Add to ORKGSpatial analysis procedures based on one-dimensional and two-dimensional (separable) ARIMA (Auto Regressive Integrated Moving Average) processes were used to analyze several yield trials. Two criteria were used to determine the best spatial model: 1) standard error of the treatment difference (SED) and 2) mean squared error (MSE) of prediction based on a cross-validation approach. It is found that spatial models with two-dimensional exponential covariance functions are frequently the best models regarding SED and MSE. Differenced models are frequently the best models regarding SED and the worst with respect to MSE
This paper reviews methods for spatial analysis of field trials in one and two dimensions with a par...
Precision agricultural technology promises to move crop production closer to a manufacturing paradig...
Several spatial analyses of neighboring plots are now available for improving the precision of varie...
Methodology for precision agriculture is, perhaps, too focused on methods that allow for spatial cor...
Regressions such as Grain yield=f(soil,landscape) are frequently reported in precision agriculture r...
The most common procedure for analyzing multi-environmental trials is based on the assumption that t...
Precision agricultural technology promises to move crop production closer to a manufacturing paradig...
The use of nearest neighbors and spatial models (SPAT) to analyze field trial data has become common...
Four types of covariates are used to account for spatial variability in data from a field experiment...
An important aim of the analysis of agricultural field trials is to obtain good predictions for geno...
This publication is with permission of the rights owner freely accessible due to an Alliance licence...
In the presence of spatial heterogeneity in experimental fields, the traditional random blocking has...
In the presence of spatial heterogeneity in experimental fields, the traditional random blocking has...
In the presence of spatial heterogeneity in experimental fields, the traditional random blocking has...
The advantages of using spatial analysis in annual crop experiments are well documented. There is mu...
This paper reviews methods for spatial analysis of field trials in one and two dimensions with a par...
Precision agricultural technology promises to move crop production closer to a manufacturing paradig...
Several spatial analyses of neighboring plots are now available for improving the precision of varie...
Methodology for precision agriculture is, perhaps, too focused on methods that allow for spatial cor...
Regressions such as Grain yield=f(soil,landscape) are frequently reported in precision agriculture r...
The most common procedure for analyzing multi-environmental trials is based on the assumption that t...
Precision agricultural technology promises to move crop production closer to a manufacturing paradig...
The use of nearest neighbors and spatial models (SPAT) to analyze field trial data has become common...
Four types of covariates are used to account for spatial variability in data from a field experiment...
An important aim of the analysis of agricultural field trials is to obtain good predictions for geno...
This publication is with permission of the rights owner freely accessible due to an Alliance licence...
In the presence of spatial heterogeneity in experimental fields, the traditional random blocking has...
In the presence of spatial heterogeneity in experimental fields, the traditional random blocking has...
In the presence of spatial heterogeneity in experimental fields, the traditional random blocking has...
The advantages of using spatial analysis in annual crop experiments are well documented. There is mu...
This paper reviews methods for spatial analysis of field trials in one and two dimensions with a par...
Precision agricultural technology promises to move crop production closer to a manufacturing paradig...
Several spatial analyses of neighboring plots are now available for improving the precision of varie...