Abstract :
[en] The definition of nitrogen (N) use efficiency (NUE) for lactating dairy cows traditionally is milk N divided by N intake. The NUE of dairy cows is associated negatively with N pollution and positively with economic results of dairy producers. The N pollution from dairy cows refers to N from manure and urine, which are mainly produced from undigested protein from the feed. Protein is the most expensive component of dairy cattle feed, and its loss reduces the profits of dairy farms. Therefore, measuring and improving the NUE of dairy cows can promote the sustainable economic development of dairy production and strengthen its social acceptability. Although the NUE in dairy cows can be affected by many factors, the economic importance of genetic improvement for efficiency traits in cattle is recognized by dairy producers.
The ultimate objective of this study was to contribute to the breeding of dairy cows that are both more economical and more respectful of the environment. However, the NUE is difficult to measure, which is why few studies have been conducted on genetic selection for NUE in dairy cows. So, the use of proxies of NUE to conduct genetic selection could be a good choice for its ease to be measured on a large scale. Different proxies of NUE were genetically analyzed in this thesis and their genetic relationships with other already selected traits were explored in Holstein cows in the Walloon Region of Belgium.
In the first step, milk urea concentration (MU), as the traditional proxy of NUE, was genetically analyzed, and its genetic correlations with 11 other traits of economic interest were estimated. The results showed that the average daily heritability and repeatability of MU in the first 3 parities ranged from 0.19 to 0.22 and from 0.47 to 0.48, respectively. High genetic correlations (0.94 to 0.97) were found among MU in the first 3 parities. The genetic correlation between MU and the 11 traits of interest ranged from −0.28 (milk yield) to 0.28 (somatic cell score).
Then, predict NUE (PNUE) and N losses (PNL), as alternative and novel proxies for NUE, predicted by milk mid-infrared spectra, milk yield, and parity. Genetic analyses were performed on these two phenotypes and genetic correlations were estimated between them and 30 other traits of interest. The estimated heritability and repeatability of PNUE and PNL in primiparous and multiparous ranged from 0.12 to 0.14, and from 0.40 to 0.55, respectively. The approximate genetic correlations between PNUE and 30 traits of interest ranged from −0.46 (udder depth) to 0.47 (milk yield). Additionally, the approximate genetic correlations between PNL, lower values representing less N pollution, and 30 traits of interest ranged from −0.32 (angularity) to 0.57 (direct calving ease).
Since the genetic correlations estimated between MU and PNUE were very low; the estimated breeding values (EBV) of the three features (N intake (NINT), milk true protein N (MTPN), and milk urea N yield (MUNY)) were used to build a new N efficiency index (NEI), the purpose of which was to quantitatively combine MU and PNUE. The approximate genetic correlations between the NEI and 37 other traits (economic indices) of interest were investigated. The NEI showed positive genetic correlations with production yield traits (0.08 to 0.46), and negative genetic correlations with the investigated functional traits and economic indices (−0.71 to −0.07), except for production and functional type economic indices. In addition, increasing NEI in early lactation favors a reduction in the intensity of methane emissions and increases dry matter intake but is detrimental to energy balance (given in general discussion). We then explored the potential impact of genetic selection for NEI on bulls. The daughters of the bulls with higher NEI had lower NINT and MUNY, and higher MTPN.
Genomic selection is commonly applied in animal breeding, so whether NEI and its composition traits can be used for genomic prediction was verified to select at earlier stage the dairy cows and bulls. The prediction accuracies of the NEI and its composition traits performed, using single-step genomic best linear unbiased prediction (ssGBLUP) analyses, varied from 0.48 to 0.66 for genotyped cows, from 0.35 to 0.55 for non-genotyped cows, and from 0.39 to 0.56 for bulls.
Finally, we investigated the genomic background of NEI to understand better its genetic variability. So, the genomic regions associated with NEI and its composition traits were identified and the functional annotation of the identified genomic regions was analyzed. The largest explanatory genomic region of NEI was located at position 1.52-2.09 Mb of Bos taurus autosome 14, which explained 0.58% of the total additive genetic variance. The 16 key candidate genes were identified as related to NEI and its composition traits, which are mainly expressed in the milk cells, mammary, and liver tissues. Annotated quantitative trait loci (QTLs) are mostly reported to be related to milk, health, and production traits based on the identified genomic regions.
In conclusion, this study showed that it is possible to develop genetic selection for dairy cows that are both more economical and more respectful of the environment. Moreover, the developed NEI has the advantage of available phenotypes through large-scale prediction. Therefore, the NEI has the potential for routine application in dairy cattle breeding in the future, contributing a novel possibility to reduce N pollution and improve economic results for dairy producers.
