scholarly journals Spatial dependence in experiments of progeny selection for bean ( Phaseolus vulgaris L.) yield

Revista CERES ◽  
2016 ◽  
Vol 63 (4) ◽  
pp. 477-485
Author(s):  
Michele Jorge da Silva ◽  
Antonio Policarpo Souza Carneiro ◽  
Andréia Luiza Gonzaga Feres ◽  
José Eustáquio Souza Carneiro ◽  
Nerilson Terra Santos ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Field Experiments ◽  
Model Parameters ◽  
Ratio Test ◽  
Geostatistical Model ◽  
Breeding Programs ◽  
Lattice Design ◽  
Geostatistical Models ◽  
Watson Test

ABSTRACT In field experiments, it is often assumed that errors are statistically independent, but not always this condition is met, compromising the results. An inappropriate choice of the analytical model can compromise the efficiency of breeding programs in preventing unpromising genotypes from being selected and maintained in the next selection cycles resulting in waste of time and resources. The objective of this study was to evaluate the spatial dependence of errors in experiments evaluating grain yield of bean progenies using analyses in lattice and randomized blocks. And also evaluate the efficiency of geostatistical models to describe the structure of spatial variability of errors. The data used in this study derived from experiments arranged in the lattice design and analyzed as lattice or as randomized blocks. The Durbin-Watson test was used to verify the existence of spatial autocorrelation. The theoretical semivariogram was fitted using geostatistical models (exponential, spherical and Gaussian) to describe the spatial variability of errors. The likelihood ratio test was applied to assess the significance of the geostatistical model parameters. Of the eight experiments evaluated, five had moderate spatial dependence for the randomized blocks analysis and one for both analyses, in lattice and randomized blocks. The area of the experiments was not a determinant factor of the spatial dependence. The spherical, exponential and Gaussian geostatistical models with nugget effect were suitable to represent the spatial structure in the randomized block analysis. The analysis in lattice was efficient to ensure the independence of errors.

Spatial Probabilistic Modeling of Corrosion in Ship Structures

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Jesus Luque ◽  
Rainer Hamann ◽  
Daniel Straub
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Measurement Data ◽  
Corrosion Process ◽  
Model Parameters ◽  
Plate Element ◽  
Structural Element ◽  
Ship Structures ◽  
Operational Conditions ◽  
Corrosion Model

Corrosion in ship structures is influenced by a variety of factors that are varying in time and space. Existing corrosion models used in practice only partially address the spatial variability of the corrosion process. Typical estimations of corrosion model parameters are based on averaging measurements for one ship type over structural elements from different ships and operational conditions. Most models do not explicitly predict the variability and correlation of the corrosion process among multiple locations in the structure. This correlation is of relevance when determining the necessary inspection coverage, and it can influence the reliability of the ship structure. In this paper, we develop a probabilistic spatiotemporal corrosion model based on a hierarchical approach, which represents the spatial variability and correlation of the corrosion process. The model includes as hierarchical levels vessel–compartment–frame–structural element–plate element. At all levels, variables representing common influencing factors (e.g., coating life) are introduced. Moreover, at the lowest level, which is the one of the plate element, the corrosion process can be modeled as a spatial random field. For illustrative purposes, the model is trained through Bayesian analysis with measurement data from a group of tankers. In this application, the spatial dependence among corrosion processes in different parts of the ships is identified and quantified using the proposed hierarchical model. Finally, how this spatial dependence can be exploited when making inference on the future condition of the ships is demonstrated.

scholarly journals LOG-VEROSSIMILHANÇA COMBINADA PARA COMPARAÇÃO DE ESTRUTURAS DE CONTINUIDADE ESPACIAL EM FLORESTA ESTACIONAL DECIDUAL

FLORESTA ◽  
2015 ◽  
Vol 45 (4) ◽  
pp. 797
Author(s):  
Julio Cesar Wojciechowski ◽  
Julio Eduardo Arce ◽  
Saulo Henrique Weber ◽  
Paulo Justiniano Ribeiro Júnior ◽  
Carlos Alberto da Fonseca Pires
Keyword(s):  
Spatial Dependence ◽  
Deciduous Forest ◽  
Geostatistical Model ◽  
Post Intervention ◽  
Spatial Continuity ◽  
Log Likelihood ◽  
Geostatistical Models ◽  
Akaike Criterion ◽  
Spatial Volume ◽  
Fragmented Forests

O presente estudo teve como objetivo verificar a dependência espacial e distribuição do volume em três fragmentos de Floresta Estacional Decidual, geograficamente separados e com idades pós-intervenção distintas, utilizando um modelo geoestatístico único ou combinado. Os dados foram coletados em 56 unidades amostrais de 250 m2, distribuídas sistematicamente em uma malha de 40 x 40 m, onde foram medidos os indivíduos com DAP ≥ 10 cm a partir do centro da unidade conforme metodologia descrita por Prodan. Os dados foram submetidos a dois tipos de análise, sendo o primeiro um ajuste individual das áreas a título de comparação entre seus modelos e o segundo, um ajuste proposto pelo método combinado, ambos utilizando modelos geoestatísticos, com ajuste pela função da maximização do logaritmo da verossimilhança. Os modelos foram comparados pelo critério de informação de Akaike (AIC) e a relação do parâmetro alcance como indicação do grau de dependência espacial. Os resultados mostram que os modelos combinados foram superiores em relação aos ajustes dos modelos para as áreas individuais. Indica-se a aplicação de modelos geoestatísticos de log-verossimilhança combinados em formações florestais fragmentadas para uma melhor análise e detecção da estrutura de correlação espacial do volume. AbstractCombined log-likelihood for comparison of spatial continuity structures in deciduous forest. The present study aimed to examine spatial dependence and distribution of volume in three fragments of Subtropical forest, geographically separated and the different post-intervention ages, using a single geostatistical model or combined model. Data were collected from 56 sampling units of 250 m2 systematically distributed in a grid of 40 x 40 m. Trees with DBH ≥ 10 cm were measured according to Prodan’s methodology. Two types of analysis were applied to the data. The first one was an individual adjustment for comparison between their models and the second one consisted in the proposed combined approach adjustment. Both analysis used geostatistical models with adjustment function maximizing log-likelihood. Models were compared using Akaike criterion (AIC) and relational range parameter as an indication of spatial dependence degree. Results show that combined models had lower AIC values as well as greater spatial dependence degree on adjustments of individual areas models. This research indicates the use of combined log-likelihood geostatistical models to study fragmented forests for analysis and detection of spatial volume correlation structure.Keywords: Geostatistics; forest inventory; mixed models; Akaike criterion.

scholarly journals Defining lithic patterns within river-dominated delta deposits for geostatistical simulation

2020 ◽  
Vol 27 (1) ◽  
pp. petgeo2019-105
Author(s):  
Brian J. Willis ◽  
Subhash Kalla ◽  
Tao Sun
Keyword(s):  
Reservoir Simulation ◽  
Process Model ◽  
Geostatistical Simulation ◽  
Rock Properties ◽  
Model Parameters ◽  
Geostatistical Model ◽  
Geostatistical Methods ◽  
Reservoir Development ◽  
Geostatistical Models ◽  
The Impact

Reservoir development forecasts depend on accurate descriptions of the spatial distribution of rock properties that impact subsurface fluid-flow pathways and volume connectivity. Reservoir models constructed using geostatistical methods combine analogous facies dimension data with sparse subsurface data to predict spatial variations in rock properties. This study uses a physics-based depositional process model to define realistic facies variations within a river-dominated delta deposit formed during multiple shoreline regressions and transgressions. Geostatistical models are conditioned to varying amounts of information extracted from the depositional model to examine how well they reproduce the facies patterns. Reservoir simulation is used to examine the impact of analogous dimension data and varying conditioning constraints on reservoir performance predictions of water displacing oil. The dimensions of surface depositional features underestimate the continuity of preserved facies patterns, proportional grids following major flooding surfaces allow significantly better predictions than uniform rectangular grids, and trend constraints are more important when defined facies correlation length is significantly less than well spacing. When geostatistical model parameters are poorly chosen, reservoir simulation of the resulting weakly-structured facies patterns overpredict recovery and water breakthrough time. It is demonstrated that process-based depositional models can be used to optimize geostatistical model construction methods and input parameters to reduce uncertainty of reservoir development assessments.

Uncertainty in the model parameters due to spatial variability of rainfall

1999 ◽  
Vol 220 (1-2) ◽  
pp. 48-61 ◽  
Author(s):  
I. Chaubey ◽  
C.T. Haan ◽  
S. Grunwald ◽  
J.M. Salisbury
Keyword(s):  
Spatial Variability ◽  
Model Parameters

scholarly journals Temporal stability of electrical conductivity in a sandy soil

2016 ◽  
Vol 30 (3) ◽  
pp. 349-357 ◽  
Author(s):  
Aura Pedrera-Parrilla ◽  
Eric C. Brevik ◽  
Juan V. Giráldez ◽  
Karl Vanderlinden
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Precision Agriculture ◽  
Temporal Stability ◽  
Principal Component ◽  
Model Parameters ◽  
Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

scholarly journals Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Gabriel Soropa ◽  
Olton M. Mbisva ◽  
Justice Nyamangara ◽  
Ermson Z. Nyakatawa ◽  
Newton Nyapwere ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Ph ◽  
Strong Evidence ◽  
Spatial Dependence ◽  
Random Component ◽  
Smallholder Farming ◽  
Mineral N ◽  
Spatial Trend ◽  
Spatial Trends

AbstractA study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe.

Effect of Savanna windrow wood burning on the spatial variability of soil properties

2021 ◽  
Vol 51 ◽  
Author(s):  
Diogo Neia Eberhardt ◽  
Robélio Leandro Marchão ◽  
Pedro Rodolfo Siqueira Vendrame ◽  
Marc Corbeels ◽  
Osvaldo Guedes Filho ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Spatial Arrangement ◽  
Nugget Effect ◽  
Tropical Savannas ◽  
Geostatistical Methods ◽  
Exchangeable Ca ◽  
Spherical Models

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.

scholarly journals Elasto-plastic-adhesive DEM model for simulating hillslope debris flows: cross comparison with field experiments

2018 ◽  
Author(s):  
Adel Albaba ◽  
Massimiliano Schwarz ◽  
Corinna Wendeler ◽  
Bernard Loup ◽  
Luuk Dorren
Keyword(s):  
Numerical Model ◽  
Flow Velocity ◽  
Debris Flows ◽  
Field Experiments ◽  
Initial Conditions ◽  
Experimental Tests ◽  
Height Velocity ◽  
Model Parameters ◽  
Fine Content ◽  
Adhesive Model

Abstract. This paper presents a Discrete Element-based elasto-plastic-adhesive model which is adapted and tested for producing hillslope debris flows. The numerical model produces three phases of particle contacts: elastic, plastic and adhesion. The model capabilities of simulating different types of cohesive granular flows were tested with different ranges of flow velocities and heights. The basic model parameters, being the basal friction (ϕb) and normal restitution coefficient (ϵn), were calibrated using field experiments of hillslope debris flows impacting two sensors. Simulations of 50 m3 of material were carried out on a channelized surface that is 41 m long and 8 m wide. The calibration process was based on measurements of flow height, flow velocity and the pressure applied to a sensor. Results of the numerical model matched well those of the field data in terms of pressure and flow velocity while less agreement was observed for flow height. Those discrepancies in results were due in part to the deposition of material in the field test which are not reproducible in the model. A parametric study was conducted to further investigate that effect of model parameters and inclination angle on flow height, velocity and pressure. Results of best-fit model parameters against selected experimental tests suggested that a link might exist between the model parameters ϕb and ϵn and the initial conditions of the tested granular material (bulk density and water and fine contents). The good performance of the model against the full-scale field experiments encourages further investigation by conducting lab-scale experiments with detailed variation of water and fine content to better understand their link to the model's parameters.

scholarly journals GEOSTATISTICAL MODELING OF TIMBER VOLUME SPATIAL VARIABILITY FOR Tectona grandis L. F. PRECISION FORESTRY

CERNE ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Allan Libanio Pelissari ◽  
Marcelo Roveda ◽  
Sidney Fernando Caldeira ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Tectona Grandis ◽  
Wood Products ◽  
Geostatistical Modeling ◽  
Timber Volume ◽  
Silvicultural Treatments ◽  
Punctual Kriging ◽  
Precision Forestry ◽  
Taper Models

ABSTRACT Considering the hypothesis that the wood volumes present spatial dependence, whose knowledge contributes for the precision forestry, the aim of this work was to estimate the volume spatial variability for timber assortments and identify their spatial patterns on Tectona grandis stands. A dataset of 1,038 trees was used to fit taper models and estimate the total stem, sawlog, and firewood volumes in 273 plots allocated on T. grandis stands at eight years old, which represents the second thinning that enables commercial volumes. Semivariograms models was applied to fit the spatial dependence, and punctual kriging was used to compose volume maps. Geostatistical modeling allowed us to estimate the T. grandis spatial variability and develop timber volume maps. Thus, silvicultural treatments, such as thinning and pruning, as well as for planning spatial interventions, are possible to be recommended for aimed wood products.

scholarly journals Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

2018 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Julian Koch ◽  
Gorka Mendiguren ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Performance Measures ◽  
Sampling Strategy ◽  
Behavioral Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

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