ABSTRACT
Xylella fastidiosa subsp. pauca is the causal agent of "olive quick decline syndrome" in Salento (Apulia, Italy). On April 2015, we started interdisciplinary studies to provide a sustainable control strategy for this pathogen that threatens the multi-millennial olive agroecosystem of Salento. Confocal laser scanning microscopy and fluorescence quantification showed that a zinc-copper-citric acid biocomplex-Dentamet®-reached the olive xylem tissue either after the spraying of the canopy or injection into the trunk, demonstrating its effective systemicity. The biocomplex showed in vitro bactericidal activity towards all X. fastidiosa subspecies. A mid-term evaluation of the control strategy performed in some olive groves of Salento indicated that this biocomplex significantly reduced both the symptoms and X. f. subsp. pauca cell concentration within the leaves of the local cultivars Ogliarola salentina and Cellina di Nardò. The treated trees started again to yield. A 1H-NMR metabolomic approach revealed, upon the treatments, a consistent increase in malic acid and γ-aminobutyrate for Ogliarola salentina and Cellina di Nardò trees, respectively. A novel endotherapy technique allowed injection of Dentamet® at low pressure directly into the vascular system of the tree and is currently under study for the promotion of resprouting in severely attacked trees. There are currently more than 700 ha of olive groves in Salento where this strategy is being applied to control X. f. subsp. pauca. These results collectively demonstrate an efficient, simple, low-cost, and environmentally sustainable strategy to control this pathogen in Salento.
ABSTRACT
An index to benchmark pesticide mobility relevant to surface water runoff and soil erosion (surface water mobility index, or SWMI) was derived based on two key environmental fate parameters: degradation half-life and organic carbon-normalized soil/water sorption coefficient (Koc). Values assigned with the index of each individual compound correlate well with the concentration trend of 13 pesticides monitored in six Lake Erie, USA, tributaries from 1983 to 1991. Regression using a power function of SWMI fits concentration data well at various percentiles in the database for each tributary and all six tributaries combined, with r2 ranging from 0.71 to 0.94 for the concentrations at the 95th percentile. Good agreement was also obtained between SWMI and the time-weighted annual mean concentrations (r2 = 0.67-0.87). Although concentrations at or near peaks tend to be driven by rare hydrological events (intense precipitation immediately after application), SWMI explains the peak concentration data generally well (r2 = 0.53-0.86). The SWMI-concentration relationship was further evaluated with two other pesticide monitoring databases: the U.S. Geological Survey National Water Quality Assessment Program White River Study Unit (1991-1996) at Hazelton, Indiana, USA, and the Syngenta (previously Novartis) Voluntary Monitoring Program with Community Water Systems at the Higginsville City Lake, Missouri, USA (1995-1997). The ability of the proposed SWMI to discriminate pesticide runoff mobility and its correlation with surface water monitoring data can be significant in the development of screening methodologies and data-based models for government agencies and/or practitioners in general facing increasing pressure to assess pesticide occurrence in aquatic environments.
