Spatial Distribution of Eggs of Bactericera cockerelli (Hemiptera: Trizoidae) in Tomatillo Using Geostatistical Techniques

2018 ◽  
Vol 53 (4) ◽  
pp. 422-433 ◽  
Author(s):  
J.F. Ramírez-Dávila ◽  
A.D. Acosta-Guadarrama ◽  
M. Martínez-Quiroz ◽  
D.K. Figueroa-Figueroa ◽  
F. Lara-Vazquez
Keyword(s):  
Spatial Distribution ◽  
Bactericera Cockerelli ◽  
Geostatistical Techniques

scholarly journals Modelización espacial de ninfas de Bactericera cockerelli Sulc. en tomate de cáscara (Physalis ixocarpa Brot.) por medio de técnicas geoestadísticas//Spatial modeling of Bactericera cockerelli Sulc. nymphs on husk tomato (Physalis ixocarpa Brot.) using of geostatistical techniques

Biotecnia ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 142-152
Author(s):  
Roberto Rivera Martínez ◽  
José Francisco Ramírez Dávila ◽  
Marisol Martínez Quiroz ◽  
Andrés González Huerta
Keyword(s):  
Spatial Modeling ◽  
Cross Validation ◽  
Control Measures ◽  
Effective Management ◽  
Bactericera Cockerelli ◽  
Tomato Crop ◽  
Pests And Diseases ◽  
Density Maps ◽  
Geostatistical Techniques ◽  
Husk Tomato

El cultivo de tomate de cáscara es afectado por la presencia de diversas plagas y enfermedades en donde figura la presencia del psílido Bactericera cockerelli Sulc., al cual se le atribuye el amarillamiento y aborto floral. El control de este psílido no ha sido eficiente por el desconocimiento de la distribución espacial del mismo, por lo tanto, el presente estudio se realizó con la finalidad de conocer la distribución espacial de ninfas de Bactericera cockerelli, en tomate de cáscara por medio de técnicas geoestadísticas. Se determinó el semivariograma experimental y se ajustó a un modelo teórico, el ajuste se validó con el método de validación cruzada y se obtuvieron los mapas de agregación de la plaga a través del krigeado. Los resultados mostraron que las poblaciones de ninfas de Bactericera cockerelli, presentan una distribución del tipo agregada, la cual fue corroborada con los mapas de densidad. La plaga no infestó el 100% de la superficie de las parcelas estudiadas, lo cual nos ayuda a crear programas de manejo eficaces y dirigir las medidas de control a las áreas específicas de infestación.ABSTRACTThe husk tomato crop is affected by the presence of several pests and diseases which includes the presence of Bactericera cockerelli Sulc., psyllid to which, yellowing and floral abortion is attributed. The control of this psyllid has not been efficient; therefore, the present study was carriedout with the purpose to know the spatial distribution of B. cockerelli nymphs, on husk tomato by using geostatistical techniques. The experimental semivariogram was determined and a theoretical model adjusted, and validated with the cross validation method and the aggregation maps of the plate were obtained through krigeado. The results show that the populations of B. cockerelli nymphs exhibits an aggregate type distribution, corroborated with density maps. The pest did not infest 100 % of the plots studied, which helps us to create effective management programs and direct control measures to specific areas of infestation.

scholarly journals DISTRIBUCIÓN ESPACIAL DE SEDIMENTOS EN LA REGIÓN NORTE DEL CARIBE COLOMBIANO

2016 ◽  
Vol 41 (1) ◽  
Author(s):  
Jorge Paramo ◽  
Luisa Espinosa ◽  
Blanca Posada ◽  
Samuel Núñez ◽  
Seydi Benavides
Keyword(s):  
Spatial Distribution ◽  
Calcium Carbonate ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Caribbean Region ◽  
Sediment Type ◽  
Sediment Samples ◽  
Sediment Types ◽  
Caco3 Content ◽  
Geostatistical Techniques

The spatial distribution of sediments in the continental shelf, their granulometry (phi) and composition (content of calcium carbonate, CaCO3) is described, taking into account the localization (depth, latitude and longitude) to explain their source and distribution and to establish their relationship with the more productive areas in the northern Colombian Caribbean region. Sediment samples were collected in 68 stations during two surveys carried out in December 2005 and February 2006. Granulometry was determined with sieving separation method and medium grains values of sediment (PHI = F) were calculated. Additionally calcium carbonate (CaCO3) contents were determined. Cluster analysis was performed to characterize groups of similar stations in terms of sediment types and sediment type maps were made using geostatistical techniques. Analysis of relationship between sediment types, according to their PHI, with depth, latitude and longitude, and CaCO3 content with sediment types and depth, was made with Generalized Additive Models (GAM). According to spatial distribution of sediments was possible characterize three sectors in agreement with the values of PHI: 1) from Río Buritaca to Río Camarones with fine sands and muds, 2) from Riohacha to Cabo de la Vela with very coarse sands and sands, 3) from Cabo de la Vela to Puerto Estrella with fine sands and muds.

Spatial Distribution of Pacific Halibut (Hippoglossus stenolepis): An Application of Geostatistics to Longline Survey Data

1994 ◽  
Vol 51 (7) ◽  
pp. 1506-1518 ◽  
Author(s):  
Dominique Pelletier ◽  
Ana M. Parma
Keyword(s):  
Spatial Distribution ◽  
Spatial Structure ◽  
Large Scale ◽  
Survey Design ◽  
Gulf Of Alaska ◽  
Small Scale ◽  
Spatially Correlated ◽  
Pacific Halibut ◽  
Hippoglossus Stenolepis ◽  
Geostatistical Techniques

The spatial distribution of Pacific halibut (Hippoglossus stenolepis) in the Gulf of Alaska was analyzed using longline catch per unit of effort (CPUE) data collected during three grid surveys in 1984, 1985, and 1986. Geostatistical techniques were used: (i) a variographic analysis to model and estimate the spatial structure of halibut abundance and (ii) ordinary kriging to predict local abundance. Available small-scale information made it possible to model satisfactorily the spatial structure. Results show (i) a persistent large-scale east–west difference in average CPUE and (ii) spatially correlated CPUE data with an average covariance decreasing as the distance between observations increased, over a range of 0–20 nautical miles (nmi) in 1984 and 1985, and 50 nmi in 1986. The survey design had limitations in that it was too unbalanced, with stations very close together along north–south transects, and transects too far apart from each other. Consequently, prediction error was small close to the transects and large in between in a clear banded pattern. To achieve a more regular coverage of the same area, a new survey design was developed: the global variances obtained with this new design using the variogram parameters for 1985 and 1986 were 20% lower than those based on the old design.

scholarly journals Geostatistical techniques applied to spatial distribution of macroorganisms in soybean crop

2018 ◽  
Vol 12 (03) ◽  
pp. 357-364
Author(s):  
Lenon Henrique Lovera ◽  
◽  
Elizeu De Souza Lima ◽  
Rafael Montanari ◽  
Zigomar Menezes de Souza ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soybean Crop ◽  
Geostatistical Techniques

scholarly journals Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

2016 ◽  
Vol 46 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Fátima L. BENÍTEZ ◽  
Liana O. ANDERSON ◽  
Antônio R. FORMAGGIO
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
High Resolution ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Remote Sensing Data ◽  
Amazon Rainforest ◽  
Weighted Regression ◽  
Sensing Data ◽  
Geostatistical Techniques

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

scholarly journals Spatial Distribution of Nymphs Populations Bactericera cockerelli Sulc in Tomato Crops (Physalis ixocarpa Brot)

Phyton ◽  
2019 ◽  
Vol 88 (4) ◽  
pp. 449-458
Author(s):  
Roberto Rivera-Mart韓ez ◽  
Agust韓 David Acosta-Guadarrama ◽  
Jos�Francisco Ram韗ez-D醰ila ◽  
Fidel Lara Vazquez ◽  
Dulce Karen Figueroa Figueroa
Keyword(s):  
Spatial Distribution ◽  
Bactericera Cockerelli

scholarly journals Spatial Variability of Irrigated Garlic (Allium sativum L.) Production Components

HortScience ◽  
2020 ◽  
Vol 55 (3) ◽  
pp. 300-303
Author(s):  
Job Teixeira de Oliveira ◽  
Rubens Alves de Oliveira ◽  
Lucas Allan Almeida Oliveira ◽  
Paulo Teodoro ◽  
Rafael Montanari
Keyword(s):  
Spatial Distribution ◽  
Spatial Correlation ◽  
Economic Value ◽  
Dry Mass ◽  
Leaf Length ◽  
Garlic Bulb ◽  
Geostatistical Techniques ◽  
Total Plant ◽  
Number Of Leaves ◽  
Red Latosol

Among the crops that are usually grown under irrigation, one can mention garlic, which is a product with high demand in Brazil and the world, it is highly valued in the cuisine of several countries, and is an aggregated crop with high economic value. In 2018, this work was conducted in Yellow Red Latosol. The objective was to characterize the structure and magnitude of the spatial distribution of garlic production components and to map the productive components to visualize spatial distribution and to evaluate the spatial correlation between garlic bulb yield (BY) and other variables of the crop: total plant mass (TPM), number of leaves (NL), floral tassel length (FTL), leaf length (LL), leaf width (LW), pseudostem diameter (PD), shoot wet mass (SWM), shoot dry mass (SDM), number of cloves per bulb (NCB), clove mass (CM), root dry mass (RDM), and irrigation (IRR). All these traits were sampled in a 90-point grid georeferenced. Data analysis using statistical and geostatistical techniques made it possible to verify that the production components and BY, TPM, NL, FTL, LL, LW, PD, SWM, SDM, CM, and IRR presented special dependence. The spatial correlation between BY and TPM, LW, and CM showed a moderate spatial dependence.

Spatial analysis and abundance estimation of the southernmost population of purple clam, Amiantis purpurata in Patagonia (Argentina)

2003 ◽  
Vol 83 (5) ◽  
pp. 1073-1082 ◽  
Author(s):  
Enrique Mario Morsan
Keyword(s):  
Spatial Distribution ◽  
Environmental Variables ◽  
Spherical Model ◽  
Abundance Estimation ◽  
Total Biomass ◽  
Demographic Structure ◽  
Data Density ◽  
Geostatistical Techniques ◽  
Variographic Analysis ◽  
Oriented Parallel

Spatial distribution of the southernmost population of the purple clam, Amiantis purpurata (Bivalvia: Veneridae), was described from systematic survey data (density and local biomass), and was related to environmental variables (depth and sediment). Geostatistical techniques were used to model and map density, and to estimate absolute biomass.Spatial distribution was highly contagious: half of the population lives at densities up to 240 clams m−2. Higher clam abundance occur only at sites with finer and well sorted sediment and intermediate depth. Even when the demographic structure of the population was composed of three year-classes recruited between 1978 and 1980, local biomass and density exhibited a non-linear relationship between them, and discrepancies between their distributions of frequency, their relationship with depth, and variographic analysis.The spherical model was fitted to the experimental variograms. Anisotropy was explored and introduced into the model. The resulting map shows the location and extension of the patches in the study area; they conform a fringe where the highest concentrations are oriented parallel to the coast line, interrupted by small shoals. No anisotropism was evident in the variographic analysis of local biomass. The kriged mean was 3369 g m−2; and estimated total biomass was 53,290 tn.Such differences between density and local biomass seem to be linked to compensatory processes involving biomass, growth and mortality, originated at the scale of individual overlapping neighbourhoods.

scholarly journals SPATIAL REPRESENTATION OF SURFACE WATER MONITORING AND ITS ASSESSMENT USING GEOSTATISTICAL AND NON-GEOSTATISTICAL TECHNIQUES IN GIS

2019 ◽  
Vol 45 (4) ◽  
pp. 177-189 ◽  
Author(s):  
Edon Maliqi ◽  
Petar Penev
Keyword(s):  
Water Pollution ◽  
Heavy Metals ◽  
Time Series ◽  
Spatial Distribution ◽  
Surface Water ◽  
Statistical Models ◽  
Pollution Indices ◽  
Mean Square ◽  
Surface Water Pollution ◽  
Geostatistical Techniques

Continuous monitoring of surface water is essential in terms of heavy metals investigation. Therefore, surface water quality is an environmental aspect which should be analyzed and monitored depending on its spatial distribution. The aim of this study is to provide an overview for evaluation of surface water pollution in the Mitrovica area by applying spatial distribution using Geographic Information System (GIS), geostatistical and non-geostatistical techniques. Nowadays, GIS with the geostatistics and non-geostatistics are very frequently used techniques in environmental monitoring studies. By providing the spatial distribution, there is possibility to place the pollution values in space. The surface water pollution caused by heavy metals (As, Cr, Cu, Ni, Pb, Zn and Cd) were sampled and analyzed from six monitoring stations in Sitnica river on different time series within three months countineously. The monitoring stations (samples) in Sitnica river were been distributed randomly. Pollution maps were produced using geostatistical and non-geostatistical (Spline and Kriging) approach. There were produced different pollution values in Sitnica river during the period of monitoring. Mainly the north part of Sitnica river has been poluted mostly with Heavy Metal Pollution Index (HPI) from 50 to 85 in the month of May, from 125 to 265 in the month of June and from 320 to 535 in the month of July. As well as the Metal Index (MI) from 0.60 to 2.05 in the month of May, June and July. The different statistical models were tested for geostatistical and non-geostatistical techniques in order to identify the best fitted technique for the pollution indices and the best interpolation techniques were selected on the basis of Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). These statistical tested model have shown that the best fitted interpolation technique is Kriging because of the lowest values of MSE, MAD, RMSE, MAE and MAPE. In the study were involved statistical models such as correlation and regression, for showing the relation between time series datasets and interpolated pollution indices as well. The cartographic output derived from the study were raster maps (15m spatial resolution) which represent the spatial distribution of surface water pollution as a result of monitoring process on time series. It is our believe that the present study will be used as a reference study for further environmental investigation and monitoring in Mitrovica since.

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