Data/hora: 16/06/2024 06:47:52

Provedor de dados: 	70	
País: 	Brazil	
Título: 	GAUSSIAN SPATIAL LINEAR MODEL OF SOYBEAN YIELD USING BOOTSTRAP METHODS	
Autores: 	Dalposso,Gustavo H.; Uribe-Opazo,Miguel A.; Johann,Jerry A.; Galea,Manuel; Bastiani,Fernanda De.	
Data: 	2018-01-01	
Ano: 	2018	
Palavras-chave: 	Geostatistics; Spatial resampling; Spatial variability.	
Resumo: 	ABSTRACT This study aims to quantify the uncertainties associated to the parameters of a Gaussian spatial linear model (GSLM) and the assumption of normality residuals in the modeling of the spatial dependence of the soybean yield as a function of soil chemical attributes. The spatial bootstrap methods were used to determine the point and interval estimators associated with the model parameters. Hypothesis tests were carried out on the significance of the model parameters and the quantile-quantile probability plot was elaborated to verify the data normality. The uncertainties associated to the parameters of the spatial dependence structure were quantified and the potassium content, phosphorus content and soil pH covariates were significant to explain the soybean yield mean. These covariates were used in the elaboration of a new model, which provided the elaboration of a contour map of soybean yield. Analysis of the quantile-quantile plot indicated that soybean yield data follow a normal probability distribution.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100110	
Editor: 	Associação Brasileira de Engenharia Agrícola	
Relação: 	10.1590/1809-4430-eng.agric.v38n1p110-116/2018	
Formato: 	text/html	
Fonte: 	Engenharia Agrícola  v.38 n.1 2018	
Fonte: 	info:eu-repo/semantics/openAccess