Growth performance, carcass traits, meat quality, and blood variables of small-sized meat ducks with different feed efficiency phenotypes

Abstract

The study investigated the effects of feed efficiency (residual feed intake, RFI and residual intake and gain, RIG) on the production performance of small-sized meat ducks. Ninety ducks with intermediate and extreme (high and low) RFI values were selected from 1,083 male ducks of similar body weight, and the 3 groups were then redivided according to RIG. For both efficiency measures, the feed conversion ratio (FCR) and average daily feed intake (ADFI) of efficient ducks were significantly lower than those of inefficient ducks (P < 0.05), while the residual body weight gain (RG) was significantly higher in efficient ducks (P < 0.05). Inefficient-RFI animals showed greater skin fat yield (P < 0.05), but no other differences in carcass traits were observed (P > 0.05). RIG had positive effects on the pH₁ value of the breast muscle (P < 0.05), but feed efficiency did not affect the other meat quality traits (P > 0.05). With regard to blood biochemical parameters, efficient ducks had significantly lower triglycerides (TG) (P < 0.05). Correlation analysis demonstrated that RFI was positively correlated with average daily feed intake and feed conversion ratio (P < 0.05), while RIG exhibited a strong negative correlation with both (P < 0.05). The average daily body weight gain was positively correlated with RIG (P < 0.05). RIG had a positive effect on the pH₁ value of the breast muscle (P < 0.05). Furthermore, triglyceride and high-density lipoprotein cholesterol levels correlated with both efficiency classifications (P < 0.05). Overall, the efficiency measures did not affect the carcass and meat quality of small-sized meat ducks but could identify ducks with lower feed consumption and fast growth.

INTRODUCTION

Meat duck production is an important industry in China, accounting for 68% of the duck meat production worldwide in 2021, according to statistics from the Food and Agriculture Organization (Hou and Liu, 2022). Feed consumption represents the foremost cost in duck meat manufacturing, and improving feed efficiency is important for decreasing feed costs in duck meat production. However, the selection of feed efficiency may also affect other vital economic traits. In China, most meat ducks are large-sized meat ducks. In the current mainstream consumer market, the demand for high-quality meat products continues to increase, and well-priced small-sized meat ducks are preferred. Compared with the large-sized meat ducks raised by the traditional poultry breeding industry, small-sized meat ducks developed from indigenous duck lines in China have not only the advantages of good flavor and high nutritional content, but also a high meat yield. Nevertheless, the problems of long growth periods and low feed conversion rates remain unresolved. At present, most feed efficiency research has focused on large-sized meat ducks, while small-sized meat ducks have been understudied. At present, poultry breeding aims for reducing production costs by increasing the feed efficiency of birds without affecting performance (Lin and Aggrey, 2013; Fathi et al., 2021).

By dividing the total energy of livestock and poultry into growth energy and maintenance energy, residual feed intake (RFI), first used in poultry by Luiting (1990), can accurately reflect metabolic differences among individuals within the same population (Berry and Crowley, 2012). However, RFI has its drawbacks, namely, slow-growing animals that consume a relatively small amount of feed may exhibit superior RFI values and smaller body sizes, which are unfavorable for breeding efforts (Martin et al., 2021). Therefore, net feed efficiency was selected as a new feed efficiency indicator. Unlike RFI, it describes the efficiency with which animals divide feed according to production and maintenance needs, independent of animal production levels (Musigwa et al., 2021). It is measured using RFI, residual body weight gain (RG), and residual intake and weight gain (RIG) estimated from a linear combination of growth, body weight (BW), and feed intake traits. Similar to RFI, RG is derived from the difference between actual weight gain and predicted weight gain, using a regression equation fitted to medium-term metabolic weight and body weight gain. RG is associated with growth rate, independent of feed intake, and efficient RG animals exhibit higher growth rates (Nascimento et al., 2016).

The objective of this study was to evaluate the growth performance, carcass traits, meat quality, and blood variables of ducks classified according to RFI and RIG as well as the correlation between these parameters. The current findings provide valuable insights for the selection of economic characteristics for RFI and RIG in small-sized meat ducks for greater meat production and quality.

MATERIALS AND METHODS

Ethics Statement

All experimental procedures were approved by the China Council on Animal Care and Ministry of Science and Technology of the People's Republic of China. All experimental ducks were managed and handled according to the guidelines established and approved by the Animal Care and Use Committee of the Yangzhou University (No. SYDW-2019015). All efforts were made to minimize animal suffering.

Location, Management of Animals, and Experimental Diet

A total of 1,729 mixed-sex, 1-day-old male small-sized meat ducks (H strain with black beak, black shank, and white feather) were obtained from the Ecolovo Group, China. For the first 3 wk, prior to the experimental period, all ducks were raised in pens (15 birds/m²). At 21 d of age, 1,083 birds with similar BW were selected and transferred to individual cages, which measured 73 × 55 × 80 cm (approximately 1 m²). Each cage was equipped with an individual feeder and drinker. All experimental ducks were raised in the same shed and provided water and feed ad libitum. The feed compositions are listed in Table 1.

Table 1.

Compositions and nutrients of the experimental diets.

Item	0–7 d	8–21 d	22–42 d
Ingredient (%)
Corn	10.32	10.63	47.18
Wheat middling	15.41	15.00	6.89
Wheat bran	-	-	20.00
Rice noodles	35.21	34.99	-
Rice bran	15.81	15.00	3.00
Peanut meal	-	-	3.00
Corn gluten meal	-	-	5.00
Soybean meal	12.63	13.70	5.94
Nucleotide slag	2.00	2.00	-
Limestone powder	1.52	1.58	1.90
Calcium hydrogen phosphate	1.10	1.10	1.09
Compound premix1	6.00	6.00	6.00
Total	100	100	100
Formulated nutrient profile (g/kg)
Crude protein	210.00	180.00	150.00
Crude fat	20.00	30.00	35.00
Crude fiber	50.00	50.00	70.00
Crude ash	70.00	80.00	100.00
Calcium	10.00	10.00	10.00
Phosphorus	6.00	5.50	4.50
Sodium chloride	6.00	6.00	6.00
Methionine	4.00	4.00	2.80
Moisture	140.00	140.00	140.00

¹Supplied per kilogram of total diet: bentonite, 44.46 g; lysine, 3.24 g; DL-MHA-FA (88%), 0.99 g; threonine, 0.73 g; sodium chloride, 4.40 g; sodium bicarbonate, 2.00 g; sodium sulfate, 2.00 g; herbalife, 0.20 g; choline chloride (60%), 1.00 g; Jin Duowei, 0.53 g; Jin Yvkang, 0.15 g; C-811 enzyme, 0.30 g.

Bird Husbandry

The whole experiment lasted 21 d, the initial BW (iBW) was measured at 22 d of age, the total feed intake and average daily feed intake (ADFI) were measured and calculated at 22 to 42 d of age, the final BW (fBW) was measured at 42 d of age, ADG was calculated according to the measured data, whereafter the metabolic body weight (MBW^0.75) and feed conversion ratio (FCR) were finally obtained. MBW^0.75 was determined as final the BW raised to the power of 0.75. RFI was calculated according to the method described by Aggrey et al. (2010), as per the following equation:

$$\mathrm{RFI}=\mathrm{ADFI}-\left(\mathrm{a}+{\mathrm{b}}_1\times \text{MB}{\mathrm{W}}^{0.75}+{\mathrm{b}}_2\times \text{ADG}\right)$$

where a is the intercept, while b₁ and b₂ are the partial regression coefficients of FI on MBW^0.75 and ADG, respectively.

Growth Performance

The PROC REG program of Statistical Analysis Software (version 9.4, SAS Institute Inc., Cary, NC) was used to perform multiple linear regression modeling of RFI and RG, with ADG and MBW^0.75 as independent variables for RFI and ADFI, respectively, while MBW^0.75 was the independent variable for RG (Koch et al., 1963), resulting in the equations described in Table 2. The expected ADFI (ADFIe) and ADG (ADGe) were calculated as per respective equations. The difference between the ADFI and the ADFIe was used to determine residuals for RFI. The difference between the ADG and ADGe was used to determine the residuals for RG. The RIG values were calculated using the formula RIG = (RFI × −1) + RG, as described by Berry and Crowley (2012). Using the SAS STANDARD method with a mean of 0 and a standard deviation of 1, the residuals of the RFI and RG indices were normalized. All experimental ducks were ordered according to their RFI levels after the outlier data (total 1.8%) were removed. The 30 ducks with the lowest RFI values overall (mean RFI = 24.99 g/d) were put into the low-RFI group; the 30 ducks with the highest overall RFI values (mean RFI = 25.37 g/d) were put into the high-RFI group; and the 30 ducks with the middle RFI values (mean RFI = 0.00 g/d) were put into the medium-RFI group. There were a total of 90 experimental ducks, and each duck was rated based on its RIG score. The low, medium, and high class consisted of 31, 32, and 27 birds, respectively.

Table 2.

Regression equations to predict ADFI and estimated ADG in growing small-sized meat ducks.

Equation1	n	r2	P value
ADFIe = −0.86 + 0.44*ADG + 0.45*MBW0.75	1083	0.52	<0.001
ADGe = −30.65 + 0.03*ADFI + 0.28*MBW0.75	1083	0.80	<0.001

¹ADFIe = expected average daily feed intake; ADG = average daily gain; MBW^0.75 = metabolic body weight; ADGe = expected average daily gain; ADFI = average daily feed intake.

Carcass Characteristics

Ninety selected ducks were fasted for 6 h, weighed (live weight, LW), and then sent to a poultry processing plant for slaughter and hair removal. The weight of the defeathered carcass was determined as the carcass weight (CW). The carcass was then manually eviscerated, weighed after removing viscera including the crop, trachea, esophagus, spleen, pancreas, gallbladder, gonads, and intestinal tract, which was recorded as semieviscerated weight (SEW). The eviscerated weight (EW) was measured as SEW after removing the heart, liver, gizzard, proventriculus, and abdominal fat. Carcass yield was calculated as the percentage of live weight. The breast muscle, thigh muscle, gizzard, and abdominal fat pad, including the leaf fat surrounding the cloaca and gizzard, were separated and weighed, with their weights denoted as breast muscle weight (BMW), thigh muscle weight (TMW), gizzard weight (GW), and abdominal fat weight (AFW), respectively. Carcass skin and subcutaneous fat were separated and weighed, which are denoted as skin fat weight (SFW). Breast and thigh muscle yields were calculated as the percentage of EW. Heart and liver yields were calculated as percentages of LW. According to the standard established by the Ministry of Agriculture and Rural Affairs of the People's Republic of China (2020), the lean meat percentage was calculated as (BMW + TMW)/EW × 100%, the gizzard percentage as GW/(GW + EW) × 100%, the abdominal fat percentage as AFW/(AFW + EW) × 100%, and the skin fat percentage as (AFW + SFW)/(AFW + EW) × 100%.

Meat Quality

Color, pH, rate of water loss, and shear force of the meat were all assessed in the left breast and thigh muscles. Muscle lightness (L*), redness (a*), and yellowness (b*), as determined by the Commission Internationale de l'Eclairage, were determined using a chroma meter (CR-400, Konica Minolta, Tokyo, Japan). The data were expressed according to CIELAB coordinates, that is, L* = 0 (black) to L* = 100 (white) represents luminosity, and −a* (green), +a* (red), −b* (blue), and +b* (yellow). The surfaces of all the samples were freshly trimmed and free of fat or connective tissue. A pH meter was used to record the pH at 1 h (pH₁) and 24 h (pH₂₄, muscle was stored for 24 h at 4°C) after the animal was killed. Before and during pH determinations, the pH meter (pH-STAR, Matthaus, Berlin, Germany) was calibrated using phosphate buffers with pH values of 4.01 and 7.01. Three readings were used to calculate the average pH.

The water loss rate was measured using a meat quality pressure meter (Meat-1, Tenovo Food, Beijing, China), according to the method of Tang et al. (2009). Collected meat samples (0.125 cm³) were wrapped in absorbent paper and placed into the machine for testing. The program was set to 300 N for 5 min, and all samples were measured 3 times, with the final result determined by averaging the results. To determine cooking loss, the muscle samples were cut into 1.0 (width) × 0.5 (thickness) × 2.5 (length) cm strips. The samples were weighed (WTM), stored in plastic bags, and cooked in a water bath (15 min at 80°C). Following cooking, the samples were allowed to cool to room temperature before being gently patted dry with a paper towel without being squeezed and weighed again (WTC). The cooking loss was calculated as [(WTM − WTC)/WTM] × 100. Using a digital tenderness meter (C-LM3B, Tenovo Food, Beijing, China), muscle strips with fibers perpendicular to the blade were sheared to determine shear force.

The proximate composition was determined using the left breast and thigh muscle surplus samples, with moisture, protein, intramuscular fat (IMF), and collagen in the meat and meat products determined using a near-infrared spectrophotometer with an artificial neural network calibration model and database within the FoodScan Meat Analyzer (FOSS FoodScan 78800; Dedicated Analytical Solutions, Hilleroed, Denmark). All exterior fat and connective tissue were removed prior to proximate analysis. Each sample was coarsely ground using a tabletop grinder to obtain a sample of approximately 180 g. The ground sample was then placed in a 140 mm round sample dish, and the dish was placed in a FoodScan. Protein, IMF, collagen, and moisture percentages (g/100 g) are shown. The final reported values were calculated by taking independent readings for each sample and averaging them. All measurements were carried out in triplicate.

Blood Biochemical Parameters

Before slaughtering (42 d of age), blood samples were obtained from the wing veins of 90 ducks (30 high-RFI birds, 30 medium-RFI birds, and 30 low-RFI birds) using vacutainer tubes after 6 h of fasting (Karisa et al., 2014). Serum was collected via centrifugation at 3,500 × g for 5 min at 4°C and stored at −20°C for subsequent analysis. Serum samples were collected to determine hormone concentrations. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glucose (GLU), creatine kinase (CK), and free fatty acid (FFA) concentrations in serum were measured using a colorimetric method with an automatic biochemical analyzer (Hitachi 7080, Tokyo, Japan) and commercial kits (Biosino Bio-Technology and Science Inc., Beijing, China). The serum concentrations of insulin (INS), growth hormone (GH), adrenocorticotropic hormone (ACTH), cholecystokinin (CCK), cortisol (COR), ghrelin (GHR), and leptin (LEP) were measured using an enzyme immunoassay analyzer (Diatek DR200BS, Wuxi, China) and commercial ELISA kits (Beijing Sino-UK Institute of Biological Technology, Beijing, China).

Statistical Analysis

All data were analyzed using SAS version 9.4. Prior to the analysis, the normality of the data was verified, and the homogeneity of variance was examined. Data were analyzed using histograms, formal statistical tests, and quantile-quantile plots of the UNIVARIATE procedure in SAS. For statistical analysis, the PROC GLM procedure in SAS was used, applying the statistical model described below.

$$Y_i=\mu +{\beta }_i+\epsilon$$

where Y_i is the response variable of the ith animal, μ is the population mean, β_i is the effect of the RFI or RIG class (low, medium, high) of the ith animal, and Ԑ is the residual error. Least-squares means were calculated and compared using Duncan's test. Pearson's correlation analysis was used to examine the relationship between the various phenotypes. At P < 0.05, differences were declared statistically significant.

RESULTS AND DISCUSSION

Growth Performance

The growth and feed efficiency data for small-sized meat ducks are presented in Table 3. In this study, iBW, fBW, and MBW^0.75 were not influenced by RFI or RIG, which is in agreement with the definition of the 2 indicators proposed by Berry and Crowley (2013) because animals divide feed based on production and maintenance requirements that are independent of productivity. The mean RFI values of the low-, medium-, and high-RFI groups were −24.99, 0.00, and 25.37 g/d, respectively. Moreover, the mean RIG of low-, medium-, and high-RIG birds was 26.62, 0.00, and −26.53 g/d. The RFI and RIG had averages close to zero, which was to be expected as this measure was the residue of a prediction equation. The low-RFI group (most efficient) gained 21.64 and 46.28 g (P < 0.001) less ADFI than the medium and high groups, yet the ADG for the low group was close to that of the high group (61.2 vs. 61.57 g). In comparison to the medium and low groups, the high-RIG group (most efficient) had the lowest (P < 0.001) feed intake (134.62 vs. 156.04 and 181.91 g) and maximum (P > 0.05) BW gain (61.90 vs. 60.30 and 60.64 g). RFI was positively correlated with ADFI (r = 0.257, P < 0.05) and FCR (r = 0.949, P < 0.05), whereas RIG was negatively correlated with ADFI (r = −0.942, P < 0.05) and FCR (r = −0.987, P < 0.05). Moreover, ADG was positively correlated with RIG (r = 0.275, P < 0.05), while not significantly correlated with RFI (P > 0.05). The feed efficiency of low-RFI ducks was better than that of high-RFI ducks, and that of high-RIG Ducks was better than that of low-RIG ducks. RG in the low-RIG group was lower than that in the high- and medium-RIG groups, while RG in the low-RIG group was lower than that in the medium-RIG group (P < 0.01). RG was negatively correlated with RFI (r = −0.231, P < 0.05) and positively correlated with RIG (r = 0.405, P < 0.05). The relative RG value increased with higher RIG values. However, no statistically significant difference was noted between low-RFI and high-RFI ducks. RG, like RFI, is defined as the difference between actual and expected BW gain and is connected with faster growth rates while staying independent of FI changes, with positive RG animals developing faster and consuming more (Crowley et al., 2010). In the 2 efficiency measures, the ADFI and FCR of efficient ducks were greater than those of medium ducks (P < 0.05), whereas the ADFI and FCR of inefficient ducks were lower than those of medium ducks (P < 0.05). Nevertheless, the ADG was not correlated with RFI and RIG classifications. Low-RFI ducks required 0.81 g less feed for each increase of 1 g of BW gain compared to high-RFI ducks. High-RIG ducks required 0.86 g less feed for each 1 g increase in BW gain than low-RIG ducks. The experimental results are consistent with theoretical studies, indicating that RIG combines the advantages of RFI and RG, increasing the growth rate while maintaining low feed intake (Berry and Crowley, 2012). The results indicated that, based on RIG, individuals exhibited better production performance than those screened based on RFI and RG, highlighting the advantages of a reduced ADFI and an increased ADG. When looking at the average individual, differences in feed intake between the top-ranked ducks based on different residual feed efficiency qualities may be minor. However, when considering the production level, lower feed intake values could lead to significant feed cost savings over time.

Table 3.

Mean, standard error of the mean (SEM), and phenotypic correlation for performance traits of small-sized meat ducks classified as low-, medium-, and high-residual feed intake (RFI) or low-, medium-, and high-residual intake and gain (RIG).

Trait1	RFI					RIG					Correlation coefficients
	Low	Medium	High	SEM	P value	Low	Medium	High	SEM	P value	RFI	RIG
Initial BW (g)	774.13	765.57	769.87	12.893	0.898	773.43	765.05	779.58	13.277	0.752	−0.063	−0.007
Final BW (g)	2071.87	2026.83	2056.30	20.908	0.311	2047.64	2031.33	2087.96	21.853	0.188	−0.159	0.212
MBW0.75 (g)	307.03	301.97	305.26	2.332	0.306	304.30	302.50	308.83	2.434	0.185	0.042	0.213
ADFI (g/d)	134.15c	155.79b	182.41a	1.018	<0.001	181.91a	156.04b	134.62c	1.057	<0.001	0.257*	−0.942⁎⁎
ADG (g/d)	61.20	60.06	61.57	0.859	0.440	60.64	60.30	61.90	0.743	0.302	−0.135	0.275*
FCR (g/g)	2.19c	2.61b	3.00a	0.025	<0.001	3.02a	2.59b	2.16c	0.021	<0.001	0.949⁎⁎	−0.987⁎⁎
RFI (g/d)	−24.99c	0.00b	25.37a	0.527	<0.001	25.65a	−0.00b	−25.63c	0.513	<0.001	-	−0.994⁎⁎
RG (g/d)	0.91	−0.00	−0.67	0.536	0.118	−0.97c	0.01	0.90a	0.493	0.024	−0.231*	0.405⁎⁎
RIG (g/d)	25.90a	−0.12	−26.04c	0.798	<0.001	−26.62c	0.00b	26.53a	0.691	<0.001	−0.991⁎⁎	-

^aDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^bDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^cDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^⁎Indicates a significant correlation (P < 0.05).

^⁎⁎Indicates an extremely significant correlation (P < 0.01).

¹BW = body weight; MBW^0.75 = metabolic body weight; ADFI = average daily feed intake; ADG = average daily gain; FCR = feed conversion ratio; RFI = residual feed intake; RG = residual gain; RIG = residual intake and gain.

Carcass Characteristics

The impacts of RFI and RIG divergence on carcass characteristics are shown in Table 4. The examination of feed efficiency variations revealed disparities between efficient and inefficient animals. The abdominal fat percentage of low-RFI ducks was slightly lower than that of medium- and high-RFI ducks (P > 0.05), and the abdominal fat percentage of high-RIG ducks was also slightly lower than that of medium- and low-RIG ducks (P > 0.05). The liver yield showed a downward tendency (P > 0.05) in the efficient ducks based on the RFI index. The percentage of skin fat yield from high-RFI ducks was 1.75 and 1.19 percentage points higher (P < 0.05) than that of medium- and low-RFI ducks, respectively. Prior research has demonstrated that efficient animals had different BW gain patterns, including lower fat deposition and visceral weight (Nascimento et al., 2016; Fathi et al., 2019; Yang et al., 2020). The RFI trait was positively correlated with skin fat yield (r = 0.200), and no correlation existed between RIG and skin fat yield. Furthermore, there were no significant differences in other carcass traits between the different RFI groups (P > 0.05). Similarly, no significant difference was observed between the low- and high-RIG ducks (P > 0.05). RFI Furthermore, feed intake and RIG were not correlated with any of the carcass characteristics evaluated (P > 0.05). Defeathered, semieviscerated, and eviscerated carcass yields were higher in this study than in our previous study, at 80.70, 76.27, and 70.34%, respectively (Bai et al., 2022). Animal weight, yield, and size at slaughter are all positively connected to carcass traits (Santos et al., 2016). The trends in liver yield with respect to RFI were consistent with earlier findings by Fathi et al. (2019) and Gabarrou et al. (1998), who found that RFI was genetically and phenotypically associated with lower heart, liver, and gizzard yields. This is due to the fact that less efficient chicken spent more resources developing their organs than more effective poultry. In the present study, heart yield, liver yield, and gizzard yield, showed no correlation with RFI and RIG. This could be due to the fact that the variation in the relationship between body composition and efficiency measures between studies could be due to a variety of factors such as genotype or phenotype, interspecies differences, relative growth rates of the body and its tissues, stage of maturity, and the environment (Begli et al., 2017). Furthermore, animals with less subcutaneous fat deposition develop later and require more time to mature, increasing production costs (Yamamoto et al., 2013). The greater fat deposition of High-RFI animals was due to a larger ad libitum feed consumption compared with the Low-RFI animals (Lines et al., 2018). Inefficient poultry may also consume more feed, resulting in increased fat deposition (Zhang et al., 2018; Li et al., 2020). Because RG identifies animals with high growth rates and adequate finishing, the combination of RG and RFI may eliminate the problem of insufficient subcutaneous fat at slaughter, which is consistent with the skin fat yield traits measured in the study.

Table 4.

Mean, standard error of the mean (SEM), and phenotypic correlation for carcass trait of small-sized meat ducks classified as low-, medium-, and high-residual feed intake (RFI) or low-, medium-, and high-residual intake and gain (RIG).

Trait	RFI					RIG					Correlation coefficients
	Low	Medium	High	SEM	P value	Low	Medium	High	SEM	P value	RFI	RIG
Carcass yield (%)	80.39	80.69	81.02	0.21	0.130	80.62	80.74	80.80	0.231	0.827	0.205	−0.085
Semieviscerated yield (%)	76.20	76.11	76.48	0.237	0.538	76.28	76.01	76.34	0.235	0.600	0.092	−0.114
Eviscerated yield (%)	70.44	69.76	70.79	0.336	0.110	70.09	70.61	70.51	0.378	0.570	0.067	−0.071
Breast muscle yield (%)	8.38	8.36	8.32	0.176	0.973	8.40	8.15	8.39	0.199	0.632	0.008	−0.159
Thigh muscle yield (%)	12.16	12.52	12.04	0.161	0.110	12.21	12.33	12.39	0.189	0.786	−0.059	−0.021
Abdominal fat yield (%)	1.74	1.81	1.79	0.090	0.853	1.75	1.86	1.67	0.104	0.451	0.051	0.107
Gizzard yield (%)	3.11	3.23	3.22	0.075	0.478	3.22	3.17	3.10	0.074	0.587	0.092	−0.107
Heart yield (%)	0.491	0.485	0.50	0.010	0.585	0.50	0.49	0.49	0.011	0.696	0.170	−0.117
Liver yield (%)	1.794	1.835	1.82	0.025	0.434	1.82	1.84	1.81	0.028	0.708	0.070	−0.069
Lean meat yield (%)	20.44	20.86	20.46	0.222	0.343	20.48	20.63	20.78	0.256	0.681	0.027	−0.163
Skin fat yield (%)	27.02b	26.46b	28.21a	0.411	0.013	27.06	27.58	27.08	0.464	0.685	0.200	0.083

^aDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^bDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

Meat Quality

Tables 5 and 6 indicate the differences in breast and thigh meat quality traits between the RFI and RIG groups. In addition to improved performance, efficient ducks should have acceptable carcass production and quality. Prior research has sought to link animal performance and carcass traits to RIG levels (Carneiro et al., 2019). However, data on poultry breeds are scarce. Thus, we evaluated the meat quality of ducks divided into 3 groups based on RFI and RIG. In this study, pH₁ of high-RFI ducks was slightly lower than that of low-RFI and high-RFI ducks (P > 0.05), low-RIG ducks had lower pH₁ compared to medium- and high-RIG ducks (P < 0.05), and no significant difference was observed between medium-RIG and high-RIG ducks (P > 0.05). The value of pH₁ was negatively associated with RFI (r = −0.240, P < 0.05) and positively associated with RIG (r = 0.270, P < 0.05) which is in agreement with results in Korat chickens (Poompramun et al., 2022) as well as high-, medium-, and low-RFI Dorper × Santa Inês male lambs (Montelli et al., 2020). Further, a higher growth rate was positively correlated with the pH of breast meat. In our study, there were no differences in pH₂₄ between RFI or RIG groups. In addition, RFI or RIG had no effect on thigh muscle quality (P > 0.05) (Table 6). There were no significant differences between the efficiency measures for pH at 1 and 24 h, color (reflectance coordinates L*, a*, b*), cooking loss, shear force, or drip loss of duck meat from each efficiency measure (P > 0.05). Intramuscular moisture, protein, fat, and collagen of duck meat did not differ between RFI classes of feed efficiency and RIG classes (P > 0.05). Similarly, there was no correlation between RFI and RIG classes for the variables analyzed (P > 0.05). Le Bihan-Duval et al. (2008) analyzed the physiological changes of muscle after slaughter and pointed out that the pH change as well as muscle glycogenolysis rate play a key role affecting meat quality after slaughter, mainly the rate and amplitude of acidification, which influences the functional properties of poultry meat. It has been noted that the link between pH₁ and final pH during the acidification of postmortem muscle fibers does not have a genetic basis (Le Bihan-Duval et al., 2008). However, our findings and the covariation between these 2 attributes can be attributed to distinct underlying processes. Changes to the sensory qualities of poultry meat are of particular significance as color is a key component of meat product appearance and influences customer purchasing behavior. Meat quality flaws might affect color and tenderness since they have been associated with selection based on chicken growth rate and muscle development (Mudalal et al., 2015). However, despite the fact that rigorous selection of growth performance and breast yield has led to variations in muscle histology and metabolism, only slight alterations in meat color and no negative impact on meat quality were observed. The tenderness of meat is an important functional attribute during food processing, as the quality of processed duck meat products is affected by the ability of the flesh to retain water during storage and cooking (Duclos et al., 2007). In the early postmortem phase, a sudden drop in pH and high temperatures can cause myofiber shrinking and disrupt protein functioning, which lowers the muscles' ability to store water (Wilhelm et al., 2010). Meat tenderness is determined by shear force, which is a result of water loss and is strongly related to the intramuscular water component and, consequently, to the meat's ability to hold water (Joo et al., 2013). Recent studies have shown that the higher the water content in muscle, the higher the tenderness of meat. In our results, the correlation between pH₁ and feed efficiency did not affect the water-holding capacity and tenderness of breast muscle. For low- and high-RIG ducks, the traits of water-holding capacity, cooking loss, and shear force were not different, and neither attribute was impacted by RFI class. These traits were not expressed in thigh muscle quality in correlation with feed efficiency (Tables 5 and 6). Paiva et al. (2018) indicated that selection to enhance feed conversion ratio may diminish L*, water-holding capacity, and shear force in broilers to varying degrees, which is not in line with our current findings. In the production of meat ducks, the relationship between RFI or RIG and meat quality traits appeared to be weak, although the idea that selection for greater growth rates is deleterious to meat quality per se could not be substantiated. Therefore, genetic selection for RFI and RIG in small-sized meat ducks can reduce feed intake, lower total production costs, and improve feed efficiency without affecting meat quality traits.

Table 5.

Mean, standard error of the mean (SEM), and phenotypic correlation for breast muscle quality of small-sized meat ducks classified as low-, medium-, and high-residual feed intake (RFI) or low-, medium-, and high-residual intake and gain (RIG).

Trait1	RFI					RIG					Correlation coefficients
	Low	Medium	High	SEM	P value	Low	Medium	High	SEM	P value	RFI	RIG
pH1	6.11	6.11	6.05	0.021	0.057	6.05b	6.13a	6.12a	0.023	0.021	−0.240*		0.270*
pH24	6.14	6.11	6.08	0.025	0.219	6.08	6.14	6.13	0.027	0.176	−0.199		0.231
L*	40.42	40.08	39.70	0.393	0.422	39.55	40.26	40.36	0.409	0.288	−0.140		0.106
a*	14.91	14.70	14.16	0.361	0.328	14.17	14.81	15.01	0.408	0.302	−0.162		0.135
b*	5.71	5.45	5.26	0.220	0.359	5.23	5.60	5.65	0.254	0.416	−0.150		0.156
Shear force	25.14	24.34	25.44	1.381	0.851	25.97	23.55	25.22	1.479	0.529	0.052		0.130
Drip loss (%)	23.92	25.74	24.90	1.343	0.635	24.98	25.29	24.16	1.493	0.859	0.058		0.017
Cook loss (%)	29.56	29.72	29.00	0.941	0.855	29.13	29.72	29.53	0.948	0.906	−0.048		0.191
Moisture (%)	74.10	74.28	74.10	0.103	0.398	74.02	74.24	74.10	0.120	0.470	−0.007		0.033
Protein (%)	23.66	23.44	23.51	0.106	0.342	23.53	23.35	23.64	0.113	0.268	−0.101		0.032
Intramuscular fat (%)	2.41	2.47	2.62	0.076	0.126	2.64	2.50	2.41	0.084	0.120	0.218		−0.147
Collagen (%)	0.37	0.34	0.40	0.043	0.641	0.40	0.31	0.38	0.048	0.416	0.042		−0.026

^aDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^bDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^⁎Indicates a significant correlation (P < 0.05).

¹pH₁ = pH value measured 1 h after slaughter; pH₂₄ = pH value measured 24 h after slaughter; L* = lightness; a* = redness of red; b* = yellowness.

Table 6.

Mean, standard error of the mean (SEM), and phenotypic correlation for thigh muscle quality of small-sized meat ducks classified as low-, medium-, and high-residual feed intake (RFI) or low-, medium-, and high-residual intake and gain (RIG).

Trait1	RFI					RIG					Correlation coefficients
	Low	Medium	High	SEM	P value	Low	Medium	High	SEM	P value	RFI	RIG
pH1	6.39	6.38	6.38	0.022	0.876	6.39	6.38	6.39	0.025	0.903	−0.050	0.014
pH24	6.26	6.28	6.29	0.031	0.744	6.30	6.26	6.26	0.034	0.634	0.031	−0.074
L*	42.63	42.92	44.22	0.592	0.150	44.56	42.75	42.87	0.616	0.095	0.025	−0.079
a*	15.41	15.12	14.69	0.454	0.536	15.06	15.00	15.40	0.531	0.849	−0.088	0.025
b*	9.10	8.89	8.82	0.340	0.820	9.03	9.09	9.18	0.386	0.954	−0.166	0.148
Shear force	31.06	32.75	32.03	2.422	0.884	30.46	35.26	30.55	2.659	0.403	−0.012	0.047
Drip loss (%)	25.30	25.23	25.97	0.900	0.821	25.53	25.23	25.66	1.022	0.957	0.218	−0.132
Cook loss (%)	32.77	28.09	30.70	1.835	0.209	30.01	28.72	33.01	2.159	0.348	−0.125	0.145
Moisture (%)	72.32	72.71	72.51	0.184	0.326	72.62	72.52	72.32	0.214	0.581	−0.002	−0.055
Protein (%)	21.56	21.79	21.50	0.145	0.374	21.46	21.78	21.43	0.161	0.278	−0.023	−0.040
Intramuscular fat (%)	4.23	4.25	4.44	0.199	0.704	4.41	4.21	4.14	0.195	0.563	0.015	−0.020
Collagen (%)	0.64	0.67	0.61	0.049	0.706	0.63	0.69	0.62	0.056	0.726	0.010	−0.024

¹pH₁ = pH value measured 1 h after slaughter; pH₂₄ = pH value measured 24 h after slaughter; L* = lightness; a* = redness of red; b* = yellowness.

Hormone and Metabolite Concentrations

The data in Table 7 show that the serum TG level in the low-RIG group was greater than that in the high- and medium-RIG groups (P < 0.05), whereas the TG of the high-RIG group was lower than that of the medium-RIG group (P < 0.05) (Table 7). The serum TG level of the high-RFI birds was higher than that of the low-RFI birds (P < 0.05), but the RIG group showed the opposite tendency. RFI exhibited a tendency (P = 0.076) for lower HDL-C levels. In addition, HDL-C levels exhibited a tendency (P = 0.064) toward improvement in high-RIG ducks based on the RIG index. The serum INS level in the low-RIG group was lower than that of the high-RIG groups, while lower than that of the medium-RIG group (P < 0.05). However, there was no significant difference between the low-, medium-, and high-RFI ducks (P > 0.05). Low-RIG birds had higher serum GH levels than high-RIG birds (P < 0.05). Similarly, the low-RFI group had slightly lower GH levels than the medium- and high-RFI groups (P > 0.05). RFI was found to be positively related to serum TG levels (r = 0.323, P < 0.05) and negatively related to serum HDL-C level (r = −0.332, P < 0.05). RIG levels were moderately related to serum TG levels (r = −0.415, P < 0.05) and positively related to serum HDL-C level (r = 0.370, P < 0.05). Correlation analysis of serum hormone levels showed that RFI and RIG were not correlated with any of the indices (P > 0.05).

Table 7.

Mean, standard error of the mean (SEM), and phenotypic correlation for serum metabolite and hormone concentrations of small-sized meat ducks classified as low-, medium-, and high-residual feed intake (RFI) or low-, medium-, and high-residual intake and gain (RIG).

Trait1	RFI					RIG					Correlation coefficients
	Low	Medium	High	SEM	P value	Low	Medium	High	SEM	P value	RFI	RIG
Serum metabolites
TC (mmol/L)	4.75	4.95	4.83	0.10	0.373	4.83	4.87	4.72	0.11	0.597	0.084	−0.090
TG (mmol/L)	0.63b	0.67ab	0.73a	0.03	0.024	0.71a	0.69b	0.62c	0.03	0.039	0.323*	−0.415⁎⁎
HDL-C (mmol/L)	2.94	2.93	2.74	0.07	0.076	2.72	2.83	2.96	0.07	0.064	−0.332*	0.370*
LDL-C (mmol/L)	1.38	1.41	1.30	0.05	0.277	1.31	1.41	1.36	0.05	0.419	−0.165	0.130
GLU (mmol/L)	11.52	11.55	11.64	0.09	0.579	11.64	11.51	11.52	0.09	0.547	0.149	−0.110
CK (U/L)	1458.44	1438.29	1463.69	68.88	0.965	1458.76	1380.43	1445.65	62.06	0.650	−0.009	0.029
FFA (mmol/L)	0.67	0.64	0.62	0.04	0.710	0.61	0.66	0.67	0.04	0.498	−0.134	0.197
Serum hormones
INS (uIU/mL)	15.49	16.14	15.09	0.38	0.144	14.93b	16.70a	15.17ab	0.37	0.005	−0.113	0.180
GH (ng/mL)	4.88	4.75	5.24	0.15	0.082	5.29a	4.82b	4.88b	0.14	0.043	0.042	−0.065
ACTH (pg/mL)	24.74	24.06	26.16	0.73	0.142	25.51	24.60	24.44	0.85	0.637	0.105	−0.122
CCK (pmol/L)	4.03	4.17	3.84	0.19	0.477	3.83	4.20	4.10	0.20	0.406	−0.184	0.198
COR (ng/mL)	5.43	5.65	5.35	0.13	0.322	5.29	5.65	5.46	0.15	0.296	−0.170	0.133
GHR (ng/mL)	81.57	81.27	84.76	2.15	0.443	84.77	80.91	81.30	2.26	0.389	0.189	−0.214
LEP (ng/mL)	4.64	4.89	4.82	0.08	0.075	4.82	4.80	4.78	0.07	0.927	0.064	−0.044

^aDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^bDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^cDifferent letters within a row and of the RFI or RIG classification indicate significant differences (P < 0.05).

^⁎Indicates a significant correlation (P < 0.05).

^⁎⁎Indicates an extremely significant correlation (P < 0.01).

¹TC = total cholesterol; TG = triglycerides; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; GLU = glucose; CK = creatine kinase; FFA = free fatty acid; INS = insulin; GH = growth hormone; ACTH = adrenocorticotropic hormone; CCK = cholecystokinin; COR = cortisol; GHR = ghrelin; LEP = leptin.

Similarly, a prior study found greater levels of TG in pigs with low feed efficiency, and the gene expression patterns of pigs with low feed efficiency increased cholesterol absorption (Horodyska et al., 2019). RFI is the difference between an animal's actual and projected feed intake depending on production. It has been claimed that some of the variation in RFI is due to differences in energy efficiency caused by changes in heat production, which are in part due to differences in protein metabolism (Herd and Arthur, 2009). An animal's immune system requires dietary energy and proteins. In theory, the immune system will work better if these nutritional requirements are met. The individuals were screened according to the index of feed efficiency, and individuals with high feed efficiency may be at an increased risk for infectious diseases as the above-mentioned nutritional needs cannot be met by the reduced intake (Van Eerden et al., 2004). Nonefficient birds have more energy available to maintain their "thermostats" of non–antigen-specific immune reactivity at a greater level than highly efficient birds. In the study, Low-RIG birds had higher serum GH levels than high-RIG birds (P < 0.05). In this regard, researchers have opposing viewpoints, arguing that changes in RFI can be ascribed to the animal's appetite or amount of energy consumed, resulting in divergence of fat deposition in genotypes or phenotypes (Roberts et al., 2007; Lines et al., 2018). All of the excess energy used by efficient animals above and beyond protein maintenance and deposition was associated with increased energy retained as fat. Basal or underlying metabolic rates were not altered by selection for RFI. Leptin synthesis occurs in the liver and adipose tissues of poultry (Taouis et al., 2001; Murugesan and Nidamanuri, 2022). While the high-RFI group had very low abdominal fat compared to the low-RFI group, the enhanced plasma leptin levels in the high-RFI group suggested that the liver was the primary location of leptin synthesis. The involvement of leptin in lipogenesis is assumed to be connected with hepatic production of leptin in chickens (Friedman-Einat and Seroussi, 2019). Selection for RFI had no effect on liver weight but genes related to fatty acid oxidation are downregulated in the liver of high-RFI ducks, while genes associated with lipid synthesis are upregulated (Drouilhet et al., 2016; Jin et al., 2019). This is consistent with prior investigations on liver yield and TG levels (Tables 4 and 7). In the current investigation, however, LEP were considerably higher in the high-RFI group. This contradicts experimental findings on the satiating impact of leptin in mammals (Pandit et al., 2017). Several factors that stimulate feed intake appear to outweigh leptin's satiating impact, which may be related to the hypothesized genetic effect (Cassy et al., 2004; Swennen et al., 2007). Thus, the role of LEP in residual feed intake in these duck strains should be investigated further.

CONCLUSIONS

Our study determined that by combining the advantages of RFI and RG, RIG increased the growth rate without affecting growth performance and meat quality while maintaining a low feed intake. Regarding carcass traits, efficient ducks displayed different BW gain patterns, including reduced fat deposition and lower visceral weight. The abdominal fat percentage of efficient ducks was slightly lower than that of inefficient ducks. The association between RG and RFI may eliminate the problem of insufficient subcutaneous fat at slaughter, with no correlation between RIG and skin fat yield. The effects of TG, LDL-C, INS and GH on the feed efficiency of ducks should be carefully examined and validated in future experiments. The present study provides valuable insights into the selection of economic characteristics for RFI and RIG for higher meat production and quality in small-sized meat ducks.