Phytophthora infestans (Montagne) de Bary, the fungus that causes late blight in potatoes, can practically completely destroy all of the above-ground sections of sensitive cultivars in the presence of favourable environmental factors and in the absence of any preventative measures. Understanding and contrasting the four nonlinear models and empirical model for disease progressive curves of five year data are the main goals of the current study. Data on the progress of the late blight were investigated statistically. The area under the disease progress curve (AUDPC), disease rates, and disease progression curves were estimated. The estimation of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), R-square, MAE, MAPE, RMSE, standard error, and other regression parameters was done. The monomolecular model and the logistic model yielded the lowest standard errors and the highest R-square values. Additionally, the results demonstrated that, for each year, the monomolecular model and the logistic model with the lower AIC and BIC values provided a good fit for the disease progression curve. The area under the disease progression curve was calculated to determine the degree of response to the disease, and the monomolecular model and logistic allow computation of the disease progression rate. 2018 was the lowest prevalence of the Phytophthora infestans disease ever observed. A maximum area under the disease progress curve (AUDPC) value was determined in 2021. Breeding programmes targeted at creating varieties with improved resistance to Phytophthora infestans may benefit from year or varieties with low disease incidence. Empirical models showed the partial occurrence of the Late Blight Disease Incidence, so, we can conclude that all these model cannot fit for this region. Potato blight forecasting is important to protect the potato yield. If the favourable weather conditions can be forecast and communicated to the growers early with sufficient time for a control sprayed, the crop will be protected.

Phytophthora infestans, non linear, disease rate, area under the disease progress curve

The relatively cool nights during December and January within potato growing area makes a big drop for blight occurrence. There was a big problem in these seasons. With the moving-graph method blight would always have been forecast before it occurred, although in some cases it was first observed quite a while after the first favorable period. Based on the results non linear regression models were the most appropriate for description the disease progress data. Late Blight was expected to appear within 7-10 days after ten consecutive disease incidence by the Beamount, Cook and Hyre & Bond’s system.

Vaidheki M., D.S. Gupta, S. Hembram, M.K. Debnath, T.K. Hath and P. Basak (2022). Epidemiological Model for Potato Late Blight in the Northern Part of West Bengal. Biological Forum – An International Journal, 14(4a): 297-306.