Correlation and path analysis of body weight and biometric traits of Nguni cattle breed

Genetic variations of Toll-like receptor 4 gene in exon 2 of South African Dorper sheep

Objective

The study was conducted to identify the sequence variation of Toll-like receptor 4 (TLR4) in exon 2 of South African Dorper sheep.

Materials and Methods

Blood samples were collected from fifty (n = 50) South African Dorper sheep aged between 3 and 4 years. The Deoxyribonucleic acid (DNA) was extracted, amplified, and sequenced for the TLR4 gene. DNA sequencing was used to identify the sequence variations of the TLR4 gene in South African Dorper sheep.

Results

The results showed that one synonymous single nucleotide polymorphism (SNP) of the TLR4 gene in exon 2 position T2249C was identified. Two genotypes (TT and TC) were discovered from the identified SNP. The dominant genotype was TT (0.60) over TC (0.40), with the dominant allele T (0.80) over C (0.20). The results also indicated that the used population was in the Hady-Weinberg Equilibrium. Polymorphism genetic analysis findings suggest that the identified sequence variation of TLR4 in exon 2 of South African Dorper sheep was moderate polymorphism.

Conclusion

TLR4 gene at exon 2 of South African Dorper sheep had the SNP (T>C) at position 2249 bp with two genotypes (TT and TC).

Body weight prediction of Belgian Blue crossbred using random forest

Objective

The aim of this study was to predict the body weight (BW) of a Belgian Blue X Friesian Holstein (BB X FH) crossbred in Indonesia based on morphometrics using random forest.

Materials and Methods

A total of 26 BB X FH crossbreds were observed for BW, chest weight (CW), body length (BL), hip height (HH), wither height (WH), and chest girth (CG) from 0, 30, 60, 90, 120, 150, 180, 210, 240, 270, and 300 days of age. Stepwise regression and random forest were performed using R 3.6.1.

Results

The random forest results show that CG is an important variable in estimating BW, with an important variable value of 24.49%. Likewise, the results obtained by stepwise regression show that CG can be an indicator of selection for the BB X FH crossbred. The R squared value obtained from the regression is 0.83, while the R squared value obtained from the random forest (0.86) is greater than the regression.

Conclusion

In conclusion, random forest produces a better model than stepwise regression. However, a good simple equation to use to estimate BW is CG.

The meta-analysis of beef cattle body weight prediction using body measurement approach with breed, sex, and age categories

Objective

The aim of the study was to use a meta-analysis to identify the correlation between linear body measurements, including body length (BL), wither height (WH), heart girth (HG), and body volume (BV), and body weight in beef cattle by breed, sex, and age as categories.

Materials and methods

These results can be used as a method for predicting beef cattle body weight. This study used systematic review and meta-analysis guidelines to create a checklist. The first stage was searching for papers relevant to the study objectives. The second stage was searching using the keywords beef cattle, body weight, body measurement, and correlation. The third stage was reviewing the title and abstract. The fourth stage was abstracting information from selected papers, and the last stage was tabulating data.

Results

The results from this study were obtained, and 32 papers were eligible for the meta-analysis stage. The correlation between linear body measurement and body weight of beef cattle showed that HG (r = 0.88) and BV (r = 0.97) were significantly (p < 0.05) different compared to BL (r = 0.74) and WH (r = 0.72). The correlation between HG and body weight, and the categorization of cattle breeds showed significantly (p < 0.05) different results. The correlation between BV and body weight of cattle according to breed categories showed results that were not significantly (p > 0.05) different, while age was significantly (p < 0.05).

Conclusion

In conclusion, to predict beef cattle body weight, it is necessary to use HG or BV, with breed, sex, and age of cattle as categories.

Morphological characterization of Sussex cattle at Huntersvlei farm, Free State Province, South Africa

Sussex cattle breed is characterized by their distinctive solid red coat colour and white tail switch. Sussex cattle are known for being easy to handle and manage, making them an ideal choice for cattle farmers. The phenotypic characterization of this cattle breed in South Africa is unknown. Hence, the objective of this study was to characterize the morphological structure, phenotypic and body indices traits of Sussex cattle in South Africa at Huntersvlei farm, Frere State province of South Africa. One hundred and one weaners (n = 101) between 6 and 8 months old (female = 57 and male = 44) and fifty yearlings between 12 and 15 months old (female = 15 and male = 35) were used in this study. Body weight at weaning, yearling and linear body measurements such as head length (HL), head width (HW), ear length (EL), ear width (EW), sternum height (SH), withers height (WH), heart girth (HG), hip height (HH), body length (BL), rump length (RL), and rump width (RW) were measured. Moreover, the animals were assessed for coat colour and horn presence. Descriptive statistics, Pearson's correlation and Principal Component Analysis (PCA) were used to describe the Sussex cattle breed. The results indicated that male Sussex cattle had highly significant (p < 0.01) mean numeric values for the BW and morphometric traits. The results further showed that Sussex cattle had highly significant (p < 0.01) increase for the BW and morphometric traits as age advances in all sexes. Interaction effect of sex and age showed a highly significant (p < 0.01) effect with BW and measured morphometric traits, while moderately significant (p < 0.05) with EW. Male Sussex cattle showed highly significant (p < 0.01) higher mean numeric values for the body index (BI), length index (LI) and compact index (CI) indices. While female Sussex animals showed highly significant (p < 0.01) mean numeric values for area index (AI) and proportionality (PR). Body weight showed a positive highly significant (p < 0.01) correlation with the measured morphometric traits except for the moderate significant (p < 0.05) correlation with EL. Coat colour traits ranged from 15 (9.93%), 103 (68.21%) to 33 (21.85%) for light, moderate and dark colours, respectively. While horn presence traits ranged from 48 (31.79%), 42 (27.81%) to 61 (40.40%) for polled, scur and horned respectively. The PCA results extracted only two components in both sexes of the animals. The morphological variations obtained in this study could be complemented by performance data and molecular markers of single nucleotide polymorphism (SNP) to guide the overall breed characterization, conservation and development of appropriate breeding and selection strategies.

Characterization of a unique Sikkimese yak population of India: a multivariate approach

Native Sikkimese yak in Sikkim state of India is a pastoral treasure being raised through centuries-old transhumance practices and has evolved in response to natural and man-made selection. Currently, the population of Sikkimese yak is at risk with about five thousand total headcounts. Characterization is essential for taking appropriate decisions for conservation of any endangered population. In an attempt to phenotypically characterize the Sikkimese yaks, this study recorded phenotypic morphometric traits information, viz., body length (LG), height at withers (HT), heart girth (HG), paunch girth (PG), horn length (HL), horn circumference (HC), distance between horns (DbH), ear length (EL), face length (FL), face width (FW), and tail length with switch (TL), on 2154 yaks of both sexes. Multiple correlation estimation highlighted that HG and PG, DbH and FW, and EL and FW were highly correlated. Using principal component analysis, LG, HT, HG, PG, and HL were found to be the most important traits for phenotypic characterization of Sikkimese yak animals. Discriminant analysis based on different locations of Sikkim hinted at the existence of two separate clusters, however, broadly, phenotypic uniformity could be observed. Subsequent genetic characterization can offer greater insights and can pave the way for future breed registration and conservation of the population.

Using Multivariate Adaptive Regression Splines to Estimate the Body Weight of Savanna Goats

The Savanna goat breed is an indigenous goat breed in South Africa that is reared for meat production. Live body weight is an important tool for livestock management, selection and feeding. The use of multivariate adaptive regression splines (MARS) to predict the live body weight of Savanna goats remains poorly understood. The study was conducted to investigate the influence of linear body measurements on the body weight of Savanna goats using MARS. In total, 173 Savanna goats between the ages of two and five years were used to collect body weight (BW), body length (BL), heart girth (HG), rump height (RH) and withers height (WH). MARS was used as a data mining algorithm for data analysis. The best predictive model was achieved from the training dataset with the highest coefficient of determination and Pearson's correlation coefficient (0.959 and 0.961), respectively. BW was influenced positively when WH > 63 cm and HG >100 cm with a coefficient of 0.51 and 2.71, respectively. The interaction of WH > 63 cm and BL < 75 cm, WH < 68 cm and HG < 100 cm with a coefficient of 0.28 and 0.02 had a positive influence on Savanna goat BW, while male goats had a negative influence (-4.57). The findings of the study suggest that MARS can be used to estimate the BW in Savanna goats. This finding will be helpful to farmers in the selection of breeding stock and precision in the day-to-day activities such as feeding, marketing and veterinary services.

Predicting body weight of Kalahari Red goats from linear body measurements using data mining algorithms

Background and Aim:

The Kalahari Red goat breed is the finest meat-producing species in South Africa, and its coat color ranges from light to dark red-brown. A practical approach to estimating their body weight (BW) using linear body measurements is still scarce. Therefore, this study aimed to determine the best data mining technique among classification and regression trees (CART), Chi-square automatic interaction detection (CHAID), and exhaustive CHAID (Ex-CHAID) for predicting the BW of Kalahari Red goats.

Materials and Methods:

This study included 50 Kalahari Red goats (does = 42 and bucks = 8) aged 3–5 years. Body length (BL), heart girth (HG), rump height (RH), height at withers (WH), sex, and age were the essential indicators to estimate BW. The best model was chosen based on the goodness of fit, such as adjusted coefficient of determination (Adj. R²), coefficient of determination (R²), root mean square error (RMSE), standard deviation ratio (SD ratio), mean absolute percentage error, Akaike information criteria, relative approximation error, and coefficient of variation.

Results:

The SD values of the ratio ranged from 0.32 (CART) to 0.40 (Ex-CHAID). The greatest R² (%) was established for CART (89.23), followed by CHAID (81.99), and the lowest was established for Ex-CHAID (81.70). CART was established as the preferred algorithm with BL, HG, and WH as critical predictors. The heaviest BW (73.50 kg) was established in four goats with BL higher than 92.5 cm.

Conclusion:

This study reveals that CART is the optimum model with BL, HG, and WH as the essential linear body measurements for estimating BW for Kalahari Red goats. The updated records will assist the rural farmers in making precise judgments for various objectives, such as marketing, breeding, feeding, and veterinary services in remote areas where weighing scales are unavailable.

Mapping Growth Hormone Gene of Body Weight Krui Cattle in Pesisir Barat Regency Lampung, Indonesia

&lt;b&gt;Background and Objective:&lt;/b&gt; The growth hormone (GH) gene plays a role in meat growth and has been shown to increase the growth rate and carcass composition after being given GH. For this function, this gene is used as a strong candidate for genetic markers for meat growth traits. The research objective was to map the growth hormone (GH) gene of the bodyweight of Krui cattle in the Pesisir Barat Regency. &lt;b&gt;Materials and Methods:&lt;/b&gt; This research used 30 blood samples of 30 Krui cattle. The method used was by taking quantitative data and blood samples from adult Krui cattle in Pesisir Barat Regency and then the blood samples were analyzed by DNA isolation method. PCR amplification used was a pair of GH-Forward primers: 5 'ATC CAC ACC CCC TCC ACA CAGT 3' and GH- reverse: 5 'CAT TTT CCA CCC TCC CCT ACA G 3', as well as digestion using the RFLP method at the Laboratory of Animal Breeding and Genetics of Universitas Gadjah Mada, Yogyakarta. Association between genotype and body weight was analyzed descriptively. &lt;b&gt;Results:&lt;/b&gt; The results showed that Krui cattle had polymorphic genes with three genotypes found, namely: CC, CT and TT. Cattle with CT genotype had the largest average body weight or meat production compared to those with other genotypes. &lt;b&gt;Conclusion:&lt;/b&gt; These results indicated that the GH gene identifier has strong evidence that it can be used as a selection tool with the help of genotypes of body weight traits of Krui meat production in the Pesisir Barat Regency. Krui cattle with CT genotype can be developed further because it has high economic value with high average body weight and meat production.