Getting the most out of sorghum low-input field trials in West Africa using spatial adjustment

Abstract

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 Mali, West Africa, were analysed for grain yield using different mixed models including models with autoregressive spatial correlation terms. Spatial models (AR1, AR2) 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, AR1 × AR1, captured most of the gains for heritability and relative efficiency provided by the best model identified for each environment using Akaike's Information Criterion. 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 West Africa, 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