Multivariate analysis of morphometric traits of Malaimadu cattle – Autochthonous draught cattle of south India

doi:10.21203/rs.3.rs-2191383/v1

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Multivariate analysis of morphometric traits of Malaimadu cattle – Autochthonous draught cattle of south India

https://doi.org/10.21203/rs.3.rs-2191383/v1

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published 21 Oct, 2023

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Malaimadu cattle is a less explored indigenous cattle germplasm reared in the Western Ghats area of Tamil Nadu, south India. This study aimed to analyse the morphological structure of the Malaimadu cattle using a multivariate approach that explains best represent body conformation. Sixteen body measurements were obtained from 156 adult female cattle from their native breeding tract. The Nearest Neighbor Method of Hierarchical Cluster analysis was used to group the morphometric traits into clusters. Pearson’s coefficients of correlation (r) were estimated to determine the phenotypic correlations between different body measurements. Traits were analysed using varimax rotated principal component analysis (PCA) with Kaiser normalization. Cluster analysis categorises morphometric traits into two distinct clusters with the first cluster formed by traits related to horns and face, while the second cluster denotes general body conformation. Out of 120 phenotypic correlations, 55 were significant of which 51 were positive correlations. The estimated correlation coefficient ranges between − 0.50 (body length and face width) to 0.85 (chest girth and body weight). PCA extracted six components representing 70.19% of total variance, while the first principal component comprises of chest girth (CG), body weight (BW), Height at wither (HW) and Paunch girth (PG) alone accounts for 23.70% thereby describing the general size and shape of the animal. The multivariate approach has proven to be effective in differentiating Malaimadu cattle from other indigenous breeds of south India with clear morphometric differences that helps in identifying the pure phenotype for future propagation and also for devising breeding strategies.

Cluster analysis

Malaimadu cattle

Morphometric traits

Multivariate analysis

PCA

India accounts for 22.6% (209.96 MT per annum) of the Global milk production (927.8 MT). Cattle alone contribute 51% of the country’s total milk production with the average milk yield per day of 6.62 kg in crossbred cows and 2.72 kg in indigenous/non-descript cattle (Basic Animal Husbandry Statistics, 2021). India is also bestowed with 53 registered indigenous cattle breeds that are appreciated for their adaptive traits like tolerance to heat stress, thriving on low input system and disease resistance (NBAGR, 2022). In developing countries, indigenous animal genetic resources (AnGRs) are being eroded through the significant transformation of agricultural systems like mechanization of agriculture, indiscriminate introduction of exotic germplasm before proper characterization, utilization and conservation of indigenous AnGRs and lack of awareness among the farmers to breed true to the type animals. To mitigate the adverse effects of global warming, climate change in near future and for sustainable livestock farming, conservation and evaluation of local breeds which are evolved and well adapted to the local environment is the way forward.

Malaimadu cattle is an indigenous germplasm reared in the adjoining Western Ghats area of Theni, Tenkasi, Virudhunagar, Tirunelveli and Dindigul districts of south Tamil Nadu, India. They are short to medium sized, slender, sturdy animals and fit to climb up the hilly areas for grazing throughout the year. This genetic group is known for its endurance and farmers rearing this cattle fetch revenue by sale of male calves to the bull tamer enthusiast who inturn train them to participate and win the traditional sport events like Jallikattu (Bull taming) and reckla (Bull cart) races, as a part of cultural heritage of Tamil Nadu. Apart from this, farmers also utilise Malaimadu cattle for penning and manuring the agricultural field thereby helps in organic farming (Vivekanandan and Alagumalai, 2013).

Karthickeyan et al. (2019) assessed the within population genetic diversity of Malaimadu cattle through microsatellite markers and observed that this population possess high number of allelic variability and was non-bottlenecked. Karyotype profiling of Malaimadu cattle reveals the standard cytogenetic parameters that are in agreement with the other registered native cattle breeds of India (Selvan et al. 2021).

Body conformation is an important parameter in phenotypic characterization of any breed or population. Morphometric studies are used to characterize the body conformation of different animal breeds, compare the growth of different individuals and describe individuals or population more effectively that have a significant role in breed characterization which seems to be the first approach in the conservation of animal genetic resources (Bousbia et al. 2021).

Hence this study was undertaken to provide a detailed description of the Morphometric traits using a multivariate approach that explains best represent body conformation in Malaimadu cattle.

Sampling and Data collection

Morphometric data on 156 adult Malaimadu cattle was collected from their native breeding tract of south districts of Tamil Nadu, India by following stratified stage sampling method (Fig. 1). To avoid age and sex effects, only adult female Malaimadu cattle of age above 3 years were included in this study. The animals were extensively managed as a herd in an open type enclosure made up of locally available thorny plant stem and stones stacked up. They graze on natural pasture containing forages in hilly forest such as Red oat grass (Themeda triandra), Bermuda Grass (Cynodon dactylon) Hill mango tree (Commiphora caudata) during daytime and scavenged on pulses by-products when available. Sixteen body measurements which can explain the body conformation in cattle were assessed following the Food and Agriculture Organization (FAO) guidelines for phenotypic characterization of animal genetic resources (FAO, 2012).

Morphometric traits included in this study were Body length (BL), Chest girth (CG), Height at wither (HW), Face length (FL), Face width (FW), Ear length (EL), Tail length with switch (TLS), Paunch girth (PG), Horn length (HL), Horn circumference at base (HCB), Horn circumference at midpoint (HCM), Horn circumference at tip (HCT), Horn distance at base (HDB), Horn distance at midpoint (HDM), Horn distance at tip (HDT) and Body weight (BW). The body measurements were carried out using a measuring tape when the animals were standing on a flat ground and maintained in upright posture. All the measurements were recorded by the same technical person to avoid effects between recorders. Body weight of each animal was calculated from their CG and BL measurement as per the Schaeffer’s formula.

Statistical analysis

Morphometric data were processed and analysed using the IBM SPSS Statistics for Windows, Version 13.0 (IBM SPSS Statistics for Windows, IBM Corporation, Armonk, NY). The descriptive statistics (mean, standard error and coefficient of variation) were calculated for all the morphometric traits under study. The Nearest Neighbor Method of Hierarchical Cluster analysis was performed to group the various body traits into clusters. A paired sample t-test was done to compare the linear traits like HW - BL; CG - PG and FL - FW.

Pearson’s coefficients of correlation (r) were estimated to determine the phenotypic correlations between different body measurements, which serve as the basis for employing principal component analysis (PCA) for quantifying trait variability and characterizing breeds. PCA is a method of reducing the dimensionality of multivariate data set that accounts for the maximum portion of the variance present in the original set of variables with a minimum number of composite variables. In order to test the validity of the factor analysis of the data set, the Kaiser-Meyer-Olkin (KMO) sampling adequacy criterion and Bartlett’s sphericity test were calculated at a significance level of 1%. The factor matrix was subjected to Varimax rotation in order to minimize the number of variables with strong correlations, especially with the first main component.

Morphometric measurements were recorded from the representative set of 156 adult females Malaimadu cattle as per the FAO guidelines (FAO, 2012). The descriptive statistics for all the morphometric traits were given in the Table 1. The coefficient of variation (CV) for different body measurements ranges between 5.64 (HW) to 50.07 (HDT). The morphometric measurements pertaining to the horns (HL, HCB, HCM, HCT, HDB, HDM and HDT) represent the most heterogeneous parameters, while traits like BL, CG, HW, FL and PG showed more homogeneity in Malaimadu cattle.

Table 1

Descriptive statistics of morphometric traits of adult Malaimadu cattle **(N = 156)**
Traits	Mean ± S.E	CV (%)
Body length	104.80 ± 0.71	8.38
Chest girth	133.03 ± 0.73	6.81
Height at wither	110.48 ± 0.50	5.64
Face length	40.71 ± 0.19	5.69
Face width	16.88 ± 0.40	29.63
Ear length	18.21 ± 0.15	10.35
Tail length with switch	86.87 ± 0.66	9.55
Paunch girth	134.17 ± 0.86	7.99
Horn length	28.57 ± 0.58	25.30
Horn circumference at base	14.90 ± 0.21	17.52
Horn circumference at midpoint	12.39 ± 0.17	17.53
Horn circumference at tip	4.61 ± 0.07	20.00
Horn distance at base	11.89 ± 0.26	27.11
Horn distance at midpoint	23.20 ± 0.37	19.95
Horn distance at tip	18.51 ± 0.74	50.07
Body weight	171.91 ± 2.27	16.52
Measurements expressed in cm, except for body weight (kg)

The Nearest Neighbor Method of Hierarchical Cluster analysis showed the formation of two distinct clusters from the linear morphometric traits of which the first cluster comprised of ten traits. The first agglomeration occurred between HCB, HCM, HDB, EL, FW, which then combined with HDM, followed by HCT, HL, HDT and finally FL to form the first cluster. The second cluster resulted from the agglomeration of CG, PG, BL, HW, TLS and BW (Fig. 2). A t-test between HW and BL revealed that the animal was significantly higher at withers by 5.68 cm indicating its stature as taller than longer, while between FL and FW shows cattle has longer face (Table 2).

Table 2

Paired t-test of selected linear traits of Malaimadu cattle
Paired Traits	Mean difference	t-statistic	p value
HW - BL	5.68	6.57	< 0.000
PG - CG	1.15	1.48	0.142
FL - FW	23.83	56.28	< 0.000

A total of 120 correlations between different morphometric traits were estimated. Among these, 55 were significant of which 51 were positive correlations. The magnitude of estimated correlation coefficient ranges between − 0.50 (BL and FW) to 0.85 (CG and BW). Both CG and BW were found to be positively correlated with most of studied morphometric traits (Table 3).

Table 3

Phenotypic correlation among different morphometric traits in adult Malaimadu cattle
	BL	CG	HW	FL	FW	EL	TLS	PG	HL	HCB	HCM	HCT	HDB	HDM	HDT	BW
BL	1
CG	0.023	1
HW	0.004	0.545**	1
FL	0.068	0.214**	0.334**	1
FW	-0.503**	0.289**	0.257**	0.111	1
EL	0.075	0.168*	-0.072	-0.134	-0.061	1
TLS	0.121	0.238**	0.172*	0.176*	0.119	0.105	1
PG	-0.062	0.529**	0.437**	0.141	0.391**	0.05	0.195*	1
HL	-0.001	0.156	0.069	0.13	0.03	0.074	0.001	0.184*	1
HCB	-0.182*	0.37**	0.204*	0.122	0.398**	0.082	0.181*	0.321**	0.304**	1
HCM	0.04	0.245**	0.176*	0.236**	0.132	0.03	0.069	0.295**	0.505**	0.544**	1
HCT	0.027	0.092	0.085	-0.066	0.1	-0.181*	-0.001	0.232**	0.008	0.1	0.185*	1
HDB	-0.21**	0.326**	0.322**	0.152	0.296**	0.131	0.202*	0.153	0.068	0.175*	0.166*	0.157	1
HDM	-0.021	0.186	0.111	0.116	-0.014	0.238**	0.071	0.048	0.267**	0.175*	0.197*	0.13	0.333**	1
HDT	-0.139	0.003	0.072	-0.049	0.105	0.023	0.016	-0.001	-0.046	0.143	0.027	0.117	0.186*	0.506**	1
BW	0.547**	0.845**	0.444**	0.208**	-0.026	0.181*	0.248**	0.395**	0.133	0.212**	0.229**	0.087	0.163*	0.142	-0.075	1
The lower triangle shows the phenotypic correlation among different morphometric traits with superscripts showing their respective
level of significance - *p < 0.01 and p < 0.05

The PCA was applied to sixteen morphometric traits in Malaimadu cattle and KMO measure of sampling adequacy (MSA) was obtained as 0.562, which support the use of the data set for factor analysis. The overall significance of the correlation matrix tested with Bartlett’s sphericity test for the morphometric traits was highly significant (p < 0.0001) which allows a reduction in the dimension of all sixteen morphometric traits. Six components were extracted out of sixteen principal components whose eigenvalue is greater than one using Kaiser Rule criterion (Johnson and Wichern 1982). The identified six components represent 70.19% of total variance, while the first component alone accounts for 23.70% of the variance (Table 4). The coefficients of the PCA of varimax (orthogonal) rotated component matrix of the six extracted components are given in Table 5. The first component was represented by significant positive high loading of CG, BW, HW and PG. The second component explained 12.67% of the total variance with positive high loading of HCM, HL and HCB. The remaining four components explained 33.82% of the total variance. The communality ranged from 0.289 (TLS) to 0.919 (BW) and unique factors ranged from 0.711 to 0.081 for all the 16 different morphometric traits (Table 6). The component plot of the six components in rotated space is shown in Fig. 3.

Table 4

Total variance explained by different components in adult Malaimadu cattle
Component	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
Component	Total	Percentage of variance	Cumulative percentage	Total	Percentage of variance	Cumulative percentage	Total
1.	3.792	23.701	23.701	3.792	23.701	23.701	3.049
2.	2.028	12.672	36.374	2.028	12.672	36.374	2.012
3.	1.617	10.109	46.482	1.617	10.109	46.482	1.942
4.	1.457	9.107	55.589	1.457	9.107	55.589	1.755
5.	1.219	7.621	63.210	1.219	7.621	63.210	1.244
6.	1.117	6.978	70.188	1.117	6.978	70.188	1.228
7.	0.904	5.649	75.837
8.	0.796	4.974	80.812
9.	0.630	3.940	84.752
10.	0.580	3.623	88.375
11.	0.492	3.075	91.450
12.	0.404	2.527	93.977
13.	0.364	2.277	96.254
14.	0.310	1.938	98.192
15.	0.286	1.788	99.980
16.	0.003	0.020	100.000

Table 5

Varimax rotated component matrix of different factors for biometry traits in adult Malaimadu cattle
Traits	Principal component
Traits	1	2	3	4	5	6
Chest girth	0.883	0.174	0.06	0.066	0.005	-0.037
Body weight	0.851	0.148	-0.413	-0.007	0.026	-0.03
Height at wither	0.664	0.007	0.136	0.109	0.101	0.385
Paunch girth	0.64	0.253	0.27	-0.102	0.265	-0.061
Tail length with switch	0.438	-0.032	0.017	0.107	-0.261	0.128
Horn circumference at midpoint	0.15	0.836	0.01	0.06	0.138	0.118
Horn length	-0.015	0.815	-0.066	0.053	-0.077	0.046
Horn circumference at base	0.284	0.633	0.361	0.088	0.028	-0.067
Body length	0.23	-0.004	-0.869	-0.111	0.044	0.014
Face width	0.292	0.094	0.81	0.006	0.075	0.045
Horn distance at midpoint	0.072	0.256	-0.114	0.842	-0.04	-0.024
Horn distance at tip	-0.071	-0.051	0.109	0.789	0.167	-0.034
Horn distance at base	0.361	0.003	0.305	0.522	-0.061	0.118
Horn circumference at tip	0.124	0.073	-0.01	0.181	0.836	-0.079
Face length	0.238	0.19	-0.037	0.063	-0.201	0.804
Ear length	0.221	0.112	-0.096	0.221	-0.537	-0.606

Table 6

Communalities and unique factor of various biometry traits in adult Malaimadu cattle
Traits	Communalities	Unique factor
Body length	0.823	0.177
Chest girth	0.819	0.181
Height at wither	0.629	0.371
Face length	0.784	0.216
Face width	0.758	0.242
Ear length	0.774	0.226
Tail length with switch	0.289	0.711
Paunch girth	0.631	0.369
Horn length	0.680	0.32
Horn circumference at base	0.625	0.375
Horn circumference at midpoint	0.758	0.242
Horn circumference at tip	0.759	0.241
Horn distance at base	0.514	0.486
Horn distance at midpoint	0.795	0.205
Horn distance at tip	0.671	0.329
Body weight	0.919	0.081

In developing countries, sustainability in livestock farming requires monitoring of native domestic animal population which are well adapted to the local environment. Use of multivariate methods in analysing the morphometric traits to access the within and between population diversity will play a significant role in upkeeping of autochthonous genetic resources (Birteeb et al., 2014). Multivariate analyses of morphometric traits have been reported to be effective for a precise and objective discrimination of livestock species (Vohra et al. 2015; Mishra et al. 2017; Alhajeri et al. 2019; Silva-Jarquin et al. 2019; Bousbia et al. 2021).

Based on the BL, CG, HW, Malaimadu cattle can be categorised as small to medium sized animals. Similar type of short stature breeds of cattle reared close to the Western ghats of south India like Alamadi (Parameswari et al. 2021), Kasargod (Iype et al. 2016), Bargur (Ganapathi et al. 2013) and Pulikulam (Srinivasan and Sathiamoorthy 2020) were already registered as recognised indigenous breeds of India by National Bureau of Animal Genetic Resources (NBAGR), Karnal, Haryana, India.

High CV pertaining to horn parameters (HL, HCB, HCM, HCT, HDB, HDM, HDT) and FW may be due to the fact that selection was not applied or influenced more by the environmental factors for these traits, which was in concordance with Bousbia et al. (2021). Lesser variability in BL, CG, HW, FL and PG reveals that the Malaimadu cattle are of similar in their body size. This homogeneity might be due to natural selection favouring particular size and shape that is well adapted to local environment. Cluster analysis shows the first cluster was associated with variables related to horns and face, while the second cluster was formed by variables pertaining to bone growth providing insight into general body conformation of Malaimadu cattle. A paired t-test between selected linear traits indicates that the cattle is taller than longer and has a narrow face.

Traits like CG, BW, HCB, HCM and HDB were found to be strongly correlated with the other studied traits, whereas HDT was not correlated with most of the traits except HDB and HDM indicating the independence of this trait. Higher correlation of body weight with other morphometric traits indicates the fact that the development of various anatomical structures directly or indirectly influences the growth of the animal. The highest negative correlation between BL and FW indicates that increase in the length of the animal tends to have a narrow face, which supports the findings of paired t-test in the present study. Correlation among morphometric traits serve as the basis for employment of further multivariate techniques like PCA for distinguishing breeds of livestock (Yakubu et al. 2011).

The KMO measure of sampling adequacy computed across all variables was equal to 0.562 for Malaimadu cattle. Kaiser (1974) recommended that the variables with MSA above 0.50 could be proceded for factor analysis. However, Tolenkhomba et al. (2013) reported KMO-MSA as 0.60 in Manipur local cattle, Pundir et al. (2011) reported 0.89 in Kankrej cows and Verma et al. (2015) reported 0.75 in hilly cattle of Himachal Pradesh.

In this study, Varimax rotation method was applied to maximize the sum of the variances of the squared loadings. Six components with Eigenvalue greater than 1 were estimated and they cumulatively accounted for 70.19% of total variance. The remaining unexplained variation may be ascribing to the segregation of casual alleles at contributory loci, measurement error and environmental factors (Brooks et al. 2010). In accordance with the present study, Tolenkhomba et al. (2013) extracted six components which accounted for 69.77% of total variance in Manipur local cattle, whereas Pundir et al. (2011) and Verma et al. (2015) extracted three and five components accounted for 66.02% and 65.95% of total variance in Kankrej cows and hilly cattle of Himachal Pradesh respectively. The variables most closely associated with first component (CG, BW, HW and PG) tend to describe the general size and shape of the animal which was in agreement with Bousbia et al. (2021), Yakubu et al. (2009) and Carpenter et al. (1978).

The communalities after extraction of principal components gives the common variance that is shared between the variables (Yunusa et al. 2013). The lower and higher communality of TLS (0.289) and BL (0.823) indicates that these traits were less and more effective in explaining the total variation in body conformation of Malaimadu cattle compared to other traits.

Thus Multivariate approach of morphometric traits, which are easy to measure, makes it possible to explore areas like breed structure, between population variability and to develop morphological scale for any given breed. The six components obtained in this study explain the morphological structure of this cattle and hence could be used to select or define the Malaimadu cattle based on a group of variables rather than on isolated traits. Thus, the morphometric differences observed between Malaimadu cattle with other short indigenous cattle of south India may support the fact that a large part of the morphological variation is under genetic control, indicating that they can be subjected to further genetic improvement program. Morphometric characterization in this study along with the earlier research findings of molecular and cytogenetic characterization of Malaimadu cattle define it as a separate genetic group that deserve a status of a distinct autochthonous cattle breed of Indian origin.

Acknowledgements The authors were thankful to Tamil Nadu Veterinary and Animal Sciences University, Chennai, India for their financial support under the TANUVAS Sub-Project scheme.

Author contribution All authors contributed to manuscript review, editing, final proofreading and approving for submission. SSA and KSMK: contributed to the study conception, methodology and design. SSA, TS, and KA: carried out preliminary data collection and data curation. SSA, SD and KA: carried out the data analysis. SSA and KA wrote the manuscript.

Funding Tamil Nadu Veterinary and Animal Sciences University, Chennai, provided financial support under the TANUVAS Sub-Project scheme to the First author.

Data availability The datasets analyzed during the current study are available from the corresponding author upon request.

Ethics approval Not applicable.

Consent to participate Not applicable.

Consent for publication Not applicable.

Conflict of interest The authors declare no competing interests.

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Multivariate analysis of morphometric traits of Malaimadu cattle – Autochthonous draught cattle of south India

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