Search Results

You are looking at 1 - 10 of 11 items for :

  • "semivariogram" x
  • All content x
Clear All
Free access

E.A. Guertal and C.B. Elkins

Photosynthetically active radiation (PAR) was measured at two times of day (8:00 am and noon Central Standard Time) in a 915 × 915-cm area of a 1006 × 915-cm gable roof greenhouse. PAR measurements were taken across a grid at 40-cm intervals, a total of 529 data points. Spatial variation of PAR in the greenhouse was evaluated through contour plots and the geostatistical technique of semivariogram construction. Semivariograms provide a visual guide to the degree of spatial correlation of a variable, allowing a quantification of the distance at which variables cease to be spatially correlated (the range) Measured PAR contained distinct zones of lowered values, a function of overhead greenhouse structures, wall-hung electrical boxes, and tall plants in adjacent greenhouses. Although the amount of PAR changed over time, zones of high and low PAR remained relatively constant, except at the sides of the greenhouse. Constructed semivariograms revealed that PAR contained strong spatial correlation (up to a 350-cm separation) as measured in the north-south direction, moving parallel to greenhouse bench placement. When PAR measurements perpendicular to benches (east-west) were used in directional semivariograms PAR was found to be completely random, plotting as a horizontal line called a nugget effect. Thus, plants placed perpendicular to the greenhouse benches (east-west) would not be affected by the spatial correlation of PAR.

Open access

Job Teixeira de Oliveira, Rubens Alves de Oliveira, Domingos Sarvio Magalhães Valente, Isabela da Silva Ribeiro, and Paulo Eduardo Teodoro

bivariate indexes were calculated among the attributes studied. For each attribute, their spatial dependence was analyzed by the simple semivariogram calculation. To obtain the ideal number of neighbors, the kriging and cokriging maps were obtained through

Open access

Job Teixeira de Oliveira, Rubens Alves de Oliveira, Lucas Allan Almeida Oliveira, Paulo Teodoro, and Rafael Montanari

population with the normal distribution. To characterize the structure and magnitude of the spatial dependence of the ( Fig. 1 ) components, semivariogram adjustments and semivariance estimation were performed, estimating the coefficients of the theoretical

Open access

Job Teixeira de Oliveira, Rubens Alves de Oliveira, Mario Puiatti, Paulo Teodoro, and Rafael Montanari

values were expressed in cm 3 . Descriptive analysis was performed. Semi-variogram adjustments and semi-variance estimation were performed to estimate the coefficients of the theoretical model for the semi-variogram called the nugget effect (C 0 ), sill

Free access

Chase M. Straw, Rebecca A. Grubbs, Kevin A. Tucker, and Gerald M. Henry

relationship between sampling devices. Spatial maps were used to visually compare the variability of each field property. The maps were generated by first plotting the empirical semivariograms (half the squared difference of the value between two points for all

Free access

Susan C. Miyasaka, Charles E. McCulloch, Graham E. Fogg, and James R. Hollyer

, and tissue-cultured plantlets were used as propagating materials. Optimum plot size was calculated based on Eq. [2] and Smith’s (1938) “b” calculated from Eq. [1]. Calculation of semivariograms to quantify spatial autocorrelation. Often observations

Free access

M. Joseph Stephens, Jessica Scalzo, Peter A. Alspach, Ron A. Beatson, and Ann Marie Connor

magnitudes of the estimates relative to their se s, the likelihood ratio test of nested models, and the row × plant position semivariograms. For FRAP, the semivariograms suggested the presence of a linear trend along the rows and this was fitted as a fixed

Full access

Valdinar Ferreira Melo, Edvan Alves Chagas, Raphael Henrique da Silva Siqueira, Olisson Mesquita de Souza, Luís Felipe Paes de Almeida, Diogo Francisco Rossoni, Pollyana Cardoso Chagas, and Carlos Abanto-Rodríguez

( Jorge and Silva, 2010 ). With the results obtained for each grid unit, we proceeded to estimate the sample semivariance. For this purpose, the centroid of each plot was used, comprising 100 sample points. After the model adjustment to the semivariogram

Free access

M. Joseph Stephens, Peter A. Alspach, Ron A. Beatson, Chris Winefield, and Emily J. Buck

correlations relative to their se s, the semivariograms, and the likelihood ratio test (data not shown) and were therefore not included in the final models. The residual plots indicated that the assumption of normality was tenable for all traits except for

Free access

Gerald M. Henry, Michael G. Burton, and Fred H. Yelverton

semivariogram model with a variable radius type set to 12 was performed by the Kriging method. Kriging uses prior knowledge about the spatial distribution of a variable to predict values of said variable at unobserved points and turning the data into a raster