Evaluation and reparametrization of mathematical models for prediction of the leaf area of <i>Megathyrsus maximus</i> cv. BRS Zuri

Authors

  • Patrick Bezerra Fernandes Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
  • Rodrigo Amorim Barbosa Empresa Brasileira de Pesquisa Agropecuária, Embrapa Gado de Corte, Campo Grande, MS, Brazil.
  • Maria da Graça Morais Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
  • Cauby de Medeiros-Neto Universidade do Estado de Santa Catarina, Lages, SC, Brazil.
  • Antonio Leandro Chaves Gurgel Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
  • Carolina Marques Costa Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
  • Ana Beatriz Graciano da Costa Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
  • Juliana Caroline Santos Santana Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
  • Manoel Gustavo Paranhos da Silva Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
  • Gelson dos Santos Difante Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.

DOI:

https://doi.org/10.17138/tgft(8)214-219
Abstract How to Cite

Abstract

The aim of this study was to verify the precision and accuracy of 5 models for leaf area prediction using length and width of leaf blades of Megathyrsus maximus cv. BRS Zuri and to reparametrize models. Data for the predictor variables, length (L) and width (W) of leaf blades of BRS Zuri grass tillers, were collected in May 2018 in the experimental area of Embrapa Gado de Corte, Mato Grosso do Sul, Brazil. The predictor variables had high correlation values (P<0.001). In the analysis of adequacy of the models, the first-degree models that use leaf blade length (Model A), leaf width × leaf length (Model B) and linear multiple regression (Model C) promoted estimated values similar to the leaf area values observed (P>0.05), with high values for determination coefficient (>80%) and correlation concordance coefficient (>90%). Among the 5 models evaluated, the linear multiple regression (Model C: β0 = -5.97, β1 = 0.489, β2 = 1.11 and β3 = 0.351; R² = 89.64; P<0.001) and as predictor variables, width, length and length × width of the leaf blade, are the most adequate to generate precise and exact estimates of the leaf area of BRS Zuri grass.

How to Cite

Fernandes, P. B., Barbosa, R. A., Morais, M. da G., de Medeiros-Neto, C., Gurgel, A. L. C., Costa, C. M., Costa, A. B. G. da, Santana, J. C. S., Silva, M. G. P. da, & Difante, G. dos S. (2020). Evaluation and reparametrization of mathematical models for prediction of the leaf area of <i>Megathyrsus maximus</i> cv. BRS Zuri. Tropical Grasslands-Forrajes Tropicales, 8(3), 214–219. https://doi.org/10.17138/tgft(8)214-219

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Published

2020-09-30

Issue

Vol. 8 No. 3 (2020): September 2020

Section

Research Papers