Shoot Trait Variability The ANOVA-II results ( Table 1 ) reveal significant effects of population for all the shoot traits ( p < 0.001) and highly significant population × salinity interactions ( p < 0.001), indicating that the magnitude and direction of responses differed among the populations.
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The findings ( Figure 5 ) indicated highly significant negative correlations ( p < 0.001) between PEG-6000 concentrations and shoot dry biomass, Cd concentration in roots and shoots, and Cd accumulation in roots, shoots, and entire plants.
PERMANOVA analysis revealed extremely significant differences between fallen leaf and fecal bacterial communities (F = 13.47, R 2 = 0.53, p < 0.001).
Notably, shoot total phosphorus content consistently exceeded root content, showing a highly significant difference ( p < 0.001) under both P1 and P2 treatments.
Plant height The overall linear mixed-effects model (LMM) for the spring (Experiment 1) revealed a highly significant main effect of time on plant height (F(8, 360) = 523.91, p < 0.001), whereas the main effect of treatment was marginally non-significant (F(4, 45) = 2.37, p = 0.07).
Marker–trait association results revealed a highly significant association between both markers and the phenotypes ( p -value < 0.001) ( Figure 3 A,B).
Key population genetic differentiation parameters showed a highly significant genetic differentiation coefficient ( Fst ) of 0.21 ( p < 0.001) among populations, indicating a moderate level of genetic differentiation across geographic origins.
Among the agronomic traits, the plant height, stem diameter, and leaf number exhibited a highly significant positive correlation with fresh herbage yield ( p < 0.001), with the correlation coefficients exceeding 0.8.
The model revealed highly significant exponential growth (β = 0.108; p < 0.001; R 2 = 0.952), indicating strong explanatory power and confirming a robust pattern of expansion in the field under study.
The non-hierarchical F ST is high, 0.329, and highly significant ( p < 0.001), indicating the presence of genetic structure among populations.
With the increase in yield, the total Si uptake had an increasing trend (r = 0.773, p < 0.001), and the Si uptake per 100 kg of grains had a decreasing trend (r = 0.800, p < 0.001).
Further analysis of the year-to-year CT variation in Kentville revealed that year had a marginal effect on extractable CT levels ( p = 0.057), while genotype remained a highly significant factor ( p < 0.001), and no significant year × genotype interaction was observed ( p = 0.910) ( Table 1 ).
The comparison of travelled distances showed highly significant differences between sample types (H 2,299 = 158.585, p < 0.001, Kruskal–Wallis one-way ANOVA on ranks; Figure 5 ).
Baseline PSQI scores were comparable between groups, but the intervention group showed significantly greater improvements thereafter, with mean PSQI decreasing to 8.60 ± 2.28 at week 1 and 5.20 ± 1.87 at week 2, compared with 11.08 ± 2.73 and 10.68 ± 2.88 in controls at the same time points, respectively, indicating a marked, time-dependent improvement in subjective sleep quality with between-group differences reported as highly significant ( p < 0.001) [ 134 ].
The global PERMANOVA revealed a highly significant effect of treatment on subpopulation structure ( p = 0.001), indicating that at least one condition differed in multivariate space.
In this way, the validity of the models was evaluated using independent samples in the discriminant models and was found to be highly significant ( p < 0.001).
Pairwise Pearson’s coefficients of correlation among traits were calculated and highly significant ( p < 0.001) positive correlations were detected between RS and RSTS ( R = 0.999), RS and AAC ( R = 0.464), and RSTS and AAC ( R = 0.461; Figure 2 ).
Thousand-Seed Weight and Hectoliter Mass For thousand-seed weight (TSW), two-way ANOVA revealed a highly significant effect of Year (F 1 , 16 = 434.87, p < 0.001) and a significant Year × Treatment interaction (F 3 , 16 = 8.86, p = 0.0011), whereas the main effect of Treatment was not statistically significant (F 3 , 16 = 1.52, p = 0.2380) ( Table S1 ).
Linear regression analysis indicated a highly significant correlation ( p < 0.001) between root rot severity at the seedling stage and at maturity.
The ANCOVA also revealed a highly significant effect of the covariate ( p < 0.001), i.e., leaf size, with respect to disease severity, sporulation severity, sporangia produced per leaf, and Pv DNA concentration in the DM lesions.
The effects of different levels of these factors on the differentiation coefficient of ‘11-C-2’ shoot segments are highly significant ( p < 0.001).
AMMI (IPCA1) was highly significant ( p < 0.001) according to the AMMI model’s analysis of variance ( Table 1 ).
Contrariwise, biostimulant treatments had a highly significant effect ( p -value ≤ 0.001) on tomato productive and qualitative traits.
Genotypes (G) significantly influenced all the physiological traits studied (Phi2, PhiNPQ, PhiNO, SPAD, LEF, NPQt, FvP/FmP, and LT), as indicated by the highly significant p -values ( p < 0.001) and large mean-square values.
In the fungal community ( Figure 6 b), Glomeromycota showed a highly significant negative correlation with TK (blue, p < 0.001), representing the strongest correlation among all fungal phyla, indicating that TK content exerts a strong regulatory effect on the distribution of this group.