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Feed efficiency measures and their relationships with production and meat quality traits in slower growing broilers
Feed efficiency measures and their relationships with production and meat quality traits in slower growing broilers Chaoliang Wen,∗ Wei Yan,∗ Jiangxia Zheng,∗ Congliang Ji,† Dexiang Zhang,† Congjiao Sun,∗,1 and Ning Yang∗,1 ∗
National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; and † Guangdong Wen’s Nanfang Poultry Breeding Co. Ltd, Yunfu 527400, Guangdong Province, China correlations between FCR and production traits than with RFI, while RFI showed a moderate and positive phenotypic correlation with abdominal fat. FCR was weakly correlated with FI and slightly positively correlated with IMF content. The correlation coefficient between RFI and FI was 0.62, and that between RFI and IMF content was close to zero. Without increasing FI, decreasing FCR could effectively enhance the growth rate and market weight with no adverse effect on meat quality. In contrast, by improving RFI, FI and abdominal fat mass were significantly reduced and thus increased the yield with no unfavorable effects on meat quality. In consideration of consumer preference and overall economical benefits, RFI is a more suitable index to improve feed efficiency in slower growing broilers.
ABSTRACT Feed consumption accounts for the major cost of broiler production. Improving the efficiency of feed utilization is a primary goal in breeding strategies, although few studies have focused on slower growing broilers. Here, we recorded the feed intake (FI) during the fast-growing period (d 56 to 76) and measured the live weight, body measurements, carcass characteristics, and intramuscular fat (IMF) content of Chinese yellow broilers. Then, the residual feed intake (RFI) and feed conversion ratio (FCR) were calculated for each individual. Pair-wise phenotypic correlations were subsequently calculated between feed efficiency traits and others. Finally, we separately selected the more efficient individuals based on RFI and FCR values to evaluate the impacts on the traits of FI, growth, carcass characteristics, and meat quality. The results showed higher
Key words: feed efficiency, slower growing broiler, meat quality, carcass composition, growth 2018 Poultry Science 0:1–9 http://dx.doi.org/10.3382/ps/pey062
INTRODUCTION
was originally proposed by Koch et al. (1963) in beef cattle and first applied in chickens by Luiting (1990). Both FCR and RFI are moderately heritable in poultry (Begli et al., 2016; Xu et al., 2016; Liu et al., 2017; SellKubiak et al., 2017). Consequently, selection for either of these 2 indices can effectively improve feed usage efficiency. However, since the FCR is strongly correlated with FI and BW gain (BWG), it is difficult to select for this trait for the prediction of an actual response (Gunsett, 1984), whereas RFI appears to be independent of production traits, but moderately correlated to FCR and FI (Zhang and Aggrey, 2003; Aggrey et al., 2010; Case et al., 2012). Thus, RFI has been regarded as a desirable criterion for the genetic improvement of energy efficiency in chicken breeding in many studies (Yuan et al., 2015; Begli et al., 2016; Xu et al., 2016). Consumers, especially in East Asia, desire slower growing broilers more than commercial fast-growing strains, due to their traditional culinary customs, which demand specific meat qualities (Quentin et al., 2003; Rizzi et al., 2007; Jiang et al., 2017; Zhang et al., 2017). Relatively higher meat quality and richer flavor were the characteristics of slower growing broilers
Feed accounts for about 70% of the overall cost in the poultry industry (Willems et al., 2013), and, thus, improving feed efficiency is an important goal in poultry production. Genetic and breeding approaches are effective to enhance feed efficiency. In particular, feed utilization of commercial broilers has been dramatically improved over the last several decades through intensive artificial selection of feed efficiency traits (Siegel, 2014; Zuidhof et al., 2014; Tallentire et al., 2016), which commonly include the feed conversion ratio (FCR) and residual feed intake (RFI). The FCR of broilers is succinctly expressed as the ratio of feed intake (FI) to live weight gain (Titus et al., 1953), and RFI is defined as the difference between the true and predicted feed consumption based on multiple linear regression equations of the requirements for production and body weight (BW) maintenance over a specific period. RFI C 2018 Poultry Science Association Inc. Received November 27, 2017. 1 Corresponding author: [email protected], [email protected]
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WEN ET AL.
(Quentin et al., 2003; Zhao et al., 2007), while poor feed efficiency is their major deficiency compared to commercial fast-growing broilers. To our knowledge, however, few studies have focused on the feed efficiency of slower growing broilers, resulting in the lack of an accurate and reliable theoretical foundation for the selection of target traits in breeding programs to improve feed utilization. Therefore, the main objectives of this study were 1) to estimate phenotypic correlations between feed efficiency traits and the relevant ones, and 2) to assess the impacts of selection for FCR and RFI on the traits of FI, growth rate, carcass characteristics, and meat quality of slower growing broilers to determine more appropriate selection indices to improve the efficiency of feed use. The findings of this study are expected help to comprehensively elucidate the relative response and efficiency of direct selection for feed utilization traits and contribute towards the breeding of slower growing broilers.
MATERIALS AND METHODS Ethics Statement All the experimental procedures were conducted in accordance with the Guidelines for Experimental Animals established by the Animal Care and Use Committee of China Agricultural University.
Experimental Population and Management The slower growing broiler is a mid-sized Chinese breed that is characterized by yellow feathers and excellent meat quality with an average slaughter age of 78 days. Eggs were obtained from Guangdong Wen’s Nanfang Poultry Breeding, Co., Ltd. (Yunfu City, Guangdong Province, China) and hatched in brooders. The chicks were reared in a closed coop to regulate light and temperature during the early growth stage (wk 0 to 5). At the end of wk 5, each bird was equipped with a unique electronic chip on the middle of the shank after routine selection for feather color, physical condition, and body conformation, and then transferred to a halfopen poultry facility located in southern China (latitude 23◦ N, longitude 112◦ E). The poultry facility was equipped with a ventilation system and enclosed watering system (“nipple drinkers”). A total of 223 male chickens from one hatch was raised in an enclosed area measuring 24 m long by 5.5 m wide, which was equipped with 30 automated electronic feeding systems, 70 nipple drinkers, and a layer of wood shavings covering the floor. The floor space, as well as the feeding and watering systems, met the needs of the birds. All broilers were allowed ad libitum access to an automated electronic feeder that dispensed conventional pellets containing 2,900 kcal/kg of metabolizable energy and 190 g/kg of crude protein. Only one chicken at a time was able to enter an individual feeder. The birds had ample ac-
cess to feed, as the daily feeder occupancy throughout the trial period was less than 45%. The feeding system was validated and previously applied by Howie et al. (2009). In brief, the feeder automatically identified the electronic chip and sent an identification code to a central computer when a broiler entered and left a feeder. The system also recorded live weight, feed consumption, and the start and end times of each visit. The precision of the feeder scale was to the nearest 0.1 g, and that of the clock was to the nearest 1 second. Roughly 20 g of feed were available from the trough of each feeder at any time and were automatically replenished from the hopper of each feeder, thus effectively avoiding waste. Birds were placed immediately into the enclosed area on equipping with a unique electronic chip and were housed, but no data were recorded until 56 d of age (the start of the feeding trial) to allow the broilers to adapt to the experimental conditions and feeding system. During the experimental period, the temperature was maintained at 19 to 28◦ C with a constant 20:4-h light:dark photoperiod. Individual FI was continuously recorded during the fast-growing period from 56 to 76 d of age. Then, daily FI and total feed intake (TFI) were calculated for each bird based on the recorded data. Inaccurate and incomplete intake and output records during the experimental period were excluded from analysis. After exclusions, a total of 207 broilers was included for the subsequent studies.
Body Measurements The live weight of each cock was measured with an electronic scale (to the nearest 5 g) at the beginning (56 d of age) and end (76 d of age) of the feeding trial. The traits of body slope length (BSL; length from the front of the shoulder blade to the body trunk to the tail), fossil bone length (FBL; the length from the hypocleido-clavical joint to the caudal end of the sternum while the bird was held horizontally), breast width (BrW; the length at the widest point of the anterior end of the keel while the chicken was held on its back), shank length (SL; the length from the top of the hock joint to the foot pad), and shank circumference (SC) of all birds were measured using a measuring tape (to the nearest 1 mm) and digital calipers (to the nearest 0.01 mm) at 77 d of age.
Slaughter Surveys All chickens were euthanized by cervical dislocation followed by decapitation at the age of 78 d, and the following carcass traits were measured: subcutaneous fat thickness (SFT), abdominal fat weight (AFW, surrounding the gizzard, cloaca, and adjacent abdominal muscles), left breast muscle weight (BMW, pectoralis major and minor), and left leg weight (LW, thigh and drumstick). All body measurements were acquired with an electronic balance with a precision of 0.1 g, apart
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FEED EFFICIENCY IN SLOWER GROWING BROILERS
from AFW, which was measured with a digital caliper (to the nearest 0.01 mm). About 40 g of the pectoralis minor and thigh muscles were separately collected and stored at −20◦ C for subsequent determination of the intramuscular fat (IMF) content. The data that fell outside the mean ± 3 standard deviations (SD) were regarded as outliers and excluded from analysis. The abdominal fat percentage (AFP), single breast muscle percentage (BMP), and single leg percentage (LP) were seriatim calculated based on the records of BW (at 76 d of age), AFW, BMW, and LW.
Determination of IMF Content Before the determination of IMF content, the IMF and epimysium were trimmed from the muscle samples at room temperature, minced with a meat grinder, and dried in an oven at 65 and 105◦ C for 12 h, respectively (Cheng et al., 2015). The dried samples were then cooled to room temperature in an exsiccator and weighed to the nearest 0.1 mg. Finally, the IMF content of the dried muscle was extracted with anhydrous ether in a Soxhlet extractor, as described by Cui et al. (2012), and expressed as: IMF content =
fat weght after extraction (mg) dry weight of the muscle (g)
Calculation of Feed Efficiency Before calculation of the FCR and RFI, the data for TFI, body measurements, and IMF content lying outside 3 SD of the mean were regarded as outliers and excluded from analysis. Then, the BWG, metabolic midweight (MMW), and FCR were calculated. The RFI was estimated based on linear regression, as first proposed by Koch et al. (1963) and formerly used for chickens by Yi et al. (2015), with the following equations:
TFI ; BW G RFI = TFI − (b0 + b1 MMW + b2 BWG)
FCR =
Where: TFI was measured from 56 to 76 d of age (kg), and BWG is the BW at 76 d of age (BW76 , kg) minus BW at 56 d of age (BW56 , kg); and MMW = ( B W 56 (k g )+2 B W 76 (k g ) )0. 75 . Where: b0 is the intercept, and b1 and b2 are partial regression coefficients.
Statistical Analysis and Selection Procedure Pair-wise phenotypic correlations of feed efficiency traits with other traits were quantified using the psych package of R Project for Statistical Computing online software (https://www.r-project.org/). A probability (P) value of < 0.05 was considered statistically signif-
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icant. Genetic parameters were not estimated for each trait, since the study was based on a small population, and the chickens used in this experiment had no pedigree. To comprehensively understand the consequences of phenotypic selection for RFI and FCR on FI, growth, carcass traits, meat quality, and flavor characteristics, the 207 cocks were ranked by RFI and FCR values. Then, the lowest 10 and 20% of RFI-ranked birds, respectively, and the lowest 10 and 20% of FCR-sorted individuals were separately selected for subsequent analyses. The unpaired two-tailed t test with RStudio (Racine, 2012) was used to examine each trait between the chosen efficient individuals and the population.
RESULTS Descriptive Statistics of Traits The phenotypic mean, SD, coefficient of variation (CV), and minimum and maximum values of each trait are summarized in Table 1. Due to the missing observations and exclusion of outliers, the number of observations differed among the measured traits. The CV of all traits in the population had a wide range of 3.64 to 41.47%. The CV of FCR, FI, BWG, AFW, AFP, SFT, IMFB , and IMFL were all greater than 15%, indicating large phenotypic variations in the traits of feed efficiency, FI, growth, and fatty acid metabolism in this breed, and reflecting the absence of directional selection for these traits. The mean RFI was approximately equal to zero, since it represented residuals of a linear model. It is worth noting that the average value of FCR was 3.22, which was in good agreement with previous studies of a yellow-plumage dwarf broiler strain (3.31) by Xu et al. (2016) and a slow-growing line (3.15) by N’Dri et al. (2006). But, the FCR value of modern commercial broilers is currently < 2.0 (Siegel, 2014; Brameld and Parr, 2016; Lee and Aggrey, 2016). Therefore, improving feed efficiency is an extremely urgent task in breeding strategies for slower growing broilers.
Phenotypic Correlations The phenotypic correlation coefficients of feed efficiency traits with others are presented in Table 2. A moderate and positive correlation (0.34) was found between RFI and FCR. The Pearson correlation coefficient between RFI and TFI was positive (0.62), while that between FCR and TFI was low and negative (−0.26). As expected, RFI was phenotypically uncorrelated with BWG, BMW, and LW. However, there was a high negative relationship (−0.73) between FCR and BWG, and FCR had a moderate and negative correlation coefficient with BMW (−0.42) and LW (−0.42). Also, RFI had a moderate and positive phenotypic correlation with AFW (0.41) and AFP (0.44), and a weak negative correlation with BMP (−0.18) and LP (−0.15), while the correlation coefficients of FCR with AFW, AFP, BMP, and LP were close to zero (−0.11
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WEN ET AL. Table 1. Descriptive statistics for all measured traits. Traits1 RFI (kg) FCR TFI (kg) BWG (kg) AFW (g) AFP (%) SFT (mm) BMW (g) BMP (%) LW (g) LP (%) IMFB (mg/g) IMFL (mg/g) BW76 (kg) BSL (cm) FBL (cm) BrW (cm) SL (cm) SC (cm) BW56 (kg) MMW(kg0.75 )
Number
Mean
SD
CV (%)
204 204 204 207 206 206 205 205 205 206 206 198 198 207 206 204 203 205 206 207 207
0.00 3.22 2.30 0.75 45.56 1.94 2.23 118.70 5.13 240.32 10.38 22.10 31.55 2.32 25.69 10.34 9.02 9.28 5.43 1.57 1.64
0.20 0.77 0.36 0.18 18.89 0.73 0.56 16.08 0.51 26.88 0.67 5.24 9.26 0.23 1.38 0.58 0.81 0.34 0.29 0.12 0.10
Maximum
Minimum
0.98 8.72 3.26 1.15 97.90 3.73 3.60 152.80 6.53 300.10 13.51 39.03 57.61 2.89 28.50 12.00 11.20 10.15 6.18 1.83 1.89
− 0.65 2.27 1.33 0.18 3.40 0.20 1.09 65.90 2.45 179.90 6.75 12.50 13.61 1.65 22.00 9.00 6.50 8.10 4.70 1.17 1.31
– 23.91 15.74 23.77 41.47 37.71 24.98 13.55 9.86 11.18 6.47 23.71 29.36 10.02 5.38 5.60 9.03 3.64 5.26 7.87 6.34
1 RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight.
Table 2. Pearson correlation coefficients between feed efficiency traits and others. Traits1 FCR RFI FCR
TFI
BWG
AFW
AFP
SFT
BMW
BMP
LW
LP
IMFB IMFL
BW76
BSL
FBL
SL
SC
BrW BW56 MBW
0.34∗∗ 0.62∗∗ 0.07 0.41∗∗ 0.44∗∗ 0.07 − 0.05 − 0.18∗∗ − 0.01 − 0.15∗ 0.13 0.07 0.09 − 0.11 0.02 − 0.05 0.05 0.11 0.07 0.09 – − 0.26∗∗ − 0.73∗∗ − 0.11 − 0.02 − 0.15∗ − 0.42∗∗ − 0.11 − 0.42∗∗ 0.01 0.16∗ 0.15∗ − 0.46∗∗ − 0.27∗∗ − 0.12 − 0.23∗∗ − 0.13 − 0.07 0.19∗∗ − 0.26∗∗
∗
P < 0.05, ∗∗ P < 0.01. RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight. 1
to 0.01, P > 0.05). In contrast to RFI, which was not obviously phenotypically correlated with IMFB and IMFL (0.13 and 0.07, P > 0.05), FCR showed slight and positive correlations with IMFB and IMFL (0.16 and 0.15, respectively, P < 0.05). As opposed to the irrelevant relationships between RFI and body measurements (−0.11 to 0.09, P > 0.05), FCR was slightly and positively correlated to BSL (−0.27) and SL (−0.23).
Phenotypic Differences Between the Efficient Birds and the Population There were 7 (35%) and 17 birds (42.5%) both in RFI- and FCR-ranked groups of the top 10 and 20%, respectively. The daily FI of RFI-efficient birds (individuals with the lowest 10 and 20% RFI values) was clearly lower than the population mean during the feeding experiment, while there was no distinct difference between the FCR-efficient birds (individuals with the lowest 10 and 20% FCR values) and the population mean (Figure 1A and B). Meanwhile, the top 20 and
40 chickens with low RFI had lower TFI than the population with a comparatively large (1.92 and 2.03 vs. 2.30 kg, respectively, P < 0.01, Table 3; Figure 2A and B), whereas this situation was not found between FCR-efficient bird groups and the population (2.30 and 2.32 vs. 2.30 kg, respectively, P > 0.05, Table 4; Figure 2A and B). The change curves of BW based on the automated feeding system records are shown in Figure 1C and D. The linear curves of BW indicated that the population was in a rapid growth period during the feeding trial. Furthermore, RFI- and FCR-efficient birds exerted different growth rates with the aging process. Specifically, the RFI-efficient birds seemingly displayed lower growth rates than the FCR-efficient birds. The FCR-efficient groups had both higher (P < 0.01) BWG (0.92 and 0.90 vs. 0.75 kg, respectively) and BW76 (2.42 and 2.42 vs. 2.32 kg, respectively) than the population mean (Table 4; Figure 2C–F). However, there were no obvious differences (P > 0.05) between the RFI-efficient birds and the population mean for BW and BW76 (Table 3; Figure 2C–F).
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Figure 1. Change curves of feed intake (FI) and body weight (BW) with the aging process. The left plots displays the daily FI change over the 3-week test period among the population and favorable broilers selected by RFI and FCR values. The right plots describe BW changes based on automated feeding system records. The mean values for each curve are indicated in the legend. The chicken FI was relatively low at 56 and 76 d of age, because the feeding systems began recording weight at 56 d of age and stopped recording before 76 d of age; thus, the valid recording time was < 24 hours.
Some discrepancies were observed (P < 0.05) in AFW and AFP between the RFI-efficient birds and the population (34.90 and 35.43 vs. 45.56 g; and 1.53 and 1.53 vs. 1.94%, respectively) (Table 3). There were no clear differences (P > 0.05) in SFT, BMW, BMP, LW, LP, IMFB , IMFL , and body measurements (BSL, FBL, BrW, SL, and SC) between the RFI-efficient birds and the population. The comparisons between the FCRefficient birds and the population are displayed in Table 4. BMW and LW were greater (P < 0.05) in the 2 FCR-efficient groups than the population (124.64 and 124.46 vs. 118.70 g; and 253.34 and 253.43 vs. 240.32 g, respectively), while we found no differences (P > 0.05) in BMP and LP between the FCR-efficient birds and the population. Meanwhile, there were no obvious differences (P > 0.05) in AFW, AFP, SFT, FBL, BrW, SL, and SC between the FCR-efficient groups and the population. No obvious discrepancy (P > 0.05) of IMFB was found between the FCR-efficient group (top 20%) and the population, while an apparent difference was seen in IMFB between the FCR-efficient group (top 10%) and the population (19.63 vs. 22.10 mg/g, respectively, P < 0.05). Although there was no statistical difference (P > 0.05) in IMFL between the population and the 2 FCR-efficient groups, IMFL clearly decreased in the FCR-efficient (20%) birds (28.83 vs. 31.55 mg/g, respectively, P = 0.103). In additional, an apparent discrepancy was observed in BSL between the FCRefficient (10%) birds and the population (26.47 vs. 25.69 cm, respectively, P < 0.01).
DISCUSSION In agreement with previous studies with slow-growing broilers (N’Dri et al., 2006; Xu et al., 2016), the FCR values in the current study were much higher than that of commercial fast-growing broilers (Siegel, 2014; Brameld and Parr, 2016; Lee and Aggrey, 2016). Thus, the poor feed efficiency was indeed the major deficiency of slow-growing broilers, which had enormous potential to improve. According to estimated heritability data of FCR and RFI in literatures, heritability of RFI in broilers was about 0.41, and FCR heritability ranged from 0.22 to 0.49 (Aggrey et al., 2010; Howie et al., 2011; Xu et al., 2016; Begli et al.; Liu et al., 2017). Hence, RFI and FCR have moderate heritability, indicating the selection for either of these 2 indices (FCR and RFI) can undoubtedly increase the feed efficiency. Then, the next problem is to identify which index is more appropriate to be used in breeding strategy for the benefit maximization. Deep reasons for the comparison of RFI and FCR in breeding strategy are the discrepant correlations between feed efficiency traits (RFI and FCR) and other traits, indicating that the direct selection of RFI or FCR would influence different traits incidentally. Our results showed a highly negative correlation between FCR and BW, which also phenotypically and genetically existed in commercial broilers (Zhang and Aggrey, 2003) and turkeys (Case et al., 2012; Willems et al., 2013). Besides, FCR was also negatively
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WEN ET AL. Table 3. Comparisons between the lowest RFI birds and the population. Mean ± SD1 Traits
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RFI (kg) FCR TFI (kg) AFW (g) AFP (%) BWG (kg) SFT (mm) BMW (g) BMP (%) LW (g) LP (%) IMFB (mg/g) IMFL (mg/g) BW76 (kg) BSL (cm) FBL (cm) BrW (cm) SL (cm) SC (cm) BW56 (kg) MMW (kg0.75 )
Lowest 10% of RFI − 0.30 2.84 1.92 34.90 1.53 0.70 2.10 114.68 5.12 233.35 10.39 20.91 31.94 2.24 25.70 10.24 8.64 9.27 5.41 1.54 1.61
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
2
∗∗
0.11 0.41∗∗ 0.30∗∗ 20.48∗ 0.86∗ 0.19 0.76 14.80 0.45 29.33 0.53 5.16 10.48 0.22 1.29 0.72 0.87 0.28 0.28 0.11 0.09
Lowest 20% of RFI3 − 0.24 2.85 2.03 35.43 1.53 0.73 2.15 118.80 5.20 240.79 10.51 21.00 31.01 2.29 25.84 10.34 8.76 9.28 5.43 1.56 1.63
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
∗∗
0.10 0.34∗∗ 0.28∗∗ 17.88∗∗ 0.74∗∗ 0.16 0.65 13.76 0.46 27.46 0.50 5.51 9.95 0.20 1.40 0.65 0.86 0.31 0.27 0.11 0.09
Population 0.00 3.22 2.30 45.56 1.94 0.75 2.23 118.70 5.12 240.32 10.38 22.10 31.55 2.32 25.69 10.34 9.02 9.28 5.43 1.57 1.64
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.20 0.77 0.36 18.89 0.73 0.18 0.56 16.08 0.51 26.88 0.67 5.24 9.26 0.23 1.38 0.58 0.81 0.34 0.29 0.12 0.10
Within each row, ∗ P < 0.05 and ∗∗ P < 0.01 for RFI-sorted group vs. the population (t test). n = 20. 3 n = 40. 4 RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight. 1 2
correlated to FI in phenotype, while the genetic relationship was slightly positive both in commercial broilers and turkeys (Zhang and Aggrey, 2003; Willems et al., 2013). Therefore, selection for lower FCR can significantly increase BW, but it had no distinct effect on FI. On the contrary, we found a high and positive correlation between RFI and FI in our research, while the phenotypic correlation coefficient between RFI and BW was almost zero. These results were consistent with those of a former study between RFI and FI, and RFI and BW in yellow broilers (Xu et al., 2016). In general, selection for RFI can improve the feed efficiency of slower growing broilers by less FI and supplying the same amount of meat product, while if selecting FCR, birds would produce more meat product with the same amount of FI. The success of poultry meat production is not only to improve feed efficiency, but also to increase the proportion of muscle and reduce that of fat (Zerehdaran et al., 2004). Muscle and fat are the main components of the broiler carcass. However, abdominal and subcutaneous fat with little economic value are recognized as the main sources of waste in poultry production (Tavaniello et al., 2014). Also, excessive fat affects consumer acceptance and product sales. Abdominal fat grows at a faster rate than other fat tissues and is highly correlated to total carcass lipids (Fouad and El-Senousey, 2014), hence abdominal fat mostly reflects excessive fat deposition (Zerehdaran et al., 2004). In the current study,
FCR had high and positive phenotypic correlations with BMW and LW, but not with BMP, LP, AFW, and AFP. Moreover, single BMW and LW were significantly increased when selecting FCR, while there were no distinct changes in the percentages of breast and leg muscle, or abdominal and subcutaneous fat. These results implied that the carcass compositions of muscle and fat remained relatively unchanged. We observed abdominal fat had a moderate and positive phenotypic correlation to RFI in the present study. When selecting low RFI, the weight and percentage of abdominal fat significantly reduced. These results indicated that RFI improvement was accompanied with increased yield of edible carcass meat and reduced deposition of abdominal fat (Siegel, 2014). Several studies have reported that lower FI can effectively reduce the level of undesirable fat in broilers (Richards et al., 2003; Yang et al., 2010). Richards et al. (2003) and Yang et al. (2010) suggested that lower feed consumption can reduce fat deposition by inhibiting the activity of some lipogenic enzymes in the livers of broilers. It can be concluded that the accumulation of abdominal fat in chickens could be indirectly reduced by selecting lower RFI. Although abdominal and subcutaneous fat are useless, IMF is favorable. As fat is the precursor of flavor substance in meat and the IMF can enhance juiciness and tenderness of the meat, it has been established that IMF plays a major role in determining the quality and flavor of meat (Tavaniello et al., 2014; Zhang
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Figure 2. Phenotypic differences in total feed intake (TFI), body weight (BW), and BW at 76 d of age between the more efficient birds based on RFI or FCR values and the population. ∗∗ P < 0.01. The dashed line in each plot represents the average value of the corresponding traits of the population.
et al., 2017). The IMF content has a low to moderate heritability, ranging from 0.11 to 0.22 (Zhao et al., 2007; Chen et al., 2008; Jiang et al., 2017). A previous study indicated that IMF content could effectively be improved through appropriate selection strategy (Zhao et al., 2007). In the current study, a weak phenotypic correlation was observed between FCR and IMF content; the birds with the lowest 20% of FCR had a lower IMF content of the thigh muscle than the population. And when we selected the lowest 10% of FCR from the population, the IMF content of the breast muscle was significantly reduced. Whereas the correlation between RFI and IMF content was approximately zero, selection for low RFI did not change the IMF content. Because IMF is an important indicator of meat quality (Cui et al., 2012; Jiang et al., 2017), we inferred that improving feed efficiency by selecting FCR may have a negative effect on meat quality, while selection for RFI would have no influence on meat quality. In recent yr, more and more consumers prefer the specific meat qualities of slower growing broilers over those
of commercial fast-growing ones (Quentin et al., 2003; Rizzi et al., 2007; Jiang et al., 2017; Zhang et al., 2017). Therefore, the foci of slower growing chicken production are first and foremost meat quality and feed consumption, and secondly, carcass yield. However, growth rate is often associated with commercial broiler production (Zuidhof et al., 2014; Brameld and Parr, 2016), due to the correlation between growth rate and the age at market weight. The live BW over a specific period and morphometric measurements are the primarily factors in growth rate measurements (Sheng et al., 2013; Siegel, 2014; Tallentire et al., 2016). According to the change curve of BW, the lowest-RFI chickens seemingly exhibited lower growth rates than the lowest-FCR birds, while there were no significant differences in BW between the lowest-RFI birds and the population. In addition, FCR had a moderate and positive correlation with live weight, and the BW in our study was in good agreement with previous results of an intercross population (Sheng et al., 2013). The market weight and BSL were significantly increased by selecting low FCR.
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WEN ET AL. Table 4. Comparisons between the lowest FCR birds and the population. Mean ±SD1 Traits
4
FCR RFI (kg) TFI (kg) BWG (kg) BMW (g) BMP (%) LW (g) LP (%) AFW (g) AFP (%) SFT (mm) IMFB (mg/g) IMFL (mg/g) BW76 (kg) BSL (cm) FBL (cm) BrW (cm) SL (cm) SC (cm) BW56 (kg) MMW(kg0.75 )
Lowest 10% of FCR 2.50 − 0.19 2.30 0.92 124.64 5.14 253.34 10.44 41.31 1.69 2.33 19.63 30.35 2.42 26.47 10.44 9.05 9.30 5.55 1.50 1.66
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
2
∗∗
0.09 0.15∗∗ 0.26 0.10∗∗ 13.58∗ 0.43 24.86∗ 0.53 15.86 0.58 0.60 4.80∗ 10.12 0.16∗∗ 1.27∗ 0.60 0.75 0.33 0.28 0.10∗∗ 0.08
Lowest 20% of FCR3 2.59 − 0.15 2.32 0.90 124.46 5.15 253.43 10.47 42.36 1.73 2.23 20.95 28.83 2.42 26.01 10.44 9.01 9.33 5.53 1.52 1.66
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
∗∗
0.12 0.13∗∗ 0.29 0.11∗∗ 13.43∗ 0.49 23.94∗∗ 0.53 16.99 0.62 0.61 6.03 9.26 0.19∗∗ 1.34 0.57 0.76 0.29 0.27 0.11∗ 0.09
Population 3.22 0.00 2.30 0.75 118.70 5.13 240.32 10.38 45.56 1.94 2.23 22.10 31.55 2.32 25.69 10.34 9.02 9.28 5.43 1.57 1.64
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.77 0.20 0.36 0.18 16.08 0.51 26.88 0.67 18.89 0.73 0.56 5.24 9.26 0.23 1.38 0.58 0.81 0.34 0.29 0.12 0.10
Within each row, ∗ P < 0.05 and ∗∗ P < 0.01 for FCR-sorted group vs. the population (t test). n = 20. 3 n = 40. 4 RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight. 1 2
Nevertheless, improving RFI would result in a slight decrease in growth rate, but no significant decrease in market weight, whereas decreasing the FCR would obviously enhance the growth rate. In conclusion, selection for both RFI and FCR can effectively increase feed efficiency, while improving the FCR can clearly enhance the growth rate and market weight without increasing FI and having no significant effect on carcass composition. Moreover, a lower FCR has a tendency to reduce the IMF content, thereby decreasing meat quality. By contrast, selecting low RFI could significantly reduce FI and abdominal fat content, and had no unfavorable impact on meat quality. Improving feed efficiency and maintaining meat quality were both of significance in the production of slower growing broilers. Therefore, as compared with the FCR, the RFI may be a more suitable index to improve feed efficiency in slower growing broiler. These results are important for the continued development of strategies to improve feed efficiency in broiler breeding and production.
ACKNOWLEDGMENTS The current research was funded by Programs for Changjiang Scholars and Innovative Research in Universities ( IRT 15R62) and Chinese Universities Scientific Fund ( 2017TC034).
REFERENCES Aggrey, S. E., A. B. Karnuah, B. Sebastian, and N. B. Anthony. 2010. Genetic properties of feed efficiency parameters in meattype chickens. Genet. Sel. Evol 42:25. Begli, H. E., R. V. Torshizi, A. A. Masoudi, A. Ehsani, and J. Jensen. 2016. Longitudinal analysis of body weight, feed intake and residual feed intake in F2 chickens. Livest. Sci 184:28– 34. Brameld, J. M., and T. Parr. 2016. Improving efficiency in meat production. Proc. Nutr. Soc. 75:242–246. Case, L. A., B. J. Wood, and S. P. Miller. 2012. The genetic parameters of feed efficiency and its component traits in the turkey (Meleagris gallopavo). Genet. Sel. Evol. 44:2. Chen, J. L., G. P. Zhao, M. Q. Zheng, J. Wen, and N. Yang. 2008. Estimation of genetic parameters for contents of intramuscular fat and inosine-5’-monophosphate and carcass traits in Chinese Beijing-You chickens. Poult. Sci 87:1098–1104. Cheng, W., J. Cheng, D. Sun, and H. Pu. 2015. Marbling analysis for evaluating meat quality: Methods and techniques. Compr. Rev. Food. Sci. F. 14:523–535. Cui, H., R. Liu, G. Zhao, M. Zheng, J. Chen, and J. Wen. 2012. Identification of differentially expressed genes and pathways for intramuscular fat deposition in pectoralis major tissues of fastand slow-growing chickens. BMC Genomics 13:213. Fouad, A. M., and H. K El-Senousey. 2014. Nutritional factors affecting abdominal fat deposition in poultry: A review. Asian Austral J. Anim. Sci. 27:1057–1068. Gunsett, F. C. 1984. Linear index selection to improve traits defined as ratios. J. Anim. Sci. 59:1185–1193. Howie, J. A., B. J. Tolkamp, S. Avendano, and I. Kyriazakis. 2009. The structure of feeding behavior in commercial broiler lines selected for different growth rates. Poult. Sci. 88: 1143–1150. Howie, J. A., S. Avendano, B. J. Tolkamp, and I. Kyriazakis. 2011. Genetic parameters of feeding behavior traits and their
Downloaded from https://academic.oup.com/ps/advance-article-abstract/doi/10.3382/ps/pey062/4970823 by Serials Section, Dixson Library user on 21 April 2018
FEED EFFICIENCY IN SLOWER GROWING BROILERS relationship with live performance traits in modern broiler lines. Poult. Sci. 90:1197–1205. Jiang, M., W. L. Fan, S. Y. Xing, J. Wang, P. Li, R. R. Liu, Q. H. Li, M. Q. Zheng, H. X. Cui, J. Wen, and G. P. Zhao. 2017. Effects of balanced selection for intramuscular fat and abdominal fat percentage and estimates of genetic parameters. Poult. Sci. 96:282–287. Koch, R. M., L. A. Swiger, D. Chambers, and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486–494. Lee, J., and S. E. Aggrey. 2016. Transcriptomic differences of genes in the avian target of rapamycin (avTOR) pathway in a divergent line of meat-type chickens selected for feed efficiency. Genet. Mol. Res. 15. doi: 10.4238/gmr.15027120. Liu, T., C. Luo, J. Wang, J. Ma, D. Shu, M. S. Lund, G. Su, and H. Qu. 2017. Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens. PloS One 12:e173620. Luiting, P. 1990. Genetic variation of energy partitioning in laying hens: Causes of variation in residual feed consumption. Worlds Poult. Sci. J. 46:133–152. N’Dri, A. L., S. Mignon-Grasteau, N. Sellier, M. Tixier-Boichard, and C. Beaumont. 2006. Genetic relationships between feed conversion ratio, growth curve and body composition in slow-growing chickens. Br. Poult. Sci. 47:273–280. Quentin, M., I. Bouvarel, C. Berri, E. Le Bihan-Duval, E. Baeza, Y. Jego, and M. Picard. 2003. Growth, carcass composition and meat quality response to dietary concentrations in fast-, mediumand slow-growing commercial broilers. Anim. Res. 52:65–77. Racine, J. S. 2012. RStudio: A platform-independent IDE for R and Sweave. J. Appl. Econ. 27:167–172. Richards, M. P., S. M. Poch, C. N. Coon, R. W. Rosebrough, C. M. Ashwell, and J. P. McMurtry. 2003. Feed restriction significantly alters lipogenic gene expression in broiler breeder chickens. J. Nutr. 133:707–715. Rizzi, C., A. Marangon, and G. M. Chiericato. 2007. Effect of genotype on slaughtering performance and meat physical and sensory characteristics of organic laying hens. Poult. Sci. 86: 128–135. Sell-Kubiak, E., K. Wimmers, H. Reyer, and T. Szwaczkowski. 2017. Genetic aspects of feed efficiency and reduction of environmental footprint in broilers: A review. J. Appl. Genet. 58: 487–498. Sheng, Z., M. E. Pettersson, X. Hu, C. Luo, H. Qu, D. Shu, X. Shen, O. Carlborg, and N. Li. 2013. Genetic dissection of growth traits in a Chinese indigenous × commercial broiler chicken cross. BMC Genomics 14:151. Siegel, P. B. 2014. Evolution of the modern broiler and feed efficiency. Annu. Rev. Anim. Biosci. 2:375–385.
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Tallentire, C. W., I. Leinonen, and I. Kyriazakis. 2016. Breeding for efficiency in the broiler chicken: A review. Agron. Sustain. Dev. 36:66. Tavaniello, S., G. Maiorano, M. Siwek, S. Knaga, A. Witkowski, D. Di Memmo, and M. Bednarczyk. 2014. Growth performance, meat quality traits, and genetic mapping of quantitative trait loci in 3 generations of Japanese quail populations (Coturnix japonica). Poult. Sci. 93:2129–2140. Titus, H. W., J. A. L. Mehring, and J. H. Brumbaugh. 1953. Variation of feed conversion. Poul. Sci. 32:1074–1077. Willems, O. W., S. P. Miller, and B. J. Wood. 2013. Aspects of selection for feed efficiency in meat producing poultry. Worlds Poult. Sci. J. 69:77–88. Willems, O. W., S. P. Miller, and B. J. Wood. 2013. Assessment of residual body weight gain and residual intake and body weight gain as feed efficiency traits in the turkey (Meleagris gallopavo). Genet. Sel. Evol. 45:26. Xu, Z., C. Ji, Y. Zhang, Z. Zhang, Q. Nie, J. Xu, D. Zhang, and X. Zhang. 2016. Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens. BMC Genomics 17:594. Yang, X., J. Zhuang, K. Rao, X. Li, and R. Zhao. 2010. Effect of early feed restriction on hepatic lipid metabolism and expression of lipogenic genes in broiler chickens. Res. Vet. Sci. 89:438–444. Yi, G., J. Yuan, H. Bi, W. Yan, N. Yang, and L. Qu. 2015. In-depth duodenal transcriptome survey in chickens with divergent feed efficiency using RNA-Seq. PLoS One 10:e136765. Yuan, J., T. Dou, M. Ma, G. Yi, S. Chen, L. Qu, M. Shen, L. Qu, K. Wang, and N. Yang. 2015. Genetic parameters of feed efficiency traits in laying period of chickens. Poult. Sci. 94:1470–1475. Zerehdaran, S., A. L. Vereijken, J. A. van Arendonk, and E. H. van der Waaijt. 2004. Estimation of genetic parameters for fat deposition and carcass traits in broilers. Poul. Sci. 83:521–525. Zhang, M., F. B. Yan, F. Li, K. R. Jiang, D. H. Li, R. L. Han, Z. J. Li, R. R. Jiang, X. J. Liu, X. T. Kang, and G. R. Sun. 2017. Genome-wide DNA methylation profiles reveal novel candidate genes associated with meat quality at different age stages in hens. Sci. Rep. 7:45564. Zhang, W., and S. E. Aggrey. 2003. Genetic variation in feed utilization efficiency of meat-type chickens. Worlds Poult. Sci. J. 59:328–339. Zhao, G. P., J. L. Chen, M. Q. Zheng, J. Wen, and Y. Zhang. 2007. Correlated responses to selection for increased intramuscular fat in a Chinese quality chicken line. Poul. Sci. 86:2309–2314. Zuidhof, M. J., B. L. Schneider, V. L. Carney, D. R. Korver, and F. E. Robinson. 2014. Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 20051. Poult. Sci. 93:2970–2982.
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National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; and † Guangdong Wen’s Nanfang Poultry Breeding Co. Ltd, Yunfu 527400, Guangdong Province, China correlations between FCR and production traits than with RFI, while RFI showed a moderate and positive phenotypic correlation with abdominal fat. FCR was weakly correlated with FI and slightly positively correlated with IMF content. The correlation coefficient between RFI and FI was 0.62, and that between RFI and IMF content was close to zero. Without increasing FI, decreasing FCR could effectively enhance the growth rate and market weight with no adverse effect on meat quality. In contrast, by improving RFI, FI and abdominal fat mass were significantly reduced and thus increased the yield with no unfavorable effects on meat quality. In consideration of consumer preference and overall economical benefits, RFI is a more suitable index to improve feed efficiency in slower growing broilers.
ABSTRACT Feed consumption accounts for the major cost of broiler production. Improving the efficiency of feed utilization is a primary goal in breeding strategies, although few studies have focused on slower growing broilers. Here, we recorded the feed intake (FI) during the fast-growing period (d 56 to 76) and measured the live weight, body measurements, carcass characteristics, and intramuscular fat (IMF) content of Chinese yellow broilers. Then, the residual feed intake (RFI) and feed conversion ratio (FCR) were calculated for each individual. Pair-wise phenotypic correlations were subsequently calculated between feed efficiency traits and others. Finally, we separately selected the more efficient individuals based on RFI and FCR values to evaluate the impacts on the traits of FI, growth, carcass characteristics, and meat quality. The results showed higher
Key words: feed efficiency, slower growing broiler, meat quality, carcass composition, growth 2018 Poultry Science 0:1–9 http://dx.doi.org/10.3382/ps/pey062
INTRODUCTION
was originally proposed by Koch et al. (1963) in beef cattle and first applied in chickens by Luiting (1990). Both FCR and RFI are moderately heritable in poultry (Begli et al., 2016; Xu et al., 2016; Liu et al., 2017; SellKubiak et al., 2017). Consequently, selection for either of these 2 indices can effectively improve feed usage efficiency. However, since the FCR is strongly correlated with FI and BW gain (BWG), it is difficult to select for this trait for the prediction of an actual response (Gunsett, 1984), whereas RFI appears to be independent of production traits, but moderately correlated to FCR and FI (Zhang and Aggrey, 2003; Aggrey et al., 2010; Case et al., 2012). Thus, RFI has been regarded as a desirable criterion for the genetic improvement of energy efficiency in chicken breeding in many studies (Yuan et al., 2015; Begli et al., 2016; Xu et al., 2016). Consumers, especially in East Asia, desire slower growing broilers more than commercial fast-growing strains, due to their traditional culinary customs, which demand specific meat qualities (Quentin et al., 2003; Rizzi et al., 2007; Jiang et al., 2017; Zhang et al., 2017). Relatively higher meat quality and richer flavor were the characteristics of slower growing broilers
Feed accounts for about 70% of the overall cost in the poultry industry (Willems et al., 2013), and, thus, improving feed efficiency is an important goal in poultry production. Genetic and breeding approaches are effective to enhance feed efficiency. In particular, feed utilization of commercial broilers has been dramatically improved over the last several decades through intensive artificial selection of feed efficiency traits (Siegel, 2014; Zuidhof et al., 2014; Tallentire et al., 2016), which commonly include the feed conversion ratio (FCR) and residual feed intake (RFI). The FCR of broilers is succinctly expressed as the ratio of feed intake (FI) to live weight gain (Titus et al., 1953), and RFI is defined as the difference between the true and predicted feed consumption based on multiple linear regression equations of the requirements for production and body weight (BW) maintenance over a specific period. RFI C 2018 Poultry Science Association Inc. Received November 27, 2017. 1 Corresponding author: [email protected], [email protected]
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WEN ET AL.
(Quentin et al., 2003; Zhao et al., 2007), while poor feed efficiency is their major deficiency compared to commercial fast-growing broilers. To our knowledge, however, few studies have focused on the feed efficiency of slower growing broilers, resulting in the lack of an accurate and reliable theoretical foundation for the selection of target traits in breeding programs to improve feed utilization. Therefore, the main objectives of this study were 1) to estimate phenotypic correlations between feed efficiency traits and the relevant ones, and 2) to assess the impacts of selection for FCR and RFI on the traits of FI, growth rate, carcass characteristics, and meat quality of slower growing broilers to determine more appropriate selection indices to improve the efficiency of feed use. The findings of this study are expected help to comprehensively elucidate the relative response and efficiency of direct selection for feed utilization traits and contribute towards the breeding of slower growing broilers.
MATERIALS AND METHODS Ethics Statement All the experimental procedures were conducted in accordance with the Guidelines for Experimental Animals established by the Animal Care and Use Committee of China Agricultural University.
Experimental Population and Management The slower growing broiler is a mid-sized Chinese breed that is characterized by yellow feathers and excellent meat quality with an average slaughter age of 78 days. Eggs were obtained from Guangdong Wen’s Nanfang Poultry Breeding, Co., Ltd. (Yunfu City, Guangdong Province, China) and hatched in brooders. The chicks were reared in a closed coop to regulate light and temperature during the early growth stage (wk 0 to 5). At the end of wk 5, each bird was equipped with a unique electronic chip on the middle of the shank after routine selection for feather color, physical condition, and body conformation, and then transferred to a halfopen poultry facility located in southern China (latitude 23◦ N, longitude 112◦ E). The poultry facility was equipped with a ventilation system and enclosed watering system (“nipple drinkers”). A total of 223 male chickens from one hatch was raised in an enclosed area measuring 24 m long by 5.5 m wide, which was equipped with 30 automated electronic feeding systems, 70 nipple drinkers, and a layer of wood shavings covering the floor. The floor space, as well as the feeding and watering systems, met the needs of the birds. All broilers were allowed ad libitum access to an automated electronic feeder that dispensed conventional pellets containing 2,900 kcal/kg of metabolizable energy and 190 g/kg of crude protein. Only one chicken at a time was able to enter an individual feeder. The birds had ample ac-
cess to feed, as the daily feeder occupancy throughout the trial period was less than 45%. The feeding system was validated and previously applied by Howie et al. (2009). In brief, the feeder automatically identified the electronic chip and sent an identification code to a central computer when a broiler entered and left a feeder. The system also recorded live weight, feed consumption, and the start and end times of each visit. The precision of the feeder scale was to the nearest 0.1 g, and that of the clock was to the nearest 1 second. Roughly 20 g of feed were available from the trough of each feeder at any time and were automatically replenished from the hopper of each feeder, thus effectively avoiding waste. Birds were placed immediately into the enclosed area on equipping with a unique electronic chip and were housed, but no data were recorded until 56 d of age (the start of the feeding trial) to allow the broilers to adapt to the experimental conditions and feeding system. During the experimental period, the temperature was maintained at 19 to 28◦ C with a constant 20:4-h light:dark photoperiod. Individual FI was continuously recorded during the fast-growing period from 56 to 76 d of age. Then, daily FI and total feed intake (TFI) were calculated for each bird based on the recorded data. Inaccurate and incomplete intake and output records during the experimental period were excluded from analysis. After exclusions, a total of 207 broilers was included for the subsequent studies.
Body Measurements The live weight of each cock was measured with an electronic scale (to the nearest 5 g) at the beginning (56 d of age) and end (76 d of age) of the feeding trial. The traits of body slope length (BSL; length from the front of the shoulder blade to the body trunk to the tail), fossil bone length (FBL; the length from the hypocleido-clavical joint to the caudal end of the sternum while the bird was held horizontally), breast width (BrW; the length at the widest point of the anterior end of the keel while the chicken was held on its back), shank length (SL; the length from the top of the hock joint to the foot pad), and shank circumference (SC) of all birds were measured using a measuring tape (to the nearest 1 mm) and digital calipers (to the nearest 0.01 mm) at 77 d of age.
Slaughter Surveys All chickens were euthanized by cervical dislocation followed by decapitation at the age of 78 d, and the following carcass traits were measured: subcutaneous fat thickness (SFT), abdominal fat weight (AFW, surrounding the gizzard, cloaca, and adjacent abdominal muscles), left breast muscle weight (BMW, pectoralis major and minor), and left leg weight (LW, thigh and drumstick). All body measurements were acquired with an electronic balance with a precision of 0.1 g, apart
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FEED EFFICIENCY IN SLOWER GROWING BROILERS
from AFW, which was measured with a digital caliper (to the nearest 0.01 mm). About 40 g of the pectoralis minor and thigh muscles were separately collected and stored at −20◦ C for subsequent determination of the intramuscular fat (IMF) content. The data that fell outside the mean ± 3 standard deviations (SD) were regarded as outliers and excluded from analysis. The abdominal fat percentage (AFP), single breast muscle percentage (BMP), and single leg percentage (LP) were seriatim calculated based on the records of BW (at 76 d of age), AFW, BMW, and LW.
Determination of IMF Content Before the determination of IMF content, the IMF and epimysium were trimmed from the muscle samples at room temperature, minced with a meat grinder, and dried in an oven at 65 and 105◦ C for 12 h, respectively (Cheng et al., 2015). The dried samples were then cooled to room temperature in an exsiccator and weighed to the nearest 0.1 mg. Finally, the IMF content of the dried muscle was extracted with anhydrous ether in a Soxhlet extractor, as described by Cui et al. (2012), and expressed as: IMF content =
fat weght after extraction (mg) dry weight of the muscle (g)
Calculation of Feed Efficiency Before calculation of the FCR and RFI, the data for TFI, body measurements, and IMF content lying outside 3 SD of the mean were regarded as outliers and excluded from analysis. Then, the BWG, metabolic midweight (MMW), and FCR were calculated. The RFI was estimated based on linear regression, as first proposed by Koch et al. (1963) and formerly used for chickens by Yi et al. (2015), with the following equations:
TFI ; BW G RFI = TFI − (b0 + b1 MMW + b2 BWG)
FCR =
Where: TFI was measured from 56 to 76 d of age (kg), and BWG is the BW at 76 d of age (BW76 , kg) minus BW at 56 d of age (BW56 , kg); and MMW = ( B W 56 (k g )+2 B W 76 (k g ) )0. 75 . Where: b0 is the intercept, and b1 and b2 are partial regression coefficients.
Statistical Analysis and Selection Procedure Pair-wise phenotypic correlations of feed efficiency traits with other traits were quantified using the psych package of R Project for Statistical Computing online software (https://www.r-project.org/). A probability (P) value of < 0.05 was considered statistically signif-
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icant. Genetic parameters were not estimated for each trait, since the study was based on a small population, and the chickens used in this experiment had no pedigree. To comprehensively understand the consequences of phenotypic selection for RFI and FCR on FI, growth, carcass traits, meat quality, and flavor characteristics, the 207 cocks were ranked by RFI and FCR values. Then, the lowest 10 and 20% of RFI-ranked birds, respectively, and the lowest 10 and 20% of FCR-sorted individuals were separately selected for subsequent analyses. The unpaired two-tailed t test with RStudio (Racine, 2012) was used to examine each trait between the chosen efficient individuals and the population.
RESULTS Descriptive Statistics of Traits The phenotypic mean, SD, coefficient of variation (CV), and minimum and maximum values of each trait are summarized in Table 1. Due to the missing observations and exclusion of outliers, the number of observations differed among the measured traits. The CV of all traits in the population had a wide range of 3.64 to 41.47%. The CV of FCR, FI, BWG, AFW, AFP, SFT, IMFB , and IMFL were all greater than 15%, indicating large phenotypic variations in the traits of feed efficiency, FI, growth, and fatty acid metabolism in this breed, and reflecting the absence of directional selection for these traits. The mean RFI was approximately equal to zero, since it represented residuals of a linear model. It is worth noting that the average value of FCR was 3.22, which was in good agreement with previous studies of a yellow-plumage dwarf broiler strain (3.31) by Xu et al. (2016) and a slow-growing line (3.15) by N’Dri et al. (2006). But, the FCR value of modern commercial broilers is currently < 2.0 (Siegel, 2014; Brameld and Parr, 2016; Lee and Aggrey, 2016). Therefore, improving feed efficiency is an extremely urgent task in breeding strategies for slower growing broilers.
Phenotypic Correlations The phenotypic correlation coefficients of feed efficiency traits with others are presented in Table 2. A moderate and positive correlation (0.34) was found between RFI and FCR. The Pearson correlation coefficient between RFI and TFI was positive (0.62), while that between FCR and TFI was low and negative (−0.26). As expected, RFI was phenotypically uncorrelated with BWG, BMW, and LW. However, there was a high negative relationship (−0.73) between FCR and BWG, and FCR had a moderate and negative correlation coefficient with BMW (−0.42) and LW (−0.42). Also, RFI had a moderate and positive phenotypic correlation with AFW (0.41) and AFP (0.44), and a weak negative correlation with BMP (−0.18) and LP (−0.15), while the correlation coefficients of FCR with AFW, AFP, BMP, and LP were close to zero (−0.11
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WEN ET AL. Table 1. Descriptive statistics for all measured traits. Traits1 RFI (kg) FCR TFI (kg) BWG (kg) AFW (g) AFP (%) SFT (mm) BMW (g) BMP (%) LW (g) LP (%) IMFB (mg/g) IMFL (mg/g) BW76 (kg) BSL (cm) FBL (cm) BrW (cm) SL (cm) SC (cm) BW56 (kg) MMW(kg0.75 )
Number
Mean
SD
CV (%)
204 204 204 207 206 206 205 205 205 206 206 198 198 207 206 204 203 205 206 207 207
0.00 3.22 2.30 0.75 45.56 1.94 2.23 118.70 5.13 240.32 10.38 22.10 31.55 2.32 25.69 10.34 9.02 9.28 5.43 1.57 1.64
0.20 0.77 0.36 0.18 18.89 0.73 0.56 16.08 0.51 26.88 0.67 5.24 9.26 0.23 1.38 0.58 0.81 0.34 0.29 0.12 0.10
Maximum
Minimum
0.98 8.72 3.26 1.15 97.90 3.73 3.60 152.80 6.53 300.10 13.51 39.03 57.61 2.89 28.50 12.00 11.20 10.15 6.18 1.83 1.89
− 0.65 2.27 1.33 0.18 3.40 0.20 1.09 65.90 2.45 179.90 6.75 12.50 13.61 1.65 22.00 9.00 6.50 8.10 4.70 1.17 1.31
– 23.91 15.74 23.77 41.47 37.71 24.98 13.55 9.86 11.18 6.47 23.71 29.36 10.02 5.38 5.60 9.03 3.64 5.26 7.87 6.34
1 RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight.
Table 2. Pearson correlation coefficients between feed efficiency traits and others. Traits1 FCR RFI FCR
TFI
BWG
AFW
AFP
SFT
BMW
BMP
LW
LP
IMFB IMFL
BW76
BSL
FBL
SL
SC
BrW BW56 MBW
0.34∗∗ 0.62∗∗ 0.07 0.41∗∗ 0.44∗∗ 0.07 − 0.05 − 0.18∗∗ − 0.01 − 0.15∗ 0.13 0.07 0.09 − 0.11 0.02 − 0.05 0.05 0.11 0.07 0.09 – − 0.26∗∗ − 0.73∗∗ − 0.11 − 0.02 − 0.15∗ − 0.42∗∗ − 0.11 − 0.42∗∗ 0.01 0.16∗ 0.15∗ − 0.46∗∗ − 0.27∗∗ − 0.12 − 0.23∗∗ − 0.13 − 0.07 0.19∗∗ − 0.26∗∗
∗
P < 0.05, ∗∗ P < 0.01. RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight. 1
to 0.01, P > 0.05). In contrast to RFI, which was not obviously phenotypically correlated with IMFB and IMFL (0.13 and 0.07, P > 0.05), FCR showed slight and positive correlations with IMFB and IMFL (0.16 and 0.15, respectively, P < 0.05). As opposed to the irrelevant relationships between RFI and body measurements (−0.11 to 0.09, P > 0.05), FCR was slightly and positively correlated to BSL (−0.27) and SL (−0.23).
Phenotypic Differences Between the Efficient Birds and the Population There were 7 (35%) and 17 birds (42.5%) both in RFI- and FCR-ranked groups of the top 10 and 20%, respectively. The daily FI of RFI-efficient birds (individuals with the lowest 10 and 20% RFI values) was clearly lower than the population mean during the feeding experiment, while there was no distinct difference between the FCR-efficient birds (individuals with the lowest 10 and 20% FCR values) and the population mean (Figure 1A and B). Meanwhile, the top 20 and
40 chickens with low RFI had lower TFI than the population with a comparatively large (1.92 and 2.03 vs. 2.30 kg, respectively, P < 0.01, Table 3; Figure 2A and B), whereas this situation was not found between FCR-efficient bird groups and the population (2.30 and 2.32 vs. 2.30 kg, respectively, P > 0.05, Table 4; Figure 2A and B). The change curves of BW based on the automated feeding system records are shown in Figure 1C and D. The linear curves of BW indicated that the population was in a rapid growth period during the feeding trial. Furthermore, RFI- and FCR-efficient birds exerted different growth rates with the aging process. Specifically, the RFI-efficient birds seemingly displayed lower growth rates than the FCR-efficient birds. The FCR-efficient groups had both higher (P < 0.01) BWG (0.92 and 0.90 vs. 0.75 kg, respectively) and BW76 (2.42 and 2.42 vs. 2.32 kg, respectively) than the population mean (Table 4; Figure 2C–F). However, there were no obvious differences (P > 0.05) between the RFI-efficient birds and the population mean for BW and BW76 (Table 3; Figure 2C–F).
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Figure 1. Change curves of feed intake (FI) and body weight (BW) with the aging process. The left plots displays the daily FI change over the 3-week test period among the population and favorable broilers selected by RFI and FCR values. The right plots describe BW changes based on automated feeding system records. The mean values for each curve are indicated in the legend. The chicken FI was relatively low at 56 and 76 d of age, because the feeding systems began recording weight at 56 d of age and stopped recording before 76 d of age; thus, the valid recording time was < 24 hours.
Some discrepancies were observed (P < 0.05) in AFW and AFP between the RFI-efficient birds and the population (34.90 and 35.43 vs. 45.56 g; and 1.53 and 1.53 vs. 1.94%, respectively) (Table 3). There were no clear differences (P > 0.05) in SFT, BMW, BMP, LW, LP, IMFB , IMFL , and body measurements (BSL, FBL, BrW, SL, and SC) between the RFI-efficient birds and the population. The comparisons between the FCRefficient birds and the population are displayed in Table 4. BMW and LW were greater (P < 0.05) in the 2 FCR-efficient groups than the population (124.64 and 124.46 vs. 118.70 g; and 253.34 and 253.43 vs. 240.32 g, respectively), while we found no differences (P > 0.05) in BMP and LP between the FCR-efficient birds and the population. Meanwhile, there were no obvious differences (P > 0.05) in AFW, AFP, SFT, FBL, BrW, SL, and SC between the FCR-efficient groups and the population. No obvious discrepancy (P > 0.05) of IMFB was found between the FCR-efficient group (top 20%) and the population, while an apparent difference was seen in IMFB between the FCR-efficient group (top 10%) and the population (19.63 vs. 22.10 mg/g, respectively, P < 0.05). Although there was no statistical difference (P > 0.05) in IMFL between the population and the 2 FCR-efficient groups, IMFL clearly decreased in the FCR-efficient (20%) birds (28.83 vs. 31.55 mg/g, respectively, P = 0.103). In additional, an apparent discrepancy was observed in BSL between the FCRefficient (10%) birds and the population (26.47 vs. 25.69 cm, respectively, P < 0.01).
DISCUSSION In agreement with previous studies with slow-growing broilers (N’Dri et al., 2006; Xu et al., 2016), the FCR values in the current study were much higher than that of commercial fast-growing broilers (Siegel, 2014; Brameld and Parr, 2016; Lee and Aggrey, 2016). Thus, the poor feed efficiency was indeed the major deficiency of slow-growing broilers, which had enormous potential to improve. According to estimated heritability data of FCR and RFI in literatures, heritability of RFI in broilers was about 0.41, and FCR heritability ranged from 0.22 to 0.49 (Aggrey et al., 2010; Howie et al., 2011; Xu et al., 2016; Begli et al.; Liu et al., 2017). Hence, RFI and FCR have moderate heritability, indicating the selection for either of these 2 indices (FCR and RFI) can undoubtedly increase the feed efficiency. Then, the next problem is to identify which index is more appropriate to be used in breeding strategy for the benefit maximization. Deep reasons for the comparison of RFI and FCR in breeding strategy are the discrepant correlations between feed efficiency traits (RFI and FCR) and other traits, indicating that the direct selection of RFI or FCR would influence different traits incidentally. Our results showed a highly negative correlation between FCR and BW, which also phenotypically and genetically existed in commercial broilers (Zhang and Aggrey, 2003) and turkeys (Case et al., 2012; Willems et al., 2013). Besides, FCR was also negatively
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WEN ET AL. Table 3. Comparisons between the lowest RFI birds and the population. Mean ± SD1 Traits
4
RFI (kg) FCR TFI (kg) AFW (g) AFP (%) BWG (kg) SFT (mm) BMW (g) BMP (%) LW (g) LP (%) IMFB (mg/g) IMFL (mg/g) BW76 (kg) BSL (cm) FBL (cm) BrW (cm) SL (cm) SC (cm) BW56 (kg) MMW (kg0.75 )
Lowest 10% of RFI − 0.30 2.84 1.92 34.90 1.53 0.70 2.10 114.68 5.12 233.35 10.39 20.91 31.94 2.24 25.70 10.24 8.64 9.27 5.41 1.54 1.61
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
2
∗∗
0.11 0.41∗∗ 0.30∗∗ 20.48∗ 0.86∗ 0.19 0.76 14.80 0.45 29.33 0.53 5.16 10.48 0.22 1.29 0.72 0.87 0.28 0.28 0.11 0.09
Lowest 20% of RFI3 − 0.24 2.85 2.03 35.43 1.53 0.73 2.15 118.80 5.20 240.79 10.51 21.00 31.01 2.29 25.84 10.34 8.76 9.28 5.43 1.56 1.63
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
∗∗
0.10 0.34∗∗ 0.28∗∗ 17.88∗∗ 0.74∗∗ 0.16 0.65 13.76 0.46 27.46 0.50 5.51 9.95 0.20 1.40 0.65 0.86 0.31 0.27 0.11 0.09
Population 0.00 3.22 2.30 45.56 1.94 0.75 2.23 118.70 5.12 240.32 10.38 22.10 31.55 2.32 25.69 10.34 9.02 9.28 5.43 1.57 1.64
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.20 0.77 0.36 18.89 0.73 0.18 0.56 16.08 0.51 26.88 0.67 5.24 9.26 0.23 1.38 0.58 0.81 0.34 0.29 0.12 0.10
Within each row, ∗ P < 0.05 and ∗∗ P < 0.01 for RFI-sorted group vs. the population (t test). n = 20. 3 n = 40. 4 RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight. 1 2
correlated to FI in phenotype, while the genetic relationship was slightly positive both in commercial broilers and turkeys (Zhang and Aggrey, 2003; Willems et al., 2013). Therefore, selection for lower FCR can significantly increase BW, but it had no distinct effect on FI. On the contrary, we found a high and positive correlation between RFI and FI in our research, while the phenotypic correlation coefficient between RFI and BW was almost zero. These results were consistent with those of a former study between RFI and FI, and RFI and BW in yellow broilers (Xu et al., 2016). In general, selection for RFI can improve the feed efficiency of slower growing broilers by less FI and supplying the same amount of meat product, while if selecting FCR, birds would produce more meat product with the same amount of FI. The success of poultry meat production is not only to improve feed efficiency, but also to increase the proportion of muscle and reduce that of fat (Zerehdaran et al., 2004). Muscle and fat are the main components of the broiler carcass. However, abdominal and subcutaneous fat with little economic value are recognized as the main sources of waste in poultry production (Tavaniello et al., 2014). Also, excessive fat affects consumer acceptance and product sales. Abdominal fat grows at a faster rate than other fat tissues and is highly correlated to total carcass lipids (Fouad and El-Senousey, 2014), hence abdominal fat mostly reflects excessive fat deposition (Zerehdaran et al., 2004). In the current study,
FCR had high and positive phenotypic correlations with BMW and LW, but not with BMP, LP, AFW, and AFP. Moreover, single BMW and LW were significantly increased when selecting FCR, while there were no distinct changes in the percentages of breast and leg muscle, or abdominal and subcutaneous fat. These results implied that the carcass compositions of muscle and fat remained relatively unchanged. We observed abdominal fat had a moderate and positive phenotypic correlation to RFI in the present study. When selecting low RFI, the weight and percentage of abdominal fat significantly reduced. These results indicated that RFI improvement was accompanied with increased yield of edible carcass meat and reduced deposition of abdominal fat (Siegel, 2014). Several studies have reported that lower FI can effectively reduce the level of undesirable fat in broilers (Richards et al., 2003; Yang et al., 2010). Richards et al. (2003) and Yang et al. (2010) suggested that lower feed consumption can reduce fat deposition by inhibiting the activity of some lipogenic enzymes in the livers of broilers. It can be concluded that the accumulation of abdominal fat in chickens could be indirectly reduced by selecting lower RFI. Although abdominal and subcutaneous fat are useless, IMF is favorable. As fat is the precursor of flavor substance in meat and the IMF can enhance juiciness and tenderness of the meat, it has been established that IMF plays a major role in determining the quality and flavor of meat (Tavaniello et al., 2014; Zhang
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Figure 2. Phenotypic differences in total feed intake (TFI), body weight (BW), and BW at 76 d of age between the more efficient birds based on RFI or FCR values and the population. ∗∗ P < 0.01. The dashed line in each plot represents the average value of the corresponding traits of the population.
et al., 2017). The IMF content has a low to moderate heritability, ranging from 0.11 to 0.22 (Zhao et al., 2007; Chen et al., 2008; Jiang et al., 2017). A previous study indicated that IMF content could effectively be improved through appropriate selection strategy (Zhao et al., 2007). In the current study, a weak phenotypic correlation was observed between FCR and IMF content; the birds with the lowest 20% of FCR had a lower IMF content of the thigh muscle than the population. And when we selected the lowest 10% of FCR from the population, the IMF content of the breast muscle was significantly reduced. Whereas the correlation between RFI and IMF content was approximately zero, selection for low RFI did not change the IMF content. Because IMF is an important indicator of meat quality (Cui et al., 2012; Jiang et al., 2017), we inferred that improving feed efficiency by selecting FCR may have a negative effect on meat quality, while selection for RFI would have no influence on meat quality. In recent yr, more and more consumers prefer the specific meat qualities of slower growing broilers over those
of commercial fast-growing ones (Quentin et al., 2003; Rizzi et al., 2007; Jiang et al., 2017; Zhang et al., 2017). Therefore, the foci of slower growing chicken production are first and foremost meat quality and feed consumption, and secondly, carcass yield. However, growth rate is often associated with commercial broiler production (Zuidhof et al., 2014; Brameld and Parr, 2016), due to the correlation between growth rate and the age at market weight. The live BW over a specific period and morphometric measurements are the primarily factors in growth rate measurements (Sheng et al., 2013; Siegel, 2014; Tallentire et al., 2016). According to the change curve of BW, the lowest-RFI chickens seemingly exhibited lower growth rates than the lowest-FCR birds, while there were no significant differences in BW between the lowest-RFI birds and the population. In addition, FCR had a moderate and positive correlation with live weight, and the BW in our study was in good agreement with previous results of an intercross population (Sheng et al., 2013). The market weight and BSL were significantly increased by selecting low FCR.
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WEN ET AL. Table 4. Comparisons between the lowest FCR birds and the population. Mean ±SD1 Traits
4
FCR RFI (kg) TFI (kg) BWG (kg) BMW (g) BMP (%) LW (g) LP (%) AFW (g) AFP (%) SFT (mm) IMFB (mg/g) IMFL (mg/g) BW76 (kg) BSL (cm) FBL (cm) BrW (cm) SL (cm) SC (cm) BW56 (kg) MMW(kg0.75 )
Lowest 10% of FCR 2.50 − 0.19 2.30 0.92 124.64 5.14 253.34 10.44 41.31 1.69 2.33 19.63 30.35 2.42 26.47 10.44 9.05 9.30 5.55 1.50 1.66
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
2
∗∗
0.09 0.15∗∗ 0.26 0.10∗∗ 13.58∗ 0.43 24.86∗ 0.53 15.86 0.58 0.60 4.80∗ 10.12 0.16∗∗ 1.27∗ 0.60 0.75 0.33 0.28 0.10∗∗ 0.08
Lowest 20% of FCR3 2.59 − 0.15 2.32 0.90 124.46 5.15 253.43 10.47 42.36 1.73 2.23 20.95 28.83 2.42 26.01 10.44 9.01 9.33 5.53 1.52 1.66
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
∗∗
0.12 0.13∗∗ 0.29 0.11∗∗ 13.43∗ 0.49 23.94∗∗ 0.53 16.99 0.62 0.61 6.03 9.26 0.19∗∗ 1.34 0.57 0.76 0.29 0.27 0.11∗ 0.09
Population 3.22 0.00 2.30 0.75 118.70 5.13 240.32 10.38 45.56 1.94 2.23 22.10 31.55 2.32 25.69 10.34 9.02 9.28 5.43 1.57 1.64
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.77 0.20 0.36 0.18 16.08 0.51 26.88 0.67 18.89 0.73 0.56 5.24 9.26 0.23 1.38 0.58 0.81 0.34 0.29 0.12 0.10
Within each row, ∗ P < 0.05 and ∗∗ P < 0.01 for FCR-sorted group vs. the population (t test). n = 20. 3 n = 40. 4 RFI = residual feed intake; FCR = feed conversion ratio; TFI = total feed intake from 56 to 76 d of age; BWG = body weight gain; AFW = abdominal fat weight; AFP = percentage of AFW; SFT = subcutaneous fat thickness; BMW = single breast muscle weight; BMP = percentage of BMW; LW = single leg weight; LP = percentage of LW; IMFB and IMFL = intramuscular fat content of the pectoralis minor and thigh muscle, respectively; BW76 and BW56 = body weight at 76 and 56 d of age, separately; BSL = body slope length; FBL = fossil bone length; BrW = breast width; SL = shank length; SC = shank circumference; MMW = metabolic mid-weight. 1 2
Nevertheless, improving RFI would result in a slight decrease in growth rate, but no significant decrease in market weight, whereas decreasing the FCR would obviously enhance the growth rate. In conclusion, selection for both RFI and FCR can effectively increase feed efficiency, while improving the FCR can clearly enhance the growth rate and market weight without increasing FI and having no significant effect on carcass composition. Moreover, a lower FCR has a tendency to reduce the IMF content, thereby decreasing meat quality. By contrast, selecting low RFI could significantly reduce FI and abdominal fat content, and had no unfavorable impact on meat quality. Improving feed efficiency and maintaining meat quality were both of significance in the production of slower growing broilers. Therefore, as compared with the FCR, the RFI may be a more suitable index to improve feed efficiency in slower growing broiler. These results are important for the continued development of strategies to improve feed efficiency in broiler breeding and production.
ACKNOWLEDGMENTS The current research was funded by Programs for Changjiang Scholars and Innovative Research in Universities ( IRT 15R62) and Chinese Universities Scientific Fund ( 2017TC034).
REFERENCES Aggrey, S. E., A. B. Karnuah, B. Sebastian, and N. B. Anthony. 2010. Genetic properties of feed efficiency parameters in meattype chickens. Genet. Sel. Evol 42:25. Begli, H. E., R. V. Torshizi, A. A. Masoudi, A. Ehsani, and J. Jensen. 2016. Longitudinal analysis of body weight, feed intake and residual feed intake in F2 chickens. Livest. Sci 184:28– 34. Brameld, J. M., and T. Parr. 2016. Improving efficiency in meat production. Proc. Nutr. Soc. 75:242–246. Case, L. A., B. J. Wood, and S. P. Miller. 2012. The genetic parameters of feed efficiency and its component traits in the turkey (Meleagris gallopavo). Genet. Sel. Evol. 44:2. Chen, J. L., G. P. Zhao, M. Q. Zheng, J. Wen, and N. Yang. 2008. Estimation of genetic parameters for contents of intramuscular fat and inosine-5’-monophosphate and carcass traits in Chinese Beijing-You chickens. Poult. Sci 87:1098–1104. Cheng, W., J. Cheng, D. Sun, and H. Pu. 2015. Marbling analysis for evaluating meat quality: Methods and techniques. Compr. Rev. Food. Sci. F. 14:523–535. Cui, H., R. Liu, G. Zhao, M. Zheng, J. Chen, and J. Wen. 2012. Identification of differentially expressed genes and pathways for intramuscular fat deposition in pectoralis major tissues of fastand slow-growing chickens. BMC Genomics 13:213. Fouad, A. M., and H. K El-Senousey. 2014. Nutritional factors affecting abdominal fat deposition in poultry: A review. Asian Austral J. Anim. Sci. 27:1057–1068. Gunsett, F. C. 1984. Linear index selection to improve traits defined as ratios. J. Anim. Sci. 59:1185–1193. Howie, J. A., B. J. Tolkamp, S. Avendano, and I. Kyriazakis. 2009. The structure of feeding behavior in commercial broiler lines selected for different growth rates. Poult. Sci. 88: 1143–1150. Howie, J. A., S. Avendano, B. J. Tolkamp, and I. Kyriazakis. 2011. Genetic parameters of feeding behavior traits and their
Downloaded from https://academic.oup.com/ps/advance-article-abstract/doi/10.3382/ps/pey062/4970823 by Serials Section, Dixson Library user on 21 April 2018
FEED EFFICIENCY IN SLOWER GROWING BROILERS relationship with live performance traits in modern broiler lines. Poult. Sci. 90:1197–1205. Jiang, M., W. L. Fan, S. Y. Xing, J. Wang, P. Li, R. R. Liu, Q. H. Li, M. Q. Zheng, H. X. Cui, J. Wen, and G. P. Zhao. 2017. Effects of balanced selection for intramuscular fat and abdominal fat percentage and estimates of genetic parameters. Poult. Sci. 96:282–287. Koch, R. M., L. A. Swiger, D. Chambers, and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486–494. Lee, J., and S. E. Aggrey. 2016. Transcriptomic differences of genes in the avian target of rapamycin (avTOR) pathway in a divergent line of meat-type chickens selected for feed efficiency. Genet. Mol. Res. 15. doi: 10.4238/gmr.15027120. Liu, T., C. Luo, J. Wang, J. Ma, D. Shu, M. S. Lund, G. Su, and H. Qu. 2017. Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens. PloS One 12:e173620. Luiting, P. 1990. Genetic variation of energy partitioning in laying hens: Causes of variation in residual feed consumption. Worlds Poult. Sci. J. 46:133–152. N’Dri, A. L., S. Mignon-Grasteau, N. Sellier, M. Tixier-Boichard, and C. Beaumont. 2006. Genetic relationships between feed conversion ratio, growth curve and body composition in slow-growing chickens. Br. Poult. Sci. 47:273–280. Quentin, M., I. Bouvarel, C. Berri, E. Le Bihan-Duval, E. Baeza, Y. Jego, and M. Picard. 2003. Growth, carcass composition and meat quality response to dietary concentrations in fast-, mediumand slow-growing commercial broilers. Anim. Res. 52:65–77. Racine, J. S. 2012. RStudio: A platform-independent IDE for R and Sweave. J. Appl. Econ. 27:167–172. Richards, M. P., S. M. Poch, C. N. Coon, R. W. Rosebrough, C. M. Ashwell, and J. P. McMurtry. 2003. Feed restriction significantly alters lipogenic gene expression in broiler breeder chickens. J. Nutr. 133:707–715. Rizzi, C., A. Marangon, and G. M. Chiericato. 2007. Effect of genotype on slaughtering performance and meat physical and sensory characteristics of organic laying hens. Poult. Sci. 86: 128–135. Sell-Kubiak, E., K. Wimmers, H. Reyer, and T. Szwaczkowski. 2017. Genetic aspects of feed efficiency and reduction of environmental footprint in broilers: A review. J. Appl. Genet. 58: 487–498. Sheng, Z., M. E. Pettersson, X. Hu, C. Luo, H. Qu, D. Shu, X. Shen, O. Carlborg, and N. Li. 2013. Genetic dissection of growth traits in a Chinese indigenous × commercial broiler chicken cross. BMC Genomics 14:151. Siegel, P. B. 2014. Evolution of the modern broiler and feed efficiency. Annu. Rev. Anim. Biosci. 2:375–385.
9
Tallentire, C. W., I. Leinonen, and I. Kyriazakis. 2016. Breeding for efficiency in the broiler chicken: A review. Agron. Sustain. Dev. 36:66. Tavaniello, S., G. Maiorano, M. Siwek, S. Knaga, A. Witkowski, D. Di Memmo, and M. Bednarczyk. 2014. Growth performance, meat quality traits, and genetic mapping of quantitative trait loci in 3 generations of Japanese quail populations (Coturnix japonica). Poult. Sci. 93:2129–2140. Titus, H. W., J. A. L. Mehring, and J. H. Brumbaugh. 1953. Variation of feed conversion. Poul. Sci. 32:1074–1077. Willems, O. W., S. P. Miller, and B. J. Wood. 2013. Aspects of selection for feed efficiency in meat producing poultry. Worlds Poult. Sci. J. 69:77–88. Willems, O. W., S. P. Miller, and B. J. Wood. 2013. Assessment of residual body weight gain and residual intake and body weight gain as feed efficiency traits in the turkey (Meleagris gallopavo). Genet. Sel. Evol. 45:26. Xu, Z., C. Ji, Y. Zhang, Z. Zhang, Q. Nie, J. Xu, D. Zhang, and X. Zhang. 2016. Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens. BMC Genomics 17:594. Yang, X., J. Zhuang, K. Rao, X. Li, and R. Zhao. 2010. Effect of early feed restriction on hepatic lipid metabolism and expression of lipogenic genes in broiler chickens. Res. Vet. Sci. 89:438–444. Yi, G., J. Yuan, H. Bi, W. Yan, N. Yang, and L. Qu. 2015. In-depth duodenal transcriptome survey in chickens with divergent feed efficiency using RNA-Seq. PLoS One 10:e136765. Yuan, J., T. Dou, M. Ma, G. Yi, S. Chen, L. Qu, M. Shen, L. Qu, K. Wang, and N. Yang. 2015. Genetic parameters of feed efficiency traits in laying period of chickens. Poult. Sci. 94:1470–1475. Zerehdaran, S., A. L. Vereijken, J. A. van Arendonk, and E. H. van der Waaijt. 2004. Estimation of genetic parameters for fat deposition and carcass traits in broilers. Poul. Sci. 83:521–525. Zhang, M., F. B. Yan, F. Li, K. R. Jiang, D. H. Li, R. L. Han, Z. J. Li, R. R. Jiang, X. J. Liu, X. T. Kang, and G. R. Sun. 2017. Genome-wide DNA methylation profiles reveal novel candidate genes associated with meat quality at different age stages in hens. Sci. Rep. 7:45564. Zhang, W., and S. E. Aggrey. 2003. Genetic variation in feed utilization efficiency of meat-type chickens. Worlds Poult. Sci. J. 59:328–339. Zhao, G. P., J. L. Chen, M. Q. Zheng, J. Wen, and Y. Zhang. 2007. Correlated responses to selection for increased intramuscular fat in a Chinese quality chicken line. Poul. Sci. 86:2309–2314. Zuidhof, M. J., B. L. Schneider, V. L. Carney, D. R. Korver, and F. E. Robinson. 2014. Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 20051. Poult. Sci. 93:2970–2982.
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