Rate and timing Determine rate and timing response of Sero-X in inhibiting microsclerotia. DNA as a diagnostic tool Assess the appropriateness of DNA abundance measurement for Sero-X's mode of action. Data Provide enough data points from replicated & randomised treatments for statistical analysis Spatial variability Minimise the effect of spatial variability across the paddock.

Treatments 4 Various rates of Sero-X and 1 Untreated Control Replications Random complete block 0.96 ha each GPS locations per replicate 1 data point for SD1 (sample date 1) and SD2. 3 data points for SD3, SD4 and SD5. Cores per GPS datapoint 4 × 4 grid of 10cm soil core all combining to return one data point Broad Sampled point per replicate 50 cores collected in a zig zag pattern up the middle of the replicate and combined to give one data point

Treatment Name Rate L/ha Number of Sero-X Applications Total Sero-X Applied (L/ha) Description 1 Lo T1 2 x 500ml + 1L/ha 3 2 Low label rate 2 Do T2 1 x 2L/ha 1 2 Defoliation only 3 La T3 3 x 2L/ha 3 6 Current label Rate 4 Hr T4 3 x 1L/ha 3 3 Half label rate 5 Utc T5 Untreated Control Not applicable Not applicable No Treatment Timing of Applicatons Sero-X Application 1 1st Flower Sero-X Application 2 Peak Flowering Sero-X Application 3 Defoliation

Season Sample Date ID Time Date 21-22 SD1 Pre cotton planting - season 1 18-Oct-21 21-22 SD2 Post Picking BEFORE Measurable Treatment effect - season 1 6-Jan-22 21-22 & 22-23 SD3 Post incorporation of the stubble in the soil for season 1, and also pre plant for season 2 11-Nov-22 22-23 SD4 Post incorporation of the stubble in the soil - season 2 9-May-23 23-24 SD5 Fallow Year, one year after last collection - season 3 2-May-24

Microsclerotial inhibition lasts ~14-21 days after each Sero-X treatment, leaving gaps in growing season where microsclerotia may develop. Parasitic life stages, (hyphae and conidia), not affected by Sero-X, can survive in soil for up to 4 months, depending on conditions. After this period, a 2-3 week timeframe exists where DNA from dead hyphae and conidia cells is still detectable by qPCR. This results in an approximate 5 month window where Sero-X treatment effects cannot be accurately measured through DNA comparison, thus efficacy of Sero-X is not measured at SD2

At Sample Date 1 and SD 2: 1 GPS Loc Data Point Each DP is 1 composite made up of 16 sub samples from 1 Sqm GPS precision from 5-30m (no fixed base station/RTK) 4 GPS per Treatment Per treatment block 4 GPSlocated Data Points (GPS DP) 1 Broad sampled Data point Broad sampled Data Point (Broad DP) 1 composite made up of 50 sub samples taken in a zig zag pattern across the whole treatment block 5 DPs per treatment, 5 Treatments & 3 replicates 5 Data Points x 5 Treatment blocks x 3 replicates = Total of 75 for the trial area Results from SD1 and SD2 showed us that the variation in the data would not give us meaningful results so at Sample Date 3 onwards, a different sampling protocol was implemented: At Sample Date 3, SD 4 and SD 5: 3 Data points per GPS Location Each DP is 1 composite made up of 16 sub samples from 1 sqM 4 GPS locations per Treatment 4 GPS location per treatment block × 3 Data Points (GPSloc DP) =12 1 Broad sampled Data point Broad sampled Data Point (Broad DP) 1 composite made up of 50 sub samples taken in a zig zag pattern across the whole treatment block 13 DPs per Treatment, 5 treatments 3 replicated 13 Data Points × 5 Treatments × 3 Replicants = 195 Data Points for the trial area per sample date

To quantify the efficacy of Sero-X in mitigating Verticillium dahliae exposure in cotton crops, we employed a comparative Area Under the Curve (AUC) analysis. AUC analysis allows us to assess the cumulative impact of the treatment over the entire growing season. Trapezoidal Rule This analytical approach was selected because it excels at estimating the total effect over time, even when working with data collected at specific intervals throughout the growing season. This illustration demonstrates how the trapezoidal rule is applied to estimate the area under a curve: The blue curve represents the changing pgDNA levels over time. The red dots represent our actual data points (measurements taken at specific times). The colored trapezoids (red, green, and blue) show how we approximate the area under the curve between each pair of data points. By connecting our measurement points with straight lines to form trapezoids, we can calculate an area that closely represents the cumulative impact of the treatment, as if we had continuous measurements. Our approach involved the following steps: Steps in Analysis Data Collection We measured pgDNA levels of Verticillium dahliae per gram of soil at regular intervals throughout the growing season in both Sero-X treated plots and untreated control (UTC) plots. UTC Regression Model Using the data from the UTC plots, we developed a regression model to describe how pgDNA levels change over time without treatment. Expected Outcome Projection For each Sero-X treated plot, we applied this UTC regression model to its initial pgDNA level. This projection represented the expected outcome if the plot had been left untreated, accounting for different starting conditions across treatments. Curve Generation We plotted time-series curves for both the actual Sero-X treated results and the projected untreated outcomes for each plot. AUC Calculation We calculated the area under each curve using the trapezoidal rule. This method estimates the area by dividing the curve into trapezoids based on our sampling intervals, providing a good approximation of the total exposure over time. Comparison and Risk Quantification For each treated plot, we compared the AUC of the actual Sero-X treated curve to the AUC of its projected untreated curve. The difference between these areas over just cotton Season 22-23 represents the reduction in Verticillium dahliae exposure achieved by the Sero-X treatment. Percentage Calculation We expressed this reduction as a percentage of the projected untreated exposure: % Risk Mitigated = (AUCprojected - AUCsero-X) / AUCprojected × 100 This method allows us to account for varying initial conditions across treatments while providing a comprehensive assessment of Sero-X's efficacy in reducing Verticillium dahliae exposure throughout the growing season. The analysis and regression modeling was performed using Minitab 21.1.0, with the AUC calculations carried out using Python with SciPy