Livestock Research for Rural Development 22 (10) 2010  Notes to Authors  LRRD Newsletter  Citation of this paper 
2264 data observations on 8 body metric traits: Head Length, Neck Circumference, Shoulder to Tail Drop, Thoracic Circumference, Body Width, Leg Length, Hip to Knee Length, and Leg Circumference obtained from 108 local guinea pigs reared in the Teaching and Research Farm of the University of Dschang were used in the analysis. The data were analyzed to establish regression equations for the metric traits. A test of linear models with different combinations of independent variables revealed that the general body size and the appendage factors contributed significantly to live weights.
Regression equations to evaluate live weights were sought for at birth, weaning (3 weeks), 10, 15 and 20 weeks of age and were: WT_{0 } =_{ }75.4 + 0.86PC1 + 0.82PC2; WT_{3 }=_{ }135 + 0.90PC1; WT_{10 }=_{ }261 + 0.95PC1; WT_{15 } =_{ }345 + 0.91PC1 and WT_{20 }=_{ }439 + 0.95PC1, respectively. Linear body measurements could be used to predict live weights of local guinea pigs for selection purposes.
Keywords: Linear models, growth, predictions, selection
Over the years, emphasis has shifted from subjective methods of appraising animals to more objective methods like the use of linear body measurements of different body parts (Essien and Adesope 2003). Linear body measurements can be used in assessing growth rate, weight, feed utilization, and carcass characteristics in farm animals (Brown et al 1973). They can be obtained with relatively lower costs with a high relative accuracy and consistency. Essien and Adesope (2003) ascertained that linear body measurements described an animal more completely than conventional methods of weighing and grading. Linear body measurements have been divided into skeletal and tissue measurements (Blackmore et al 1995). Skeletal measurements include all the height and length measurements while tissue measurements include heart girth, chest depth, punch girth, and width of hips. These body measurements can be further divided into horizontal measurements like body length and head to shoulder length, and vertical measurements like height at withers and chest depth (Blackmore et al 1995). In skeletal development, there is a faster bone growth in length than in width and circumference (Brown et al 1956). It has been found out that wither height is about 50% matured at birth and that skeletal growth ceases at 3040 months in Angus cattle (Brown et al 1956). Brown et al (1983) showed that linear body measurements matured in the order; hip height, shoulder width, hip width, wither height, heart girth, chest depth, body length, and height at withers. Orheruata (1988) observed that body length, height at withers and shoulder to tail drop were about 4050% matured in N’Dama cattle at birth and put forward the following order of maturity among five body dimensions; head to shoulder, height at withers, heart girth, shoulder to tail drop, and body length. Green and Carmen (1978) showed that skeletal development within a population became relatively more uniform with age.
Certain body dimensions have been helpful in evaluating the pattern of development of the skeletal frame (Berg and Butterfield 1976; Berg 1978). As maturity for bone and muscle is approached, fattening process increases. Monitoring these changes objectively with body measurements can aid in guiding a breeding program, as fattening process is synonymous with a decline in the efficiency of food use (Berg and Butterfield 1976; Berg 1978). Orheruata (1988) found that in beef cattle, a high girth measurement meant more muscle in meat. From studies on linear body measurements, Spencer and Eckert (1988) derived production equations for weight. A study of linear body measurements in local guinea pigs is important because most traditional farmsteads lack weighing machines and adequate knowledge to understand their manipulation. The use of calibrated weight band which is very common in developed countries is not common in the developing world because their calibrations are based on temperate animal breeds. But simple linear measuring devices will be easy to handle and will assist in selecting animals to become parents of the next generations. This study therefore envisages the establishment of relationship amongst metric traits.
Four boars and 40 does of local guinea pigs were used in the study. They were purchased in local markets in Belo and Bafut in the North West Region, and in Dschang in the Western Region of the Western Highlands of Cameroon. The acquired animals were fairly homogenous in size and shape. They were characterized by tricolored coat pattern; of black and yellow pigmentation with varying degrees of spotting white. The animals were housed in groups of ten does to one boar in suspended bamboo cages measuring approximately 1.2 x 0.7 x 0.6m in a cement block house for seclusion. They were fed ad libitum with fresh forage composed principally of Pennisetum purpureum and Tripsacum laxum, and supplemented daily with a compounded ration. The quantity of supplement given was adjusted to 5% of the average live weight of the animals in each treatment according to Fonteh et al (2005).
Prior to parturition, each doe was transferred to an individual cage measuring 40 x 40 x 60cm to avoid post partum breeding, thereby avoiding inbreeding and to ensure effective monitoring of each sow and its litter. Litters were examined at birth for defects and kids identified with numbered necklace tags and pedigreed with sire and dam. Young animals were nursed by their dams up to weaning (21 days). Weanlings were sexed and separated at 21 days to avoid indiscriminate breeding. This procedure was repeated for the first and second parturitions.
On weaning at 21 days, the does were removed and transferred to the harem while their kids remained in the cages. All cages were cleaned daily to avoid accumulation of urine and faeces. Animals were treated against endoparasites with PIPERAZINE^{®} and with DIDETEKI^{®} against ectoparasites every five weeks, and coccidiosis with an anticoccidian (VETACOX^{®}) every month for three consecutive days according to Fonteh et al (2005). By the time the experiment ended 57 and 35; 48 and 30; 29 and 15; 26 and 13 and 20 and 10 males and females off springs were identified and pedigreed by sire and dam at birth, weaning (3 weeks), 10 weeks, 15 weeks and 20 weeks, respectively.
Linear body measurements that included; Head Length (HL), Neck Circumference (NC), Shoulder to Tail Drop (ST), Thoracic Circumference (TC), Body Width (BW), Leg Length (LL), Hip to Knee Length (HK), and Leg Circumference (LC) were measured using a measuring tape graduated in cm as described for linear body measurements by Hassan and Chiroma (1991). The measurements were carried out at birth, weaning (21 days), 10, 15 and 20 weeks of age.
Live body weight was measured with an Ohuastriple beam balance (with a sensitivity of 0.1g). Birth weight was recorded within 12 hours after parturition. Weaning weight, 10week, 15week and 20week weights were measured on days 21, 70,105 and 140, respectively.
Data analysis
Data were analyzed for principal component analysis (PCA) and the first and second factor scores at birth, and the first factor at weaning (3 weeks), 10, 15 and 20 weeks were considered as independent variables according to Takashi and Anthony (1989). As the factor scores were obtained from an orthogonal solution (Kaiser’s varimax method), they are theoretically independent, thus fitting the linear regression model,
Model: Weight = β0 + β1.PC1 + ………………. + βp.PCp
This model was chosen as a reference for guinea pig body weight evaluation at various ages. Practically, regression analysis aims at estimating parameters for β0, β1 … βp when n (>p) sets of dependent and independent variables are given. The goodness of fit to the regression model can be judged by the coefficient of determination (R^{2}).
The first two principal components at birth and the first component at weaning, 10, 15 and 20 weeks from a Principal Component Analysis (PCA) represented as PC1 and PC2 are interpreted as follows;
PC1 General Body Size
PC2 Appendage Factor
As a first step, we used factor scores of all these factors for the regression analysis, presuming a linear model,
Model: Live weight = β0 + β1.PC1 + β2.PC2
Table 1 depicts multiple correlation coefficients, R change and the coefficient of determinism (R^{2}). At all the ages studied, R change are sufficiently large and about 74% to 91% of the variance in live weights are explained by the models.
Table 1. Multiple correlation coefficient (R), R change and R^{2} at birth, weaning, 10, 15 and 20 weeks of age 

Age 
R 
R change 
R^{2} 
0 
0.86 
0.73 
0.74 
0 
0.82 
0.66 
0.66 
3 
0.90 
0.81 
0.82 
10 
0.95 
0.89 
0.89 
15 
0.91 
0.82 
0.83 
20 
0.95 
0.90 
0.91 
The general body size (PC1), which is related to the inherent genetic potential shows much contribution to live weight (table 2).
Table 2. Regression for estimation of weights at birth, weaning, 10, 15 and 20 weeks 

Age 
Intercept 
Partial coefficient 
tvalue 
Standard tvalue 
At birth 
β0 
75.4 
71.4 
t_{66, .010 }= 2.36 t_{66, .050 }= 1.66 

β1 
0.86 
13.4 


β2 
0.82 
11.2 

Regression equation 
WT_{0 }=_{ }β0 + β1.PC1 + β2.PC2 

Weaning 
β0 
135 
63.5 
t_{66, .010 }= 2.36 

β1 
0.90 
16.9 

Regression equation 
WT_{3 }=_{ }β0 + β1.PC1 

10 weeks 
β0 
261 
90.3 
t_{66, .010 }= 2.36 

β1 
0.95 
23.3 

Regression equation 
WT_{10 }=_{ }β0 + β15PC1 

15 weeks 
β0 
345 
17.5 
t_{66, .010 }= 2.36 

β1 
0.91 


Regression equation 
WT_{15 }=_{ }β0 + β1.PC1 

20 weeks 
β0 
439 
13.4 
t_{66, .010 }= 2.36 

β1 
0.95 


Regression equation 
WT_{20 }=_{ }β0 + β1.PC1 
The appendage factor (PC2) also had significant contributions to live weight at birth.
The final retained regression equations are;
At birth, WT_{0 }=_{ }75.4 + 0.86PC1 + 0.82PC2
At weaning, WT_{3 }=_{ }135 + 0.90PC1
At 10 weeks, WT_{10 }=_{ }261 + 0.95PC1
At 15 weeks, WT_{15 }=_{ }345 + 0.91PC1
At 20 weeks, WT_{20 }=_{ }439 + 0.95PC1,
Where live weights are in terms of grams, and PC1 and PC2 are the standardized factor scores.
Assuming that the residuals were due to factors other than the first two scores at birth and the first score at weaning, 10, 15 and 20 weeks, the predicted weights (Table 3) computed by the above equations can be very reliable as depicted by the high R^{2}values.
Table 3. Predicted live weights for local guinea pigs according to regression equations at various ages versus observed weights 

Regression model No 
Age, weeks 
Observed weight, g 
Predicted weight, g 
1 
0 
73 
76 
1 
3 
129 
136 
1 
10 
256 
262 
1 
15 
339 
346 
1 
20 
438 
440 
Starting from these records, relevant information on live weights at farm level may be obtained. Regression models were found that estimate live weights with high reliabilities (Figure 1).


The regression models to predict weights did not capture sufficient variation in live weights. This could be due to the smallness of the dataset in this study and / or the non inclusion of other variables like the dam and litter size that are known to affect weight in local guinea pigs. Although it is obvious that estimation errors might occur, the models with sufficient reliabilities are good candidates to be used as benchmarks in overall farm management and selection. Weights of guinea pigs that are far below values which one may consider as standard averages within the population is an indication that farm management should be improved upon in order to achieve at least average weight at a given age. Animals showing performances far above the expected figures might be considered as potential breeding animals.
The empirical models to predict live weights at various ages could be used as benchmarks for guinea pig weight predictions in selection schemes.
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Received 19 April 2010; Accepted 16 June 2010; Published 1 October 2010