Using the Small Ruminant Nutrition System to develop and evaluate an alternative approach to estimating the dry matter intake of goats when accounting for ruminal fiber stratification

• Autor(a) principal: Regadas Filho, J. G. L.
• Data de Publicação: 2014
• Outros Autores: Tedeschi, L. O., Cannas, A., Vieira, R. A. M., Rodrigues, M. T.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://doi.org/10.3168/jds.2014-8632; http://www.locus.ufv.br/handle/123456789/21647
• topic: Heterogeneous fiber pool; Nonlinear opti- mization; Rumen capacity

The first objective of this research was to assess the ability of the Small Ruminant Nutrition System (SRNS) mechanistic model to predict metabolizable energy intake (MEI) and milk yield (MY) when using a heterogeneous fiber pool scenario (GnG1), compared with a traditional, homogeneous scenario (G1). The second objective was to evaluate an alternative approach to estimating the dry matter intake (DMI) of goats to be used in the SRNS model. The GnG1 scenario considers an age-dependent fractional transference rate for fiber particles from the first ruminal fiber pool (raft) to an escapable pool (λr), and that this second ruminal fiber pool (i.e., escapable pool) follows an age-independent fractional escape rate for fiber particles (ke). Scenario G1 adopted only a single fractional passage rate (kp). All parameters were estimated individually by using equations published in the literature, except for 2 passage rate equations in the G1 scenario: 1 developed with sheep data (G1-S) and another developed with goat data (G1-G). The alternative approach to estimating DMI was based on an optimization process using a series of dietary constraints. The DMI, MEI, and MY estimated for the GnG1 and G1 scenarios were compared with the results of an independent dataset (n = 327) that contained information regarding DMI, MEI, MY, and milk and dietary compositions. The evaluation of the scenarios was performed using the coefficient of determination (R^2) between the observed and predicted values, mean bias (MB), bias correction factor (Cb), and concordance correlation coefficient. The MEI estimated by the GnG1 scenario yielded precise and accurate values (R^2 = 0·82; MB = 0.21 Mcal/d; Cb = 0.98) similar to those of the G1-S (R^2 = 0.85; MB = 0.10 Mcal/d; Cb = 0.99) and G1-G (R^2 = 0.84; MB = 0.18 Mcal/d; Cb = 0.98) scenarios. The results were also similar for the MY, but a substantial MB was found as follows: GnG1 (R^2 = 0.74; MB = 0.70 kg/d; Cb = 0.79), G1-S (R^2 = 0.71; MB = 0.58 kg/d^1; Cb = 0.85) and G1-G (R^2 = 0.71; MB = 0.65 kg/d; Cb = 0.82). The alternative approach for DMI prediction provided better results with the G1-G scenario (R^2 = 0.88; MB = −71.67 g/d; Cb = 0.98). We concluded that the GnG1 scenario is valid within mechanistic models such as the SRNS and that the alternative approach for estimating DMI is reasonable and can be used in diet formulations for goats.