Five SNPs in close proximity to the major association signal on chromosome Vu11 displayed highly significant deviations (P ≤ 0.01) from the null expectation of no marker–phenotype correlation, supporting their strong association with genomic regions influencing Striga resistance in cowpea ( Table 6 ).
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Generally, the VIs had a better relationship with Pn at the jointing–booting stage, but the r value became worse as the growth stage advanced; however, it might be that the number of samples was relatively small and therefore no VIs passed the highly significant correlation test ( p < 0.01).
Effect on dry matter quality of quinoa under different irrigation and nitrogen application treatments The analysis of variance results ( Table 2 ) revealed that both irrigation level and nitrogen application rate had highly exerted highly significant effects on dry matter accumulation in quinoa across all growth stages ( p < 0.01).
Two loci, RM8006 on Chr 7 and RM6105 on Chr 11 showed extremely significant associations ( P < 0.01), and another two loci, RM132 and RM16153 on chromosome 3, were detected at P < 0.01 in one field condition but at P < 0.05 in the other one.
Year (Y), GT, MM and Y×GT had a highly significant ( P ≤ 0.01) effect on crude fat content, crude ash content, acid detergent fiber (ADF) and neutral detergent fiber (NDF) content of the forage.
The content of 1,8-cineole in leaves was about 8.3 times higher than that in stems, and 13.5 times higher than that in roots, showed an extremely significant difference ( p- value < 0.01).
The cultivar × treatment interaction was highly significant (F = 136.57, p < 0.01), indicating differential responses of the two cultivars under the second harvest conditions. 3.2.
The difference between AUC CT across the treatments were highly significant (p <0.01; Figure 8 , top row), but this difference progressively disappeared within the first 4–5 d.
2.5 Correlation Under drought stress at the vegetative stage, the percent reduction in SDW (%R_SDW) had a highly significant (p ≤ 0.01) negative correlation with WU, SDW, WUE, RDW, CRN, and CRPT.
Differences in root morphological traits across root orders Root order had an extremely significant effect on the average diameter, SRL, SSA, and RTD of T. chinensis fine roots ( p < 0.01; Supplementary Table 2 ).
Statistical analysis was carried out using one way ANOVA that identified significant difference between the samples, with significant ( p < 0.05), highly significant ( p < 0.01) and very highly significant ( p < 0.001).
Additionally, the interaction between genotype and environment (G × E) and environmental variance were also highly significant (P< 0.01) across all environments analyzed ( Zhang et al., 2022 ). 2.2 Population structure, kinship, and linkage disequilibrium (LD) We obtained high-quality datasets after filtering, and the average missing rates were 0.073, 0.069, and 0.070 for SNPs from DArT, GBS and the combination of GBS and DArT (GBS - DArT) ( Table 1 ).
The results revealed that all vegetation indices showed a correlation coefficient with LAI above 0.74, indicating a highly significant relationship (p<0.01).
The correlation coefficients of coleoptile length among environments were highly significant and ranged from 0.23 to 0.96 ( P < 0.01), which also indicated that coleoptile length was affected by the environment.
However, the interaction between them has different influences on them, specifically, it has extremely significant influence on VBD and VBD/RD ( p < 0.01, Table 3 ), significant influence on XA and NV ( p < 0.05, Table 2 ), but no influence on RD, CT and C/R ( p > 0.05, Table 2 ).
Differences in expression were considered significant ( * ) when P < 0.05 and highly significant ( ** ) when P < 0.01.
Under cold stress, highly significant positive correlations were observed between LTG and SR ( r = 0.37, p ≤ 0.01), and between PH and FLL ( r = 0.39, p ≤ 0.01), respectively.
Results Components of Variance The REML ANOVA indicated that the variance due to genotypes was highly significant ( p ≤ 0.01) for all agronomic and grain nutrients for individual cropping years and pooled analysis over cropping years except for P, K, and Mn of the 2019 rainy season ( Table 1 ).
* Significant difference ( P < 0.05); ** Highly significant difference ( P < 0.01).
final rust severity average coefficient of infection area under disease progress curve heat tolerance indices yield traits cluster analysis principal component analysis heatmap and hierarchical clustering Yield traits Highly significant differences (P< 0.01) were detected among genotypes for all studied agronomic traits.
In addition, drought type, precipitation, and temperature had extremely significant partial correlations with the drought response of GY ( P ≤ 0.01), with partial correlation coefficients of −0.60, 0.57, and −0.44, respectively ( Figure 3B ).
The Hap1 and Hap2 of Glyma.01G005900 in different years of ELS( P =0.0039) and ELT ( P =0.039)showed extremely significant differences (P<0.01).
The ANOVA resulted in highly significant differences across cereal species and design ( p < 0.01).
The significance of the differences was tested by t -tests or one-way (ANOVA; * , p < 0.05, significant; ** , p < 0.01, extremely significant).
(1987) showed that plant height, tiller number and plant density of forage had extremely significant effects on forage yield ( p < 0.01), among which tiller number had the greatest effect.