scholarly journals Mapping of soil micronutrients in an european atlantic agricultural landscape using ordinary kriging and indicator approach

Bragantia ◽  
2010 ◽  
Vol 69 (suppl) ◽  
pp. 175-186 ◽  
Author(s):  
Jorge Dafonte Dafonte ◽  
Montserrat Ulloa Guitián ◽  
Jorge Paz-Ferreiro ◽  
Glécio Machado Siqueira ◽  
Eva Vidal Vázquez
Keyword(s):  
Spatial Distribution ◽  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Management Practices ◽  
Agricultural Landscape ◽  
Interpolation Method ◽  
Indicator Kriging ◽  
Climatic Conditions ◽  
Nugget Effect ◽  
Indicator Approach

Nutrient maps based on intensive soil sampling are useful to develop site-specific management practices. Geostatistical methods have been widely used to determine the spatial correlation and the range of spatial dependence at different sampling scales. If spatial dependence is detected, the modelled semivariograms can then be used to map the interested variable by kriging, an interpolation method that produces unbiased estimates with minimal estimation variance. The objectives of this paper were to examine and to map the spatial distribution of the micronutrients Cu, Zn, Fe and Mn on an agricultural area in Galicia, Spain, under European Atlantic climatic conditions. The ordinary kriging was first used to determine the values for the non-sampled locations, then the indicator approach was used to transform the micronutrient content values into binary values having the mean values of each nutrient as the threshold content. All four elements analyzed showed spatial dependence using the indicator semivariograms. The strength of spatial dependence was assessed using the values of nugget effect and range from the semivariogram, the fitted range values decreased in the order Mn >Fe >Zn >Cu. The spatial dependence of the combination of two or more of the studied micronutrients was also examined using indicator semivariograms. In opposition to spatial analysis of individual microelements, indicator semivariograms obtained for the binary coding of the variables showed a great nugget effect value or a low proportion of sill. The maps for each nutrient obtained using indicator kriging showed some similarity in the spatial distribution, suggesting the delimitation of uniform management areas.

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 Species richness and diversity in shrub savanna using ordinary kriging

2016 ◽  
Vol 51 (8) ◽  
pp. 958-966 ◽  
Author(s):  
Anderson Pedro Bernardina Batista ◽  
José Márcio de Mello ◽  
Marcel Régis Raimundo ◽  
Henrique Ferraço Scolforo ◽  
Aliny Aparecida dos Reis ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Species Richness ◽  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Diversity Index ◽  
Spatial Models ◽  
Shannon Diversity Index ◽  
Shannon Diversity ◽  
Species Richness And Diversity ◽  
Number Of Species

Abstract: The objective of this work was to analyze the spatial distribution and the behavior of species richness and diversity in a shrub savanna fragment, in 2003 and 2014, using ordinary kriging, in the state of Minas Gerais, Brazil. In both evaluation years, the measurements were performed in a fragment with 236.85 hectares, in which individual trees were measured and identified across 40 plots (1,000 m2). Species richness was determined by the total number of species in each plot, and diversity by the Shannon diversity index. For the variogram study, spatial models were fitted and selected. Then, ordinary kriging was applied and the spatial distribution of the assessed variables was described. A strong spatial dependence was observed between species richness and diversity by the Shannon diversity index (<25% spatial dependence degree). Areas of low and high species diversity and richness were found in the shrub savanna fragment. Spatial distribution behavior shows relative stability regarding the number of species and the Shannon diversity index in the evaluated years.

scholarly journals Spatial Distribution of Weed Seed Banks in the Agricultural Field and Anthropized Cerrado

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Klayton Antonio do Lago Lopes ◽  
Marcelo De Sousa Silva ◽  
Leandro Dos Santos Costa ◽  
Taciella Fernandes Silva ◽  
Tiago Vieira da Costa ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Seed Bank ◽  
Ordinary Kriging ◽  
Management Practices ◽  
Total Density ◽  
Agricultural Field ◽  
Weed Seed ◽  
Eleusine Indica ◽  
Distribution Maps

Characterization of the seeds bank is an essential tool for decision making on weed control and management practices and the study and maintenance of the ecological dynamics of natural areas. In this context, the present study aimed to characterize the spatial variability of the seeds bank in an experimental agricultural field and an anthropized cerrado area, using the ordinary kriging geostatistical technique. Sampling was carried out on 10x10 regular grids in two different environments. Area 01 consisted of an experimental agricultural field of annual crops (soy and corn); area 02 represented the anthropized cerrado. The sample grids consisted of 25 points collected at a depth of 0.00-0.20 m. The soil samples were placed in 6.38 dm3 and 0.05 m² plastic containers. Spatial distribution maps of the species found have been drawn up, grouped in dicotyledonous, monocotyledonous, and total density, in addition, the density of three weeds most found in each area. The weed seed bank present strong spatial variability to 01 and 02, which indicates behavior in spots or in patches for both dicotyledonous and monocotyledonous plants, especially Mollugo verticillata L. and Eleusine indica (L.) Gaertn. in the experimental field, and for Richardia scabra L. and Eleusine indica (L.) Gaertn. in the anthropized cerrado. The ordinary kriging technique made it possible to map the weed seed bank and, therefore, it may work as an efficient tool in controlling weeds in agricultural fields, especially in its pre-emergence phase. Furthermore, it can assist in the recovery of native anthropized vegetation. 

scholarly journals Mapping The Distribution of Iron Content in Nickel Laterite Deposits Using Indicator Kriging and Ordinary Kriging Interpolation Method

PROMINE ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 29-36
Author(s):  
Hendro Purnomo
Keyword(s):  
Root Mean Square ◽  
Iron Content ◽  
Ordinary Kriging ◽  
Interpolation Method ◽  
Indicator Kriging ◽  
Kriging Interpolation ◽  
Mean Square ◽  
Kriging Method ◽  
Nickel Laterite ◽  
Kriging Interpolation Method

Beside containing nickel (Ni), nickel laterite deposits also contain other elements, including iron (Fe) which have varying levels in each layer. In this study, the distribution of Fe content in the limonite layers was carried out using the indicator kriging method to analyze the probability distribution of iron levels and ordinary kriging to analyze the variability of iron levels spatially. Fitting the variogram was undertaken by using spherical, exponential and gaussian models. The selection of the best variogram model was carried out based on the smallest root mean square error (RMSE) value, while the estimation of resource potential was calculated by the polygon extended area method. The results of the interpolation show that the distribution of iron anomaly occupies ± 83,3% of the research area with a potential resource of ±64.522.110 ton of iron. The evaluation of the interpolation results base on the root mean square standardized prediction error (RMSP) indicates that the estimation results of iron content using the ordinary kriging method are underestimated.

Mapping of Soil Micronutrients in Kashmir Agricultural Landscape Using Ordinary Kriging and Indicator Approach

2012 ◽  
Vol 41 (2) ◽  
pp. 319-329 ◽  
Author(s):  
Mushtaq A. Wani ◽  
J. A. Wani ◽  
M. A. Bhat ◽  
N. A. Kirmani ◽  
Zahid M. Wani ◽  
...  
Keyword(s):  
Ordinary Kriging ◽  
Agricultural Landscape ◽  
Indicator Approach

scholarly journals Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China

2019 ◽  
Vol 31 (6) ◽  
pp. 2385-2394 ◽  
Author(s):  
Xiong Yao ◽  
Kunyong Yu ◽  
Yangbo Deng ◽  
Jian Liu ◽  
Zhuangjie Lai
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Southern China ◽  
Kriging Interpolation ◽  
Intrinsic Factors ◽  
Geostatistical Approach

Abstract To obtain accurate spatial distribution maps of soil organic carbon (SOC) and total nitrogen (TN) in the Hetian Town in Fujian Province, China, soil samples from three depths (0–20, 20–40, and 40–60 cm) at 59 sampling sites were sampled by using traditional analysis and geostatistical approach. The SOC and TN ranged from 2.26 to 47.54 g kg−1, and from 0.28 to 2.71 g kg−1, respectively. The coefficient of variation for SOC and TN was moderate at 49.02–55.87% for all depths. According to the nugget-to-sill ratio values, a moderate spatial dependence of SOC content and a strong spatial dependence of TN content were found in different soil depths, demonstrating that SOC content was affected by both extrinsic and intrinsic factors while TN content was mainly influenced by intrinsic factors. Indices of cross-validation, such as mean error, mean standardized error, were close to zero, indicating that ordinary kriging interpolation is a reliable method to predict the spatial distribution of SOC and TN in different soil depths. Interpolation using ordinary kriging indicated the spatial pattern of SOC and TN were characterized by higher in the periphery and lower in the middle. To improve the accuracy of spatial interpolation for soil properties, it is necessary and important to incorporate a probabilistic and machine learning methods in the future study.

scholarly journals COMPARISON ANALYSIS OF INTERPOLATION TECHNIQUES FOR DEM GENERATION USING CARTOSAT-1 STEREO DATA

2014 ◽  
Vol 9 (2) ◽  
Author(s):  
Andie Setiyoko ◽  
Anil Kumar
Keyword(s):  
Ordinary Kriging ◽  
Interpolation Method ◽  
Indicator Kriging ◽  
Stereo Image ◽  
Single Frequency ◽  
Polynomial Method ◽  
Data Sets ◽  
Differential Mode ◽  
Simple Kriging ◽  
Interpolation Techniques

Digital Elevation Model (DEM) can be generated using several techniques such as photogrammetric technique, interferometry, Lidar, etc. In photogrammetric technique, a DEM generation using stereo images, accuracy of generated DEM is also dependent on interpolation techniques. The process of interpolation is conducted to generate DEM as a continuous data from the point map that contained height information as a discrete data. In this research, point map was extracted from Cartosat-1 stereo image and from geodetic single frequency GPS in differential mode. Different interpolation techniques were applied on these data sets with different combination within these data sets. In this study, analysis of DEM interpolation was conducted with deterministic interpolators such as inverse distance weighted (IDW), global polynomial, local polynomial, and radial basis functions (RBF); and probabilistic interpolators such as simple kriging, ordinary kriging, universal kriging, indicator kriging, probabilistic kriging, disjunctive kriging, and cokriging. The accuracy of generated DEMs through different interpolation techniques were evaluated with ground point data collected from geodetic single frequency GPS in differential mode. Based on the analysis, the range error of DEMs generated was between 1.29 m to 2.96 m. Interpolation method with the least error was ordinary kriging using point map data and GPS points, while the highest error was obtained from global polynomial method.

scholarly journals Spatio-temporal distribution patterns of Plutella xylostella (Lepidoptera: Plutellidae) in a fine-scale agricultural landscape based on geostatistical analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian-Yu Li ◽  
Yan-Ting Chen ◽  
Meng-Zhu Shi ◽  
Jian-Wei Li ◽  
Rui-Bin Xu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Plutella Xylostella ◽  
Agricultural Landscape ◽  
Seasonal Pattern ◽  
Distribution Patterns ◽  
Geostatistical Analysis ◽  
Distribution Model ◽  
Detailed Knowledge ◽  
Fine Scale ◽  
Spatio Temporal

AbstractA detailed knowledge on the spatial distribution of pests is crucial for predicting population outbreaks or developing control strategies and sustainable management plans. The diamondback moth, Plutella xylostella, is one of the most destructive pests of cruciferous crops worldwide. Despite the abundant research on the species’s ecology, little is known about the spatio-temporal pattern of P. xylostella in an agricultural landscape. Therefore, in this study, the spatial distribution of P. xylostella was characterized to assess the effect of landscape elements in a fine-scale agricultural landscape by geostatistical analysis. The P. xylostella adults captured by pheromone-baited traps showed a seasonal pattern of population fluctuation from October 2015 to September 2017, with a marked peak in spring, suggesting that mild temperatures, 15–25 °C, are favorable for P. xylostella. Geostatistics (GS) correlograms fitted with spherical and Gaussian models showed an aggregated distribution in 21 of the 47 cases interpolation contour maps. This result highlighted that spatial distribution of P. xylostella was not limited to the Brassica vegetable field, but presence was the highest there. Nevertheless, population aggregations also showed a seasonal variation associated with the growing stage of host plants. GS model analysis showed higher abundances in cruciferous fields than in any other patches of the landscape, indicating a strong host plant dependency. We demonstrate that Brassica vegetables distribution and growth stage, have dominant impacts on the spatial distribution of P. xylostella in a fine-scale landscape. This work clarified the spatio-temporal dynamic and distribution patterns of P. xylostella in an agricultural landscape, and the distribution model developed by geostatistical analysis can provide a scientific basis for precise targeting and localized control of P. xylostella.

scholarly journals Environmental Drivers and Age Trends in Site Productivity for Oak in Southern Poland

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 209
Author(s):  
Luiza Tymińska-Czabańska ◽  
Jarosław Socha ◽  
Marek Maj ◽  
Dominika Cywicka ◽  
Xo Viet Hoang Duong
Keyword(s):  
Nitrogen Deposition ◽  
Management Practices ◽  
Additive Model ◽  
Climatic Conditions ◽  
Environmental Indicators ◽  
Environmental Drivers ◽  
Site Productivity ◽  
Site Factors ◽  
Wide Range ◽  
Si Model

Site productivity provides critical information for forest management practices and is a fundamental measure in forestry. It is determined using site index (SI) models, which are developed using two primary groups of methods, namely, phytocentric (plant-based) or geocentric (earth-based). Geocentric methods allow for direct site growth modelling, in which the SI is predicted using multiple environmental indicators. However, changes in non-static site factors—particularly nitrogen deposition and rising CO2 concentration—lead to an increase in site productivity, which may be visible as an age trend in the SI. In this study, we developed a geocentric SI model for oak. For the development of the SI model, we used data from 150 sample plots, representing a wide range of local topographic and site conditions. A generalized additive model was used to model site productivity. We found that the oak SI depended predominantly on physicochemical soil properties—mainly nitrogen, carbon, sand, and clay content. Additionally, the oak SI value was found to be slightly shaped by the topography, especially by altitude above sea level, and topographic position. We also detected a significant relationship between the SI and the age of oak stands, indicating the long-term increasing site productivity for oak, most likely caused by nitrogen deposition and changes in climatic conditions. The developed geocentric site productivity model for oak explained 77.2% of the SI variation.

scholarly journals Spatial Dependence Evaluation of Agricultural Technical Efficiency—Based on the Stochastic Frontier and Spatial Econometric Model

2021 ◽  
Vol 13 (5) ◽  
pp. 2708
Author(s):  
Ziqi Yin ◽  
Jianzhai Wu
Keyword(s):  
Spatial Distribution ◽  
Technical Efficiency ◽  
Spatial Dependence ◽  
Stochastic Frontier ◽  
Hebei Province ◽  
Agricultural Technology ◽  
Agricultural Product ◽  
Spatial Econometric ◽  
Frontier Analysis ◽  
The Impact

In recent years, through the implementation of a series of policies, such as the delimitation of major grain producing areas and the construction of advantageous and characteristic agricultural product areas, the spatial distribution of agriculture in China has changed significantly; however, research on the impact of such changes on the efficiency of agricultural technology is still lacking. Taking 11 cities in Hebei Province as the research object, this study examines the spatial dependence of regional agricultural technical efficiency using the stochastic frontier analysis and spatial econometric analysis. The results show that the improvement in agricultural technical efficiency is evident in all cities in Hebei Province from 2008 to 2017, but there is scope for further improvement. Industrial agglomeration has statistical significance in improving the efficiency of agricultural technology. Further, there is an obvious spatial correlation and difference in agricultural technical efficiency. Optimizing the spatial distribution of agricultural production, promoting the innovation, development, and application of agricultural technology, and promoting the expansion of regional elements can contribute to improving agricultural technical efficiency.

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