Use Of Geostatistics Research Articles

Spatio-temporal distribution of the leaf crown borer Eupalamides cyparissias in coconut (Cocus nucifera L.) plantation in a tropical region

The leaf crown borer Eupalamides cyparissias (Fabricius, 1777) is an agricultural pest that deserves attention in the cultivation of coconut (Cocos nucifera L.) and other economically important palm trees (Areacaceae). Several studies address the use of geostatistics to understand the attack of pest insects in agriculture, but there are no studies on the spatial and temporal patterns of E. cyparissias in coconut trees. The presence or absence of the borer was verified monthly in all coconut plants of a plot measuring 12.5 hectares, from July 2018 to October 2020. The occurrence of the borer was verified by the appearance of two main symptoms in the coconut trees. Dispersion patterns of E. cyparissias were studied using semivariograms to determine the most suitable spatial distribution model for the species. Kriging maps were created. Exponential and spherical semivariogram models showed the best fit to the patterns of spatial dispersion of the borer, showing that the attack on the coconut plantation occurs in an aggregate, and in foci, with a range of 20 to 160 metres. The climatic data had no significant correlation with the infestation rate. These results show that geostatistics are an important tool to define reliable sampling plans for integrated pest management. It is useful to assess the spatial and temporal distribution of E. cyparissias

Use of Geostatistics in the sugarcane context in Brazil: a review

Use of Geostatistics in the sugarcane context in Brazil: a review

GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation

Abstract. The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to a perform geostatistical simulation, which is used to generate multiple non-unique realizations that reproduce the variability in measurements and are constrained by observations. Despite the broad utility of this approach, there are few open-access geostatistical simulation software applications. To address this accessibility issue, we present GStatSim, a Python package for performing geostatistical interpolation and simulation. GStatSim is distinct from previous geostatistical tools in that it emphasizes accessibility for non-experts, geostatistical simulation, and applicability to remote sensing data sets. It includes tools for performing non-stationary simulations and interpolations with secondary constraints. This package is accompanied by a Jupyter Book with user tutorials and background information on different interpolation methods. These resources are intended to significantly lower the technological barrier to using geostatistics and encourage the use of geostatistics in a wider range of applications. We demonstrate the different functionalities of this tool for the interpolation of subglacial topography measurements in Greenland.

GEOSPATIAL DISTRIBUTION OF FOREST SPECIES IN THE BOTANICAL GARDEN OF RECIFE, PERNAMBUCO, BRAZIL

The adoption of appropriate strategies for the conservation and management of forest ecosystems associated with the Atlantic Forest depends on understanding the pattern of spatial distribution of forest species, mainly in protected areas. Thus, this study aimed to evaluate the geospatial distribution of forest species in a fragment of Atlantic Forest (Lowland Dense Ombrophilous Forest) in the Botanical Garden of Recife, Pernambuco, Brazil. The phytosociological study was carried out by the plot method, with 40 plots of 10 m x 25 m, systematically distributed, where all living individuals that showed circumference at 1.3 m above the ground ≥ 15 cm were identified and georeferenced. To identify the existence of floristic similarity between the plots, the obtained data were subjected to hierarchical cluster analysis. The spatial variability of forest species was characterized using geostatistical analysis. The data were interpolated by kriging, followed by the construction of thematic maps of spatial distribution. Hierarchical cluster analysis revealed the formation of two groups of plots. The forest species Artocarpus heterophyllus, Dialium guianense, Helicostylis tomentosa, Hevea brasiliensis, Pouteria durlandii and Thyrsordium spruceanum showed spatial dependence. The use of geostatistics allowed visualizing the spatial distribution arrangements and identifying the concentration sites of these species. Information on the geospatial distribution of forest species can be used for the preservation, conservation and management of forest remnants.

Use of Geostatistics as a Tool to Study Spatial-Temporal Dynamics of Leucoptera coffeella in Coffee Crops

Coffee is considered one of the most important commercial commodities globally, and in 2020, it moved to a global market of USD 102.02 billion. However, the attack of pests in coffee production can cause significant economic losses. Leucoptera coffeella is a critical pest in coffee-producing countries, with productivity losses reaching 87%. The knowledge of the spatial distribution patterns of L. coffeella is essential to developing an efficient sampling and control plan. Moreover, it allows us to target for control specific locations/seasons where L. coffeella occurrence is at its highest density before reaching the economic injury level. Therefore, our objective in this study was to determine the spatial distribution of L. coffeella in coffee crops through geostatistical analysis. Data on the population density of L. coffeella were collected over four years on a farm with 18 center pivots located in the Brazilian Cerrado. The presence of L. coffeella was recorded in all 18 pivots during the entire time of the study (2016 to 2020). The highest densities were from July to November. These high densities of L. coffeella positively correlated with maximum air temperatures and wind speed. It was also verified to negatively correlate with minimum air temperatures and rainfall. The surrounding vegetation does not affect the pest densities. The pest hotspots appeared in different pivots and different locations inside pivots. Furthermore, L. coffeella showed an aggregated distribution pattern. For three years, the colonization started at the edge of the crop. The sampling should be performed equidistant as the pest is distributed equally in all directions. The information found in this study provides valuable information to initiate timely management and control methods in coffee crops with a high incidence of L. coffeella, thus reducing production costs and the harmful effects of pesticide use.

Use of Geostatistics for Multi-Scale Spatial Modeling of Xylella fastidiosa subsp. pauca (Xfp) Infection with Unmanned Aerial Vehicle Image

In recent years, the use of Unmanned Aerial Vehicles (UAVs) has been spreading widely, as in plant pest control. The collection of huge amounts of spatial data raises various issues including that of scale. Data from UAVs generally explore multiple scales, so the problem arises in determining which one(s) may be relevant for a given application. The objective of this work was to investigate the potential of UAV images in the fight against the Xylella pest for olive trees. The data were a multiband UAV image collected on one date in an olive grove affected by Xylella. A multivariate geostatistics approach was applied, consisting firstly of estimating the linear coregionalization model to detect the scales from the data; and secondly, of using multiple factor kriging to extract the sets of scale-dependent regionalized factors. One factor was retained for each of the two selected scales. The short-range factor could be used in controlling the bacterium infection while the longer-range factor could be used in partitioning the field into three management zones. The work has shown the UAV data potential in Xylella control, but many problems still need to be solved for the automatic detection of infected plants in the early stages.

The Use of Lightweight Penetrometer PANDA for the Compaction Control of Classified Sand Tailings Dams

Sand tailings dams have historically been the most commonly used technology for tailings storage in Chile. Although engineering advances have resulted in the construction of approximately 250-m-high facilities, some operational challenges still remain, including compaction control. Control is currently performed at a few control points in a dam embankment, without considering a series of factors that affect its mechanical behavior (e.g.,layer thickness and material variability). Within this context, geostatistics can be applied in combination with low-cost geotechnical tools as an alternative to improve compaction control in tailings storage facilities. In this study, an extensive field investigation was carried out. A total of 91 PANDA penetrometer tests were conducted to monitor the degree of compaction in an experimental classified sand tailings dam. The results were analyzed using stochastic interpolation for ordinary kriging and considering the spatial distribution of the cone resistance and the degree of compaction determined for the dam. The results showed that spatial variability was associated with the material variability of sand tailings and the compaction method used, and deviations from design requirements. The article shows the value of the use of geostatistics in decision-making in the case of classified sand tailings dams. This is mainly due to the fact that it allows optimization of the compaction process used in these tailings dams. Additionally, a useful database is generated to continue deepening studies of physical stability during the useful life of the tailings storage facilities.

GSTools v1.3: a toolbox for geostatistical modelling in Python

Abstract. Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.

The use of geostatistics to estimate missing data in a spatial econometric model of housing prices

Housing prices have been the subject of many studies, and some of them have tried to determine the influencing structural and location factors through hedonic econometric models. One of the main factors considered in the literature on real estate appraisals is the location of the dwellings. For this reason, this study combines the spatial methodologies of geostatistics and spatial econometrics. On the one hand, this work uses geostatistics to estimate missing data to account for the lack of information in the sampled real estate websites. On the other hand, the explanatory factors of prices are determined through spatial econometrics. The combination of both methods facilitates estimating housing prices in Santa Marta (Colombia), solving the problem of missing data. In the modeling, the problems of spatial heteroscedasticity and multicollinearity are corrected. This combination of methods could be of great interest to company ies and public agencies related to real estate activity, which is sustained by the information available on these real estate websites.

Ґрунтознавство в Україні: досягнення, пріоритети та перспективи

Goal. Based on generalizations and analysis of modern achievements to determine the main priorities and prospects of soil research, further ways of systematic management of soil resources in the context of climate change and market economy. Methods. Structural and functional approach: collection and synthesis of an information database on soils and their functions, systematic and comparative analysis of soil science achievements, their expert assessment. Results. The general characteristic and assessment of scientific and practical achievements in the field of acquiring soil science knowledge as fundamental for balanced land use, formation of civilized land market relations, management of soil and land resources, their productive, ecological, and social functions are given. The most difficult and debatable issues are described, their importance in solving current problems in obtaining reliable information about the current transformation of soils and soil cover. Prospects for their solution are as follows: the development and use of modern fundamentally new methodological approaches and technical means of monitoring, the creation of concession systematics, and methods of soil mapping on a fundamentally new basis. The achievements in the field of physics, land reclamation, fertility and protection of soils and soil cover, achievements in the use of geostatistics, pedotransfer modeling, modern bio diagnostics of soils, their microbiological condition, “resuscitation of their health” are highlighted. The prospects of in-depth study of the suppressive ability of soils on the methodological basis of molecular biomarkers and metagenomics are shown. Conclusions. The analysis of soil science achievements identified priority areas of research: systematics and cartography of soils on a new technical and methodological basis, land valuation improvements, soil reclamation innovations, the suppressive ability of soils, their carbon balance, neutral safety degradation, and environmental degradation.

Probability elicitation using geostatistics in hydrocarbon exploration

The exploratory phase of a hydrocarbon field is a period when decision-supporting information is scarce while the drilling stakes are high. Each new prospect drilled brings more knowledge about the area and might reveal reserves, hence choosing such prospect is essential for value creation. Drilling decisions must be made under uncertainty as the available geological information is limited and probability elicitation from geoscience experts is key in this process. This work proposes a novel use of geostatistics to help experts elicit geological probabilities more objectively, especially useful during the exploratory phase. The approach is simpler, more consistent with geologic knowledge, more comfortable for geoscientists to use and, more comprehensive for decision-makers to follow when compared to traditional methods. It is also flexible by working with any amount and type of information available. The workflow takes as input conceptual models describing the geology and uses geostatistics to generate spatial variability of geological properties in the vicinity of potential drilling prospects. The output is stochastic realizations which are processed into a joint probability distribution (JPD) containing all conditional probabilities of the process. Input models are interactively changed until the JPD satisfactory represents the expert’s beliefs. A 2D, yet realistic, implementation of the workflow is used as a proof of concept, demonstrating that even simple modeling might suffice for decision-making support. Derivative versions of the JPD are created and their effect on the decision process of selecting the drilling sequence is assessed. The findings from the method application suggest ways to define the input parameters by observing how they affect the JPD and the decision process.

Distribution Analysis of Heavy Metal Contaminants in Soil With Geostatistic Methods; Paper Review

Heavy metal contaminants in the soil will have a direct effect on human life. The spatial distribution of naturally occurring heavy metals is highly heterogeneous and significantly increased concentrations may be present in the soil at certain locations. Heavy metals in areas of high concentration can be distributed to other areas by surface runoff, groundwater flow, weathering and atmospheric cycles (eg wind, sea salt spray, volcanic eruptions, deposition by rivers). More and more people are now using a combination of geographic information science (GIS) with geostatistical statistical analysis techniques to examine the spatial distribution of heavy metals in soils on a regional scale. The most widely used geostatistical methods are the Inverse Distance Weighted, Kriging, and Spatial Autocorrelation methods as well as other methods. This review paper will explain clearly the source of the presence of heavy metals in soil, geostatistical methods that are often used, as well as case studies on the use of geostatistics for the distribution of heavy metals. The use of geostatistical models allows us to accurately assess the relationship between the spatial distribution of heavy metals and other parameters in a map.

Distribution Analysis of Heavy Metal Contaminants in Soil With Geostatistic Methods; Paper Review

Heavy metal contaminants in the soil will have a direct effect on human life. The spatial distribution of naturally occurring heavy metals is highly heterogeneous and significantly increased concentrations may be present in the soil at certain locations. Heavy metals in areas of high concentration can be distributed to other areas by surface runoff, groundwater flow, weathering and atmospheric cycles (eg wind, sea salt spray, volcanic eruptions, deposition by rivers). More and more people are now using a combination of geographic information science (GIS) with geostatistical statistical analysis techniques to examine the spatial distribution of heavy metals in soils on a regional scale. The most widely used geostatistical methods are the Inverse Distance Weighted, Kriging, and Spatial Autocorrelation methods as well as other methods. This review paper will explain clearly the source of the presence of heavy metals in soil, geostatistical methods that are often used, as well as case studies on the use of geostatistics for the distribution of heavy metals. The use of geostatistical models allows us to accurately assess the relationship between the spatial distribution of heavy metals and other parameters in a map.

Estimation of Travel Mode Choice Using Geostatistics: a Brazilian Case Study

Traditional methods for travel demand estimation are often built on socioeconomic and travel information. The information required to conduct such studies is costly and rarely available in developing countries. Besides, some conventional methods do not consider the spatial relationship of variables and, in general, a large amount of socioeconomic and individual travel data is required. The key aim of this paper is to evaluate the importance of considering spatial information when estimating travel mode choices especially considering the lack of available data. The study area is the São Paulo Metropolitan Area (Brazil) and the dataset refers to an Origin-Destination Survey, conducted in 2007. This research paper analyzes the use of Geostatistics when estimating discrete travel mode choices. The results demonstrated a satisfactory outcome for the geostatistical approach. Finally, although socioeconomic and travel variables have greater explanatory power in predicting travel mode choices, spatial factors contribute to better understand the travel behavior and to provide further information when estimating spatially correlated data.

Landscape-scale spatial variability of kaolinite-gibbsite ratio in tropical soils detected by diffuse reflectance spectroscopy

The use of diffuse reflectance spectroscopy (DRS) has gained prominence in the quantification of soil attributes due to its ease and practicality of obtaining data. This study aimed to evaluate the potential of different methodologies applied to spectral curves given by DRS to estimate kaolinite (Kt) and gibbsite (Gb), and their spatial variability characterization for the Western Plateau of São Paulo. The Western Plateau of São Paulo has 13 million hectares, 2 million of them covered by basalt and 11 million by sandstones. A total of 600 samples were collected at a depth of 0.0–0.20 m. Calibration curves were constructed with pure minerals for x-ray diffraction (XRD) and DRS techniques. The Kt/(Kt + Gb) ratio and the percentages of Kt and Gb were determined by XRD and using the following three methodologies applied to spectral curves: continuum removal technique (CR), direct ratio of the valley (DRV), and multivariate analysis by partial least squares regression (PLSR). The CR procedure had means similar to those observed by XRD, i.e., 0.90 and 0.92, respectively, while DRV overestimated the ratio, with a mean of 1.32. DRS allowed the estimation of the Kt/(Kt + Gb) ratio for the different geological and landscape compartments of the Western Plateau of São Paulo for the CR and DRV procedures. CR procedure allowed constructing models to be more efficient compared to those obtained by DRV and PLSR. The use of geostatistics to interpolate the data of the ratio Kt/(Kt + Gb) by DRS provided important information to define specific management zones accurately and more economically.

Geostatistics and its potential in Agriculture 4.0

In order to meet future food demands and ensure sustainability new technologies have been incorporated into agriculture. Some researchers believe that we are living in the fourth agricultural revolution or Agriculture 4.0. Among the many technologies involved in the Agriculture 4.0, it is necessary to highlight the importance of geostatistics in the implementation of those technologies. Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena, and it is very important to understand the spatial distribution of agricultural variables. Therefore, the objective of this review is to show the potential of geostatistics in Agriculture 4.0. The article presents an exhaustive literature review of geostatistics and its potential in agriculture, by showing a brief of geostatistical approaches, some practical use of geostatistics in agriculture, and a description of multivariate geostatistics for multi-source data fusion using some case studies. This review showed that geostatistics has been used for agricultural purposes and has been producing exciting results. In addition, more advanced analysis such as multivariate geostatistics in fuse heterogeneous data can be easily adapted to any experimental conditions and type of sensor data and/or sampling data to increase estimation accuracy.

Soil sampling optimization using spatial analysis in irrigated mango fields under brazilian semi-arid conditions

Abstract Soil sampling is a fundamental procedure in the decision making regarding the management of the soil, thus, a sampling plan should represent as accurately as possible the evaluated crop field. Therefore, the objectives of this study were to suggest a soil sampling approach and soil sampling point allocation using spatial analyses and compare to the classic statistic method in irrigated mango orchards in the Brazilian semi-arid region. The experiment was carried out in three commercial mango orchards located in the region of the São Francisco Valley, Brazil. Soil samples were collected in 0-0.2 m and 0.2-0.4 m depths following regular grids where the number of samples varied from 50 to 56. Soil texture, soil bulk density, soil total porosity, microporosity, macroporosity, pH, Ca, Mg, Na, K, Al, P, potential acidity, and the sum of basis were evaluated. Classical and geostatistical statistics were used to determine the ideal number of soil samples. Fuzzy c-means clustering technique was used to separate the areas into homogeneous zones and to allocate the sampling points. The wide method of 20 individual soil samples proved to be inefficient. On the other hand, the use of geostatistics proved to be efficient and is required for each crop field. The c-means clustering was adequate to separate the areas into homogeneous zones and, thus, to assist the sampling point allocation.

Analysis of Rainfall Trends and Extreme Precipitation in the Middle Adriatic Side, Marche Region (Central Italy)

Extreme precipitation trends and events are fundamental for the definition of the region’s climate and allow the subsequent analysis of the risk for the territory and the possible countermeasures. This study takes into account the Marche Region (Central Italy) with 128 rain gauges from 1921 to 2017. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. In addition, to obtain a comparable analysis, the average precipitations grouped in climatological standard normals were analyzed. Finally, the main purpose of the research was achieved by analyzing extreme events in the middle Adriatic side. In addition, forecasts of extreme precipitation events, with a return period of 100 years, were made using the theory of “generalized extreme value” (GEV). The innovation of this research is represented by the use of geostatistics to spatialize the variables investigated, through a clear and immediate graphic representation performed through GIS software. This study is a necessary starting point for the study of climate dynamics in the region, and it is also a useful tool for land use planning.

MANAGEMENT SYSTEMS: SOIL COVER AND COMPACTION, LONGITUDINAL DISTRIBUTION, AND YIELD OF SOYBEAN CROP

The way the soil is managed can influence its structuring and, consequently, crop yield. Thus, this study aimed to evaluate the effect caused by the management systems plowing followed by two intermediate harrowing operations, intermediate harrowing, chiseling, chiseling followed by intermediate harrowing, cross chiseling followed by intermediate harrowing, and non-tillage on soil and agronomic attributes of the soybean crop. A randomized block design with four replications was used. The percentage of soil cover, soil resistance to penetration, number of plants per meter, longitudinal distribution of seedlings, and soybean yield were evaluated. The data were submitted to analysis of variance by the Tukey test at 5% probability, and use of geostatistics for soil resistance to penetration. The system without soil tillage provides the best straw preservation but affects the longitudinal distribution of soybean seedlings. The use of intermediate harrowing for managing crop residues or soil tillage leads to the greatest compaction problems. Chiseling is efficient in maintaining compaction values below critical values up to a depth of 0.20 m when working at 0.35 m. Soil yield is not affected by soil management systems when the pluviometric regime is adequate to crop requirements.

Use of Geostatistics and Geographical Information Systems Techniques in the Management of Gökhöyük Agricultural Farm

Arazi kullanımı, ana materyal ve topoğrafyaya bağlı olarak büyük değişkenlik gösteren toprak özelliklerinin mesafeye bağlı değişkenlikleri; toprağın verimliliği, kalitesi ve genel olarak sürdürülebilirliğini önemli düzeyde etkilemektedir. Bu çalışma; yaklaşık 1900 hektar genişliğindeki Gökhöyük Tarım İşletmesi arazilerinin bir kısım toprak özelliklerinin mesafeye bağlı değişkenliklerini belirlemek, haritalamak ve işletme arazilerinin sürdürülebilir kullanımlarını etkileyecek sorunların tespit edilerek çözüm önerilerini ortaya koymak amacı ile gerçekleştirilmiştir. Bu amaçla, çalışma alanını temsil edecek şekilde 63 noktadan ve 4 farklı derinlikten (0-30, 30-60, 60-90 ve 90-120 cm) toprak ve 19 noktadan da taban suyu örnekleri alınmıştır. Toprak örneklerinde elektriksel iletkenlik (EC), pH, kil, kum ve silt içerikleri ile hidrolik iletkenlik değerleri; taban suyu örneklerinde ise pH ve EC değerleri belirlenmiştir. Klasik istatistik ve jeoistatistik yöntemler ile çalışılan özelliklerin, arazideki genel durumu ve mesafeye bağlı değişkenlikleri karakterize edilmiştir. Araştırma sonucuna göre, yüksek kil içeriğine sahip olan hem yüzey hem de yüzey altı topraklarında hidrolik iletkenlik değerleri (<20 mm h-1) oldukça düşük bulunmuştur. Bitki besin elementi alımı üzerine önemli bir etkisi olan pH değerlerinin tüm derinliklerde ve arazinin önemli bir kısmında 8.5’in üzerinde; çalışma alanının orta kısmında bir hatta yer alan toprakların 60-120 cm derinliğinde EC değerleri (>4 dS m-1) sorun oluşturabilecek düzeylerde olduğu tespit edilmiştir. Bu bölgedeki su örneklerinin de EC değerleri 20 dS m-1’nin üzerinde olduğu görülmüştür. Kurak ve sıcak dönemlerde tuz içeriği yüksek taban suyunun kapilarite ile yüzeye taşınması, toprağın üretkenlik fonksiyonuna zarar verebilir. Toprak özelliklerinin mesafeye bağlı değişimini gösteren haritaların kullanımı ile çiftlik arazisinde bitkisel üretimin geliştirilmesine ve toprak kalitesinin iyileştirilmesine katkı sağlayacak kararların doğru bir şekilde alınması mümkün olabilecektir.