Phenotypic characterization is used to identify and document diversity within and between distinct breeds, based on their observable attributes (FAO, 2012). Phenotypic characterization, including information of body biometry, is the prerequisite of all lesser known buffalo populations in order to provide overall picture of buffalo genetic diversity. Unless we know the physical and biometric characters besides accurate geographic location, population size, animal husbandry management practices, and utility of the particular animal breed their overall improvement cannot be properly implemented. In India, the buffalo population is 105.34 million (56.6% of total world buffalo population) which contributes 51.1% of total milk produced in the country. About 56.0% were classified as nondescript/uncharacterized buffaloes (DAHDF, 2007). This estimate of non-descript buffaloes includes many unique but distinct buffalo populations, which needs to studied and classified. Till date, 13 breeds of buffalo have been recognized, studied and registered in India (NBAGR, 2013). One unique lesser known buffalo population which have not been studied so far is Gojri buffalo from Punjab and Himachal Pradesh (Vohra et al., 2012). “Gojri” means “Gujjars’ animal” and is recognised due to the community who rear them, since time immemorial. Gojri buffaloes are well adapted in hot and humid climate of India and have a unique ability to graze on hill tops without fall, a characteristic not common to riverine buffaloes. Physical appearance of Gojri buffaloes include black coat colour with brown thick hairs; white patches may be present on black face, and muzzle is black in colour. Males are brownish to black in colour. Horns are characteristic medium to large sized with curved orientation which moves backwards and then towards front to complete the loop, called ‘Pattih wale seengh’. Udder is small round shaped but well-placed with cylindrical teats and milk vein is visible indicating the potential for milk production. The temperament of these animals is docile (Figure 1).

Body conformation by recording a minimum number of body measurements/biometric traits which reduce the cost, labour and time is the need of the day. FAO have used height at withers (HT) as a prime indicator for their type (Simon and Buchenauer, 1993). Body shapes measured objectively could improve selection for growth by enabling the breeder to recognize early-maturing and late-maturing animals of different size (Brown et al., 1973; 1974). Significant differences in different body measurement/biometric traits due to age and sex were reported by many workers in different breeds and species. i.e. Gilbert et al. (1993), Shahin et al. (1995), Pundir et al. (2007a, b, c; 2008), Singh et al. (2008) and Yakubu et al. (2009) in cattle; Shahin et al. (1993) in Egyptian buffalo; Biedermann and Schmucker (1989), Jakubec et al. (1999), Miserani et al. (2002) and Sadak et al. (2006) in horses; and Sarako et al. (2006) in sheep. In the present study different body biometry traits, relationships among different body measurements and to develop unobservable components (latent) to define which of these measures best represent body conformation in Gojri buffalo were studied using principal component analysis.

Data consisted of 13 different body measurements which can explain the body conformation in buffaloes were taken on a lesser known buffalo sample, popularly known as Gojri, from Punjab and Himachal Pradesh regions of North India. Traits included in the study were height at wither or body height (HT); body length (BL); chest girth (CG); paunch girth (PG); face length (FL); face width (FW); horn length (HL); horn circumference (HC); ear length (EL); distance between hip bone (HB); distance between pin bone (PB); tail length (TL); tail length up to switch (TUS). To avoid age and sex effects only adult Gojri buffaloes (3.5 yrs and above) were taken in the study. Biometric data on 233 Gojri buffaloes was collected from four districts of their breeding tract. All the measurements were recorded once in upright animal standing on a level ground and by the same technical person to avoid between-recorder effects. Measurements were recorded by using tape measure.

Mean of biometric traits (cm) studied in Gojri buffaloes were 128.66±0.32 for HT, 133.33±0.35 BL, 195.91±0.67 CG, 213.91±1.34 PG, 48.58±0.11 FL, 22.33±0.09 FW, 44.61±0.61 HL, 19.82±0.12 HC, 28.76±0.09 EL, 53.58 ±0.24 HB, 24.29±0.29 PB, 90.57±1.15 TL and 104.15± 0.67 tail up to switch, respectively. In Nilli Ravi breed of river buffalo, Nivsarkar et al. (2000) reported average HT, CG, and BL as 134.2, 207.7, and 165.4 cm respectively, whereas Ahmad et al. (2013) reported HT to be 140.2±7.2 cm in Nilli Ravi females. Based on comparison of biometric traits like average HT, CG and BL of Gojri buffaloes with Nilli Ravi buffaloes, which are also sharing their breeding tract, Gojri buffalo is lighter, smaller and shorter than Nilli Ravi buffaloes. Coefficient of variation for different biometric traits in Gojri buffaloes ranged from 3.32% (FL) to 19.41% (HL). It was observed that HL and distance between PB (17.05%), had more variability which may be due to the fact that selection was not applied for these traits or that these parts respond more to the environment than others. Similarly, the TL with switch (18.01%) had more variability than without the switch (9.11%), indicating that tail switch had more variability. Face length had little variability which may be due to the fact that it is a cephalic measurement and its close association with cranial bone. Majority of body measurements showed less variability, indicating that the Gojri buffaloes are almost similar in their body size. Correlation coefficient estimated ranged between −0.41 (EL and distance between PB) to 0.77 (HT and BL) among various biometric traits. A total of 91 correlations (in all combinations) were estimated. Among these 72 were significant of which 54 were positive correlations (Table 1). Height at withers had significant higher correlations with BL (0.77), FL (0.50), and CG (0.50) while it showed lowest correlation with PG (0.10) which was non-significant. Body length had higher correlations with CG (0.55) and FL (0.49), while BL had the lowest phenotypic correlation with TL. The FL had positive correlation with FW (0.62) and HC (0.55). The correlation between FW and HC (0.52), distance between HB and EL (0.67), TL and tail up to switch (0.51) were also high. The PG had small negative correlations with FL (−0.24), HC (−0.18), FW (−0.07), and TL (−0.05). Tail length showed negative correlation with PG (−0.58), HL (−0.05) and distance between PB (−0.31). The positive and significant (p<0.05/0.01) correlations among different biometric traits suggest high predictability among the different traits.

The PCA was applied to 13 body conformation traits in Gojri buffalo and KMO measure of sampling adequacy (MSA) was obtained as 0.74. The widely used and accepted method of rotation (varimax) was applied (Fernandez, 2002) as it maximizes the sum of the variances of the squared loadings (squared correlations between variables and components). The estimate of sampling adequacy KMO revealed the proportion of the variance in different biometric traits caused by the underlying components (Kaiser, 1958). The overall significance of the correlation matrix was tested with Bertlett’s test of sphericity for the biometric traits (chi-square was 1.149E3, p<0.01) was significant, it means correlation matrix is not an identity matrix and provided enough support for the validity of the factor analysis of data. The MSA below 0.5 was not accepted, and KMO-MSA greater than 0.5 is must for satisfactory factor analysis to proceed. Pundir et al. (2011) reported higher estimates of sampling adequacy as 0.891 in Kankrej cows and also Yakuba et al. (2009) as their estimates of sampling adequacy were 0.90 and 0.92 in age groups of 1.5 to 2.4 years and 2.5 to 3.6 years, respectively, in White Fulani cattle.

Out of 13 principal components, four components were extracted using Kaiser Rule criterion (Johnson and Wichern, 1982) to determine the number of components i.e. retaining only the components that have eigenvalue greater than 1 (Table 2). There could be more than one criterion to decide how many components can be used. Scree plot can also depict the various components and could can be used to decide the actual number of the components to be included for analysis, components having eigenvalues up to the point “bent of elbow” are usually considered (Figure 2). The identified four components could explain cumulative percentage of variance of 70.86%. First component accounted for 31.45% of the variation. It was represented by significant positive high loading of height at wither, BL, FL, FW, HL, and CG. First component seemed to be explaining the maximum of general body conformation in Gojri buffaloes. The second component explained 17.15% of total variance with high loading of distance between HB, TL, and EL. Third component explained 12.59% of variance and showed high component loadings for PG and TL up to switch. The fourth factor accounted for 9.66% of total variability with comparatively higher loading for tail characteristics (Table 3). In Egyptian buffalo bull Shahin et al. (1993) reported that most of the common variability (88%) in body dimensions could be accounted for by components representing general size, body depth and height and head width. Tolenkhomba et al. (2013) used factor analysis with promax rotation revealed 6 components which explained about 69.77% almost similar to our finding but the total variation with first component explained 21.93% of total variation and was represented by significant positive high loading of height at wither, BL, heart girth, PG, and EL in local cattle of Manipur. The proportion of total variance explained by first component was more in our study (31.45%) compared to by Tolenkhomba et al. (2013). Yakubu et al. (2009) extracted 2 factors in the age group of 2.5 to 3.6 years explained 86.47% of the total variation by studying the 14 morpho-structural traits of White Fulani cattle. Salako (2006) extracted two factors from 10 different biometric traits in Uda sheep which accounted for 75% of total variation. Sadek et al. (2006) extracted three factors for Arabian mares and stallions separately by studying 14 different traits and these explained 66% and 67% of total variation.

The communality ranged from 0.44 (HL) to 0.83 (HB distance) and unique factors ranged from 0.55 to 0.16 for all these 13 different biometric traits (Table 4). Body conformation appeared to be controlled by common and unique factors in Egyptian buffalo bulls Shahin et al. (1993). In Egyptian buffalo the communalities ranged from 0.96 (height at hips, HT) to 0.78 (rump width) and the uniqueness (special size factors) made the balance Shahin et al. (1993). Approximate range of communality i.e. 0.42 to 0.87 was reported by Sadek et al. (2006) in Arabian mares. Higher estimates of communality (ranged from 0.79 to 0.93) were observed by Yakubu et al. (2009). The lower estimates of communalities of HL and PG indicates that they did not explain the body conformation in Gojri buffaloes.

All inter-factor correlations between different components were positive and ranged from 0.42 to 0.03, having maximum between first and second component and minimum between second and third component. Pattern Matrix indicate that the first principal component can be used in the evaluation and comparison of body biometry in female buffaloes using head and horn traits (HC, FW, and FL) while second principal component has shown high component loadings of heart girth, paunch girth, body length and height at wither (Table 5). Our findings were similar to Pundir et al. (2011) where they reported that first factor explained 38.89% of total variation and second factor accounted for 12.36% of total variability which had comparatively higher loading for horn characteristics in Kankrej cows. However, scanty literature is available for adult riverine buffaloes on body conformation traits.

Phenotypic characterization and multivariate analysis of body conformation traits in Gojri buffalo is a novel report. The positive and significant correlations among different biometric traits suggest high predictability among the different traits, and also making them amenable for analysis through PCA. Results of PCA suggest that the use of orthogonal synthetic variables principal component one (PC1) and two (PC2) provided a means of reduction in the number of biometric traits to be recorded in Gojri buffaloes which could be used in explaining body conformation and in distinguishing different breeds. The PC1 may be used in phenotypic selection as a means to explain body conformation of better early maturing buffaloes to be used in breeding programmes.