Body weight components of Nellore steers finished in tropical pastures

Almeida, Vítor Visintin Silva de; Oliveira, Aline Cardoso; Oliveira, Hellenn Cardoso; Silva, Robério Rodrigues; Júnior, Dorgival Morais de Lima

doi:10.4025/actascianimsci.v41i1.39005

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…In the estimation of condemnation losses, carcass weights and viscera were determined using the literature (IBGE, 2018b;Almeida et al, 2019), based on the prices of products sold in the local market in Thailand ( Table 1). The real and dollar prices were corrected to eliminate the inflationary effect through the Fundaç ã o Getúlio Vargas (FGV, 2019) and the Consumer Price Index (IGP-DI) CPI) (US Inflation Calculator, 2019), respectively; both based on December 2018.…”

Section: Methodsmentioning

confidence: 99%

Economic Losses’ Estimation of carcass and Organ Condemnations From Slaughter Cattle in Thailand, Pará State, Brazilian Amazon

Noronha

Santos

Pereira

et al. 2019

JAS

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The article identifies the main pathologies found and estimates the economic losses generated in a slaughterhouse in the Thailand municipality, Pará, during the period from March 2010 to October 2014. 55,169 animals were slaughtered, with total economic losses of R$ 1,221,035.90 and ischemia was the most frequent lesion (41.86%). The most frequent condemnation organs were lungs (48.75%), kidneys (41.66%) and liver (3.61%), which produced economic loss of R$ 76,405.65 (6.26%). Tuberculosis was the main cause of carcass condemnation, responsible for R$ 872,783.64 of economic losses. The bovine tuberculosis control can begin in production, with examinations on the farm to slaughter lines, with careful carcass inspection, as advocated by the National Program for the Control and Eradication of Animal Brucellosis and Tuberculosis (PNCEBT), which increases disease control and minimizes condemnations and economic losses at slaughter.

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Section: Methodsmentioning

confidence: 99%

Economic Losses’ Estimation of carcass and Organ Condemnations From Slaughter Cattle in Thailand, Pará State, Brazilian Amazon

Noronha

Santos

Pereira

et al. 2019

JAS

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“…Equations evaluated to predict carcass chemical composition assuming carcass water is known in the Carcass dataset [ 21 , 22 , 33 , 36 ]; Table S4. Equations evaluated to predict empty body chemical composition assuming carcass chemical composition is known in the Empty Body-Carcass dataset [ 21 , 22 ]; Literature used to construct the Weight Dataset [ 64 , 66 , 67 , 68 , 70 , 73 , 85 , 86 , 87 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , …”

mentioning

confidence: 99%

Assessment of Equations to Predict Body Weight and Chemical Composition in Growing/Finishing Cattle and Effects of Publication Year, Sex, and Breed Type on the Deviation from Observed Values

Lancaster

2022

Animals

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Body weight and chemical composition are important aspects of beef cattle nutrition and management; however, existing equations estimating relationships among empty body and carcass chemical components were developed over 40 years ago using different cattle genetics and production systems. The objective of this analysis was to evaluate existing equations in predicting empty body and carcass chemical composition and determine the effect of sex, breed type, and publication year. A dataset was developed from published literature that contained 388 treatment means from 46 studies published between 1970 and 2020. Two equations relating shrunk body weight (SBW) to empty body weight (EBW), and 8 equations relating EBW and hot carcass weight (HCW) were found in the literature and evaluated using the developed dataset. Three sets of equations relating empty body chemical components, 4 sets of equations relating carcass chemical components, and 2 sets of equations relating carcass with empty body chemical components were found in the literature and evaluated using the dataset. Precision and accuracy of the equations were evaluated by simple linear regression of observed on predicted values, mean bias (MB), and concordance correlation coefficient (CCC). Additionally, the fixed effects of publication year, sex, and breed type on the deviation from observed values were evaluated using a general linear model. Both equations relating SBW to EBW and all equations relating EBW to HCW had high precision, but accuracy varied from −3.22 to −0.11% and −9.35 to −3.73% MB, respectively, and all the equations were affected by sex and breed type with 8 out of the 10 equations affected by publication year. For prediction of empty body chemical composition assuming empty body water is known, the 3 sets of equations varied in precision for protein (0.18 to 0.46), but not for fat (0.88 to 0.96) or ash (0.06 to 0.13) based on CCC, although the precision of prediction of protein and ash were poor. Accuracy of the 3 sets of equations varied for predicting empty body fat, protein, and ash with MB of −19.73 to −3.81, 1.67 to 15.91, and −0.16 to 15.75%, respectively. All 3 sets of equations were affected by publication year and breed type for predicting empty body fat, protein, and ash, and by sex for ash. For prediction of carcass chemical components assuming carcass water is known, the precision was similar among the 4 sets of equations for predicting fat (0.92 to 0.95), protein (0.34 to 0.40), and ash (−0.02 to −0.01) based on CCC, although precision was poor for protein and ash, but accuracy varied for prediction of carcass fat, protein and ash with MB of −11.20 to −2.52, 2.72 to 8.92, and −4.66 to 20.12%, respectively. Publication year and breed type affected the prediction of carcass fat and protein, and publication year, sex, and breed type affected the prediction of carcass ash for all 4 sets of equations. The precision of predicting empty body chemical components assuming carcass chemical components are known was high for water (0.96 and 0.98), fat (0.97 and 0.98), protein (0.97 and 0.97), and ash (0.98 and 0.96) and similar between the 2 sets of equations based on CCC. The accuracy of predicting empty body water (−1.68 and −0.33%), fat (6.38 and 2.70%), protein (0.85 and −0.54%), and ash (−0.65 and −4.54%) was moderate to high, but differed between sets of equations for fat and ash. Publication year influenced the prediction of empty body water for both sets of equations and ash for one of the equations, whereas, breed type influenced the prediction of water, protein, and ash, but not fat for both equations. Overall, existing equations may have major limitations to predicting empty body protein and ash unless carcass protein and ash are known. Additionally, all the equations were affected by some combination of publication year, sex, and breed type for one or more chemical components. Thus, a more robust set of equations should be developed to account for sex, breed type, and more recent cattle genetics and management systems.

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Body weight components of Nellore steers finished in tropical pastures

Cited by 2 publications

References 15 publications

Economic Losses’ Estimation of carcass and Organ Condemnations From Slaughter Cattle in Thailand, Pará State, Brazilian Amazon

Economic Losses’ Estimation of carcass and Organ Condemnations From Slaughter Cattle in Thailand, Pará State, Brazilian Amazon

Assessment of Equations to Predict Body Weight and Chemical Composition in Growing/Finishing Cattle and Effects of Publication Year, Sex, and Breed Type on the Deviation from Observed Values

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