This dataset originates from the 2024 long coleoptile wheat trial conducted at Dookie College, Victoria, Australia. The trial investigates the interactions between wheat genotype, sowing depth, and presswheel pressure (as a proxy for soil strength) on early crop establishment and development. The core experimental design is a factorial combination of six wheat genotypes (Scepter, Calibre, Mace, Mace18, Magenta, Magenta13), two sowing depths (shallow: 30–40 mm; deep: 80–100 mm), and three presswheel pressures (light, standard, heavy), resulting in 36 treatment combinations replicated across multiple blocks. The dataset is structured across multiple Excel file (.xlsx) sheets, each representing different aspects of the trial, including experimental design, field maps, seed characteristics, soil measurements, and emergence data. Key files include treatment layouts, seed packing details, soil strength and moisture data at various depths and time points (pre-sowing, 0 days after sowing, and 12 days after sowing), temperature logger data, and detailed emergence counts over time. Soil strength was measured using a Geotester penetrometer, while gravimetric moisture and matric potential were assessed through laboratory analysis of soil cores taken at specified depths. Temperature sensors recorded hourly data at 0, 3–4, and 8–10 cm depths in selected plots. Seed characteristics such as thousand seed weight, seed size grading, and germination/vigour assessments were recorded for each genotype. The emergence data includes counts from two seeding rows per plot, tracked over multiple dates post-sowing, allowing for analysis of emergence dynamics. The dataset supports investigations into how genotype and agronomic practices influence wheat establishment under varying soil mechanical resistance and moisture conditions. All data is labelled with consistent identifiers for plot, treatment, genotype, depth, and pressure, facilitating integration across sheets. This comprehensive dataset enables robust analysis of genotype by environment by management interactions relevant to improving wheat establishment under challenging sowing conditions.

This dataset captures results from a 2024 field experiment conducted at Wynarka, South Australia, to investigate the interaction between sowing depth, fertiliser placement, and seed size on the establishment and performance of long coleoptile wheat. The trial involved three experiments using the Mace18 genotype (Experiments 1 and 2) and Calibre (Experiment 3), with treatments including two sowing depths (shallow: 4–5 cm and deep: 10–12 cm), two seed sizes (standard and small), and multiple fertiliser placements (above, with, below seed, sideband, and twin-row configurations). The experiment was laid out in a randomised block design with four replicates, using 12 m × 2.2 m plots and 28 cm row spacing. The Excel file (.xlsx) contains seven sheets. “Trial details” outlines the experimental design, site conditions, and agronomic practices including sowing date (7 May 2024), fertiliser regimes, and rainfall data. “Trial Plan Exp1” and “Trial Plan Exp2&3” detail plot layouts and treatment combinations for each experiment. The “Metafile” provides metadata including sowing depths, assessment dates, and measured variables. The dataset in “Exp1_data” includes 40 plots with variables such as plant density at five time points, emergence percentage, sowing depth, coleoptile length, root and shoot biomass, NDVI across ten dates, dry biomass at GS31, GS65, and GS89, spike number, grain yield, harvest index, and 1000-grain weight. The “Exp1_soildata” sheet provides gravimetric soil moisture content at three depths (0–5, 5–10, 10–15 cm) for selected plots, measured on two dates post-sowing. The dataset includes over 50 variables. Treatment labels (e.g., “deep_Small_fert below”) encode depth, seed size, and fertiliser placement. Biomass is recorded in g/m², NDVI as unitless indices, and grain yield in t/ha. Soil moisture is expressed as percent weight/weight. Data were collected using standard field protocols, with plant assessments based on averages from 10 plants per plot. This dataset enables detailed analysis of how agronomic practices influence early wheat establishment, biomass accumulation, and final yield under low rainfall conditions.

This dataset comprises detailed agronomic data from three coordinated field experiments conducted at Beelbangera, NSW. The trials investigate the effects of genotype, sowing depth, fertiliser placement and rate, and seed size on wheat establishment and yield performance. The Excel workbook contains 14 sheets, which include trial details, site maps, treatment layouts, packet plans, raw plot-level data, processed metrics, and harvest index calculations. The data is structured with consistent identifiers such as run number, genotype, sowing depth, fertiliser treatment, and seed size category. Raw data entries include grain yield per plot, sowing depth, genotype, fertiliser rate, and plot dimensions, enabling calculation of yield per hectare. Additional sheets provide derived metrics such as grain count, canopy cover, SAVI (Soil Adjusted Vegetation Index), and harvest index. Data was collected using standard field trial methodologies. Sowing was performed on 29 April 2024 using a Morris seeder at two depths: shallow (4 cm) and deep (12–14 cm). Fertiliser was applied either with the seed or above the seed, at safe (80 kg/ha MAP) and unsafe (160 kg/ha MAP) rates. Trials were replicated three times with consistent plot sizes (12 m × 1.75 m) and row spacing (25 cm × 7 rows). Environmental conditions were monitored, including rainfall (362.5 mm total, 246 mm in the growing season), and soil tests were conducted pre-sowing (pH 6.2, total N 57 kg/ha, Colwell P 38 ppm). Herbicide and fertiliser applications were logged with dates and rates. The dataset includes over 100 variables. Key fields include genotype (e.g., Calibre, Mace18, Magenta13), sowing depth, fertiliser rate and placement, seed size (e.g., small, medium, large), grain yield, biomass, spike count, and vegetation indices. Codes such as “SS” and “DS” denote sowing depth, while “Std”, “Lge”, “Sml”, “Med”, and “Vlge” refer to seed size categories. Fertiliser treatments are coded as “Cont”, “Withseed”, and “Aboveseed”. The dataset also includes image references for visual assessments and positional metadata for canopy measurements.

This dataset comprises detailed agronomic measurements from a series of wheat field trials conducted in Roma, Queensland, designed to investigate the effects of sowing depth, coleoptile type, soil strength, and other factors on plant emergence, growth, and yield. The collection includes two primary Excel (.xlsx) files: a master data sheet containing raw and processed measurements from individual plots across multiple trials (MET, Pressure, Seed Size), and an analysis workbook summarizing statistical outputs and model selections. These main files are complemented by MET Deep Tiny Tag and MET Shallow Tiny Tag .csv files. The master sheet documents plot-level data for each trial, including sowing conditions (depth, date, soil strength at multiple depths), plant traits (coleoptile length and diameter), emergence counts at multiple intervals (7, 14, 21 days after sowing), and final emergence. It also includes biomass and grain yield metrics, harvest index, grain quality parameters (protein, moisture, test weight, screenings), and maturity dates. Each plot is identified by location, replicate, treatment, and variety, with coleoptile type (long or conventional) and seed size (standard or large) noted where relevant. The analysis workbook provides statistical summaries from ANOVA and regression models, highlighting significant effects and interactions among depth, variety, coleoptile type, and soil strength. It includes model selection outputs for emergence and coleoptile traits, with R² values and p-values for various combinations of predictors. Environmental conditions such as soil strength was measured at sowing and at multiple intervals post-sowing using gravimetric and pressure-based methods. Drone imagery, EM38 surveys, and weather station data were also collected to support spatial and temporal analysis. Data was processed using GenStat with fixed and random effects models, and transformations were applied where necessary to meet distributional assumptions. The dataset includes over 70 variables, with definitions embedded in column headers and trial documentation. Codes such as LCW (long coleoptile wheat) and conventional types are used to distinguish genetic traits. The dataset is structured to support multivariate analysis and is suitable for evaluating genotype by environment interactions, emergence dynamics, and yield formation under varying agronomic conditions.

This dataset comprises soil, plant, climatic, and management data from a 2023 field experiment conducted at Ungarra, Eyre Peninsula, South Australia. The trial aimed to evaluate the establishment and performance of long coleoptile wheat genotypes compared to short coleoptile varieties under varying sowing depths and fertiliser regimes. Three distinct experiments were conducted side-by-side: 1. Systems Trial (Water Balance): Investigated water balance at sowing using tarp treatments and sowing moisture assessments. 2. Core Genotype × Sowing Depth Trial: Compared eight wheat genotypes across three sowing depths (shallow, mid, deep). 3. Genotype × Depth × Nutrition Trial: Explored interactions between two genotypes, two sowing depths, and three fertiliser rates (45, 100, 150 kg/ha Monoammonium phosphate). Data was collected through field-based measurements including plant counts, seeding depth, NDVI (via Greenseeker), canopy cover (via Canopeo), biomass, spike counts, and grain yield using a plot header. Soil chemistry was analyzed by Eurofins APAL using standardized test codes, and rainfall data were sourced from a nearby soil moisture probe. All data was manually recorded and digitised for further analysis. The data is presented in an Excel workbook (.xlsx) contains trial details, metadata, raw experimental data and soil chemistry. The sheets are interrelated through shared identifiers such as trial number, sowing depth, genotype, and treatment number. Test variables across trials, include a range of agronomic, physiological, and soil metrics, such as grain yield, harvest ratio, biomass, coleoptile length, plant density, seeding depth, NDVI, estimated canopy cover and soil pH, EC, N, P). Codes and Symbols: - GS: Growth Stage (e.g., GS10–11) - NF: Nil Found (used in plant emergence data) - WB: Water Balance treatment codes (e.g., WB1, WB2)

This dataset originates from a comprehensive field trial conducted in Birchip, Victoria, during the 2024 winter cropping season. The trial, identified as 24_Nulla_0124_Long Coleoptile, involved 108 plots across six ranges and 26 rows, with GPS-referenced layout and border plots included. Treatments combined three factors: wheat genotype (including Mace18, Magenta13, Scepter, Calibre), sowing depth (shallow or deep), and presswheel pressure (light, standard, heavy). The dataset comprises multiple Excel sheets detailing agronomic measurements, environmental conditions, and sensor data. Soil water content was assessed gravimetrically and via matric potential across 12 plots at six depth increments (0–12 cm), both pre- and post-sowing. Soil strength was measured using a penetrometer, with readings taken in 2 cm increments and converted to kg/cm². Soil temperature was monitored using sensors placed at the surface, 3–4 cm, and 8–10 cm depths in two plots, with high-frequency time-series data recorded over several days. Emergence was tracked biweekly and then weekly from sowing through six weeks, with counts taken from 1 m sections of seeding rows. Coleoptile length and sowing depth were measured from 20 seedlings per tyne per plot, with values ranging from approximately 1.3 to 8.6 cm and 2.8 to 10.8 cm respectively. NDVI readings were collected using a handheld Greenseeker across 10 dates, showing progressive canopy development with values ranging from 0.09 to 0.79 and CV% from 0.5 to 29.5. Zadoks scores were recorded weekly from early September to October, capturing phenological stages from stem elongation to anthesis (Z43–Z71). Rabbit damage was assessed on 06/07/2024, with severity scores (1–5) and percent chewed recorded per plot. Final harvest data includes grain yield (raw and corrected), moisture content, protein percentage, test weight, screenings, and emergence rates. The dataset is structured for analytical modeling, enabling genotype, depth, pressure comparisons and supporting time-series analysis of emergence, growth, and yield. It includes both raw and processed data, with consistent formatting and minimal missing values. Some metadata corruption is present but does not affect core data usability.

This dataset comprises detailed agronomic and phenotypic data from a series of wheat trials conducted in 2024 at the Narrabri, New South Wales, focusing on the performance of long coleoptile wheat genotypes under varying sowing depths, seed sizes, water regimes, and disease treatments. The collection includes multiple Excel (.xlsx) sheets, each representing different aspects of the trial: experimental diary, plot layout, multi-environment trials (MET), durum wheat trials, seed size trials, Victrado trials (irrigated and dryland), temperature logger data, GS32 dry matter biomass, and multiple plant count records across time points. The MET and Durum trials were conducted across two times of sowing (TOS1 and TOS2), with sowing depths of 4 cm and 12 cm, and included genotypes such as Mace, Scepter, Magenta, and Westcourt. Seed size trials compared small and large seeds, while Victrado trials assessed genotype responses under well-watered and dryland conditions with factorial combinations of depth and Fusarium crown rot (FCR) treatments. Data collection included emergence counts, seedling depth and length, biomass at GS32, head counts, plant height, lodging, and final harvest metrics such as grain yield, test weight, protein content, and grain size. Soil moisture was monitored using Tiny Tag and disposable probes, and neutron moisture meters (NMM) at depths from 0 to 150 cm. The dataset includes over 70 variables, with consistent identifiers for site, trial, genotype, depth, treatment, and replication. Codes such as “4cm_Mace” or “12cm_Sunchaser+” denote sowing depth, genotype, and treatment combinations. The data were collected using standard field trial protocols, with sowing conducted using tyned implements and press wheels at 25 cm row spacing. Fertiliser applications included 238 kg/ha urea and 70 kg/ha Granulock Z Extra. Herbicide applications were recorded per TOS. This dataset enables comprehensive analysis of genotype × environment × management interactions, particularly for traits associated with early vigour, drought adaptation, and disease resistance in wheat.

This dataset originates from a 2024 field experiment conducted at Waikerie, South Australia, designed to investigate the interaction between wheat genotype, sowing depth, and presswheel pressure on crop establishment and performance. The trial included six wheat genotypes (e.g., Mace18, Scepter, Calibre, Magenta, Magenta13, Mace) sown at two depths (shallow: 30–40 mm and deep: 80–100 mm) under three presswheel pressure treatments (light, standard, heavy). The experiment was laid out in a randomized block design with four replicates, using 12 m × 2.2 m plots and 28 cm row spacing. Fertiliser treatments varied by placement (above, with, or below seed) and rate (standard, high), and seed size (standard or small) was recorded for each plot. The Excel file (.xlsx) contains seven sheets. The “Trial details” sheet outlines the experimental design, site conditions, soil test results, and assessment schedule. The “Trial plan” sheet maps plot layout and treatment combinations. The “Metafile” provides metadata including sowing date, genotype list, and measured variables. The “Exp_data” sheet is the core dataset, containing 56 plots with variables such as plant density at three time points, sowing depth, coleoptile length, root and shoot biomass, NDVI at two dates, dry biomass at GS31 and GS65, grain yield, protein content, 1000-grain weight, and harvest index. The “Exp_soildata” sheet includes gravimetric soil water content at three depths (0–5, 5–10, 10–15 cm) for selected plots. The “predicta B” sheet reports soil-borne pathogen DNA levels and nematode counts across depths and blocks. The “soil fertility” sheet provides detailed chemical and physical soil properties (e.g., pH, organic carbon, macro- and micronutrients, salinity, texture) across five depth intervals (0–100 cm). The dataset includes over 40 variables. Codes such as “deep_Small_fert with_High rate” describe treatment combinations. Depths are in mm, biomass in t/ha, and nutrient concentrations in standard agronomic units. The dataset supports analysis of genotype performance under varying mechanical and environmental constraints, with implications for optimizing deep sowing strategies in low rainfall environments.

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.

This dataset captures detailed agronomic and phenological data from a 2024 field trial conducted at Yanco, New South Wales, to evaluate the performance of durum wheat genotypes with long coleoptiles under varying sowing depths, sowing times, and Fusarium crown rot (FCR) inoculation treatments. The trial includes three durum wheat varieties—Scepter, Vittaroi, and V190245-6—each subjected to two sowing depths (shallow and deep), two sowing times (early and late), and two FCR treatments (plus and minus inoculation), resulting in a factorial design. The dataset is structured in a single sheet containing 93 entries, each representing a unique plot. Variables include identifiers such as entry number, genotype, treatment combinations, sowing and emergence dates, and positional data (replicate, range, row). Agronomic measurements include plant density (plants/m²), biomass at GS30 (g/m²), plot height (cm), head count per m², and key growth stages (GS51, GS65, GS92) with corresponding calendar and time since sowing. Yield-related metrics include hand and header grain yield (t/ha), total biomass yield (t/ha), harvest index, 1000 grain weight, screenings percentage, test weight (kg/hL), and grain protein content (%). The dataset enables analysis of genotype by environment by management interactions, particularly focusing on the influence of deep sowing and FCR pressure on crop establishment, development, and yield. All variables are consistently labelled, and treatments are encoded using descriptive names (e.g., “Deep Early Scepter”, “Shallow Late Vittaroi”) to facilitate filtering and analysis. The dataset supports statistical modelling and agronomic decision-making for optimizing durum wheat production under variable sowing conditions and disease pressures.