Willyerd, K. T., Li, C., Madden, L. V., Bradley, C. A., Bergstrom, G. C., Sweets, L. E., McMullen, M., Ransom, J. K., Grybauskas, A., Osborne, L., Wegulo, S. N., Hershman, D. E., Wise, K., Bockus, W. W., Groth, D., Dill-Macky, R., Milus, E., Esker, P. D., Waxman, K. D., Adee, E. A., Ebelhar, S. E., Young, B. G., and Paul, P. A. 2012 [MR_UT]) were used in multivariate meta-analyses, and mean log response ratios across trials were estimated and transformed to estimate mean percent control ( C ) due to the management combinations relative to S_UT. All combinations led to a significant reduction in index and DON (P < 0.001). MR_TR was the most effective combination, with a C of 76% for index and 71% for DON, followed by MS_TR (71 and 58%, respectively), MR_UT (54 and 51%, respectively), S_TR (53 and 39%, respectively), and MS_UT (43 and 30%, respectively). Calculations based on the principle of treatment independence showed that the combination of fungicide application and resistance was additive in terms of percent control for index and DON. Management combinations were ranked based on percent control relative to S_UT within each trial, and nonparametric analyses were performed to determine management combination stability across environments (trials) using the Kendall coefficient of concordance (W). There was a significant concordance of management combinations for both index and DON (P < 0.001), indicating a nonrandom ranking across environments and relatively low variability in the within-environment ranking of management combinations. MR_TR had the highest mean rank (best control relative to S_UT) and was one of the most stable management combinations across environments, with low rank stability variance (0.99 for index and 0.67 for DON). MS_UT had the lowest mean rank (poorest control) but was also one of the most stable management combinations. Based on Piepho's nonparametric rank-based variance homogeneity U test, there was an interaction of management combination and environment for index (P = 0.011) but not for DON (P = 0.147), indicating that the rank ordering for index depended somewhat on environment. In conclusion, although the magnitude of percent control will likely vary among environments, integrating a single tebuconazole + prothioconazole application at anthesis with cultivar resistance will be a more effective and stable management practice for both index and DON than either approach used alone.

Soybean (Glycine max (L.) Merr.) is produced across a vast swath of North America, with the greatest concentration in the Midwest. Root rot diseases and damping-off are a major concern for production, and the primary causal agents include oomycetes and fungi. In this study, we focused on examination of oomycete species distribution in this soybean production system and how environmental and soil (edaphic) factors correlate with oomycete community composition at early plant growth stages. Using a culture-based approach, 3,418 oomycete isolates were collected from 11 major soybean-producing states and most were identified to genus and species using the internal transcribed spacer region of the ribosomal DNA. Pythium was the predominant genus isolated and investigated in this study. An ecology approach was taken to understand the diversity and distribution of oomycete species across geographical locations of soybean production. Metadata associated with field sample locations were collected using geographical information systems. Operational taxonomic units (OTU) were used in this study to investigate diversity by location, with OTU being defined as isolate sequences with 97% identity to one another. The mean number of OTU ranged from 2.5 to 14 per field at the state level. Most OTU in this study, classified as Pythium clades, were present in each field in every state; however, major differences were observed in the relative abundance of each clade, which resulted in clustering of states in close proximity. Because there was similar community composition (presence or absence) but differences in OTU abundance by state, the ordination analysis did not show strong patterns of aggregation. Incorporation of 37 environmental and edaphic factors using vector-fitting and Mantel tests identified 15 factors that correlate with the community composition in this survey. Further investigation using redundancy analysis identified latitude, longitude, precipitation, and temperature as factors that contribute to the variability observed in community composition. Soil parameters such as clay content and electrical conductivity also affected distribution of oomycete species. The present study suggests that oomycete species composition across geographical locations of soybean production is affected by a combination of environmental and edaphic conditions. This knowledge provides the basis to understand the ecology and distribution of oomycete species, especially those able to cause diseases in soybean, providing cues to develop management strategies.