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1.
Artículo en Inglés | LILACS-Express | LILACS | ID: biblio-1551102

RESUMEN

The infiltration of water in the soil, and its variation in space, is essential to establish the irrigation schedule for crops and to evaluate the possible degrading effects on the soil. The objective was to develop an integrated processing methodology in Rstudio to identify the spatial variability of the accumulated infiltration, in two phases related to pea crops. Field sampling was carried out on a rectangular mesh with 48 points per moment, using double infiltrometer rings. The data were evaluated by means of geostatistical tools adjusted with programming code in Rstudio, defining the relationships between the magnitudes of the accumulated infiltration, for different test instants, without the need to make statistical adjustments to the normality of variables, discriminated over a period between 1 and 80 minutes. The results suggest the existence of spatial variability of the accumulated infiltration in the two evaluated phases, considering that most of the analyzed data were adjusted to multiple variance models, maintaining a degree of spatial dependence, and validating the effectiveness of the adjusted methodology developed and implemented. The spatial relationships were corroborated by means of contour maps, where the spatial variation of the accumulated infiltration between the two identified cultivation moments was observed. The reliability of the interpolation by the Ordinary Kriging method was verified by generating variance maps, establishing the degree of homogeneity of the interpolation. The variability of infiltration confirms the validity of the adjusted methodology implemented.


La infiltración del agua en el suelo y su variación espacial es fundamental para establecer la programación de riego en los cultivos y evaluar los posibles efectos degradativos en el suelo. El objetivo fue desarrollar una metodología de procesamiento integrado en Rstudio, para identificar la variabilidad espacial de la infiltración acumulada, en dos fases para un cultivo de arveja. El muestreo de campo se adelantó sobre una malla rectangular georreferenciada con 48 puntos, por cada momento, utilizando anillos infiltrómetros dobles. Los datos fueron evaluados por medio de herramientas geoestadísticas, ajustadas con código de programación en Rstudio, definiendo las relaciones entre las magnitudes de la infiltración acumulada, para diferentes instantes de prueba, sin la necesidad de realizar ajustes estadísticos de normalidad de variables, discriminados en un periodo entre 1 y 80 minutos. Los resultados sugieren la existencia de variabilidad espacial de la infiltración acumulada en las dos fases evaluadas, considerando que, la mayoría de los datos analizados, se ajustaron a múltiples modelos de semivarianza, manteniendo grados de dependencia espacial, particularmente, respecto al máximo valor acumulado de infiltración, validando la eficacia de la metodología ajustada. Las relaciones espaciales fueron corroboradas con mapas de contorno, en donde se observó la variación espacial de la infiltración acumulada entre los momentos de cultivo identificados. La confiabilidad de la interpolación por el método Kriging ordinario, se verificó mediante la generación mapas de varianza, estableciendo el grado de homogeneidad de la interpolación. La variabilidad de la infiltración confirma la validez de la metodología ajustada implementada.

2.
Artículo en Inglés | LILACS-Express | LILACS | ID: biblio-1537053

RESUMEN

The sustainable management of water and soil resources for agricultural purposes is related to the ability to store and mobilize available water for crops, particularly under a spatial analysis. The objective of the study was to design and evaluate a methodology for spatial analysis of resistance to soil penetration and infiltration on loamy-clay textures. The basic methodological principles included sampling grid planning, data capture at defined points, data fitting to empirical models, data processing, and spatial representation. A defining moment was evaluated for an established feijoa crop with permanent production. With a georeferenced rectangular sampling grid of 40m x 40m, an area of 1.36 ha was covered. Penetration resistance was measured with a penetrometer, covering 4 depths per node (sampled point). Infiltration was evaluated with ring infiltrometers. The results allowed validation of the methodology implemented through a single processing environment through RStudio. Resistance to penetration sensitively affected the variation in infiltration rates, adjusting planning activities for irrigation activities. The methodological proposal was designed to reduce processing times and graphic responses, tabulated, and integrated with a single script in the R tool, compared to traditional geostatistical techniques, which articulate the implementation of multiple tools for the generation of results.


La gestión sostenible de los recursos agua y suelo, con fines agrícolas, tiene relación con la capacidad para almacenar y movilizar agua disponible para los cultivos, particularmente, bajo un análisis espacial. El objetivo del estudio fue diseñar y evaluar una metodología de análisis espacial de la resistencia a la penetración e infiltración del suelo sobre texturas franco-arcillosas. Los principios básicos metodológicos incluyeron planificación de grilla de muestreo, captura de datos en puntos definidos, ajuste de datos a modelos empíricos, procesamiento y representación espacial de datos. Se evaluó un momento definido para un cultivo de feijoa establecido con producción permanente. Con una grilla de muestreo rectangular georreferenciada de 40m x 40m, se abarcó una superficie de 1,36 ha. La resistencia a penetración, se midió con un penetrómetro, cubriendo 4 profundidades por nodo (punto muestreado). La infiltración fue evaluada con anillos infiltrómetros. Los resultados permitieron validar la metodología implementada, mediante un entorno de procesamiento único, a través de RStudio. La resistencia a la penetración afectó sensiblemente la variación en las tasas de infiltración, ajustando actividades de planeación de actividades de riego. La propuesta metodológica fue diseñada para disminuir tiempos de procesamiento y respuestas gráficas, tabuladas e integradas en un único script en la herramienta R, comparado con técnicas tradicionales geoestadísticas, que articulan la implementación de múltiples herramientas para la generación de resultados.

3.
Biosci. j. (Online) ; 34(5): 1188-1199, sept./oct. 2018.
Artículo en Inglés | LILACS | ID: biblio-967306

RESUMEN

Having in mind the importance of knowing the variability and spatial correlation of soil properties in Indian Dark Earth (IDE), we evaluated in this study the variability and the spatial correlation of aggregates and carbon in an Ultisol under coffee cultivation in southern Amazonas. It was established a 48 x 88 m sampling grid spaced 06 x 08 m, totalling 88 sampling points. Then soil samples were collected at: 0.0-.05, 0.05-0.10, and 0.10-0.20 m layers. The spatial variability of the Mean Weighted Diameter (MWD) attributes, aggregates > 2 mm, < 2 mm, bulk density (BD) and organic carbon (OC) was analyzed by adjusting the simple semivariograms, while spatial correlations of the OC with aggregates and BD were analyzed by cross-semivariogram. We could conclude that there was spatial dependence in the variables, wherein the largest ones were observed at 0.0-0.05 m, except for Mean Weighted Diameter (MWD) and aggregates greater than 2.00 mm with larger range of values in depth from 0.05-0.10 and 0.10-0.20 m. The mean weight diameter and aggregate class attributes greater than 2.00 mm had negative spatial correlation with organic carbon at 0.0-0.05 m, while the smaller aggregates than 2.00 mm classes and bulk density correlated positively with organic carbon at 0.0-0.05 m and 0.10-0.20 m.


Considerando a importância do conhecimento da variabilidade e correlação espacial dos atributos do solo em Terra Preta de Índio (TPIs), avaliou-se neste trabalho a variabilidade e a correlação espacial de agregados e carbono em um Argissolo Amarelo eutrófico sob cultivo de café na região sul do Amazonas. Foi estabelecido um grid amostral com dimensões de 48 x 88 m e espaçamentos de 06 x 08 m, totalizando 88 pontos amostrais. Em seguida, foram coletadas amostras de solos nas profundidades: 0,0-0,05, 0,05-0,10, e 0,10-0,20 m. A variabilidade espacial dos atributos diâmetro médio ponderado (DMP), agregados > 2 mm, < 2 mm, densidade do solo (Ds) e carbono orgânico (CO) foi analisada ajustando os semivariogramas simples, enquanto as correlações espaciais do CO com agregados e Ds foram analisadas por semivariogramas cruzados. Concluiu-se que as variáveis apresentaram dependência espacial, e os maiores alcances são constatados na profundidade 0,0-0,05 m, exceto para DMP e agregados maiores que 2,00 mm com maiores valores de alcance na profundidade 0,05-0,10 e 0,10-0,20 m. Os atributos diâmetro médio ponderado e classes de agregados maior que 2,00 mm apresentam correlação espacial negativa com carbono orgânico na profundidade 0,0-0,05 m, enquanto a classes de agregados menor que 2,00 mm e densidade do solo apresentam correlação positiva com carbono orgânico nas profundidades 0,0-0,05 m e 0,10-0,20 m.


Asunto(s)
Suelo , Carbono , Café
4.
Chinese Journal of Epidemiology ; (12): 1201-1205, 2017.
Artículo en Chino | WPRIM | ID: wpr-737804

RESUMEN

Objective To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources.Methods The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected.Downscaling interpolation was conducted by using area-to-area Poisson Kriging method.The interpolation results were visualized by using geographic information system (GIS).The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township.Results In the downscaling interpolation,the range of the fitting semi-variance equation was 20.38 km.Within the range,the incidence had correlation with each other.The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y=1.053 1x,R2=0.99.The incidences at different scale were consistent.Conclusions The incidence of HFMD had spatial autocorrelation within 20.38 km.When HFMD occurs in one place,it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km.Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.

5.
Chinese Journal of Epidemiology ; (12): 1201-1205, 2017.
Artículo en Chino | WPRIM | ID: wpr-736336

RESUMEN

Objective To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources.Methods The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected.Downscaling interpolation was conducted by using area-to-area Poisson Kriging method.The interpolation results were visualized by using geographic information system (GIS).The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township.Results In the downscaling interpolation,the range of the fitting semi-variance equation was 20.38 km.Within the range,the incidence had correlation with each other.The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y=1.053 1x,R2=0.99.The incidences at different scale were consistent.Conclusions The incidence of HFMD had spatial autocorrelation within 20.38 km.When HFMD occurs in one place,it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km.Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.

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