Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows

Abstract

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (−0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (−0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.

Incorporation of feeding behaviour traits to increase the genetic gain of feed efficiency in Pietrain pigs

“…The genetic correlation between TPD and FR was strongly negative (−0.85), which indicates that the animals that spent less time feeding per visit “compensated” by higher FR. The same correlation was reported in Holstein cows (Cavani et al, 2022) (−0.97), beef cattle (Kelly et al, 2021) (−0.65), Duroc pigs (Do et al, 2013; Santiago et al, 2021) (−0.90), and Finnish Yorkshire pigs Kavlak and Uimari (2019) (−0.70). Within the group of feed efficiency traits (Table 7), a positive genetic correlation was found between FCR and RFI (0.80), similar to those obtained by Do et al (2013) and by Kavlak and Uimari (2019).…”

“…The genetic correlation between TPD and FR was strongly negative (−0.85), which indicates that the animals that spent less time feeding per visit "compensated" by higher FR. The same correlation was reported in Holstein cows (Cavani et al, 2022) T A B L E 5 Posterior mean of genetic (r g ) and phenotypic (r p ) correlations between the first period and total period of study of analysed traits with their 95% highest posterior density intervals (HPD95%). Abbreviations: DFI, daily feed intake; FBW, final body weight; FPV, feed intake per visit; FR, feeding rate per visit; NVD, number of visits per day; TPD, time spent in feeding per day; TPV, time spent in feeding per visit.…”

“…In addition, individual feeding behaviour traits such as duration and rate of feeding and consumption at each visit are recorded (de Haer et al, 1993). In this context, several studies have shown genetic and phenotypic correlations between feeding behaviour traits and feed efficiency in dairy cattle (Cavani et al, 2022), beef cattle (Kelly et al, 2021), and pigs (Chen et al, 2010; de Haer et al, 1993; Do et al, 2013; Herrera‐Cáceres et al, 2019; Kavlak & Uimari, 2019; Lu et al, 2017; Santiago et al, 2021). However, these results are inconsistent, due to the different factors that affect feeding traits, which can be related to the individual, the environment or the interaction between them.…”

Genetic analysis of feed efficiency and novel feeding behavior traits measured in group-housed broilers using electronic feeders

“…Accordingly, different animal breeding schemes have been focused on improving FE-related traits ( Herd et al, 2003 ; Willems et al, 2013 ; Patience et al, 2015 ). With the adoption of sensor technologies for automatic intake recording, it is currently possible to measure feed efficiency massively across different livestock species ( Lu et al, 2017 ; Li et al, 2020b ; Cavani et al, 2022 ). The use of electronic feeders also opens novel possibilities to investigate behavioral patterns that can be associated with feed intake in group-housed animals ( Howie et al, 2009 ).…”

Livestock species as emerging models for genomic imprinting

“…The increasing capacity to record behaviour parameters automatically for livestock precision farming makes it possible to monitor feeding behaviours of individual animals over time ( Cellier et al, 2021 ). Nowadays, investigation of feeding behaviours in livestock species is merely used as a proxy of feed efficiency phenotypes that are major traits in breeding selection schemes ( Cavani et al, 2022 ). For example, feed efficiency represents up to 50% of the breeding objectives in some European pig paternal lines ( Gilbert et al, 2017 ).…”