Abstract

The soybean crop is a source of great economic and social importance in Brazil, being an extremely profitable agricultural product. However, such profitability can be limited by a variety of factors, including plant disease. The more efficient the crop treatment applications, the greater the reduction in incidence of disease, resulting in higher and better-quality yield, as well as increased profitability. Thus, the objective of this study was to identify the better of two methods for determining optimal fungicide application timing as it relates to soybean (Glycine max) crops utilizing a ground bar sprayer. The experiment examined fungicide spraying efficiency based on a calendar schedule as compared to application as indicated by the AgroDetecta® program which monitors agrometeorological data. The study design involved randomized blocks with three treatments and twelve replicates. The treatments consisted of a control (without application of fungicides), application of fungicides on a calendar-based schedule (sprayed every 15 days after the phenological stage V5), and application of fungicides as indicated by the program AgroDetecta® (based on agrometeorological data). The experiment was carried out at Mutuca Farm (Arapoti-PR, Brazil) during crop year 2013-14. The planting of BMX Apollo® RR variety soybean seeds occurred on November 20, 2013 and December 12, 2013. The variables evaluated were disease incidence and severity, area under the disease progress curve (AUDPC), plant height, total and viable internodes, final population, pods per plant, grains per pod, and 1000-grain mass and yield. In the November sowing, the decisions regarding when to spray fungicides according to the calendar and agrometeorological data were similar, resulting in four sprays. Three applications of fungicides based on the calendar, and four sprays recommended by the AgroDetecta® program, were performed for the December sowing. It was concluded that the application of fungicide to the soybean crop utilizing ground bar sprayer technology produced similar results for the analyzed variables regardless if the application decision was based on following the calendar or on agrometeorological data.

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