Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control

Author(s)

Negrisoli, Matheus Mereb ; da Silva, Flavio Nunes ; Negrisoli, Raphael Mereb ; Lopes, Lucas da Silva ; Souza Junior, Francisco de Sales ; de Freitas, Bianca Rezende ; Velini, Edivaldo Domingues ; Raetano, Carlos Gilberto

• Document type: Journal article
• Source: AGRONOMY-BASEL; v. 12, n. 9, p. 23-pg., 2022-09-01.

Abstract

The application of remote sensing techniques and prediction models for soybean rust (SBR) monitoring may result in different fungicide application timings, control efficacy, and spraying performance. This study aimed to evaluate the applicability of a prediction model as a threshold for disease control decision-making and to identify the effect of different application timings on SBR control as well as on the spraying technology. There were two experimental trials that were conducted in a 2 x 4 factorial scheme: 2 cultivars (susceptible and partially resistant to SBR); and four application timings (conventional chemical control at a calendarized system basis; based on the prediction model; at the appearance of the first visible symptoms; and control without fungicide application). Spray deposit and coverage at each application timing were evaluated in the lower and upper region of the soybean canopy through quantitative analysis of a tracer and water-sensitive papers. The prediction model was calculated based on leaf reflectance data that were collected by remote sensing. Application timings impacted the application technology as well as control efficacy. Calendarized system applications were conducted earlier, promoting different spray performances. Spraying at moments when the leaf area index was higher obtained poorer distribution. None of the treatments were capable of achieving high spray penetration into the canopy. The partially resistant cultivar was effective in holding disease progress during the crop season, whereas all treatments with chemical control resulted in less disease impact. The use of the prediction model was effective and promising to be integrated into disease management programs. (AU)

• FAPESP's process: 18/26486-0 - Monitoring and detection of Asian soybean rust by spectroradiometry and construction of prediction model for control decision making
• Grantee: Matheus Mereb Negrisoli
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 18/24869-0 - Practical approach of spectroradiometry on the spectral signature identification of Asian soybean rust and construction of prediction model
• Grantee: Carlos Gilberto Raetano
• Support Opportunities: Regular Research Grants