Consequences of genetic selection for environmental impact traits on economically important traits in dairy cows

Abstract

The authors advise that during the study period the definition (not the evaluation) of reported udder health (UDH) changed and the scale was reversed (i.e. the negative (-) associated with UDH should be positive (+) and vice versa). However, this correction was omitted in a few places in the published paper. Therefore, the correct text in the 'Expected genetic changes under selection scenarios' section should read:A relative weight of 25% of PME (selection scenario IV) generated a response of PME by -6%, MY by 15%, FY by 6%, PY by 11%, fertility by -4%, BCS by -11%, UDH by 13% and longevity by 22%. For example by the addition of 25% of LMI, the resulting response would be for LMI by -24%, MY by 29%, FY by 16%, PY by 28%, fertility by -10%, BCS by -13%, UDH by 13% and longevity by 23%.The correct versions of Tables 5 and 6 are as follows: Table 6. Selection responses (percentage of change) of environmental, production and functional traits to LMI selection scenarios LMI, log-transformed methane intensity; MY, milk yield; FY, fat yield; PY, protein yield; Fertility, Combined female fertility; BCS, body condition score; UDH, udder health (reversed somatic cell score); Selection scenario 1 = current Walloon dairy cattle selection program, from second to fifth selection scenarios were addition of PME by 5%, 12.5%, 25% and 50% and proportional decrease on other traits respectively Table 5. Selection responses (percentage of change) of environmental, production and functional traits to PME selection scenarios PME, predicted methane emissions; MY, milk yield; FY, fat yield; PY, protein yield; Fertility, Combined female fertility; BCS, body condition score; UDH, udder health (reversed somatic cell score); Selection scenario 1 = current Walloon dairy cattle index (VeG), from second to fifth selection scenarios were addition of PME by 5%, 12.5%, 25% and 50% and proportional decrease on other traits respectively Abstract. Methane (CH 4 ) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH 4 emissions require the estimation of genetic correlations with other economically important traits and the prediction of their selection response. In this study, test-day CH 4 emissions were predicted from milk mid-infrared spectra of Holstein cows. Predicted CH 4 emissions (PME) and log-transformed CH 4 intensity (LMI) computed as the natural logarithm of PME divided by milk yield (MY). Genetic correlations of PME and LMI with traits used currently were approximated from correlations between estimated breeding values of sires. Values were for PME with MY 0.06, fat yield (FY) 0.09, protein yield (PY) 0.13, fertility 0.17; body condition score (BCS) -0.02; udder health (UDH) 0.22; and longevity 0.22. As expected by its definition, values were negative for LMI with production traits (MY -0.61; FY -0.15 and PY -0.40) and positive with fertility (0.36); BCS (0.20); UDH (0.08) and longevity (0.06). The genetic correlations of 33 type traits with PME ranged from -0.12 to 0.25 and for LMI ranged from -0.22...

Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods

“…Studying the effect of the introduction of MIR CH 4 trait in the breeding selection index is therefore interesting. Kandel et al (2014) studied the consequences of selection for environmental impact traits in dairy cows. The authors used CH 4 intensity (g of CH 4 per kg of milk) and calculated several approximate genetic correlations from estimated breeding values to other traits.…”

Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions

“…Moraes et al (2014) identified milk fat content as a key explanatory variable for predicting CH 4 emissions of North American dairy and beef cattle. Kandel et al (2014), however, found a low negative genetic correlation between infrared-predicted MeI (calibrated from SF 6 tracer data) and fat yield (g/d; r = −0.13). Using essentially the same SF 6 tracer data, Vanlierde et al (2015) found low phenotypic correlations between fat yield (g/d) and SF 6 MeP or MeI of 0.08 and −0.13, respectively.…”

“…Using essentially the same SF 6 tracer data, Vanlierde et al (2015) found low phenotypic correlations between fat yield (g/d) and SF 6 MeP or MeI of 0.08 and −0.13, respectively. Additionally, Kandel et al (2014) found a moderate genetic correlation between infrared-predicted MeI (calibrated from SF 6 tracer data) and protein yield (g/d; r = −0.47). Based on same SF 6 tracer data, Vanlierde et al (2015) found low to moderate phenotypic correlations between protein yield (g/d) and SF 6 MeP or MeI of 0.53 and −0.09, respectively.…”

“…Several studies have shown that CH 4 emissions by ruminants have a genetic component, with heritability in the range of 0. 20 to 0.30 (e.g., de Haas et al, 2011;Donoghue et al, 2013;Pinares-Patiño et al, 2013;Kandel et al, 2014;Lassen and Løvendahl, 2016). Breeding for reduced CH 4 emissions, alone or together with other mitigation strategies, could therefore be effective in reducing the environmental impact of cattle farming and, possibly, also in increasing feed efficiency.…”

Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying

“…Studying the effect of the introduction of the MIR CH 4 trait in the breeding selection index is therefore interesting. Kandel et al (2014) investigated the consequences of selection for traits of environmental impact in dairy cows. These authors used methane intensity (g/kg of milk) and calculated the approximate genetic correlation from estimated breeding values.…”