scholarly journals Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

2021 ◽  
Author(s):  
Kuo-Szu Chiang ◽  
Clive H. Bock
Keyword(s):  
Plant Pathology ◽  
Disease Severity ◽  
Statistical Power ◽  
Plant Diseases ◽  
Ordinal Scale ◽  
Disease Assessment ◽  
Ordinal Scales ◽  
Scale Design ◽  
Data Design ◽  
Scale Data

AbstractThe severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement—often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.

scholarly journals Effects of Quantitative Ordinal Scale Design on the Accuracy of Estimates of Mean Disease Severity

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 565 ◽  
Author(s):  
Hung I. Liu ◽  
Jia Ren Tsai ◽  
Wen Hsin Chung ◽  
Clive H. Bock ◽  
Kuo Szu Chiang
Keyword(s):  
Disease Severity ◽  
Plant Disease ◽  
Mean Squared Error ◽  
Simulation Method ◽  
Ordinal Scale ◽  
Disease Assessment ◽  
The Mean ◽  
Scale Design ◽  
Number Of Classes ◽  
Assessment Scales

Estimates of plant disease severity are crucial to various practical and research-related needs in agriculture. Ordinal scales are used for categorizing severity into ordered classes. Certain characteristics of quantitative ordinal scale design may affect the accuracy of the specimen estimates and, consequently, affect the accuracy of the resulting mean disease severity for the sample. The aim of this study was to compare mean estimates based on various quantitative ordinal scale designs to the nearest percent estimates, and to investigate the effect of the number of classes in an ordinal scale on the accuracy of that mean. A simulation method was employed. The criterion for comparison was the mean squared error of the mean disease severity for each of the different scale designs used. The results indicate that scales with seven or more classes are preferable when actual mean disease severities of 50% or less are involved. Moreover, use of an amended 10% quantitative ordinal scale with additional classes at low severities resulted in a more accurate mean severity compared to most other scale designs at most mean disease severities. To further verify the simulation results, estimates of mean severity of pear scab on samples of leaves from orchards in Taiwan demonstrated similar results. These observations contribute to the development of plant disease assessment scales to improve the accuracy of estimates of mean disease severities.

scholarly journals In the Eye of the Beholder: The Effect of Rater Variability and Different Rating Scales on QTL Mapping

2011 ◽  
Vol 101 (2) ◽  
pp. 290-298 ◽  
Author(s):  
Jesse A. Poland ◽  
Rebecca J. Nelson
Keyword(s):  
Disease Resistance ◽  
Qtl Mapping ◽  
Disease Severity ◽  
Rating Scales ◽  
Ordinal Scale ◽  
Disease Assessment ◽  
True Value ◽  
Accuracy And Precision ◽  
Rater Variability ◽  
The Impact

The agronomic importance of developing durably resistant cultivars has led to substantial research in the field of quantitative disease resistance (QDR) and, in particular, mapping quantitative trait loci (QTL) for disease resistance. The assessment of QDR is typically conducted by visual estimation of disease severity, which raises concern over the accuracy and precision of visual estimates. Although previous studies have examined the factors affecting the accuracy and precision of visual disease assessment in relation to the true value of disease severity, the impact of this variability on the identification of disease resistance QTL has not been assessed. In this study, the effects of rater variability and rating scales on mapping QTL for northern leaf blight resistance in maize were evaluated in a recombinant inbred line population grown under field conditions. The population of 191 lines was evaluated by 22 different raters using a direct percentage estimate, a 0-to-9 ordinal rating scale, or both. It was found that more experienced raters had higher precision and that using a direct percentage estimation of diseased leaf area produced higher precision than using an ordinal scale. QTL mapping was then conducted using the disease estimates from each rater using stepwise general linear model selection (GLM) and inclusive composite interval mapping (ICIM). For GLM, the same QTL were largely found across raters, though some QTL were only identified by a subset of raters. The magnitudes of estimated allele effects at identified QTL varied drastically, sometimes by as much as threefold. ICIM produced highly consistent results across raters and for the different rating scales in identifying the location of QTL. We conclude that, despite variability between raters, the identification of QTL was largely consistent among raters, particularly when using ICIM. However, care should be taken in estimating QTL allele effects, because this was highly variable and rater dependent.

scholarly journals Quantitative Ordinal Scale Estimates of Plant Disease Severity: Comparing Treatments Using a Proportional Odds Model

2020 ◽  
Vol 110 (4) ◽  
pp. 734-743 ◽  
Author(s):  
K. S. Chiang ◽  
H. I. Liu ◽  
Y. L. Chen ◽  
M. El Jarroudi ◽  
C. H. Bock
Keyword(s):  
Disease Severity ◽  
Hypothesis Test ◽  
Standard Procedure ◽  
Simulation Method ◽  
Superior Performance ◽  
Ordinal Scale ◽  
Proportional Odds ◽  
Ordinal Scales ◽  
Proportional Odds Models ◽  
Percent Area

Studies in plant pathology, agronomy, and plant breeding requiring disease severity assessment often use quantitative ordinal scales (i.e., a special type of ordinal scale that uses defined numeric ranges); a frequently used example of such a scale is the Horsfall-Barratt scale. Parametric proportional odds models (POMs) may be used to analyze the ratings obtained from quantitative ordinal scales directly, without converting ratings to percent area affected using range midpoints of such scales (currently a standard procedure). Our aim was to evaluate the performance of the POM for comparing treatments using ordinal estimates of disease severity relative to two alternatives, the midpoint conversions (MCs) and nearest percent estimates (NPEs). A simulation method was implemented and the parameters of the simulation estimated using actual disease severity data from the field. The criterion for comparison of the three approaches was the power of the hypothesis test (the probability to reject the null hypothesis when it is false). Most often, NPEs had superior performance. The performance of the POM was never inferior to using the MC at severity <40%. Especially at low disease severity (≤10%), the POM was superior to using the MC method. Thus, for early onset of disease or for comparing treatments with severities <40%, the POM is preferable for analyzing disease severity data based on quantitative ordinal scales when comparing treatments and at severities >40% is equivalent to other methods.

scholarly journals Ordinal outcome analysis improves the detection of between-hospital differences in outcome

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
I. E. Ceyisakar ◽  
N. van Leeuwen ◽  
Diederik W. J. Dippel ◽  
Ewout W. Steyerberg ◽  
H. F. Lingsma
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Outcome Measures ◽  
Statistical Power ◽  
Ordinal Scale ◽  
Efficiency Gain ◽  
Number Of Patients ◽  
Dichotomous Outcomes ◽  
Ordinal Outcome

Abstract Background There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. Methods We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. Results In the IMPACT study (9578 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37 to 63% less patients. Conclusions Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements. Trial registration We do not report the results of a health care intervention.

IDEMPOTENT UNINORMS ON FINITE ORDINAL SCALES

2009 ◽  
Vol 17 (01) ◽  
pp. 1-14 ◽  
Author(s):  
BERNARD DE BAETS ◽  
JÁNOS FODOR ◽  
DANIEL RUIZ-AGUILERA ◽  
JOAN TORRENS
Keyword(s):  
Ordinal Scale ◽  
Neutral Element ◽  
Ordinal Scales ◽  
One To One

In this paper we characterize all idempotent uninorms defined on a finite ordinal scale. It is proved that any such discrete idempotent uninorm is uniquely determined by a decreasing function from the set of scale elements not greater than the neutral element to the set of scale elements not smaller than the neutral element, and vice versa. Based on this one-to-one correspondence, the total number of discrete idempotent uninorms on a finite ordinal scale of n + 1 elements is equal to 2n.

scholarly journals Extension Plant Pathology: Strengthening Resources to Continue Serving the Public Interest

2012 ◽  
Vol 102 (7) ◽  
pp. 652-655 ◽  
Author(s):  
K. L. Everts ◽  
L. Osborne ◽  
A. J. Gevens ◽  
S. J. Vasquez ◽  
B. K. Gugino ◽  
...  
Keyword(s):  
Plant Pathology ◽  
Plant Disease ◽  
Crop Yields ◽  
Economic Value ◽  
The United States ◽  
Emerging Diseases ◽  
Plant Diseases ◽  
Economic Losses ◽  
Global Competitiveness ◽  
Horticultural Crops

Extension plant pathologists deliver science-based information that protects the economic value of agricultural and horticultural crops in the United States by educating growers and the general public about plant diseases. Extension plant pathologists diagnose plant diseases and disorders, provide advice, and conduct applied research on local and regional plant disease problems. During the last century, extension plant pathology programs have adjusted to demographic shifts in the U.S. population and to changes in program funding. Extension programs are now more collaborative and more specialized in response to a highly educated clientele. Changes in federal and state budgets and policies have also reduced funding and shifted the source of funding of extension plant pathologists from formula funds towards specialized competitive grants. These competitive grants often favor national over local and regional plant disease issues and typically require a long lead time to secure funding. These changes coupled with a reduction in personnel pose a threat to extension plant pathology programs. Increasing demand for high-quality, unbiased information and the continued reduction in local, state, and federal funds is unsustainable and, if not abated, will lead to a delay in response to emerging diseases, reduce crop yields, increase economic losses, and place U.S. agriculture at a global competitive disadvantage. In this letter, we outline four recommendations to strengthen the role and resources of extension plant pathologists as they guide our nation's food, feed, fuel, fiber, and ornamental producers into an era of increasing technological complexity and global competitiveness.

Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements

2006 ◽  
Vol 1 (2) ◽  
pp. 275-288 ◽  
Author(s):  
Simone Graeff ◽  
Johanna Link ◽  
Wilhelm Claupein
Keyword(s):  
Powdery Mildew ◽  
Disease Severity ◽  
Near Infrared ◽  
Plant Stress ◽  
Stress Factors ◽  
Gaeumannomyces Graminis ◽  
Disease Assessment ◽  
Leaf Reflectance ◽  
Reflectance Measurements ◽  
Take All

AbstractThe ability to identify diseases in an early infection stage and to accurately quantify the severity of infection is crucial in plant disease assessment and management. A greenhouse study was conducted to assess changes in leaf spectral reflectance of wheat plants during infection by powdery mildew and take-all disease to evaluate leaf reflectance measurements as a tool to identify and quantify disease severity and to discriminate between different diseases. Wheat plants were inoculated under controlled conditions in different intensities either with powdery mildew or take-all. Leaf reflectance was measured with a digital imager (Leica S1 Pro, Leica, Germany) under controlled light conditions in various wavelength ranges covering the visible and the near-infrared spectra (380–1300 nm). Leaf scans were evaluated by means of L*a*b*-color system. Visual estimates of disease severity were made for each of the epidemics daily from the onset of visible symptoms to maximum disease severity. Reflectance within the ranges of 490780 nm (r2 = 0.69), 510780nm (r2 = 0.74), 5161300nm (r2 = 0.62) and 5401300 nm (r2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490780nm showed most sensitive response to damage caused by powdery mildew and take-all infestation. The results of this study indicated that disease detection and discrimination by means of reflectance measurements may be realized by the use of specific wavelength ranges. Further studies have to be carried out, to discriminate powdery mildew and take-all infection from other plant stress factors in order to develop suitable decision support systems for site-specific fungicide application.

scholarly journals An information theoretic measure for the evaluation of ordinal scale data

2006 ◽  
Vol 38 (3) ◽  
pp. 487-494 ◽  
Author(s):  
W. J. Tastle ◽  
M. J. Wierman
Keyword(s):  
Ordinal Scale ◽  
Information Theoretic ◽  
Scale Data

scholarly journals Human Pathogens on Plants: Designing a Multidisciplinary Strategy for Research

2013 ◽  
Vol 103 (4) ◽  
pp. 306-315 ◽  
Author(s):  
Jacqueline Fletcher ◽  
Jan E. Leach ◽  
Kellye Eversole ◽  
Robert Tauxe
Keyword(s):  
Plant Pathology ◽  
Food Safety ◽  
Microbial Contamination ◽  
Management Strategies ◽  
Defense Responses ◽  
Plant Diseases ◽  
Food Microbiology ◽  
Food Plants ◽  
Human Pathogens ◽  
Salmonella Spp

Recent efforts to address concerns about microbial contamination of food plants and resulting foodborne illness have prompted new collaboration and interactions between the scientific communities of plant pathology and food safety. This article provides perspectives from scientists of both disciplines and presents selected research results and concepts that highlight existing and possible future synergisms for audiences of both disciplines. Plant pathology is a complex discipline that encompasses studies of the dissemination, colonization, and infection of plants by microbes such as bacteria, viruses, fungi, and oomycetes. Plant pathologists study plant diseases as well as host plant defense responses and disease management strategies with the goal of minimizing disease occurrences and impacts. Repeated outbreaks of human illness attributed to the contamination of fresh produce, nuts and seeds, and other plant-derived foods by human enteric pathogens such as Shiga toxin-producing Escherichia coli and Salmonella spp. have led some plant pathologists to broaden the application of their science in the past two decades, to address problems of human pathogens on plants (HPOPs). Food microbiology, which began with the study of microbes that spoil foods and those that are critical to produce food, now also focuses study on how foods become contaminated with pathogens and how this can be controlled or prevented. Thus, at the same time, public health researchers and food microbiologists have become more concerned about plant–microbe interactions before and after harvest. New collaborations are forming between members of the plant pathology and food safety communities, leading to enhanced research capacity and greater understanding of the issues for which research is needed. The two communities use somewhat different vocabularies and conceptual models. For example, traditional plant pathology concepts such as the disease triangle and the disease cycle can help to define cross-over issues that pertain also to HPOP research, and can suggest logical strategies for minimizing the risk of microbial contamination. Continued interactions and communication among these two disciplinary communities is essential and can be achieved by the creation of an interdisciplinary research coordination network. We hope that this article, an introduction to the multidisciplinary HPOP arena, will be useful to researchers in many related fields.

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