A Simulation Model for Assessing Soybean Rust Epidemics

Abstract

A soybean rust (causal agent Phakopsora pachyrhizt) simulation model was developed for assessing disease epidemics as a part of pest risk analysis. Equations describing environmental effects on disease components were developed by re‐analyzing previous data with a view toward a systems approach. The infection rate was predicted well using dew period and temperature after inoculation as independent variables (R2=0.88, P < 0.0001). The exponential models which used physiological day as an independent variable explained 98% of the variations of latent period and senescence of disease lesions. The simulation model was validated with data from 72 sequential planting experiments during 1980 and 1981 in Taiwan. Time of onset for these epidemics varied from 25—60 days and 50—80 days after planting soybean cultivars TK 5 and G 8587, respectively. The epidemic periods were 75—95 for TK 5 and 100—120 days for G 8587. Variation of epidemics was accurately predicted by the simulator. Predicted disease curves fit well the observed disease curves for the recognized cropping seasons, spring‐ and autumn‐seeded crops. For G 8587, which is very sensitive to photoperiod, the data from spring and autumn gave a better fit compared with data from pre‐summer planting. The model underestimated disease epidemics during the winter, probably because the plant growth model failed to reflect the photoperiod rection of soybean. The simulation model was validated with data from other experiments conducted in three cropping seasons in 1979 and 1980. Determination coefficients of the regression between observed and predicted disease severity were significant.

Models and applications for risk assessment and prediction of Asian soybean rust epidemics

“…The study in risk assessment is a macroscale, long-term prediction that encompasses assessment of potential for entry, establishment, epidemic, and/or crop losses in a region or country once an epidemic occurs (Yang, 2006). Several mathematical and computer models have been developed for predicting Asian soybean rust epidemics (Yang , 1991a, Batchelor , 1997, Isard , 2005, Kim , 2006) with applicability in risk assessment studies and prediction frameworks. Recently, plant pathologists, in collaboration with climatologists, have paid new attention to the development, testing and application of models to assess the risk and predict ASR.…”

“…Fuzzy Logic Apparent Infection Rate (models 4-6, Table 1) and, in some cases, the subsequent host infection and colonization after entry (model 7, Table 1). (Yang , 1991a) was developed and validated with experiments conducted at the Asian Vegetable Research and Development Center (AVRDC) in southern Taiwan in the early 80s. SOYRUST is a process-driven computer model that simulates daily increase of disease severity in two soybean varieties (Yang , 1991a).…”

Meteorological factors and Asian soybean rust epidemics: a systems approach and implications for risk assessment

“…Recently, wind speed, wind direction, and solar radiation have been used in the aerobiological models for ASR forecasting (Isard et al, 2005;Pan et al, 2006). Historically, the first ASR epidemiological models used hourly measurements of leaf wetness duration and temperature during the wet period (Yang et al, 1991b). Since those variables influence infection (Figure 1), it is reasonable to use those models when the goal is forecast the risk of infection (Reis et al, 2004;Canteri et al, 2005) or to integrate them into a mechanistic approach for predicting disease severity increase from multiple secondary infections (Yang et al, 1991b).…”

“…Historically, the first ASR epidemiological models used hourly measurements of leaf wetness duration and temperature during the wet period (Yang et al, 1991b). Since those variables influence infection (Figure 1), it is reasonable to use those models when the goal is forecast the risk of infection (Reis et al, 2004;Canteri et al, 2005) or to integrate them into a mechanistic approach for predicting disease severity increase from multiple secondary infections (Yang et al, 1991b). Thus, the types and number of weather variables used for ASR forecasting are strongly dependent on the approach and the disease component estimated.…”

“…A major step forward in increasing epidemiological knowledge of ASR was based on a cooperative agreement between the U.S. and Taiwan, where an extensive sequential planting experiment in Taiwan helped to shed light on the disease epidemiology under natural conditions at plot and field scales (Tchanz, 1984;Yang et al, 1990). Those studies provided detailed information that was used to develop first epidemiological models aimed to understand and assess the risk of the disease and potential losses in the United States (Yang et al, 1991a(Yang et al, , 1991b.…”