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88 results for “Winter Wheat”
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PROMET winter wheat simulations
This data set contains output data from simulations with the model PROMET 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, anthesis day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the ERA-Interim (Dee et al. 2011) 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 (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from CARAIB winter wheat simulations
<p>This data set contains output data from simulations with the model CARAIB 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 (Ruane et al. 2015) 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 (A0= 'none', A1='regain original growing season').</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE winter wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE 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, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) 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 (A0= 'none', A1='regain original growing season').
ChinaWheatYield30m: A 30-m annual winter wheat yield dataset from 2016 to 2021 in China
<p>Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Most existing public yield datasets have a coarse spatial resolution. Although these datasets are useful for analyzing regional temporal and spatial change, they cannot deal with spatial heterogeneity, which happens to be the most significant characteristic of the Chinese small-scale farmers' economy. Hence, we generated a 30-m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter wheat-producing provinces in China for the period 2016-2021. </p>
Influence of small-scale spatial variability of soil properties on yield formation of winter wheat
<p>This is a data set of soil properties and plant properties of winter wheat.</p> <p>The data derived from a long-term field trial for the year 2016 at the Asendorf field station 70 km north of Hanover, Germany (49 m above sea level, 52°45′48.4′′N 9°01′24.3′′E) and a field site in Triesdorf, located in Northern Bavaria (450 m a.s.l., 49°12'36.5"N 10°38'33.9"E).</p> <p>Data includes soil (OC, bulk density, texture, pH-value) and plant data (grain yield, thousand grain weight, tillers per m², spikes per m²). All methods and data will be described in an upcoming journal article in the Journal Plant and Soil (DOI:10.1007/s111104-023-06212-2).</p>
QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations
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Data from: Validation of grain yield QTL from soft winter wheat using a CIMMYT spring wheat panel
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Data from: Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery
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High buffering potential of winter wheat composite cross populations to rapidly changing environmental conditions
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Determining haploblocks and haplotypes in the MAGIC winter wheat population WM-800 based on the wheat 15k Infinium and the 135k Affymetrix SNP arrays
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Data for: Biochar co-compost improves nitrogen retention and reduces carbon emissions in a winter wheat cropping system
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Nitrous oxide emission and grain yield in Chinese winter wheat-summer maize rotation: A meta-analysis
<p>Collected data for the meta-analysis of the N2O emissions and grain yields in Chinese winter wheat-summer maize rotation. The manuscript is submitted to Agronomy.</p>
Characterization of N variations in different organs of winter wheat and mapping NUE using UAV-based remote sensing
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Supplemental Data- Figures and Tables-Soft Red Winter Wheat Elite Germplasm Screening and Evaluation for Strip Rust in the US Southeast Region
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Effects of winter wheat irrigation on local climate and extreme events over the North China by using the high resolution non-hydrostatic regional climate model
<p>The control and irrigation simulation dataset from RegCM4.7.</p>
Data from: Spatial variation of soil respiration in a cropland under winter wheat and summer maize rotation in the North China Plain
Spatial variation of soil respiration (Rs) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of Rs in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected Rs data from 23 sample plots in the cropland. At the late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2 s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2 s-1). However, the spatial variation of Rs in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson's correlation analyses based on the sampling data revealed that the spatial variation of Rs was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between Rs and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between Rs and EVI in each cluster for both winter wheat and summer maize. The spatial variation of Rs in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical Rs models and benefit the acquisition of Rs in cropland ecosystems at large scales.
Data from: A field-based analysis of genetic improvement for grain yield in winter wheat cultivars developed in the us central plains from 1992 to 2014
[No abstract entered]
The genotypic data of elite European cultivar panel comprising 358 winter and 14 summer wheat varieties released from 1975 to 2007 at different marker densities
<p>This submission contains the genotpying data corresponding to the GABI-WHEAT and its subset TROST panel, at different densities i.e 35k, 90k for GABI-WHEAT and 135k for TROST panel. Additionally, the marker oligo sequences, envisioned to be used for mapping to wheat reference genome for obtaining marker phyical positions and thus assist genomic interoperability, are included. </p> <p>(35k, 135k and 90k are names given to markers originating from Affymetrix [Allen et al., 2017*; Muqaddasi et al., 2019, Muqaddasi et al., 2020**] and 90k iSELECT [Wang et al., 2014***] SNP array chips)</p> <p>*https://doi.org/10.1111/pbi.12635</p> <p>**https://doi.org/10.3835/plantgenome2018.05.0029</p> <p>***https://doi.org/10.1111/pbi.12183</p>
Dataset on: Incidence and geographical distribution of cereal cyst nematode (CCN, Heterodera spp.) in winter wheat fields in Algeria
<p>Cereal cyst nematodes (CCN, <em>Heterodera</em> spp.) are the most damaging plant-parasitic nematode species on wheat, causing severe economic loss in global wheat production. In summer 2015, we analyzed samples collected from 22 wheat fields in Algeria using the Fenwick can technique. The study revealed that 54.55 % of wheat fields were infested with cereal cyst nematodes. The species was observed in several locations in the northern part of Algeria but not in the southern desert area. Population densities of CCNs in soil varied between the regions at an infestation rate of between 0.6 ± 0.54 and 86.6 ± 19.96 cysts per 500 g of dried soil. Furthermore, we found an average of 56.33±15.18 and 364.70 ± 81.93 second-stage juveniles and eggs/cyst. The infestation was most severe in cereal fields in Draa Semar and Djendel with 86.6 ± 19.96 cyst/500g of soil and 57.4±17.55 cysts/500g of soil, respectively. Infestation was lowest in fields in Ras Elouad, Sidi Mbarek and Sedraia with 0.6 ± 0.54 cysts/500g of soil; 1.6 ± 1.67 cysts/500g of soil and 2.4 ± 1.67 cysts/500g of soil, respectively. <em>Heterodera</em> spp. was distributed throughout the cereal growing province in Algeria and could cause economic loss in these regions.</p>
IMPORTANT FACTORS IN IMPROVING WINTER WHEAT GRAIN QUALITY
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International Brain Laboratory public data
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OpenNeuro
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