Evaluation of parental dam birth weights associated with live weights and calving ease of female progeny of Indonesian-grade cattle

Manopo Jouke Hendrik and Umar Paputungan

Faculty of Animal Sciences, Sam Ratulangi University, Manado 95115, Indonesia
manopo_hendrik@yahoo.com

Abstract

Records of dam birth weights used as the parental generation (G₀) and weight records from birth to calving of 308 two and half-year-old heifers as the first generation (G₁) accumulated over five years (2011-2016) mated by artificial insemination (AI) were used to evaluate dam birth weight (G₀) associated with live weights and calving ease of their female progeny (G₁) of Indonesian-grade cattle. All heifers were reared in private areas belonging to 153 farmers. Dam birth weights were classified into high, medium and low birth weights. Dams (G₀) and heifers (G ₁) were from two parental Ongole sire groups (Krista, Kr and Tunggul, Tu) and were mated by AI using straws of Kr and Tu to produce the first generation (G₁) and second generation (G₂), respectively. The breeding herds were on range pasture year around. Farmers supervised their heifers and the animals showing signs of estrus were brought to the rural AI service center of government to be mated by AI using thawed straw of frozen germ plasma of Ongole-breed sires. The data were analyzed using a covariance model.

Low birth weight dams (G₀) produced lighter female progeny (G₁), at birth, yearling, eighteen-month and two and half-years old, compared with those delivered by high birth weight dams. Dam birth weights (G₀) of high, medium and low did not affect calving difficulty (dystocia) among the female progeny (G₁). Low birth weight dams (G₀) produced lighter grand progeny (G₂) at birth and at 18-month old compared with those generated by high birth weight dams (G₀). Furthermore, dam birth weight did not affect grand progeny’s weaning and yearling live weights.

Keywords: AI, artificial insemination, dystokia, pasture, Ongol, urea-palm sugar blocks

Introduction

The goal of animal breeders is rapid genetic improvement, for which accurate prediction of parental performance record is the most crucial factors. Cattle and buffaloes are important on smallholder farms in most developing countries to provide meat, milk, traction power and manure in integrated crop and livestock farming systems (Preston and Leng 2009). Breeders can rank the animals and cull those with the poorest evaluations while selecting those with the best evaluation as replacements. Low birth weight sires produced female progeny which were lighter than those sired by high birth weight bulls at birth, yearling, and two years of age (Paputungan et al 2000). In addition, sire birth weight did not affect the level of calving difficulty among the female progeny calving at two-year of age (Paputungan et al 2000). Selection and use of sires with high growth rates and mature weights reproduced correlated responses in birth weight and accordingly higher incidence of calving difficulty of dams (Naazie et al 1989).

Growth traits of animals are always of primary concern during breeding for its determinant economical value in animal industry. In government cattle development program, it is becoming common in North Sulawesi province of Indonesia (at district cattle breeding station) to breed cows by artificial insemination (AI) technique using germ plasmas (semen) of the Kirsta (Kr)and Tunggul (Tu) sires taken from “the artificial insemination (AI) sire germ plasma center” at the Singosari district, East Java province of Indonesia. Genetically, the DNA band using restricted enzyme for specific growth hormone (GH) gene produced by the bacterium of “Moraxella species” (Msp) with one enzyme resulted in an Ongole-breed sire called “Tunggul” represented by GH genotype Tu_B^-/- and sire called “Krista” represented by GH genotype Kr_B^+/+(Paputungan et al 2016). Furthermore, the random effects of these sire (G₀) genotypes were not significantly associated with all growth traits of the progeny (G₁), except the heterozygous GH genotype (BC^+/-) excelled over their homozygous GH genotypes (BC^+/+ and BC^-/-) in respect of progeny live weight gain as described by Paputungan et al (2016).

Thus, the question posed by this practice was what were the risks of selecting female progeny from low birth weight dams for breeding replacements mated by artificial insemination (AI) method? Therefore, the objectives of this study were to asses direct effects of dam birth weight (G₀) on the weights and calving performance of their female calves (G₁) and the subsequent weights of the second generation of progeny (G₂) relative to their ages of the Indonesian-grade cattle.

Materials and Methods

Experimental procedures

This study was carried out involving Indonesian-grade cattle at Tumaratas Village at the artificial insemination (AI) service center of Minahasa regency, North Sulawesi province of Indonesia. Records of weights (birth, weaning, yearling and two and half-year weight) belonging  to 308 two and half-year-old heifers after calving, accumulated over five years (2011-2016) were used in this study. All heifers were reared in private areas belonging to 153 farmers.

The dam populations as the parental generation (G₀) were born from the parental cows mated by artificial insemination (AI) using germ plasmas (semen) of Kirsta (Kr) with genotype of B^+/+and Tunggul (Tu) with genotype of B^{–/ –}as described by Paputungan et al (2016). Breeding by AI method was supported by the government breeding program development for Indonesian-grade cattle.The dams (G₀) were classified into three groups according to their birth weights. The dam (G₀) birth weights were adjusted for the age of their parental cows using the formula (Zulkharnaimet al 2010) as follows:

Data of age adjustment were analyzed using simple software of the statistical program function in Excel XP 2007.

Adjusted dams’ birth weight (G₀) above 36.0 kg were classified as high (H), from 31.5 to 35.9 kg as medium (M) and less than 31.5 kg as low (L). The number of dams (G₀) and their average birth weight and birth weight class and the number of their female progeny (G₁) raising a calf (G₂) were mated by sire group (AI method) as presented in Table 1. Mating systems were in single sire groups, within each sire group by AI method to produce grand progeny (G₂) of the original dams (G₁).

Management of experimental animals

The Indonesian local-grade cattle were raised by 153 smallholders, in rural areas of Tumaratas village, West Langowan district, North Sulawesi province of Indonesia, under traditional management using local grass around coconut plantations and open grass fields surrounding rural areas as described by Paputungan et al (2016). The cows (G₀) and their progeny (G₁) grazed from 07.00 am to 17.30 pm without supplementary concentrate. The breeding herds were on the pasture all year round. The farmers supervised their cows and when they showed signs of estrus, the cows were brought to the rural artificial insemination (AI) service center of government to be mated using thawed straw of frozen germ plasmas of the Ongole-breed bulls stored in liquid nitrogen.  Calves (G₂) grazed on local grass pasture with supplementary feeding of a urea-palm sugar block at the animal pens in the afternoon (Paputungan et al 2015).

The average conception rate (C/R) was 55.6 % and the services per conception (S/C) were 1.44, based on the annual data of the AI service center of Minahasa regency, North Sulawesi province 2013-2014 (Kasehung et al 2015). The value of C/R indicated that 55.6 % of cows were pregnant for the first AI service and 44.4 %  were pregnant for the second AI service at the next estrus period. The value of S/C indicated that 100 pregnant cows needed 144 services of AI using straws of frozen germ plasma. These values were classified as moderate reproductive performance of local-grade cattle using AI method (Winarti and Supriyadi 2010). This moderate reproductive performance of local-grade cows might be due in part to late supervision of the farmers on the signs of animal estrus causing open cows at the time of AI application.

Dam birth weights of 308 animals were determined by using a digital weighing scale when animals were standing still as described by Paputungan et al (2000) and Ozkaya and Bozkurt (2008). At the same time, measurements of chest girth and body length were taken by tape measure (Table 1). Calving difficulty of the heifers was scored on a scale of 0 to 5 (0= normal calving, 1= slightly difficult calving delivery, 2= difficult calving delivery without assistance, 3= more difficult calving delivery requiring simple assistance, 4= the most difficult calving delivery requiring intensive assistance, and 5= the most difficult calving delivery requiring surgery). These scores were transformed to Snell score (ranging from 0= normal to 100= the most difficult calving delivery) as described by Tong et al (1977) for normal approximation. The calves (G ₂)were weighed within 24 hours after birth, at weaning and finally at 18-month of age.

Statistical analysis

Data on the phenotypic weights of female progeny (G₁) were analyzed by covariance analysis, using the General Linear Models (GLM) procedure of SAS (2003) with mathematical model as follows (Steel and Torrie 1993),

Y_ijkl = µ + B_i + C_j + P (BC)_ijkl+ e_ijkl

Where:Y_ijkl = weight observation of the 1^thfemale progeny (G₁) within the k^th mating interaction associated with the j^th dam birth weightclasses (G₀) and the i^th parental sire genotypic groups (G₀);µ = general mean common to all animals in the experiment; B_i= the fixed effect associated with the i^th genotypic parental sire groups(i=2, Kr-B^+/+, Tu-B^–/–); C_j= the fixed effect associated with the j ^th parental dam birth weightclasses (j=3, High, Medium, Low); P(BC)_ijkl = the random effect of the l^th weights of female progeny (l=3, High, Medium, Low) within the k^th mating interaction [k=6, (Kr-B^+/+ xHigh), (Tu-B^–/– x High), (Kr-B^+/+ x Medium), (Tu-B^–/– x Medium), (Kr-B^+/+ x Low), (Tu-B ^–/–x Low)] associated with the j^th parental dam birth weightclasses and the i^th genotypic parental sire groups;e_ijkl = random effects peculiar to each individual female progeny.

Fixed linear models used for analysis included the effects of dam birth-weight class, sire-group and daughter or grand progeny’s year of birth. The above mentioned factors were considered for all the traits analyzed. Other effects such as the age of heifers (G₁) and the sex of the grand progeny (G₂) were trait specific and considered for the analysis of particular traits. Least-squares analyses of variance, means and standard errors were derived and least-square means tested using PDIFF option.

Results

Dam (G₀) birth weight classes of high and medium produced different daughters’ birth weights of 30.2 and 28.5 kg, respectively (Table 2). The same patterns were found for dam (G₀) birth weight classes of high and medium producing daughters’ 18-month weights of 298 and 290 kg; respectively, and daughters’ 2.5-year weight means of 369 and 352 kg, respectively. However, dam (G₀) birth weight classes of medium and low produced the same daughters’ birth weights of 28.5 and 28.1 kg; respectively, the same daughters’ 18-month weight means of 290 and 286 kg;  and the same daughters’ 2.5-year weight of 352 and 348 kg (Table 2).

Weaning weights of daughters  delivered by high, medium and low classes of dam (G₀) birth weights were not significantly different.Furthermore, dam (G₀) birth weight classes of high, medium and low did not affect calving difficulty of their female progeny scores, which ranged from 9.3 to 11.3, indicating normal calving delivery. In contrast, dam (G₀) birth weight classes of high, medium and low produced daughters’ yearling weight means of 207, 198 and 192 kg, respectively (Table 2).The progeny live weights from the two sire groups of Krista and Tunggul were not significantly different as reported by Paputungan et al (2016). Least-squares means and standard errors of weights of grand progeny (G₂) by dam birth weight classes are presented in Table 3.

Grand progeny (G₂) birth weight and 18-month weight of high and low classes of dam (G₀) birth weights were different;  29.9 and 27.8 kg, respectively for birth weight, and 296 and 288 kg, respectively for 18-month weight. However, grand progeny’s (G₂) weaning weight and yearling weight from high, medium and low classes of dam (G₀) birth weights were not different: 124, 123 and 122 kg, respectively for weaning weight, and 207, 201 and 199 kg, respectively for yearling weight (Table 3).

Discussion

Dams with high birth weight produced heifers (G₁) that were heavier (p<0.05) at birth, at yearling, at 18-month old and at 2.5-year old compared with the daughters of low birth weight dams (Figure 1). Weights at weaning of the daughters (G₁) derived from three dam birth weight class were not different. The results revealed that although the female progeny (G₁) delivered by low birth weight dam (G₀) had lighter body weight at 18-month and 2.5-year old than those delivered by high birth weight dam (G₀), the progeny (G ₁) did not have higher incidence of calving difficulty (dystocia), because their calves (G₂) were also lighter at birth as shown in Figure 2. These results are in agreement with those reported by Meijering and Postma (1985) and Paputungan et al (2000). Grand progeny (G ₂) of the high birth weight dams were heavier at birth (P<0.05) compared with those from low birth weight dams (G₀) (29.9 vs 27.8 kg), while the difference was not significant between the low and medium dam birth weight (G₀) classes.

Figure 1. Association of dam birth weight (G0) classes with weights and calving difficulty scores of female progeny (G1); a,b,c superscript within the same color bar chartswithout common letter are different at p<0.05

There were no differences among the three dam birth weight (G₀) classes for grand progeny’s (G₂) weaning weight (Figure 2). This was probably due to the negative genetic correlation between direct and maternal genetic effects on weaning weight (Trus and Wilton 1988). Yearling weights of the grand progeny of the three dam birth weight classes were also not different (Figure 2). However, the grand progeny (G₂) of the high birth weight dams (G₀) were heavier at 18-months compared with those from low birth weight dams (G₀) (296 vs 288 kg), while the difference was not significant between the low and medium dam birth weight (G₀) classes. It was indicated that there was no difference among the three dam birth weight classes of high, medium and low for grand progeny’s (G ₂) weaning weight (Figure 2). This was probably due to the lack of genetic association between direct and maternal genetic effect on weaning and yearling weights (Hohenboken and Brinks 1971; Trus and Wilton 1988). Furthermore, yearling weights of the grand progeny (G₂) of the three dam birth weight classes (G₀) were not different. This also might be due to contribution of better environmental effect on rapid growth from weaning to yearling weight period. This was indicated by the fact that 18-month weights of the grand progeny (G₂) of high dam birth weight class (G₀) were heavier than those of low dam birth weight class (G₀).

Figure 2. Association of dam birth weight (G0) classes with weights of grand progeny (G2); a,b superscript within the same color bar charts without common letter are different at p<0.05

Conclusions

• Low birth weight dams (G₀) produced lighter female progeny (G₁)  at birth, yearling, eighteen-month and two and half-year of age compared with those delivered by high birth weight dams.
  
  
• Parental dam birth weight (G₀) classes of high, medium and low did not affect  incidence of calving difficulty (dystocia) among the female progeny (G₁).
  
  
• Low birth weight dams (G₀) produced lighter grand progeny (G₂) at birth and at 18-months compared with those generated by high birth weight dams (G₀). Dam birth weight classes did not affect grand progeny’s weaning and yearling weights.
  
  
• Higher parental dam birth weights can be used as the potential replacement stocks in Indonesian grade cattle for live weight gain improvement in their adult progeny without incidence of calving difficulties. High weight records of each individual animal combined with the application of AI technique should be maintained for breeding development program in increasing the favorable large population of animals supporting the local farmer community development in the rural area.

Acknowledgment

The financial support of the Ministry of Research, Technology and Higher Education, Republic of Indonesia through their Research Finance Program is gratefully acknowledged. The authors also acknowledge Mr. Jan Kuhu and his farmer group members for their assistance in animal data collection at the artificial insemination service center at Tumaratas village, district of West Langowan, Minahasa regency, North Sulawesi province of Indonesia.

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Received 16 April 2016; Accepted 1 May 2016; Published 2 June 2016

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