scholarly journals Behavior of Soil Chemical Attributes in Field-Forest Succession in Southern Amazonas

2020 ◽  
Vol 8 (3) ◽  
pp. 807
Author(s):  
Maria Clécia Gomes Sales ◽  
Elilson Gomes de Brito Filho ◽  
Milton César Costa Campos ◽  
José Maurício da Cunha ◽  
Guilherme Abadia Silva ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Random Distribution ◽  
Forest Succession ◽  
Spherical Model ◽  
Natural Field ◽  
Geostatistical Methods ◽  
Sample Spacing ◽  
Chemical Attributes ◽  
And Behavior ◽  
Field Area

The use of geostatistical methods in the identification of the size and structure of the spatial variability of soil chemical attributes has been a very important tool in the evaluation and behavior of soil attributes. This research aimed to evaluate the spatial variability of chemical attributes in natural field and forest areas, in the Humaitá region (AM). In these areas, meshes with dimensions of 70 m x 70 m were established at regular intervals of 10 minutes in the 0.0-0.2 m layers, totaling 64 samples per layer. It was determined: soil pH, phosphorus (P), potassium (K+), calcium (Ca2+), magnesium (Mg2+), aluminum (Al3+) and potential acidity (H++Al3+). Base saturation (V%) and sum of bases (SB) were calculated. The data were evaluated by descriptive statistics and spatial dependence analysis, based on the best models and semivariograms adjustment. The chemical attributes are spatially dependent, they present random distribution of ideal sample spacing, considering that the variables that showed dependence were adjusted to the exponential and spherical model. Geostatistic was presented as an appropriate tool, providing information that allows the understanding of the spatial distribution. The degree of dependence was strong and moderate. The highest reaches were recorded in the natural field area.

scholarly journals Spatial variability of soil chemical properties after coffee tree removal

2009 ◽  
Vol 33 (5) ◽  
pp. 1507-1514 ◽  
Author(s):  
Sidney Rosa Vieira ◽  
Osvaldo Guedes Filho ◽  
Márcio Koiti Chiba ◽  
Heitor Cantarella
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Crop Yields ◽  
Management Strategies ◽  
Chemical Properties ◽  
Spherical Model ◽  
Soil Chemical Properties ◽  
Kriging Interpolation ◽  
Geostatistical Methods ◽  
Plant Removal

Assessing the spatial variability of soil chemical properties has become an important aspect of soil management strategies with a view to higher crop yields with minimal environmental degradation. This study was carried out at the Centro Experimental of the Instituto Agronomico, in Campinas, São Paulo, Brazil. The aim was to characterize the spatial variability of chemical properties of a Rhodic Hapludox on a recently bulldozer-cleaned area after over 30 years of coffee cultivation. Soil samples were collected in a 20 x 20 m grid with 36 sampling points across a 1 ha area in the layers 0.0-0.2 and 0.2-0.4 m to measure the following chemical properties: pH, organic matter, K+, P, Ca2+, Mg2+, potential acidity, NH4-N, and NO3-N. Descriptive statistics were applied to assess the central tendency and dispersion moments. Geostatistical methods were applied to evaluate and to model the spatial variability of variables by calculating semivariograms and kriging interpolation. Spatial dependence patterns defined by spherical model adjusted semivariograms were made for all cited soil properties. Moderate to strong degrees of spatial dependence were found between 31 and 60 m. It was still possible to map soil spatial variability properties in the layers 0-20 cm and 20-40 cm after plant removal with bulldozers.

scholarly journals Spatial and multivariate analysis of soybean productivity and soil physical-chemical attributes

2019 ◽  
Vol 23 (6) ◽  
pp. 446-453
Author(s):  
Ricardo N. Buss ◽  
Raimunda A. Silva ◽  
Glécio M. Siqueira ◽  
Jairo O. R. Leiva ◽  
Osmann C. C. Oliveira ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Carbon Stock ◽  
Management Strategies ◽  
Covariance Structure ◽  
Principal Component ◽  
Spherical Model ◽  
Soybean Yield ◽  
Soil Attributes ◽  
Physical Attributes ◽  
Chemical Attributes

ABSTRACT The objective of this study was to evaluate the spatial variability of soybean yield, carbon stock, and soil physical attributes using multivariate and geostatistical techniques. The attributes were determined in Oxisols samples with clayey and cohesive textures collected from the municipality of Mata Roma, Maranhão state, Brazil. In the study area, 70 sampling points were demarcated, and soybean yield and soil attributes were evaluated at soil depths of 0-0.20 and 0.20-0.40 m. Data were analysed using multivariate analyses (principal component analysis, PCA) and geostatistical tools. The mean soybean yield was 3,370 kg ha-1. The semivariogram of productivity, organic carbon (OC), and carbon stock (Cst) at the 0-0.20 m layer were adjusted to the spherical model. The PCA explained 73.21% of the variance and covariance structure between productivity and soil attributes at the 0-0.20 m layer [(PCA 1 (26.89%), PCA 2 (24.10%), and PCA 3 (22.22%)] and 68.64% at the 0.20-0.40 m layer [PCA 1 (31.95%), PCA 2 (22.83%), and PCA 3 (13.85%)]. The spatial variability maps of the PCA eigenvalue scores showed that it is possible to determine management zones using PCA 1 in the two studied depths; however, with different management strategies for each of the layers in this study.

scholarly journals Spatial variability of Regosol chemical atributes in guava management with neem under semi-arid conditions

2016 ◽  
Vol 20 (7) ◽  
pp. 618-624 ◽  
Author(s):  
Douglas B. Castro ◽  
Elvira M. R. Pedrosa ◽  
Abelardo A. A. Montenegro ◽  
Mario M. Rolim ◽  
Diego A. H. S. Leitão ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Gaussian Model ◽  
Spherical Model ◽  
Psidium Guajava ◽  
Northeastern Brazil ◽  
Soil C ◽  
Neem Cake ◽  
Chemical Attributes ◽  
Semi Arid Region ◽  
Semi Arid

ABSTRACT Considering the relevant importance of guava (Psidium guajava) in Northeastern Brazil along with the benefits of neem cake amendments on soil characteristics, this work evaluated the effects of neem cake on chemical attributes of a Regosol under irrigated guava orchard in an alluvial valley of Pernambuco semi-arid region. Evaluations were carried out in two areas (area 1 - with neem cake; area 2 - without neem cake) at three periods: before the first application of neem cake, 90 days after the first application and 90 days after the second application. A regular 8 × 6-point grid was designed in each area and the soil was sampled for total organic carbon, pH, soluble salts (Na+, K+, Ca2+ and Mg2+) and total nitrogen contents, as well as soil C-CO2 evolution rate in soil. Geostatistical analysis pointed out the spherical model as the best fit to the studied variables, followed by the Gaussian model, with ranges from 12 to 60.5 m. Neem cake incorporation increased spatial variability and the contents of the evaluated soil chemical attributes.

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 Magnetic susceptibility for characterizing areas with different potentials for sugarcane production

2016 ◽  
Vol 51 (9) ◽  
pp. 1349-1358 ◽  
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.

Spatial dynamics of soil moisture and temperature in a black spruce boreal chronosequence

2006 ◽  
Vol 36 (11) ◽  
pp. 2794-2802 ◽  
Author(s):  
Ben Bond-Lamberty ◽  
Karen M Brown ◽  
Carol Goranson ◽  
Stith T Gower
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Sampling Design ◽  
Black Spruce ◽  
Structural Characteristics ◽  
Spatial Dynamics ◽  
Spherical Model ◽  
Local Scale ◽  
Spatial Dependencies ◽  
The Mean

This study analyzed the spatial dependencies of soil moisture and temperature in a six-stand chronosequence of boreal black spruce (Picea mariana (Mill.) BSP) stands. Spatial variability of soil temperature (TSOIL) was evaluated twice during the growing season using four transects in each stand, employing a cyclic sampling design with measurements spaced 2–92 m apart. Soil moisture (θg) was measured on one occasion. A spherical model was used to analyze the geostatistical correlation structure; θg and TSOIL at the 7- and 21-year-old stands did not exhibit stable ranges or sills. The fits with stable ranges and sills modeled the spatial patterns in the older stands reasonably well, although unexplained variability was high. Calculated ranges varied from 3 to 150 m for these stands, lengths probably related to structural characteristics influential in local-scale energy transfer. Transect-to-transect variability was significant and typically 5%–15% of the mean for TSOIL and 10%–70% for θg. TSOIL and θg were negatively correlated for most stands and depths, with TSOIL dropping 0.5–0.9 °C for every 1% rise in θg. The results reported here provide initial data to assess the spatial variability of TSOIL and θg in a variety of boreal forest stand ages.

scholarly journals Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

10.29007/glj1 ◽  
2019 ◽  
Author(s):  
Felipe-Omar Tapia-Silva
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Field Measurements ◽  
Auxiliary Variable ◽  
Measured Parameter ◽  
Geostatistical Methods ◽  
Variability Pattern ◽  
Distance Weighted ◽  
Precipitation Estimates ◽  
Leave One Out

Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

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

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.

Spatial variability analysis and mapping of soil physical and chemical attributes in a salt-affected soil

2019 ◽  
Vol 12 (3) ◽  
Author(s):  
Masoomeh Delbari ◽  
Peyman Afrasiab ◽  
Bahram Gharabaghi ◽  
Meysam Amiri ◽  
Armand Salehian
Keyword(s):  
Spatial Variability ◽  
Variability Analysis ◽  
Salt Affected Soil ◽  
Chemical Attributes ◽  
Physical And Chemical

scholarly journals Spatial variability of soil fertility and its relation with cocoa yield

2017 ◽  
Vol 21 (2) ◽  
pp. 88-93 ◽  
Author(s):  
Railton O. dos Santos ◽  
◽  
Laís B. Franco ◽  
Samuel A. Silva ◽  
George A. Sodré ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Spatial Variability ◽  
Soil Fertility ◽  
Soil Properties ◽  
Crop Yield ◽  
Chemical Properties ◽  
Western Region ◽  
Chemical Attributes ◽  
Cocoa Yield

ABSTRACT The knowledge on the spatial variability of soil properties and crops is important for decision-making on agricultural management. The objective of this study was to evaluate the spatial variability of soil fertility and its relation with cocoa yield. The study was conducted over 14 months in an area cultivated with cocoa. A sampling grid was created to study soil chemical properties and cocoa yield (stratified in season, off-season and annual). The data were analyzed using descriptive and exploratory statistics, and geostatistics. The chemical attributes were classified using fuzzy logic to generate a soil fertility map, which was correlated with maps of crop yield. The soil of the area, except for the western region, showed possibilities ranging from medium to high for cocoa cultivation. Soil fertility showed positive spatial correlation with cocoa yield, and its effect was predominant only for the off-season and annual cocoa.

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