Nonlinear Regression Analysis to Determine Infection Models of Colletotrichum acutatum Causing Anthracnose of Chili Pepper Using Logistic Equation

Abstract

A logistic model for describing combined effects of both temperature and wetness period on appressorium formation was developed using laboratory data on percent appressorium formation of Colletotrichum acutatum. In addition, the possible use of the logistic model for forecasting infection risks was also evaluated as compared with a first-order linear model. A simplified equilibrium model for enzymatic reactions was applied to obtain a temperature function for asymptote parameter (A) of logistic model. For the position (B) and the rate (k) parameters, a reciprocal model was used to calculate the respective temperature functions. The nonlinear logistic model described successfully the response of appressorium formation to the combined effects of temperature and wetness period. Especially the temperature function for asymptote parameter A reflected the response of upper limit of appressorium formation to temperature, which showed the typical temperature response of enzymatic reactions in the cells. By having both temperature and wetness period as independent variables, the nonlinear logistic model can be used to determine the length of wetness periods required for certain levels of appressorium formation under different temperature conditions. The infection model derived from the nonlinear logistic model can be used to calculate infection risks using hourly temperature and wetness period data monitored by automated weather stations in the fields. Compared with the nonlinear infection model, the linear infection model always predicted a shorter wetness period for appressorium formation, and resulted in significantly under-and over-estimation of response at low and high temperatures, respectively.

Global Warming and Its Possible Impact on Agriculture in India

“…They explained that climate change would affect the optimal conditions for infection, host specificity, and mechanisms of plant infection. Plant pathogens are strongly affected by environment; therefore, the survival, rate of multiplication, vigor, sporulation, direction, distance of dispersal of inocula, rate of spore germination, and penetration of pathogens can be affected in the climate-change scenario (Kang et al, 2010). The authors suggested that climate-induced changes would affect the measures farmers use to effectively control diseases and the viability of particular cropping systems in particular regions.…”

Modelling approaches for addressing complexity in plant health management

“…X is an independent variable, usually representing the effect of weather. Kang et al (2010) have estimated the percentage of appressorium formation of the fungus Colletotrichum acutatum, the causal agent of anthracnose of chili pepper, using a multiple linear regression model as follows: P = -13.3 + 0.612T + 0.928W where: P = percentage of appressorium formation; T = level of temperature (°C); W = period of wetness (h).…”

A Three-Year Field Validation Study to Improve the Integrated Pest Management of Hot Pepper

“…Hot pepper ( Capsicum annum L.) is cultivated on more than 55,000 ha in Korea and is one of the country’s most important cash vegetable crops ( Kim, 2010 ). Pests of hot pepper can cause severe damage to yields ( Kang et al, 2010 ; Myung et al, 2009). Its diseases include anthracnose ( Colletotrichum acutatum J. H. Simmonds), Phytophthora blight ( Phytophthora capsici Leonian), bacterial wilt ( Ralstonia solanacearum (Smith) Yabuuchi et al), and bacterial spot ( Xanthomonas campestris pv.…”

“…Each program has a critical model for advising the proper time to treat hot peppers with pesticides. The anthracnose infection model calculates a cumulative infection risk (IR) value every hour based on the hourly temperature and moisture characteristics ( Ahn et al, 2008 ; Huber and Gillespie, 1992 ; Kang et al, 2010 ; Wilson et al, 1990 ). The tobacco budworm program has a population model which calculates the cumulative eclosion rates for the first and second generations based on the degree-day model to control eggs and the younger stages of larvae, respectively ( Jung, 2010 ).…”