A Flexible Model for Studying Plant Disease Progression

Pennypacker, S. P.; Knoble, H. D.; Antle, C. E.; Madden, Laurence V.

doi:10.1094/phyto-70-232

Cited by 70 publications

(35 citation statements)

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“…The Weibull probability density function and cumulative distribution can be successfully used to describe a wide range of disease progress curves (Pennypacker et al 1980;Mora-Aguilera et al 1996). In this formula b is a scale parameter, which is inversely related to the rate of disease increase.…”

Section: Field Experimentsmentioning

confidence: 99%

Quantitative resistance and its components in 16 barley cultivars to yellow rust, Puccinia striiformis f. sp. hordei

et al. 2006

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Sixteen barley cultivars with a susceptible infection type (IT = 7-8) in the seedling stage to an isolate of race 24 of Puccinia striiformis f. sp. hordei were planted at two locations in Mé xico. Disease severity (DS) parameters were assessed for the flag leaf and for the upper three leaves. The cultivars represented at least five levels of quantitative resistance ranging from very susceptible to quite resistant. ''Granado'', ''Gloria/Copal'' and ''Calicuchima-92'' represented the most resistant group and had an IT of 7 or 8. The cultivar · environment interaction variance, although significant, was very small compared with the cultivar variance. The disease severity parameters were highly correlated. The monocyclic parameter DS m , measured when the most susceptible cultivar had reached its maximum DS, was very highly correlated with the area under the disease progress curve (AUDPC), r being 0.98.Components of quantitative resistance were evaluated in two plant stages. In the seedling stage small cultivar effects for the latency period were observed, which were not correlated with the quantitative resistance measured in the field. In the adult plant stage the latency period (LP), infection frequency (IF) and colonization rate (CR) were measured in the upper two leaves. The LP was much longer than in the seedling stage and differed strongly between cultivars. The differences in IF were too large, those in CR varied much less. The components showed association with one another. The LP and IF were well correlated with the AU-DPC (r = 0.7-0.8).

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Section: Field Experimentsmentioning

confidence: 99%

Quantitative resistance and its components in 16 barley cultivars to yellow rust, Puccinia striiformis f. sp. hordei

et al. 2006

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“…Se describió la ocurrencia de temperaturas máximas y mínimas con base en intervalos de confianza para la media (Sincich et al, 2002) y número de días con temperaturas mínimas inferiores a 4 o C y superiores a 30 o C. Con un software denominado SICA (Medina et al, 2004), se calcularon unidades calor diarias con el método de seno simple con temperatura mínima crítica de 4 o C (Asante et al, 1991). Las unidades calor diarias y la precipitación, se acumularon por año y durante el periodo de evaluación y se ajustaron con el modelo Weibull modificado (Pennypacker et al, 1980), que es flexible a los parámetros Y=1−exp(−UC o -pp)/b) c ; donde Y= proporción acumulada de unidades calor u ocurrencia acumulada de precipitación(mm); UC, pp= unidades calor (o precipitación acumulada); b= estimador de la tasa de crecimiento en su forma inversa (1/b); c= parámetro de la forma de la curva. Para una misma variable, el estimador de la tasa de incremento por año, permite detectar diferencias significativas entre ellos.…”

Section: Methodsunclassified

“…With a software called SICA (Medina et al, 2004), daily heat units were calculated using the simple method with minimum temperature within critical of 4 °C (Asante et al, 1991). Daily heat and precipitation, accumulated units per year and during the evaluation period and adjusted to the modified Weibull model (Pennypacker et al, 1980), which is flexible to the parameters Y=1-exp(-UC o -pp)/b) c ; where Y= cumulative proportion of units heat or accumulated occurrence of precipitation (mm); UC, pp = heat units (or accumulated rainfall); b = estimator growth rate in its inverse (1 / b); c = parameter curve shape. For the same variable, the estimate of the rate of increase per year, to detect significant differences between them.…”

Section: Ocurrencia Temporal De Pulgón Lanígero En áRboles De Manzanomentioning

confidence: 99%

Pulgón lanígero e impacto ambiental por el uso de pesticidas en manzano en Chihuahua, México

Beltrán

Cuéllar

López³

et al. 2017

Remexca

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El manzano (Malus sylvestris var domestica Bork) es uno de los frutales caducifolios más importantes en el mundo.Anivel nacional, Chihuahua es el principal productor de manzana con una aportación de 66%. En este cultivo se hospedan diversos fitófagos entre los que se encuentra el pulgón lanígero del manzano, y cuyo combate es con base en insecticidas. En este estudio se analizó la información generada durante los años 2010 y 2011 sobre temperatura y precipitación, ocurrencia de pulgón lanígero en ascenso por el tronco del árbol, número de colonias aéreas y se calculó el índice de impacto ambiental por la aplicación de plaguicidas en tres huertos de manzano ubicados en Cuauhtémoc, Chihuahua, con la variedad ‘Golden Delicious’sobreportainjertofrancoycondiferentemanejode plagas (f itófagos y f itopatógenos). Los resultados permitieron detectar un diferencial de 96 mm en la precipitación ocurrida entre el año 2010 y 2011, diferencias signif icativas en la tasa de acumulación de calor para estos mismos años y un diferencial significativo en la ocurrencia de pulgón lanígero entre años y huertos. En relación al coeficiente de impacto ambiental, se detectó variación entre años y huertos con valores que f luctuaron entre 16 y 436 unidades de impacto ambiental. La mayor acumulación de unidades calor y menor precipitación pluvial se relacionaron con mayor número de pulgones en ascenso y colonias aéreas, además de un mayor impacto ambiental por el uso de pesticidas en manzano.

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“…Epidemics in each of the three regions were characterized by the model of simplified Weibull distribution with two parameters (b and c) (Pennypacker et al, 1980;Thal et al, 1984): Y = 1-e-(t/b) c , t>0; where Y = incidence ratio, t = time in days after planting, b = parameter estimator of the epidemic rate in its inverse form, and c = parameter of the curve shape. Additionally, the intensity of epidemics was estimated by calculating the absolute area under the disease progress curve (AUDPCa) by the trapezoidal integration method: AUDPCa = Σ1 n-i [(Yi + Yi +1) / 2] (t i +1 -ti), where: Yi = proportion of disease in the i-th evaluation, ti = time at the i-th observation, n = number of evaluations (Campbell and Madden, 1990;Jeger and Viljanen-Rollinson, 2001).…”

Section: Temporal Analysismentioning

confidence: 99%

Agronomical management influence on the spatiotemporal progress of strawberry dry wilt in Michoacan, Mexico

Ceja-Torres¹,

Mora-Aguilera²,

Mora-Aguilera³

2014

Afr. J. Agric. Res.

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A Flexible Model for Studying Plant Disease Progression

Cited by 70 publications

References 0 publications

Quantitative resistance and its components in 16 barley cultivars to yellow rust, Puccinia striiformis f. sp. hordei

Quantitative resistance and its components in 16 barley cultivars to yellow rust, Puccinia striiformis f. sp. hordei

Pulgón lanígero e impacto ambiental por el uso de pesticidas en manzano en Chihuahua, México

Agronomical management influence on the spatiotemporal progress of strawberry dry wilt in Michoacan, Mexico

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