The Impact of a Commercial Biostimulant on the Grape Mycobiota of Vitis vinifera cv. Barbera

Abstract

Reducing the use of fungicides, insecticides, and herbicides in order to limit environmental pollution and health risks for agricultural operators and consumers is one of the goals of European regulations. In fact, the European Commission developed a package of measures (the European Green Deal) to promote the sustainable use of natural resources and strengthen the resilience of European agri-food systems. As a consequence, new plant protection products, such as biostimulants, have been proposed as alternatives to agrochemicals. Their application in agroecosystems could potentially open new scenarios regarding the microbiota. In particular, the vineyard microbiota and the microbiota on the grape surface can be affected by biostimulants and lead to different wine features. The aim of this work was to assess the occurrence of a possible variation in the mycobiota due to the biostimulant application. Therefore, our attention has been focused on the yeast community of grape bunches from vines subjected to the phytostimulant BION^®50WG treatment. This work was carried out in the CREA-VE experimental vineyard of Vitis vinifera cv. Barbera in Asti (Piedmont, Italy). The composition of fungal communities on grapes from three experimental conditions such as IPM (integrated pest management), IPM+BION^®50WG, and IPM+water foliar nebulization was compared by a metabarcoding approach. Our results revealed the magnitude of alpha and beta diversity, and the microbial biodiversity index and specific fungal signatures were highlighted by comparing the abundance of yeast and filamentous fungi in IPM and BION^®50WG treatments. No significant differences in the mycobiota of grapevines subjected to the three treatments were detected.

Keywords: Acibenzolar-S-Methyl; ITS region; biodiversity; grape; mycobiota; next-generation sequencing; yeast.

Figure 1

Sampling area details. (A) GPS picture of the vineyard area; (B) top view of the CREA—Viticoltura Enologia experimental vineyard. Vitis vinifera cv. Barbera was highlighted by a red line; (C) area of V. vinifera cv. Barbera with the indication of the three treatment areas; (D) sampling points.

Figure 2

Rarefaction curves. Control group in red; Bion group in green; Water group in blue; x-axis: species richness or number of OTUs; y-axis: sequence sample size or number of reads.

Figure 3

Alpha diversity. Alpha diversity analysis at the species level is estimated as the number of species observed (p-value = 0.04999) in (A), a Shannon’s index (p-value = 0.73345) in (B), as Simpson’s index (p-value = 0.56553) in (C). The p-value cut-off for significance is 0.05. The black dot indicates the mean value while the insides of the colored rectangles represent the median value. (In red, the Control; in green, Bion; in blue, Water.)

Figure 4

Beta diversity. PcoA based on Bray–Curtis metrics shows the dissimilarity of the fungal communities in the different samples according to the three theses (p = 0.688). (In red is the Control; in green is the Bion; in blue is the Water.)

Figure 5

Core mycobiota. Representation of the main yeast at the species level for each treatment considering the median data from five samples for each treatment.

Figure 6

Heatmap. The graphical representation of the abundance at the species and higher taxa levels with respect to each sample (five per treatment). The resulting cluster shows the distance measure using Euclidean and clustering algorithms using ward.D. Hierarchical clustering is performed with the hclust function in the stat package of Microbiome-Analyst.