Descriptive study and non-genetic effects on production traits of Nellore breed in the Amazon

The study and determination of the importance of non-genetic factors on production traits of the Nellore breed are essential to genetic evaluation programs, because they define which traits should or should not be addressed in the analysis model. There are very few descriptions of which are the traits of economical relevance to the Legal Amazon region, which provide a sign of the genetic variability present in the herds and the potential for the breeding programs.

This study showed significant differences among the different States, mainly in relation to the P450 and IPP averages. The results confirmed that non-genetic factors should be taken into consideration in the analysis models for the studied traits.

Key words: analysis of variance, bovine, environmental effects, performance traits, reproductive traits

Introduction

In an animal breeding program, the selection for maximization of production is one of the most important decisions to be taken by the modern breeder. A prerequisite for success is the attainment of accurate estimates of genetic parameters (Gonçalves et al 2011). The knowledge of environmental and genetic factors, as well as the non-genetic sources of variation which act over traits of economical significance is essential to the effectiveness of the breeding program, since it interferes in growth through weight gain and reproduction of the commercial herds in Brazil.

In 1953, the Law 1.806, from 06/01/1953, changed the Brazilian Amazon name to Legal Amazon, due to a political concept rather than a geographical imperative. In Brazil, the Legal Amazon is composed by the States of Acre (AC), Amapá (AP), Amazonas (AM), Mato Grosso (MT), Pará (PA), Rondônia (RO), Roraima (RR), Tocantins (TO) and part of (west of the 44º meridian) Maranhão (MA), corresponding to 61% of the Brazilian territory, or 5.217.423 km². The pressure of deforestation in the Amazon region and the worldwide looks on the Forest Reserves force the livestock enterprises to rethink its production systems. Therefore, the use of the genetic improvement and of technologies related to reproduction and nutrition can contribute for the production of bovine herds of better quality and higher precocity in areas already altered, avoiding more deforestation for formation of new pastures.

Factors such as herd, sire, year and month of birth, sex of calves and age of cow at parturition, have been pointed as important sources of variation for performance traits and growth rate in the Nellore breed (Silveira et al 2004; Conceição et al 2005; Santos et al 2011).

Material and Methods

The database analyzed consisted of 211,744 records of animals of Nellore breed, associated to measurements of standardized weight at 120, 210 and 450 days of age, scrotal circumference at 450 days of age and age at first parturition. All animals were part of the Program for Genetic Improvement of the Nellore Breed, born during the period of 1995 to 2008, raised at pasture and distributed in 44 herds located at the following States: Acre (AC), Maranhão (MA), Mato Grosso (MT), Pará (PA), Rondônia (RO), and Tocantins (TO).

The climate in the participant farms is typical to the Brazilian Amazon region, characterized by two seasons, a dry one (from May to September), and a rainy one (from October to April), with average annual rainfall usually between 1,250 mm and 2,500 mm, decreasing from Southwest to Northeast, and subjected to important fluctuations. Under influence of the low latitude, temperatures remain high during all months of the year, and annual thermal averages are higher than 22º C in more elevated areas, and in the west and north sectors, increasing from north to south until 27º C, when it is closer to the Equator, with small annual thermal amplitude. Due to geographical factors (latitude and relief) and dynamic factors, the region presents high levels of thermal efficiency during the whole year, reason why is characterized by mega thermal climate in the west sector. In the north sector (heights of over 800 m), climate is mesothermal, tending to mega thermal. The annual relative humidity average varies from 60% to 85%, increasing from northern to southern regions (SUDAM 2013).

The PMGRN–Nelore Brasil standardizes the animal weights at a determined standard ages (120 days, 205 days, 365 days, 450 days, or 550 days of age), which requires each animal to have both a previous and a posterior weighing data to the standardized date. The following formula was used for calculating weight at standardized date: Weight at standardized age = Previous weight + (Average daily gain x number of days between previous weighing and standardized age).

Each animal had a unique and permanent identification, which contained the following data: parents registration number, number of farm of origin (NFO), number of present farm (NPA), state (UF), sex (SX), year (ANO) and month (MES) of birth, standardized weight at 120, 210, and 450 days of age (P120, P210, and P450), scrotal circumference standardized at 450 days of age (PE450), age at first parturition (IPP), lot at 120, 210, and 450 days (LOTE120, LOTE210, and LOTE450), contemporary group (GC) at 120, 210, and 450 days (GC120, GC210 and GC450), and contemporary group for IPP (GCIPP).

The data consistency, the descriptive analysis, the analysis of variance and of choice of model for each one of the traits were performed using the Statistical Analysis System (SAS 2002) software. Measurements equal to zero or above/below three standard deviation were not used in the analysis, as well as contemporary groups (GC450) with less than seven animals. Tables and Figures were built in Microsoft Excel.

For the normality test of the traits, the CAPABILITY normaltest procedure of SAS (2002) was used. Such procedure provided the critical values for the traits (Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling), with significance level set at P<0,01. Furthermore, according to these values, the evaluated traits presented normal distribution of residues.

The choice of the best model to be used in the analysis of each trait was done by the PROC REG and STEPWISE procedures from SAS. As far as the P120, P210, P450, and PE450, the following factors were taken into consideration: State (UF), sex (SX), class of age of cow at parturition (CIVP), number of farm of origin (NFA), year, month, contemporary group at 120 days (GC120), as well as GC210 for P210 and P450, and the GC450 for P450 and PE450.

As far as the IPP trait, the model considered the following factors: UF, CIVP, NFA, year, month and GCIPP. Age of the cow at parturition was divided in nine classes, created according to the frequency of parturitions; classes from 1 to 7 correspond to ages of 2 to 8 years, class 8 includes cows from 9 to 11 years old, and class 9 has animals with more than 11 years of age. The factor SX was not considered in models for IPP and PE450.

The analyses of variance were carried out with the GLM procedure of SAS, adjusted for each trait, according to the results from the previous analysis of choice of the best model. Average comparisons for traits with two classes were performed by the t-Student test. For traits with more than two classes, it was used the Scheffé test.

Results and Discussion

The actual number of animals in the studied States corresponds to 32.1% of the Brazilian herd. Despite the high representation, the number of participant animals in the PMGRN–Nelore Brasil is still low, mainly in the states of Maranhão (0.01%), Tocantins (0.04%), Rondônia (0.07%), and Acre (0.18%). The higher participation of the states of Mato Grosso (0.73%), and Pará (0.40%) is justified, possibly, by its numbers in the Brazilian herd (19582504 and 12807706 animals, respectively), as well as the technification of the rural enterprises, promoted by breeders coming from the South and the Southeast to areas such as the south of Para and North of Mato Grosso, opening a niche market for animals tested to be used in commercial herds of these regions.

Table 1 shows the overall averages for the growth traits (weights at different ages), scrotal circumference and age at first parturition for the studied animals. The observed values are close to the overall averages from the PMGRN – Nelore Brasil in 2009, which means that the region represents adequately the average animal of a breeding program performed for more than 20 years in the country.

Table 1. Descriptive statistics by sex and overall for growth and reproductive traits of Nellore animals raised in the Legal Amazon.
Trait		N	Average	SD	Minimun	Maximun
P120 (kg)	*Overall	94,822	123	19.0	46	227
	M	47,463	127	18.8	66	180
	F	46,932	119	17.3	66	180
P210 (kg)	Overall	88,176	180	28.1	70	359
	M	44,195	187	27.6	95	264
	F	43,592	172	25.1	95	264
P450 (kg)	Overall	63,580	262	45.5	110	612
	M	31,589	279	47.3	111	612
	F	31,375	245	36.3	110	528
PE450 (mm)	M	23,901	227	28.4	140	315
IPP (months)	F	38,376	37	4.7	23	49
N = number of animals; P120 = Weight at 120 days of age; P210 = Weight at 210 days of age; P450 = Weight at 450 days of age; PE450 = scrotal circumference at 450 days; IPP = age at first parturition; M = males; F = females. *Overall includes the values before data consistency.

It can be observed the superiority of males as far as weights at different ages and average PE450 of 22.7 ± 2.9 cm, which is inferior to the average of 26.4 cm of 549 males studied by Silveira et al (2004), in Mato Grosso do Sul.

The IPP equal to 37.1 ± 4.7 months is similar to values cited by Pereira et al (2000), Gunski et al (2001), Dias et al (2004), Boligon et al (2007), and Boligon et al (2008), which were of 35.7; 36.0; 34.6; 36.2 and 36.5 months, respectively, for females of the Nellore breed. Values close to 23 months for IPP were observed, which highlights the variability existent in the region that can be worked for higher precocity and productivity.

Boligon et al (2008) observed average weaning and yearling weight of 178 ± 29.0 kg and 270 ± 52.3 kg for males and females raised in the South and Southeast regions of the country, which are similar to the values observed in this study for animals raised in the Legal Amazon. For animals raised in the Middle West, Southeast and Northeast regions, Garnero et al (2001) reported average weights at 120, 240, and 550 days, respectively, of 121, 191, and 309 kg.

Figures 1 to 3 show the overall averages of weights at different ages, PE450, and IPP by State.

In Figure 1, it can be observed that overall average weights are higher in Acre, followed by Pará, Mato Grosso, Maranhão, Rondônia andTocantins. As far as PE450, there were no records in the State of Acre for average calculations. Meanwhile, Pará and Mato Grosso presented the higher average values of PE450 (Figure 2), and Tocantins presented the higher average value for IPP (Figure 3).

The environmental effects and regional particularities, such as rain season, availability and quality of pastures, management, existence of insemination centers, presence of trained technicians for insemination and number of participant animals are reflected in the phenotypical averages observed among States. Such effects are important in the determination of a trait and are called fixed effects in a genetic evaluation.

As far as the analysis of variance, the proposed analysis models for each trait were significant (P<0.0001), presenting an R-squared that varied from 0.21 (for IPP) to 0.65 (for P450), which highlights a higher influence of other environmental factors not controlled on the IPP reproductive trait. Every source of variation considered in the models for P120, P210, P450, and IPP were statistically significant (P<0.01).

The effect of State (UF) was significant (P<0.001) only for P450 and IPP, indicating that, at older ages the maternal effects decreased and the peculiarities of each State would have a higher influence in the animals performance. The animals from Rondônia and Tocantins were not different (P>0.05) as far as averages for P450 (Figure 1). IPP averages were not statistically different (P>0.05) between the States of Rondônia and Mato Grosso (difference of 0.15 months), nor between the States of Maranhão and Pará (difference of 0.18 months), which are geographically closer.

The month of birth of calves reflects the availability of food in the studied year, so that, if cows in the last trimester of gestation have access to good sources of food, it should give birth to heavier calves. When comparing the averages for P120, differences were not significant (P>0.05) between the months of January through June, and between the months of July through December. Overall, in the studied region, the dry period starts during the Amazonian Summer (July through December), and the period with higher rainfall occurs during the months from January through June, which corroborates the observed results.

The sex effect was statistically significant (P<0.001) in the three standardized weights. There are striking differences in animals linked to sex, with males being approximately 10% heavier than females. This probably occurs due to a higher capacity to gain weight presented by males and because of a more developed body structure (Souza et al 2000).

According to Martins et al (2000), bovine males are heavier not only at birth, but also at all ages; such difference can be assigned to the males’ genetic capacity to present higher growth indices pre and post birth, due to hormonal factors. McManus et al (2002) reported that the superior weight at birth of males is justified by the premature action of testosterone, which determines the more pronounced metabolic rate of the fetus during the gestation period. The lighter weight in females can also be influenced by the fact that they have a larger accumulation of body fat, decreasing feed intake. Abdominal fat, according to Nkrumah et al (2005), could exert physical limitations on rumen and on feed intake, mainly by leptin secretion by the adipocytes. The leptin hormone has been associated with decreased feed intake. Average comparisons by t-Student test highlighted the statistical differences (P<0.05) for the three studied weights in males and females.

The farm effect (NFA) was non-significant (P>0.28) only for the P120, because growth traits during preweaning phase in the herds studied were strongly influenced by maternal ability.

The CIVP had significant influence on every trait studied (P<0.0001). This happened because during the first years of life of a cow, milk yield increases, decreasing gradually in the course of time. When averages for the trait P120 were compared, for example, there was no significant differences (P>0.05) among averages of offspring weight of cows in classes 1 and 9, or among offspring weight of cows in classes 5 and 6. Table 2 presents the averages for traits by class of age of cows at parturition (CIVP).

Table 2. Averages by class of age of cows at parturition (CIVP) for some Nellore traits raised in the Legal Amazon.
Trait	CVIP=1	CVIP=2	CVIP=3	CVIP=4	CVIP=5	CVIP=6	CVIP=7	CVIP=8
P120(days)	111	125	122	127	129	129	123	118
P210(days)	171	180	181	184	187	187	179	171
P450(days)	278	262	264	266	266	263	255	242
IPP(months)	35	36	36	35	36	36	36	37
PE450(mm)	251	229	228	231	231	228	221	218
P120 = Weight at 120 days of age; P210 = Weight at 210 days of age; P450 = Weight at 450 days of age; PE450 = scrotal circumference at 450 days; IPP = age at first parturition.

A study on the effect of environment on preweaning traits in Nellore animals in Maranhão, the authors observed that herd effect and sex of offspring were not significant for weight at birth (P>0.05). Age of cow at parturition, used as a covariate in the analyses models, was not significant (P>0.05) for any of the studied traits, while month and year of birth of offspring did influence (P>0.05) the performance of the animals, even in the Amazon climate, which is less changeable throughout the year (Martins et al 2000).

Santos et al (2012) evidenced significant effects (P<0.0001) of herd, year, birth season of the offspring (grouped in quarters of the year) and feeding regimen, when studying the standardized weight of Nellore herds in the North region of Brazil.

Conclusions

Acknowledgements

To CAPES for the students grants granted. To ANCP for granting the data from PMGRN – Nelore Brasil.

References

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Descriptive study and non-genetic effects on production traits of Nellore breed in the Amazon

J S S Sena, A S Matos, C R Marcondes¹, L A F Bezerra², R B Lôbo³, P R N Rorato⁴ and L C S Chaves⁵

Abstract

Introduction

Material and Methods

Results and Discussion

Conclusions

Acknowledgements

References

Descriptive study and non-genetic effects on production traits of Nellore breed in the Amazon

J S S Sena, A S Matos, C R Marcondes1, L A F Bezerra2, R B Lôbo3, P R N Rorato4 and L C S Chaves5

Abstract

Introduction

Material and Methods

Results and Discussion

Conclusions

Acknowledgements

References

J S S Sena, A S Matos, C R Marcondes¹, L A F Bezerra², R B Lôbo³, P R N Rorato⁴ and L C S Chaves⁵