Beta Regression Model for Predicting the Development of Pink Rot in Potato Tubers During Storage.
Beta Regression Model for Predicting the Development of Pink Rot in Potato Tubers During Storage.
复制标题
用于预测马铃薯块茎储藏期间粉红腐病发展的 Beta 回归模型。
DOI：
10.1094/pdis-06-15-0696-re
复制
发表时间：
2016
期刊：
影响因子：
4.5
通讯作者：
N. Gudmestad
中科院分区：
文献类型：
--
作者：
S. Yellareddygari;J. Pasche;Raymond J. Taylor;Su Hua;N. Gudmestad
粉腐病是马铃薯的一种重要病害，分布在世界各地。严重的产量和质量损失已在收获和采后储存中报告。在有利于疾病发展的条件下，从田间到贮藏，从贮藏到运输，粉腐病的严重程度可以继续增加，造成进一步的损失。在贮藏中预测粉腐病的发展对种植者在疾病发展的早期阶段进行干预以减少经济损失具有很大的潜力。粉红腐病以限定在区间（0或1，对应于0%为无病，100%为最大病）的腐病百分比估计。在本研究中，在拟合以单位区间（0,1）为界的连续响应变量时，考虑了β回归而不是传统的普通最小二乘回归（线性回归）。该方法被认为是数据转换和线性回归分析的一个很好的替代方法。用收获时、产量和收获后天数的块茎粉腐病发病率百分比作为研究协变量，预测采后32 ~ 78天内粉腐病的发展。结果表明，收获期粉腐病率与产量之间的交互作用是beta回归模型的显著预测因子（P < 0.0001）。并设计线性回归模型与提出的beta回归模型进行比较。在线性回归模型诊断图中观察到的线性预测因子不恒定，调整后的R2（0.49）。本研究的拟R2（0.56）和常方差表明，β回归函数足以预测贮藏期间粉腐病的发生。贝塔预测模型的使用可以帮助种植者决定是否在块茎储存时使用杀菌剂，或者在发生重大储存损失之前将其出售。
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.