Autor(es):
Lima, Elizeu de S. ; Souza, Zigomar M. de ; Oliveira, Stanley R. de M. ; Montanari, Rafael [UNESP] ; Farhate, Camila V. V. [UNESP]
Data: 2022
Identificador Persistente: http://hdl.handle.net/11449/218601
Origem: Oasisbr
Assunto(s): Physicochemical variables of soil; machine learning; soil phosphorus content; soil moisture; exchangeable aluminum; Physicochemical variables of soil; Physicochemical variables of soil; machine learning; machine learning; soil phosphorus content; soil phosphorus content; soil moisture; soil moisture; exchangeable aluminum; exchangeable aluminum
Descrição
Made available in DSpace on 2022-04-28T17:21:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01
Eucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning has made significant advances in most varied fields of agrarian sciences. In this context, this study aimed to use physicochemical variables of the soil as well as climatic and dendrometric variables of eucalyptus to predict its height using the random forest algorithm. The study was conducted in the municipality of Tres Lagoas, in Mato Grosso do Sul, Brazil. The original database consisted of 49 soil physicochemical variables collected at 0-0.20 m and 0.20-0.40 m, two dendrometric and four climatic variables, and one response variable related to the height of eucalyptus. A correlation matrix was applied to select variables. Furthermore, modeling was performed using the random forest algorithm, which performed well (r = 0.98, R-2 = 0.96) in predicting the height of eucalyptus. Overall, the most important variables to predict the eucalyptus plant height included diameter at breast height (DBH), phosphorus content (P1), gravimetric moisture (GM1) at a soil depth between 0.00 m and 0.20 m, and exchangeable aluminum content (Al2) between 0.20 m to 0.40 m of soil.
Univ Estadual Paulista, Fac Ciencias Agr & Vet, Jaboticabal, SP, Brazil
Univ Estadual Campinas, Fac Engn Agr, Campinas, SP, Brazil
Embrapa Agr Digital, Campinas, SP, Brazil
Univ Estadual Paulista, Fac Engn, Ilha Solteira, SP, Brazil
Univ Estadual Paulista, Fac Ciencias Agr & Vet, Jaboticabal, SP, Brazil
Univ Estadual Paulista, Fac Engn, Ilha Solteira, SP, Brazil
