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 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.

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).

생산량 및 대상 작물별 재배 최적 모델 및 비례정밀복합환경제어 시나리오별 데이터

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).