Potential use of machine learning methods in assessment of Fusarium culmorum and Fusariumproliferatum growth and mycotoxin production in treatments with antifungal agents. Issue 2 (February 2021)

• Record Type: Journal Article
• Authors: Tarazona, Andrea; Mateo, Eva M.; Gómez, José V.; Romera, David; Mateo, Fernando
• Is Part Of: Fungal biology. Volume 125:Issue 2(2021)
• Page Start: 123
• Page End: 133
• Publication Date: 2021-02
• Subjects: Effective doses -- Fumonisins -- Fungicides -- Fusarium spp -- Predictive mycology -- Zearalenone; Mycology -- Periodicals; Fungi -- Periodicals; 579.505
• DOI: 10.1016/j.funbio.2019.11.006
• Languages: English
• ISSNs: 1878-6146

Abstract

Abstract: Fusarium -controlling fungicides are necessary to limit crop loss. Little is known about the effect of antifungal formulations at sub-lethal doses, and their interaction with abiotic factors, on Fusarium culmorum and F. proliferatum development and on zearalenone and fumonisin biosynthesis, respectively. In the present study different treatments based on sulfur, trifloxystrobin and demethylation inhibitor fungicides (cyproconazole, tebuconazole and prothioconazole) under different environmental conditions, in Maize Extract Medium, are assayed in vitro . Several machine learning methods (neural networks, random forest and extreme gradient boosted trees) have been applied for the first time for modeling growth of F. culmorum and F. proliferatum and zearalenone and fumonisin production, respectively. The most effective treatment was prothioconazole, 250 g/L + tebuconazole, 150 g/L. Effective doses of this formulation for reduction or total growth inhibition ranged as follows ED50 0.49–1.70, ED90 2.57–6.02 and ED100 4.0–8.0 µg/mL, depending on the species, water activity and temperature. Overall, the growth rate and mycotoxin levels in cultures decreased when doses increased. Some treatments in combination with certain aw and temperature values significantly induced toxin production. The extreme gradient boosted tree was the model able to predict growth rate and mycotoxin production with minimum error and maximum R 2 value. Highlights: The effect of three commercial antifungal formulations against Fusarium spp. was assessed. DMI fungicides were able to inhibit Fusarium spp. growth and mycotoxin production. Partial fungal growth-inhibition by abiotic factors can induce toxin biosynthesis. Machine learning methods were a good tool to predict fungal growth and mycotoxin production. Extreme gradient boosted trees were the best machine learning methods assayed.