Prediction of voluntary dry matter intake in stall fed growing goats
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.livsci.2018.11.002 http://hdl.handle.net/11449/189885 |
Resumo: | A Monte Carlo Risk Assessment (MCRA) was used to investigate the variability of existing empirical equations to predict dry matter intake (DMI) for weaned Saanen goats. Probability distribution functions were generated for each input variable used in the investigated DMI predictive equations using the Monte Carlo technique, and Spearman correlations (ρ) among the input variables were used to maintain their observed correlation. Probability distribution functions were obtained using an evaluation database containing 515 observations from four studies with Saanen goats (14.4–48.7 kg body weight (BW)). Thus, the pattern of the probability distribution functions relied exclusively on the observed distribution of the input variables. The MCRA simulation had 5000 iterations and used the Latin hypercube sampling approach to enable a balanced sampling throughout the distribution. Subsequently, with the Monte Carlo simulations, we generated tornado plots using standardized regression coefficients to evaluate influential input variables, and estimated the overlap between observed and predicted DMI. The overlap provided the percentage similarity considering the entire distribution shape. Additionally, each extant DMI equation was challenged by varying the input variables (i.e., independent variables) within the 90% confidence intervals of the probability distribution functions to obtain the prediction range of each equation. Finally, we regressed residual (observed – predicted) values on the predicted values centered on their mean values for each extant DMI equation to assess their mean biases. Our results indicated that even though it is clear that DMI is influenced by goat size (i.e., BW, BW0.75, metabolic weight (MW)), significant biases were observed in all tested equations. Six out of ten literature equations tested did not show a mean bias, whereas only one among the ten tested equations did not have a linear bias. Sex class influenced ADG, age, DM digestibility, metabolizability, and relative size (i.e., inputs considered in some tested equations), and DMI (i.e., male goats had 8% greater DMI per unit of BW than females). Tornado diagrams revealed that BW was the most influential input in the equations commonly used for estimating DMI. Thus, goat size (i.e., BW, BW0.66, MW) is a potential reliable predictor of DMI. Given its influence in predicting intake, the dietary NDF would be considered when developing empirical equations. Future studies should focus on defining the role of environment in DMI regulation, and determining an accurate way to adjust DMI considering metabolic regulation mechanisms in goats. |
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Prediction of voluntary dry matter intake in stall fed growing goatsDMIGoatsModelingMonte Carlo Risk AssessmentSensitivity analysisTornado plotA Monte Carlo Risk Assessment (MCRA) was used to investigate the variability of existing empirical equations to predict dry matter intake (DMI) for weaned Saanen goats. Probability distribution functions were generated for each input variable used in the investigated DMI predictive equations using the Monte Carlo technique, and Spearman correlations (ρ) among the input variables were used to maintain their observed correlation. Probability distribution functions were obtained using an evaluation database containing 515 observations from four studies with Saanen goats (14.4–48.7 kg body weight (BW)). Thus, the pattern of the probability distribution functions relied exclusively on the observed distribution of the input variables. The MCRA simulation had 5000 iterations and used the Latin hypercube sampling approach to enable a balanced sampling throughout the distribution. Subsequently, with the Monte Carlo simulations, we generated tornado plots using standardized regression coefficients to evaluate influential input variables, and estimated the overlap between observed and predicted DMI. The overlap provided the percentage similarity considering the entire distribution shape. Additionally, each extant DMI equation was challenged by varying the input variables (i.e., independent variables) within the 90% confidence intervals of the probability distribution functions to obtain the prediction range of each equation. Finally, we regressed residual (observed – predicted) values on the predicted values centered on their mean values for each extant DMI equation to assess their mean biases. Our results indicated that even though it is clear that DMI is influenced by goat size (i.e., BW, BW0.75, metabolic weight (MW)), significant biases were observed in all tested equations. Six out of ten literature equations tested did not show a mean bias, whereas only one among the ten tested equations did not have a linear bias. Sex class influenced ADG, age, DM digestibility, metabolizability, and relative size (i.e., inputs considered in some tested equations), and DMI (i.e., male goats had 8% greater DMI per unit of BW than females). Tornado diagrams revealed that BW was the most influential input in the equations commonly used for estimating DMI. Thus, goat size (i.e., BW, BW0.66, MW) is a potential reliable predictor of DMI. Given its influence in predicting intake, the dietary NDF would be considered when developing empirical equations. Future studies should focus on defining the role of environment in DMI regulation, and determining an accurate way to adjust DMI considering metabolic regulation mechanisms in goats.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Animal Science UNESP - Universidade Estadual PaulistaDepartment of Animal Science Texas A&M UniversityDepartment of Agricultural Sciences University of SassariDepartment of Animal Science UNESP - Universidade Estadual PaulistaFAPESP: 2014/14734-9FAPESP: 2014/14939-0FAPESP: 2015/22600-5Universidade Estadual Paulista (Unesp)Texas A&M UniversityUniversity of SassariAlmeida, Amélia Katiane de [UNESP]Tedeschi, Luis Orlindode Resende, Kléber Tomás [UNESP]Biagioli, Bruno [UNESP]Cannas, AntonelloTeixeira, Izabelle Auxiliadora Molina de Almeida [UNESP]2019-10-06T16:55:21Z2019-10-06T16:55:21Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1-9http://dx.doi.org/10.1016/j.livsci.2018.11.002Livestock Science, v. 219, p. 1-9.1871-1413http://hdl.handle.net/11449/18988510.1016/j.livsci.2018.11.0022-s2.0-85056629035Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLivestock Scienceinfo:eu-repo/semantics/openAccess2024-06-07T18:41:17Zoai:repositorio.unesp.br:11449/189885Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:20:53.086146Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Prediction of voluntary dry matter intake in stall fed growing goats |
title |
Prediction of voluntary dry matter intake in stall fed growing goats |
spellingShingle |
Prediction of voluntary dry matter intake in stall fed growing goats Almeida, Amélia Katiane de [UNESP] DMI Goats Modeling Monte Carlo Risk Assessment Sensitivity analysis Tornado plot |
title_short |
Prediction of voluntary dry matter intake in stall fed growing goats |
title_full |
Prediction of voluntary dry matter intake in stall fed growing goats |
title_fullStr |
Prediction of voluntary dry matter intake in stall fed growing goats |
title_full_unstemmed |
Prediction of voluntary dry matter intake in stall fed growing goats |
title_sort |
Prediction of voluntary dry matter intake in stall fed growing goats |
author |
Almeida, Amélia Katiane de [UNESP] |
author_facet |
Almeida, Amélia Katiane de [UNESP] Tedeschi, Luis Orlindo de Resende, Kléber Tomás [UNESP] Biagioli, Bruno [UNESP] Cannas, Antonello Teixeira, Izabelle Auxiliadora Molina de Almeida [UNESP] |
author_role |
author |
author2 |
Tedeschi, Luis Orlindo de Resende, Kléber Tomás [UNESP] Biagioli, Bruno [UNESP] Cannas, Antonello Teixeira, Izabelle Auxiliadora Molina de Almeida [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Texas A&M University University of Sassari |
dc.contributor.author.fl_str_mv |
Almeida, Amélia Katiane de [UNESP] Tedeschi, Luis Orlindo de Resende, Kléber Tomás [UNESP] Biagioli, Bruno [UNESP] Cannas, Antonello Teixeira, Izabelle Auxiliadora Molina de Almeida [UNESP] |
dc.subject.por.fl_str_mv |
DMI Goats Modeling Monte Carlo Risk Assessment Sensitivity analysis Tornado plot |
topic |
DMI Goats Modeling Monte Carlo Risk Assessment Sensitivity analysis Tornado plot |
description |
A Monte Carlo Risk Assessment (MCRA) was used to investigate the variability of existing empirical equations to predict dry matter intake (DMI) for weaned Saanen goats. Probability distribution functions were generated for each input variable used in the investigated DMI predictive equations using the Monte Carlo technique, and Spearman correlations (ρ) among the input variables were used to maintain their observed correlation. Probability distribution functions were obtained using an evaluation database containing 515 observations from four studies with Saanen goats (14.4–48.7 kg body weight (BW)). Thus, the pattern of the probability distribution functions relied exclusively on the observed distribution of the input variables. The MCRA simulation had 5000 iterations and used the Latin hypercube sampling approach to enable a balanced sampling throughout the distribution. Subsequently, with the Monte Carlo simulations, we generated tornado plots using standardized regression coefficients to evaluate influential input variables, and estimated the overlap between observed and predicted DMI. The overlap provided the percentage similarity considering the entire distribution shape. Additionally, each extant DMI equation was challenged by varying the input variables (i.e., independent variables) within the 90% confidence intervals of the probability distribution functions to obtain the prediction range of each equation. Finally, we regressed residual (observed – predicted) values on the predicted values centered on their mean values for each extant DMI equation to assess their mean biases. Our results indicated that even though it is clear that DMI is influenced by goat size (i.e., BW, BW0.75, metabolic weight (MW)), significant biases were observed in all tested equations. Six out of ten literature equations tested did not show a mean bias, whereas only one among the ten tested equations did not have a linear bias. Sex class influenced ADG, age, DM digestibility, metabolizability, and relative size (i.e., inputs considered in some tested equations), and DMI (i.e., male goats had 8% greater DMI per unit of BW than females). Tornado diagrams revealed that BW was the most influential input in the equations commonly used for estimating DMI. Thus, goat size (i.e., BW, BW0.66, MW) is a potential reliable predictor of DMI. Given its influence in predicting intake, the dietary NDF would be considered when developing empirical equations. Future studies should focus on defining the role of environment in DMI regulation, and determining an accurate way to adjust DMI considering metabolic regulation mechanisms in goats. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T16:55:21Z 2019-10-06T16:55:21Z 2019-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.livsci.2018.11.002 Livestock Science, v. 219, p. 1-9. 1871-1413 http://hdl.handle.net/11449/189885 10.1016/j.livsci.2018.11.002 2-s2.0-85056629035 |
url |
http://dx.doi.org/10.1016/j.livsci.2018.11.002 http://hdl.handle.net/11449/189885 |
identifier_str_mv |
Livestock Science, v. 219, p. 1-9. 1871-1413 10.1016/j.livsci.2018.11.002 2-s2.0-85056629035 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Livestock Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-9 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128794746159104 |