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

Detalhes bibliográficos
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
Resumo: 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.
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spelling Regadas Filho, J. G. L.Tedeschi, L. O.Cannas, A.Vieira, R. A. M.Rodrigues, M. T.2018-09-06T10:14:54Z2018-09-06T10:14:54Z2014-1000220302https://doi.org/10.3168/jds.2014-8632http://www.locus.ufv.br/handle/123456789/21647The 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.engJournal of Dairy Sciencev. 97, n. 11, p. 7185- 7196, nov. 2014American Dairy Science associationinfo:eu-repo/semantics/openAccessHeterogeneous fiber poolNonlinear opti- mizationRumen capacityUsing 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 stratificationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALartigo.pdfartigo.pdftexto completoapplication/pdf1133216https://locus.ufv.br//bitstream/123456789/21647/1/artigo.pdf6104a3da46c1ee31430d6794a64ad3fbMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/21647/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILartigo.pdf.jpgartigo.pdf.jpgIM Thumbnailimage/jpeg4645https://locus.ufv.br//bitstream/123456789/21647/3/artigo.pdf.jpgafda163399fb2b378bec5b6bfaf8955cMD53123456789/216472018-09-06 23:01:06.28oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452018-09-07T02:01:06LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv 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
title 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
spellingShingle 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
Regadas Filho, J. G. L.
Heterogeneous fiber pool
Nonlinear opti- mization
Rumen capacity
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
author Regadas Filho, J. G. L.
author_facet Regadas Filho, J. G. L.
Tedeschi, L. O.
Cannas, A.
Vieira, R. A. M.
Rodrigues, M. T.
author_role author
author2 Tedeschi, L. O.
Cannas, A.
Vieira, R. A. M.
Rodrigues, M. T.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Regadas Filho, J. G. L.
Tedeschi, L. O.
Cannas, A.
Vieira, R. A. M.
Rodrigues, M. T.
dc.subject.pt-BR.fl_str_mv Heterogeneous fiber pool
Nonlinear opti- mization
Rumen capacity
topic Heterogeneous fiber pool
Nonlinear opti- mization
Rumen capacity
description 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.
publishDate 2014
dc.date.issued.fl_str_mv 2014-10
dc.date.accessioned.fl_str_mv 2018-09-06T10:14:54Z
dc.date.available.fl_str_mv 2018-09-06T10:14:54Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv https://doi.org/10.3168/jds.2014-8632
http://www.locus.ufv.br/handle/123456789/21647
dc.identifier.issn.none.fl_str_mv 00220302
identifier_str_mv 00220302
url https://doi.org/10.3168/jds.2014-8632
http://www.locus.ufv.br/handle/123456789/21647
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt-BR.fl_str_mv v. 97, n. 11, p. 7185- 7196, nov. 2014
dc.rights.driver.fl_str_mv American Dairy Science association
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rights_invalid_str_mv American Dairy Science association
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