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

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...

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Autores principales:	Denis A. Shah, Thomas R. Butts, Spyridon Mourtzinis, Juan I. Rattalino Edreira, Patricio Grassini, Shawn P. Conley, Paul D. Esker
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3e866ed6e642441da082bfc5398484cf
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spelling	oai:doaj.org-article:3e866ed6e642441da082bfc5398484cf2021-12-02T15:14:55ZA machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States10.1038/s41598-021-98230-22045-2322https://doaj.org/article/3e866ed6e642441da082bfc5398484cf2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98230-2https://doaj.org/toc/2045-2322Abstract 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.Denis A. ShahThomas R. ButtsSpyridon MourtzinisJuan I. Rattalino EdreiraPatricio GrassiniShawn P. ConleyPaul D. EskerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Denis A. Shah Thomas R. Butts Spyridon Mourtzinis Juan I. Rattalino Edreira Patricio Grassini Shawn P. Conley Paul D. Esker A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States
description	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.
format	article
author	Denis A. Shah Thomas R. Butts Spyridon Mourtzinis Juan I. Rattalino Edreira Patricio Grassini Shawn P. Conley Paul D. Esker
author_facet	Denis A. Shah Thomas R. Butts Spyridon Mourtzinis Juan I. Rattalino Edreira Patricio Grassini Shawn P. Conley Paul D. Esker
author_sort	Denis A. Shah
title	A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States
title_short	A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States
title_full	A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States
title_fullStr	A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States
title_full_unstemmed	A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States
title_sort	machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central united states
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3e866ed6e642441da082bfc5398484cf
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A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States

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