Breeding Dairy Cattle for Resource Efficiency and Environmental Sustainability : Final report of the A++COW -project (2019-2023)

Abstract

Cows consuming less feed than average of the population for a given level of production and body weight are considered as resource and feed efficient. The main aim of the A++Cow project, conducted during 2019 and 2023, was to develop tools to improve the feed efficiency of dairy cows through animal breeding, to increase knowledge on the genetic background of these traits and thereby improve the environmental and economic sustainability of dairy production. The development of reliable genomic breeding values for the Nordic dairy population is essential to achieve this goal. Therefore, the four main objectives of the A++Cow project were to 1) advance the development of novel phenotypes of feed efficiency, 2) model feed efficiency traits for Maintenance, Metabolic Efficiency and Metabolic Resilience breeding objectives, 3) develop single-step genomic prediction models for Nordic dairy cattle, and 4) assess the economic and environmental benefits, and disseminate the feed efficiency breeding indices. During the project data were collected from dairy cows at Luke Jokioinen research farm on i.e., feed intakes, milk production and composition, body weights and blood NEFA and BHB levels. When combining the data with the data collected in previous studies, the total data set included 148 715 feed efficiency records from 828 primiparous Nordic Red Dairy cattle cows. The first Saved Feed index was published in 2019 and the index was included into the Nordic Total Merit index in 2020. Based on the results of A++Cow project, the new single-step genomic prediction model of Maintenance, where carcass weight was included in the model, resulted in higher validation reliability and better predictive ability compared with traditional BLUP approach. Current genetic trend in metabolic body weight (MBW) appeared to be somewhat underestimated in all breeds and the new model corrected the genetic trend of MBW. The reliability of the Metabolic Efficiency genomic predictions has been rather low. This could be improved by switching to a model based on regression on expected feed intake (ReFI) that has been developed during the project and which has a better ability to describe the metabolic efficiency of a cow. In addition, more feed intake records are needed. CFIT 3D-camera imaging could offer a technological solution for recording feed intake on-farms. The accuracy of measuring feed intake by CFIT 3D-cameras was studied in the project and a correlation of 0.71 was found between the average feed intake of 4 to 7 days assessed by CFIT 3D-camera and the average feed intake measured by scales. This is a reasonable correlation, and the technology and algorithms can be further developed in future. The project assessed animal breeding as highly relevant to improve the sustainability of dairy production – a 10% improvement in resource efficiency would reduce the carbon footprint by 8% and reduce eutrophication impacts by 10%. In addition, the economic impact is significant, and we found that by including two feed efficiency traits, MBW for Maintenance and ReFI for Metabolic Efficiency, into a selection index with production traits and fertility, improved the total economic gain about 30 %. In addition, the welfare of the cows in early lactation can now be considered through the prediction of blood NEFA and BHB levels from the milk MIR spectral readings. The coefficients of determination were 0.53 and 0.63 for NEFA and BHB, respectively, indicating that the prediction models perform well for both animal breeding and herd management purposes.