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88 results for “Winter Wheat”
ECOBREED WP2 T2.1 Winter common wheat (Triticum aestivum) - Late maturity group
<p>Description of the winter common wheat (Triticum aestivum) late maturity group nursery. Tested within T2.1 in Germany (by Secobra), Czech Republic (by Selgen) and Slovakia (by NPPC) in 2019/2020.</p>
ECOBREED WP2 T2.1 Winter durum wheat (Triticum durum) nursery
<p>Description of the winter durum wheat (Triticum durum) nursery. Tested within T2.1 in Austria (by BOKU), Hungary (by MTA-ATK) and Italy (by UNITUS). Results included from the season 2018/19.</p>
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
AgriCarbon-EO Winter wheat Net Ecosystem Exchange and Biomass over South-west France at 10 m resolution
<p>Dataset contains the outputs of the AgriCarbon-EO</p> <p>An agronomical modeling tool for the carbon and water flux estimates by Bayesian assimilation of S2 and LandSat8 remote sensing data into the Prosail radiative transfer model and the SAFYE-CO2 crop model.<br> -----------------------<br> -for TILE : T31TCJ <br> -for year: 2017<br> -for Winter wheat crops<br> - at 10 m resolution</p> <p> </p> <p>Maps:<br> -file: "GLA_statmap.tif"<br> Description: A raster with 4 bands containing respectively:<br> *The R2 of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The RMSE of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The Bias of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The number of images that are assimilated into SAFYE-CO2 from 2016/11/01 until 2017/08/01</p> <p>-file: "emerg_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of emerg retrieved by the SAFYE-CO2 inversion in days of simulation (the simulation begins the 01/01/2016).<br> *The standard deviation of emerg retrieved by the SAFYE-CO2 inversion.<br> <br> -file: "LUEa_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ.<br> *The standard deviation of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ.</p> <p>-file: "SENa_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of Sena retrieved by the SAFYE-CO2 inversion in °C.<br> *The standard deviation of Sena retrieved by the SAFYE-CO2 inversion in °C.</p> <p>-file: "SENb_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of SENb retrieved by the SAFYE-CO2 inversion.<br> *The standard deviation of SENb retrieved by the SAFYE-CO2 inversion.</p> <p>-file: "PRT_La_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of DAM retrieved by the SAFYE-CO2 inversion.<br> *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion.</p> <p>-file: "DAM_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of DAM retrieved by the SAFYE-CO2 inversion in g/m2.<br> *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NEP_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NEP retrieved by the SAFYE-CO2 inversion in g/m2.<br> *The standard deviation of NEP retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NECB_exportG_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2 , considering an export scénario with grains export only.<br> *The standard deviation of NECB_exportG retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NECB_exportGLS_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2, considering an export scénario with grains, stems and leaves.<br> *The standard deviation of NECB_exportGLS retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p> </p> <p>Shapefiles a GIS: <br> -file: "S2_TILE_T31TCJ.shp"<br> Description: shape file of the contour of the T231 TCJ sentinel2 tile <br> -file: "FR_AUR.shp"<br> Description: shape file of the contour of AURADE experimental field <br> -file: "FR_AUR_TOWER.shp"<br> Description: shape file of the location of the AURADE eddy covariance flux tower<br> -file: "POI_2017.shp"<br> Description: shape file of the location of points of interest that illustrate the ... paper<br> -file: "ESU_DAM.shp"<br> Description: shape file of the contour of the plots where dry biomass samples were taken.<br> -file: "ESU_DAM_points.shp"<br> Description: shape file of the location of the points where dry biomass samples were taken.<br> -file: "mapT31TCJ_spamaps.qgz"<br> QGIS project file for the visualisation of the NEP maps.<br> </p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Impact of spatio-temporal shade dynamics on winter wheat growth and yield
<p>During two growing seasons (2013-2014, 2014-2015), an artificial shade structure was installed on the experimental farm of Gembloux Agro-Bio Tech to evaluate winter wheat growth, productivity and quality under shade. During both seasons, global radiation (MJ/m²/days) at crop canopy level was measured with quantum sensors (CS300- Campbell Scientific Inc., USA – accuracy ± 5 % for the daily global radiation) and recorded every minute by a data logger (CR1000 - Campbell Scientific Inc., USA). These data are compiled at a daily time scale for each treatment (CS: constant shade, PS: periodic shade, NS: no shade) into the tables “<em>GR_2013_2014.txt</em>” and “<em>GR_2014_2015.txt</em>”.</p> <p>During the cropping season, we sampled winter wheat to assess aboveground biomass, dry matter dynamics, final yield, yield components (thousand grain yield, grain size, and spike per m²) and grain protein content. These data are compiled in the two tables “<em>sampling_2013_2014.txt</em>” and “<em>sampling_2014_2015.txt</em>”. Samples were taken from three adjacent sowing lines of 40 cm. To assess dry matter distribution (g/m²), wheat plants were subdivided into spikes (<em>DM_Spike_g_m2</em>) and straw (<em>DM_Straw_g_m2</em>), dried and weighed. The final yield is expressed in t/ha at 0% humidity (<em>Yield_t_ha_0%</em>). We assessed the proportion of grain size using 3 sieves: 2.2, 2.5, 2.8 mm (<em>Grain_weight_seive_2.2mm, Grain_weight_seive_2.5mm, Grain_weight_seive_2.8mm</em>). Thousand grain weight at 0% humidity was calculated on subsamples from the harvested plots (<em>TGW_g_0%</em>). Protein content (%) analysis was performed with near-infrared reflectance spectroscopy technique (<em>Protein_content_%).</em></p> <p>Detailed information on the experimental design will be available in the following paper: “Impact of spatio-temporal shade dynamics on wheat growth and yield, perspectives for temperate agroforestry” in European Journal of Agronomy.</p>
The first 500-meter, long-term winter wheat grain protein content dataset for China from multi-source data
<p>In China, the demand for precise perception of wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diversified geographical landscape in China introduces spatiotemporal heterogeneity and intricacy to the influence of wheat GPC, further amplifying the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS.</p>
Data from: Carry-over effect of leguminous winter cover crops and living mulches on winter wheat as a second main crop following white cabbage
<p><strong>Background: </strong>In trials on two strategies for the integration of legumes in a vegetable crop rotation (leguminous winter cover crops and living mulches), data were collected on the two subsequent crops white cabbage and winter wheat. The data on biomass and soil mineral nitrogen content are made publicly available here. </p> <p> </p> <p><strong>Abstract:</strong> <span>The direct effect of winter cover crops (WCC) or living mulches (LM) on a first vegetable crop has already been investigated. However, little is known about the effect on growth and yield of a second cash crop. </span><span>The aim of the study was to assess the carry-over effect of legumes grown as WCC or LM on winter wheat as a second crop after cabbage measured in yield and nitrogen release.</span><span> Two field trials were carried out in Germany between 2019 and 2022. In the WCC trial rye, rye with vetch, vetch, pea and faba bean were used as WCC and compared to bare soil. The WCC biomass was incorporated before cabbage planting in late spring. For the LM trial, perennial ryegrass or white clover were used as LM during cabbage cultivation and compared to bare soil. The LM biomass was incorporated together with the cabbage residues (STU/STT) and compared to an early incorporation of LM biomass before cabbage planting (RT). Winter wheat in both trials was seeded as the second main crop in the rotation in the fall.</span></p>
ChinaWheat30L: Long-term winter wheat maps of China at 30-m resolution from 2000 to 2023
<p>This is the long-term winter wheat map product of China at 30-m resolution from 2000 to 2023 (ChinaWheat30L). The product was generated using a knowledge-guided machine learning approach and the integration of satellite remote sensing and environmental datasets.</p>
Data from: Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat
<div> <div> <p>Barley yellow dwarf is one of the major viral diseases of cereals. Phenotyping barley yellow dwarf in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment; (2) identify genomic regions associated with barley yellow dwarf resistance, and (3) evaluate the ability of genomic selection models to predict barley yellow dwarf resistance. Up to 107 wheat lines were phenotyped during each of 5 field seasons under both insecticide treated and untreated plots. Across all seasons, barley yellow dwarf severity was lower within the insecticide treatment along with increased plant height and grain yield compared with untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying the resis- tance gene Bdv2. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a poten- tially novel genomic region for barley yellow dwarf resistance on chromosome 5AS. Given the variable heritability of the trait (0.211–0.806), we obtained a predictive ability for barley yellow dwarf severity ranging between 0.06 and 0.26. Including the presence or absence of Bdv2 as a covariate in the genomic selection models had a large effect for predicting barley yellow dwarf but almost no effect for other ob- served traits. This study was the first attempt to characterize barley yellow dwarf using field-high-throughput phenotyping and apply geno- mic selection to predict disease severity. These methods have the potential to improve barley yellow dwarf characterization, additionally identifying new sources of resistance will be crucial for delivering barley yellow dwarf resistant germplasm.</p> </div> </div>
Winter wheat yield analysis using the Light Use Efficiency model in LandKlif project
<p><span>The crop yield of Winter wheat is calculated using the light use efficiency (LUE) model for the state of Bavaria (adopted from Dhillon et al 2020). The yield output is received by inputting the synthetic dataset of Sentinel-2-MODIS with 10-meter spatial and 8-day temporal resolution (generated by Dhillon et al 2022) plus climate elements. The model output is validated using the Landesamt regional crop yield data of Bavaria for 2019 with an R2 of 0.86 and RMSE of 5.03 dt/ha. The NDVI and the yield product was created by Carina Kübert-Flock, Thorsten Dahms, and Maninder Singh Dhillon from TP 7 in LandKlif project.<br></span></p> <p><span>LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Figure 3 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 3. Relationships between yield components and Vulpia myuros density at two sowing times and crop densities in the growing seasons of 2017–2018 (A and C) and 2018– 2019 (B and D). Number of crop ears per square meter (A and B) and 1,000-kernel weight (C and D) data are shown with fitted curves. Data from the growing seasons of 2017–2018 and 2018–2019 were fit to the linear regression (Equation 2) and asymptotic nonlinear regression (Equation 3) models, respectively.
Figure 4 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 4. Relationships between the per-plant seed production and Vulpia myuros density at two crop densities solely at normal sowing time in the growing season of 2017–2018 (A) and at two sowing times and crop densities in 2018–2019 (B). Data from the growing seasons of 2017–2018 and 2018–2019 were fit to the linear regression (Equation 2) and asymptotic nonlinear regression (Equation 3) models, respectively.
Figure 2 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 2. Relationships between crop grain yield (kg ha−1) and Vulpia myuros density at two sowing times and crop densities in winter wheat in the growing seasons of 2017–2018 (A) and 2018–2019 (B). Data were fit to the rectangular hyperbola model (Equation 1).
Figure 1 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 1. Cumulative emergence dynamics of Vulpia myuros at normal sowing time and late sowing time in relation to thermal time (C) in 2017–2018 (A) and 2018–2019 (B). Regression equation and parameter estimates described in Table 2.
Biomass estimation of winter wheat using the Light Use Efficiency model in Bavaria (Atlas) in LandKlif project
<p>This dataset shows the predicted biomass (g/m2) of winter wheat (WW) using the Light Use Efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.82 with an RMSE of 5.46 dt/ha. </p> <p>This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Fig. 3 in The Impact Of Sowing Time On Sugar Content And Snow Mould Development In Winter Wheat
Fig. 3. The content of carbohydrates depending Fig. 4. The average content of residual carbohyon the sowing time. drates in comparison with the carbohydratecon- tent in autumn (2005-2007).
Agronomic performance of cultivar mixtures and pure stands of 8 winter wheat varieties, obtained from mixture field trials in Switzerland from 2021 to 2023, together with associated functional traits measurements
<p>This dataset contains agronomic performance data for 8 Swiss winter wheat cultivars, grown in pure stands and in mixtures at 3 locations in Switzerland during 3 growing seasons (2021-2023). The dataset has been used to analyse the effects of cultivar mixtures on agronomic performance and stability, which is published in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eja.2024.127504" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.eja.2024.127504</a>. </p> <p>The dataset contains notably grain yield, protein content, thousand kernel weight, specific weight, and Zeleny sedimentation value, as well as functional traits measured at flowering for each mixture and pure stand plot. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h3>Methods </h3> <p> <em>Field trials </em></p> <div>Field trials were set up over the course of three growing seasons – 2020/2021, 2021/2022 and 2022/2023 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Utzenstorf (47°97′ N 7°33′ E, 483m a.s.l.). </div> <div>Experimental communities consisted of pure stand plots, 2-cultivars mixtures, and one plot with the 8 cultivars mixed. We sowed every possible combination of 2-cultivar mixtures, amounting to a total of 28 2-cultivar mixtures treatments, to which we added the 8-cultivar mixture. Each community was grown in a plot of 7.1 m<sup>2</sup> (1.5m∗4.7m). We used a complete randomized block design, with 3 replicates, the plots being randomized at each site within each block. Sowing was performed with a small plot drill (Wintersteiger plotseed TC). Density of sowing was 350 viable seeds/m<sup>2</sup>. For the mixtures, seeds were mixed beforehand at a 2 × 50 % mass ratio for 2-cultivars mixtures and 8 × 12.5 % for the 8-cultivar mixture. We chose this method of mixing as this is what is commonly done by farmers in Switzerland. Plots were sowed mechanically each autumn and fertilized with ammonium nitrate at a rate of 140 N/ha in 3 applications (40 N/ha at tillering stage/BBCH 22–29; 60 N/ha at the beginning of stem elongation/BBCH 30–31; 40 N/ha at booting stage/BBCH 45–47). The trials were grown according to the Swiss <em>Extenso</em> scheme, i.e. without any fungicide, insecticide, and growth regulator. Weeds were regulated twice or thrice per season with the application of herbicides commonly used in Switzerland.</div> <div> </div> <div><em>Ear density</em></div> <div> </div> <div>Before harvest, we manually harvested horizontal bands of 1.5 × 0.3 square meters per plot. The location of the band was randomly chosen but we avoided plot edges (i.e. the band was located at more than 0.5 m from the lower and upper edge of each plot). We counted the heads, and obtained ear density from the head counts.</div> <div> </div> <div><em>Trait measurements </em></div> <div> </div> <div>At flowering time, we randomly sampled 6 healthy leaves per plot. We immediately wrapped this leaf in moist cotton; this was stored overnight at room temperature in open plastic bags. The following day, we removed excess surface water on the leaf and weighted it to obtain its water saturated weight. This leaf was then scanned with a flatbed scanner (Perfection V39II, Epson), oven-dried in a paper envelope at 80°C for 72 hours, and subsequently weighed again to obtain its dry weight. Leaf Dry Matter Content (LDMC) was calculated as the ratio of leaf dry mass (g) to water saturated leaf mass (g). Using the leaf scans, we measured leaf area with the image processing software ImageJ. Specific Leaf Area (SLA) was calculated as the ratio of leaf area (cm2) to leaf dry mass (g).</div> <div> </div> <div><em>Phenology and height </em></div> <div> </div> <div>For each plot, we recorded the heading date as the day of the year, in which 50 % of the ears of the plot had fully emerged from the flag leaf. Plant height was measured in each plot at BBCH 59–75, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</div> <div> </div> <p><em>Harvest and post harvest measurements</em></p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured hectoliter weight (test weight, HLW, kg/hl) and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15 % of humidity. Protein content (% of dry matter) was measured at the site level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g) was measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany). Zeleny sedimentation value was measured by the laboratory of Delley Seeds and Plants. </p> <p> </p>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
CIMMYT winter wheat product profiles 2022
<p>Detailed target product profiles for CIMMYT winter wheat breeding 2022. The product profiles are the trait specifications for selection and release of material from the two CIMMYT winter wheat breeding pipelines: (<em>1) Hard winter, cold tolerant & irrigated, normal maturity</em> (denoted HWW-CTI-NM) and <em>(2) Hard winter, cold tolerant, drought tolerant, normal maturity </em>(denoted HWW-CTD-NM). Product profiles are defined as in the Excellence in Breeding Toolbox for “product design and management” (https://excellenceinbreeding.org/toolbox/tools/cgiar-seed-product-market-segment-database).</p> <p>Traits include grain and processing traits, nutritional enhancement, agronomic and disease traits. For winter wheat, no specific production, multiplication, or unique product registration traits apply in 2022 so these fields in the product profiles are marked as not applicable (NA). Traits are listed by category and assigned a measurement scale which is used in selection along with a minimum score. The traits are differentiated by requirement: either as “must have” or “nice to have” as well as the requirement for improvement (vs. maintenance). Indication of a trait as a threshold trait indicates it is a requirement for release of material from the breeding pipeline.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.