4 results on '"Borges ALCC"'
Search Results
2. Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.
- Author
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Souza VC, Congio GFS, Rodrigues JPP, Valadares Filho SC, Silva FAS, Rennó LN, Reis RA, Cardoso AS, Rodrigues PHM, Berchielli TT, Messana JD, Cajarville C, Granja-Salcedo YT, Borges ALCC, Kozloski GV, Rosero-Noguera JR, Gonda H, Hristov AN, and Kebreab E
- Abstract
The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake ( DMI ; kg/d) and nitrogen intake ( NI ; g/d), concentrations of dietary components, as well as average daily gain ( ADG ; g/d) and average body weight ( BW ; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error ( RMSE ) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient ( CCC ) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy., Competing Interests: The authors report no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)
- Published
- 2024
- Full Text
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3. Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.
- Author
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Congio GFS, Bannink A, Mayorga OL, Rodrigues JPP, Bougouin A, Kebreab E, Carvalho PCF, Berchielli TT, Mercadante MEZ, Valadares-Filho SC, Borges ALCC, Berndt A, Rodrigues PHM, Ku-Vera JC, Molina-Botero IC, Arango J, Reis RA, Posada-Ochoa SL, Tomich TR, Castelán-Ortega OA, Marcondes MI, Gómez C, Ribeiro-Filho HMN, Gere JI, Ariza-Nieto C, Giraldo LA, Gonda H, Cerón-Cucchi ME, Hernández O, Ricci P, and Hristov AN
- Subjects
- Animals, Cattle, Latin America, Diet veterinary, Eating, Methane, Animal Feed analysis
- Abstract
On-farm methane (CH
4 ) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1 ), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)- Published
- 2023
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4. Predicting enteric methane production from cattle in the tropics.
- Author
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Ribeiro RS, Rodrigues JPP, Maurício RM, Borges ALCC, Reis E Silva R, Berchielli TT, Valadares Filho SC, Machado FS, Campos MM, Ferreira AL, Guimarães Júnior R, Azevêdo JAG, Santos RD, Tomich TR, and Pereira LGR
- Subjects
- Animal Feed analysis, Animals, Brazil, Cattle, Energy Intake, Female, Milk chemistry, Diet veterinary, Lactation, Methane analysis
- Abstract
Accurate estimates of methane (CH4) production by cattle in different contexts are essential to developing mitigation strategies in different regions. We aimed to: (i) compile a database of CH4 emissions from Brazilian cattle studies, (ii) evaluate prediction precision and accuracy of extant proposed equations for cattle and (iii) develop specialized equations for predicting CH4 emissions from cattle in tropical conditions. Data of nutrient intake, diet composition and CH4 emissions were compiled from in vivo studies using open-circuit respiratory chambers, SF6 technique or the GreenFeed® system. A final dataset containing intake, diet composition, digestibility and CH4 emissions (677 individual animal observations, 40 treatment means) obtained from 38 studies conducted in Brazil was used. The dataset was divided into three groups: all animals (GEN), lactating dairy cows (LAC) and growing cattle and non-lactating dairy cows (GCNL). A total of 54 prediction equations available in the literature were evaluated. A total of 96 multiple linear models were developed for predicting CH4 production (MJ/day). The predictor variables were DM intake (DMI), gross energy (GE) intake, BW, DMI as proportion of BW, NDF concentration, ether extract (EE) concentration, dietary proportion of concentrate and GE digestibility. Model selection criteria were significance (P < 0.05) and variance inflation factor lower than three for all predictors. Each model performance was evaluated by leave-one-out cross-validation. The Intergovernmental Panel on Climate Change (2006) Tier 2 method performed better for GEN and GCNL than LAC and overpredicted CH4 production for all datasets. Increasing complexity of the newly developed models resulted in greater performance. The GCNL had a greater number of equations with expanded possibilities to correct for diet characteristics such as EE and NDF concentrations and dietary proportion of concentrate. For the LAC dataset, equations based on intake and animal characteristics were developed. The equations developed in the present study can be useful for accurate and precise estimation of CH4 emissions from cattle in tropical conditions. These equations could improve accuracy of greenhouse gas inventories for tropical countries. The results provide a better understanding of the dietary and animal characteristics that influence the production of enteric CH4 in tropical production systems.
- Published
- 2020
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