In full-light drought conditions (T 1 , T 2 ), dry matter accumulation and photosynthetic capacity were affected, with drought duration (D n ) showing highly significant negative correlations ( p < 0.01) with most physiological parameters ( RWC , LDW , G s , T r ).
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Correlation Analysis Between Nutrient Contents and Stoichiometric Characteristics of Different Components of Main Shrubs and Herbs in Karst Forests As shown in Figure 5 , for the stoichiometric characteristics of shrub leaves, the positive correlation groups were as follows: C content was significantly ( p < 0.05) positively correlated with C/K (r = 0.54); there were significant ( p < 0.05) or extremely significant ( p < 0.01) positive correlations between N content and P (r = 0.68), Ca (r = 0.62), and Mg (r = 0.60) contents, as well as between P content and Ca (r = 0.67) and Mg (r = 0.46) contents; K content was extremely significantly ( p < 0.01) positively correlated with K/P (r = 0.73); Ca content was significantly ( p < 0.05) or extremely significantly ( p < 0.01) positively correlated with Mg (r = 0.79), N/K (r = 0.61), and Ca/Mg (r = 0.47).
Responses of Whole-Plant Dry Matter and Plant Nitrogen Concentration to Nitrogen Fertilization Analysis of variance showed that year, site, N rate, and cultivar had highly significant effects on whole-plant dry matter and plant N concentration at all growth stages ( p < 0.01) ( Table 3 ).
The highly significant correlations ( p < 0.01) with yield were ZRs, leaf N content, leaf K content, PWP and SPAD, and the significant correlations ( p < 0.05) with yield were IAA, GA, and Pn.
Moreover, Spearman’s rank correlation analysis demonstrated highly significant positive correlations ( p < 0.01) among all pairs of methods ( Table 4 ).
Joint ANOVA and Genetic Parameters Joint ANOVA for the treatments (environmental indices (E) and genotypes (G)) displayed highly significant differences ( p < 0.01) for all studied traits, and the interactions were significant for 12 traits and insignificant for 8 traits (DH, RWC, E, POD, Pn, Gs, PPO, and CAT) ( Table 2 ).
The results also showed a highly significant difference ( p < 0.01) among bacteria inoculation treatments in the bud number, fresh and dry weight, and seed dry weight.
Response surface modeling demonstrated a highly significant regression ( p < 0.01; R 2 = 0.9941; Table 1 and Table 2 ), with selenium concentration exerting the strongest influence on particle size.
Association analysis between three phenotypic traits (DBH, annual height increment, and branch number) and SNPs identified 25 highly significant SNP loci ( p < 0.01).
Further analysis of fruit quality showed highly significant positive correlations (between peel L* value and peel b* value, r = 0.98, ** p < 0.01), a highly significant negative correlation (between pH value and aroma compounds, r = −0.97, ** p < 0.01), and a highly significant positive correlation (between total phenols and tannins, r = 0.99, ** p < 0.01).
It was also 7.2% thicker than “Caddo”, 13.0% thicker than “Nacono”, and 20.35% thicker than “Mandan”, all of which were extremely significant ( p < 0.01) ( Figure 2 A).
Sources of Variance for Yield Components and Grain Yield of Four Soybean Genotypes Across 13 Environments The combined analysis of variance across thirteen environments revealed highly significant effects ( p < 0.01) for environment (E), genotype (G), and genotype-by-environment interactions (G × E) for all measured traits ( Table 2 ).
In addition, except for the early two years, a highly significant ( p < 0.01) correlation was also observed between the WC and wheat yield during 2018–2019, showing that a higher WC was consistent with a higher wheat yield ( Figure 5 ). 2.3.
The needle soluble sugars showed highly significant positive correlations ( p < 0.01) with the root soluble sugars, root starch, root NSCs, and stem starch.
A value of p < 0.05 was considered significantly different, and a highly significant difference was identified with p < 0.01.
Analysis of variance (ANOVA) for MDS revealed highly significant differences ( p < 0.01) among RILs, environments, and line × environment interactions ( Table A1 ), indicating that stripe rust resistance is influenced by both genetic and environmental factors.
Effects of Harvesting Intensity and Recovery Time on Understory Plant Diversity As shown in Table 3 , both harvesting intensity, recovery time, and their interaction had highly significant effects ( p < 0.01) on all four diversity indices: the Shannon–Wiener index, Simpson index, Pielou evenness index, and Margalef richness index.
Our data revealed a highly significant positive correlation between Gs and Tr ( p < 0.01), whereas WUE showed no statistically significant variation among cultivars ( p > 0.05).
Correlation Analysis of Soil Nutrients and Soil Enzymes From Pearson correlation analysis ( Figure 8 ), soil available P showed a highly significant positive correlation ( p < 0.01) with total p , with a correlation coefficient of 0.809; avail-able N showed a significant positive correlation ( p < 0.05) with organic matter and catalase, with correlation coefficients of 0.712 and 0.669, respectively; and organic matter showed a highly significant positive correlation ( p < 0.01) with catalase, with a correlation coefficient of 0.810.
(3) Irrigation and nitrogen application had highly significant effects on alfalfa hay yield ( p < 0.01).
In deciduous broad-leaved forests, the Shannon–Wiener index showed a significant negative correlation ( p < 0.05) with available potassium (AP), while the Simpson index and Pielou index showed a highly significant negative correlation ( p < 0.01) with available potassium (AP).
The Correlations Among Biomass, Root Morphology and Architecture, and Non-Structural Carbohydrates Across both periods, the biomass of leaves, stems, and roots exhibited highly significant positive correlations ( p < 0.01) with both SS and ST ( Figure 6 ).
Analysis of Year and Treatment Interaction Effects on Sugarcane Yield-Related Traits The treatment had extremely significant effects on all yield-related traits ( p < 0.01), indicating that treatment is the primary factor influencing sugarcane yield and associated agronomic characteristics ( Table 1 ).
Phenotypic Analysis of Salinity Tolerance Analysis of Variance and Mean Performance of the Studied Traits The analysis of variance revealed highly significant ( p < 0.01) differences for the 13 measured traits among the wheat genotypes in the control and salinity treatments.
Interactive Effects of Nitrogen Levels and Growth Stages on Rhizome and Tiller Bud Density of Kentucky Bluegrass Nitrogen addition, phenological stage, and their interaction had highly significant effects on the rhizome bud density, tiller bud density, and total belowground bud density of Kentucky bluegrass ( Figure 2 a–c) ( p < 0.01).