A genetic algorithm for resizing and sampling reduction of non-stationary soil chemical attributes optimizing spatial prediction

Aim of study: To evaluate the influence of the parameters of the geostatistical model and the initial sample configuration used in the optimization process; and to propose and evaluate the resizing of a sample configuration, reducing its sample size, for simulated data and for the study of the spatial variability of soil chemical attributes under a non-stationary with drift process from a commercial soybean cultivation area. Area of study: Cascavel, Brazil Material and methods: For both, the simulated data and the soil chemical attributes, the Genetic Algorithm was used for sample resizing, maximizing the overall accuracy measure. Main results: The results obtained from the simulated data showed that the practical range did not influence in a relevant way the optimization process. Moreover, the local variations, such as variance or sampling errors (nugget effect), had a direct relationship with the reduction of the sample size, mainly for the smaller nugget effect. For the soil chemical attributes, the Genetic Algorithm was efficient in resizing the sampling configuration, since it generated sampling configurations with 30 to 35 points, corresponding to 29.41% to 34.31% of the initial configuration, respectively. In addition, comparing the optimized and initial configurations, similarities were obtained regarding spatial dependence structure and characterization of spatial variability of soil chemical attributes in the study area. Research highlights: The optimization process showed that it is possible to reduce the sample size, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in future experiments.

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Spatial variability and sampling density of chemical attributes in archaeological black earths under pasture in southern Amazonas, Brazil

10.5194/soil-2019-26 ◽

2019 ◽

Author(s):

Alan F. L. de Lima ◽

Milton C. C. Campos ◽

José M. da Cunha ◽

Laércio S. Silva ◽

Flávio P. de Oliveira ◽

...

Keyword(s):

Spatial Variability ◽

Spatial Dependence ◽

Spatial Behavior ◽

Dependence Structure ◽

Sampling Efficiency ◽

Sampling Density ◽

Agricultural Planning ◽

Chemical Attributes ◽

Spatial Dependence Structure ◽

Potential Acidity

Abstract. Spatial mapping of soil chemical attributes is essential for sampling efficiency and agricultural planning management, ensuring a regional development and sustainability of the unique characteristics of archaeological black earths (ABEs). Thus, this study was developed aiming at assessing the spatial variability and sampling density of chemical attributes in soils of ABEs under pasture in southern Amazonas, Brazil. A sampling grid of 56 × 80 m with regular spacings of 8 m was installed in the experimental area and samples were taken from the crossing points at depths of 0.0–0.05, 0.05–0.10, and 0.10–0.20 m, totaling 264 georeferenced points. The chemical attributes pH in water, organic carbon, Ca, Mg, K, P, Al, and potential acidity were determined in these samples, while CEC, SB, V, t, T, and m were calculated. The attributes present a spatial dependence varying from strong to moderate, being Al3+ the only chemical attribute that does not present a spatial dependence structure in the assessed depths. Scaled semivariograms satisfactorily reproduce the spatial behavior of attributes in the same pattern of individual semivariograms, allowing their use to estimate the variability of soil attributes. Sampling density is higher at a depth of 0.0–0.05 m, requiring 2 and 1 point ha−1 at depths of 0.05–0.10 and 0.10–0.20 m, respectively, to represent the spatial pattern of chemical attributes.

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REDUCTION OF SAMPLE SIZE IN THE ANALYSIS OF SPATIAL VARIABILITY OF NONSTATIONARY SOIL CHEMICAL ATTRIBUTES

Engenharia Agrícola ◽

10.1590/1809-4430-eng.agric.v39nep56-65/2019 ◽

2019 ◽

Vol 39 (spe) ◽

pp. 56-65

Author(s):

Tamara C. Maltauro ◽

Luciana P. C. Guedes ◽

Miguel A. Uribe-Opazo

Keyword(s):

Spatial Variability ◽

Sample Size ◽

Chemical Attributes

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Spatial variability-based sample size allocation for stratified sampling

CATENA ◽

10.1016/j.catena.2021.105509 ◽

2021 ◽

Vol 206 ◽

pp. 105509

Author(s):

Shuangshuang Shao ◽

Huan Zhang ◽

Manman Fan ◽

Baowei Su ◽

Jingtao Wu ◽

...

Keyword(s):

Spatial Variability ◽

Sample Size ◽

Stratified Sampling ◽

Sample Size Allocation

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

Pesquisa Agropecuária Tropical ◽

10.1590/1983-40632021v5166853 ◽

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.

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Magnetic susceptibility for characterizing areas with different potentials for sugarcane production

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2016000900034 ◽

2016 ◽

Vol 51 (9) ◽

pp. 1349-1358 ◽

Cited By ~ 6

Author(s):

Diego Silva Siqueira ◽

José Marques Júnior ◽

Daniel De Bortoli Teixeira ◽

Sammy Sidney Rocha Matias ◽

Livia Arantes Camargo ◽

...

Keyword(s):

Magnetic Susceptibility ◽

Spatial Variability ◽

Exchange Capacity ◽

Strongly Correlated ◽

Soil Attributes ◽

Sugarcane Production ◽

Sugarcane Yield ◽

Chemical Attributes ◽

Physical And Chemical ◽

Variability Model

Abstract The objective of this work was to evaluate the use of magnetic susceptibility for characterizing the spatial variability of soil attributes and identifying areas with different potentials for sugarcane (Saccharum spp.) production. Samples were collected at 110 points (1 per 7 ha) in the layers of 0.00-0.20 and 0.20-0.40 m, to determine the magnetic susceptibility and physical and chemical attributes of the soil. Fiber content, sucrose polarization (POL), and sugarcane yield were determined in 33 points. The spatial variability model for magnetic susceptibility was 63 and 22% more accurate in delimiting soil potential for sugarcane production than soil physical and chemical attributes at the 0.0-0.2 and 0.2-0.4-m layers, respectively. The spatial variability map for magnetic susceptibility was strongly correlated with clay (0.83 and 0.89, respectively, for the layers) and sand contents (-0.84 and -0.88); moderately correlated with organic matter (-0.25 and -0.35), sum of bases (-0.46 and 0.37), cation exchange capacity (0.22 and 0.47), pH (-0.52 and 0.13), and POL (0.43 and 0.53); and weakly correlated with sugarcane yield (0.26 and 0.23). Magnetic susceptibility can be used to characterize the spatial variability of soil attributes and to identify areas with different potentials for sugarcane production.

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The effects of data adequacy and calibration size on the accuracy of presence-only species distribution models

10.1101/775700 ◽

2019 ◽

Author(s):

Truly Santika ◽

Michael F. Hutchinson ◽

Kerrie A. Wilson

Keyword(s):

Performance Measures ◽

Species Distribution ◽

Species Distribution Models ◽

Simulated Data ◽

List Type ◽

Model Accuracy ◽

Distribution Models ◽

Accuracy Measure ◽

Data Coverage ◽

True Distribution

ABSTRACTPresence-only data used to develop species distribution models are often biased towards areas that are frequently surveyed. Furthermore, the size of calibration area with respect to the area covered by the species occurrences has been shown to affect model accuracy. However, existing assessments of the effect of data inadequacy and calibration size on model accuracy have predominately been conducted using empirical studies. These studies can give ambiguous results, since the data used to train and test the model can both be biased.These limitations were addressed by applying simulated data to assess how inadequate data coverage and the size of calibration area affect the accuracy of species distribution models generated by MaxEnt and BIOCLIM. The validity of four presence-only performance measures, Contrast Validation Index (CVI), Boyce index, AUC and AUCratio, was also assessed.CVI, AUC and AUCratio ranked the accuracy of univariate models correctly according to the true importance of their defining environmental variable, a desirable property of an accuracy measure. Contrastingly, Boyce index failed to rank the accuracy of univariate models correctly and a high percentage of irrelevant variables produced models with a high Boyce index.Inadequate data coverage and increased calibration area reduced model accuracy by reducing the correct identification of the dominant environmental determinant. BIOCLIM outperformed MaxEnt models in predicting the true distribution of simulated species with a symmetric dominant response. However, MaxEnt outperformed BIOCLIM in predicting the true distribution of simulated species with skew and linear dominant responses. Despite this, the standard performance measures consistently overestimated the performance of MaxEnt models and showed them as always having higher model accuracy than the BIOCLIM models.It has been acknowledged that research should be directed towards testing and improving species distribution modelling tools, particularly how to handle the inevitable bias and scarcity of species occurrence data. Simulated data, as demonstrated here, provides a powerful approach to comprehensively test the performance of modelling tools and to disentangle the effects of data properties and modelling options on model accuracy. This may be impossible to achieve using real-world data.

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EFFECTS OF SPATIAL VARIABILITY OF SOIL CHEMICAL PARAMETERS ON TIFTON 85 GRASS YIELD1

Revista Caatinga ◽

10.1590/1983-21252020v33n125rc ◽

2020 ◽

Vol 33 (1) ◽

pp. 236-245

Author(s):

EUDOCIO RAFAEL OTAVIO DA SILVA ◽

MURILO MACHADO DE BARROS ◽

MARCOS GERVASIO PEREIRA ◽

JOÃO HENRIQUE GAIA GOMES ◽

STEPHANY DA COSTA SOARES

Keyword(s):

Decision Making ◽

Spatial Variability ◽

Dry Matter ◽

Irregular Sampling ◽

Descriptive Statistics ◽

Dry Matter Yield ◽

Chemical Parameters ◽

Tropical Pastures ◽

Chemical Attributes ◽

Soil Chemical Parameters

ABSTRACT Studies on spatial variability of soil attributes of tropical pastures gather information that can assist in decision making about managements of these soils. The objective of the present study was to evaluate the spatial variability of soil chemical attributes and their effects on grass yield of Tifton 85. The experiment was carried out in an area of 3.91 ha at the Feno Rio Farm of the Federal Rural University of Rio de Janeiro, Seropédica, RJ, Brazil. Soils of the 0-0.20 and 0.20-0.40 m layers were sampled considering an irregular sampling mesh, making a total of 50 georeferenced points. The parameters evaluated were: the soil chemical attributes pH, Al+3, Ca+2, Mg+2, Na+, K+, P, H+Al, and total organic carbon (TOC); and the Tifton 85 dry matter yield (DMY). The results of these parameters were subjected to descriptive statistics, linear correlation, and geostatistics, and maps were developed for the analyses. Regions with grass yields different from the general mean were found in the area, which presented mean grass yield of 2248 kg ha-1. The soil chemical parameters Na+, Ca+2, TOC, and H+Al were significantly correlated with DMY, confirming that they are important and affect the Tifton 85 grass yield. The mapping of the Tifton 85 cycle is important for understanding the variability of DMY. The investigation of areas with different productive potentials should be followed by development of maps of soil chemical attributes to correlate and understand the ratios that may be involved with these variations.

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Use of Correlated Data for Nonparametric Prediction of a Spatial Target Variable

Mathematics ◽

10.3390/math8112077 ◽

2020 ◽

Vol 8 (11) ◽

pp. 2077

Author(s):

Pilar García-Soidán ◽

Tomás R. Cotos-Yáñez

Keyword(s):

Spatial Data ◽

Lead Level ◽

Simulated Data ◽

Auxiliary Variable ◽

Correlated Data ◽

Dependence Structure ◽

Nonparametric Approach ◽

Additional Information ◽

Squared Prediction Error ◽

Kernel Approach

The kriging methodology can be applied to predict the value of a spatial variable at an unsampled location, from the available spatial data. Furthermore, additional information from secondary variables, correlated with the target one, can be included in the resulting predictor by using the cokriging techniques. The latter procedures require a previous specification of the multivariate dependence structure, difficult to characterize in practice in an appropriate way. To simplify this task, the current work introduces a nonparametric kernel approach for prediction, which satisfies good properties, such as asymptotic unbiasedness or the convergence to zero of the mean squared prediction error. The selection of the bandwidth parameters involved is also addressed, as well as the estimation of the remaining unknown terms in the kernel predictor. The performance of the new methodology is illustrated through numerical studies with simulated data, carried out in different scenarios. In addition, the proposed nonparametric approach is applied to predict the concentrations of a pollutant that represents a risk to human health, the cadmium, in the floodplain of the Meuse river (Netherlands), by incorporating the lead level as an auxiliary variable.

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