scholarly journals Use of Body Linear Measurements to Estimate Live Weight in Communal Beef Cattle

2021 ◽  
Vol 2 (2) ◽  
pp. 11-20
Author(s):  
Soul Washaya ◽  
Wesley Bvirwa ◽  
Godfrey Nyamushamba
Keyword(s):  
Body Weight ◽  
Principal Component Regression ◽  
Live Weight ◽  
Principal Component ◽  
Body Measurements ◽  
Linear Regression Models ◽  
Linear Measurements ◽  
Smallholder Farms ◽  
Accuracy Of Prediction ◽  
The Relationship

Body measurements are important criteria in the selection of elite animals for breeding. The objective of this study was to determine the relationship, accuracy of prediction of body weight from body measurements, and identifying multicollinearity from three beef breeds.  Four classes of stock (bull, cows, steers, and heifers) were considered. Correlation, simple, and multiple linear regression models were fitted with body weight (BW) as the dependent variable and body length (BL), heart girth (HG), height at wither (HW), muzzle circumference (MC), and shank circumference (SC) as the independent variables. The BW of the animals ranged from 218 to 630 kg, the least being heifers and bulls were the heaviest. The pairwise phenotypic correlations showed a high and significant positive relationship between BW and body dimensions (r = 0.751- 0.96; P<0.01). However, negative correlations were observed between BW with BL and MC of r = -0.733 and -0.703 and -0.660, -0.650, for cows and heifers, respectively. Regressing BW on BL, HG, and HW measurements gave statistically significant (P<0.01) equations with R2 ranging from 0.60 to 0.79. Collinearity, as portrayed by high variance inflation factors (VIFs), tolerance values, and low eigenvalues, was evident in four of the variables. It was concluded that the regression model was useful in BW prediction for smallholder farms and the relationship between BW and other body measurements was influenced by breed and class of stock. It is recommended that ridge regression or principal component regression be used in cases where multicollinearity exisists.

scholarly journals Bayesian principal component analysis of Nigerian indigenous normal feather chickens' body linear measurements

2020 ◽  
Vol 44 (1) ◽  
pp. 10-20
Author(s):  
A. E. Sonubi ◽  
A. S. Adenaike ◽  
A. A. Dauda ◽  
T. P. Alao ◽  
B. O. Shonubi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Principal Components ◽  
Body Shape ◽  
Principal Component ◽  
Component Analysis ◽  
Body Measurements ◽  
Indigenous Chicken ◽  
Body Dimension ◽  
Linear Measurements

The indigenous chicken is a store house of unique genes that could be used in other parts of the world for improving other breeds. This study was carried out using bayesian principal component analysis and aimed objectively at determining the effect of sex on Nigerian indigenous normal feather chickens' body dimension, describing their body shape, and predicting their body weights from body measurements using orthogonal conformation traits derived from the principal components score. The parameters measured at 16 weeks of age were body weight, body length, breast girth, thigh length, shank length, shank diameter, keel length, wing length, wing span, and tail length on 233 randomly selected adult chickens. Sexual dimorphism was observed in all the traits with higher values recorded for males. Bayesian correlations among body weight and biometric traits were positive (r = 0.09 to 0.651 and 0.017 to 0.579 in male and female chickens respectively). The descriptive statistics showed that the mean body weight was 1.8085 ± 0.263 kg for males and 1.403 ± 0.226 kg for females. The first two principal components (PCs) were extracted for the males, both PCs components account for 72.21%. For the females, three PCs were extracted and they account for 77%. The first PC in each case accounted for the greatest percentage of the total variation. The use of orthogonal body shape characteristics derived from components' scores was more appropriate than the use of original traits in body weight prediction as multi-collinearity problems were eliminated. This led to simultaneous analysis of these body measurements rather than on individual basis. These components could be used as selection criteria for improving body weight of indigenous Nigerian chickens.

PSIII-5 Predicting live weight using linear body measurements in growing beef calves

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 285-285
Author(s):  
Vanessa Rotondo ◽  
Dan Tulpan ◽  
Katharine M Wood ◽  
Marlene Paibomesai ◽  
Vern R Osborne
Keyword(s):  
Machine Learning ◽  
Body Weight ◽  
Linear Models ◽  
Live Weight ◽  
Body Measurements ◽  
Learning Models ◽  
Linear Measurements ◽  
Beef Calves ◽  
Machine Learning Models ◽  
Linear Body

Abstract The objective of this study is to investigate how linear body measurements relate to and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 103 Angus cross calves were enrolled in the study from wk 2 - 8. Calves were weighed and linear measurements were collected weekly, such as: poll to nose, width across the eyes (WE), width across the right ear, neck length, wither height, heart girth (HG), midpiece height (MH), midpiece circumference, midpiece width (MW), midpiece depth (MD), hook height, hook width, pin height, top of pin bones width (PW), width across the ends of pin bones, nose to tail body length, the length between the withers and pins, forearm to hoof, cannon bone to hoof. These measurements were taken using a commercial soft tape measure and calipers. To assess relationships between traits and to fit a model to predict BW, data were analyzed using the Weka (The University of Waikato, New Zealand) software using both linear regression (LR) and random forest (RF) machine learning models. The models were trained using a 10-fold cross-validation approach. The automatically derived LR model used 11 traits to fit the data to weekly BW (r2 = 0.97), where the traits with the highest coefficients were HG, PW and WE. The RF model improved further the BW predictions (r2= 0.98). Additionally, sex differences were examined. Although the BW model continued to fit well (r2 0.97), some of the top linear traits differed. The results of this study suggest that linear models built on linear measurements can accurately estimate body weight in beef calves, and that machine learning can further improve the model fit.

PSX-A-21 Late-Breaking: Predicting live weight using linear body measurements in growing dairy calves

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 371-372
Author(s):  
Vanessa Rotondo ◽  
Vern R Osborne ◽  
Marlene Paibomesai ◽  
Katharine M Wood ◽  
Sophia Jantzi
Keyword(s):  
Machine Learning ◽  
Body Weight ◽  
Linear Models ◽  
Dairy Farm ◽  
Live Weight ◽  
Dairy Calves ◽  
Body Measurements ◽  
Linear Measurements ◽  
The Right ◽  
Linear Body

Abstract The objective of this study was to explore how linear body measurements are related to body weight and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 69 Holstein calves from a commercial dairy farm were enrolled in the study from wk 2 – 8 of age. Calves were weighed and linear measurements were collected weekly. Nineteen linear measurements were obtained each week, including: poll to nose, width across the eyes, width across the right ear, neck length (NL), wither height (WH), heart girth (HG), midpiece height (MH), midpiece circumference (MC), midpiece width (MW), midpiece depth (MD), midpiece width across the 13th rib (MW13), hook height, hook width, pin height, top of pin bones width (PW), nose to tail body length, the length between the withers and pins (WPL), forearm to hoof, cannon bone to hoof. These measurements were taken using a commercial soft tape measure and calipers. Using a machine learning approach, models were generated to predict BW from calf linear measurements using Weka software 3.8.5 (University of Waikato, New Zealand) using a 10-fold cross-validation method. Both linear regression (LR) and random forest (RF) models were evaluated. Across all weeks the LR model derived 12 of the 19 traits to fit the BW model (r2 = 0.93). These included: PN, NL, WH, HG, MC, MW, MD, HW, PW, MW13, WPL. The RF model slightly reduced BW predictions (r2= 0.92). The results of this study suggest that linear models built on linear measurements can accurately estimate body weight in dairy calves. These data and models generated are important to further the development of visualized weighing systems for young dairy calves and may be used to accurately predict BW without a scale.

scholarly journals Statistical models based on morphometric traits for live body weight estimation in goats

2021 ◽  
Vol 13 (Volume 13, Issue 2) ◽  
pp. 134-140
Author(s):  
N.A. Ouchene-Khelifi ◽  
N. Ouchene
Keyword(s):  
Body Weight ◽  
Statistical Models ◽  
Live Weight ◽  
Abdominal Circumference ◽  
Body Measurements ◽  
Morphometric Traits ◽  
Linear Measurements ◽  
Strategic Plans ◽  
P 75 ◽  
Simple Regression Analysis

Abstract. The objective of this study was to develop statistical models to predict body weight from goat’s body measurements. Data on 1702 goats for circumferences of chest (CG), abdominal circumference (AC) and spiral circumference (SC), height at withers (WH), body length (BL), and body weight (BW) were analysed to study the relationship between linear body measurements and body weight. The present study revealed that in the goats from all breeds studied (Arabia, Makatia, Kabyle, M’zabite, Saanen and Alpine), the weight evolved in the same direction and at the same rate as the linear measurements chosen. The linear measurements were all significantly correlated with animal weight (p<0.001). Results indicated that Arabia goats had the highest WH (71.07 cm) and CG (17.72 cm). The highest measurements were reported in Alpine goats for AC (97.73 cm), BL (78.05 cm), SC (106.29 cm) and BW (41.60 kg). The Kabyle breed were recorded with the lowest values for the WH (64.95 cm), BL (67.58 cm) and BW (29.52 kg). The average live weight was 38.15±10.90 kg with differences according to age, sex and breed (Arabia, Makatia, Kabyle and M’Zabite). Positive and highly significant (p<0.001) correlations were observed between BW and the majority of independent variables. The highest relationship was illustrated between CG with BW (r=0.922). Linear regression analyses were performed to develop the models. The simple regression analysis found all parameters to be significant (p<0.001) (WH, BL, CG, AC and CS) and CG gave more precision on the weight when using a single measurement parameter (R2 varied between 0.950 and 0.967). Therefore, the following formula can be used to estimate the live weight of the animals using only the chest circumference (P=75*CG). The development of these equations would enable producers and researchers to predict the animal body weight and develop strategic plans for the relevant goat herds.

scholarly journals Hubungan Ukuran Tubuh dengan Bobot Badan dan Bobot Karkas Bandikut (Echymipera rufescens) Di Kampung Manawi Distrik Angkaisera Kabupaten Kepulauan Yapen

2021 ◽  
Vol 11 (3) ◽  
pp. 195
Author(s):  
Sonei G. Bonai ◽  
Frandz Pawere ◽  
Hanike Monim
Keyword(s):  
Body Weight ◽  
Live Weight ◽  
Carcass Weight ◽  
Regression Equations ◽  
Body Measurements ◽  
Alternative Source ◽  
Average Percentage ◽  
Males And Females ◽  
The Relationship ◽  
Average Body

Abstract Bandicoot  (Echypera rufescens)  is an alternative source of animal protein and a source of germplasm for humans, especially local communities in Papua. These animals are obtained by hunting and/or setting traps in the community's closest habitat.  The aim of this research was to determine the relationship between body measurements and body weight and carcass weight by utilizing 32 bandicoots, 16 males and 16 females, with a live weight range of 400 - 2000 grams in Yapen Island Regency, Papua. The study was conducted by using an explorative study and the data were analyzed using multiple correlations and regression. The carcass was obtained by slaughtering the head, removing the blood, and then removing the hair by burning (singeing). The average body weight of male bandicoots was 1403 grams and that of females was 598.75 grams, while the average carcass weights of males and females were 1050.06 grams and 415 grams, respectively. The average heart girth of male bandicoots is 23.03 cm and that of females is 17.81 cm, while the average body length of male and female bandicoots is 25.19 cm and 18.91 cm, respectively. The average percentage of male bandicoot carcasses was 73.99 cm and that of females was 69.22 cm. The correlation coefficient between body weight and body measurements was 0.911 while the carcass weight and body measurements were 0.901. The correlation between body weight and carcass of male bandicoots were 0.911 and 0.901. The correlation between body weight and carcass of female bandicoot were 0.702 and 0.747. The regression equation for male bandicoots to estimate body weight (BB) and carcass weight (BK) were BB = (-1705, 594+84,432 X1 +46,234X2) and (BK = -432,092 +71,545 X1 +33,127X2). The female bandicoot regression equations to estimate body weight (BB) and carcass weight (BK) were: (BB = -509,134+39,437 X1 +21,443X2) and (BK= -436,703 +31,720 X1 +15,164X2). Keywords: Bandicut (Echypera rufescens); Carcass length; Carcass weight; Carcass; Heart girth; Live weight; Singeing   Abstrak Bandikut (Echypera rufescens) merupakan  salah satu sumber alternatif protein hewani dan sumber plasma nutfa bagi manusia khususnya masyarakat lokal yang berada di Papua. Hewan ini diperoleh dengan cara berburu dan/atau  pemasangan jerat di habitat terdekat masyarakat. Penelitian tentang hewan ini bertujuan untuk mengetahui hubungan antara ukuran-ukuran  tubuh dengan  bobot badan dan bobot karkas dengan memanfaatkan  32 ekor bandikut masing-masing 16 ekor jantan dan 16 ekor betina dengan kisaran berat hidup 400 -  2000 gram.  Karkas diperoleh dengan cara bagian kepala disembelih, dikeluarkan darah kemudian dilakukan penghilangan bulu dengan cara dibakar (singeing). Karkas terdiri dari daging, tulang dan lemak setelah kepala, isi rongga dada dan perut, kaki belakang bagian bawah dan kaki depan bagian bawah serta ekor dikeluarkan. Rata-rata bobot badan bandikut jantan adalah 1403 gram dan betina adalah 598,75 gram, sedangkan rata-rata bobot karkas jantan dan betina masing-masing adalah 1050,06  gram dan 415 gram. Rata-rata lingkar dada bandikut  jantan adalah 23,03 cm dan betina adalah 17,81 cm, sedangkan rata-rata panjang badan bandikut jantan dan betina berturut-turut adalah 25,19 cm dan 18,91 cm. Rata-rata persentase karkas bandikut jantan adalah 73,99 cm dan betina adalah  69,22 cm. Koefisien korelasi antara bobot badan  dengan ukuran-ukuran tubuh adalah 0,911 sedangkan antara bobot karkas dengan ukuran-ukuran tubuh adalah 0,901. Korelasi bobot badan dan karkas bandikut jantan adalah  0,911 dan  0.901. Korelasi bobot badan dan karkas bandikut betina adalah sebesar 0,702 dan 0,747. Persamaan regresi bandikut jantan untuk menduga bobot badan (BB) dan bobot karkas (BK) adalah BB = (-1705, 594+84,432 X1 +46,234X2 dan BK = -432,092 +71,545 X1 +33,127X2. Persamaan regresi bandikut betina untuk menduga bobot badan (BB) dan bobot karkas (BK) adalah BB = -509,134+39,437 X1 +21,443X2 dan BK= -436,703 +31,720 X1 +15,164X2. Kata kunci: Bandicut (Echypera rufescens); Berat karkas; Berat potong; Karkas; Lingkar dada; Panjang karkas.

The development of the lamb with particular reference to the alimentary tract

1964 ◽  
Vol 6 (2) ◽  
pp. 169-178 ◽  
Author(s):  
R. V. Large
Keyword(s):  
Body Weight ◽  
Alimentary Tract ◽  
Live Weight ◽  
The Body ◽  
Root Transformation ◽  
Feeding Regimes ◽  
Gut Fill ◽  
Relationship Of ◽  
The Relationship

1. Thirty Suffolk × Half bred lambs were slaughtered at the following ages: two twin lambs at birth and two singles and two twins at 1, 2, 3, 4, 6, 8 and 16 weeks of age.2. The following weights were recorded: live-weight immediately before slaughter; and carcass, head, skin, feet, alimentary tract, heart, liver, kidneys, lungs and trachea, and blood immediately afterwards.3. The alimentary tract was emptied and weighed in four separate parts; reticulo-rumen, omasum-abomasum, small intestine, large intestine.4. The volumes of the reticulo-rumen and the omasum-abomasum were measured by immersing in water and filling the organs with water to 2 cm. pressure.5. The in vitro digestive efficiency of rumen liquor from lambs of 1, 2, 3 and 4 weeks of age was assessed.6. Empty body weight was considered to be valuable in comparing animals of different ages or from different feeding regimes or at different times of the year because variations in gut ‘fill’ were eliminated.7. There were no differences between singles and twins in the relationship of the fresh weights of the parts of the body to empty body weight, except that development of the liver and the blood was rather slower for singles.8. Little evidence was found of a difference in rate of development of the alimentary tract between singles an d twins, although the log an d square root transformation suggested a possible difference in reticulo-rumen size in favour of twins, significant at the 5% level.

INFLUENCE OF BREED, BIRTH DATE, AGE AND BODY WEIGHT ON LINEAR BODY MEASUREMENTS OF GROWING RAMS MAINTAINED IN A CONTROLLED ENVIRONMENT

1984 ◽  
Vol 64 (2) ◽  
pp. 279-291 ◽  
Author(s):  
J. N. B. SHRESTHA ◽  
D. P. HEANEY ◽  
P. S. FISER ◽  
G. A. LANGFORD
Keyword(s):  
Body Weight ◽  
Key Words ◽  
Litter Size ◽  
Body Length ◽  
Birth Date ◽  
Controlled Environment ◽  
Body Measurements ◽  
Leg Length ◽  
The Relationship ◽  
Linear Body

Heart girth (HG), body length (BL), leg length (LL), metacarpal circumference (MC), withers height (WH) and hook width (HW) of 233 growing rams of three synthetic strains, Suffolk and Finnsheep breeds were measured at 6, 8 and 10 mo of age. Thereafter, subsequent measurements were taken at 11–13 mo, 18–21 mo and 23–25 mo of age. Breed, birth period (hysterectomy derived birth date), age of ram and body weight (BW) had important effects (P < 0.05) on linear body measurements, whereas age of dam did not (P > 0.05). Significant effects of litter size on HG and BL were observed at 6 and 8 mo of age, respectively. All linear body measurements increased from 6 to 21 mo of age, whereas BL, WH and HW continued to increase to 25 mo. Rams of Strain 1, developed as a synthetic sire strain, with a large proportion of Suffolk background were generally similar to the Suffolk rams in all body measurements except for HG which was significantly larger than in Suffolk rams. The Finnsheep rams had smaller HG, BL, MC and HW than the synthetic strains and Suffolk rams, whereas LL and WH of the Finnsheep and Suffolk rams were similar. Rams of Strains 2 and 3, developed as synthetic dam strains with 50 percent Finnsheep background, were similar in body measurements. The synthetic dam strains did not differ from Strain 1 and/or Suffolk with respect to HG, BL, WH and HW. However, Suffolk rams had larger MC and shorter LL compared to those of the Strain 2 and 3 rams. Birth period had a significant effect on HG, BL, LL and MC, but no consistent trend with age of ram was apparent. Linear body measurements were positively correlated with each other and with body weight; however, the relationship varied as rams progressed in age. The importance of breed, birth date, age of ram and body weight on body measurements and the requirements for appropriate adjustments is emphasized. Key words: Sheep, body measurements, breed, birth date, age of ram

scholarly journals Validity of Weight Estimation Models in Pigs Reared under Different Management Conditions

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Marvelous Sungirai ◽  
Lawrence Masaka ◽  
Tonderai Maxwell Benhura
Keyword(s):  
Body Length ◽  
Live Weight ◽  
Linear Regression Method ◽  
Linear Measurements ◽  
Large White ◽  
High Positive Correlation ◽  
Positive Correlation ◽  
Positive Correlations ◽  
The Relationship ◽  
Selection Of

A study was carried out to determine the relationship between linear body measurements and live weight in Landrace and Large White pigs reared under different management conditions in Zimbabwe. Data was collected for body length, heart girth, and live weight in 358 pigs reared under intensive commercial conditions. The stepwise multiple linear regression method was done to develop a model using a random selection of 202 records of pigs. The model showed that age, body length, and heart girth were useful predictors of live weight in these pigs with significantly high positive correlations observed. The model was internally validated using records of the remaining 156 pigs and there was a significantly high positive correlation between the actual and predicted weights. The model was then externally validated using 40 market age pigs reared under communal conditions and there was a significantly low positive correlation between the actual and predicted weights. The results of the study show that while linear measurements can be useful in predicting pig weights the appropriateness of the model is also influenced by the management of the pigs. Models can only be applicable to pigs reared under similar conditions of management.

Modeling relevance of mobile communication services by social setting dimensions

2016 ◽  
Vol 20 (1) ◽  
pp. 311-331
Author(s):  
Elena Menichelli ◽  
Richard Ling
Keyword(s):  
Mobile Communication ◽  
Communication Channel ◽  
Mixed Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Communication Services ◽  
Channel Choice ◽  
Social Settings ◽  
Negative Messages ◽  
The Relationship

There is little research examining the confluence of what communication channel is used for which purpose with which person. This study examines the “setting” for communication that includes what is communicated (e.g. positive or negative messages), the nature of the relationship (close versus distant), and the information channel. The respondents to a web-based questionnaire ( n = 627) were Norwegian smartphone users aged 16–35 years. Respondents evaluated mobile communication services that they used in specific social settings by “checking off” all that apply. Two methods of analysis are used to examine the material. First, a Principal Component Regression validated the main method, namely a mixed model for the Analysis of Variance. Results show the probability of using a mobile communication service is based on the effects of social group, communication purpose, communication channel, and their interaction. The relationship to the interlocutor was found to have the strongest effect on channel choice.

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