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Comparison of monitoring- and weather-based risk indicators of botrytis leaf blight of onion and determination of action thresholds

  • Agriculture Canada

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Botrytis leaf blight (Botrytis squamosa) is the key disease for scheduling fungicide sprays in many onion-growing areas. Various disease predictors have been developed to identify the best time to initiate fungicide spray programs or to time spray intervals. However, individual predictors have not been rigorously evaluated based on their predictive accuracy. Receiver operating characteristic (ROC) curve analysis was used to evaluate the reliability of the following seven predictors at various damage thresholds: (1) the number of lesions on the oldest leaves; (2) the number of lesions on the youngest leaves; (3) the airborne conidia concentration (ACC); (4) the sporulation index (SI); (5) the inoculum production index (IPI); (6) the infection probability (IP); and (7) a disease severity value (DSV). Data on disease intensity were concurrently collected in sprayed and unsprayed plots. The analysis was conducted with data from the entire season (185 samplings) and for data after the critical disease level of 1 lesion per leaf was reached (107 samplings). At damage thresholds of 1 or 5 lesions per leaf, predictors based on biological monitoring generally were more reliable at predicting disease risk than weather-based predictors. At a damage threshold of 10 lesions per leaf, most pairwise comparisons showed that there were no significant differences among the areas under the ROC curves (AUCs) for any predictors except for the AUC for ACC which was significantly higher and for and IP that were significantly higher and significantly lower, respectively, than AUC for most other predictors. The difference between the two types of predictors was smaller when only data after the first fungicide spray were analysed. Best action thresholds were 8.06 to 13.79 conidia per m of air, 2.73 to 3.56 lesions per oldest leaf, and 0.16 to 0.43 lesions per youngest leaf. The most reliable weather-based predictor was SI at an action threshold of 82.55 to 86.46 SI value. Best time to initiate the spray program could be predicted using monitoring-based predictors at the lowest action threshold. The interval between sprays could be best predicted using monitoring-based predictors at a higher threshold combined with SI values above 80. Key words: disease management, epidemiology, forecasting systems, risk management.La brûlure de la feuille de l’oignon (Botrytis squamosa) est une maladie clef dans la gestion des fongicides dans la plupart des régions de production. Plusieurs indicateurs de risque ont été développés afin de déterminer le meilleur moment pour débuter les applications de fongicides ou pour déterminer l’intervalle entre les pulvérisations. Toutefois, ces indicateurs n’on jamais été évalués pour leur fiabilité à estimer les risques de maladie. L’analyse ROC « receive operating characteristic curve » a été utilisée pour évaluer la fiabilité de sept indicateurs en fonction de différents seuils de dommages: (1) nombre moyen de lésions sur la feuille du bas (OLD); (2) nombre moyen de lésions sur la feuille du haut (YNG); (3) la concentration aérienne de conidies (ACC); (4) l’indice de sporulation (SI); (5) l’indice de production d’inoculum (IPI); (6) la probabilité d’infection (IP); et (7) la valeur de sévérité (DSV). Des données sur l’intensité de la maladie ont été recueillies simultanément dans des parcelles traitées et non traitées avec des fongicides. L’analyse a été faite sur les données comprenant toute la saison et seulement la période après l’atteinte du seuil critique de une lésion par feuille pour un total de 185 et 107 échantillonnages. Au seuil de dommage de 1 et 5 lésions par feuille, les indicateurs basés sur le dépistage se sont avérés plus fiables que ceux basés sur des prédictions. Par contre, au seuil de 10 lésions par feuille, il n’y a pas de différences significatives entre les aires sous la courbe ROC (AUC) pour chaque paire d’indicateurs à l’exception de ACC dont l’AUC était significativement plus élevée et de IP dont l’AUC était significativement plus faible. Peu de différence entre les AUCs ont été observées pour les données recueillies après l’atteinte du seuil de une lésion par feuille. Le meilleur seuil d’intervention a été établit à 8,06 à 13,79 conidies par m d’air, 2,73 à 3,56 lésions par feuille du bas et 0,16 à 0,43 lésions par feuille du haut. Parmi les autres indicateurs, l’indice de sporulation (SI) s’est avéré être le plus fiable à un seuil d’intervention de 82,55 à 86,46. Le meilleur moment pour débuter les applications de fongicides devraient être déterminées en fonction du dépistage et par la suite l’intervalle entre les pulvérisations devrait être déterminé en fonction du dépistage avec un seuil plus élevé combiné avec un indice de sporulation (SI) au seuil de 80. Mots-clés : gestion des maladies, épidémiologie, lutte raisonnée, systèmes prévisionnels, gestion des risques.
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Plant Disease / April 2012 497
Remote Sensing for Assessing Rhizoctonia Crown and Root Rot Severity
in Sugar Beet
Gregory J. Reynolds, Department of Plant Pathology, University of California, Davis 95616; Carol E. Windels, Department of Plant
Pathology and Northwest Research and Outreach Center and Ian V. MacRae, Department of Entomology and Northwest Research and
Outreach Center, University of Minnesota, Crookston 56716; and Soizik Laguette, Department of Earth System Science and Policy,
University of North Dakota, Grand Forks 58202
Reynolds, G. J., Windels, C. E., MacRae, I. V., and Laguette, S. 2012. Remote sensing for assessing Rhizoctonia crown and root rot severity in
sugar beet. Plant Dis. 96:497-505.
Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani
AG-2-2, is an increasingly important disease of sugar beet in Minne-
sota and North Dakota. Disease ratings are based on subjective, visual
estimates of root rot severity (0-to-7 scale, where 0 = healthy and 7 =
100% rotted, foliage dead). Remote sensing was evaluated as an al-
ternative method to assess RCRR. Field plots of sugar beet were inocu-
lated with R. solani AG 2-2 IIIB at different inoculum densities at the
10-leaf stage in 2008 and 2009. Data were collected for (i) hyperspec-
tral reflectance from the sugar beet canopy and (ii) visual ratings of
RCRR in 2008 at 2, 4, 6, and 8 weeks after inoculation (WAI) and in
2009 at 2, 3, 5, and 9 WAI. Green, red, and near-infrared reflectance
and several calculated narrowband and wideband vegetation indices
(VIs) were correlated with visual RCRR ratings, and all resulted in
strong nonlinear regressions. Values of VIs were constant until at least
26 to 50% of the root surface was rotted (RCRR = 4, wilting of foliage
starting to develop) and then decreased significantly as RCRR ratings
increased and plants began dying. RCRR also was detected using air-
borne, color-infrared imagery at 0.25- and 1-m resolution. Remote
sensing can detect RCRR but not before initial appearance of foliar
The soilborne fungus Rhizoctonia solani Kühn AG-2-2 intra-
specific groups IIIB and IV cause Rhizoctonia crown and root rot
(RCRR) of sugar beet (Beta vulgaris L.) (11,73,74). Since the early
1990s, these pathogens have become widespread in sugar beet–
growing regions of Minnesota and North Dakota because of wet
weather (40), planting of susceptible sugar beet cultivars (Al Cat-
tanach, personal communication), and increased production of
soybean, edible bean, and corn (69), which are alternate hosts of R.
solani AG-2-2 (11,20,32,73,74). Production of these susceptible
rotation crops in the sugar beet cropping system allows R. solani
inoculum to build up in soil and contribute to disease outbreaks.
Management of RCRR is achieved through rotations of three or
more years with non-host plants (57,58,75), early planting (10),
and application of fungicides (22,27,28,67,72).
Symptoms of RCRR include a dark-brown to gray rot that typi-
cally begins near the crown and spreads over the root surface,
eventually causing cracking and sunken lesions (75). Petioles are
black and rotted at the point of attachment to the crown. Some-
times, infections occur on the root tip or laterally on the root sur-
face (75). Aboveground, foliage may show sudden and severe wilt-
ing and then chlorosis; severely infected plants eventually die.
Disease severity typically is assessed by a visual rating scale based
on the amount of rot on the tap root (45). This rating system, how-
ever, is destructive and requires removal of roots from soil.
Furthermore, visual disease assessments are subjective in nature
and affected by fatigue, bias, human error, and differences in esti-
mates among raters (38,41,43,60,61).
Remote sensing is an alternative method to nondestructively as-
sess plant diseases rapidly, repeatedly, and over a large area with-
out physical contact with the sampling unit (i.e., sugar beet foliage;
38). Remote sensing in agriculture typically involves measuring
reflectance of electromagnetic radiation from the subject of interest
(i.e., vegetation), usually in the visible (390 to 770 nm), near-infra-
red (NIR, 770 to 1,300 nm), or middle-infrared (1,300 to 2,500
nm) ranges (23). The technology is advantageous because reflec-
tance over broad electromagnetic domains may be measured in a
nondestructive manner, over a wide area, and in real time (19).
Instruments may collect either hyperspectral or multispectral re-
flectance data. Hyperspectral sensors measure reflectance continu-
ously as a series of narrow wavelength bands while multispectral
sensors measure average reflectance at a few wide bands separated
by segments where no measurements are taken (35). Both hyper-
spectral and multispectral reflectance data typically are converted
to vegetation indices, where two or more important wavebands are
mathematically combined to provide pertinent information on plant
biophysical parameters (i.e., chlorophyll content) or to correct for
background interference from soil or the atmosphere (68).
Extensive reviews on the application of remote sensing to detec-
tion of plant diseases have been written by Jackson (21), Hatfield
and Pinter (17), Nilsson (38), and West et al. (71). Nutter et al.
concluded from two studies that remote sensing-based disease
assessments were more precise and accurate than visual disease
assessments for Sclerotinia homeocarpa on bentgrass (41) and
foliar diseases in alfalfa (42). The technology also has shown
potential for detecting root stress and diseases in several crops
(9,13,44,47,70), including diseases caused by R. solani, such as
rice sheath blight (48) and blight of creeping bentgrass (49). Re-
mote sensing technology also has been applied to the detection of
sugar beet diseases, including Cercospora leaf spot (65) and Rhizo-
mania (66). Thus, there is potential for application of remote sens-
ing to detect or assess RCRR.
Aboveground symptoms of RCRR (sudden, permanent wilting
of leaves and yellowing of foliage) are the basis for remote detec-
tion of this disease. Using remote sensing technology, it also may
be possible to detect stress in sugar beet plants before visible wilt-
ing occurs, because reduced photosynthesis rates or water content
may produce subtle, detectable changes that would be invisible to
the naked eye. Chavez et al. (6) detected Potato yellow vein virus
in potato prior to the development of visible chlorosis using a re-
mote sensing-based approach. Similarly, photosynthesis rates de-
Corresponding author: G. J. Reynolds, E-mail:
Accepted for publication 19 November 2011. /PDIS-11-10-0831
© 2012 The American Phytopathological Society
498 Plant Disease / Vol. 96 No. 4
creased by as much as 14% in yellow poplar prior to development
of visible ozone injury symptoms (14). Johnsen et al. (24) detected
water stress in creeping bentgrass by remote sensing as much as 48
h before visible wilting occurred.
Laudien et al. (30,31) conducted research to determine the
potential for remote sensing to detect RCRR but focused only on
the distinction between healthy and diseased plants near the end of
the growing season. Early-season detection of the disease was not
assessed, nor was the relationship of reflectance to severity of
RCRR. Early detection of RCRR or the ability to assess disease
severity based on remote sensing may allow for rapid assessment
of entire fields.
The objectives of this study were to (i) investigate the potential
for remote sensing in early detection of RCRR on sugar beet and
(ii) identify optimal vegetation indices for correlating with visual
disease severity ratings. A brief report has been published (53).
Materials and Methods
Field trials. Experiments were established at the University of
Minnesota, Northwest Research and Outreach Center, Crookston,
in 2008 and 2009. Both sites had been sown to soybean the previ-
ous year and were fertilized following standard procedure to maxi-
mize sugar beet yield and quality (26). In 2008, the field site (60 by
65.5 m) was sown on 21 May with two nontransgenic commercial
sugar beet cultivars: one susceptible (‘Vanderhave 4653’) and the
other partially resistant (‘Hilleshog 3035’) to RCRR. In 2009, the
site was slightly smaller (47 by 65.5 m) because some 2008
Rhizoctonia inoculum density treatments resulted in similar disease
severities. Roundup Ready cultivars were selected in 2009 because
of the rapid adoption (88% hectares) of transgenic cultivars by
sugar beet producers in Minnesota and North Dakota in 2009 (64).
Plots were planted with ‘Crystal 539RR’ and ‘Crystal 658RR’ (par-
tially resistant and susceptible to RCRR, respectively).
In both years, seed were sown every 4.76 cm at a depth of 2.5
cm in rows 0.56 m apart. In 2008, each treatment was assigned to
six-row plots arranged in a two-by-eight factorial treatment design
of four replicates; in 2009, a two-by-six factorial design was used.
Each plot was 3.35 m wide by 10.7 m long, and blocks were sepa-
rated by 7.6-m alleys. Plant populations were thinned to 17.8-cm
spacing on 26 June 2008 and 18 June 2009. In both years, plots
were treated with the insecticide terbufos (Counter; BASF, Lud-
wigshafen, Germany) at planting (1.7 kg a.i. ha
) to control root
insects. In 2009, chlorpyrifos (Lorsban-4E; Dow AgroSciences,
Indianapolis, IN) also was applied postemergence (0.84 kg a.i. ha
because of higher than normal sugar beet root maggot populations.
In 2008, microrate applications of the herbicides triflusulfuron
(UpBeet, 236 to 710 ml a.i. ha
; DuPont, Wilmington, DE),
desmedipham+phenmediphan (Betamix, 3.5 g a.i. ha
; Bayer
CropScience US, Pittsburgh), clopyralid (Stinger, 25 to 30 ml a.i.
; Dow AgroSciences), clethodim (Select, 70 to 130 ml a.i. ha
Arysta LifeScience, Cary, NC), and methylated seed oil adjuvant
(473 to 592 ml a.i. ha
) were made at four intervals, beginning on
15 June and continuing every 6 days. An additional application of
desmedipham+phenmediphan and triflusulfuron (947 ml and 9.5 g
a.i. ha
, respectively) was applied 10 days later. Weeds were con-
trolled in 2009 with two applications of glyphosate (Roundup;
Monsanto, Creve Coeur, MO) in mid-June and mid-July (1.7 and
2.2 kg a.i. ha
, respectively). Application of glyphosate does not
affect RCRR severity ratings in the field (2). Cercospora leaf spot
(CLS) was controlled in 2008 by successive applications of
triphenyl tin hydroxide (Super Tin, 0.35 kg a.i. ha
; DuPont),
tetraconazole (Eminent, 0.91 kg a.i. ha
; Isagro-USA, Morrisville,
NC), and pyraclostrobin (Headline, 0.63 kg a.i. ha
; BASF) from
early August to early September. In 2009, only one fungicide appli-
cation was necessary to control CLS, and pyraclostrobin was ap-
plied at 0.63 kg a.i. ha
in early September. Chemicals were ap-
plied with a tractor-mounted sprayer and TeeJet 8002 flat fan
nozzles at 7.0 kg/cm
Inoculation with R. solani. Inoculum was prepared on corn ker-
nels (10) and also on barley grain (56). For corn kernel inoculum,
dent corn was soaked in distilled water for 12 h in 750-ml beakers,
drained, and autoclaved at 121°C for 60 min on two consecutive
days. R. solani AG-2-2 IIIB (isolate 87-36-4; 10) was grown on
acidified potato-dextrose agar (APDA) for 7 days. Four 1.5-cm-
diameter disks from the margin of an actively growing colony were
transferred to the corn kernels and incubated at 21 ± 1°C for 21 to
38 days (containers were shaken every 2 days). Barley grain inocu-
lum was prepared by combining 3,120 cm
of barley and 1,800 ml
of distilled water in aluminum pans (30.5 by 50.8 by 10.2 cm).
Grain was autoclaved for 120 min on two consecutive days, inocu-
lated with 15 1.5-cm-diameter disks from the margin of 7-day-old
cultures on APDA, and incubated at 21 ± 1°C for 14 to 21 days.
After R. solani had completely colonized barley grains, inoculum
was dried for 36 h and ground in a Wiley Mill (number 3 round-
hole screen, 3.2-mm mesh). Inoculum was stored at 21 ± 1°C in
the laboratory for 3 weeks until used. Some corn kernels were cut
in half with razor blades the day of inoculation to reduce inoculum
In both years, sugar beet plants were inoculated before closure
of the row by foliage, at the 10- to 12-leaf stage. On 10 and 11 July
2008, inoculum was applied to plots of each cultivar to attain a
range of RCRR disease severities (4). Each cultivar was treated
with seven different inoculum densities in separate plots, and each
density was applied to all plants in the center four rows of six-row
plots. Treatments included either corn kernel inoculum (at one-half
or two R. solani-infested kernels per root) or ground barley inocu-
lum at five different rates (1.5, 2.2, 3, 3.7, or 4.5 g/m of row).
Plants were inoculated with corn kernel inoculum by removing soil
from the root about 2.5 cm below the soil surface, placing inocu-
lum adjacent to the exposed tap root, and re-covering with soil.
Ground barley inoculum was deposited in sugar beet crowns with a
Gandy granule applicator calibrated to release appropriate rates per
meter of row (56). Control plots were not inoculated. Plots then
were cultivated to throw soil into crowns and cover inoculum to
favor infections (56). In 2009, plots were inoculated on 6 July as
previously described but the 2.2- and 3.7-g rates of barley inocu-
lum were not used because they were not needed to attain a range
of disease severities based on results from 2008. Plots were not
irrigated, but, within 1 week after inoculation, 1.88 cm of pre-
cipitation occurred in 2008 and 3.96 cm in 2009.
Spectral and disease assessments. To correlate spectral meas-
urements and disease severity, each plot was divided by a flag that
marked a 6.1-m length for measuring spectral reflectance and the
remaining 4.6 m for removal of plants to visually rate RCRR sever-
ity. This allowed for multiple spectral reflectance measurements of
a full sugar beet canopy, as well as destructive removal of roots.
Previous research has shown that RCRR ratings on half a research
plot are the same as across the whole plot (4). Furthermore, above-
ground symptoms in the portion rated for disease were consistent
with those in the portion measured spectrally. Reflectance and
disease severity were measured on the same day. In 2008, data
were collected on 25 July, 7 and 18 August, and 3 September at 2,
4, 6, and 8 weeks after inoculation, respectively. In 2009, data were
collected on 21 and 28 July, 11 August, and 9 September at 2, 3, 5,
and 9 weeks after inoculation, respectively. Assessments were not
obtained at equal intervals in both years because clouds were a
limiting factor; clear skies were required for the spectral measure-
Reflectance data were acquired with a FieldSpec FR hand-held
spectroradiometer (Analytical Spectral Devices, Inc.; Boulder,
CO), which is composed of three separate spectrometers in the
same enclosure. It has a sampling interval of 1.4 nm for the 350- to
1,000-nm region of the electromagnetic spectrum (3-nm spectral
resolution) and 2 nm for the 1,000- to 2,500-nm region (10-nm
spectral resolution), with a field of view of 25°. Three data meas-
urements were collected 1.2 m from the top of the sugar beet can-
opy at nadir, meaning the instrument is directed downward in line
with gravity and diametrically opposed to the zenith; this provided
a 50.8-cm-diameter field of view. Measurements were taken on
clear, sunny days between 10:00 a.m. and 2:00 p.m. CST to ensure
Plant Disease / April 2012 499
consistent sun angle and intensity for all plots and all assessment
dates (16) and obtained at 2-m intervals within the 6.1-m spectral
measurement portion of each plot, at least 1 m from plot edges.
The instrument was optimized with a calibrated spectralon white
reflectance panel every 15 min while readings were obtained,
allowing readings from different assessment dates to be compared.
The panel reflects close to 100% of all incident radiation, and re-
flectance values are calculated as a ratio of reflected radiation to
incident radiation.
At each sampling date, 10 plants were arbitrarily removed from
the four middle rows of the 4.6-m section of each plot not used for
spectral assessment (two or three plants per row evenly distributed
throughout the area). Because every plant in the four 10.7-m-length
rows was inoculated per plot, variability in disease ratings was
minimized among plants within the same plot. About 30% of
plants in the sampled area were removed by the end of the season,
and previous studies indicated that this proportion of sampling was
representative of the plot (4). Tap roots were visually assessed for
RCRR using a 0-to-7 scale (45), where 0 = no visible lesions; 1 =
superficial, scattered inactive lesions; 2 = shallow, dry rot cankers
or active lesions on 5% of root surface; 3 = deep dry-rot cankers
at crown or extensive lateral lesions affecting 6 to 25% of the root;
4 = rot affecting 26 to 50% of tap root, with cracks and cankers up
to 5 mm deep; 5 = 51 to 75% of tap root blackened, with rot
extending into interior and roots usually misshapen with cracks
and rifts; 6 = entire root blackened except extreme tip; and 7 = root
100% rotted and foliage dead. Ratings of the 10 beet plants were
averaged to estimate the overall plot RCRR severity per sampling
date. This rating scale is discontinuous because it is more difficult
to assess small changes in severity at moderate levels of disease
than at very high or very low levels of disease (18), especially on
tap roots. The rating scale, however, is well established, and
continuous data assessment scales have not been developed for
Aerial imagery. To validate the 2 years of ground-based remote
sensing data, aerial, color-infrared (CIR) digital imagery of the
2009 field trial was obtained using the Airborne Environmental
Research Observational Camera (AEROcam, Upper Midwest
Aerospace Consortium, University of North Dakota, Grand Forks),
an MS4100 High Resolution 3-CCD Digital Multispectral Camera
(Geospatial Systems Incorporated, Rochester, NY). This camera
measures reflectance in three bands that approximate Landsat
satellite bands: green (520 to 600 nm), red (630 to 690 nm), and
NIR (760 to 900 nm). The camera was mounted on a Piper Arrow
PA28-201; imagery was acquired at 457.2 m above ground level
(AGL; 0.25-m pixel size) on three dates, each within 2 days of
ground-based spectral measurements and disease assessments (3,
5, and 9 weeks after inoculation). One set of imagery also was
acquired from 1,829 m AGL (1-m pixel size) on 6 August 2009.
Global positioning system (GPS) coordinates were obtained at
corners of the four blocks of the field trial as well as at corners of
neighboring fields with an AgGPS 132 (Trimble Navigation Lim-
ited, Sunnyvale, CA) submeter, differentially corrected GPS with
combined L1, satellite, and beacon antenna for georeferencing.
Control point error for rectification of the 0.25-m spatial resolution
CIR digital imagery was 0.0164 pixels on 28 July, 0.0134 pixels on
13 August, and 0.0146 pixels on 9 September 2009.
Aerial CIR digital images were processed using ERDAS Imag-
ine (version 9.3; ERDAS, Inc., Atlanta, GA). The images were
cropped to include only the field trial to reduce processing time.
The GPS coordinates at block and field corners were used to geo-
reference and geometrically rectify the images. Maps then were
generated by calculating the optimized soil-adjusted vegetation
index (OSAVI; Table 1) (54) for each pixel. OSAVI is a vegetation
index ranging from 0 to 1 that incorporates red and NIR reflec-
tance with a universal soil transformation (0.16). It was selected
because it is a common multispectral vegetation index associated
with chlorophyll content that showed promise in both the 2008
ground-based results (53) and in preliminary research by Laudien
et al. (31). The ground-based spectral measurements and disease
assessments were used as ground truth data (on location informa-
tion collected to relate remote sensing measurements to real fea-
tures on the ground).
Statistical analyses. Hyperspectral reflectance signatures were
compared visually using ViewSpecPro (Analytical Spectral De-
vices, Inc.). First derivatives also were calculated and visually
compared using this software to qualitatively identify pertinent
wavelengths associated with RCRR infection. To identify optimal
indices for assessing RCRR severity, hyperspectral reflectance data
were combined into various narrowband and wideband vegetation
indices associated with chlorophyll or water content (Table 1).
Vegetation index values were regressed against disease severity
values using regression analysis in R, version 2.10.1 (50). Linear
regression models were initially tested with data from the suscepti-
ble and partially resistant cultivars combined, but, because cultivars
showed significant differences in 2009 for most indices assessed,
each cultivar was ultimately assessed individually. P values show-
ing the differences between cultivars were obtained from these
initial, combined linear regression models. Because relationships
between vegetation indices and disease severity frequently were
nonlinear, regressions of increasingly higher order also were as-
sessed to determine the best fit model. Significant P values (0.05)
for all coefficients were required of higher-order models for a
nonlinear model to be selected. When nonlinear models were se-
lected, a tipping point (where the vegetation index begins to de-
scend) was visually identified where index values begin to change.
To assess the difference in RCRR ratings between cultivars, analy-
sis of covariance (ANCOVA) was used to compare disease ratings
from control and barley-inoculated plots using inoculum dosage as
the continuous variable and cultivar as the categorical variable.
Corn inoculum treatments were not included because they were not
continuous with barley inoculum dosages.
Disease development. A wide range of disease ratings for
RCRR was obtained across plots at each assessment date (Table 2).
In each plot, disease symptoms developed uniformly in the por-
tions used to rate roots for disease and for spectral measurements
throughout the growing season. In both years, the range of inocu-
lum density treatments resulted in various times for onset of
belowground (Table 2) and aboveground symptoms of RCRR,
which was ideal for monitoring disease development by spectral
Table 1. Reflectance ranges and vegetation indices assessed for correlation
with Rhizoctonia crown and root rot disease ratings from sugar beet plots a
the University of Minnesota, Northwest Research and Outreach Center,
Green R
Red R
DVI NIR Red 25
NDVI (NIR – Red)/(NIR + Red) 55
OSAVI (NIR – Red)/(NIR + Red + 0.16) 54
GNDVI (NIR – Green)/(NIR + Green) 15
+ R
)/2 – R
mSR (R
+ R
) 62
+ R
) 1
Green = green reflectance, Red = red reflectance, NIR = near-infrared
reflectance, DVI = difference vegetation index, SRVI = simple ratio
vegetation index, NDVI = normalized difference vegetation index, OSAVI
= optimized soil-adjusted vegetation index, GNDVI = green normalized
difference vegetation index, PSSR
= pigment specific simple ratio
(chlorophyll-a), PSSR
= pigment specific simple ratio (chlorophyll-b),
RVSI = red edge vegetation stress index, LWI = leaf water index, mSR =
modified spectral ratio, and NDRE = normalized difference red edge.
R represents reflectance at the given wavelength or wavelength range (nm).
500 Plant Disease / Vol. 96 No. 4
measurements. Root symptoms of RCRR developed first, and
aboveground symptoms of wilt were observed when ratings
reached a value of 4 (26 to 50% of the root surface rotted). Chloro-
sis developed as RCRR values increased.
At 2 weeks after inoculation in both years, some plants in plots
with high inoculum densities (one-half and two corn kernels per
root) were beginning to wilt but no chlorosis was observed; these
symptomatic plants also had darkened petioles at the soil line. At
this time, the other inoculated plots and noninoculated controls
displayed no aboveground symptoms of RCRR, although root rot
was beginning to develop (Table 2). Plots inoculated with R. so-
lani-infested corn kernels (both rates) had RCRR ratings >4 by the
next sampling date, and most were extensively wilted and
chlorotic. In 2008, plots inoculated with the lowest density of R.
solani (infested grain at 1.5 g/m of row) had low RCRR values and
did not develop chlorosis or wilting during the growing season; in
2009, disease was more severe and RCRR values resulted in
aboveground symptoms by 9 weeks after inoculation. By compari-
son, the moderate to high rates of infested barley grain inoculum
(2.2, 3, 3.7, and 4.5 g/m) often resulted in higher RCRR ratings
(Table 2) and earlier development of aboveground symptoms after
inoculation compared with the 1.5-g rate.
In both years, ratings for RCRR consistently were higher for
both cultivars at all assessment dates when inoculum levels were
high (one-half or two infested corn kernels per root) and usually
were lower for the partially resistant than susceptible cultivar for
all rates of barley grain inoculum (Table 2). In 2008, ANCOVA
showed a significant effect of cultivar on RCRR ratings (P value <
0.0001), with the susceptible cultivar developing increasingly more
severe RCRR ratings than the partially resistant cultivar as inocu-
lum dose increased. In 2009, the effect of cultivar was somewhat
significant (P value = 0.0944), with the susceptible cultivar having
slightly higher RCRR ratings overall than the partially resistant
Spectral measurements. Several wavelengths were identified
through first derivative analysis as being associated with RCRR
severity, including some in the red (660 and 680 nm), red-edge
(730 and 740 nm), and NIR (1,130, 1,145, and 1,330 nm) ranges;
all of these bands, or close approximations, are incorporated into
one or more of the vegetation indices assessed (Table 3). Visual
inspection of hyperspectral reflectance signatures for healthy and
RCRR-diseased plots showed that wilting and chlorotic sugar beet
plot canopies were associated with increased red and red-edge
reflectance (620 to 750 nm), decreased NIR reflectance (770 to
1300 nm), and increased middle-infrared reflectance (1,300 to
2,500 nm) (data not shown). Reflectance signatures of plots with
maximum RCRR severity (disease rating = 7, where root is com-
pletely rotted and foliage dead) were closely associated with the
reflectance signatures of bare soil measured in alleys.
Although cultivars responded to RCRR infection similarly in
2008, the partially resistant and susceptible cultivars had signifi-
cantly different responses in 2009; therefore, cultivars were as-
sessed individually each year. Reflectance in the red and NIR
ranges and all vegetation indices assessed yielded statistically sig-
nificant regression models when plotted against disease ratings for
RCRR of both cultivars in 2008 and 2009 (P < 0.0001; Table 3).
Many of these indices also had R
values higher than 0.6, meaning
the index in question accounts for over 60% of the variability in the
data. Because the relationships between disease severity and many
of the indices were nonlinear, higher-order models also were as-
sessed. Models selected were linear, second-order, or third-order.
Green reflectance was the only variable assessed that did not con-
sistently have significant P values for both cultivars in both years
(Table 3).
Because P values and R
values were comparable for most
reflectance ranges and vegetation indices tested, no single vegeta-
tion index was optimal. The OSAVI consistently had the highest or
second highest R
value of indices assessed and was selected as
representative of the wideband indices (Fig. 1). Other wideband
index values followed the same relationship to RCRR as OSAVI
(data not shown).
In both years, OSAVI values generally were constant at RCRR
disease ratings of 0 until about 5 (when extensive rot, cracks, and
cankers affect more than 50% of the root surface) in the susceptible
(Fig. 1A and C) and partially resistant (Fig. 1B and D) cultivars;
then, index values dropped as RCRR severity increased. Early
foliar symptoms of wilting and chlorosis were not observed in
cultivars until RCRR reached values around 4. In 2008, the
relationship between OSAVI and RCRR values was similar for
both cultivars; disease was detected somewhat earlier in the par-
tially resistant (Fig. 1B) than in the susceptible cultivar (Fig. 1A)
but the difference was not statistically significant (P value =
0.3070). The “tipping point” (when vegetation index values began
to descend) was at a disease rating of around 5 for the susceptible
cultivar (Fig. 1A) and around 4 for the partially resistant cultivar
(Fig. 1B). In 2009, the susceptible and partially resistant cultivars
yielded significantly different OSAVI responses to RCRR (P value
= 0.0019), most likely due to differences in the y-intercept coeffi-
Table 2. Development of Rhizoctonia crown and root rot (RCRR) several weeks after inoculating the upper 2.5 cm of sugar beet roots with different numbers
of R. solani-infested corn kernels or inoculating crowns with various rates of infested ground barley inoculum when plants were at the 10- to 12-leaf stage in
two growing seasons
Average rating for RCRR (0-to-7 scale)
Number of corn kernels/root
Amount of ground barley inoculum (g/m of row)
1/2 2 1.5 2.2 3 3.7 4.5
Year, WAI
2 0.6 0.5 3.2 3.6 3.3 4.1 1.7 1.5 1.7 2.1 2.2 1.9 2.0 2.6 2.2 2.2
4 1.3 0.9 4.5 5.6 6.0 6.9 1.5 2.5 1.8 2.4 2.3 2.7 2.2 4.0 2.1 3.3
6 1.6 1.2 5.0 6.5 6.7 7.0 1.9 3.0 2.2 3.4 2.3 3.5 3.0 4.8 2.9 3.9
8 1.2 1.0 5.2 6.3 6.5 7.0 1.7 2.5 2.5 3.1 3.1 3.4 2.3 6.2 3.2 4.6
2 1.1 0.9 3.4 3.8 4.1 4.5 1.7 1.9 2.5 2.0 2.3 2.5
3 1.1 1.1 4.1 4.5 4.3 5.2 1.7 2.4 3.2 3.0 3.3 3.7
5 1.1 1.5 5.0 5.8 5.9 6.5 2.5 3.9 4.3 4.6 4.5 5.1
9 1.5 1.9 5.7 6.7 6.5 7.0 4.0 6.0 5.8 6.2 5.9 6.5
Scale where 0 = no visible lesions on root and 7 = root 100% rotted and foliage dead (45); each value is based on an average of 10 roots per plot, four
replicates. Plots were inoculated on 10 and 11 July 2008 and 6 July 2009 when foliage was in the 10- to 12-leaf stage; – = not inoculated. Rhizoctonia
solani-infested corn kernels were placed on the root surface about 2.5 cm below the soil line (10); various rates of infested, ground barley inoculum were
applied into crowns with a Gandy applicator (56). All plants in the four center rows of six-row plots were inoculated per treatment; control plots were not
inoculated. PR = partially resistant (‘Hilleshog 3035’ in 2008 and ‘Crystal 539RR’ in 2009) and S = susceptible (‘VanderHave 4653’ in 2008 and ‘Crystal
658RR’ in 2009).
WAI = weeks after inoculation.
Plant Disease / April 2012 501
cient and higher overall OSAVI values with the partially resistant
(Fig. 1D) compared with the susceptible (Fig. 1C) cultivar. The
tipping point occurred in the OSAVI at RCRR ratings of about 5
for both cultivars in 2009 (Fig. 1C and D).
The modified spectral ratio (mSR; Table 1; 62), selected as
representative of the narrowband indices, allowed for earlier detec-
tion of RCRR (Fig. 2) than the OSAVI (Fig. 1) but was more vari-
able. The mSR values were constant at RCRR disease ratings of 0
to 3, when foliage appeared healthy and 6 to 25% of the root sur-
face was rotted; then, mSR values decreased as disease severity
increased. In 2008, the susceptible (Fig. 2A) and partially resistant
(Fig. 2B) cultivars followed the same trends (P value = 0.1320). In
2009, the susceptible and partially resistant cultivars yielded
significantly different mSR responses to RCRR (P value < 0.0001),
with the partially resistant cultivar (Fig. 2D) having higher overall
index values than the susceptible cultivar (Fig. 2C). Changes in the
mSR generally occurred at the onset of mild wilting. The tipping
point occurred in the mSR at RCRR ratings of around 3.5 for both
cultivars in both years (Fig. 2).
Aerial imagery. One typical block (replicate) of the field trial is
shown at 3, 5, and 9 weeks after inoculation (Fig. 3A). At each
evaluation date, the filtered area (Fig. 3A, noted on the right side of
the image) covers the portion of plots where roots were removed
for disease assessment; the unfiltered area on the left was used for
reflectance measurements. Ratings for RCRR from selected plots
are provided as examples at each assessment date and include the
two cultivars (susceptible [S] and partially resistant [PR]) inocu-
lated with two R. solani-infested corn kernels (2k) and the non-
inoculated control (C). There was a low level of natural infestation
by R. solani AG 2-2 in the trial area. Because of the infrared filter
used on the camera, healthy vegetation is displayed as red; soil is
cyan or black, depending on moisture; and dead foliage also ap-
pears cyan. Pixels from plots infected with RCRR range in appear-
ance from red (healthy plants and plants with early root rot and no
foliar symptoms), to varying mixtures of red and cyan (healthy
plants and plants with root rot and foliar symptoms), to cyan (se-
verely diseased, completely dead plants). As plants wilt, more
background soil is exposed, causing part of the shift from red to
cyan; dead foliage may also be contributing to the change. It was
difficult to differentiate soil from dead sugar beet foliage except
when soil moisture was very high and the soil appeared much
darker than dead foliage. Severely diseased plots are indistinguish-
able from bare soil, validating the reflectance signatures obtained
from the ground where plots with the maximum RCRR severity
rating of seven had almost the same signature as bare soil.
Using CIR digital imagery (Fig. 3A), small patches of RCRR
were identified in inoculated plots as early as 3 weeks after
inoculation (WAI); some infected plants were evident in plots with
RCRR severity ratings as low as 3.8 but RCRR infections only
were consistently apparent in plots with increasingly higher RCRR
severity ratings. Disease severity increased in prevalence and
severity by 5 WAI and again by 9 WAI.
The OSAVI (Fig. 3B) was used to calculate a value for each
pixel from the images of the same plots as those shown in Figure
3A. Again, the area visually rated for disease is shown on the right
in a dark strip, and the area used for reflectance measurements is
on the left side of each plot. The OSAVI values range from 0 to 1,
with 0 represented as black and 1 represented as white; values
between 0 and 1 are shades of gray based on this continuum. Bare
soil and chlorotic or necrotic vegetation have low OSAVI values
and appear very dark, while healthy vegetation has a high OSAVI
value and appears very light. From 3 to 9 WAI, OSAVI values cal-
culated from canopy reflectance decreased in inoculated plots.
The aerial imagery (Fig. 3A) and derived OSAVI maps (Fig. 3B)
are very similar in illustrating the detection and development of
RCRR from 3 to 9 WAI. Furthermore, both images validated data
Table 3. Regression statistics for relationship between reflectance ranges and vegetation indices for root disease severity ratings of Rhizoctonia crown and
root rot (RCRR) on sugar beet at the University of Minnesota, Northwest Research and Outreach Center, Crookston
Order R
P value Order R
P value
Green S Linear 0.002 0.6095 Linear 0.044 0.0392*
PR Linear 0.072 0.0051* Linear 0.056 0.0203*
Red S Second 0.638 <0.0001* Second 0.625 <0.0001*
PR Second 0.618 <0.0001* Second 0.459 <0.0001*
NIR S Third 0.701 <0.0001* Linear 0.521 <0.0001*
PR Second 0.558 <0.0001* Linear 0.351 <0.0001*
DVI S Third 0.770 <0.0001* Third 0.691 <0.0001*
PR Second 0.651 <0.0001* Third 0.556 <0.0001*
SRVI S Linear 0.552 <0.0001* Linear 0.582 <0.0001*
PR Second 0.709 <0.0001* Linear 0.409 <0.0001*
NDVI S Third 0.858 <0.0001* Third 0.760 <0.0001*
PR Second 0.753 <0.0001* Third 0.637 <0.0001*
OSAVI S Third 0.856 <0.0001* Third 0.762 <0.0001*
PR Second 0.766 <0.0001* Third 0.662 <0.0001*
GNDVI S Second 0.732 <0.0001* Second 0.641 <0.0001*
PR Second 0.693 <0.0001* Second 0.584 <0.0001*
S Linear 0.553 <0.0001* Linear 0.584 <0.0001*
PR Second 0.713 <0.0001* Linear 0.411 <0.0001*
S Linear 0.545 <0.0001* Linear 0.576 <0.0001*
PR Second 0.687 <0.0001* Linear 0.471 <0.0001*
RVSI S Third 0.828 <0.0001* Second 0.717 <0.0001*
PR Second 0.667 <0.0001* Third 0.554 <0.0001*
LWI S Linear 0.558 <0.0001* Linear 0.524 <0.0001*
PR Second 0.624 <0.0001* Linear 0.256 <0.0001*
mSR S Second 0.689 <0.0001* Second 0.671 <0.0001*
PR Second 0.708 <0.0001* Second 0.585 <0.0001*
NDRE S Second 0.680 <0.0001* Second 0.657 <0.0001*
PR Second 0.627 <0.0001* Second 0.631 <0.0001*
Asterisk (*) denotes statistical significance at P values < 0.05.
Green = green reflectance, Red = red reflectance, NIR = near-infrared reflectance, DVI = difference vegetation index, SRVI = simple ratio vegetation index,
NDVI = normalized difference vegetation index, OSAVI = optimized soil-adjusted vegetation index, GNDVI = green normalized difference vegetation
index, PSSR
= pigment specific simple ratio (chlorophyll-a), PSSR
= pigment specific simple ratio (chlorophyll-b), RVSI = red edge vegetation stress
index, LWI = leaf water index, mSR = modified spectral ratio, and NDRE = normalized difference red edge.
S = cultivar is susceptible to RCRR and PR = cultivar is partially resistant to RCRR.
502 Plant Disease / Vol. 96 No. 4
obtained from the ground, which showed that OSAVI values de-
creased after RCRR severity values reached a rating of 5 and foliar
symptoms of wilting and chlorosis developed (Fig. 1).
Vegetation indices did not consistently detect RCRR until root
ratings indicated that 25 to 50% of the root surface was rotted
(RCRR = 4) and foliar symptoms of wilt were beginning to occur,
making remote sensing based-assessment of RCRR most suitable
for late-season use after infections have developed. This agrees
with previous work on other crops with various pathogens, where
root disease was not detected until visible foliar symptoms devel-
oped. For instance, Phytophthora root rot in cranberry was most
consistently detected in late-season aerial imagery (47), and take-
all of wheat was detected during the milk stage of grain develop-
ment using a normalized difference vegetation index (7). In our
research, no single narrowband or wideband index stood out as
superior for correlation with RCRR disease severity ratings, likely
because many of the indices assessed were based on similar wave-
lengths and associated with similar biophysical parameters (pri-
marily chlorophyll content). Also, differences in R
values were
negligible and most likely due to the number of wavelengths and
width of the bands used to calculate each index. All wideband indi-
ces had better overall fits with RCRR severity than the narrowband
indices because they incorporated two 15-nm-wide bands. Most of
the narrowband indices incorporate two 1-nm wavebands and had
wide variability, while the mSR incorporates three and had the
lowest variability among narrowband indices. Although averages of
the red, green, and NIR ranges were assessed, individual narrow
wavebands were not tested because we wanted the data to be tied
to our multispectral aerial imagery. Our goal was to eventually
develop a tool useful for growers and sugar companies to rapidly
assess large geographic areas for RCRR. Using individual narrow
bands would assume that they will have access to hyperspectral
imagery, which can be quite expensive.
The OSAVI and mSR indices correlated well with severity of
RCRR, and both are associated with chlorophyll content, which is
consistent with the chlorosis commonly associated with infected
plants (75). Reduced chlorophyll content has been measured in
root rots caused by Phytophthora spp. in citrus (34) and R. solani
in broad bean (39), as well as in R. solani sheath blight in rice (37).
In our research, vegetation indices associated with water content
(i.e., leaf water index) also had similar patterns when plotted
against RCRR disease severity values. Thus, it is unclear if
changes in the vegetation indices observed with higher disease
severity ratings were associated with reduction in chlorophyll
content (which appeared to occur after wilting) or with increased
soil background reflectance and reduced moisture. The close
association between reflectance in plots with high disease severity
and bare soil reflectance may indicate that changes in all
vegetation indices were influenced by increased soil reflectance
due to a wilting canopy. However, changes in some vegetation
indices occurred at the onset of visible wilting, when foliage still
covered most of the soil background. Thus, changing canopy struc-
ture due to wilting also likely plays a major role in the early
changes observed. Reflectance in the NIR is heavily affected by
leaf angle and canopy geometry (33). Most of the vegetation indi-
ces assessed in our research incorporate an NIR band. Because
wilting was the first visible foliar symptom to occur, early changes
in these indices are probably due to changes in canopy structure.
At the canopy level, spectral responses are influenced by three
main factors: physiological state of the plants, extent of canopy or
leaf cover, and spectral characteristics of soil (63). Leaf pigments
strongly absorb visible light; therefore, reflectance in this region is
low overall for healthy vegetation and increases as foliage is
stressed (5). Chlorophyll absorbs violet-blue and red light for
photosynthesis. Green light is poorly absorbed for photosynthesis;
hence, most plants appear green. The changes observed in these
visible bands of light may indicate reduced chlorophyll content in
the sugar beet canopy, resulting in chlorotic-appearing foliage.
Fig. 1. Optimized soil-adjusted vegetation index (OSAVI) values plotted against
Rhizoctonia crown and root rot (RCRR) disease ratings for A, a susceptible (S)
cultivar in 2008 (y = –0.005x
+ 0.035x
– 0.085x + 0.741); B, a partially resistant
(PR) cultivar in 2008 (y = –0.016x
+ 0.065x + 0.633); C, an S cultivar in 2009 (y =
+ 0.043x
– 0.12x + 0.78); and D, a PR cultivar in 2009 (y = –0.006x
– 0.156x + 0.82) from field trials at the University of Minnesota, Northwes
Research and Outreach Center, Crookston. Ratings for RCRR range from 0 to 7 and
are based on the amount of rot present on sugar beet tap roots, where 0 = healthy and
7 = 100% rotted and foliage is dead (45). “Tipping points”, where OSAVI values begin
to decline, were visually identified and are indicated by dashed lines.
Fig. 2. Modified spectral ratio (mSR) values plotted against Rhizoctonia crown and
root rot (RCRR) disease ratings for A, a susceptible (S) cultivar in 2008 (y =
+ 0.308x + 2.96); B, a partially resistant (PR) cultivar in 2008 (y = –0.088x
0.289x + 3.09); C, an S cultivar in 2009 (y = –0.068x
+ 0.233x + 2.76); and D, a
PR cultivar in 2009 (y = –0.087x
+ 0.316x + 3.22) from field trials at the University
of Minnesota, Northwest Research and Outreach Center, Crookston. Ratings fo
RCRR range from 0 to 7 and are based on the amount of rot present on sugar beet
tap roots, where 0 = healthy and 7 = 100% rotted and foliage is dead (45). “Tipping
points”, where mSR values begin to decline, were visually identified and are indi-
cated by dashed lines.
Plant Disease / April 2012 503
Near-infrared light is not absorbed by leaf pigments; therefore,
reflectance is high in this region and depends on internal leaf struc-
ture (29), and changes observed in this region may be due to
wilting in the sugar beet canopy. Finally, the middle-infrared
reflectance region is associated with water content (5,8). The
changes observed in this region may be due to water-stressed
sugar beet foliage, which can occur when severe RCRR restricts
transpiration. As sugar beet plants die, the canopy begins to
collapse, and canopy reflectance becomes a mixture of vegetation
and soil background.
Vegetation indices associated with chlorophyll (particularly the
OSAVI and mSR) indicated loss of chlorophyll content in foliage
as severity of RCRR increased. Although wilting is very likely
playing a role in the reflectance responses observed in the various
indices and in the aerial imagery, the strong association previously
demonstrated between these indices and chlorophyll content sug-
gests that reflectance changes observed in RCRR-infected plots
were due, in some part, to reduction in chlorophyll content (55,62).
The direct impact of RCRR on chlorophyll content in sugar beet
foliage is unknown but could be useful information in refining
value of vegetation indices associated with chlorophyll in detection
of this disease.
High OSAVI and mSR values suggested that the partially resis-
tant cultivar had higher chlorophyll content than the susceptible
cultivar in 2009 (54,62). Overall, the cultivars assessed generally
showed similar trends in their regression models, possibly indicat-
ing that the OSAVI or mSR can be used to detect RCRR in plants
with foliar symptoms of wilt and chlorosis. Our tests were limited
to four cultivars, and further experimentation is needed on a wide
range of cultivars to determine whether variability among cultivars
with various traits (e.g., leaf coloration and plant architecture)
could affect results. Also, no studies have been done to correlate
the relationship of foliar and root symptoms of RCRR on a wide
range of cultivars or germplasm (Lee Panella, personal com-
munication). Perhaps certain cultivars show foliar symptoms ear-
lier or later than those we evaluated in our trials.
Although remote sensing of RCRR is a potential alternative for
detecting the disease, rather than visual assessment of roots, it has
several drawbacks. The equipment is relatively expensive and
requires a careful operator who is familiar with the accompanying
software. Measurements must be obtained on clear, sunny days
with consistent sun angles (between 10:00 a.m. and 2:00 p.m.)
between different dates (16). Research also is needed to determine
the sensitivity of remote sensing to detect RCRR compared with
Fig. 3. Sugar beet block (replicate) at the University of Minnesota, Northwest Research and Outreach Center, Crookston shown at 3, 5, and 9 weeks after inoculation (WAI)
with Rhizoctonia solani AG 2-2 IIIB as A, color-infrared (CIR) digital imagery at 0.25-m spatial resolution and B, derived optimized soil-adjusted vegetation index (OSAVI)
maps. The masked portion of plots (noted on the right side) is where roots were removed for disease assessment, and the remaining area on the left was used for aerial
imagery. Rhizoctonia crown and root rot (RCRR) was assessed at each sampling date (10 roots per treatment per date) with a 0-to-7 rating scale (45), where 0 = healthy and
7 = 100% rotted and foliage is dead. Disease ratings are provided in the masked out area of selected plots. Treatment labels refer to example plots where S = susceptible
cultivar and PR = partially resistant cultivar; 2k = each root inoculated with two corn kernels colonized by Rhizoctonia solani; and C = noninoculated control (all other plots
were low to moderate inoculum densities and are not identified). Healthy sugar beet foliage is red in the CIR imagery and light in the OSAVI maps; soil is cyan or black in the
CIR imagery and dark in the OSAVI maps. Note that OSAVI values decrease as disease levels increase.
504 Plant Disease / Vol. 96 No. 4
other maladies that cause sugar beet foliage to become chlorotic
and, thereby, complicate and compromise its application. Examples
include diseases (Aphanomyces root rot and Cercospora leaf spot),
insects (sugar beet root maggot), nutrient deficiencies, and loss of
nitrogen from foliage as sugar beet roots mature and store sucrose.
Other factors, such as chemical inputs, could also have an impact
on reflectance from sugar beet foliage. For example, glyphosate
has been shown to reduce chlorophyll content in soybean
(46,51,52) but this phenomenon has not been studied on sugar beet
in the field.
Detection of RCRR by remote sensing occurs too late for imple-
mentation of remedial management measures (i.e., fungicide appli-
cations) but aerial imaging may be helpful in identifying areas of
possible RCRR that then can be ground truthed. This would be
particularly helpful in large fields where it is not practical to walk
the entire area looking for potential problems. This approach also
could be used to develop risk maps showing areas with potentially
high R. solani inoculum for future growing seasons. This would
allow growers to plan and implement strategic management strate-
gies to control R. solani on sugar beet and rotation crops. Further-
more, because it is a nondestructive method, multiple remote sens-
ing-based assessments could be used to determine rate of increase
in disease severity or in size of severely infected patches of fields
over time. This could be a useful tool for assessing different treat-
ments or the impact of RCRR in varying conditions in future re-
search studies.
Further research is needed to determine whether RCRR has a
unique spectral signature on a wide selection of sugar beet cultivars
and whether other root or foliar problems (biotic and abiotic) cause
a similar response in grower fields. Remote sensing based on ther-
mal infrared emissivity (12) should also be assessed as a potential
means for earlier detection of RCRR than reflectance-based remote
We thank the University of Minnesota, Minnesota Agriculture Experiment
Station, and Northwest Research and Outreach Center, Crookston and the
Sugarbeet Research and Education Board of Minnesota and North Dakota for
funding of this project; J. R. Brantner, J. Nielsen, and student workers (C.
Danielson, N. Knutson, J. Reitmeier, C. Solheim, and K. Baird) for assisting
with field and laboratory work; and R. Moon for advice on statistical analyses.
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... However, subsequent studies showed that this species causes onion leaf spots and leaf blight which had been previously described as "blast" (Lorbeer, 1992;Lorbeer et al., 2004). B. squamosa epidemics can be divided into two phases: a leaf spotting phase when the number of lesions increases slowly, and a leaf blighting phase when both airborne inoculum and lesion number increase rapidly (Carisse et al., 2008). Initial visible symptoms caused by B. squamosa are small distinct necrotic spots, approximately 2 mm in diameter, surrounded by silvery halo, which could be confused with thrips injury, downy mildew, drought, excessive soil moisture, or symptoms caused by Botrytis cinerea. ...
... B. squamosa sporulates only on senescent leaf tissues; hence, disease progress is generally slow early in the season when the majority of leaves are green. However, as leaves senesce, the sporulation potential increases rapidly, influencing the rate of disease progress (Carisse et al., 2008). ...
... It reduces yield, quality and storability of bulbs (Sutton et al., 1986;Tremblay et al., 2003). In some countries, the pathogen and its distribution in onion crops have been well described, and disease epidemiology and management options have been thoroughly studied (Ellerbrock & Lorbeer, 1977;Sutton et al., 1986;Carisse et al., 2005;2008;Van der Heyden et al., 2012). However, literature data on leaf blight in Bosnia and Herzegovina and neighboring countries, are either scarce or almost nonexistent. ...
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Over the past several decades, necrotic spots, lesions and blight symptoms have been observed on onion leaves in several locations in Bosnia and Herzegovina, where the crop is grown intensively. The type of symptoms indicated a possible infection with Botrytis squamosa, a widespread pathogen of onion. As symptoms of leaf spots and necrotic lesions can also be caused by some other biotic and abiotic factors, our research focused on identifying the causal agent of the observed symptoms. The pathogen was isolated from diseased tissue using standard phytopathological procedure and identified based on pathogenic and morphological features. Identification was confirmed by amplification and sequencing of the internal transcribed spacer (ITS) region of ribosomal DNA (rDNA). The influence of temperature and growth medium on mycelial growth rate of the isolates was also studied.
... Many effective epidemiological models have been developed to predict plant diseases based on environmental conditions that are conducive to growth and reproduction of the pathogen (Dewdney et al. 2007;Gent et al. 2013;Gilles et al. 2004;Peduto et al. 2013). One potential source of error in weather-based models is the implicit assumption of the presence of pathogen inoculum (Carisse et al. 2008). While inoculum is often present in many systems, uncertainty about the presence or absence of pathogen inoculum in specific locations and times can lead to overapplication of pesticides in the absence of the pathogen. ...
... While inoculum is often present in many systems, uncertainty about the presence or absence of pathogen inoculum in specific locations and times can lead to overapplication of pesticides in the absence of the pathogen. Monitoring for the presence of airborne spores or other pathogen inoculum can help increase the accuracy of disease forecasting models (Carisse et al. 2008). Many downy mildew pathogens spread rapidly through the production of wind-blown asexual sporangia. ...
... The integration of these tools with other disease risk indicators often increases the accuracy of the disease forecasting models by assessing the local presence of the pathogen rather than assuming presence. Detection methods have significantly advanced in recent years, from passive grease-covered glass slides to solar-powered impaction spore trap samplers linked with quantitative polymerase chain reaction (qPCR) methods, enabling detection of very low numbers of spores (Carisse et al. 2008(Carisse et al. , 2009Klosterman et al. 2014;Thiessen et al. 2016). These advances have made it possible to accurately predict disease pressure in some cases, leading to more effective and efficient disease control measures (Carisse et al. 2011). ...
Peronospora effusa is an obligate oomycete that causes downy mildew of spinach. Downy mildew threatens sustainable production of fresh market organic spinach in California, and routine fungicide sprays are often necessary for conventional production. In this study, airborne P. effusa spores were collected using rotating arm impaction spore trap samplers at four sites in the Salinas Valley between late-January and early-June in 2013 and 2014. Levels of P. effusa DNA were determined by a species-specific qPCR assay. Peronospora effusa was detected prior to and during the growing season in both years. Nonlinear time series analyses on the data suggested that the within-season dynamics of P. effusa airborne inoculum are characterized by a mixture of chaotic, deterministic, and stochastic features, with successive data points somewhat predictable from the previous values in the series. Analyses of concentrations of airborne P. effusa suggest both an exponential increase in concentration over the course of the season and oscillations around the increasing average value that had season-specific periodicity around 30, 45, and 75 days, values that are close to whole multiples of the combined pathogen latent and infectious periods. Each unit increase in temperature was correlated with 1.7-6% increased odds of an increase in DNA copy numbers, while each unit decrease in wind speed was correlated with 4-12.7% increased odds of an increase in DNA copy numbers. Disease incidence was correlated with airborne P. effusa levels and weather variables, and a receiver operating characteristic curve analysis suggested that P. effusa DNA copy numbers determined from the spore traps nine days prior to disease rating could predict disease incidence.
... in part, no doubt, to the fact that time series methods have been considered relevant mostly for multi-season contexts, and multi-season datasets are scarce in plant pathology. However, with the advent of molecular probes for studying the airborne inoculum of plant pathogens, it has become much easier to capture time series data within single growing seasons [5,6,8]. ...
... Reflecting this focus, the literature on the use of monotonic growth curve models or, more recently, compartment models consisting of linked differential equations is extensive and the methodology is well developed. In contrast, the literature on how to handle long, oscillating data series for plant pathogen populations is rather thin, with only isolated case studies [3][4][5][6][7] employing a range of statistical approaches. To date, there has been no concerted effort in botanical epidemiology to establish general properties of time series data associated with pathogen populations or disease intensity. ...
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In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.
... This is due in part, no doubt, to the fact that time series methods have been considered to be relevant mostly to multi-season data, and mult-season datasets are scarce in plant pathology. However, with the advent of molecular probes for studying the airborne inoculum of plant pathogens, it has become much easier to capture time-series data within single growing seasons [5,6,8]. ...
... Airborne concentrations of pathogen inoculum have been recently monitored using vortex (spinning rod) air samplers combined with species-specific quantitative polymerase chain reaction (qPCR), in some cases used commercially for crop management decisions. Carisse and colleagues pioneered the approach in one of the first examples of commercial use in managing fungicide applications to control Botrytis leaf blight in onion in Quebec Province, Canada [5,9,10]. This work (along with characterization of effective fungicide regimes and conducive weather conditions) helped to improve monitoring and reduce disease outbreaks in onion crops in Quebec. ...
Air sampling using vortex air samplers combined with species specific amplification of pathogen DNA, was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic dynamics. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of the pathogen abundance data, but also indicated that practicality may limit the capacity for definitively classifying the dynamics of air borne plant pathogen inoculum. Over the two years of the study five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating the pathogen abundance data were increasing or not, revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves, and also (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence is positive or not.
Sometimes plant pathologists assess disease intensity when they are primarily interested in other response variables, such as yield loss or toxin concentration in harvested products. In these situations, disease intensity potentially could be considered a surrogate of yield or toxin. A surrogate is a variable which can be used instead of the variable of interest in the evaluation of experimental treatments or in making predictions. Surrogates can be measured earlier, more conveniently, or more cheaply than the variable of primary interest, but the reliability or validity of the surrogate must be shown. We demonstrate ways of quantifying two facets of surrogacy by using a protocol originally developed by Buyse and colleagues for medical research. Coefficient-of-determination statistics can be used to conveniently assess the strength of surrogacy on a unitless scale. As a case study, we evaluated whether severity of Fusarium head blight (i.e., FHB index) can be used as a surrogate for yield loss and deoxynivalenol (DON) toxin concentration in harvested wheat grain. Bivariate mixed models and corresponding approximations were fitted to data from 82 fungicide trials conducted from 2008-2013. Individual-level surrogacy-for predicting the variable of interest (yield or DON) from the surrogate (index) in plots with the same treatment-was very low. Trial-level surrogacy-for predicting the effect of treatment (e.g., mean difference) for the variable of interest based on the effect of the treatment on the surrogate (index)-was moderate for yield, and only low for DON. Challenges in using disease severity as a surrogate for yield and toxin are discussed.
The evidential basis for disease management decision making is provided by data relating to risk factors. The decision process involves an assessment of the evidence leading to taking (or refraining from) action on the basis of a prediction. The primary objective of the decision process is to identify-at the time the decision is made-the control action that provides the best predicted end-of-season outcome, calculated in terms of revenue or another appropriate metric. Data relating to disease risk factors may take a variety of forms (e.g., continuous, discrete, categorical) on measurement scales in a variety of units. Log10-likelihood ratios provide a principled basis for the accumulation of evidence based on such data and allow predictions to be made via Bayesian updating of prior probabilities. Expected final online publication date for the Annual Review of Phytopathology Volume 55 is August 4, 2017. Please see for revised estimates.
Downy mildew is the most devastating disease threatening sustainable spinach production, particularly in the organic sector. The disease is caused by the biotrophic oomycete pathogen, Peronospora effusa, and the disease results in yellow lesions that render the crop unmarketable. In this study, the levels of DNA from airborne spores of P. effusa were assessed near a field of susceptible plants in Salinas, CA during the winter months of 2013/14 and 2014/15 using rotating arm impaction spore trap samplers that were assessed with a species-specific quantitative PCR (qPCR) assay. Low levels of P. effusa DNA were detectable from December through February in both winters, but increased during January in both years, in correlation with observed disease incidence; sharp peaks in P. effusa DNA detection were associated with the onset of disease incidence. The incidence of downy mildew in the susceptible field displayed logistic-like dynamics but with considerable inter-season variation. Analysis of the area under the disease progress curves suggested that the 2013-2014 epidemic was significantly more severe than the 2014-2015 epidemic. Spatial analyses indicated that disease incidence was dependent within an average range of 5.6 m, approximately equivalent to the width of three planted beds in a typical production field. The spatial distribution of spores captured during an active epidemic most closely fit a power-law distribution, but could also be fit with an exponential distribution. These studies revealed two important results in the epidemiology of spinach downy mildew in California. First, they demonstrated the potential of impaction spore trap samplers linked with a qPCR assay for indicating periods of high disease risk, as well as the detection of long distance dispersal of P. effusa spores. Secondly, at the scale of individual crops, a high degree of spatial aggregation in disease incidence was revealed.
A thorough understanding of the epidemiology and aerobiology of Botrytis-induced diseases is essential for the design of rational management strategies. B. cinerea and other Botrytis spp. are important pathogens of ornamental, fruit, vegetable, and legume crops, and Botrytis-induced diseases can be found in nurseries, fields, greenhouses, and in storage. Although a generic outline of the life cycle of Botrytis spp. can be drawn, there are large variations depending on the host, agricultural practices, environmental conditions, and the geographical region. A Botrytis epidemic comprises a sequence of processes (perennation and infection, colonization, conidiation, and conidia dispersal), each of which is influenced by the host and the surrounding environment. The survival of Botrytis spp. between cropping seasons and the dispersal of conidia to uninfected plants are critical phases of the Botrytis-induced disease cycle, because if either phase is prevented, the disease will be delayed or will not occur. For most Botrytis-induced diseases, the pathogen reproduces profusely and asexually by air-dispersed conidia. Hence aerobiology plays a key role in understanding the epidemiology of Botrytis spp., which have developed a variety of strategies to infect and colonize their host. With a view to the design of best management practices, this chapter reviews the epidemiology and aerobiology of some important Botrytis-induced diseases and discuss the influence of the host, the environment, and agricultural practices on disease development.
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Dollar spot of bentgrass, caused by Sclerotinia homoeocarpa, was chosen as a model pathosystem to evaluate the accuracy and precision of several disease assessment methods. Quadrats were assessed by visually estimating percent disease severity, by measuring percent reflectance of sunlight at 600 and 800 nm, and by image analysis of color photographic slides. Visual and reflectance assessments were performed by four raters and repeated 24-h later to obtain a measure of intra-rater repeatability. Linear regression of the original assessments (Y) versus the repeated assessments (X) revealed that intra-rater repeatability was highest for the 600-nm radiometric method as indicated by slope values not significantly different from 1.0 (P≤0.01) and intercepts close to and not significantly different from zero (P≤0.01) [...]
New European Union regulations as well as the market demand the traceability along the value-adding chain of substances that may become components of food and feedstuffs (EU, 2000). This makes documentation of specific field provenance increasingly important for agriculture in the future. Modern computer-based geographical information systems are able to handle, spatially interpret and display the required geographical data and so can contribute to the optimization of the value-adding chain (Burrough and McDonnel, 1998). Against this background, Südzucker and the University of Hohenheim started a cooperation project in 2002, the aim of which was the development of a user-friendly, field-specific, GIS-based sugar beet management information system (Laudien, 2005). One of the points of emphasis of the project was the detection and locating of biotic stress factors in sugarbeet fields by employing hyperspectral distance exploration sensors. To this end, data were collected at weekly intervals during the growing period with a portable field spectroradiometer from a strip trial artifically inoculated with the Rhizoctonia solani sugar beet disease (cf. Büttner et al., 2002; Bürcky, 2003). In order to ensure easy management of the very voluminous data, the processed reflection data, as well as all the additional information, were archived and displayed in a Web-based spectral database. This article describes the construction and content of this Web-based spectral database, which forms part of the sugarbeet management information system.
Rhizoctonia root rot represents an increasing problem in sugar beet production in many countries. The reasons for the spread of the disease caused by Rhizoctonia solani anastomosis group (AG) 2-2IIIB are not well understood. However, an observed increase in disease incidence and severity in regions with narrow sugar beet-maize crop rotations suggested maize as a possible host plant of A solani AG 2-2IIIB. Therefore, greenhouse trials were conducted to investigate the pathogenicity of Rhizoctonia solani on maize. Trials revealed that the pathogen established well on maize and caused distinct root rot symptoms. In a successive greenhouse experiment, Rhizoctonia-infected ground maize roots were used as inoculum source for sugar beet plants and successfully induced root and crown rot symptoms. The pathogen strains reisolated from these maize-inoculated sugar beet plants were identical in PCR analysis to those strains used to inoculate maize in the first experiment. Koch's postulates were thereby completed. Maize rootstocks were used as a further inoculum source in a crop rotation experiment in the greenhouse to test the spread of Rhizoctonia solani from maize to sugar beet. Whole maize rootstocks (infected and healthy) were buried in the center of large pots. Sugar beet was sowed around these rootstocks. Within 5, weeks, a spread of Rhizoctonia solani was observed from one crop to the other. Additionally, Rhizoctonia solani was isolated from maize plants grown under field conditions, and isolates were compared, to AG 2-2IIIB isolates from sugar beet by PCR. Isolates from maize were identified as R. solani AG 2-2IIIB. This is the first time that anastomosis group 2-2 subgroup IIIB has been described as the causal agent of crown and brace root rot on maize in Europe. These results indicate that maize as a preceding crop in rotations may keep up the inoculum potential in the soil and therefore plays an important role in the epidemiology of Rhizoctonia root rot in sugar beet.
Sugarbeet and sugarcane are the major sources of sucrose, a sweetener in a vast range of foods. Total world production of sucrose was estimated at 126,500 metric tons in 1998-1999 of which 37 percent was from sugarbeet and 63 percent was from sugarcane. This Extension Circular discusses the importance of growing sugarbeets and sugarcane in the states of Nebraska, Colorado, Montana, and Wyoming.
The responses of vegetation indices to changes in water stress were evaluated in two separate laboratory experiments. In one experiment the normalized difference vegetation index (NDVI), the near-IR to red ratio (near-IR/red), the Infrared Index (II), and the Moisture Stress Index (MSI) were more highly correlated to leaf water potential in lodgepole pine branches than were the Leaf Water Content Index (LWCI), the mid-IR ratio (Mid-IR), or any of the single Thematic Mapper (TM) bands. In the other experiment, these six indices and the TM Tasseled Cap brightness, greenness, and wetness indices responded to changes in leaf relative water content (RWC) differently than they responded to changes in leaf water content (WC) of three plant species, and the responses were dependent on how experimental replicates were pooled. -from Author