Abstract
An experiment was conducted at the orchard of Chaudhary Charan Singh, Haryana, Agricultural University, Hisar (75°46′E and 29°10′N) and 215.2 a.m.s.l. The Ber powdery mildew index was recorded during the peak period (44–49 standard meteorological weeks) at different time intervals for the years 2015, 2016 and 2017 on Ber cultivar Umran. The study was carried out to find out the best fit growth models and significant weather variables in the disease progress curve. Five growth models viz. Exponential, Gompertz, Logistic, Log Logistic and Weibull were analysed for summarizing and comparing different plant disease epidemics. The statistical package R was used to illustrate models and disease progress over time. The best fit growth model was found by Akaike Information Criterion and Bayesian Information Criterion. The R code was developed and the regression analysis was performed to find out the best weather variables in the disease progress curve. It was observed that Exponential model was best fit growth model for the disease progress over time during 2015, whereas the Gompertz model was best fit during 2016 and 2017. The maximum and minimum temperature showed significant correlation with powdery mildew during 2015, whereas during 2017 the powdery mildew has significantly and negatively correlation with minimum temperature (− 0.74) and evening relative humidity (− 0.72). The coefficient of determination (R2) during 2015 and 2017 was found 78 and 68%, respectively.
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Appendix
Appendix
Fitting the growth model in R software
The following code shows how to analyze the data through R code
dataset1 < -read.csv(file.choose(),header = TRUE)
attach(dataset1)
result.m_1 < -drm(response ~ time, data = dataset1, fct = EXD.3())
summary(result.m_1)
plot(result.m_1)
result.m_2 < -drm(response ~ time, data = dataset1, fct = G.4())
summary(result.m_2)
plot(result.m_2)
result.m_3 < -drm(response ~ time, data = dataset1, fct = L.5())
summary(result.m_3)
plot(result.m_3)
result.m_4 < -drm(response ~ time, data = dataset1, fct = LL.5())
summary(result.m_4)
plot(result.m_4)
result.m_5 < -drm(response ~ time, data = dataset1, fct = W1.4())
summary(result.m_5)
plot(result.m_5)
AIC(result.m_1, result.m_2, result.m_3, result.m_4, result.m_5)
AIC(result.m_1, result.m_2, result.m_3, result.m_4, result.m_5,
+ k = Log(nrow(dataset1)))
BIC(result.m_1, result.m_2, result.m_3, result.m_4, result.m_5)
dataset2 < -read.csv(file.choose(),header = TRUE)
attach(dataset2)
result.m_1 < -drm(response ~ time, data = dataset2, fct = EXD.3())
summary(result.m_1)
result.m_2 < -drm(response ~ time, data = dataset2, fct = G.4())
summary(result.m_2)
result.m_3 < -drm(response ~ time, data = dataset2, fct = L.5())
summary(result.m_3)
result.m_4 < -drm(response ~ time, data = dataset2, fct = LL.5())
summary(result.m_4)
result.m_5 < -drm(response ~ time, data = dataset2, fct = W1.4())
summary(result.m_5)
AIC(result.m_1, result.m_2, result.m_3, result.m_4, result.m_5)
BIC(result.m_1, result.m_2, result.m_3, result.m_4, result.m_5)
dataset3 < -read.csv(file.choose(),header = TRUE)
attach(dataset3)
result.m_1 < -drm(response ~ time, data = dataset3, fct = EXD.3())
summary(result.m_1)
result.m_2 < -drm(response ~ time, data = dataset3, fct = G.4())
summary(result.m_2)
result.m_3 < -drm(response ~ time, data = dataset3, fct = L.5())
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Singh, R., Kumar, M., Mamta, D. et al. Development of growth model for Ber powdery mildew in relation to weather parameters. Indian Phytopathology 72, 235–241 (2019). https://doi.org/10.1007/s42360-019-00124-x
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DOI: https://doi.org/10.1007/s42360-019-00124-x