2023 Emerald Wheat Emergence and Agronomic Performance Dataset: Multi-Factor Analysis of Depth, Sowing Time, and Genotype Effects

Full description

This dataset comprises detailed agronomic measurements from a multi-factor wheat trial conducted in Emerald, Queensland, designed to evaluate the effects of sowing depth (shallow vs deep), time of sowing (TOS 1: April 17 and TOS 2: May 17), and genotype type (Conventional vs LCW) across 16 wheat varieties. The trial employed a split-split plot design with three replicates per treatment combination. Data were collected for emergence (plants/m²), phenology (days to flowering and maturity), tiller counts at GS65 and GS90, total biomass, grain yield (from biomass cuts and machine harvest), harvest index, and grain quality traits including protein content, test weight, screenings percentage, and 300 seed weight.

The dataset is structured across five Excel (.xlsx and .csv), each sheet corresponding to a specific trait or analysis. Each sheet includes raw measurements, statistical summaries, and model outputs from REML-based linear mixed models fitted in GenStat. Fixed effects include TOS, depth, type, variety, and their interactions, while random effects account for replication and nested plot structures.

Environmental conditions were consistent across plots, with sowing depth and timing being the primary experimental variables. Soil strength measurements and emergence counts were taken at multiple intervals post-sowing. Data transformations and residual diagnostics were applied where necessary to meet model assumptions. The dataset includes over 150 unique plot-level observations per trait, with some plots excluded due to missing or questionable data.

Variable definitions include emergence counts (plants/m²), DTF and DTM (days), tiller counts (tillers/m²), biomass (kg/ha), grain yield (kg/ha at 12.5% moisture), HI (unitless ratio), protein (%), test weight (g), screenings (% arcsine-transformed), and seed weight (g). Codes and abbreviations are consistent across sheets, and all measurements are aligned to standard agronomic protocols. This dataset enables robust analysis of genotype performance under varying sowing conditions and supports genotype selection for improved emergence and yield stability.

Lineage: Field experiment data