Beta Regression Model for Predicting the Development of Pink Rot in Potato Tubers During Storage

Abstract

Pink rot is an important disease of potato with worldwide distribution. Severe yield and quality losses have been reported at harvest and in postharvest storage. Under conditions favoring disease development, pink rot severity can continue to increase from the field to storage and from storage to transit, causing further losses. Prediction of pink rot disease development in storage has great potential for growers to intervene at an earlier stage of disease development to minimize economic losses. Pink rot disease is estimated as percent rot confined on the interval (0 or 1, corresponding to 0% as no disease and 100% as maximum disease). In this study, beta regression is considered over the traditional ordinary least squares regression (linear regression) for fitting continuous response variables bounded on the unit interval (0,1). This method is considered a good alternative to data transformation and analysis by linear regression. The percentages of incidence of pink rot in tubers at harvest, yield, and days after harvest were used as study covariates to predict pink rot development from 32 to 78 days postharvest. Results demonstrate that the interaction between percentage of pink rot at harvest and yield is a significant predictor (P < 0.0001) of the beta regression model. A linear regression model was also designed to compare the results with the proposed beta regression model. Linear predictors observed in diagnostic plots with linear regression model was found to not be constant and an adjusted R2 (0.49) was obtained. The pseudo R2 (0.56) and constant variance for this study suggests that the beta regression function is adequate for predicting the development of pink rot during storage. The use of the beta prediction model could help growers decide whether to apply a fungicide to tubers going into storage or to market their crop before significant storage losses are incurred.

Modelling Inoculum Availability ofPlurivorosphaerella nawaein Persimmon Leaf Litter with Bayesian Beta Regression

“…Busby et al (2013) used beta regression to evaluate the effects of fungal endophytes and Populus genotypes on the proportion of leaf area affected by Drepanopeziza populi . Yellareddygari et al (2016) developed a beta regression model to predict the incidence of pink rot, caused by Phytophthora erythroseptica , on potato tubers during storage based on disease incidence at harvest. Burman et al (2017) estimated the potential geographic distribution of Austropuccinia psidii in Puerto Rico with beta regression.…”

“…Previous studies with beta regression in the context of plant pathology used frequentist inference and did not include random effects (Busby et al, 2013; Yellareddygari et al, 2016; Burman et al, 2017; Xu et al, 2019). In contrast to frequentist inference, where point estimates and confidence intervals are obtained for model parameters, results of Bayesian inference are presented by their posterior distributions.…”

A time‐course investigation of resistance to the carboxylic acid amide mandipropamid in field populations of Plasmopara viticola treated with anti‐resistance strategies

“…For this reason the set of collected observations can be assumed to be a sample from beta distribution. 33 A beta-regression model accounting for overdispersion 34 was fitted according to the following equations:…”

“…Because disease severity is defined as the affected tissue surface with respect to the total leaf area, it is a continuous proportion. For this reason the set of collected observations can be assumed to be a sample from beta distribution . A beta‐regression model accounting for overdispersion was fitted according to the following equations: …”

Modelling ventilated potato bulk storage facilities

“…The recent publications by Grudzińska and Barbaś (2017) and Gancarz (2018) contain models related to this area. For studies on rot that include models, see Lui and Kushalappa (2002) and Yellareddygari et al (2016). Also, work that includes a model on dormancy break (Coleman, 1998) has been done.…”