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68 results for “grain yield”
Data from: Genome-wide association study for grain yield and component traits in wheat (Triticum aestivum L.)
<p>The study material in GWAS panel with 280 common bread wheat genotypes was selected from All India Coordinated Research Project on Wheat and Barley to map the genomic regions governing days to heading (DH), grain filling duration (GFD), grain number per spike (GNPS), grain weight per spike (GWPS), plant height (PH), and grain yield (GY).</p> <p><strong>Phenotypic data:</strong></p> <p>The GWAS panel was evaluated at five different environments during the 2020-21 <em>Rabi</em> (winter) season: E1-University of Agricultural Sciences, research farm, Dharwad (15°29'20.71"N, 74°59'3.35"E, 750m AMSL), E2-ICAR-Indian Agricultural Research Institute, New Delhi (28°38′30.5″N, 77°09′58.2″E, 228 m AMSL), E3-Indian Agricultural Research Institute, Jharkhand (24°16'58.4"N, 85°21'16.1"E, 651m AMSL), E4-ICAR-Indian Institute of Wheat and Barley, Karnal (29°41'8.2644''N, 76°59'25.9692''E, 250m AMSL), and E5-Punjab Agricultural University, Ludhiana (30o54' N, 75o48'E, 247m AMSL). The genotypes were planted in an augmented block design along with repeated checks (DBW187, MACS6222, WH1124, and WH1142). All the genotypes of a GWAS panel were phenotyped for six quantitative traits i.e. GWPS (gm), GY (gm), PH (cm) at five locations, GFD (days), DH (days) at four locations and GNPS (number) at two locations. Phenotypic data were analyzed using the R package 'augmentedRCBD'</p> <p><strong>Genotypic data:</strong></p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of 21 days-old seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 14,790 high-quality SNPs was used in GWAS analysis. </p>
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>
Data from: Plant population and maize grain yield: a global systematic review of rainfed trials
Maize (Zea mays L.) productivity has increased globally as a result of improved genetics and agronomic practices. Plant population and row spacing are two key agronomic factors known to have a strong influence on maize grain yield. A systematic review was conducted to investigate the effects of plant population on maize grain yield, differentiating between rainfall regions, N input, and soil tillage system (conventional tillage [CT] and no-tillage [NT]). Data were extracted from 64 peer-reviewed articles reporting on rainfed field trials, representing 13 countries and 127 trial locations. In arid environments, maize grain yield was low (mean maize grain yield = 2448 kg ha−1) across all plant populations with no clear response to plant population. Variation in maize grain yield was high in semiarid environments where the polynomial regression (p < 0.001, n = 951) had a maximum point at ∼140,000 plants ha−1, which reflected a maize grain yield of 9000 kg ha−1. In subhumid environments, maize grain yield had a positive response to plant population (p < 0.001). Maize grain yield increased for both CT and NT systems as plant population increased. In high-N-input (r2 = 0.19, p < 0.001, n = 2 018) production systems, the response of plant population to applied N was weaker than in medium-N-input (r2 = 0.49, p < 0.001, n = 680) systems. There exists a need for more metadata to be analyzed to provide improved recommendations for optimizing plant populations across different climatic conditions and rainfed maize production systems. Overall, the importance of optimizing plant population to local environmental conditions and farming systems is illustrated.
Data from: Genome-wide association study for grain yield and component traits in wheat (Triticum aestivum L.)
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Supporting data for: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics
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Data from: Plant population and maize grain yield: a global systematic review of rainfed trials
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Barley fasciated ear genes determine inflorescence meristem size and grain yield
<p>In flowering plants, the inflorescence meristem (IM) provides founder cells to form successive floral meristems, the precursors of fruits or seeds. Hence, the activity and developmental progression of IM are critical for yield production in cereal crops. IM size is positively associated with spikelet number in some major cereals, like rice (<em>Oryza sativa</em>) and maize (<em>Zea mays</em>). However, the relationship and regulatory mechanism between IM size and grain yield remain unknown in Triticeae tribe. Here, we report that IM size has a negative correlation with yield traits in barley (<em>Hordeum vulgare</em>), and three <em>FASCIATED EAR</em> (<em>FEA</em>) orthologs, <em>HvFEA2</em>, <em>HvFEA3</em>, and <em>HvFEA4</em>, regulates IM size and spike development and ultimately affect the grain yield. All three HvFEAs are highly expressed in developing spikes, and loss-of-function mutants exhibit enlarged IM size, shortened spike length and decreased spikelet number, which leads to the reduced grain yield. We further reveal that HvFEA4 potentially targets to multiple pathways during reproductive development, including transcriptional control, phytohormones signaling and redox status. Our findings uncover the roles of barley FEA genes in limiting IM size and promoting spikelet formation, which provides insights into yield improvement by manipulating IM activity.</p>
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]
Effect of planting density on growth characteristics and grain yield increase in successive cultivations of two rice (Oryza sativa L.) cultivars
<p><span>Rice (<i>Oryza sativa</i> L.) re-cultivation plays an important role in increasing land productivity and efficiency in crop rotation. Planting density (PD) is an important agronomic factor to achieving maximum grain yield (GY). The current study aimed to determine the best PD for the first cultivation and re-cultivation of rice. The experiment was conducted as a split plot based on the randomized complete block design with three replications at Ghaemshahr University, Mazandaran (northern Iran) from 2013 to 2014. Treatments consisted of cultivar, i.e. Tarom Hashemi and Koohsar, at two levels as the main factor and PDs at the three levels of 16, 25, and 33.3 hills m<sup>-2</sup> with planting spaces of 25 × 25, 20 × 20, and 30 × 10 cm<sup>2</sup>, respectively, as the sub factor. The results of the current study showed that cultivar for the first cultivation, and year for the re-cultivation had a significant effect on GY. PD had a significant effect on GY in both first cultivation and re-cultivation. The GY of Hashemi in the first cultivation (36.1%) and re-cultivation (18.5%) was higher than that of Koohsar. GY in the first cultivation and re-cultivation had an increasing trend; PD increased up to 33.3 hills m<sup>-2</sup> as 19.9% and 21.4% in the first cultivation and re-cultivation, respectively, due to the increase in number of panicles m<sup>-2</sup> (30.1% and 30.6%, respectively). Hashemi is suitable for the first cultivation. It is noteworthy, also, that a density of 33.3 hills m<sup>-2</sup> is recommended for the first cultivation and re-cultivation.</span></p>
Discovery of Genomic Regions Associated with Grain Yield and Agronomic Traits in Bi-parental Populations of Maize (Zea mays. L) under Optimum and Low Nitrogen Conditions
<p>Supplementary material_ BLUEs across location for grain yield and other agronomic traits for four F3 populations evaluated under optimum and low N management conditions. </p><p>Genotypic data - SNP Marker data for four F3 populations used in the manuscript "Discovery of Genomic Regions Associated with Grain Yield and Agronomic Traits in Bi-parental Populations of Maize (Zea mays. L) under Optimum and Low Nitrogen Conditions" from Front in Genet</p>
Effect of planting density on growth characteristics and grain yield increase in successive cultivations of two rice (Oryza sativa L.) cultivars
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Barley fasciated ear genes determine inflorescence meristem size and grain yield
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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
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Enhanced sucrose loading improves rice yield by increasing grain size
GEO Series GSE74204. Oryza sativa. 12 samples. Type: Expression profiling by array.
The grain yield modulator miR156 regulates seed dormancy through the gibberellin pathway in rice
GEO Series GSE131243. Oryza sativa. 7 samples. Type: Expression profiling by high throughput sequencing.
Spatiotemporal resolved leaf angle establishment improves rice grain yield via controlling population density
GEO Series GSE155932. Oryza sativa. 40 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Spatiotemporal resolved leaf angle establishment improves rice grain yield via controlling population density [mRNA]
GEO Series GSE155930. Oryza sativa. 20 samples. Type: Expression profiling by high throughput sequencing.
Ozone-induced rice grain yield loss is controlled by ABERRANT PANICLE ORGANIZATION 1 gene
GEO Series GSE65465. Oryza sativa. 4 samples. Type: Expression profiling by array.
OsCOMT, encoding a caffeic acid O-methyltransferase in melatonin biosynthesis, increases grain yield through regulating leaf senescence and vascular patterning
GEO Series GSE184400. Oryza sativa. 9 samples. Type: Expression profiling by high throughput sequencing.
Figure 3 in Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan
Figure 3. Plots of R2(a), adjusted R2(b), C (c) and MSE (d) against P. p
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