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68 results for “grain yield”
Figure 1 in Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan
Figure 1. Normal probability plot of residuals.
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
Fine tuning N recommendations requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. Recent studies have demonstrated the ability of Normalized Difference Vegetation Index (NDVI) to assess crop N status and predict yield in wheat (Tricticum aestivum L.) and maize (Zea mays L.); however, there has been relatively little such research on rice (Oryza sativa L.). The objectives of this study were to determine how well NDVI measured at the panicle initiation (PI) rice growth stage assesses crop N status and predicts final grain yield. Nitrogen response trials were established over a 4-yr period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. Additionally, the relationship between NDVI and crop N status was characterized across 28 on-farm plots representing a range of environmental conditions and management practices. The NDVI at PI was best correlated with total N uptake (NUP, r2 = 0.66), followed by N concentration (NCONC, r2 = 0.54), and aboveground biomass (AGB, r2 = 0.51). The utility of NDVI was greatest at lower values of crop N status, whereas at higher values, NDVI saturated. The NDVI at PI was positively correlated with final grain yield (r2 = 0.58) indicating utility for developing in-season yield predictions. While NDVI is a potentially useful tool to improve N fertilizer management and develop in-season yield predictions in rice, alternative indices that do not saturate would likely provide a basis for a better tool.
Rare Allele of A Novel Histone H4 Acetyltransferase Enhances Grain Weight, Yield and Plant Biomass Presumably Through the Regulation of Transcription
GEO Series GSE62554. Oryza sativa. 8 samples. Type: Expression profiling by high throughput sequencing.
Spatiotemporal resolved leaf angle establishment improves rice grain yield via controlling population density [miRNA-seq]
GEO Series GSE155931. Oryza sativa. 20 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
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Hairy Vetch–Based Green Manure Improves Yield and Grain Appearance Quality in High-Yielding Japanese Rice Cultivars with Lodging Resistance Conferred by Functionally Weak GA20ox alleles
GEO Series GSE313855. Oryza sativa. 14 samples. Type: Expression profiling by high throughput sequencing.
MSH7 confers quantitative variation in pollen fertility and boosts grain yield in maize
GEO Series GSE224891. Zea mays. 12 samples. Type: Expression profiling by high throughput sequencing.
Suppressing a phosphohydrolase of cytokinin nucleotide enhances grain yield in rice
GEO Series GSE231360. Oryza sativa. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.