Modeling and mapping potential epidemics of rice diseases globally

Savary, Serge; Nelson, Andrew; Willocquet, Laetitia; Pangga, Ireneo B.; Aunario, Jorrel Khalil

doi:10.1016/j.cropro.2011.11.009

Cited by 88 publications

(49 citation statements)

References 46 publications

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“…Such conditions may not be encountered in a field setting where, starting from a relatively low primary inoculum, the disease progresses exponentially due to its late season polycyclic nature (Savary et al. ; Barnwal et al. ).…”

Section: Discussionmentioning

confidence: 99%

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Accuracy and Reliability of Severity Estimates Using Linear or Logarithmic Disease Diagram Sets in True Colour or Black and White: a Study Case for Rice Brown Spot

Schwanck

Ponte

2014

Journal of Phytopathology

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Four disease diagram sets to aid in assessments of rice brown spot severity were developed and evaluated with regard to accuracy and reliability of the estimates. These sets had severity increments in a linear (LIN) or logarithmic (LOG) fashion with the diagrams depicted in true colour (COL) or black and white (BW). Ten inexperienced raters evaluated one of the sets, totalling forty raters. On each group of raters, the assessment was made first unaided and then aided with the respective diagram set. The minimum and the maximum severities of the diagram sets were 0.5 and 36%, respectively, and the five intermediate severity values were 6, 12, 18, 24 and 32% for the LIN diagram sets, and 1.8, 3.3, 6, 11 and 20% for the LOG sets. Overall agreement, measured by the Lin's concordance correlation coefficient (ρc), increased considerably when using the aid (unaided mean ρc = 0.53, aided mean ρc = 0.87) due to a strong reduction in the systematic bias, measured by the bias correction factor Cb (unaided mean Cb = 0.60, aided mean Cb = 0.95). All diagrams led to similar accuracy and precision, but a consistent overestimation was still observed when using the LIN sets, and variability for the absolute errors was higher for the LOG sets, compared with the LIN sets. Estimates using the diagram sets were more reliable based on the intraclass correlation (mean ρ = 0.79–0.86) compared with unaided estimates (mean ρ = 0.51–0.67). Raters exhibited preference for specific values, such as the ‘knots’ (10, 20, 30%, etc.), and the severity values represented in the diagrams, especially when using the LIN sets. The diagram sets similarly helped to improve accuracy and reliability of estimates of rice brown spot epidemics.

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Section: Discussionmentioning

confidence: 99%

“…, ), thus contributing to the complex of yield reducers among rice diseases today (Savary et al. ; Barnwal et al. ).…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Reliability of Severity Estimates Using Linear or Logarithmic Disease Diagram Sets in True Colour or Black and White: a Study Case for Rice Brown Spot

Schwanck

Ponte

2014

Journal of Phytopathology

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“…At the higher severity levels, the increasing number of small and brownish lesions without a defined chlorotic halo, which are probably of younger age, and the aggregated spatial pattern of lesions, may indicate an autoinfection process in rice brown spot, with older lesions as the focal source of inoculum. The new quantitative knowledge provided in this study can be used to improve a simulation model for rice brown spot by allowing lesion size to vary according to disease severity and setting an aggregation parameter, two aspects not previously taken into account due to lack of information (Savary et al ., ).…”

Section: Discussionmentioning

confidence: 97%

“…To the authors’ knowledge, these two aspects have never been quantified together for a leaf‐spotting disease such as rice brown spot that rarely reaches >35% in the field (Savary et al ., ; Pannu & Chahal, ; Schwanck et al ., ). Knowledge concerning the variability of lesion size and aggregation may be useful for (i) generating hypotheses about epidemiological processes occurring at the leaf level for rice brown spot, such as autoinfection (Lannou et al ., ); (ii) providing information to improve simulation models (Savary et al ., ); and (iii) developing disease assessment tools, such as standard area diagrams, for use in the field (Bock et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Measuring lesion attributes and analysing their spatial patterns at the leaf scale using digital image analysis

Schwanck

Ponte

2016

Plant Pathology

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Digital image analysis was used to quantify size, shape and relative positions of individual plant disease lesions to determine their spatial distribution pattern at the leaf scale. Rice brown spot was used as a necrotrophic pathogen causing numerous discrete lesions. A 50‐leaf subsample was selected from an existing data set of 350 images of leaves taken from the field, and analysed for disease severity using image analysis. Further measurements included size, shape and the relative positions of lesions for all leaves with severity > 8% (n = 25) and an additional 25‐leaf sample with severity <8%. A total of 3964 necrotic and/or halo areas were selected using a manually defined threshold in the computer program Assess. There were significant and positive associations (Pearson's r > 0.81; P < 0.001) between the size‐related measurements (lesion area, longest and shortest axis). Coalesced areas, formed by interconnection of lesions and associated haloes, and a high number of small lesions were found with an increase in severity, suggesting a secondary cycle and autoinfection process. Results from quadrat‐based (Poisson distribution and Spatial Analysis by Distance IndicEs) and distance‐based (point‐process Poisson) spatial methods were in good agreement and, together with a Taylor power law model, suggested a shift from random to predominantly aggregated patterns of lesions at severities approaching 10%. This framework, which is applicable to other foliar diseases, proved useful in providing quantitative knowledge of epidemic processes at the leaf scale. Finally, these results may be useful in improving simulation models and disease assessment methods.

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“…Scientists monitoring and modeling agricultural pest patterns generally used low spatial resolutions in-situ weather stations data (Landschoot et al, 2013;Savary et al, 2012;Bao et al, 2011;Pavan et al, 2011;Skelsey et al, 2009;Wharton et al, 2008;Chattopadhyay et al, 2005;Nutter et al, 2002). However, spatial and temporal weather variability and the information resolution required to quantify agricultural pest risks using weather stations normally exhibit a reduced spatial resolution; thus, monitoring agricultural pests by using conventional techniques would require a large number of weather stations to guarantee a reasonable level of geographic confidence.…”

Section: Introductionmentioning

confidence: 99%

Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

Silva

Damásio

Sousa

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

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Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby improving pest management results and

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Modeling and mapping potential epidemics of rice diseases globally

Cited by 88 publications

References 46 publications

Accuracy and Reliability of Severity Estimates Using Linear or Logarithmic Disease Diagram Sets in True Colour or Black and White: a Study Case for Rice Brown Spot

Accuracy and Reliability of Severity Estimates Using Linear or Logarithmic Disease Diagram Sets in True Colour or Black and White: a Study Case for Rice Brown Spot

Measuring lesion attributes and analysing their spatial patterns at the leaf scale using digital image analysis

Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

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