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Pythium spp. cause seed decay, damping-off, and root rot in soybean and corn; however, their diversity and importance as pathogens in Minnesota are unknown. Our objectives were to identify the Pythium spp. present in Minnesota soybean fields, determine their aggressiveness on corn and soybean, and investigate their sensitivity to seed treatment fungicides. For identification, sequences obtained using internal transcribed space ITS4 and ITS1 primers were compared with reference sequences in the National Center for Biotechnology Information database. Seedling and soil samples yielded over 30 oomycete species. Aggressiveness was determined using two methods; a seed assay, which also examined temperature effects on aggressiveness, and a seedling assay. Of 21 species evaluated, seven Pythium spp. were pathogenic on both soybean and corn, reducing root growth by 20% or more while two Pythium and one Phytopythium spp. were pathogenic only on soybean. Aggressiveness of many isolates increased as temperature increased from 15°C to 25°C. The sensitivity of 10 pathogenic species to azoxystrobin, ethaboxam, mefenoxam, pyraclostrobin, or trifloxystrobin was tested. EC50 values for mefenoxam and ethaboxam were 10−2 of those to strobilurin fungicides. Pythium spp. in Minnesota are diverse and a significant cause of seedling disease on soybean and corn. Most Pythium spp. isolated in this study were more sensitive to mefenoxam and ethaboxam than to strobilurin fungicides.

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We propose a new kernel for Metropolis Hastings called Directional Metropolis Hastings (DMH) with multivariate update where the proposal kernel has state dependent covariance matrix. We use the derivative of the target distribution at the current state to change the orientation of the proposal distribution, therefore producing a more plausible proposal. We study the conditions for geometric ergodicity of our algorithm and provide necessary and sufficient conditions for convergence. We also suggest a scheme for adaptively update the variance parameter and study the conditions of ergodicity of the adaptive algorithm. We demonstrate the performance of our algorithm in a Bayesian generalized linear model problem.