Mechanisms that contribute to variation in residual feed intake and life-cycle efficiency of beef cattle
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abstract
Since about two-thirds of the cost of producing beef is directly related to the expense of feed inputs, strategies that improve efficiency of feed utilization will substantially increase the economic viability of beef production systems. In fact, Weaber (2012) estimated that the USbeef industry could save $1 billion annually by reducing residual feed intake (RFI) by 10% (equivalent to reducing daily intake by 0.9 kg per animal). Moreover, improvements in feed efficiency will concurrently mitigate the environmental impact of beef systems through reductions in greenhouse gas emissions, and fecal excretion of nutrients. Discovery and adoption of technologies to enhance genetic merit for feed efficiency is arguably one of the most cost-effective strategies available to meet future demand for animal protein in a sustainable manner. RFI is defined as the difference between an animal's actual feed intake and expected feed intake, with efficient animals being those that consume less than expected based on requirements for maintenance (BW) and level of production (ADG). Because RFI is phenotypically independent of size and level of production, it is the preferred trait for research focused on discovery of genomic or phenotypic biomarkers associated with metabolic processes responsible for variation in feed efficiency. In beef cattle, inter-animal variation in RFI has been linked to differences in heat production, methane, composition of gain and digestibility; demonstrating that RFI is a complex trait controlled by numerous biological processes and thereby regulated by a large number of divergent genes (Herd and Arthur, 2009; Bottje and Carstens, 2009). The cost of measuring feed intake remains a key challenge to more widespread adoption of selection programs that include RFI. This has prompted numerous candidate-geneapproach (Karisa et al., 2013) and genome-wide-association (Rolf et al., 2012) studies to identify RFI QTLs for marker-assisted selection (MAS) programs. Although these studies have generated informative SNPs, their current utility for MAS programs is limited by recent research demonstrating that causative SNPs for RFI are breed or population specific (Sherman et al., 2010; Saatchi et al., 2014). Saatchi and coworkers (2014) suggested that differences in QTLs among divergent populations might be due to environmental interactions, differences in the power to detect QTLs or differences in genetic architecture of RFI variation among breeds. Thus, there is need to identify more informative genomic markers and (or) phenotypic biomarkers for more accurate and robust selection for RFI across divergent cattle populations. While our understanding of RFI in growing cattle has advanced in recent years, our knowledge of the associations between RFI in growing cattle and efficiency of mature cows is limited. Moreover, the relative ranking of phenotypic RFI in growing cattle can vary when animals are switched from low- to high-energy diets (Brown et al., 2005; Durunna et al., 2011). Given the increasing use of RFI to identify feed-efficient cattle, it will be important to better understand how phenotypic RFI is affected by changes in diet, climatic conditions and stage of maturity.
