Statistical Elements of a Successful Farm-scale Trial

Abstract

Growers and advisers increasingly run large strip trials on their own paddocks, but the rules used to design them come from small-plot research. We simulated strip trials on a spatially autocorrelated field, varying trial length (L), replication (r), randomised or systematic allocation, and long-strip or stacked layout, for a nitrogen response that was uniform or varied across within-field pseudo-environments (PEs). Analyses of strip-plot means and of yield pixels were first checked for Type I error, and designs were then compared by the smallest response that a valid analysis detects with 80% power. Follow-up runs added gaps, unequal strip lengths, trends, spatially varying responses and alternative field structures; more than 3 million models were fitted. Treating pixels as independent replicates gave false positives in 32-40% of null trials. Replication was the strongest design lever: the detectable response scaled as $r^{-0.60}$ to $r^{-1.02}$ but only as $L^{-0.45}$ or more slowly, and at equal plot area more, shorter replicates won all 66 comparisons. With complete data, strip means with an autoregressive residual were the most precise analysis; when strips contained gaps, pixel-level spatial models with a strip effect halved the detectable response, while the spatial analysis of strip means became liberal. For PE-varying responses, spatial PE models detected per-PE effects 33-46% smaller than PE means without a spatial term. Randomisation did not improve precision but kept tests valid (systematic allocation: Type I error up to 23%), whereas systematic allocation gave more accurate response maps. The results are condensed into a design table and a detectable-gain chart for use before a trial is sown.