Effects of Genotype by Environment Interactions on Milk Yield, Energy Balance, and Protein Balance

Abstract

Increases in genetic merit for milk yield are associated with increases in mobilization of body reserves. This study assessed the effects of genotype by environment (GxE) interactions on milk yield and energy and protein balances. Heifers (n = 100) with high or low genetic merit for milk yield were milked 2 or 3 times a day and received rations of low or high caloric density. The management factors were selected to induce substantial differences in milk production levels and model different management strategies. The 2 x 2 x 2 factorial arrangement enables the assessment of the effects of genotype, environment, and GxE interactions. Mean daily energy-corrected milk production in the first 100 d in milk varied between 21.8 and 35.2 kg among the groups. The experimental factors affected milk production in the presumed direction. Ration was the most determinant factor on milk production. Effects of milking frequency and genetic merit were significant only in the groups that were fed rations with high caloric density. Signs for severe negative energy balances, protein balances, and low body condition scores, all of which may be indicative of health risks, were not concentrated in the highest producing cows. Feed caloric density and milking frequency had stronger effects on energy balances and protein balances, with unfavorable effects of low caloric density feed and an extra milking. This emphasizes the possible effect of mismanagement on animal health risks. High genetic merit cows had significantly lower postpartum body condition scores. Genotype x environment interactions existed, but more information is needed to determine if cows of different genetic merit for milk yield are differently at risk for disease under specific conditions. High milk production levels per se will increase allostatic load, but need not compromise the health status of relatively young cows. Ongoing one-sided selection for high yield may be combined with good animal health, but because high genetic merit for milk yield seems intrinsically connected to the allocation of resources from maintenance toward milk, this puts increasing demands on farmers' time and management skills.

Analysis of feed intake and energy balance of high-yielding first lactating Holstein cows with fixed and random regression models

“…The trajectories of the mean lactation curves, given in Figures 1 and 2, are very similar to those reported in the literature (Veerkamp and Thompson, 1999;de Vries and Veerkamp, 2000;Coffey et al, 2001;Banos et al, 2005;Beerda et al, 2007). Surprisingly, the heifers start gaining weight on the 30th day of lactation although they are still in a negative energy balance till the 42nd day in milk.…”

Genome-wide associations for feed utilisation complex in primiparous Holstein–Friesian dairy cows from experimental research herds in four European countries

“…A detailed description of the experimental treatments imposed on the animals in the different countries is provided elsewhere, for Scotland (Veerkamp et al, 1995;Pryce et al, 1999;Coffey et al, 2004), Ireland (Horan et al, 2005), Sweden (Petersson et al, 2006), the Netherlands (Veerkamp et al, 2000;Beerda et al, 2007), and a more detailed description of the merging of the data sources and variance components across the different herds is given by Banos et al (2012). The analyses by Banos et al (2012) illustrated the benefits of combining data across herds, and therefore the Swedish data were added in this study to increase the number of records for milk yield.…”

Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries

“…Furthermore, cows were split into two diet groups fed a high and a low caloric density ration, respectively. The former comprised 49% corn silage, 30% grass silage and 21% soybeans meal, whereas the low caloric ration included 86% grass silage and 14% concentrates (Beerda et al, 2007;Windig et al, 2008). Individual records available for each cow were: daily milk yield (sum of three milkings) and dry matter intake, and weekly milk fat and protein yield, milk somatic cell count and liveweight.…”

“…Energy intake had already been calculated in each herd, separately, as net energy for the two Dutch herds (TGEN and NBZ) (Beerda et al, 2007) and metabolisable energy for Crichton (Scotland) and Moorepark (Ireland;Emmans, 1994;Friggens et al, 2003). The latter (ME) was converted to net energy (NE) using the following formula (Van Es, 1978):…”

“…Derivation of phenotypic traits At first, energy balance was calculated for each cow and week of lactation. For this purpose, predicted weekly phenotypic records were used to calculate the energy expended by the cow for yield and maintenance, based on the method described by Beerda et al (2007). This method is consistent with the net energy principle used to assess energy intake in the four herds in the present study, as described previously.…”