Sistema de previsão para o manejo da ferrugem asiática na cultura da soja safrinha

Abstract

The soybean crop is usually perceived as the most important crop, since it is one of the main sources of protein concentrates and vegetable oil. The Asian soybean rust is considered one of the most destructive diseases. The prediction systems help on the initial identification of the disease in the crop, in the planning and decision making of whether to apply pesticides; in the reduction of possible environmental contaminations and other undesirable consequences, this way showing it to be an interesting tool regarding safety and economy. In this sense, this work aims at testing a prediction system for the occurrence and handling of Asian soybean rust in the off-season crop based on weather variables and different gaps of time to apply pesticides, besides distinct plant populations. The field experiment was carried in the city of Entre-Ijuís, in the state of Rio Grande do Sul. Twelve treatments were carried, where four of them had their severity calculated (VSC) through the prediction system (6, 9, 12 e 15), a sample with no application and a treatment with scheduled applications. The other factor considered was the population by hectare: 246.666 and 451.111 plants ha -1. In order to conduct the experiment, we used the random blocks outline, with four repetitions and totalizing 48 experimental units. To calculate the VSC, the data was obtained in a forecast station installed in the location of the experiment. Data were submitted to ANOVA through F testing and the averages were compared with the Scott-Knott test with 5% of error probability. Differences were observed among the managing programs regarding productivity. Managing program 9VSC had the highest production average in both plant populations, showing that only two pesticide applications in the correct moment would be enough, allowing financial profit and lower environmental risk with the use of a disease prediction system for Ansian soybean rust during off-season.