Prediction of Energy Digestibility of Hays in Horses

Sales, J.; Homolka, P.; Koukolová, V.

doi:10.1016/j.jevs.2012.12.001

Cited by 4 publications

(3 citation statements)

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“…Results from in vivo assays with horses have demonstrated a close and linear relationship between energy digestibility and organic matter digestibility in forages, concentrates, and mixed diets [7,10,11,34]. In the present work, energy digestibility of the mixed feeds and diets in Table 1, derived from their calculated gross energy and digestible energy predicted with Equations ( 1) and ( 2), was highly correlated (r > 0.91; p < 0.001) with the observed organic matter digestibility in the dataset, which would support the reliability of summative equations based on digestible nutrients to predict digestible energy of mixed feeds and diets for horses [4].…”

Section: Characteristics Of the Dataset For The Evaluationmentioning

confidence: 99%

“…In vivo digestibility studies are the gold standard for measuring digestible energy in horse feeds, but because those assays are time consuming, labor intensive, costly, and often highly impractical, researchers have made several efforts to develop predictive equations based on the chemical composition of the feeds [6][7][8][9][10][11][12][13][14]. Some of those equations have been incorporated into the models proposed by several feed evaluation systems for horses [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Two Equations for Prediction of Digestible Energy in Mixed Feeds and Diets for Horses

Marín

Valle

Bergero

et al. 2022

Animals

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Several authors have developed equations for estimating digestible energy in horse feeds as an alternative to the inconveniences of in vivo digestibility assays. We aimed to evaluate two of such equations. A dataset was constructed from the literature with 32 mixed feeds and diets of known proximate composition, whose digestibility was measured in in vivo assays. Then, the digestible energy of the mixed feeds and diets was predicted with both equations from their proximate components. Precision, accuracy, reproducibility, bias, and decomposition of total error of predictions were determined. Both equations performed almost equally well (R2 = 0.89 vs. 0.87, root mean square error of prediction = 183 vs. 217 kcal/kg dry matter, concordance correlation coefficient = 0.91 vs. 0.86, and linear error = 24.6 vs. 33.6% of total error). Linear bias (p < 0.01 in both equations) resulted in overvaluation of low digestible energy feeds and, to a lesser extent, undervaluation of high digestible energy feeds and was significantly (p < 0.05) related to crude fiber. The obtained results indicate that the accuracy of both equations could be improved by reassessing the effects of crude fiber on the digestibility of the other proximate components.

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Section: Characteristics Of the Dataset For The Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Two Equations for Prediction of Digestible Energy in Mixed Feeds and Diets for Horses

Marín

Valle

Bergero

et al. 2022

Animals

View full text Add to dashboard Cite

show abstract

“…Asimismo, y coincidiendo con lo que expresa Sales et al (8) , los métodos de análisis de la digestibilidad in vivo tienden a necesitar muchas horas de consumo, mucha mano de obra, con costos muy altos, y resultan impracticables. Proponemos usar el instrumento comercial Nasco Digestion Analyzer para observar posibles cambios en heces frescas como una guía clave a considerar en la evaluación de la alimentación (9) , siendo ésta, una técnica de bajo costo y fácil implementación, como método complementario para la valoración nutricional de la calidad del forraje ofrecido a los equinos.…”

Section: Introductionunclassified