were censored for days open (DO) and number of inseminations to conception (INS), respectively. Heritability estimates for production traits (milk, fat, protein) ranged between 0.18 and 0.25. Heritability of days to first service (DFS) and DO was 0.05; heritability of INS on the liability scale was 0.04. Genetic correlations between fertility traits were 0.41, 0.71, and 0.87 for DFS-INS, DO-INS, and DO-DFS, respectively. Days open had a larger genetic correlation (ranging from 0.63 to 0.76) with production traits than did DFS (0.47 to 0.59) or INS (0.16 to 0.23). Greater antagonism between production and DO may be due to voluntary management decisions for high-yielding cows, resulting in longer lactation lengths. Inseminations to conception appeared to be less correlated with milk production than were the other 2 female fertility traits. Including INS in a total merit index would be expected to increase genetic gain in terms of profit, but profit would decrease if either DO or DO and DFS were included in the index. Thus, INS is the trait to be preferred when selecting for female fertility. The genetic correlation between actual milk yield and 305-d standardized milk yield was 0.96 in the present study, suggesting that some reranking of sires could occur. Because the target of attaining a 12-mo calving interval, as implied by a 305-d standardized lactation length, is changing in the dairy industry, routine genetic evaluation of actual total lactation milk yield should be considered.