Excerpts

For both barley and bean, we found a highly significant correlation between the modeled h leaf and the measured τ (p < 0.001).

The effect of “rust infection” was statistically highly significant [repeated measures ANOVA: F (1,10) = 19.815, P = 0.001], as was the interaction of time and rust infection [repeated measures ANOVA: F (4, 40) = 10.665, P < 0.001].

Organic matter and organic carbon served as primary components, with a highly significant path coefficient of 0.930 ( P< 0.001) indicating strong support for macro elements.

This efficiency was highly significant (p< 0.001) compared to manual counting, which required 76.1 (± 1.24) seconds for 100 maize seeds and over 93.59 (± 1.84) seconds for 100 smooth pigweed seeds.

Correlation analysis of quantitative traits Correlation analyses revealed varying degrees of association among oat traits in both cultivation regions, with most trait correlations reaching highly significant levels (P<0.001, Figure 3 ).

Results Selection Response Selection Based on Freezing Tests (FT) The results showed highly significant differences in freezing tolerance between genotypes ( F = 5.0, p < 0.001).

However, crude protein content had a highly significant ( p < 0.001) and strong negative correlation with dry biomass production (-88%).

Timepoint, treatment, and the timepoint * treatment interaction were all highly significant in a general linear model on log-transformed data (df: 1, 37; all P -values < 0.001; treatment, JA: F = 110.5, JA-Ile: F = 181.9; timepoint, JA: F = 87.78, JA-Ile: F = 34.88; interaction, JA: F = 32.04, JA-Ile: F = 17.60).

While tree-ring width and earlywood width exhibited a decreasing trend, an increasing trend in latewood width was observed (each p < 0.001; Supplementary Figure 2 ).

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

Associations were considered highly significant at a threshold of 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).

tianguii (middle of Tiankeng, top of the Tiankeng, orchid garden), T test of Sobs index showed that the number of fungal OTUs observed in samples collected at orchid garden was higher than that at middle of Tiankeng and top of Tiankeng, and the difference was extremely significant (P ≤ 0.001 marked as * * *).

Significance testing for fixed effects in the mixed model ( Table 3 ) revealed that location effects were highly significant (P< 0.001).

The sowing time × cultivar interaction was highly significant in 2022 ( F = 13.9, p < 0.001) and 2024 ( F = 6.9, p < 0.001), but was weaker in 2023 ( p = 0.049).

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 ).

ANOVA showed highly significant ( P < 0.001 and P < 0.01) differences among genotypes both for specific locations (Islamabad and Nowshera) and across locations (combined data) ( Table 2 ).

The analysis of variance revealed highly significant ( P = 0.001) differences between the clusters for different cross combinations.

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.

A non-parametric analysis of variance (Kruskal–Wallis test), followed by Dunn’s post-hoc test, indicated highly significant differences (p < 0.001) for VC as well as for all soil surface components.

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

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 ).

In comparison, the majority of the lines expressing ΔN ZmL (2 lines) or any of the chimeric DGAT1s (2–4 lines identified from each construct) had highly significant ( P < 0.001) increases in lipid levels compared to both wild type and their respective null siblings ( Table 2 and Supplementary Tables 4 , 5 ).

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 ).