Efficiency of Spatial Analyses of Field Pea Variety Trials
Rong-Cai Yang*,a,
Terrance Z. Yeb,
Stanford F. Bladec and
Manjula Bandarad
a Alberta Agriculture, Food, and Rural Development, Room 300, 7000–113 Street, Edmonton, AB, Canada T6H 5T6, and Dep. of Agricultural, Food, and Nutritional Sci., Univ. of Alberta, Edmonton, AB, Canada T6G 2P5
b Alberta Agriculture, Food, and Rural Development, Room 300, 7000–113 Street, Edmonton, AB, Canada T6H 5T6
c Alberta Agriculture, Food, and Rural Development, RR6, 17507 Fort Road, Edmonton, AB, Canada T5B 4K3
d Alberta Agriculture, Food, and Rural Development, Brooks, AB, Canada T1R 1E6
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Fig. 1. Distributions of the efficiency of (A) the nearest neighbor adjustment (NNA), (B) least squares smoothing (LSS), and (C) first-order autoregressive model analyses (AR1) in removing spatial trends from the residual variances in 157 field pea variety trials tested in Alberta, Canada, during 1997 to 2001. SSENNA, SSELSS, and SSERCBD are the error sums of squares from the NNA, LSS, and randomized complete block design analyses. SEDAR1 and SEDRCBD are the standard errors of difference from the AR1 and RCBD analyses.
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Fig. 2. Decomposition of yield data into variety effect, spatial trend, and residual by the least squares smoothing analysis for two field pea variety trials with high (A) and low (B) coefficients of variation.
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Copyright © 2004 by the Crop Science Society of America.
