Prediction of fresh and dry matter of the aerial part of the plant teosinte as a function of morphological traits

Authors

Keywords:

Zea mays ssp. mexicana, multiple linear regression, regression tree, forage plant

Abstract

The objective of this work was to verify whether the fresh and dry matter of the aerial part of the teosinte plant can be estimated as a function of morphological traits. An experiment was conducted in the agricultural year 2021/2022, in nine sowing dates. The following morphological traits were measured: stem length of the main stem (CC, cm); length of the main stem tassel (CP, cm); number of leaves on the main stem (NF); and number of tillers of the plant (NPF) and the productive ones: fresh mass of the aerial part of the plant (MFPA, g) and dry mass of the aerial part of the plant (MSPA, g). The parameters of the multiple linear regression model and the coefficient of determination were estimated considering the variables MFPA and MSPA as dependent and the other (CC, CP, NF, NPF) as independent. The parameters of the regression tree algorithm for predicting MFPA and MSPA were estimated as a function of the other variables (CC, CP, NF, NPF). The fresh and dry mass of the aerial part of the teosinte plant can be estimated as a function of morphological characters. The MFPA can be estimated by the following regression model: MFPA= -740.42 + 3.38(CC) + 9.70(CP) + 41.05(NF) + 85.70(NPF). While the MSPA can be estimated by the following regression model: MSPA= -84.33 + 0.85(CC) + 0.27(CP) + 3.67(NF) + 19.46(NPF). Plants with NPF greater than 8 and CC greater than 154 cm show the highest production of MFPA and MSPA.

Author Biographies

Alberto Cargnelutti Filho, Universidade Federal de Santa Maria

Docente do curso de Agronomia

Murilo Vieira Loro, Universidade Federal de Santa Maria

Discente do curso de doutorado em Agronomia

Mikael Brum dos Reis, Universidade Federal de Santa Maria

Discente do curso de Agronomia

Vithória Morena Ortiz, Universidade Federal de Santa Maria

Discente do curso de mestrado em Agronomia

João Augusto Andretta, Universidade Federal de Santa Maria

Discente do curso de Agronomia

Bruno Raul Schuller, Universidade Federal de Santa Maria

Discente do curso de Agronomia

References

AHMAD, M. et al. Estimation of correlation coefficient in oats (Avena sativa L.) for forage yield, grain yield and their contributing traits. International Journal of Plant Breeding and Genetics, v. 7, n. 3, p. 188-191, 2013.

ALVARES, C. A. et al. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711-728, 2013.

CARGNELUTTI FILHO, A. et al. Relações lineares entre caracteres de aveia preta. Ciência Rural, v. 45, n. 6, p. 985-992, 2015.

HAIR, J. F. et al. Análise multivariada de dados. 6. ed. Porto Alegre: Bookman, 2009, 688p.

JHA, A. K. et al. Impact of irrigation method on water use efficiency and productivity of fodder crops in Nepal. Climate, v. 4, n. 1, p. 1-13, 2016.

KHANAL, B. et al. Maximizing fodder yield of teosinte (Euchlaena mexicana) through sowing dates and mixed fodder cropping management. Journal of Agriculture and Forestry University, v. 4, p. 269-278, 2020.

KUMAR, B. et al. Herbage production, nutritional composition and quality of teosinte under Fe fertilization. International Journal of Agriculture & Biology, v. 18, n. 2, p. 319-329, 2016.

LOH, W. Y. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, v. 1, n. 1, p. 14-23, 2011.

MILBORROW, S. _rpart.plot: Plot ´rpart` Models: an Enhanced Version of ´plot.rpart`_. R package version 3.1.1, 2022. <https://CRAN.R-project.org/package=rpart.plot>.

MOHAN, S.; DAR, E. A.; SINGH, M. Fodder quality of teosinte fodder as influenced by nitrogen, phosphorus and zinc application. International Journal of Pure and Applied Bioscience, v. 5, p. 596-604, 2017.

ORTIZ, V. M. et al. Correlação e análise de trilha entre caracteres de teosinto. Sigmae, v. 12, n. 1, p. 29-39, 2023.

R CORE TEAM. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2023. URL https://www.R-project.org/.

SANTOS, H. G. et al. Sistema Brasileiro de Classificação de Solos. 5. ed. Brasília: Embrapa. p. 356, 2018.

THERNEAU, T.; ATKINSON, B. _rpart: Recursive Partitioning and Regression Trees_. R package version 4.1.19, 2022. <https://CRAN.R-project.org/package=rpart>

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Published

16-08-2023

How to Cite

Konrad, M., Cargnelutti Filho, A., Loro, M. V., Reis, M. B. dos ., Ortiz, V. M., Andretta, J. A., & Schuller, B. R. (2023). Prediction of fresh and dry matter of the aerial part of the plant teosinte as a function of morphological traits. Sigmae, 12(3), 10–17. Retrieved from https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/2214

Issue

Vol. 12 No. 3 (2023): Special issue: 67th RBRAS and 20th SEAGRO

Section

Applied Statistics

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