scholarly journals QUANTIFYING SIZE AND SHAPE DIFFERENCES BETWEEN MUTURU AND N'DAMA BREEDS OF CATTLE

2021 ◽  
Vol 21 ◽  
pp. 51-58
Author(s):  
S. N. Ibe ◽  
A. G. Ezekwe
Keyword(s):  
Body Weight ◽  
Correlation Matrix ◽  
Prediction Models ◽  
Pelvic Bone ◽  
Linear Measure ◽  
Discriminant Functions ◽  
Linear Measurements ◽  
Size And Shape ◽  
Optimal Balance ◽  
Linear Body

Body weight and eight linear body traits, namely heart girth, body and diagonal lengths, height at withers and at hip, width of loin and at pelvic bone, and depth at rear flank, were measured forthniylıtly on 32 Muturu bulls, 16 of which were born in the dry season (Muturu -D) and the remaining 16 in the rainy season (Muturu-R), and on 11 N'dama bulls. Correlations between all pairs of traits in all groups were high, positive and significant (r > 0.093). The first two principal components derived from the correlation matrix of the linear measurements, PCI and PC2 accounted for at least 98% of total variance in all cases and were regarded as, size' and 'shape' vectors, respectively. Whereas PC1 gave largest weight to heart girth, PC2 gave largest weight to two or more different other linear measurements in the three groups. N'dama had the best conformation, followed by Muturu -D and then Muturu - R. PC-based prediction models were more parsimonius than linear measure-based models and are considered preferable for selecting animals for "optimal" balance. Also, in addition to discriminating between the two different breeds, PC- based discriminant functions were able to discriminate among individuals within the same breed on the basis of putritional differences. These functions are therefore recommended for classifying animals according to different macro and micro criteria.

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.

Effect of Breed and Sex on Body Weight and Linear Body Measurements of Turkeys (Meleagris gallopavo)

2019 ◽  
pp. 1-5
Author(s):  
I. M. Chana ◽  
M. Kabir ◽  
O. Orunmuyi ◽  
A. A. Musa
Keyword(s):  
Body Weight ◽  
Wing Length ◽  
Meleagris Gallopavo ◽  
The Body ◽  
Body Measurements ◽  
Neck Length ◽  
Linear Measurements ◽  
Randomized Design ◽  
Chest Girth ◽  
Linear Body

Aim: The aim of this study was to investigate the effect of breed and sex on body weight and linear body measurements of 100 Turkeys which included 50 Norfolk and 50 Mammoth breeds each. Study Design and Duration: The experiment lasted for 20 weeks during which the performance parameters were monitored in 100 Turkeys using completely randomized design. Methodology: The body weight and linear measurements were taken at an interval of two weeks (i.e. day 1, 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20 weeks). Parameters monitored were shank length (cm), back length (cm), chest girth (cm), neck length (cm), thigh length, and wing length and body weight. Results: Result obtained showed that there where significant differences (P<0.05) in body weight across the breed with Norfolk having 2.70±0.04 and Mammoth 2.55±0.04. The linear measurements studied (body length, neck length, back length, shank length, thigh length, wing length, and chest girth) showed that the Norfolk had superiority over the Mammoth breed. Conclusion: Result showed remarkable and better growth performance of male turkeys than their female counterparts for all traits and ages. Also, higher values in linear body parameters noted in males.

Prediction of Body Weight and other Linear Body Measurement of Two Commercial Layer Strain Chickens

2011 ◽  
Vol 6 (1) ◽  
pp. 13-22 ◽  
Author(s):  
L.O. Ojedapo ◽  
S.R. Amao ◽  
S.A. Ameen ◽  
T.A. Adedeji ◽  
R.I. Ogundipe ◽  
...  
Keyword(s):  
Body Weight ◽  
Body Measurement ◽  
Linear Body

scholarly journals Enoxaparin in cardiovascular disease: how to improve effectiveness and safety?

2011 ◽  
Vol 10 (6) ◽  
pp. 112-123
Author(s):  
I. S. Yavelov
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Disease ◽  
Acute Coronary Syndrome ◽  
Body Weight ◽  
Standard Treatment ◽  
Decision Strategy ◽  
Elderly Age ◽  
Treatment Protocols ◽  
Coronary Syndrome ◽  
Optimal Balance

The review analyses the specifics of enoxaparin therapy in the most prevalent cardiovascular diseases, such as acute coronary syndrome, venous thromboembolism, and atrial fibrillation. The decision strategy is presented for difficult and non-standard clinical situations (renal dysfunction, elderly age, heparin medication change, or abnormal body weight), when the optimal balance between effectiveness and safety requires modifying the standard treatment protocols.

II.—Genetics of Gametes. II. Strain Differences in Characteristics of Rabbit Spermatozoa

1961 ◽  
Vol 68 (1) ◽  
pp. 17-24 ◽  
Author(s):  
R. A. Beatty ◽  
R. A. N. Napier
Keyword(s):  
Body Weight ◽  
Strain Differences ◽  
Genetic Effects ◽  
The Body ◽  
Size And Shape ◽  
Rabbit Spermatozoa

SynopsisThe study of genetic effects on the phenotype of gametes may be called the genetics of gametes. As evidence of such genetic effects, marked inter-strain differences have been demonstrated in characteristics of rabbit spermatozoa examined in nigrosin-eosin preparations. Strain differences have been found in the size and shape of the spermatozoan head, and in a measure of spermatozoan viability—the incidence of stained heads. A first comparison of the spermatozoa of specific breeds is given. The size of the spermatozoan head is to some extent independent of the body weight.

Development of the laser remote caliper as a method to estimate surface area and body weight in beef cattle

SURG Journal ◽  
2008 ◽  
Vol 1 (2) ◽  
pp. 57-72
Author(s):  
Sarah Core ◽  
Stephen Miller ◽  
Matthew Kelly
Keyword(s):  
Body Weight ◽  
Beef Cattle ◽  
Surface Area ◽  
Digital Imaging ◽  
Late Gestation ◽  
P Value ◽  
Side View ◽  
Linear Measurements ◽  
Indirect Measures ◽  
Area And Volume

Linear measurements combined with surface area and volume calculations were used to develop formulas to estimate body weight (BW) in beef cattle. These measurements were evaluated directly or estimated using a laser remote caliper (LRC) and digital imaging software. Seventy-two dry, late gestation beef cows aged 3-13 years were measured and weighed Six measurements for each cow were taken; the cattle were weighed, a body condition score (BCS) was assigned, heart girth (HG), hip width (HW), and hip height (HH) were measured directly and 3 digital pictures were taken. The digital pictures portrayed three different views; side view (restrained), rear view (restrained), and side view (free-stall). Body length, HW, HH, surface area and volume were indirectly calculated from the digital images. For each view a complete (C-) formula (direct and indirect measures) and remote (R-) formula (only indirect measures) to estimate BW was developed. The R-squared values 0.7459, 0.7937, 0.8078, 0.5016, 0.611, 0.5553 were attained for C-side view free-stall, C-side view (restrained), C-rear view (restrained), R-side view free-stall, R-side view (restrained), and R-rear view (restrained). The accuracy of these formulas was 81% on average. BCS, HG and HW were the most significant factors when developing a formula for BW (p-value < 0.001). Side view (restrained) image measurements were most accurate in estimating BW. These measurements were highly correlated with the direct measurements and digital linear body measurements were not distorted (due to poor posture/positioning) as seen with the other views. The results or this study show that linear measurements collected by digital imaging methods can be a useful tool for estimating BW.

Performance and Linear Measurements of Growing Pigs Fed on Basis of Their Body Weight

2009 ◽  
Vol 9 (1) ◽  
pp. 57-59 ◽  
Author(s):  
A.E. Onyimonyi ◽  
S.O.C. Ugwu ◽  
N.S. Machebe
Keyword(s):  
Body Weight ◽  
Growing Pigs ◽  
Linear Measurements
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