scholarly journals VARIABILIDADE ESPACIAL DOS PERCENTIS 75 DA PRECIPITAÇÃO PLUVIAL ANUAL PARA O ESTADO DO PIAUÍ

Irriga ◽  
2006 ◽  
Vol 11 (2) ◽  
pp. 178-187
Author(s):  
Francisco Edinaldo Pinto Mousinho ◽  
Aderson Soares de Andrade Júnior ◽  
Antônio Carlos Andrade Gonçalves ◽  
José Antonio Frizzone

VARIABILIDADE ESPACIAL DOS PERCENTIS 75 DA PRECIPITAÇÃO PLUVIAL ANUAL  PARA O ESTADO DO  PIAUÍ  Francisco Edinaldo Pinto Mousinho1; Aderson Soares de Andrade Júnior2; Antônio Carlos Andrade Gonçalves3; José Antonio Frizzone41Universidade Federal do Piauí, Campus Amílcar Ferreira Sobral,  Florian, -PI, [email protected] Meio-Norte, Teresina, -PI3Universidade Estadual de Maringá, Departamento de Agronomia, Maringá, PR4Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Piracicaba, SP  1 RESUMO No presente trabalho foi avaliada a variabilidade espacial dos percentis 75 da precipitação pluvial anual para o Estado do Piauí, empregando-se técnicas estatísticas descritivas e geoestatísticas. A estatística descritiva descartou a presença de valores extremos e confirmou o ajuste dos dados à distribuição normal, sendo realizada então a análise geoestatística. O semivariograma experimental foi melhor ajustado ao modelo  Gaussiano, mostrando nítida continuidade espacial do atributo estudado. Utilizando-se a krigagem ordinária, os valores dos percentis 75 foram estimados para locais não amostrados e a seguir gerado o mapa temático para o Estado do Piauí. Os percentis 75 apresentaram uma grande variabilidade espacial sendo os maiores valores observados no noroeste do Estado e os menores no sudeste, região semi-árida. A variância dos dados se aproximou do patamar do modelo de semivariograma, o que contribui para se pressupor a condição de estacionaridade do processo. O efeito pepita de 9200mm2 revelou  a variabilidade não explicada ou a possibilidade de existir dependência espacial em uma escala menor que a amostrada. Os percentis 75 da precipitação anual  apresentaram um alcance da dependência espacial da ordem de240 km, com forte continuidade espacial,  e a sua espacialização para o Estado do Piauí permitiu a visualização da sua distribuição. UNITERMOS: Precipitação pluviométrica, espacialização, geoestatística  MOUSINHO, F. E. P., ANDRADE JÚNIOR, A. S. , GONÇALVES, A. C. A., FRIZZONE, J. A. SPATIAL VARIABILITY OF ANNUAL 75 PERCENTIL PRECIPITATION  FOR PIAUI STATE  2 ABSTRACT In the present work the spatial variability of annual 75-percentile precipitation for Piauí  State, using descriptive statistical and geostatistics techniques, was evaluated. Descriptive statistics discarded the presence of extreme values and confirmed the data adjustment to normal distribution, and then geostatistical analysis was carried out. The experimental semivariogram was better adjusted to the Gaussian model, showing the spatial continuity of the studied matter. Using ordinary kriging, the 75 percentile values were estimated for non-sampled points and then a map was generated for PiauíState. 75 percentile values presented a great spatial variability with maximum values in the northwestern region and minimum values in the southeastern one, i.e., the semi-arid region.  Data semi variance was close to the semivariogram model sill and that contributes to presuppose the stationary process condition. The nugget effect value of 9200mm2 revealed the non-explained variability or the possibility of spatial dependence existence in a smaller scale than the sampled one. The annual 75-percentile precipitation presented a spatial dependence range of240 km, with strong spatial continuity, and its spatialization forPiauíState can be used for agricultural zoning state programs. KEYWORDS:  pluviometric precipitation, spatialization, geostatistics  3 INTRODUÇÃO A distribuição espacial da precipitação pluvial em uma determinada região é um dos fatores que refletem diretamente os diferentes níveis de desenvolvimento regional, principalmente o agrícola, pois dentre todas as atividades produtivas a agricultura é a que apresenta maior  dependência da ocorrência das chuvas, sendo esta a principal  responsável pela alternância das produções agrícolas anuais (Morais et al., 2001).No Estado do Piauí, onde predomina a agricultura de “sequeiro",  é de capital importância a realização de estudos sobre a distribuição espacial destas no seu território, tornando possível um planejamento regional criterioso quanto às culturas a serem exploradas e locais e épocas de cultivo, de modo a se obter, com um dado nível de probabilidade, um determinado nível de rendimento.       De acordo com Gomes & Cruz (2002), vários trabalhos têm sido feitos visando caracterizar a distribuição das precipitações pluviais, utilizando-se para tal as médias, sazonais ou anuais. Todavia, estas informações não são suficientemente confiáveis para fins de planejamento agrícola, constituindo um risco para o produtor, já a probabilidade de ocorrência das médias é de apenas 50%, justificando o uso de probabilidades não inferiores a 75% com vistas a minimizar estes  riscos (Gondim & Fernández Medina, 1980).A análise estatística clássica considera que os valores medidos de uma determinada variável são independentes, variando aleatoriamente no espaço, o que nem sempre é verdade. Contrariamente, a geoestatística considera a continuidade espacial da variável, tendo, assim, um amplo campo de utilização pois muitas variáveis têm nítida continuidade espacial e devem ser analisadas segundo a teoria das variáveis regionalizadas (Hamlett et al, 1986).De acordo com Gonçalves et al. (2001), a hipótese do ajuste dos valores de determinado atributo à distribuição normal geralmente não é testada, embora a realização de qualquer estudo estatístico ou geoestatístico assuma a condição de normalidade dos dados. Segundo Hamlett et al. (1986), a análise exploratória dos valores de uma determinada variável distribuída no espaço, é um procedimento indispensável em estudos geoestatísticos, pois, por meio deste se verifica o ajuste dos mesmos à distribuição normal. Gonçalves et al. (2001), também, ressaltaram a importância de uma cuidadosa análise de variáveis espacialmente distribuídas como etapa prévia de uma análise geoestatística.Com o conhecimento do padrão de variabilidade espacial de um atributo pode-se estimar valores  em locais não amostrados, sendo a krigagem o interpolador utilizado nos estudos geoestatísticos por ser não tendencioso e de variância mínima, assegurando a melhor estimativa. Utilizando-se uma grade regular de valores estimados através da krigagem pode-se elaborar mapas que representem a distribuição da variável em uma determinada região, os quais constituem uma das maneiras mais ilustrativas para  representar a espacialização de uma variável em uma determinada área.Face ao exposto, este trabalho teve por objetivo avaliar a variabilidade espacial dos percentis 75 da precipitação pluviométrica anual e realizar a sua espacialização para o Estado do Piauí, empregando-se técnicas estatísticas descritivas e geoestatísticas. 

Author(s):  
Douglas B. Castro ◽  
Elvira M. R. Pedrosa ◽  
Abelardo A. A. Montenegro ◽  
Mario M. Rolim ◽  
Diego A. H. S. Leitão ◽  
...  

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.


2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Gabriel Soropa ◽  
Olton M. Mbisva ◽  
Justice Nyamangara ◽  
Ermson Z. Nyakatawa ◽  
Newton Nyapwere ◽  
...  

AbstractA study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe.


2021 ◽  
Vol 51 ◽  
Author(s):  
Diogo Neia Eberhardt ◽  
Robélio Leandro Marchão ◽  
Pedro Rodolfo Siqueira Vendrame ◽  
Marc Corbeels ◽  
Osvaldo Guedes Filho ◽  
...  

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.


2013 ◽  
Vol 37 (1) ◽  
pp. 68-77 ◽  
Author(s):  
Marcela de Castro Nunes Santos ◽  
José Marcio de Mello ◽  
Carlos Rogério de Mello ◽  
Léo Fernandes Ávila

The spatial characterization of soil attributes is fundamental for the understanding of forest ecosystems. The objective of this work was to develop a geostatistical study of chemical and physical soil attributes at three depths (D1 - 0-20 cm; D2 - 20-50 cm; D3 - 50-100 cm), in an Experimental Hydrographic Micro-catchment entirely covered by Atlantic Forest, in the Mantiqueira Range region, Minas Gerais. All the considered variables presented spatial dependence structure in the three depths, and the largest degrees of spatial dependence were observed for pH in the three depths, soil cation exchange capacity potential in D3, soil organic matter in D1 and D3 and clay and soil bulk density in D2. The method most used for the adjustments of semi-variogram models was the Maximum Likelihood and the most selected model was the Exponential. Furthermore, the ordinary kriging maps allowed good visualization of the spatial distribution of the variables.


CERNE ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Allan Libanio Pelissari ◽  
Marcelo Roveda ◽  
Sidney Fernando Caldeira ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
...  

ABSTRACT Considering the hypothesis that the wood volumes present spatial dependence, whose knowledge contributes for the precision forestry, the aim of this work was to estimate the volume spatial variability for timber assortments and identify their spatial patterns on Tectona grandis stands. A dataset of 1,038 trees was used to fit taper models and estimate the total stem, sawlog, and firewood volumes in 273 plots allocated on T. grandis stands at eight years old, which represents the second thinning that enables commercial volumes. Semivariograms models was applied to fit the spatial dependence, and punctual kriging was used to compose volume maps. Geostatistical modeling allowed us to estimate the T. grandis spatial variability and develop timber volume maps. Thus, silvicultural treatments, such as thinning and pruning, as well as for planning spatial interventions, are possible to be recommended for aimed wood products.


2009 ◽  
Vol 33 (5) ◽  
pp. 1507-1514 ◽  
Author(s):  
Sidney Rosa Vieira ◽  
Osvaldo Guedes Filho ◽  
Márcio Koiti Chiba ◽  
Heitor Cantarella

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.


Author(s):  
G. S. Tagore ◽  
G. D. Bairagi ◽  
R. Sharma ◽  
P. K. Verma

A study was conducted to explore the spatial variability of major soil nutrients in a soybean grown region of Malwa plateau. From the study area, one hundred sixty two surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, EC, organic carbon, soil available nutrients (N, P, K, S and Zn) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Exponential model fits well with experimental semi-variogram of pH, EC, OC, available N, P, K, S and Zn. pH, EC, OC, N, P, and K has displayed moderate spatial dependence whereas S and Zn showed weak spatial dependence. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned.


2018 ◽  
Vol 31 (2) ◽  
pp. 434-445
Author(s):  
JUCICLÉIA SOARES DA SILVA ◽  
ÊNIO FARIAS DE FRANÇA E SILVA ◽  
GLÉCIO MACHADO SIQUEIRA ◽  
GERÔNIMO FERREIRA DA SILVA ◽  
DIEGO HENRIQUE SILVA DE SOUZA

ABSTRACT Spatial variability of soil attributes affects crop development. Thus, information on its variability assists in soil and plant integrated management systems. The objective of this study was to assess the spatial variability of the soil apparent electrical conductivity (ECa), electrical conductivity of the saturation extract (ECse), water content in the soil (θ) and soil texture (clay, silt and sand) of a sugarcane crop area in the State of Pernambuco, Brazil. The study area had about 6.5 ha and its soil was classified as orthic Humiluvic Spodosol. Ninety soil samples were randomly collected and evaluated. The attributes assessed were soil apparent electrical conductivity (ECa) measured by electromagnetic induction with vertical dipole (ECa-V) in the soil layer 0.0.4 and horizontal dipole (ECa-H) in the soil layer 0.0-1.5 m; and ECse, θ and texture in the soil layers 0.0-0.2 m and 0.2-0.4 m. Spatial variability of the ECa was affected by the area relief, and had no direct correlation with the electrical conductivity of the saturation extract (ECse). The results showed overestimated mean frequency distribution, with means distant from the mode and median. The area relief affected the spatial variability maps of ECa-V, ECa-H, ECse and θ, however, the correlation matrix did not show a well-defined cause-and-effect relationship. Spatial variability of texture attributes (clay, site and sand) was high, presenting pure nugget effect.


2013 ◽  
Vol 37 (2) ◽  
pp. 295-306 ◽  
Author(s):  
Livia Arantes Camargo ◽  
José Marques Júnior ◽  
Gener Tadeu Pereira

A good knowledge of the spatial distribution of clay minerals in the landscape facilitates the understanding of the influence of relief on the content and crystallographic attributes of soil minerals such as goethite, hematite, kaolinite and gibbsite. This study aimed at describing the relationships between the mineral properties of the clay fraction and landscape shapes by determining the mineral properties of goethite, hematite, kaolinite and gibbsite, and assessing their dependence and spatial variability, in two slope curvatures. To this end, two 100 × 100 m grids were used to establish a total of 121 regularly spaced georeferenced sampling nodes 10 m apart. Samples were collected from the layer 0.0-0.2 m and analysed for iron oxides, and kaolinite and gibbsite in the clay fraction. Minerals in the clay fraction were characterized from their X-ray diffraction (XRD) patterns, which were interpreted and used to calculate the width at half height (WHH) and mean crystallite dimension (MCD) of iron oxides, kaolinite, and gibbsite, as well as aluminium substitution and specific surface area (SSA) in hematite and goethite. Additional calculations included the goethite and hematite contents, and the goethite/(goethite+hematite) [Gt/(Gt+Hm)] and kaolinite/(kaolinite+gibbsite) [Kt/(Kt+Gb)] ratios. Mineral properties were established by statistical analysis of the XRD data, and spatial dependence was assessed geostatistically. Mineralogical properties differed significantly between the convex area and concave area. The geostatistical analysis showed a greater number of mineralogical properties with spatial dependence and a higher range in the convex than in the concave area.


2001 ◽  
Author(s):  
Maria Sallydelândia Sobral de Farias ◽  
Vera Lúcia Antunes de Lima ◽  
Tumkur Rajaro Gopinath ◽  
José Dantas Neto ◽  
Carlos Alberto Vieira de Azevedo

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