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

Authors

Denis A. Shah, Kansas State University
Thomas R. Butts, University of Arkansas
Spyridon Mourtzinis, Agstat Consulting
Juan I. Rattalino Edreira, University of Nebraska - LincolnFollow
Patricio Grassini, University of Nebraska - LincolnFollow
Shawn P. Conley, University of Wisconsin-Madison
Paul D. Esker, Pennsylvania State University

Document Type

Article

Date of this Version

9-21-2021

Comments

CC-BY

Abstract

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.