Modeling the relationship between estimated fungicide use and disease-associated yield losses of soybean in the United States II: Seed-applied fungicides vs seedling diseases

Picarbutrazox Effectiveness Added to a Seed Treatment Mixture for Management of Oomycetes that Impact Soybean in Ohio

None of the current oomycota fungicides are effective towards all species of Phytophthora, Phytopythium, Globisporangium, and Pythium that affect soybean seed and seedlings in Ohio. Picarbutrazox is a new oomyceticide with a novel mode of action towards oomycete pathogens. Our objectives were to evaluate picarbutrazox to determine (i) baseline sensitivity (EC50) to 189 isolates of 29 species, (ii) the efficacy with a base seed treatment with three cultivars with different levels of resistance in 14 field environments; and (iii) if the rhizosphere microbiome was affected by the addition of the seed treatment on a moderately susceptible cultivar. The mycelial growth of all isolates was inhibited beginning at 0.001 μg, and the EC50 ranged from 0.0013 to 0.0483 μg of active ingredient (a.i.)/ml. The effect of seed treatment was significantly different for plant population and yield in eight of 14 and six of 12 environments, respectively. The addition of picarbutrazox at 1 and 2.5 g of a.i./100 kg seed to the base seed treatment compared to the base alone was associated with higher plant populations and yield in three and one environments, respectively. There was limited impact of the seed treatment mefenoxam 7.5 g of a.i. plus picarbutrazox 1 g of a.i./100 kg seed on the oomycetes detected in the rhizosphere of soybean seedlings collected at the V1 growth stage. Picarbutrazox has efficacy towards a wider range of oomycetes that cause disease on soybean, and this will be another oomyceticide tool to combat early season damping-off in areas where environmental conditions highly favor disease development.

A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north-central United States

Foliar fungicide usage in soybeans in the north-central United States increased steadily over the past two decades. An agronomically-interpretable machine learning framework was used to understand the importance of foliar fungicides relative to other factors associated with realized soybean yields, as reported by growers surveyed from 2014 to 2016. A database of 2738 spatially referenced fields (of which 30% had been sprayed with foliar fungicides) was fit to a random forest model explaining soybean yield. Latitude (a proxy for unmeasured agronomic factors) and sowing date were the two most important factors associated with yield. Foliar fungicides ranked 7th out of 20 factors in terms of relative importance. Pairwise interactions between latitude, sowing date and foliar fungicide use indicated more yield benefit to using foliar fungicides in late-planted fields and in lower latitudes. There was a greater yield response to foliar fungicides in higher-yield environments, but less than a 100 kg/ha yield penalty for not using foliar fungicides in such environments. Except in a few production environments, yield gains due to foliar fungicides sufficiently offset the associated costs of the intervention when soybean prices are near-to-above average but do not negate the importance of disease scouting and fungicide resistance management.