The Effects of Two Species of Leucaena on In Vitro Rumen Fermentation, Methane Production and Post-ruminal Protein Supply in Diets Based on Urochloa hybrid cv. Cayman

Gaviria, Xiomara; Castro-Montoya, J.; Bolívar, Diana; Molina-Botero, Isabel Cristina; Chirinda, Ngonidzashe; Dickhoefer, Uta; Rosales, Rolando Barahona; Arango, Jacobo

doi:10.3390/agronomy12030629

Cited by 2 publications

(2 citation statements)

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“…This influence seemed particularly strong in the case of L. diversifolia fed at 30% of DM. In fact, it has been reported that whereas L. diversifolia reduced methane production, L. leucocephala did not [30]. Nonetheless, in other studies, the addition of L. leucocephala to a diet of Star grass increased DMI and productivity without increasing methane emission [31,32].…”

Section: Discussionmentioning

confidence: 86%

Evaluation of a Model (RUMINANT) for Prediction of DMI and CH4 from Tropical Beef Cattle

Ruden

Rivera

Vargas

et al. 2023

Animals

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Simulation models represent a low-cost approach to evaluating agricultural systems. In the current study, the precision and accuracy of the RUMINANT model to predict dry matter intake (DMI) and methane emissions from beef cattle fed tropical diets (characteristic of Colombia) was assessed. Feed intake (DMI) and methane emissions were measured in Brahman steers housed in polytunnels and fed six forage diets. In addition, DMI and methane emissions were predicted by the RUMINANT model. The model’s predictive capability was measured on the basis of precision: coefficients of variation (CV%) and determination (R2, percentage of variance accounted for by the model), and model efficiency (ME) and accuracy: the simulated/observed ratio (S/O ratio) and slope and mean bias (MB%). In addition, combined measurements of accuracy and precision were carried out by means of mean square prediction error (MSPE) and correlation correspondence coefficient (CCC) and their components. The predictive capability of the RUMINANT model to simulate DMI resulted as valuable for mean S/O ratio (1.07), MB% (2.23%), CV% (17%), R2 (0.886), ME (0.809), CCC (0.869). However, for methane emission simulations, the model substantially underestimated methane emissions (mean S/O ratio = 0.697, MB% = −30.5%), and ME and CCC were −0.431 and 0.485, respectively. In addition, a subset of data corresponding to diets with Leucaena was not observed to have a linear relationship between the observed and simulated values. It is suggested that this may be related to anti-methanogenic factors characteristic of Leucaena, which were not accounted for by the model. This study contributes to improving national inventories of greenhouse gases from the livestock of tropical countries.

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Section: Discussionmentioning

confidence: 86%

Evaluation of a Model (RUMINANT) for Prediction of DMI and CH4 from Tropical Beef Cattle

Ruden

Rivera

Vargas

et al. 2023

Animals

Self Cite

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“…Feeding is a critical aspect of dairy cow production since it impacts the capacity of the cattle to manifest their natural abilities. − The quality of the diet of dairy cattle is affected through its chemical content and also by how dairy cattle consume it. Insights on how dairy cattle use their feeds, such as feed digestibility, can help ration formulation be more accurate when dairy cow output is forecast.…”

Section: Introductionmentioning

confidence: 99%

Toward Modeling the In Vitro Gas Production Process by Using Propolis Extract Oil Treatment: Machine Learning and Kinetic Models

Vakili

Ehtesham

Mesgaran

et al. 2022

Ind. Eng. Chem. Res.

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To overcome challenges with in vivo digestibility assessment, in vitro digestibility techniques have been created. The biobased additive in concentrate can effectively promote the in vitro digestibility performance. The primary goal of this study was to evaluate the propolis effect on produced gas by an in vitro procedure in 25−75% proportion of concentrate. Then, machine learning (ML) models such as nonlinear regression techniques and multilayer perceptron neural networks (MLP-NNs) were applied to assess the prediction performance of gas generation during in vitro digestion using propolis treatments with diverse diet components. The MLP-NN was created using 11 nonlinear regression (NLR) and 12 training algorithms. The findings revealed that the logistic−exponential without lag time (LE 0 ) model was chosen as the best nonlinear model for eight diet interventions. Also, the achievements of the MLP-NN model evaluation revealed that the trainbr training procedure with six neurons in the hidden layer can accurately predict the gas output. The prediction errors of the MLP-NN and NLR approaches were not significantly different (R 2 ∼ 0.99). Three-dimensional response surface graphs were drawn with the help of a neural network, and the optimal value was calculated based on it in a simple and intuitive way. This route displayed the optimum propolis treatment, in which the application of 75% propolis ethanol extract in concentrate could significantly increase gas production between 1 and 4 mL/h. The MLP-NN model has more capabilities than NLR in such studies.

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The Effects of Two Species of Leucaena on In Vitro Rumen Fermentation, Methane Production and Post-ruminal Protein Supply in Diets Based on Urochloa hybrid cv. Cayman

Cited by 2 publications

References 40 publications

Evaluation of a Model (RUMINANT) for Prediction of DMI and CH4 from Tropical Beef Cattle

Evaluation of a Model (RUMINANT) for Prediction of DMI and CH4 from Tropical Beef Cattle

Toward Modeling the In Vitro Gas Production Process by Using Propolis Extract Oil Treatment: Machine Learning and Kinetic Models

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