Standard area diagram set for assessment of severity and temporal progress of apple blotch

Castellar, Camilla; Jauch, Felipe; Moreira, Rafaele Regina; Duarte, Henrique da Silva Silveira; Mio, Louise Larissa May De

doi:10.1007/s10658-021-02268-x

Cited by 6 publications

(2 citation statements)

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“…In fact, LCCC is considered a more appropriate method for purpose gauging accuracy and precision (Madden et al, 2007), but our objective depended upon the selection of studies where all statistics of bias and precision were reported for individual raters (available for at least six raters), allowing us to calculate the sample variance. This is usually not available when authors use Lin's CCC because the means across raters tends to be reported for statistical comparison between methods (Yadav et al, 2013;Rivera et al, 2020;Castellar et al, 2021). Another reason is that the values of the slopes and intercepts, which are measures of constant and systematic bias, respectively, cannot be directly compared with the location-shift and scale-shift statistics of the LCCC, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, linear regression continues to be used by some phytopathometrists for evaluating SADs (Santos et al 2017;Camara et al 2018;Trojan and Pria 2018;Arias et al 2020;Robaina et al 2020;Kublik et al 2020). The two linear regression coefficients (intercept and slope), together with the coefficient of determination (R 2 ), are usually reported for each rater in the SAD validation study, while those phytopathometrists reporting LCCC statistics have most often reported means of all raters combined (Del Ponte et al 2017;Bock et al 2020;Brás et al 2020;Nascimento et al 2020;Rivera et al 2020;Castellar et al 2021;Montero et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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How much do standard area diagrams improve accuracy of visual estimates of plant disease severity? A systematic review and meta-analysis

Ponte¹,

Cazón²,

Alves³

et al. 2021

Preprint

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Plant disease severity is commonly estimated visually without or with the aid of standard area diagram sets (SADs). It is generally believed that the use of SADs leads to less biased (more accurate) and thus more precise estimates, but the degree of improvement has not been characterized in a systematic manner. We built on a previous review and screened 153 SAD studies published from 1990 to 2021. A systematic review resulted in a selection of 72 studies that reported three linear regression statistics for individual raters, which are indicative of the two components of bias (intercept = constant bias; slope = systematic bias) and precision (Pearson's correlation coefficient, r), to perform a meta-analysis of these accuracy components. The meta-analytic model determined an overall gain of 0.07 (r increased from 0.88 to 0.95) in precision. Globally, there was a reduction of 2.65 units in the intercept, from 3.41 to 0.76, indicating a reduction in the constant bias. Slope was least affected and was reduced slightly from 1.09 to 0.966, indicating marginally less systematic bias when using SADs. A multiple correspondence analysis suggested an association of less accurate, unaided estimates with diseases that produce numerous lesions and for which maximum severities of 50% are rarely attained. On the other hand, more accurate estimates were observed with diseases that cause only a few lesions and those diseases where the lesions coalesce and occupy more than 50% of the specimen surface. This was most pronounced for specimen types other than leaves. By quantitatively exploring how characteristics of the pathosystem and how SADs affect precision and constant and systematic biases, we affirm the value of SADs for reducing bias and imprecision of visual assessments. We have also identified situations where SADs have greater or lesser effects as an assessment aid.

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