Data/hora: 27/07/2024 02:43:12

Provedor de dados: 	Scientia Agricola	
País: 	Brazil	
Título: 	Identification of patterns for increasing production with decision trees in sugarcane mill data	
Autores: 	Peloia,Paulo Rodrigues; Bocca,Felipe Ferreira; Rodrigues,Luiz Henrique Antunes.	
Data: 	2019-07-01	
Ano: 	2019	
Palavras-chave: 	Data mining; Yield variability; Regression tree; Knowledge discovery.	
Resumo: 	ABSTRACT: Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001400281	
Editor: 	São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"	
Relação: 	10.1590/1678-992x-2017-0239	
Formato: 	text/html	
Fonte: 	Scientia Agricola  v.76 n.4 2019	
Fonte: 	info:eu-repo/semantics/openAccess