This dataset comprises three Excel (.xlsx) of interrelated field trials conducted at Condobolin, NSW, during the 2024 winter cropping season, focusing on wheat genotype performance under varying agronomic treatments and environmental conditions. The trials include a Nutrition Trial, Seed Size Trial, and a Core Trial, each exploring distinct but complementary aspects of long coleoptile wheat development. Data is structured across multiple sheets detailing experimental design, treatment applications, phenological observations, soil and environmental metrics, and yield outcomes. Each file contains genotype-specific data across multiple ranges and rows, with treatments involving sowing depths (4 cm and 12 cm), fertilizer placement (with seed or above seed), and seed size (small or large). The Nutrition Trial includes fertilizer rate variations and detailed soil nutrient profiles, while the Seed Size Trial emphasizes seed size effects on emergence, biomass, and grain quality. The Core Trial integrates sowing time comparisons (TOS 1 and TOS 2), coleoptile measurements, and soil temperature logging. Methodological details include precise sowing dates, herbicide and insecticide applications, NDVI readings, frost tipping scores, and maturity assessments. Soil moisture was measured gravimetrically at multiple depths pre- and post-harvest, and nutrient analyses were conducted on samples from 0–60 cm. Environmental data from Condobolin AWS provides daily temperature and rainfall records, highlighting frost events and dry periods critical to crop development. The dataset includes over 30 variables such as genotype, sowing depth, seed size, plant counts, dry weight, grain yield, head counts, phenology scores, and grain quality metrics. Codes like “Mace18_4_W_L” or “Calibre_12_A_H” denote genotype, depth, placement, and fertilizer rate. Time-series soil temperature data is recorded at sub-minute intervals, offering high-resolution insights into thermal conditions affecting emergence. This dataset enables robust statistical analysis of genotype performance under frost-prone and moisture-limited conditions without requiring further input from the original researchers.

This dataset documents the results of a 2024 field trial conducted by CSIRO at Tabbita, NSW, to evaluate the performance of long coleoptile wheat genotypes under varying sowing depths and soil conditions. The Excel file (.xlsx) contains 12 sheets, each representing a specific data type or collection date. The trial compared wheat genotypes sown at 4 cm and 10 cm depths, with some entries extending to 15 cm, across multiple replicates and ranges. The genotypes included standard and long coleoptile lines such as Mace, Mace18, Magenta, Magenta13, Gregory, Yitpi, Calibre, and Scepter, with suffixes indicating seed size or treatment (e.g., _sml, _std, _L, _S). The dataset includes sowing layout maps, cone penetrometer readings taken on three dates (17th, 24th, and 30th May) to assess soil resistance at different depths, and multiple rounds of plant counts (28th May, 30th May, 5th June, 7th June, and 13th June) across different rows and depths. Elongation measurements were recorded on 28th and 30th May, with up to 10 readings per plot, providing data on early shoot growth. Final yield data is presented in the last sheet, including raw yield (kg) and adjusted yield per hectare (Ayld), calculated based on a 4 m plot length, 23 cm row spacing, and six harvested rows. The dataset includes over 30 variables, such as genotype, sowing depth, plant counts per row, elongation measurements (ER1–ER10), and yield metrics. Codes like “Mace18_sml” denote genotype and seed size, while “4cm SD” and “10cm SD” refer to sowing depths. The data were collected using standard field protocols, with consistent plot dimensions and measurement intervals. This dataset enables analysis of genotype × environment × management interactions, particularly the influence of sowing depth and soil strength on emergence, early growth, and yield performance in long coleoptile wheat.

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 comprehensive agronomic and phenotypic data from the 2024 long coleoptile wheat trials conducted at Breeza, New South Wales, Australia. The trials include four major experiments: N_MET_24 (multi-environment trial), N_Durum_24 (durum wheat trial), Seed_size (seed size and depth interaction), and Victrado (water regime and depth interaction). The trials were established on 36 ranges with 5 rows per bed, 2-meter bed centres, and 11-meter ranges (10.8 meters planted). Fertilisation included 238 kg/ha of urea and 70 kg/ha of Granulock Z Extra Treated starter. Herbicide applications included Axil Xtra, Starane, MCPA, and Metsulfuron. Sowing occurred in two time-of-sowing (TOS) windows: TOS1 on 28 May and TOS2 on 4 July 2024. The trials evaluated multiple genotypes including Mace, Mace18, Scepter, Magenta, Magenta13, Calibre, Westcourt, V190245-6, and Sunchaser, under varying treatments such as sowing depth (4 cm and 12 cm), seed size (small and large), FCR resistance (plus/minus), and water regimes (well-watered and dryland). The dataset includes over 450 unique plots across the trials. Variables recorded include emergence and plant counts (outside and inside row counts, plants per square meter), seedling traits (depth and length), Zadok growth stage scores, dry biomass at GS32, and final harvest metrics such as yield (T/ha), protein content, test weight, grain weight, and grain size. Temperature logger data and phenological observations are also included. Data is structured across multiple sheets, each corresponding to a specific trial or measurement type, with consistent identifiers for site, trial, TOS, genotype, depth, and treatment. The dataset enables analysis of genotype-by-environment interactions, agronomic performance under deep sowing, and the influence of seed size and water availability on wheat establishment, growth, and yield.

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 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 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 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 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.

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.