Estatística multivariada aplicada à diminuição do número de preditores no mapeamento digital do solo

Caten, Alexandre ten; Dalmolin, Ricardo Simão Diniz; Pedron, Fabrício de Araújo; Mendonça-Santos, Maria de Lourdes

doi:10.1590/s0100-204x2011000500014

Cited by 8 publications

(9 citation statements)

References 17 publications

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“…The results justify the use of PCA, as there was a decrease in the number of variables to be interpreted and an increase in the discriminating power of each new variable (PC) (TEN CATEN et al, 2011), despite the loss of 17.48% of climatic data.…”

Section: Discussionmentioning

confidence: 62%

“…Principal component analysis (PCA) was performed to summarize the number of climatic variables and to, thus, increase the discriminating power of each new variable, which are the principal components (PCs). Since the PCA was performed using standardized data (average equal to zero and variance equal to one), only PCs with an eigenvalue greater than 1 were significant, since this is the variance of each variable individually (TEN CATEN et al, 2011). The PCs were considered significant when the absolute values were higher than 0.7 (JOLLIFFE, 1973).…”

Section: Discussionmentioning

confidence: 99%

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Effect of climatic conditions on the occurrence of sheep endoparasites

et al. 2019

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Article history This study evaluated the effect of climatic conditions on the occurrence of Eimeria sp., strongylids and Moniezia sp. by analyzing the excretion of eggs and oocysts in the feces of sheep. In all, 3,509 coproparasitological examinations were performed after the monthly collection of feces of 121 sheep for 29 months. The meteorological data collected were rainfall, insolation, evapotranspiration, relative humidity, and temperature. Principal component analysis was performed to summarize the number of climatic variables. To evaluate the climatic vectors that influenced the variables, Eimeria sp., strongylids and Moniezia sp., a spatial projection of the ordination of vectors on the first two principal components was performed. To evaluate the mean values of the variables, Eimeria sp., strongylids and Moniezia sp., a cluster analysis was performed (k-means clustering), according to the categories. An association of Eimeria sp. and strongylids with relative humidity and rainfall was observed. Moniezia sp. was also related to the minimum temperature. The prevalence of Moniezia sp. was the least affected by relative humidity, rainfall, insolation, and evapotranspiration. The highest counts of endoparasites were observed in the offspring category and in the months of higher rainfall.

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Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Effect of climatic conditions on the occurrence of sheep endoparasites

et al. 2019

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“…The performance gain in MLR models, already in the selection of covariates by correlation, excluding those with highly correlated (Figure 3), is probably due to the multicollinearity problem (Kempen et al, 2009;ten Caten et al, 2011), which significantly affects the model performance. The MLR with several covariates showed a tendency to have the worst performance because of its effect of harmful multicollinearity in the parametric models; thereby impairing the model.…”

Section: -D Approachmentioning

confidence: 99%

Mapping soil properties in a poorly-accessible area

Costa¹,

Pinheiro²,

Anjos³

et al. 2020

Revista Brasileira De Ciência Do Solo

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Soil maps are important to evaluate soil functions and support decision-making process, particularly for soil properties such as pH, carbon content (C), and cation exchange capacity (CEC), but the spatial resolution and soil depth should meet the needs of users. On another hand, the efficiency of statistical models to create soil maps, with an acceptable level of accuracy, often require a large number of samples with an appropriate distribution across the area of interest. However, accessibility for sampling can be a trouble in remote areas, such as the Itatiaia National Park (INP). The hypothesis of this work is that it is possible to obtain a viable result in soil mapping of areas with limited access by using DSM tools. The general objective of this paper was to create 2-and 3-D maps of the soil properties pH, carbon content, and CEC, with the correspondent spatial uncertainty, in the INP plateau. The sampling strategy was designed using conditioned Latin Hypercube Sample (cLHS), and different methods were tested to produce the soil properties maps. For calibration of the models: linear (MLR, multiple linear regression) and nonlinear (GAM, Generalised Additive Models). The results showed differences in predictive performance for all statistical methods and covariate selection approaches. The GAM, with covariates selection based on soil formation factors, was the best method for the limited number of soil samples. The greatest uncertainty was associated with areas with the lowest accessibility and, consequently, with low sampling density and/or noises in covariates. Even though the 2-and 3-D maps of soil properties, each associated with explicit uncertainty, can contribute to the INP decision makers/ managers by providing information not available before.

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“…Analisando-se a Figura 3 observa-se que as variáveis que estiverem mais próximas ao círculo unitário, possuem uma maior contribuição em relação àquelas que estiverem mais afastadas. De acordo com Caten et al (2011), também deve ser observado o ângulo formado entre duas variáveis, demonstrando maior ou menor correlação entre as mesmas. Assim, TER e SEC; e CSIM e PCVU contribuem fortemente na análise e possuem correlação significativa, devido ao pequeno ângulo formado entre estas variáveis.…”

Section: Análise De Fatores Para Os Custos Da Fase De Pós-colheita Dounclassified

Estatística multivariada aplicada em dados de custos da fase de pós-colheita do café

Santos¹,

Vieira²,

Borém³

et al. 2017

C.Sci.

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RESUMO:A escolha do modo de processamento do café é decisiva na rentabilidade da atividade cafeeira, e dependerá de diversos fatores. Assim, em decorrência da existência de tantas variáveis é comum o produtor questionar a viabilidade de determinados tipos de processamentos. Desse modo, o objetivo deste trabalho foi realizar um estudo dos principais fatores influenciadores no custo da pós-colheita do café. Quarenta e seis fazendas das regiões do Cerrado, Matas de Minas e Sul de Minas Gerais responderam a um questionário elaborado no sentido de possibilitar esta análise. A aplicação das técnicas multivariadas de análise de agrupamento, análise de fatores e análise de componentes principais, possibilitou concluir que as fazendas com maiores custos simulados foram as que possuíam as maiores porcentagens de produção de café via úmida.Termos para indexação: Agrupamento, componentes principais, análise de fatores. MULTIVARIATE STATISTICAL APPLIED TO DATA COSTS POST-HARVEST COFFEE

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Estatística multivariada aplicada à diminuição do número de preditores no mapeamento digital do solo

Cited by 8 publications

References 17 publications

Effect of climatic conditions on the occurrence of sheep endoparasites

Effect of climatic conditions on the occurrence of sheep endoparasites

Mapping soil properties in a poorly-accessible area

Estatística multivariada aplicada em dados de custos da fase de pós-colheita do café

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