Search

Application of geostatistical techniques when evaluating mineral deposits could reflect some geological characteristics which help through the stage of planning and production. In the present study, an attempt has been done on two phosphate deposits at Elsebaiya area on both sides of the River Nile namely: Um Tondoba mine at Elsebaiya East area and Western River Nile mine in Elsebaiya West area. Depending on the available data, statistical analysis illustrated differences in the distribution of P2O5 % and ore thickness within the studied areas. Geostatistics used to start with constructing variograms for P2O5 % and thickness for the two phosphate deposits to be used with ordinary kriging models, also constructing cross variograms between P2O5 % and thickness to be used with cokriging models where the difference in the variogram parameters reflected a specific variation for each deposit horizontally and vertically. The ordinary kriging models and cokriging models illustrated different distribution behavior through both the two kriging techniques.

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: 0.0-0.05, 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 maioresvalores 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.

RESUMO Estimar a variabilidade espacial de uma cultura em campo auxilia no entendimento de alguns fenômenos que podem estar correlacionados com a sua produtividade. Este trabalho teve, como objetivo, estimar, pelo método da cokrigagem, a produtividade (kg ha-1) do cafeeiro conilon, em três safras consecutivas, tendo como covariável o número de ramos produtivos (plagiotrópicos) por planta. No centro de uma lavoura, demarcou-se uma malha amostral, com 109 pontos georreferenciados, sendo cada ponto constituído por cinco plantas. Fez-se o acompanhamento, por três safras agrícolas consecutivas, contando-se os ramos produtivos e realizando-se a colheita manual. O café colhido foi separado, para cada estádio de maturação (cereja, verdoengo e verde), e secado para umidade padrão de 12%, determinando-se a produtividade de café seco em coco (kg ha-1). A produtividade e o número de ramos produtivos por ponto amostral apresentaram correlação linear e dependência espacial nas três safras. As estimativas das produtividades pelos números de ramos produtivos apresentaram similaridades na cokrigagem, mostrando ser a covariável ramo produtivo eficiente na estimativa da produtividade do café conilon (Robusta Tropical). ABSTRACT Knowing the spatial variability of a coffee-crop helps to understand some phenomena which can be correlated with its productivity. This study aimed estimating the productivity (kg ha-1) of conilon coffee-tree in three consecutive harvests using the cokriging method, considering the number of plagiotropic branches per plant as a covariable. In the center of a coffee-crop we settled a sampling grid with 109 georeferenced points, each point consisted of five plants. Subsequently the vegetative branches were counted for three consecutive growing seasons before performing manual harvesting of coffee beans. The harvested coffee was separated into cherry, yellowish and green and dried to a standard moisture of 12%. The dry cherry productivity (kg ha-1) was then evaluated. The three harvests evaluated showed linear correlation and spatial dependence among productivity and number of vegetative branches. Estimation of productivity by the number of vegetative branches showed similarities in cokriging, indicating that vegetative branches is an efficient covariable to estimate conilon coffee productivity.

The variance-based cross-variogram between two spatial processes, Z1 (·) and Z2 (·), is var (Z1 ( u ) − Z2 ( v )), expressed generally as a bivariate function of spatial locations uandv. It characterizes the cross-spatial dependence between Z1 (·) and Z2 (·) and can be used to obtain optimal multivariable predictors (cokriging). It has also been called the pseudo cross-variogram; here we compare its properties to that of the traditional (covariance-based) cross-variogram, cov (Z1 ( u ) − Z1 ( v ), Z2 ( u ) − Z2 ( v )). One concern with the variance-based cross-variogram has been that Z1 (·) and Z2 (·) might be measured in different units (“apples” and “oranges”). In this note, we show that the cokriging predictor based on variance-based cross-variograms can handle any units used for Z1 (·) and Z2 (·); recommendations are given for an appropriate choice of units. We review the differences between the variance-based cross-variogram and the covariance-based cross-variogram and conclude that the former is more appropriate for cokriging. In practice, one often assumes that variograms and cross-variograms are functions of uandv only through the difference u − v. This restricts the types of models that might be fitted to measures of cross-spatial dependence.

Submitted by Guilherme Lemeszenski (guilherme@nead.unesp.br) on 2013-08-22T19:01:18Z No. of bitstreams: 1 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) Made available in DSpace on 2013-08-22T19:01:18Z (GMT). No. of bitstreams: 1 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) Previous issue date: 2013-06-01 Made available in DSpace on 2013-09-30T20:01:11Z (GMT). No. of bitstreams: 2 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) S0103-90162013000300008.pdf.txt: 32928 bytes, checksum: b7dc84a9ce8cd0a3fa163634c029a6ea (MD5) Previous issue date: 2013-06-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:13:31Z No. of bitstreams: 2 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) S0103-90162013000300008.pdf.txt: 32928 bytes, checksum: b7dc84a9ce8cd0a3fa163634c029a6ea (MD5) Made available in DSpace on 2014-05-20T13:13:31Z (GMT). No. of bitstreams: 2 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) S0103-90162013000300008.pdf.txt: 32928 bytes, checksum: b7dc84a9ce8cd0a3fa163634c029a6ea (MD5) Previous issue date: 2013-06-01 Soil CO2 emission (FCO2) is governed by the inherent properties of the soil, such as bulk density (BD). Mapping of FCO2 allows the evaluation and identification of areas with different accumulation potential of carbon. However, FCO2 mapping over larger areas is not feasible due to the period required for evaluation. This study aimed to assess the quality of FCO2 spatial estimates using values of BD as secondary information. FCO2 and BD were evaluated on a regular sampling grid of 60 m × 60 m comprising 141 points, which was established on a sugarcane area. Four scenarios were defined according to the proportion of the number of sampling points of FCO2 to those of BD. For these scenarios, 67 (F67), 87 (F87), 107 (F107) and 127 (F127) FCO2 sampling points were used in addition to 127 BD sampling points used as supplementary information. The use of additional information from the BD provided an increase in the accuracy of the estimates only in the F107, F67 and F87 scenarios, respectively. The F87 scenario, with the approximate ratio between the FCO2 and BD of 1.00:1.50, presented the best relative improvement in the quality of estimates, thereby indicating that the BD should be sampled at a density 1.5 time greater than that applied for the FCO2. This procedure avoided problems related to the high temporal variability associated with FCO2, which enabled the mapping of this variable to be elaborated in large areas. UNESP FCAV Depto. de Ciências Exatas USP ESALQ Depto. de Ciência do Solo UNESP FCAV Depto. de Ciências Exatas

Buscou-se, com este estudo, estimar, através do uso da cokrigagem, valores de cálcio e magnésio, utilizando como co-variável valores de pH. Em um Latossolo Vermelho Distroférrico, amostrou-se a camada de 0-0,2 m, utilizando grade de 25 x 25 m, totalizando 60 pontos em 5,02 ha. Realizou-se, inicialmente, análise descritiva e de correlação e, posteriormente, análise geoestatística, para identificar a dependência espacial das variáveis estudadas. Utilizando a técnica da cokrigagem, os valores de pH mostraram-se eficientes na estimativa de teores de Ca e Mg, devido a elevada correlação entre esta co-variável e as variáveis a serem estimadas. Sought, with this study, estimating, through the use of cokriging, values of calcium and magnesium, using as covariate pH. In a Rhodic Hapludox, sampled the layer of 0-0.2 m, using the grid of 25 x 25 m, totaling 60 points from 5.02 ha. Was held, initially, descriptive analysis and correlation, and subsequently, geostatistical analysis to identify the spatial dependence of variables. Using the technique of cokriging pH values were efficient in the estimation of Ca and Mg, due to the high correlation between this covariate and the variables to be estimated.