Predicting live weight of grasscutters using their linear body measurements

B A Hagan, J K Nyameasem, A Asafu-Adjaye and K A Daffour-Oduro

CSIR-Animal Research Institute, P. O. Box AH 20 Achimota, Accra
bernardatohagan@yahoo.com

Abstract

A study was undertaken to predict the live weight (LW) of domestically kept grasscutters from body length (BL), tail length (TL), heart girth (HG) and head length (HL) using linear, quadratic and cubic functions. Data on LW, BL, TL, HG and HL were taken from 80 grasscutters between the ages of 12 and 18 months old. Linear, quadratic and cubic functions were fitted with LW as independent variable and BL, TL, HG or HL as predictor variables using the Generalized Linear Model (GLM) procedure of SAS. The correlations between the LW and linear body measurements were also determined for male, female and pooled data (both males and females).

The correlation coefficients between LW and body measurements ranged from 0.33 to 0.76 for pooled data. The coefficients of determination (R²) for the prediction equations ranged from 8.2% to 81.4% for pooled data. Using the linear function, BL (54.3%) and HG (58.2%) were better predictors of LW than TL (10.8%) and HL (8.2%). The use of both BL and HG in a given function explained better the variation in LW than the use of just one body measurement. In all three functions, prediction equations involving males gave the best R²  values compared with those from females and pooled data.

Keywords: body length, coefficient of determination, correlation, head length, heart girth, tail length

Introduction

The grasscutter is an important source of animal protein in Ghana (Opara 2010) and has one of the most expensive meats in the West African sub-region (Okeke and Mogbo 2013). The meat is a delicacy for both the rural and urban inhabitants. The successful domestication of the grasscutter has created a vibrant grasscutter farming business for the young and old in Ghana.

Knowledge of the live weight of livestock including grasscutter is important, as it is required in determining their feed requirement, breeding management, correct administration of drugs and marketing of animals (Baffour-Awuah et al 2000; Thiruvenkandan 2005; Sowande et al 2010). Live weight and changes in live weight could also be used in determining responses to genetic selection (Touchberry and Lush 1950). The accepted method of measuring live weight of animals is the use of calibrated electronic or mechanical scales. However, in most rural communities and villages in Ghana, where majority of the grasscutter farmers are found, poverty rates are very high. It is estimated that about 30% of the populace, mostly rural inhabitants, live on less than $1.00 a day. In these communities, weighing scales are either non available or unaffordable. Also in cases where cheap scales are present, farmers complain of frequent breakdowns of these scales. This results in frequent purchasing of these scales making them eventually expensive. Most grasscutter farmers have therefore resorted to the use of visual appraisal in the marketing of their animals. This has led to either underpricing or overpricing of animals during their sale.

Apart from morphological body measurements being used as indicators of sexual dimorphism in animals (Lindenfors et al 2007), they can also be used in predicting the live weight of livestock. This will enable farmers who cannot afford expensive weighing scales or frequently purchasing scales to still be in a position to estimate the approximate weight of their animals for whatever husbandry practices they need the weight for using simple measuring tapes.

Linear body measurements have been used to predict live weight of several livestock species including goats (Fajemilehin and Salako 2008; Slippers et al 2000; Tsegaye et al 2013), sheep (Thys and Hardouin 1991; Baffour-Awuah et al 2000; Afolayan et al 2006), cattle (Ahulu and Kpesese 1995; Francis et al 2002; Lukuyu et al 2016), rabbits (Chineke 2005; Egena et al 2012) and pigs (Daffour-Oduro and Naazie 2010; Adeola et al 2013). Information is however, scanty with respect to predicting live weight of grasscutters using morphological traits of the animal (Annor et al 2011; Udeh and Okonta 2013). Using a smaller data set (9 animals), Ikpeze and Ebenebe (2004) reported an almost unitary correlation coefficient between body weight and body length of grasscutter whilst Udeh and Okonta (2013) also reported that tail length and heart girth were best predictors of live weight of 5-month old female grasscutters.

The objective of this study was to predict the live weight of domestically kept grasscutters from different linear body measurements using linear, quadratic and cubic functions.

Materials and methods

Study area and management of animals

The study was carried out at the Grasscutter Unit, Pokuase Research Station of the Council for Scientific and Industrial Research-Animal Research Institute (CSIR-ARI), Accra. The area lies within the transitional agro-ecological zone on latitude 5° 41´N and longitude 0° 17´W. The mean monthly temperature and rainfall range from 23.3 to 24.7°C and 12.6 to 204.6 mm respectively (Ghana Meteorological Services Department 2009).

Animals were housed in 3-tier metallic wire mesh cages with each unit measuring 60×60×40 cm in dimension. Each animal was kept in a cage except during the breeding season where a female is joined to a male for mating. All animals were identified with metallic ear tags with unique codes. Cages were exposed to natural light, ventilation and ambient temperature.

Feed and feeding

Animal were fed freshly cut Panicum maximum daily. Fresh water was given in concrete made troughs. Both feed and water were given ad libitum. Left over feed and water were changed the next day.

Data collection

Measurements of live weight (LW), body length (BL), tail length (TL), heart girth (HG) and head length (HL) were taken from 35 male and 45 female grasscutters between the ages of 12 and 18 months old. All the measurements were taken in the morning before the animals were fed and watered. Live weights were taken with a digital weighing scale (in kilograms) after restraining the animals with nylon net. The linear body measurements were defined as described by Annor et al (2011) and were taken with a measuring tape in centimetres.

Body length is the distance from the tip of the nose to the tip of the tail.

Tail length is the distance from the base to the tip of the tail.

Heart girth is the circumference of the chest directly below the forelimb.

Head length is the distance from the tip of the nose to the level of the 7^th cervical vertebrae.

Statistical analysis

Data were analysed using the generalized linear model (GLM) procedure of SAS (SAS 2008). First, data were subjected to least square analysis using GLM Type III procedure on the fixed effect of sex:

Y_ij = m + S_i + e_ij

Where Y_ij = the trait under consideration (LW, BL, TL, HG, HL)

m = overall mean

S_i = fixed effect of the i^th sex of animal (male, female)

e_ij = random error term associated with each observation

Differences between means were separated by probability of difference (PDIFF) procedure of SAS (SAS 2008) at P<0.05.

Live weight of grasscutters were regressed on BL, TL, HG and HL separately for males and females and pooled data (both sexes) and analysed as follows:

i. Linear function

Y = a + bx

Multiple regression of LW on BL and HG for males, females and mixed sex were also carried out.

ii. Quadratic function

Y = a + bx + cx²

iii. Cubic function

Y = a + bx + cx² + dx³

Where Y = Live weight

a = constant of the regression equation

b, c, d = regression coefficients

x = body length, tail length, heart girth or head length

The quadratic and cubic functions were fitted to only BL and HG because they were the only morphological traits that were significant (P<0.05) after stepwise regression of all four traits (BL, TL, HG and HL).

Correlations among all the study traits were also estimated for males, females and pooled data (regardless of sex). Coefficients of determination (R²) values were used to evaluate the functions.

Results

Table 1 shows the descriptive statistics of LW and linear body traits of grasscutter. Both LW and HL showed appreciable spread around their means. Males were higher than females in all the traits studied though the differences were not significant (P>0.05) for TL and HL (Table 2).

Table 3 presents the phenotypic correlation among all the traits studied. Live weight of grasscutter was positive and significantly correlated with all the linear body measurements in all three categories of sexes except for the correlation between LW and TL in females. Generally, the correlations between LW and BL or HG were stronger for all categories of sexes than those between LW and TL or HL. Body length is positive and significantly correlated with TL, HG or HL for all groups except for the correlation between BL and HG in females (P>0.05). The correlations between TL and HG, HL and HG were all low and not significant (P>0.05). Moderately positive correlations existed between TL and HL though that for females was not significant (P>0.05).

Table 4 shows LW prediction from BL, TL, HG or HL using linear functions including multiple regression function with both BL and HG at the same time. Males had prediction equations with higher coefficients of determination (R²) than those of the females and mixed sexes for all the linear functions. The use of both BL and HG in predicting LW using linear functions yielded the highest R² values. With the exception of models involving TL and HL, all the models under the linear functions were highly significant (P<0.01).

Table 5 shows LW prediction from either BL or HG or both using quadratic function for the various categories of sexes. In all cases, the prediction equations for males had higher R² values than those for females and mixed sexes. Also, quadratic equations involving both BL and HG at the same time yielded higher R² values than those involving only one linear measurement, that is, BL or HG. All the models under the quadratic function were highly significant (P<0.01).

Prediction of LW from either BL or HG using cubic function for the various sex categories is presented in Table 6. The R² values for females were lower than those of the males and mixed sexes. All the models under the cubic function were also highly significant (P<0.01).

Discussion

Males outperformed female grasscutters in LW and all the linear body measurements. Other workers have also reported similar findings in this same species (Adu et al 2002; Ikpeze and Ebenebe 2004; Jayeaola et al 2009; Annor et al 2011). This confirms the general notion that sexual dimorphism in body weight and body measurements indeed exist in animals including grasscutters. In grasscutters, where sex determination is a major challenge (Annor et al 2009), farmers could use differences in their linear body measurements to determine the sexes of animals of similar ages especially animals above 1-day old. At birth, there is no significant difference in body weight and linear body measurements of male and female grasscutters (Annor et al 2011).

With the exception of HL, all the mean linear body measurements for males and female grasscutters were slightly higher than those reported by Annor et al (2011) for 180-day old grasscutters. The differences in the trait means could possibly be due to the age differences of the animals. On the other hand, the BL of both male and female grasscutters in this study were within the range of 42-58 cm reported by Schrage and Yewadan (1999) for animals over 12 months old.

The generally medium to high positive correlations between LW and linear body measurements in this study agree with Annor et al (2011) and Jayeola et al (2009) in grasscutters, Daffour-Oduro and Naazie (2010) in pigs and Baffour-Awuah et al (2000) and Afolayan et al (2006) in sheep. This suggests that body measurements could be used as predictors of LW of matured grasscutters. Body length and HG will however, be better predictors of LW than TL and HL due to their relatively high correlation coefficients (r) with LW. The high correlation coefficients between LW and body measurements in males compared to females indicate that these body measurements can predict LW better in males than their female counterparts. Daffour-Oduro and Naazie (2010) reported similar findings in 3 to 6 months old indigenous pigs in Ghana. The medium to high positive correlations among BL, TL and HL suggest that these traits could be used in predicting each other. Likewise, selection for one trait could lead to the corresponding improvement in the other traits and males are more likely to respond to this selection. Heart girth had low and non-significant (P>0.05) correlations with TL and HL but not BL. This is contrary to report by Annor et al (2011) in relatively younger grasscutters (1 to 180-day old) where high correlations existed between HG and TL, HL or BL. The difference in the findings could be attributed to the ages of the animals involved. Since HG measures muscle tissues and TL, HL and BL measure skeletal tissues, it is possible that these tissues could be growing at a similar rate in young animals compared to older ones hence the lower correlations between HG and both HL and TL in older grasscutters (12 to 18 months old).

The coefficients of determination (R²) were used as measures of the goodness of fit for the models used in predicting LW from linear body measurements. In all three functions, there were differences in the way the same traits predicted LW in different sex categories of grasscutters. In all cases, the R² values were higher for equations involving males than those of females and mixed sex categories. Indeed, a similar trend was observed for the correlations between body measurements and LW. These therefore suggest that body measurements predict LW better in adult male grasscutters than their female counterparts. This finding collaborates those of Ozluturk et al (2006), Daffour-Oduro and Naazie (2010) and Tsegaye et al (2013) in cattle, pigs and goats respectively.

Among the linear functions, modeling only one trait at a time, HG explained best the variation in LW followed by BL, TL and HL in that order. For the quadratic and cubic functions, where only HG and BL were used, the predictive ability of HG was also higher than BL. These findings agree with those of Birteeb and Ozoje (2012) in sheep, Tsegaye et al (2013) in goats, Francis et al (2002) in cattle and Annor et al (2011) and Udeh and Okota (2013) in grasscutters. The R² values increased when both BL and HG were used to estimate LW in both linear and quadratic functions. This is an indication that LW could be predicted better using both BL and HG in the same prediction equation. Several workers have also reported similar results indicating that adding additional independent variables to the prediction equation increases the goodness of fit (Baffour-Awuah et al 2000; Yakubu 2010; Tsegaye et al 2013). For more reliable estimates of LW of matured grasscutters, farmers should use prediction equations involving both HG and BL.

Generally, the quadratic functions explained much of the variations in LW of matured grasscutters compared to the linear and cubic functions. However, where the prediction equation involved the use of only one body measurement, the cubic functions were slightly ahead of the other functions. This is in agreement with results of Ojedapo et al (2012) in layer chicken where cubic functions gave the highest goodness of fit compared with linear and quadratic functions.

In summary, the prediction equation with the highest R² value was the quadratic equation involving both BL and HG to predict LW in male grasscutters (LW = -11.51 + 0.06BL + 0.58HG – 0.0072HG²). However, it is recommended that farmers use much simpler but reliable prediction equations like the linear equation involving both BL and HG (LW = -5.68 + 0.07BL + 0.14HG).

Conclusion

• Linear body measurements could be used in predicting the live weight of matured grasscutters with heart girth being the best predictor of live weight.
  
  
• The use of both heart girth and body length gives a much accurate estimate of live weight than just one linear body measurement.
  
  
• The quadratic and cubic functions explained better the variation in live weight of matured grasscutters compared to the linear function.

Acknowledgement

We are grateful to all the members of the Grasscutter Unit at the Pokuase station of Animal Research Institute, Ghana and particularly Messrs Yram Atipoe, Noble Defoe, Bright Ababio and Peter Hafudza for their assistance in collecting data for this study.

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Received 16 June 2016; Accepted 16 July 2016; Published 1 August 2016

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