Forrest W. Nutter

Quantifying the temporal dynamics of plant virus epidemics: a review

Abstract The ongoing development of more sensitive and reliable plant virus detection methods offers new opportunities to accurately and reliably monitor the temporal dynamics of plant viruses within plant populations. This review provides operational definitions and examples for concepts pertaining to the sampling and assessment of host populations to quantify disease and/or pathogen incidence within host populations over time. The linear, monomolecular, exponential, logistic, and Gompertz population growth models are presented and discussed with regards to their use in modeling the temporal dynamics of plant viruses. Other quantitative measures of temporal disease (or virus pathogen) spread, such as the disease (pathogen) progress curve, area under the disease (or pathogen) progress curve, and time to reach 50% incidence ( t 50 ), are also presented and discussed.

Germination and Sporulation of Colletotrichum acutatum on Symptomless Strawberry Leaves

ABSTRACT The germination and sporulation of Colletotrichum acutatum were characterized over time on strawberry leaves (cv. Tristar) and plastic coverslips incubated at 26 degrees C under continuous wetness. Conidia germinated within 3 h after inoculation and formed melanized appressoria with pores by 9 h after inoculation. Host penetration was not observed up to 7 days after inoculation. Production of secondary conidia on conidial and hyphal phialides began within 6 h after inoculation. Secondary conidiation was responsible for up to a threefold increase in the total number of conidia within 7 days after inoculation. Primary conidia and hyphae began to collapse 48 h after inoculation, whereas melanized appressoria remained intact. These findings suggest that appressoria and secondary conidia of C. acutatum produced on symptomless strawberry foliage may be significant sources of inoculum for fruit infections.

Terms and Concepts for Yield, Crop Loss, and Disease Thresholds

The initial report (14) of a subcommittee of the APS Plant Disease Losses Committee dealt with terms and concepts relating to the measurement of disease intensity to obtain accurate and precise quantitative information on the relationship between disease intensity (stimulus = X) and yield or yield loss (response = Y). In addition to standardizing the terms and concepts for the measurement of disease intensity, members of the full committee identified a need to clarify and standardize terms and concepts pertaining to yield, crop loss, and disease thresholds. A second subcommittee was formed to accomplish this task. This report describes concepts concerning reference points for yield and crop loss as well as a hierarchy for threshold terms, then presents a list of terms and definitions to standardize terminology for crop loss assessment.

Disease assessment concepts and the advancements made in improving the accuracy and precision of plant disease data

New concepts in phytopathometry continue to emerge, such as the evolution of the concept of pathogen intensity versus the well-established concept of disease intensity. The concept of pathogen severity, defined as the quantitative measurement of the amount of pathogen per sampling unit has also emerged in response to the now commonplace development of quantitative molecular detection tools. Although the concept of disease severity, i.e., the amount of disease per sampling unit, is a well-established concept, the accuracy and precision of visual estimates of disease severity is often questioned. This article will review disease assessment concepts, as well as the methods and assessment aides currently available to improve the accuracy and precision of visually-based disease severity data. The accuracy and precision of visual disease severity assessments can be improved by quantitatively measuring and comparing the accuracy and precision of rates and/or assessment methods using linear regression, by using computer-based disease assessment training programmes, and by developing and using diagrammatic keys (standard area diagrams).

Quantifying Alfalfa Yield Losses Caused by Foliar Diseases in Iowa, Ohio, Wisconsin, and Vermont

Although foliar diseases of alfalfa occur throughout the United States wherever alfalfa is grown, little work has been done to quantify yield losses caused by foliar pathogens since the late 1980s. To quantify the yield losses caused by foliar diseases of alfalfa, field experiments were performed in Iowa, Ohio, Vermont, and Wisconsin from 1995 to 1998. Different fungicides and fungicide application frequencies were used to obtain different levels of foliar disease in alfalfa. Visual disease and remote sensing assessments were performed weekly to determine the relationships between disease assessments and alfalfa yield. Visual disease assessments of percentage of defoliation, disease incidence, and disease severity were performed weekly, approximately five to six times during each alfalfa growth cycle. Remote sensing assessments also were obtained weekly by measuring the percentage of sunlight reflected from alfalfa canopies using handheld, multispectral radiometers. Yield loss estimates were calculated as the yield difference between the fungicide treatment with the highest yield and the nonfungicide control, divided by the yield obtained from the highest yielding fungicide treatment × 100. Over the 4-year period, significant alfalfa yield losses (P ≤ 0.05) occurred on 22 of the 48 harvest dates for the four states. The average significant yield loss for the 22 harvests was 19.3%. Both visual and percentage of reflectance assessments were used as independent variables in linear regression models to quantify the relationships between assessments and alfalfa yield. From 1995 to 1998, visual disease assessments were performed for a total of 209 dates and remote sensing assessments were performed on 198 dates from the four states. Yield models were developed for each of these assessment dates. There were 26/209, 26/209, and 17/209 significant yield models based on percentage of defoliation, disease incidence, and disease severity, respectively. Most of the significant models were for disease assessments performed on or within 1 or 2 weeks of the date of alfalfa harvest. When the significant models were averaged, percentage of defoliation, disease incidence, and disease severity explained 51, 55, and 52% of the variation in alfalfa yield, respectively. There were a total of 68/198 significant alfalfa yield models based on remote sensing assessments, and the significant models (averaged) explained 62% of the variation in alfalfa yield. Alfalfa foliar diseases continue to have a significant negative impact on alfalfa yields in the United States and remote sensing appears to offer a better means to quantify the impact of foliar diseases on alfalfa yield compared with visual assessment methods.

Influence of Temperature and Wetness Duration on Conidia and Appressoria of Colletotrichum acutatum on Symptomless Strawberry Leaves.

ABSTRACT Strawberry leaves (cv. Tristar) inoculated with Colletotrichum acuta-tum conidia were incubated at 10, 15, 20, 25, 30, and 35 degrees C under continuous wetness, and at 25 degrees C under six intermittent wetness regimes. The number of conidia and appressoria was quantified on excised leaf disks. In order to assess pathogen survival, inoculated leaves were frozen and incubated to induce acervular development. Germination, secondary3 conidiation, and appressorial development were significantly (P </= 0.05) affected by temperature and wetness treatments. Under continuous wetness, the optimum temperature range for conidial germination was 23.0 to 27.7 degrees C, whereas the optimum temperature for appressorial development ranged from 17.6 to 26.5 degrees C. Secondary conidiation showed an optimum temperature range of 21.3 to 32.7 degrees C and was most abundant between 12 and 36 h after inoculation. Conidial germination, appressorial production, and secondary conidiation were favored by increasing wetness duration and more than 4 h of wetness were required for secondary conidiation. In a greenhouse, C. acutatum survived up to 8 weeks on leaves. The number of acervuli formed on leaves after freezing and incubation was closely (r(2) >/= 0.95) related to appressorial populations prior to this treatment and was greatest following periods of continuous wetness. Production of secondary conidia and appressoria of C. acutatum on symptomless strawberry leaves under a range of environmental conditions suggests that these processes also occur under field conditions and contribute to inoculum availability during the growing season.

Temporal and Spatial Analysis of Grapevine Leafroll-Associated Virus 3 in Pinot Noir Grapevines in Australia

An epidemic of grapevine leafroll disease (GLD), caused by grapevine leafroll-associated virus 3 (GLRaV-3), was monitored over an 11-year period in Nuriootpa, South Australia. Inoculum originated from infected budwood, and initial GLD incidence at the time of transplanting in 1986 was 23.1%. Infected vines were planted in a random spatial pattern. Change in disease incidence was not observed until 8 years after planting, when disease incidence increased to 27.9%. Disease incidence increased to 51.9% by 1996. Disease progress and rate curves (dy/dt versus time) indicated that the logistic (R2 = 96.2) and Gompertz (R2 = 96.3) growth models would best describe disease progress. However, the logistic model, which has a simpler data transformation with fewer model assumptions, was chosen for the purpose of comparing this epidemic (South Australia) with a GLRaV-3 epidemic in Cabernet Sauvignon grapevines in New Zealand. The logistic rate of GLD spread with respect to time was 0.35 logit/year in South Australia and was nearly three times faster (1.19 logits/year) for GLRaV-3 spread in New Zealand. Ordinary runs analyses indicated that the arrangement of infected vines within rows in South Australia was random up to 8 years after transplanting but subsequently became highly aggregated. Thus, GLD-infected plants are contributing to new infections (i.e., there is evidence for plant-to-plant spread), and a biotic vector with a steep dispersal gradient from each point source is likely to be involved.

Diseases of Pyrethrum in Tasmania: Challenges and Prospects for Management

Pyrethrum (Tanacetum cinerariifolium (Trevir.) Sch. Bip.) is a perennial plant and member of the Asteraceae that is endemic to the Dalmatian region of the former Yugoslavia (36). Pyrethrum is cultivated commercially solely for the production of six closely related esters called pyrethrins. The plant is tufted, slender, and herbaceous, growing to a height of approximately one meter (18). Leaves are alternate and pinnately lobed/narrowly lanceolate to oblong lanceolate. The daisy-like flowers are produced at the termini of stems and consist of a cluster of 40 to 100 bisexual, yellow disk florets encircled by a ring of 18 to 22 pistillate white ray florets atop a moderately convex to subglobose receptacle (Fig. 1; 100). Disk and ray florets both possess 3 to 10 ribbed achenes located between the floret and receptacle. Involucres generally range between 12 and 18 mm in diameter (17,18). Approximately 94% of the pyrethrins are produced within secretory ducts and oil glands of the achenes of the mature pyrethrum flower, with a minor percentage of oil glands and secretory ducts also found in leaves, stems, and roots (99). Pyrethrins can be separated into two groups of three ester compounds: pyrethrins I and II. The pyrethrin I fraction contains chrysanthemic acid products, including pyrethrin I, cinerin I, and jasmolin I. The pyrethrin II fraction is derived from pyrethric acid and made up of pyrethrin II, cinerin II, and jasmolin II (19,27). Compounds within both fractions contain insecticidal properties used in household and commercial pest control products. These compounds are referred to as “knockdown” and kill agents for many arthropods, yet are of low toxicity to mammals. Pyrethrins also have the advantage over other synthetic insecticides of being rapidly broken down upon exposure to light and air, are metabolized quickly, and can be used in the production of organic farm products. Thus, natural pyrethrins are generally considered to be nonpolluting (19,27). The major areas of pyrethrum production worldwide are located in East Africa (Kenya, Rwanda, and Tanzania), Tasmania (Australia), China, and Papua New Guinea (85,94). Production of pyrethrum in Kenya began in 1928, and despite some fluctuations in annual supply, Kenya is still one of the major suppliers to the world’s market. Pyrethrum cultivation in Kenya is centered in four production areas: the northern and southern Rift Valleys, Mount Kenya, and near Lake Victoria (94). Tasmania is the other major world producer and grows approximately 2,000 hectares. In Tasmania, pyrethrum is grown predominantly along the northwest coast of the island, between Deloraine (41° 31′ S; 146° 39′ E) and Table Cape (40° 56′ S; 145° 43′ E). The cultivation of pyrethrum differs markedly between Tasmania and the other production areas of the world. For example, in 2001 approximately 200,000 growers were involved in pyrethrum production

The Role of Psychophysics in Phytopathology: The Weber–Fechner Law Revisited

The accuracy and precision of disease severity assessment data might be improved if there was a better understanding of how the laws of psychophysics actually relate to the theory and practice of phytopathometry. In this regard, we utilized a classical method developed in the field of psychophysics (the method of comparison stimuli) to test Horsfall and Barratt’s claim that raters cannot accurately discriminate disease severity levels between 25% and 50% because, according to the Weber–Fechner law, visual acuity is proportional to the logarithm of the intensity of the stimulus. We show for two pathosystems, wheat leaf rust and grapevine downy mildew, that raters can accurately discriminate disease severity levels between 25% and 50%, and that although Weber’s law appears to hold true, Fechner’s law does not. Furthermore, based upon our results, the relationship between actual (true) disease severity (X) and disease severity estimated by raters (Y) is linear, not logarithmic as proposed by Horsfall and Barratt.

Spatiotemporal Description of Epidemics Caused by Phoma ligulicola in Tasmanian Pyrethrum Fields

ABSTRACT Spatial and temporal patterns of foliar disease caused by Phoma ligulicola were quantified in naturally occurring epidemics in Tasmanian pyrethrum fields. Disease assessments (defoliation incidence, defoliation severity, incidence of stems with ray blight, and incidence of flowers with ray blight) were performed four times each year in 2002 and 2003. Spatial analyses based on distribution fitting, runs analysis, and spatial analysis by distance indices (SADIE) demonstrated aggregation in fields approaching their first harvest for all assessment times between September and December. In second-year harvest fields, however, the incidence of stems with ray blight was random for the first and last samplings, but aggregated between these times. Spatiotemporal analyses were conducted between the same disease intensity measures at subsequent assessment times with the association function of SADIE. In first-year harvest fields, the presence of steep spatial gradients was suggested, most likely from dispersal of conidia from foci within the field. The importance of exogenous inoculum sources, such as wind-dispersed ascospores, was suggested by the absence of significant association between defoliation intensity (incidence and severity) and incidence of stems with ray blight in second-year harvest fields. The logistic model provided the best temporal fit to the increase in defoliation severity in each of six first-year harvest fields in 2003. The logistic model also provided the best fit for the incidence of stems with ray blight and the incidence of flowers with ray blight in four of six and three of six fields, respectively, whereas the Gompertz model provided the best fit in the remaining fields. Fungicides applied prior to mid-October (early spring) significantly reduced the area under disease progress curve (P < 0.001) for defoliation severity, the incidence of stems with ray blight, and the incidence of flowers with ray blight for epidemics at all field locations. This study provides information concerning the epidemiology of foliar disease and ray blight epidemics in pyrethrum and offers insight on how to best manage these diseases.