Previous View
APSnet Home
Plant Disease Home



Predictive Model for "Mal de Rio Cuarto" Disease Intensity. G. J. March, Instituto de Fitopatologia y Fisiologia Vegetal, Cno. 60 Cuadras km 51/2 Cnel. Olmedo, 5119 Cordoba, Argentina. M. Balzarini, Facultad de Ciencias Agropecuarias, Universidad Nacional de Cordoba; C.C. 509, Cordoba, Argentina; and J. A. Ornaghi, J. E. Beviacqua, and A. Marinelli, Facultad de Agronomfa y Veterinaria, Universidad Nacional de Rio Cuarto, Estafeta Postal 9, Rfo Cuarto 5800, Argentina. Plant Dis. 79:1051-1053. Accepted for publication 6 July 1995. Copyright 1995 The American Phytopalhological Society. DOI: 10.1094/PD-79-1051.

“Mai de Rio Cuarto” (MRC) virus disease is the most important virus disease of maize (Zea mays L.) in Argentina with the rural areas near Chajan, Sampacho, and Suco (in Rio Cuarto, province of Cordoba) being the most affected. A predictive model for MRC before planting a crop was developed based on the disease intensity over nine agricultural years (1981-82 to 1989-90) and a series of weather variables for that period (such as minimum, mean, and maximum temperatures, number of frosts, and amount of rainfall). To build the model, agricultural years were divided into two groups according to the percentage of severely affected plants (intensity). A year was considered “mild” if the percentage of severely affected plants was less than 20% and “severe” if the percentage was higher. A discriminant stepwise procedure was used to analyze data. The average maximum temperatures in June, July, and August, the average maximum temperatures in July and August, and the total rainfall in June, July, and August were found to be significant forecasters of disease intensity. The model was validated in the agricultural years of 1990-91, 1991-92, 1992-93, and 1993-94. The relative intensity of the disease was adequately forecasted and confirmed for those years. Results support the feasibility of forecasting MRC intensity prior to planting maize in the area under study.

Keyword(s): epidemiology