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Title: Standard area diagrams for aiding severity estimation scientometrics, pathosystems and methodological trends in the last 25 years

Author
item DEL PONTE, EMBERSON - Universidade Federal De Vicosa
item PETHYBRIDGE, SARAH - Cornell University
item Bock, Clive
item MICHEREFF, SAMI - Federal University Of Pernambuco
item MACHADO, F.J. - Universidade Federal De Vicosa
item SPOLTI, P. - Universidade Federal De Vicosa

Submitted to: Journal of Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/9/2017
Publication Date: 10/1/2017
Citation: Del Ponte, E.M., Pethybridge, S.J., Bock, C.H., Michereff, S.J., Machado, F., Spolti, P. 2017. Standard area diagrams for aiding severity estimation scientometrics, pathosystems and methodological trends in the last 25 years. Journal of Phytopathology. 107(10):1161-1174. https://doi.org/10.1094/PHYTO-02-17-0069-FI.
DOI: https://doi.org/10.1094/PHYTO-02-17-0069-FI

Interpretive Summary: Estimates of disease severity are critical in many studies in Plant Pathology. Standard area diagrams (SADs) are diagrams of plant parts (leaves fruit, roots or stems) showing specific severity of disease to be used as a guide for allowing more accurate estimation of severity. SADs must be validated as an aid to demonstrate that they offer an improvement in accuracy, a process started in the early 1990s. Peer-reviewed literature post-1990 was identified, selected and cataloged in bibliographic software for further analysis of scientometric, pathosystem- and methodological-related data. A total of 105 studies (127 SADs) were found and authored by 327 researchers from 10 countries. The scientific impact of a SAD article, based on annual citations after publication year was affected by disease significance, the journal’s impact factor and methodological innovation. The reviewed SADs encompassed 48 crops and 103 unique diseases across a range of plant organs. Illustrated severity in the SADS was quantified largely by image analysis software. Typical SADs comprised five to eight black and white drawings of leaf diagrams. Increases in accuracy and reliability were demonstrated using various statistical methods. The implications of the findings and knowledge gaps are discussed. A website called SADBank for hosting SAD research data and a list of best practices for designing and implementing SADs are proposed.

Technical Abstract: Standard area diagrams (SADs) have long been used as a tool to aid the estimation of plant disease severity, an essential variable in phytopathometry. Formal validation of SADs was not considered prior to the early 1990s, when considerable effort began to be invested developing SADs and assessing their value for improving accuracy of estimates of disease severity in many pathosystems. Peer-reviewed literature post-1990 was identified, selected and cataloged in bibliographic software for further scrutiny and extraction of scientometric, pathosystem- and methodological-related data. A total of 105 studies (127 SADs) were found and authored by 327 researchers from 10 countries, but mainly from Brazil. The six most prolific authors published at least seven studies. The scientific impact of a SAD article, based on annual citations after publication year was affected by disease significance, the journal’s impact factor and methodological innovation. The reviewed SADs encompassed 48 crops and 103 unique diseases across a range of plant organs. Severity was quantified largely by image analysis software, such as QUANT, APS-Assess® or a LI-COR® leaf area meter. The most typical SADs comprised five to eight black and white drawings of leaf diagrams with severity increasing non-linearly. However, there was a trend towards using true color photographs or stylized representations in a range of color combinations and more linear (equally spaced) increments of severity. A two-step SAD validation approach was used in 78/105 studies for which linear regression was the preferred method, but a trend towards using Lin’s correlation concordance analysis and hypothesis tests to detect the effect of SADs on accuracy was apparent. Reliability measures, when obtained, mainly considered variation among, rather than within raters. The implications of the findings and knowledge gaps are discussed. A website called SADBank for hosting SAD research data and a list of best practices for designing and implementing SADs are proposed.