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Table 4 Spatial and non-spatial models used for the first stage

From: The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye

Label

Model

Variance-covariance

  

structure for error

M1

Y h i j k v =(G T) h v +S i +R i j +B i j k +e h i j k v

ID

M2

Y h i j k o q v =(G T) h v +S i +R i j +B i j k

ID

 

+W i j o +V i j q +e h i j k o q v

 

M3

Y h i j k v =(G T) h v +S i +R i j +B i j k +e h i j k v

AR(1) within B

M4

Y h i j k v =(G T) h v +S i +R i j +B i j k +e h i j k v

LV within B + nugget

M5

Y h i j k o q v =(G T) h v +S i +R i j +B i j k

AR(1) × AR(1) within R

 

+W i j o +V i j q +e h i j k o q v

 

M6

Y h i j k v =(G T) h v +S i +R i j +B i j k +e h i j k v

AR(1) × AR(1) within R

M7

Y h i j k v =(G T) h v +S i +R i j +B i j k +e h i j k v

Model 3 + nugget

M8

Y h i j k v =(G T) h v +S i +R i j +B i j k +e h i j k v

Model 5 + nugget

M9

Y h i j k o q v =(G T) h v +S i +R i j +B i j k

Model 6 + nugget

 

+W i j o +V i j q +e h i j k o q v

 
  1. Y h i j k v is the observed dry matter yield of the h-th genotype testcrossed with the v-th tester in the k-th block within the j-th replicate of the i-th trial, (G T) h v is the effect of the h-th genotype testcrossed with the v-th tester, S i is the effect of the i-th trial [ S i N(0, σ S 2 )], R i j is the effect of the j-th replicate nested within the i-th trial [ R ij N(0, σ R 2 )], B i j k is the effect of the k-th block nested within the j-th replicate of the i-th trial [ B ijk N(0, σ B 2 )] and e h i j k v is the plot error associated with the Y h i j k v observation [ e hijkv N(0, σ e 2 )]. In the models including row and column effects, W i j o is the effect of the o-th row within the j-th replicate of the i-th trial [ W ijo N(0, σ W 2 )] and V i j q is the effect of the q-th column within the j-th replicate of the i-th trial [ V ijq N(0, σ V 2 )]. Spatial variance-covariance structure were independent (ID), autoregressive in one direction [AR(1)], one-dimension linear variance (LV) and two-dimension autoregressive [AR(1) × AR(1)].