The use of geoadditive models to estimate the spatial distribution of grain weight in an agronomic field: a comparison with kriging with external drift

2011 ◽  
Vol 22 (6) ◽  
pp. 769-780 ◽  
Author(s):  
Barbara Cafarelli ◽  
Annamaria Castrignanò
Keyword(s):  
Spatial Distribution ◽  
Grain Weight ◽  
Kriging With External Drift ◽  
External Drift

scholarly journals Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 830
Author(s):  
Gabriele Buttafuoco ◽  
Massimo Conforti
Keyword(s):  
Spatial Distribution ◽  
Ordinary Kriging ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Block Kriging ◽  
Kriging With External Drift ◽  
Elevation Data ◽  
Support Size ◽  
Ordinary Cokriging ◽  
External Drift

Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial data. The paper was aimed to compare block ordinary cokriging and block kriging with an external drift in estimating the annual precipitation using elevation as covariate. Block ordinary kriging was used as reference of a univariate geostatistical approach. In addition, the different support sizes associated with precipitation and elevation data were also taken into account. The study area was the Calabria region (southern Italy), which has a spatially variable Mediterranean climate because of its high orographic variability. Block kriging with elevation as external drift, compared to block ordinary kriging and block ordinary cokriging, was the most accurate approach for modelling the spatial distribution of annual mean precipitation. The three measures of accuracy (MAE, mean absolute error; RMSEP, root-mean-squared error of prediction; MRE, mean relative error) have the lowest values (MAE = 112.80 mm; RMSEP = 144.89 mm, and MRE = 0.11), whereas the goodness of prediction (G) has the highest value (75.67). The results clearly indicated that the use of an exhaustive secondary variable always improves the precipitation estimate, but in the case of areas with elevations below 120 m, block cokriging makes better use of secondary information in precipitation estimation than block kriging with external drift. At higher elevations, the opposite is always true: block kriging with external drift performs better than block cokriging. This approach takes into account the support size associated with precipitation and elevation data. Accounting for elevation allowed to obtain more detailed maps than using block ordinary kriging. However, block kriging with external drift produced a map with more local details than that of block ordinary cokriging because of the local re-evaluation of the linear regression of precipitation on block estimates.

scholarly journals Production components of corn as function of seed distribution along the planting row

2015 ◽  
Vol 19 (12) ◽  
pp. 1172-1177
Author(s):  
Flavio H. da Silva ◽  
Paulo C. R. da Cunha ◽  
André C. de S. Almeida ◽  
Lucas da S. Araújo ◽  
Adriano Jakelaitis ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Grain Weight ◽  
High Tech ◽  
Corn Hybrids ◽  
Corn Production ◽  
Coefficients Of Variation ◽  
Stalk Diameter ◽  
Seed Distribution ◽  
Ear Number ◽  
Corn Grain

ABSTRACT High corn yields in high-tech systems are related to proper crop implementation. This study aimed to evaluate the effects of variability in the distribution of seeds along the planting row on corn production components. The study was conducted under Cerrado conditions in the municipality of Urutaí, GO, Brazil. The experimental design was randomized blocks in a 5 x 2 factorial scheme, with four replicates. The effects of five coefficients of variation (0, 20, 40, 60 and 80%) of non-uniformity in the spatial distribution of seeds along the planting row of two corn hybrids (P30F53HX and P3646HX) were evaluated. No interactions were observed for the analysed corn variables. However, as the non-uniformity in seed distribution along the planting row increased, stalk diameter, hundred-grain weight, number of rows per ear, number of kernels per row and ear length decreased. Additionally, linear reductions were observed in corn grain yield with the increase in the coefficient of variation of the spatial distribution of seeds along the planting row. Between the hybrids, the 30F53HX showed higher hundred-grain weight.

scholarly journals Estimation of transit trip production using Factorial Kriging with External Drift: an aggregated data case study

2016 ◽  
Vol 19 (4) ◽  
pp. 245-254 ◽  
Author(s):  
Anabele Lindner ◽  
Cira Souza Pitombo ◽  
Samille Santos Rocha ◽  
José Alberto Quintanilha
Keyword(s):  
Aggregated Data ◽  
Kriging With External Drift ◽  
Factorial Kriging ◽  
Trip Production ◽  
External Drift

A linear mixed effect (LME) model for soil water content estimation based on geophysical sensing: a comparison of an LME model and kriging with external drift

2014 ◽  
Vol 73 (5) ◽  
pp. 1951-1960 ◽  
Author(s):  
Barbara Cafarelli ◽  
Annamaria Castrignanò ◽  
Daniela De Benedetto ◽  
Angelo Domenico Palumbo ◽  
Gabriele Buttafuoco
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Kriging With External Drift ◽  
External Drift

scholarly journals Spatial Interpolation for Real-Time Rainfall Field Estimation in Areas with Complex Topography

2013 ◽  
Vol 14 (1) ◽  
pp. 85-104 ◽  
Author(s):  
M. C. Rogelis ◽  
M. G. F. Werner
Keyword(s):  
Real Time ◽  
Ordinary Kriging ◽  
Kriging With External Drift ◽  
Time Operation ◽  
Spatially Distributed ◽  
Interpolation Procedure ◽  
Real Time Operation ◽  
Distributed Hydrological Models ◽  
Gaussian Simulation ◽  
External Drift

Abstract For many hydrological applications interpolation of point rainfall measurements is needed. One such application is flood early warning, particularly where spatially distributed hydrological models are used. Operation in real time poses challenges to the interpolation procedure, as this should then both be automatic and efficiently provide robust interpolation of gauged data. The differences in performance of ordinary kriging, universal kriging, and kriging with external drift with individual and pooled variograms were assessed for 139 daily datasets with significant precipitation in a study area in Bogotá, Colombia. Interpolators were compared using the percentage of variability explained and the root-mean-square error found in cross validation, aiming at identifying a procedure for real-time interpolation. The results showed that interpolators using pooled variograms provide a performance comparable to when the interpolators were applied to the storms individually, showing that they can be used successfully for interpolation in real-time operation in the study area. The analysis identified limitations in the use of kriging with external drift. Only when the adjusted R2 between the secondary variables and precipitation is higher than the percentage of variability explained found in ordinary kriging, then kriging with external drift provided a consistent improvement. This interpolator was found to give a lower performance in all other cases. The distribution of precipitation over basins of interest for each of the storms, derived through sampling rainfall fields generated through conditional Gaussian simulation, shows that, while differences between the interpolators may appear to be significant, the variability of the precipitation volume is less significant.

Factorial kriging with external drift: a case study on the Penedono Region, Portugal

2001 ◽  
Vol 16 (7-8) ◽  
pp. 921-929 ◽  
Author(s):  
A.C. Batista ◽  
A.J. Sousa ◽  
M.J. Batista ◽  
L. Viegas
Keyword(s):  
Kriging With External Drift ◽  
Factorial Kriging ◽  
External Drift

GIS-based analysis for soil moisture estimation via kriging with external drift

2021 ◽  
pp. 391-408
Author(s):  
Akash Anand ◽  
Prachi Singh ◽  
Prashant K. Srivastava ◽  
Manika Gupta
Keyword(s):  
Soil Moisture ◽  
Kriging With External Drift ◽  
Moisture Estimation ◽  
External Drift
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