Generation of models from existing models composition: An application to agrarian sciences

English version

Autoři: André Luiz Pinto dos Santos ^aff001; Guilherme Rocha Moreira ^aff001; Frank Gomes-Silva ^aff001; Cícero Carlos Ramos de Brito ^aff002; Maria Lindomárcia Leonardo da Costa ^aff003; Luiz Gustavo Ribeiro Pereira ^aff004; Rogério Martins Maurício ^aff005; José Augusto Gomes Azevêdo ^aff006; José Marques Pereira ^aff007; Alexandre Lima Ferreira ^aff005; Moacyr Cunha Filho ^aff001
Působiště autorů: Department of statistics and informatics, Federal Rural University of Pernambuco, Recife, Pernambuco, Brazil ^aff001; Federal Institute of Pernambuco, Recife, Pernambuco, Brazil ^aff002; Animal Science Department, Federal University of Paraíba, Areia, Paraíba, Brazil ^aff003; Embrapa Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil ^aff004; Bioengineering Department, Federal University of São Joãodel-Rei, São João del-Rei, Brazil ^aff005; Universidade Estadual de Santa Cruz, Ilhéus, BA, Brazil ^aff006; CEPLAC-ESSUL, Itabela, Bahia, Brazil ^aff007
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0214778

Souhrn

Mathematical models that describe gas production are widely used to estimate the rumen degradation digestibility and kinetics. The present study presents a method to generate models by combining existing models and to propose the von Bertalanffy-Gompertz two-compartment model based on this method. The proposed model was compared with the logistic two-compartment one to indicate which best describes the kinetic curve of gas production through the semi-automated in vitro technique from different pinto peanut cultivars. The data came from an experiment grown and harvested at the Far South Animal Sciences station (Essul) in Itabela, BA, Brazil and gas production was read at 2, 4, 6, 8, 10, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 h after the start of the in vitro fermentation process. The parameters were estimated by the least squares method using the iterative Gauss-Newton process in the software R version 3.4.1. The best model to describe gas accumulation was based on the adjusted coefficient of determination, residual mean squares, mean absolute deviation, Akaike information criterion, and Bayesian information criterion. The von Bertalanffy-Gompertz two-compartment model had the best fit to describe the cumulative gas production over time according to the methodology and conditions of the present study.

Klíčová slova:

Grasses – Maize – Carbohydrates – Curve fitting – Mathematical models – Peanut – Fermentation – Two-compartment models

Zdroje

1. JACOVETTI R. Agronomic and nutritional performance of "Mulato II" grass under doses and nitrogen sources. Tese, Goias Federal University. 2016. https://repositorio.bc.ufg.br/tede/handle/tede/6793

2. MERTENS DR. Rate and extension of digestion. In: FORBES J.M.; FRANCE J. (Eds.). QUALITATIVE ASPECTS OF RUMINANT DIGESTION AND METABOLISM, Cambridge. [Proceedings …], Cambridge-UK: CAB International; 1993. pp. 13–51.

3. FARIAS LN, VASCONCELOS VR, CARVALHO FFR, SARMENTO JLR. Avaliation of two-compartment logistic and Gompertz mathematical models to estimate gas production from babassu (Orbignya martiana) meal and pie using the semi-automated in vitro technique. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 2011; 63:136–142.

4. SILVA NA, LIMA RR, SILVA FF, MUNIZ JA. Bayesian hierarchical model applied to genetic evaluation of beef cattle growth curves. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 2010; 62:409–418.

5. KOOPS WJ. Multiphasic growth curve analysis. Growth, Bar Harbor, 1986; 50:169–177. 3792903.

6. SCHOFIELD P PITT RE, PELL AN. Kinetics of fiber digestion from in vitro gas production. Journal of Animal Science, Champaign, v. 72, p. 2980–2991, 1994. https://doi.org/10.2527/1994.72112980x

7. FERRO MM, CASTRO WR, ZANINE ADM, SOUZA AL. Kinetics of sugarcane silage with dehydrated barley residue in vitro ruminal fermentation. Archivos de zootecnia, 2017; 66:237–242.

8. PERETTI J, HENRIQUE DS, MAYER LRR, MILITÃO ER, SCHIMITZ R, BOGER DT, et al. Chemical composition and ruminal degradability of white oat (“Avena sativa” L.) cv. IPR126 under different nitrogen levels. Revista Brasileira de Saúde e Produção Animal, 2017; 18:89–102.

9. FRANCE J, DIJKSTRA J, DHANOA MS, LÓPEZ S, BANNINK A. Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: derivation of models and other mathematical considerations. British Journal of Nutrition. 2000; 83:143–150. https://doi.org/10.1017/S0007114500000180

10. WANG M, TANG SX, TAN ZL. Modeling in vitro gas production kinetics: derivation of logistic–exponential (LE) equations and comparison of models. Animal Feed Science and Technology, 2011; 165:137–150.

11. BRITO CCR, Da SILVA JAA, FERREIRA RLC, DE SOUZA SANTOS E, FERRAZ I. Growth resulting from the variation and combination of models of Chapman;Richards and Silva-Bailey applied to Leucaena leucocephala (Lam.) of Wit. Ciência Florestal, 2007; 17:175–185.

12. SANTANA TJS, SCALON JD, BITTENCOURT TCC, de SANTANA ASA. A von bertalanffy model with response plateau to describe growth curves of beef cattle. Biometric Brazilian Journal, 2016; 34:646–655.

13. SANTOS ALP, MOREIRA GR, BRITO CCR, SILVA FSG, COSTA MLL, PIMENTEL PG, et al. Method to generate growth and degrowth models obtained from differential equations applied to agrarian sciences. SEMINA. CIÊNCIAS AGRÁRIAS (ONLINE), 2018.

14. ASSOCIATION OF OFFICIAL ANALYTICAL CHEMISTS–AOAC. OFFICIAL METHODS OF ANALYSIS. 15. edition. Arlington, Virginia. 1117p. 1990.

15. VAN SOEST PJ, ROBERTSON JB, LEWIS BA. Methods for dietary fiber, neutral detergent fiber, and nonstarter polysaccharides in relation to animal nutrition. Journal of dairy science, 1991; 74:3583–3597. doi: 10.3168/jds.S0022-0302(91)78551-2 1660498

16. MAURÍCIO RM, PEREIRA LGR, GONÇALVES LC, RODRIGUEZ NM, BORGES A, BORGES I, et al. Obtenção da equação quadrática entre volume e pressão para a implantação da técnica “in vitro” semi-automática de produção de gás para avaliação de forrageiras tropicais. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, Piracicaba, SP. Anais …Piracicaba, SBZ, 2001; 38:1340–1346.

17. MELLO R, MAGALHÃES ALR, BRENDA FC, REGAZZI AJ. Models for fit of gas production in sunflower and corn silages. Pesquisa Agropecuária Brasileira, 2008; 43:261–269.

18. UCKARDES F, EFE E. Investigation on the usability of some mathematical models in in vitro gas production techniques. Slovak Journal of Animal Science, 2014; 47:172–179.

19. ZWIETERING MH, JONGENBURGER I, ROMBOUTS FM, VAN’T RIET K. Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 1990; 56:1875–1881. 16348228.

20. PEREIRA MB. Ajustes de modelos à cinética de fermentação em equinos. Dissertation, Federal Rural University of Rio de Janeiro, Seropédica. 2013. http://r1.ufrrj.br/wp/ppgz/files/2015/05/Disserta%C3%A7%C3%A3o_-_Marcos_Barreto_Pereira.pdf

21. R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2017. http://www.r-project.org.

22. CAVALCANTE M, LIRA MDA. Genetic Variability in Pennisetum purpureum Schumacher. Revista Caatinga, Mossoro, 2010; 23:153–163.

23. SCHUNEMANN AP, TREPTOW R, LEITE DL, VENDRUSCOLO JL. Pungência e características químicas em bulbos de genótipos de cebola (Allium cepa L.) cultivados no Alto Vale do Itajaí, SC, Brasil. Current Agricultural Science and Technology, v. 12, n. 1, 2006; 12:77–80.

24. FERREIRA AL, MAURÍCIO RM, PEREIRA LGR, AZEVÊDO JAG, OLIVEIRA LS, PEREIRA JM. Nutritional divergence in genotypes of forage peanut. Revista Brasileira de Zootecnia, 2012a; 41:856–863.

25. FERREIRA AL, MAURÍCIO RM, FERNANDES FD, CARVALHO MA, RAMOS AKB, JUNIOR R G. Ranking contrasting genotypes of forage peanut based on nutritive value and fermentation kinetics. Animal feed science and technology, 2012b; 175:16–23.

26. ARAGADVAY-YUNGÁN RG, RAYAS AMOR AA, HEREDIA-NAVA D, ESTRADA-FLORES JG, MARTÍNEZ-CASTAÑEDA FE, ARRIAGA-JORDÁN C.M. Evaluación in vitro del ensilaje de girasol (helianthus annuus l.) solo y combinado con ensilaje de maíz. Revista Mexicana de ciencias pecuarias, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, México, 2015, 6: 315–327.

27. SÁ JF, PEDREIRA MS, SILVA FF, FIGUEIREDO MP, REBOUÇAS GMN, DE SOUZA DR. Cinética da fermentação in vitro do capim-Marandu em diferentes idades de corte. Acta Scientiarum Animal Sciences. 2011; 33:225–231.

28. DOS ESL, RUFINO LD, RIBEIRO KG, PEREIRA OG. Microbial characterization of Arachis pintoi and its silages at different fermentation periods. Molecular biology reports. 2019. https://doi.org/10.1007/s11033-019-04952-y

29. NOGUEIRA UT, MAURÍCIO RM, GONÇALVES LC. Comparison among substrates with different soluble carbohydrates concentration using the in vitro semi-automatic gas production technique. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 2006; 58:633–641 http://dx.doi.org/10.1590/S0102-09352006000400027

30. DA SILVA AL, DE SOUSA DB, AMORIM DS, DA SILVA SANTOS M, DA SILVA KB, DO NASCIMENTO RR. Carboidratos de plantas forrageiras para ruminantes: importância e avaliação qualitativa: uma revisão. Nucleus Animalium. 2019; 11:1–12. http://dx.doi.org/10.3738/21751463.2945.

31. CABRAL LS, VALADARES FILHO SC, MALAFAIA PAM, LANA RP, SILVA JFC, VIEIRA RAM, et al. Forage Carbohydrate Fractions and its Degradation Rates Estimated by Gas Production Technique. Revista Brasileira de Zootecnia, 2000; 29:2087–2098.

32. MALAFAIA PAM, VALADARES FILHO SDC, VIEIRA RAM, SILVA JD, PEREIRA JC. Ruminal Kinetics of Some Feeds Investigated by Gravimetric and Metabolic Techniques. Revista Brasileira de Zootecnia. 1998; 27:370–380.

33. RICACHESKI ST, HENRIQUE DS, MAYER LRR, OLIVEIRA JG, ROSLER JA, FLUCK AC. Chemical composition and ruminal degradation kinetics of white oat (Avena sativa L.) IPR 126. Revista Brasileira de Saúde e Produção Animal, 2017; 18:50–61.

34. KRISHNAMOORTHY U, RYMER C, ROBINSON PH. The in vitro gas production technique: Limitations and opportunities. Animal Feed Science and Technology, 2005; 123–124:1–7.

35. ARRÉ FA, CAMPELO JEG, SARMENTO JLR, FIGUEIREDO FILHO LAS, CAVALCANTE DH. A comparison of nonlinear models for describing weight-age data in anglo-nubian does. Revista Caatinga, 2019; 32: 251–258.

36. SILVEIRA FGD, SILVA FF, CARNEIRO PLS, MALHADO CHM, MUNIZ JA. Cluster analysis applied to nonlinear regression models selection to growth curves of crossed lambs. Ciência Rural, 2011; 41:692–698.

37. SILVEIRA FG, SILVA FF, CARNEIRO PLS, MALHADO CHM, PETERNELLI LA, SOUZA JR AAO. Classificação multivariada de modelos de crescimento para grupos genéticos de ovinos de corte. In: 54ª RBRAS (Reunião Anual da Região Brasileira da Sociedade Internacional de Biometria) e 13° SEAGRO (Simpósio de Estatística Aplicada à Experimentação Agronômica), 2009, São Carlos-SP. Anais da 54ª RBRAS e 13° SEAGRO. São Carlos-SP: UFScar, 2009.

38. GONDIM FILHO AGC. Genetic divergence and evaluation of degradation models in forage peanut genotypes (Arachis pintol). Thesis, Federal Rural University of Pernambuco. 2018. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7239

39. GROOT JCJ, CONE JW, WILLIAMS BA, DEBERSAQUES FMA, LANTINGA EA. Multiphasic analysis of gas production kinetics for in vitro fermentation of ruminant feeds. Animal Feed Science and Technology, 1996; 64:77–89.

Generation of models from existing models composition: An application to agrarian sciences

Souhrn

Klíčová slova:

Zdroje

PLOS One

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