Field trial analyses of wheat and cassava benefit from spatial correction

Spatial variation is a major source of error in agricultural field experiments affecting genotype performance prediction. Implementing statistical models that account for spatial effects can improve the prediction of genotype performance. This study evaluated the impact of the B-spline spatial correction method on the estimation of genetic parameters and AIC values in two distinct crops – wheat and cassava – using four models: Block, Block + Spatial, Block + Marker, and Block + Marker + Spatial. Analyses were performed on data from 136 and 68 trials obtained from the T3/WheatCAP and Cassavabase databases, respectively. The results demonstrated that correcting for spatial variation, regardless of marker information, increased the heritability estimate of grain yield, test weight, plant height, powdery mildew, stripe rust, and bacterial streak disease in wheat. Similar increases were observed in cassava for dry matter content, dry yield, and plant height. However, no increase was observed for cassava mosaic disease or bacterial blight. Models incorporating spatial correction in both crops consistently provided the best fit based on AIC values across all traits in wheat and cassava. These results were consistent whether or not marker effects were fitted in the models. This showed the importance of spatial correction in field experiment analysis.