A Logistic Regression Model for Predicting Risk of White Mold Incidence on Dry Bean in North Dakota

Abstract

White mold, caused by Sclerotinia sclerotiorum, is the most important disease affecting dry bean production in North Dakota. This disease currently is managed mainly through fungicides applied during the flowering stage. A disease-forecasting model was developed to help growers with their decision to apply these fungicides. The model was built using weather variables collected during eight consecutive half-month periods between 1 May and 31 August 2003 to 2005 and white mold incidence data obtained from 150 fields. The model was produced using logistic regression analysis, and includes total rainfall, average minimum temperature, and number of rainy days in the first half of June, July, and August, respectively, as predictors and explained 85% of the variability. The model was validated using an independent disease data set collected from 100 fields during the same years. The model exhibited high true positive ratio (0.79) and very high accuracy (0.91) between observed and predicted probabilities of white mold incidence. Results from this study suggest that in-season macro-weather variables could be used to predict the risk of white mold, which in-turn could help growers make better-informed decisions on whether or not to apply fungicides for white mold control.

Comparison of logistic regression and growth function models for the analysis of the incidence of virus infection

“…Logistic regression has been extensively used in medicine, for example in clinical studies (Petrie and Sabin, 2000). However, in spite of its potential for many types of studies, its use is not generalized in plant epidemiology (Mila et al, 2003;Musaka et al, 2003;Weiland et al, 2003;Thebaud et al, 2006;Harikrishnan and Del Río, 2007). We have compared logistic regression with three functions frequently used in plant epidemiology: the logistic, Gompertz and log-logistic growth functions (Campbell and Madden, 1990;Jeger, 2004).…”

“…Thus, logistic regression should be a more general and better approach for fitting bivariate, binomially distributed variables, such as count data of disease infection. Whereas logistic regression has been extensively used in case-control analysis in clinical epidemiology (Petrie and Sabin, 2000;Agresti, 2002), it is infrequent in plant epidemiology studies (Mila et al, 2003;Musaka et al, 2003;Weiland et al, 2003;Thebaud et al, 2006;Harikrishnan and Del Río, 2007). In this paper, we discuss the results of fitting logistic, Gompertz and log-logistic models, or a logistic regression model, to data sets describing Alfalfa mosaic virus (AMV) infection in one to five years-aged lucerne fields in the Ebro Valley, Northeast Spain.…”

Sclerotinia sclerotiorum disease prediction: A review and potential applications in South Africa

“…7 Once released, ascospores are viable for approximately 17 hours but, if conducive conditions are not present, host tissue infection will not occur. 23 Larger sclerotia produce greater numbers of apothecia and, thus, greater amounts of ascospores.…”

“…2,29 Mycelium is more tolerant to desiccation than ascospores and thus is more tolerant at lower RH. 7 Once infection has been initiated, S. sclerotiorum has the ability to remain inactive (latent) in host tissue in the absence of free moisture, resulting in delayed or arrested lesion development until favourable moisture levels re-occur. 30 It is evident that survival, inoculum production and infection of plants by S. sclerotiorum involves numerous stimuli and pathogen responses to weather conditions, agronomic activities and host growth stages during critical phases of pathogen survival and growth.…”

“…1 In 2003, an estimated loss of USD1.9 million in the state of North Dakota (United States of America) was incurred as a result of Sclerotinia white mold on dry bean. 7 In South Africa during 2014, the effects of Sclerotinia stem rot of canola gained more attention as a consequence of the greater prevalence of the disease during that season compared to previous years. 8 Sunflower and soybean epidemics causing up to 60% severity and 65% yield losses, respectively, were recorded in South Africa over the 2013/2014 season.…”