Abstract:

: Irrigation is important in many crop production systems. However, irrigation water can be a carrier of plant pathogens that can enter the system and spread to fields, resulting in crop damage and yield losses. The Lower Rio Grande Valley of South Texas is an important area for agricultural production which depends on the Rio Grande River as a source of water for irrigation. Thus, the presence of plant pathogens in the Rio Grande River could have important implications for crop productivity in the region. Cultured-based methods and molecular identification methods are used for monitoring plant pathogens in irrigation water. However, these methods are labor-intensive and just detect targeted pathogens. To overcome these limitations, in this study, the ITS2 amplicon metagenomic method was applied for evaluating the fungal diversity, composition, and presence of fungal plant pathogens in irrigation water from the Rio Grande River as it leaves the water reservoir (WR) and it arrives at an irrigation valve at a farm (FA). Results from the Shannon (WR = 4.6 ± 0.043, FA = 3.63 ± 0.13) and Simpson indices (WR = 4.6 ± 0.043, FA = 3.63 ± 0.13) showed that there are significant differences in the fungal diversity and community structure between the two locations and the PCA analysis showed a clear differentiation between both fungal communities. Several OTUs identified in both locations included potential plant pathogens from diverse genera including Cladosporium, Exserohilum, and Nigrospora, while others such as Colletotrichum and Plectosphaerella were found only in one of the two locations assessed. This work indicates that microbes, including plant pathogens, may enter or exit throughout the irrigation-water distribution system, thereby modifying the microbial community composition along the way. Understanding the dynamics of plant pathogen movement in irrigation water systems can help growers identify risk factors to develop measures to mitigate those risks. This study also shows the usefulness of the metagenomic approach for detecting and monitoring plant pathogen in irrigation water.

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