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 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 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 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.

Genotype data for 372 wheat breeding lines that were introduced to Australia as part of the CAIGE project and tested in yield trials between 2011 and 2020.

This dataset will contain phenotypic observations of a large number of wheat genotypes evaluated in 2016-2017 and 2017-2018 at the International Maize and Wheat Improvement Center in Ciudad Obregon, Mexico.

,Intermediate wheatgrass [Thinopyrum intermedium (Host) Barkw. & D.R. Dewey subsp. intermedium] is a high-yielding cool-season grass with adaptable uses for grazing, haying, and soil restoration. Despite its adaptability, adoption of intermediate wheatgrass has been limited due to inadequate stand longevity under grazing stress. A study was conducted near Mandan, ND USA to investigate if stand longevity of intermediate wheatgrass was affected by changes in soil properties due to grazing. Soil data from this study included measurements of soil bulk density, soil pH, soil organic carbon, and total soil nitrogen on a Wilton silt loam soil (USDA: Fine-silty, mixed, superactive frigid Pachic Haplustoll). Measurements were made in May 1997 (baseline) and again in May 2004 following four years of grazing. Data may be used to understand soil property responses to grazed perennial forages. Data are generally applicable to rainfed conditions under a semiarid Continental climate for the following associated soil types: Temvik, Grassna, Linton, Mandan, and Williams.,Resources in this dataset:,Resource title: Intermediate Wheatgrass Grazing Study Data Dictionary File name: IWGS_Data Dictionary.xlsx Resource description: Data dictionary for associated dataset.,Resource title: Intermediate Wheatgrass Grazing Study_Soil Data for Aggregated Depths File name: IWGS_Soil Data_Aggregated Depths.xlsx Resource description: File includes data for 0-30 cm depth.,Resource title: Intermediate Wheatgrass Grazing Study_Soil Data for Separated Depths File name: IWGS_Soil Data_Separated Depths.xlsx Resource description: Soil data for 0-5, 5-10, 10-20, and 20-30 cm depths.,Resource title: Intermediate Wheatgrass Grazing Study_Soil Data_Aggregated Depths File name: IWGS_Soil Data_Aggregated Depths.csv Resource description: Data for aggregated depths in csv format.,Resource title: Intermediate Wheatgrass Grazing Study_Metadata_Aggregated Depths File name: IWGS_Soil Data_Aggregated Depths_Metadata.csv Resource description: Metadata for aggregated depths.,Resource title: Intermediate Wheatgrass Grazing Study_Soils Data_Separated Depths File name: IWGS_Soil Data_Separated Depths.csv Resource description: Soil data for 0-5, 5-10, 10-20, and 20-30 cm depths.,Resource title: Intermediate Wheatgrass Grazing Study_Metadata_Separated Depths File name: IWGS_Soil Data_Separated Depths_Metadata.csv Resource description: Metadata for soils data separated by depth increment.,

,Correlations between plant available soil and grain mineral concentrations are often assumed, yet few studies examine these associations. Here, soil and wheat grain samples were analyzed from a semi-arid dryland cropping study in the northern Great Plains conducted between 2006 and 2011. Continuous spring wheat (fertilized) (Triticum aestivum L; CSW) was compared with wheat following 5 yr of perennial forages of either alfalfa (Medicago sativa L.), intermediate wheatgrass (fertilized) (Thinopyrum intermedium (Host) Barkw. & D.R. Dewey sbsp. Intermedium; IWG), or an alfalfa/intermediate wheatgrass mixture (fertilized; MIX). Wheat performance (yield, 1,000 kernel weight [TKW], and crude protein [CP] concentration), and associations between 11 plant available soil mineral concentrations and 11 wheat grain mineral concentrations were assessed. Wheat following alfalfa had greater yield than all treatments, greater TKW than CSW, greater CP than IWG and CSW, but lower grain Zn concentration than IWG (p ≤ .05). Wheat grain following IWG had greater Fe and Mn concentration than MIX, greater Mg concentration than CSW, and lower S concentration than all treatments (p < .05). Multivariate correlation analysis showed positive correlations between plant available soil and grain B, Mg, Mn, and S concentrations (p ≤ .02), while plant available soil and grain Zn and Ca concentrations showed negative associations (p ≤ .05). Rotating perennial forage phases into wheat cropping systems increased wheat yield and CP but reduced certain plant available soil minerals. Although rotating perennials into annual cropping systems can benefit some soil quality parameters it may also diminish plant available soil minerals, influencing fertility recommendations.,

,Phenotypic Data A subset of 264 lines from the National Small Grains Collection global hexaploid winter wheat germplasm collection was evaluated under controlled growth chamber conditions for reaction to the pathogens Parastagonospora nodorum and Pyrenophora tritici-repentis. Both infiltrations and inoculations were performed on plants planted in plastic cones and when seedlings were at the second leaf stage. Plants were infiltrated with the P. nodorum necrotrophic effectors (NEs) SnTox1, SnToxA, SnTox3, SnTox267, and SnTox5; and the P. tritici-repentis NE Ptr ToxB. The scoring system was 0-3, with reaction types of 2 and 3 considered sensitive and 0 to 1 were insensitive. Plants were inoculated with the P. nodorum isolates Sn4, Sn2000, AR2-1, SnIr05H71a, and NOR4 and P. tritici-repentis isolates Pti2, 86-124, DW5, and AR CrossB10. After inoculation, plants were placed in a 100 % humidity growth chamber at 21 °C for 24 hours under constant light, then moved to a controlled growth chamber at 21 °C with a 12 h photoperiod. Plants were scored at 7 days post inoculation. For P. nodorum, plants were scored using a 0 to 5 scale, with 0 being highly resistant and 5 being highly susceptible. For P. tritici-repentis, plants were scored using a 1 to 5 scale, with 1 being highly resistance and 5 being highly susceptible. Three homogeneous replicates (determined by Bartlett’s chi squared analysis) were used to calculate an average value for each trait. This value was used for the rest of the analysis.,Genotypic Data DNA of the winter wheat panel was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. Clustering data was analyzed using GenomeStudio 2.0.5 from Illumina, Inc. SNPs were ordered based on their physical position in the Chinese Spring IWGSC RefSeq v2.0. In TASSEL v5.2, SNP markers were filtered with a minor allele frequency greater than 0.01 and missing data less than 50%. For the remaining markers, missing values were imputed using the LD-KNNi method.,Genome-wide association analysis data Association mapping was conducted using the R package GAPIT v.3. The filtered hapmap file was used for the association mapping, along with the average value for each phenotypic trait. The models GLM, MLM, MLMM, FarmCPU, and Blink were run on the averages for each trait. ** Resources in this dataset:,