Residual feed intake and gain changes the performance and carcass traits in Nellore cattle

doi:10.21203/rs.3.rs-3813325/v1

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Residual feed intake and gain changes the performance and carcass traits in Nellore cattle

https://doi.org/10.21203/rs.3.rs-3813325/v1

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The residual intake and gain (RIG) aims to select animals with fast growth development that present low feed intake in relation to the expected. This study aimed to evaluate the associations of selection for RIG with performance and carcass traits in Nellore cattle. Initially, residual feed intake (RFI) and residual gain (RG) were determined. From this, the RIG was calculated, and the animals were classified as efficient and inefficient for RIG. The data were subjected to analysis of variance, and the means were compared by the Tukey test at 5% probability. Pearson's correlations were estimated between performance, carcass characteristics, and RIG classes. The efficient animals for RIG showed higher daily weight gain (DWG), Longissimus muscle area (LMA), and mean metabolic body weight (MMBW)(P < 0.001) than the inefficient ones. No significant correlations (P > 0.05) were found between subcutaneous fat thickness (SFT), marbling (MAR), LMA, MMBW, and the RIG. The RIG is a measure that can be used to identify and select animals with higher rates of DWG and LMA without changes in the dry matter intake (DMI), SFT, and MAR. However, this index should still be observed with caution, as it may be dependent on body size. The selection of animals according to the RIG can be an important factor to generate phenotypic evolution in characteristics such as weight gain and rib eye area without adverse effects on the carcass fat deposition.

Animal Science

beef cattle

dry matter intake

feed efficiency

subcutaneous fat thickness

marbling

The livestock profitability is associated with the genetic gain of characteristics of economic interest, so it is influenced by the selection of animals efficient in the use of feed. The feeding of animals represents 55–75% of the total expenses in the cattle production unit (Arthur et al. 2001). Given that production costs impact the system profitability, feed efficiency is of vital importance in beef cattle production.

Residual Feed Intake (RFI) is one of the most used indexes for selection for feed efficiency. However, some studies have shown unfavorable phenotypic correlations between this index and carcass characteristics and meat quality (Robinson and Oddy, 2004; Herd and Arthur, 2009; Basarab et al. 2011; Crowley et al. 2011). As a result, Berry and Crowley (2012) proposed the residual intake and gain (RIG). This index was proposed as an alternative to RFI since RFI is phenotypically independent of daily weight gain (DWG) (Bonilha et al. 2013), although it is used for its calculation (Koch et al. 1963).

The use of RIG allows recognizing animals of fast development, which present low feed intake in relation to the expected, without changes in the weight at maturity (Berry and Crowley, 2012). These authors also reported that animals efficient for RFI could grow slowly and need more time in the feedlot than animals efficient for RIG. Studies that relate the influence of selection from the RIG on other economic impact characteristics in beef cattle are still scarce. Thus, this study aimed to evaluate the associations of selection for RIG with performance and carcass traits in Nellore cattle.

Data from seven feed efficiency tests with 895 Nellore steers born between 2010 and 2013 were analyzed (Table 1). These tests were conducted between 2011 to 2014 had an average duration of 73 ± 3.6 days and consisted of 15-day acclimation period (including the 73 days) to the diets and facilities.

Table 1

Description of the feed efficiency tests of male Nellore cattle.
Test	Year	N	Test duration - days	Initial age - days Mean ± SD	IBW – kg Mean ± SD
01	2011	119	74	348 ± 27	371 ± 34
02	2012	134	78	273 ± 31	281 ± 28
03	2012	114	74	324 ± 32	318 ± 34
04	2013	141	71	276 ± 12	298 ± 29
05	2013	128	78	332 ± 25	318 ± 46
06	2014	132	70	263 ± 18	278 ± 27
07	2014	127	68	322 ± 48	317 ± 50
Mean	-	895	73 ± 3.6	304 ± 43.2	310 ± 46.4
n – number; SD - standard deviation; IBW – Initial body weight; kg – kilogram

The feed efficiency was conducted at the Rancho da Matinha Farm, located in Uberaba, Minas Gerais, Brazil. Animals were housed in two paddocks, with the same solar incidence, to measure dry matter intake (DMI), using the GrowSafe® systems (GrowSafe Systems Ltd., Airdrie, Alberta, Canadá). The troughs were filled with the diet periodically in the morning and afternoon, assuring ad libitum intake. The paddocks were provided with drinking fountains and animals had free-choice access to water.

The animals were weighed on a calibrated digital balance, attached to the squeeze chute, at the beginning and the end of the tests, and every 2 weeks after the beginning of each test. Weighing data were used to determine the initial body weight (IBW, kg), final body weight (FBW, kg), and daily weight gain (DWG, kg/day). The mid-test metabolic body weight (MMBW) was calculated as the average between IBW and FBW. The DWG was calculated for each animal from the slope coefficient of the straight line resulting from the regression of individual body weight measurements without fasting as a function of time. For this, the REG procedure of SAS (SAS INSTITUTE, 2009) statistical package was used.

In all feed efficiency tests, diets were formulated to be isoenergetic and isonitrogenous, to have approximately 70% of total digestible nutrients and 13% of crude protein and were supplied twice daily. Composite samples of the diet and refusals were analyzed to determine the concentrations of dry matter in an oven at 105°C, as proposed by the Association of Analytical Chemists - AOAC (2006). The dry matter intake (DMI) was calculated based on the average of all intake values obtained during the test.

The subcutaneous fat thickness (SFT), Longissimus muscle area (LMA), and marbling (MAR) on the animal's back, between the 12th and 13th rib. These traits were evaluated on the last day of each test using a real-time ultrasound scanner (Aquila Pie Medical, Inc. Maastricht, The Netherlands), with ultrasound pulses in frequency from 3 to 3.5 MHz, performed by a certified technician accredited by the Ultrasound Guidelines Council.

Residual feed intake (RFI, kg/day) and residual gain (RG, kg/day) were obtained by regression equations of DMI, MMBW, and DWG (Koch et al. 1963), using the MIXED procedure of SAS (SAS INSTITUTE, 2009) with the following equations:

_predDMI = -0.9680 + 0.07065MMBW + 2.7013DWG + ε1

_predDWG = 0.1383–0.00224MMBW + 0.1401DMI + ε2

In which _predDMI represents the predicted dry matter intake, _predDWG is the predicted daily weight gain, ε1 the RFI, and ε2 the RG.

For the RIG determination, the equation proposed by Berry and Crowley (2012) was used, in which the values of RFI and RG are associated, according to the expression:

RIG= ((-1) * RFI)) + RG

To categorize the efficient, intermediate and inefficient classes of animals in terms of RFI and RIG, they were classified using the classical limits of the literature (Koch et al. 1963), namely ± 0.5 standard deviations in relation to the mean RFI and RIG. The animals that presented RFI and RIG between − 0.5 to 0.5 SD (intermediate animals) were not included in the statistical calculation.

The analysis of the relationships between the efficient and inefficient groups regarding RIG, performance, and carcass traits was performed using the PROC MIXED of SAS (SAS INSTITUTE, 2009). Each of the seven efficiency tests consisted of two batches of animals evaluated (one and two). The model included the fixed effects of RIG groups (efficient and inefficient), feed efficiency tests (one to seven), batch (one and two), and initial age as a covariate. The means were compared by the Tukey test at 5% probability, and, in all analyzes, the null hypothesis was rejected when the probability was ≤ 0.05. Pearson's correlation estimates among feed efficiency, performance, and carcass characteristics were obtained using the PROC CORR procedure of SAS (SAS INSTITUTE, 2009).

The animals gained 0.99 ± 0.25 kg/day and consumed an average of 7.35 ± 1.37 kg/day (Table 2). The average value observed for RIG was 0.00 ± 1.79, with a range from − 5.69 to 5.31. The variables RFI and RIG showed phenotypic variability between the animals evaluated (Table 2).

Table 2

Descriptive statistics of performance and carcass traits of Nellore cattle submitted to feed efficiency tests.
Trait	Mean	SD	Minimum	Maximum
N = 895
Initial age (days)	304.26	43.20	195.00	472.00
FBW (kg)	381.48	55.55	260.00	570.00
IBW (kg)	310.64	46.45	218.00	499.00
MMBW (kg)	80.06	8.68	57.72	111.20
DWG (kg/day)	0.99	0.25	0.18	1.63
DMI (kg DM/day)	7.35	1.37	4.41	11.76
RFI (kg DM/day)	0.00	1.00	-2.98	3.14
RG (kg/day)	0.00	1.00	-3.02	2.59
RIG	0.00	1.79	-5.69	5.31
SFT (mm)	3.97	1.27	1.52	10.41
LMA (cm²)	64.01	9.48	39.41	102.32
MAR	1.99	0.48	1.39	5.56
n = number; SD = standard deviation; FBW = final body weight; IBW = initial body weight; MMBW = mid-test metabolic body weight; DWG = daily weight gain; DMI = dry matter intake; RFI = residual feed intake; RG = residual gain; RIG = residual intake and gain; SFT = subcutaneous fat thickness; LMA = longissimus muscle area; MAR = Marbling; kg - kilogram; DM = dry matter; mm = millimeter; cm² = square centimeter

Considering the classification for RIG, 30.72% of the animals (N = 275 animals) showed efficient RIG, and 32.51% (N = 291 animals) were classified as inefficient. From the total number of efficient RIG animals, 219 were classified as an efficient RFI and 56 as an intermediate RFI (Fig. 1).

It was observed that animals efficient for RIG had higher FBW and MMBW (P < 0.001; Table 3). Thus, the efficient RIG animals showed 31% more FBW than the inefficient RIG animals (P < 0.001) without changes in DM intake (P = 0.1021; Table 3). Besides, inefficient RIG animals had an average LMA 4% lower than efficient RIG animals (P < 0.001; Table 3).

Table 3

Classification for Nellore males maintained in feed efficiency tests classified as efficient and inefficient for residual intake and gain (RIG). N = 895
Traits	Efficient	Inefficient	SEM	P-value
IBW (kg)	313.4	309.8	2.80	0.1930
FBW (kg)	397.2	367.6	3.22	< 0.0001
MMBW (kg)	81.71	78.72	0.51	< 0.0001
DWG (kg/day)	1.17	0.80	0.01	< 0.0001
DMI (kg/day)	7.28	7.41	0.07	0.1021
LMA (cm²)	65.60	62.76	0.68	< 0.0001
SFT (mm)	4.11	3.90	0.12	0.0597
MAR	2.00	1.99	0.03	0.8500
RFI (kg/DM/day)	-0.88	0.81	0.04	< 0.0001
RG (kg/day)	1.10	-1.08	0.05	< 0.0001
IBW = initial body weight; FBW = final body weight; MMBW = mid-test metabolic body weight; DWG = daily weight gain; DMI = dry matter intake; LMA = longissimus muscle area; SFT = subcutaneous fat thickness; MAR = Marbling; RFI = residual feed intake; RG = residual gain; SEM = standard error of the mean; kg - kilogram; DM = dry matter; mm = millimeter; cm² = square centimeter.

There was a difference (P < 0.001) between the variables of RFI and RG for the groups of animals that were efficient and inefficient for RIG (Table 3). The animals efficient for RIG showed an average value of -0.88 kg DM/day and 1.10 kg/day for RFI and RG, respectively. Thus, most animals efficient for RIG were also efficient for RFI (Fig. 1) and for RG (positive RG). The RIG was directly proportional to the RG and indirectly proportional to the RFI (P < 0.001). Despite the difference in FBW and MMBW between RIG groups (Table 3), no phenotypic correlation was observed between RIG and these variables (P > 0.05).

Although no difference was observed in the DMI between the RIG groups, a favorable phenotypic correlation was observed between RIG and DMI (P < 0.001; r = -0.32) (Table 4), that is, the more efficient the animal, the smaller DMI would be. A positive phenotypic correlation (P < 0.001; r = 0.39) between DWG and RIG was found (Table 4). Among carcass characteristics (LMA, SFT, and MAR) and the RIG, however, no phenotypic correlations were observed (Table 4).

Table 4

Phenotypic correlations between characteristics measured in Nellore males maintained in feed efficiency tests.
	IBW	FBW	DWG	LMA	SFT	MAR	RFI	RIG	RG	MMBW
DMI	0.55***	0.73***	0.69***	0.51***	0.31***	-0.23***	0.58***	-0.32***	0.00	0.66***
IBW		0.93***	0.24***	0.74***	0.42***	0.00	-0.01	-0.06	-0.13***	0.98***
FWB			0.54***	0.73***	0.43***	-0.08	0.02	0.04	0.09	0.98***
DWG				0.29***	0.20***	-0.20***	0.00	0.39***	0.71***	0.41***
LMA					0.38***	-0.13	0.04	-0.05	-0.04	0.75***
SFT						0.03	0.02	-0.01	0.00	0.43***
MAR							-0.15***	0.04	-0.07	-0.04
RFI								-0.89***	-0.61***	0.00
RIG									0.89***	0.00
RG										0.00
DMI = dry matter intake (kg DM/day); IBW = initial body weight (kg); FBW = final body weight (kg); DWG = daily weight gain (kg/day); LMA = Longissimus muscle area (cm²); SFT = subcutaneous fat thickness (mm); MAR = Marbling; RFI = residual feed intake (kg DM/day); RIG = residual intake and gain; RG = residual gain (kg/day); MMBW = mid-test metabolic body weight (kg); ***P < 0.001. kg – kilogram; DM = dry matter; mm = millimeter; cm² = square centimeter

The values found for the RIG are close to those reported in other studies (Nascimento et al. 2016; Figueiredo et al. 2018). Nascimento et al. (2016) carried out a study with 575 steers born from 34 different sires, chosen to represent the main genealogies of the Nellore breed. In this study, the authors used mixed models for the RFI and the RIG and observed relationships of both indexes with performance characteristics, carcass traits, and meat quality. Among the animals classified as efficient and inefficient for RIG, Nascimento et al. (2016) reported a range of phenotypic variation from − 4.91 to 3.45. Figueiredo et al. (2018) studied the phenotypic correlations between RIG and other measures of feeding efficiency and found 0.003 of average value in 610 Nellore animals kept in confinement.

In this study, it is suggested that higher body weight, associated with higher DWG, in efficient RIG animals, results from the fact that the RIG is calculated taking into account the RG. The RG is highly related to weight gain and faster growth rates, making these two indices phenotypically dependent on body weight. This result is in agreement with Berry and Crowley (2012), who also observed higher body weight in animals efficient for RIG. However, other studies (Santana et al. 2014; Figueiredo et al. 2018; Carneiro et al. 2019; Montelli et al. 2019), found no difference in body weight between groups of animals that were efficient and not efficient for RIG. This fact suggests that this index should be used with caution in the animal selection, as it may be phenotypically dependent on the MMBW and, consequently, on the herd's adult size.

In the current study, the selection based on the RIG was able to identify cattle with higher weight gain (Fig. 2), which consumes a similar amount of food to the other animals evaluated. This can be attributed to the higher efficiency in the energy use for gain and low maintenance requirements in efficient animals, a clear benefit of the combination between RFI and RG.

In the case of carcass characteristics, in the current study, efficient RIG animals have higher LMA than inefficient ones, which corroborates with Nascimento et al. (2016). It is then suggested that animals with accelerated development and low intake than expected present higher capacity to deposit muscle. However, previous studies have been inconsistent about the relationship between RIG and carcass characteristics (Santana et al. 2014; Carneiro et al. 2019). Santana et al. (2014) estimated genetic parameters in 1038 Nellore animals for feed efficiency and did not observe any effect for LMA in animals classified for RIG. Carneiro et al. (2019), although conducted a study with sheep, evaluated carcass characteristics for animals classified as high, intermediate, and low RIG, and also found no effect on LMA between classes.

There is no phenotypic dependence between RFI and DWG, so it is possible to select efficient RFI animals with low weight gain. At the same time, there is no phenotypic dependence between the RG and the DMI, which results in a situation similar to that of the RFI (Baker et al. 2006; Ahola et al. 2011). The RIG then presents itself as an alternative for selection based on feed efficiency, as it identifies animals with higher daily gain without effects in the DMI. This fact has a substantial impact on the optimization of production costs, and consequently, good potential for economic return. Another important factor to note is the possibility of increasing beef production without expanding the cultivated pasture areas (Basarab et al. 2003; Nkrumah et al. 2006).

The moderate and favorable correlation between DMI and RIG found in the present study is close to the studies developed by Berry and Crowley (2012), Santana et al. (2014), Grion et al. (2014), and Ceacero et al. (2016), who reported values between − 0.34 and − 0.87. Estimates of the phenotypic correlation between RIG and DWG also moderate and favorable suggest the possibility of selecting animals with lower intake and higher weight gain, in parallel with RIG's use, as an index to assess feed efficiency. Santana et al. (2014), Grion et al. (2014), and Ceacero et al. (2016) also observed positive phenotypic correlations between DWG and RIG. Grion et al. (2014) estimated genetic parameters for characteristics indicative of feed efficiency in Nellore cattle and reported a correlation of 0.37 between RIG and DWG. Ceacero et al. (2016) found a 0.34 correlation between the two variables, values very close to those observed in the current study.

The high correlations between RFI, RG, and RIG were expected, given the equation that gives rise to the RIG index (Berry and Crowley, 2012; Santana et al. 2014; Figueiredo et al. 2018; Takeda et al. 2018). Takeda et al. (2018), when studied 4.578 Japanese black cattle found correlations between the RIG and the RFI and RIG and the RG of -0.80 and 0.86, respectively. Figueiredo et al. (2018) observed values of 0.70 (RG and RIG) -0.98 (RFI and RIG) for 610 Nellore animals.

As no correlations were observed between the RIG and LMA, SFT, MAR, and MMBW, it is possible to show that the selection based on the RIG does not negatively alter these characteristics. Santana et al. (2014) did not observe RIG and LMA and SFT correlations in Nellore animals. Ceacero et al. (2016) reported phenotypic correlation values of 0.01, -0.08, and − 0.05 for LMA, fat cover thickness, and rump fat thickness (RFT), respectively, showing that despite the correlation found, the values were low. The fact that the characteristics of SFT and MAR are not affected by the selection for RIG is of great relevance. The fat deposit acts as a body reserve in periods of seasonal rain and impacts the reproductive rates (Santana et al. 2012). Besides, when it comes to carcass quality, fat deposition acts as an efficient thermal insulator in refrigeration chambers (Baldassini et al. 2017). In this context, it is essential that technological innovations created to decrease production costs do not damage the carcass traits and, consequently, the quality of the animals' meat.

The animals classified as efficient for RIG showed higher DWG and LMA for the same dry matter intake. However, this index should still be observed with caution as it may be dependent on body size and, consequently, high weight at maturity.

Acknowledgments

Farm Rancho da Matinha and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES).

Funding

This study was financed in part by the “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES)” - Finance Code 001

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

Author contribution

Luís Miguel Gonçalves Fernandes and Amália Saturnino Chaves conceived and designed the research. Giovanna Faria de Moraes provided the data for this research together with the Rancho da Matinha farm.

Luiza Rodrigues Alves Abreu and Amália Saturnino Chaves analyzed the data and carried out the statistical analyses. Luis Miguel Gonçalves Fernandes, Giovanna Faria de Moraes and Idael Matheus Goes Lopes wrote, corrected and edited the article to manuscript standards. All authors read and approved the final manuscript.

Availability of data and materials

The data sets generated and/or analyzed during the current study are available through the corresponding author upon reasonable request.

Code availability

Not applicable.

Ethical approval

Not applicable.

Consent to publish

The owners of the animals were consent so that the data could be published, provided it was done anonymously.

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The authors declare no competing interests.

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