Excerpts

Strong and highly significant positive correlations were observed among GLA and DB (r: 0.56-0.70, ***p < 0.001) across control and stress environments indicating that genotypes with larger leaf area tends to accumulate higher biomass irrespective of conditions.

Analysis of variance The analysis of variance (ANOVA) revealed a highly significant differentiation (P< 0.001) among the three sources of variation: genotype (G), environment (E), and the genotype-environment (G x E) interaction, across all agronomic traits ( Table 1 ).

A moderately significant negative correlation was observed for DF-DLF ( r = -0.58; P < 0.001).

C showed a highly significant positive correlation with the C:N ratio (P < 0.001) and a significant positive correlation with the C:P ratio (P < 0.05).

A two-way ANOVA revealed a highly significant effect on the total number of plant leaves, influenced by plant species ( p < 0.001), treatment groups ( p < 0.001), and the interaction between species and treatment groups ( p < 0.001). 3.2.

Trait-specific responses of okra genotypes under three PEG 6000 levels The effects of genotypes, treatments, and their interaction were highly significant (p< 0.001) for all the estimated growth, root, and biochemical parameters ( Tables 1 , 2 ).

Both Wilk’s Λ and Pillai’s trace were highly significant ( p < 0.001) for each of the single IVs (treatment, exposure time and cultivar), for the two-way interactions (treatment ∗ exposure time, treatment ∗ cultivar, exposure time ∗ cultivar) and the three-way interaction treatment ∗ exposure time ∗ cultivar.

The high coefficient of determination (R2 = 0.98), highly significant p-value (p <0.001), low Root Mean Square Error (RMSE = 0.90 kg m - 2), and high Modeling Efficiency (EF = 0.98) collectively demonstrate an excellent model fit for the calibration dataset.

Highly significant genetic variations ( p < 0.001) were observed for BSR disease incidence (DI) in all field screening tests and disease rating and area under the disease progress curve in the greenhouse.

Type-III ANOVA identified highly significant main effects of grazing (F = 62.67, P < 0.001, partial η 2 = 0.68) and fertilization (F = 14.44, P < 0.001, partial η 2 = 0.49), with a moderate interaction effect (F = 4.00, P = 0.029, partial η 2 = 0.21; Supplementary Table 1 ).

A random permutation test indicated that the proportion of variance attributable for all hierarchical levels were highly significant ( p < 0.001).

A highly significant difference ( P <0.001) was found for Trt among Pht, Mat, Sdw, Pln, and Yld.

Highly significant correlations between the SA/JA ratio with redox status [GSH/GSSG ( P < 0.001) and NADPH/NADP + ( P < 0.01) ratios] and the expression of phenylpropanoid synthesis-related genes [ CHS ( P < 0.01), ANR ( P < 0.001), and F5H ( P < 0.01)] were observed ( Table 2 ).

The relationship between trophic preference (ICM) and historical change was very similar with no significant interaction (likelihood ratio test, p = 0.75) but showed a highly significant positive effect of ICM on historical change (ordinal logistic regression, p < 0.001, Figure 6 ).

The correlations of reproductive traits of winter wheat in different environments were compared in the figure, from which it can be seen that most of the winter wheat reproductive traits reached the highly significant level (p<0.001).The correlations of S1 stage traits in E1 environment were -0.02-0.67, and the correlations of S2 stage traits in E1 environment were 0.22-0.55; and the correlations of S1 stage traits in E2 environment were 0.37-0.55; and the correlations of S2 stage traits in E3 and E4 environments were 0.37-0.55. correlation was 0.37-0.63 for S1 stage trait in E2 environment and 0.45-0.62 for S2 stage trait in E2 environment; correlation of S1 stage trait in E3 environment was 0.41-0.62 and correlation of S2 stage trait in E4 environment was 0.36-0.62.

Experiment 3 Within the doubled haploid population, when using the “target” growth stages for transfer as a fixed effect there was a very highly significant interaction ( P < 0.001) between temperature, growth stage, and DH line for grain number.

AMOVA was used to determine that variation among and within groups was highly significant ( P < 0.001), with the clusters capturing 31.5% of the total genetic variations, while 68.3% was explained by individuals within populations ( Table 2 ).

The genotypes showed very highly significant differences ( p< 0.001) was measured in each and across environments at all growth stages.

In addition, a significant interaction effect between year and treatment was detected for the Margalef index (p < 0.05), whereas treatment exerted a highly significant main effect on species richness (p < 0.001).

In most cases, this difference was highly significant ( p < 0.001), with the exception of SNP assay 5 (Wnt-4), SNP assay 9 (Spr-1) and SNP assay 11 (Spr-2) (Figure 4 , Table 5 ).

Genotypes and the main effects of the treatment were highly significant ( p < 0.001) for NDVI over time; however, a G × T interaction was non-significant.

Results Agronomical and physiological traits For SM2, KS and TKW the genotype x environment (GxE) interaction was highly significant ( P < 0.001) in both growing seasons, whereas for GY, PH, and KM2 was only in one growing season (Table 1 ).

Responses of net primary productivity and relative growth rate to different grazing intensities in a herbaceous marsh wetland Grazing intensity exerted a highly significant effect on aboveground net primary productivity (ANPP) (F 2,42 = 27.21, P < 0.001) ( Figure 4A ).

leaf N:P ES relationship ( Figure 5D ) showed more scatter than this regression for CER and g s but was still highly significant ( p < 0.001; Table 2 ).

fulvum line gave a highly significant variation, P = 0.001 ( Table 4 ).