2024 Lipson Long Coleoptile Wheat Trial: Multi-Factor Analysis of Genotype, Sowing Depth, and Soil Strength Effects on Emergence, Growth, and Yield

This dataset originates from the 2024 Lipson Long Coleoptile Wheat Trial conducted at Lipson, Eyre Peninsula, South Australia. It captures a comprehensive multi-factor field experiment designed to evaluate the effects of wheat genotype, sowing depth, and soil strength (via presswheel pressure) on crop emergence, early growth, and yield. The trial includes two core components: a genotype × sowing depth factorial and a systems experiment incorporating soil strength. Six wheat genotypes (Mace, Mace18, Magenta, Magenta13, Calibre, Scepter) were sown at two depths (shallow: 4–6 cm; deep: 8–10 cm) under three presswheel pressures (light, mid, heavy), resulting in 72 treatment combinations replicated across four blocks. The dataset is organised across multiple Excel (.xlsx) sheets, each representing specific data domains: trial metadata, treatment layout, sowing depth and coleoptile measurements, soil moisture and strength profiles, and crop performance metrics. Soil moisture was measured gravimetrically at multiple depths (0–12 cm) and time points (seeding, 6, 14, 21, and 35 days after sowing), while soil strength was assessed using a penetrometer at the same intervals. Crop assessments include emergence counts (29–44 DAS), NDVI and canopy cover (via Greenseeker and Canapeo), booting/head emergence (Zadoks scale), biomass and spike number, and grain yield and quality. Variables include plant density, coleoptile length, sowing depth, biomass, harvest index, grain yield, 1000 grain weight, screenings, test weight, and protein content. Soil and environmental data are linked to treatment identifiers and plot coordinates, enabling integration across sheets. Rainfall data from the Tumby Bay weather station is included to contextualize environmental conditions. The dataset is structured to support genotype by environment by management interaction analysis and is suitable for agronomic modelling, trait evaluation, and soil-plant interaction studies.