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Getting the Most Out of Sorghum Low‐Input Field Trials in W est A frica Using Spatial Adjustment
Author(s) -
Leiser W. L.,
Rattunde H. F.,
Piepho H.P.,
Parzies H. K.
Publication year - 2012
Publication title -
journal of agronomy and crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.095
H-Index - 74
eISSN - 1439-037X
pISSN - 0931-2250
DOI - 10.1111/j.1439-037x.2012.00529.x
Subject(s) - heritability , sorghum , replicate , spatial variability , statistics , mathematics , spatial heterogeneity , random effects model , grain yield , spatial ecology , ranking (information retrieval) , agronomy , biology , ecology , computer science , machine learning , medicine , genetics , meta analysis
Breeding sorghum for low‐input conditions is hindered by soil heterogeneity. Spatial adjustment using mixed models can help account for this variation and increase precision of low‐input field trials. Large small‐scale spatial variation ( CV 39.4 %) for plant available phosphorus was mapped in an intensely sampled low‐input field. Spatial adjustments were shown to account for residual yield differences because of this and other growth factors. To investigate the potential of such models to increase the efficiency of low‐ and high‐input field trials, 17 experiments with 70 sorghum genotypes conducted in M ali, W est A frica, were analysed for grain yield using different mixed models including models with autoregressive spatial correlation terms. Spatial models ( AR 1, AR 2) improved broad sense heritability estimates for grain yield, averaging gains of 10 and 6 % points relative to randomized complete block ( RCB ) and lattice models, respectively. The heritability estimate gains were even higher under low phosphorus conditions and in two‐replicate analyses. No specific model was best for all environments. A single spatial model, AR 1 × AR 1, captured most of the gains for heritability and relative efficiency provided by the best model identified for each environment using A kaike's I nformation C riterion. Spatial modelling resulted in important changes in genotype ranking for grain yield. Thus, the use of spatial models was shown to have potentially important consequences for aiding effective sorghum selection in W est A frica, particularly under low‐input conditions and for trials with fewer replications. Thus, using spatial models can improve the resource allocation of a breeding program. Furthermore, our results show that good experimental design with optimal placement and orientation of blocks is essential for efficient statistical analysis with or without spatial adjustment.