No abstract

Phenazine-1-carboxylic acid (PCA) is a broadspectrum antibiotic produced by rhizobacteria in the dryland wheat fields of the Columbia Plateau. PCA and other phenazines reductively dissolve Fe and Mn oxyhydroxides in bacterial culture systems, but the impact of PCA upon Fe and Mn cycling in the rhizosphere is unknown. Here, concentrations of dithionite-extractable and poorly crystalline Fe were approximately 10% and 30−40% higher, respectively, in dryland and irrigated rhizospheres inoculated with the PCAproducing (PCA + ) strain Pseudomonas synxantha 2−79 than in rhizospheres inoculated with a PCA-deficient mutant. However, rhizosphere concentrations of Fe(II) and Mn did not differ significantly, indicating that PCA-mediated redox transformations of Fe and Mn were transient or were masked by competing processes. Total Fe and Mn uptake into wheat biomass also did not differ significantly, but the PCA + strain significantly altered Fe translocation into shoots. X-ray absorption near edge spectroscopy revealed an abundance of Fe-bearing oxyhydroxides and phyllosilicates in all rhizospheres. These results indicate that the PCA + strain enhanced the reactivity and mobility of Fe derived from soil minerals without producing parallel changes in plant Fe uptake. This is the first report that directly links significant alterations of Fe-bearing minerals in the rhizosphere to a single bacterial trait.

Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the U.S. Pacific Northwest (PNW). Apothecia develop from mummified berries overwintering on soil surfaces and produce ascospores that infect tissue emerging from floral and vegetative buds. Disease control currently relies on fungicides applied on a calendar basis rather than inoculum availability. To establish a prediction model for ascospore release, apothecial development was tracked in three fields, one in western Oregon and two in northwestern Washington in 2015 and 2016. Air and soil temperature, precipitation, soil moisture, leaf wetness, relative humidity and solar radiation were monitored using in-field weather stations and Washington State University's AgWeatherNet stations. Four modeling approaches were compared: logistic regression, multivariate adaptive regression splines, artificial neural networks, and random forest. A supervised learning approach was used to train the models on two data sets: training (70%) and testing (30%). The importance of environmental factors was calculated for each model separately. Soil temperature, soil moisture, and solar radiation were identified as the most important factors influencing ascospore release. Random forest models, with 78% accuracy, showed the best performance compared with the other models. Results of this research helps PNW blueberry growers to optimize fungicide use and reduce production costs.