In the MMU region of Ambala,
Parthenium hysterophorus was affected by leaf spot diseases caused by four fungal isolates cultured on PDA. Morphological and microscopic analysis identified them as
Alternaria species with distinct variations
(Pavicich et al., 2022). Molecular characterization
via 18S rRNA gene sequencing and ITS region PCR confirmed isolates as
Alternaria alternata strain PHMMU2 and three
Alternaria macrospora strains (PHMMU1, PHMMU3, PHMMU4) (Table 1). Sequences were submitted to NCBI GenBank (PQ483096, PQ433119, PQ483101, PQ483102). Phylogenetic analysis validated these identifications (Fig 1). Pathogenicity was tested using the Parthenium disc plate technique (Fig 2). Spore germination occurred within 24 hours on healthy leaves, with maximum leaf defoliation at 72 hours (25±5°C).
Alternaria macrospora PHMMU1 caused the highest disease incidence (67.27%), followed by
A.
alternata PHMMU2 (50.06%),
A.
macrospora PHMMU3 (40.70%) and PHMMU4 (35.18%). Re-isolation confirmed their identity, fulfilling Koch’s postulates and establishing pathogenicity to
Parthenium hysterophorus.
Statistical analysis
Four fungal isolates (PHMMU1–PHMMU4) were tested for pathogenicity against
Parthenium hysterophorus using the disc plate method. Disease progression, including necrotic lesions and leaf degradation, varied among strains over 72 hours. Multiple regression analysed the effects of incubation time and fungal strain on disease incidence, while ANOVA confirmed significant differences in virulence, validating the observed infection patterns.
Evaluation of disease incidence through multiple regression
Multiple regression analysis assessed the impact of incubation time and fungal isolates on
Parthenium hysterophorus disease incidence. The model showed a strong fit (high R
2) and significant predictors (p<0.05). Disease incidence increased with time, especially for isolate PHMMU1, which had the highest infection rates-from 33.21% at 24 hours to 67.27% at 72 hours-demonstrating its strong virulence. These results confirm incubation time’s significant role in disease progression and validate PHMMU1 as the most pathogenic isolate (Fig 3).
Multiple regression model building report
The multiple regression model predicting
Parthenium hysterophorus disease incidence was developed using incubation time (X1) and fungal isolate (X2) as predictors (Fig 4). The fungus variable (X2) contributed most, increasing adjusted R
2 to 50.06%. Adding incubation time (X1) improved the model accuracy to 99.84%. Including the interaction term (X1*X2) further raised adjusted R² to 99.99%, indicating a strong synergistic effect. All predictors were statistically significant (p<0.05). The incremental impact chart showed fungus had the highest contribution, followed by incubation time, with their interaction enhancing model performance. Regression models for each isolate revealed quadratic disease progression over time, characterized by a negative squared time coefficient (-0.002951), showing an initial increase then decline in severity.
Alternaria macrospora PHMMU1 had the highest linear coefficient (1.5569), indicating the fastest, most severe infection, followed by PHMMU2 (1.3604), PHMMU3 (1.1569) and PHMMU4 (1.0951). The control group exhibited minimal disease incidence (0.2833), confirming the isolates’ pathogenic effectiveness (Table 2).
Analysis of pathogenicity using ANOVA
The significance of one-way ANOVA lies in its ability to detect and validate statistical differences in disease incidence among fungal isolates, enabling a reliable interpretation of their pathogenic potential. This statistical method was chosen to evaluate whether the observed variation in pathogenic effects among the treatments was statistically significant. The results revealed notable differences in the degree of infection produced by each isolate, indicating that the effectiveness of the fungal strains in inducing disease symptoms varied considerably. This analysis provides clear evidence supporting the differential virulence of the tested isolates.
Effect of fungal treatments on disease incidence
One-way ANOVA showed a highly significant difference in disease incidence among fungal treatments (f = 15.10; p = 0.001). The control had no symptoms (0.00%), while all fungal treatments significantly increased disease.
A.
macrospora PHMMU1 caused the highest incidence (67.27%), followed by
A.
alternata PHMMU2 (50.06%), PHMMU3 (40.70%) and PHMMU4 (35.18%). The results highlight PHMMU12’s strong pathogenicity, with moderate variability within treatments (SD = 19.14) but consistent differences across groups (Fig 5).
Dunnett multiple comparisons with control
Dunnett’s multiple comparisons test further substantiated the significance of these findings. Each fungal treatment group (PHMMU4, PHMMU3, PHMMU2 and PHMMU1) was found to be significantly different from the control group at the 95% confidence level (p<0.05). Notably, the control group was distinctly separated in grouping from all fungal treatments, confirming that disease incidence following fungal inoculation was not due to random variation but was attributable to treatment effects (Fig 6). The data indicate a dose-response-like trend where certain fungal isolates, particularly
A.
macrospora strain PHMMU1, induced more aggressive disease symptoms compared to others.
Effect of incubation time on disease incidence
The effect of incubation duration on disease incidence was also evaluated through a separate one-way ANOVA. Analysis revealed a statistically significant difference among the three incubation periods (f = 9.30; p = 0.001), indicating that the length of incubation played a critical role in disease progression. Mean disease incidence was observed to increase substantially with longer incubation times (Fig 7). At 24 hours post-inoculation, the mean disease incidence was relatively low (18.70%). However, after 48 hours, the mean incidence nearly doubled to 39.77% and further increased to 57.45% at 72 hours. This stepwise increase suggests a time-dependent enhancement in disease severity, with the most substantial disease symptoms manifesting at the longest incubation period tested. The pooled standard deviation for the incubation time analysis was 24.64, indicating a moderate spread of disease incidence values within each incubation group. However, the distinct upward trend across 24, 48 and 72 hours indicates that prolonged exposure significantly facilitates fungal pathogen establishment and symptoms development.
Taken together, the results clearly demonstrate that both fungal treatment and incubation time are critical determinants of disease incidence. The highest disease severity was observed when the most aggressive fungus (
A.
macrospora strain PHMMU1) was combined with the longest incubation time (72 hours), highlighting a synergistic effect between pathogen virulence and environmental conditions favouring disease progression.
This study identified four fungal isolates as pathogenic to
Parthenium hysterophorus under
in-vitro conditions. Among them,
Alternaria macrospora strain PHMMU1 exhibited the highest virulence, with a 67.27% disease incidence, rapid spore germination within 24 hours and maximum leaf defoliation by 72 hours, suggesting a highly efficient infection cycle. These findings indicate its strong potential as a promising candidate for mycoherbicide development. The results align with earlier studies, such as those by
Kaur and Kumar (2019),
Kumar et al., (2021), Kausar et al. (2022) which demonstrated the effectiveness of
A.
macrospora MKP1 against Parthenium.
Singh (2020) also reported
Fusarium,
Alternaria,
Cephalosporium and
Cladosporium species as pathogens, with
F.
equiseti showing the highest defoliation. Similarly,
Naseem et al., (2016) found
Nigrospora oryzae to induce severe symptoms in Parthenium. Overall, these findings support the potential of
Alternaria and other fungal pathogens as biocontrol agents in integrated weed management.