scholarly journals Simultaneous identification and correction of systematic error in bioenergetics models: demonstration with a white crappie (Pomoxis annularis) model

2004 ◽  
Vol 61 (11) ◽  
pp. 2168-2182 ◽  
Author(s):  
Przemyslaw G Bajer ◽  
Robert S Hayward ◽  
Gregory W Whitledge ◽  
Richard D Zweifel
Keyword(s):  
Body Weight ◽  
Systematic Error ◽  
Prediction Error ◽  
Laboratory Experiments ◽  
Regression Equations ◽  
Consumption Level ◽  
White Crappie ◽  
Pomoxis Annularis ◽  
Consumption Prediction ◽  
Bioenergetics Models

Recent evidence indicates that important systematic error exists in many fish bioenergetics models (BEMs). An approach for identifying and correcting this error is demonstrated with a white crappie (Pomoxis annularis) BEM. Model-predicted trajectories of growth and cumulative consumption for 39 individual white crappie obtained from six 60-day laboratory experiments diverged from observed values by up to 42.5% and 227%, respectively, indicating systematic error in the BEM. To evaluate correlates of the systematic error, model prediction errors were regressed against three major input/output variables of BEMs that were covered by the laboratory experiments: fish body weight (80–341 g), temperature (23–30 °C), and consumption level (0.5%–6.2% daily). Consumption level explained >80% of the prediction error for growth and consumption. Two multiple regression equations containing body weight, temperature, and consumption variables were developed to estimate growth prediction error (R2 = 0.96) and consumption prediction error (R2 = 0.86), and incorporated into the white crappie BEM to correct its predictions. Cross-validation indicated that growth and consumption prediction error was reduced 2- to 4-fold by correction. Given recent evidence of widespread systematic error and increasing application rates of BEMs, the efficient error-identification and -correction approach described appears broadly applicable and timely.

scholarly journals Widespread consumption-dependent systematic error in fish bioenergetics models and its implications

2004 ◽  
Vol 61 (11) ◽  
pp. 2158-2167 ◽  
Author(s):  
Przemyslaw G Bajer ◽  
Gregory W Whitledge ◽  
Robert S Hayward
Keyword(s):  
Body Weight ◽  
Systematic Error ◽  
Consumption Rate ◽  
Fish Growth ◽  
Model Parameters ◽  
Feeding Rates ◽  
Daily Consumption ◽  
Consumption Level ◽  
Fish Bioenergetics ◽  
Bioenergetics Models

Data from laboratory evaluations of seven fish bioenergetics models (BEMs) were used to investigate possible associations between BEM prediction error in relative growth rate (RGRerror) and levels of model input variables: mean daily food-consumption rate and fish body weight. Correlation between RGRerror and fish body weight was found in three BEMs applied under submaintenance feeding conditions. A strong correlation between RGRerror and mean daily consumption level was observed in all models over full consumption ranges; consumption level explained 70%–96% of variation in RGRerror. All BEMs underestimated (by 2- to 5-fold) growth at lower consumption levels and overestimated (by 2- to 3-fold) growth at higher consumption levels. RGRerror values associated with higher consumption levels were greater (up to 22 cal·g–1·day–1) than those at lower consumption levels (up to 10 cal·g–1·day–1). Correlation between consumption rate and RGRerror in all seven models indicates widespread systematic error among BEMs that likely arises from deficiencies in consumption-dependent model parameters. Results indicate that many BEMs are substantially inaccurate when predicting fish growth from higher feeding rates or estimating consumption from higher growth rates, even when higher consumption levels or growth episodes are of short duration. Findings obtained under submaintenance feeding conditions indicate that additional body-weight- and consumption-dependent terms should be added to BEM subequations for routine metabolism to account for metabolic reduction.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Zhuoyi Wang ◽  
Saeed Shadpour ◽  
Esther Chan ◽  
Vanessa Rotondo ◽  
Katharine M Wood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Body Weight ◽  
Optical Sensors ◽  
Genetic Selection ◽  
Medium Size ◽  
Regression Equations ◽  
Infrared Cameras ◽  
Applications Of Machine Learning ◽  
Corrosive Environments ◽  
Partial Weight

Abstract Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

Digestive Response of Rainbow Trout, Salmo gairdneri, to Pellet Diets

1969 ◽  
Vol 26 (7) ◽  
pp. 1801-1812 ◽  
Author(s):  
John T. Windell ◽  
David O. Norris ◽  
James F. Kitchell ◽  
James S. Norris
Keyword(s):  
Body Weight ◽  
Rainbow Trout ◽  
Dry Matter ◽  
Laboratory Experiments ◽  
Corn Oil ◽  
Saturated Fat ◽  
Methyl Cellulose ◽  
Salmo Gairdneri ◽  
Gastric Evacuation ◽  
Gastric Contents

Quantitative data are presented for three laboratory experiments concerning rate of gastric evacuation of pellets (fed in gelatin capsules) and pellet components. Rainbow trout, Salmo gairdneri, acclimated to a constant water temperature of 15 C were killed 12 hr after consuming an experimental meal. Subtraction of amount of dry matter remaining at autopsy from dry matter consumed yielded amount of food digested or evacuated or both, from the stomach per unit time.Meals of encapsulated pellet were evacuated from the stomach more rapidly (65.8% decrease in 12 hr) than encapsulated corn oil (42.6%), gelatin (50.8%), a combination of corn oil and gelatin (47.3%), saturated fat (28.8%), or methyl cellulose (50.3%).Groups of fish consuming five capsules equal to approximately 0.65 % of their body weight evacuated 35.9, 45.1, 64.2, 95.5, and 100% at intervals after killing from 6 to 36 hr, respectively. Similar groups consuming seven capsules equal to approximately 1.0% of their body weight evacuated 23.7, 57.9, 70.5, and 86.6% at intervals after killing from 4 to 20 hr, respectively.Ten groups of trout consuming amounts of dry matter equal to 0.24, 0.40, 0.74, 1.11, 1.31, 1.19, 1.59, 1.56, 1.91, and 2.26% of their body weight evacuated 90.7, 81.3, 64.2, 57.9, 56.6, 52.5, 53.4, 51.3, 58.7, and 50.0% in 12 hr, respectively. Gastric motility remains relatively constant once food has entered the stomach. However, when larger meals are fed a greater quantity is evacuated per unit time. This could only be accomplished by changes in volume of gastric contents pumped per peristaltic stroke or number of strokes per unit time.

scholarly journals Body composition and net and dietary macrominerals requirements of Nellore steers under grazing

2009 ◽  
Vol 38 (6) ◽  
pp. 1081-1087 ◽  
Author(s):  
Vitor Visintin Silva de Almeida ◽  
Augusto César de Queiroz ◽  
Robério Rodrigues Silva ◽  
Fabiano Ferreira da Silva ◽  
Aline Cardoso Oliveira ◽  
...  
Keyword(s):  
Body Weight ◽  
Reference Group ◽  
Average Daily Gain ◽  
The Body ◽  
Regression Equations ◽  
Animal Body ◽  
Randomized Design ◽  
Dietary Requirements ◽  
Mineral Mixture ◽  
Daily Gain

This experiment was carried out with the objective of determining the macrominerals (Ca, P, Mg, K and Na) requirements of Nellore steers under grazing. Twenty four Nellore steers (371 ± 14 kg of BW and 26 mo old) were used. Four steers were slaughtered at the beginning of the experiment (reference group), serving as a reference in subsequent study. The remaining 20 animals were weighed and distributed into a completely randomized design with four supplementation levels offer: 0.0 (mineral mixture - control), 0.3, 0.6 and 0.9% of BW, with five replications. The supplements, based on ground corn, soybean meal and/or urea, were previously balanced to achieve an average daily gain of 350, 650 and 850g, respectively, for the different supplementation levels offer. The contents of macrominerals retained in the animal body were determined by regression equations of the macrominerals body content logarithm in function of the empty body weight logarithm (EBW). Net macrominerals requirements for a gain of 1kg of EBW were obtained using the equation Y'= b.10ª.Xb-1, with a and b, respectively, the intercept and the regression coefficient of the prediction equations of macrominerals in the animal body contents for each macromineral considered. The concentrations of all macrominerals, in the empty body weight and gain of the empty body weight, decreased with the increase in the body weight. Total calcium and phosphorus dietary requirements are higher than those recommended in the literature.

scholarly journals Morphological characterization of Arab and Oromo goats in northwestern Ethiopia

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Oumer Sheriff ◽  
Kefyalew Alemayehu ◽  
Aynalem Haile
Keyword(s):  
Body Weight ◽  
Coat Color ◽  
Strong Association ◽  
Correlation Coefficients ◽  
Field Research ◽  
Morphological Characterization ◽  
The Body ◽  
Regression Equations ◽  
Body Measurements ◽  
Chest Girth

Abstract Background An exploratory field research was conducted in northwestern Ethiopia, to characterize the morphological features of Arab and Oromo goat populations as an input to design community-based breeding programs. Ten qualitative and nine quantitative traits were considered from 747 randomly selected goats. All data collected during the study period were analyzed using R statistical software. Results Plain white coat color was predominantly observed in Arab goats (33.72%) while plain brown (deep and light) coat color was the most frequent in Oromo goats (27.81%). The morphometric measurements indicated that Oromo goats have significantly higher body weight and linear body measurements than Arab goats. Positive, strong and highly significant correlations were obtained between body weight and most of the body measurements in both goat populations. The highest correlation coefficients of chest girth with body weight for Arab (r  =  0.95) and Oromo (r  =  0.92) goat populations demonstrated a strong association between these variables. Live body weight could be predicted with regression equations of y  =  − 33.65  +  0.89  ×  for Arab goats (R2  =  90) and y  =  − 37.55  +  0.94  ×  for Oromo goats (R2  =  85), where y and x are body weight and chest girth, respectively, in these goat types. Conclusions The morphological variations obtained in this study could be complemented by performance data and molecular characterization using DNA markers to guide the overall goat conservation and formulation of appropriate breeding and selection strategies.

scholarly journals Verification of regression equations for estimating pork carcass meatiness using CMG, IM-03, fat-o-meat’er ii and ultrafom 300 devices

2012 ◽  
Vol 12 (4) ◽  
pp. 585-596 ◽  
Author(s):  
Dariusz Lisiak ◽  
Karol Borzuta ◽  
Piotr Janiszewski ◽  
Fabian Magda ◽  
Eugenia Grześkowiak ◽  
...  
Keyword(s):  
Prediction Error ◽  
Muscle Thickness ◽  
Testing Procedure ◽  
Raw Material ◽  
Longissimus Dorsi ◽  
Meat Industry ◽  
Regression Equations ◽  
Meat Content ◽  
Different Types ◽  
Manual Classification

AbstractFour manual classification devices for estimating pork carcass meat content, i.e. CGM, Fat-OMeat’er II, IM-03 and UltraFom 300 were tested. The experiment was carried out with properly selected raw material (n=141 pigs) from current deliveries for pig slaughter at the Meat Plant SKIBA S.A. in Chojnice. Pork raw material was derived from three different Polish regions and represented different types of fatness, different carcass weights (from 60 to 120 kg) and different sexes (half were gilts and half were barrows). The applied testing procedure was consistent with European Union regulations. The research resulted in the development of regression equations for estimating pork carcass meat content in Poland. These equations are of rectilinear type and use four (in the case of UltraFom 300) or two (in the case of other devices) measurements of backfat and longissimus dorsi muscle thickness located at a distance of 6 cm (CGM, IM-03) or 7 cm (Fat-OMeat’er II, UltraFom 300) from the backfat edge at the section between 3rd and 4th rib, counting ribs from the end (CGM, IM-03, Fat-O-Meat’er II) and also at the height of the last rib section (UltraFom 300). The prediction error does not exceed the termination value of 2.50% established by EU regulations and amounts to 2.16% for CGM, 2.18% for Fat-O-Meat’er II, 1.89% for IM-03 and 2.07% for UltraFom 300. New regression equations have been applied in the meat industry since 12 December 2011.

Energy costs of feeding activities and energy expenditure of grazing sheep

1964 ◽  
Vol 15 (6) ◽  
pp. 969 ◽  
Author(s):  
N McCGraham
Keyword(s):  
Body Weight ◽  
Food Intake ◽  
Energy Expenditure ◽  
Laboratory Experiments ◽  
Energy Costs ◽  
Body Weights ◽  
Daily Energy ◽  
Feeding Activities ◽  
The Cost ◽  
Grazing Behaviour

The energy costs of standing, of rumination, of eating prepared meals, and of grazing were determined in laboratory experiments by indirect calorimetry. Sheep with body weights ranging from 30 to 110 kg were used. Energy expenditure due to standing amounted to 0.34 ± 0.02 kcal/hr/kg body weight. The energy cost of rumination was 0.24 ± 0.03 kcal/hr/kg. Rate of food intake varied from 60 g dry matter/hr with sheep grazing a poor sward to 800 g/hr with sheep eating hay, but in general this did not affect energy expenditure appreciably. The cost of eating prepared meals of either fresh herbage or hay was 0.54 ± 0.05 kcal/hr/kg body weight. It tended to be greatest when rate of food intake was greatest. Energy expenditure due to grazing was also 0.54 ± 0.05 kcal/hr/kg, irrespective of the type of sward and associated grazing behaviour. It is estimated that muscular work, mainly standing and eating, could account for nearly 40% of the daily energy expenditure of a sheep at maintenance, grazing a poor but level pasture, with drinking water available, and only 10% of that of a caged animal. Such a grazing animal could thus have requirements over 40% greater than those of a caged one. With sheep on hilly pasture or a long way from water, the cost of walking could become a major item.

Corrigendum - Comparative breed studies of beef cattle. III. Carcass composition

1964 ◽  
Vol 15 (2) ◽  
pp. 333
Author(s):  
NM Tulloh
Keyword(s):  
Body Weight ◽  
Age Groups ◽  
Carcass Composition ◽  
Carcass Weight ◽  
Published Data ◽  
Regression Equations ◽  
Comparison Study ◽  
Differential Growth ◽  
Breed Differences ◽  
Breed Comparison

An investigation was made of published data on the carcass composition of cattle, based on dissection of carcasses into bone, muscle, and fat. The data included females and castrate males, without regard to breed, age, or nutritional history. It was found that the relation between each carcass component and empty body weight could be described by a linear regression equation by using logarithmic values for the variables. The differential growth ratios given by the regression equations indicated, as empty body weight increased, that: (a) the weight of each of the dissected carcass components (i.e. bone, muscle, and fat) also increased; (b) the proportion of carcass bone fell, that of fat increased, and that of muscle remained almost constant. The relations between dissected bone, muscle, and fat and carcass weight were similar to those obtained between dissected carcass components and empty body weight. To obtain evidence on whether the differential growth ratios between dissected carcass components and empty body weight or carcass weight showed any change throughout post-natal life, quadratic equations were computed by using logarithmic values for the variables. These ratios fell for all carcass components, but in only three out of six equations were the quadratic terms statistically significant. This re-examination of published data indicates that any comparisons of the carcass composition of cattle may be invalid unless they are made at the same body (or carcass) weights. In addition, a comparison made by using regression equations, with the variables expressed as percentages, is confusing because it may not reveal abnormal composition in animals of particular weights. A satisfactory type of analysis can be made by using regression techniques with the original data. The above principles of analysis were applied in a breed comparison study of the carcass composition of 28 Hereford, 25 Angus, and 18 Shorthorn steers. These cattle comprised two age groups, born in 1957 and 1958 respectively. Carcass composition was estimated by dissecting, into bone, muscle and fat, the left and right 11th ribcuts from the carcasses of the 1957 steers, and the 9th–10th–11th rib-cuts from the left sides of the carcasses of the 1958 steers. When the rib-cut data were plotted, the relations appeared linear; the data were therefore analysed by using linear regressions with arithmetical values for the variables. Results showed that the fat content was greater and the muscle content smaller in the rib-cuts of the Shorthorns in both years than in those of either Hereford or Angus steers. Differences between Herefords and Angus were small. In view of the high correlations found by other workers between the results of rib-cut dissections and carcass composition, it is assumed that the breed differences reported here in rib-cut composition were reflections of breed differences in carcass composition. The carcass compositions of the cattle used in the breed comparison study were also estimated from hot carcass weight by using regression equations derived from the literature. A comparison of the two methods of estimating carcass composition suggests that, if hot carcass weight is to be used, regression equations will need to be developed for each breed in various environments.

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