4.2.7. Statistical Analysis

All statistical analyses were performed using the R program (www.r-project.org (accessed on 30 September 2021)). The experimental data for both in vitro assays (mycelial growth inhibition and dual culture antagonism) were compared using an analysis of variance (ANOVA) followed by a Tukey's test (*p* = 0.05). Prior to this analysis, Levene's test was performed in order to verify the homogeneity of variance. Results of the dual culture antagonism assay were plotted using the R package ggplot2. The efficacy of the wound protectants was calculated as the mean percentage recovery (MPR) of the isolates under study. Normality and homogeneity of variance were tested using Levene's test and when necessary, data were transformed into the arcsine of the square root of the proportion to verify the assumption of homogeneity of variance. For both years and cultivars, an ANOVA was used to compare the differences in the mean percentage of recovery (MPR). The means were compared using Tukey's test at the 5% significance level (*p* = 0.05). The mean percentage of disease control was also calculated according to Sosnowski et al. [49,50] and Martínez-Diz et al. [41], using the formula MPDC = 100 × [1 − (MPR treatment/MPR inoculated control)]. To better visualize the results for all the treatments (15 variables, including all the product/isolate combinations, as well as inoculated controls) on both cultivars and their interaction with the meteorological variables (temperature and rainfall), a principal component analysis (PCA) was performed on the results of all the variables. For this analysis, data obtained from the treatments and meteorological variables were considered as two individual data sets or quantitative blocks, and cultivar was considered as a qualitative variable. This analysis was also performed using the R program with the Factoshiny v2.4 package [81].

**Author Contributions:** P.R., C.R., A.A. and F.F. designed the experiments; P.R. and A.G. implemented the methodology; P.R. formal analysis; P.R. original draft preparation; A.A., F.F. and C.R. review of the original draft; A.A., F.F. and C.R. supervision; A.A. and C.R. funding acquisition. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by FEDER funding through COMPETE program (POCI-01-0145- FEDER-016788) and Programa Operacional Regional de Lisboa-POR Lisboa (LISBOA-01-0145-FEDER-016788) and by national funding through FCT within the research project ALIEN (PTDC/AGR-PRO/2183/2014). The authors are thankful to FCT/MCTES for financing CESAM (UIDB/50017/2020 +UIDP/50017/2020), LEAF—Linking Landscape, Environment, Agriculture and Food (UIDB/04129/ 2020 and UIDP/04129/2020), and the PhD grant of Pedro Reis (SFRH/BD/131766/2017).

**Acknowledgments:** The authors would like to thank Natural development Group®, for providing the LC2017 product used during this work, Agrauxine S A, for providing the Esquive®, BASF BASF Agricultural Solutions Portugal for providing Tessior®, and to the Portuguese Institute for Sea and Atmosphere (IPMA-Instituto do Mar e da Atmosfera) for providing the meteorological data used on this work.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
