Spatial Variability of Soybean Yield Through a Reparameterized T-Student Model

Schemmer, Rosangela Carline; Uribe-Opazo, Miguel Ángel; Galea, Manuel; Assumpção, Rosangela Aparecida Botinha

doi:10.1590/1809-4430-eng.agric.v37n4p760-770/2017

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…The increase in the standard error of the parameters after sample resizing indicates that the reduction in the number of sample points influenced the spatial dependence structure of the physical-chemical attributes of the soil (Table 2). Moreover, regarding the standard error values following the magnitude of the estimated parameter, this characteristic is also perceived in the results of Schemmer et al (2017) and Fagundes et al (2018), who used in their studies both the Gaussian linear spatial model (as well as this study), and the Slash and t-Student linear spatial models, applied to variables related to soil and plants.…”

Section: Analysis Of the Physical-chemical Attributes Of The Soilmentioning

confidence: 76%

Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size

Canton

Guedes

Uribe-Opazo

2021

JAS

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Financial investment with collection and laboratory analysis of soil samples is an important factor to be considered when mapping agricultural areas with soybean planting. One of the alternatives is to use the spatial autocorrelation between the sample points to reduce the number of elements sampled, thus restricting the collection of redundant information. This work aimed to reduce the sample size of this agricultural area, composed of 102 sample points, and use it to analyze the spatial dependence of soil macro- and micro- nutrients, as well as the soil penetration resistance. The agricultural area used in this study has 167.35 ha, cultivated with soybean, which the soil is Red Dystroferric Latosol, and the sampling design has used in this agricultural area is the lattice plus close pairs. The reduction of the sample size was made by the multivariate effective sample size (ESSmulti) methodology. The studies with the simulation data and the soil attributes showed an inverse relationship between the practical range and the estimated value of the univariate effective sample size. With the calculation of ESSmulti, the sample configuration was reduced to 53 points. The Overall Accuracy and Tau concordance index showed differences between the thematic maps elaborated with the original and reduced sampling designs. However, the analysis of the variance inflation factor and the standard error of the spatial dependence parameters showed efficient results with the resized sample size.

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Section: Analysis Of the Physical-chemical Attributes Of The Soilmentioning

confidence: 76%

Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size

Canton

Guedes

Uribe-Opazo

2021

JAS

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“…The Σ is used to account for spatial dependencies. Moreover, the covariance matrix can further account for spatially structured and unstructured (nugget) effects [127]. The main challenge of the Student-t process is the difficulty of assigning appropriate prior specification on v to make inferences in a Bayesian analysis.…”

Section: Appendix A4 Class Of Non-gaussian Random Fields Modelsmentioning

confidence: 99%

Spatial Statistical Models: An Overview under the Bayesian Approach

Louzada

Nascimento

Egbon

2021

Axioms

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Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local.

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“…The Σ is used to account for spatial dependencies. Moreover, the covariance matrix can further account for spatially structured and unstructured (nugget) effects [88]. The main challenge of the Student-t process is the difficulty of assigning appropriate prior specification on v to make inferences in a Bayesian analysis.…”

Section: -Multivariate Log-gamma Processmentioning

confidence: 99%

Spatial Statistical Models: an overview under the Bayesian Approach

Louzada¹,

Nascimento²,

Egbon³

2020

Preprint

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Funding informationFrancisco Louzada acknowledges support from the Brazilian institutions FAPESP and CNPq. Osafu Augustine Egbon acknowledges support from the Brazilian institution CAPES. Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local.

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Spatial Variability of Soybean Yield Through a Reparameterized T-Student Model

Cited by 10 publications

References 18 publications

Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size

Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size

Spatial Statistical Models: An Overview under the Bayesian Approach

Spatial Statistical Models: an overview under the Bayesian Approach

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