In this review, disease warning models for brown rot fungi, including Monilinia fructigena, M. laxa and M. fructicola, weresummarized. Few studies have been made to relate epidemiology and disease warning in brown rot infection caused by M. fructicola and M.laxa in order to predict infections or develop decision support models for fungicide applications during the growing season. More recently adisease warning model and a decision support system were also performed for M. fructigena for organic apple orchards. This review gives anoverview on some details of the above disease warning models and decision support system

J., Holb I.

University of Debrecen Electronic Archive

International Journal of Horticultural Science 2013, 19 (1-2): 19-22
Agroinform Publishing House, Budapest, Printed in Hungary
ISSN 1585-0404
Introduction
Today, there is little doubt that disease warning system as a 
part of plant disease epidemiology provides the key to the most 
effective approach to disease control. Plant disease epidemiology 
is the overall study of disease epidemics, the comprehensive 
analysis of the interaction among three constituents: the host, 
the pathogen and the environment (Van der Plank, 1963; 
Zadoks & Schein, 1979). Since the early nineteenth century, the 
epidemiology of fruit pathogens including Monilinia spp. has 
been studied thoroughly. Rapid development of our knowledge 
on the epidemiology of brown rot fungi started at the end of 
World War II. For fifty years, research into several parts of the 
brown rot disease cycles has been conducted and the results 
have been involved in disease management practice. For the last 
two decades, the complexity of brown rot epidemics, including 
molecular examinations, the spatio-temporal modelling 
approach, disease threshold and disease warning, has been 
involved in most epidemiological studies. 
The aim of this review paper was to summarise disease 
warning models for brown rot fungi, including Monilinia 
fructigena (Aderh. & Ruhl.) Honey, M. laxa (Aderh. & Ruhl.) 
Honey and M. fructicola (Wint.) Honey (Holb, 2003ab). 
Disease warning models
Disease warning is partly about defining the conditions 
under which a pathogen, when in contact with a susceptible 
host, can infect and become established (Zadoks & Schein, 
1979, Batra, 1991). However, disease warning schemes 
require, as their components, data on and an understanding of 
the epidemiology of the diseases and pathogens. Moreover, 
understanding the interaction of host, inoculum and 
environment is essential for devising suitable disease warning 
or decision support systems. Such a system was developed for 
fruit diseases, such as apple scab, apple powdery mildew and 
fire blight. Few studies have been made to relate epidemiology 
and disease warning in brown rot infection caused by M. 
fructicola and M. laxa in order to predict infections or develop 
decision support models for fungicide applications during the 
growing season. More recently disease warning model and 
decision support system were performed for M. fructigena for 
organic apple orchards. However, no disease warning model 
has been constructed for M. polystroma.
Luo et al. (2001a,b) developed a risk analysis system 
for blossom blight of prune caused by M. fructicola. A risk 
assessment table (Figure 1) of blossom blight was produced 
for different environmental conditions to guide the control of 
prune brown rot. 
The diagram is based on bloom stage, inoculum 
concentration, and temperature. The risks were classified into 
four levels: no risk, low, moderate, and high risk; according 
to values of relative risk. There was no risk of blossom blight 
at early bloom stage (from popcorn to full bloom) when 
temperature was below 10 °C and wetness duration was shorter 
than 4 hours (Figure 1). When wetness duration was 24 h, there 
was a high relative risk of blossom blight at 20 and 25 °C when 
inoculum potential was low or at 15 and 20 °C when inoculum 
potential was high. Fewer cases with a high relative risk occur 
at late bloom stage (from late full bloom to petal fall) than at 
early bloom stage. Only the 24-hour wetness duration at 20 °C 
with either low or high inoculum potential may be associated 
with severe blossom blight. Most conditions resulted in low 
to moderate risk of blossom blight. Luo & Michailides (2001) 
developed a similar risk analysis system for latent infection of 
prune caused by M. fructicola. Another study on phenological 
analysis of brown rot blossom blight of sweet cherry, caused 
by M. laxa, was made by Tamm et al. (1995). They investigated 
the influence of temperature and wetness duration on infection 
incidence of sweet cherry blossoms in Switzerland. They 
combined Richards and Analytis’ beta models into a nonlinear 
Disease warning models for brown rot fungi of fruit crops 
Holb, I.J.
Centre for Agricultural Sciences, University of Debrecen 138 Böszörményi St., 4032 Debrecen, Hungary
Summary: In this review, disease warning models for brown rot fungi, including Monilinia fructigena, M. laxa and M. fructicola, were 
summarized. Few studies have been made to relate epidemiology and disease warning in brown rot infection caused by M. fructicola and M. 
laxa in order to predict infections or develop decision support models for fungicide applications during the growing season. More recently a 
disease warning model and a decision support system were also performed for M. fructigena for organic apple orchards. This review gives an 
overview on some details of the above disease warning models and decision support system.
Key words: Monilinia spp., disease warning, forecasting, brown rot forecasting and management strategy, BRFMS
20 Holb, I.J.
model to describe the infection incidence as a function of 
both temperature and wetness duration. The model was used 
to predict the possible range of incidence of blossom blight 
resulting from combinations of different temperatures and 
wetness durations. 
In a recent study, Luo & Michailides (2003) developed 
a preliminary decision support model to guide fungicide 
application to reduce risk of prune fruit rot caused by 
Monilinia fructicola. The estimated percentage of branches 
with fruit rot and relative probability of leading latent 
infection to fruit rot were used to develop the decision process 
(Figure 2). Four recommendations for disease management 
were used: 
•  safe, no need of fungicide application in the season; 
•  wait, continue to investigate latent infection; 
•  check reference of historical weather to decide if 
fungicide application is needed; 
•  and spray fungicide immediately.
In a more recent study Holb et al. (2011) prepared a 
brown rot forecasting disease management strategy for M. 
fructigena for organic apple orchard. In this five-year-study, 
temporal development of brown rot on fruits was analysed 
in two organic apple orchards. Disease variables of Y
f
, the 
final disease incidence; b relative rate of disease progress; 
AUDPC
S
, standardized area under disease progress curve; 
T
1.5
, is the time when disease incidence reaches 1.5% (day), 
and M, is the inflection point were derived from the three-
parameter logistic function. The disease variables of Y
f
, b, and 
AUDPC
S
 were used in a computer simulation for predicting 
temporal brown rot development, and the disease variables 
of T
1.5
, M, and Y
f
 were used to determine threshold values 
for epidemic intensity. Then these were used to construct a 
fundamental model for developing a brown rot forecasting and 
management strategy (BRFMS, Figure 3). The fundamental 
model contained four parts i) data insertation and analyses by 
computer simulation of pathogen submodels, ii) calculation 
of yield loss threshold levels based on disease incidence, 
iii) determination epidemic intensity levels and iv) decision 
modul with suggestions of disease management practices for 
each epidemic intensity level. 
Centre of the model is the computer simulated pathogen 
submodels, which requires insertation of field data and data 
analyses methods for prediction purposes. Collected data 
form the field of a prediction year (weekly assessed fruit 
brown rot incidence, weather parameters) and analyses 
methods inluding mathematical functions and derived 
parameters (AR6 model for spore dispersal; correlation 
coefficients for weather parameters; three-parameter 
Gompertz function for relationship between spore dispersal 
and corresponding disease incidence, maximum values 
of AUDPC
S
, Y
f
 and b derived from temporal brown rot 
progress curves) need to be inserted into the computer-based 
simulation program (Fig. 3). A preliminary three-parameter 
logistic function is generated by the simulation program 
after the fourth assessment date, and AUDPC
S
, Y
f
 and b are 
calculated from the function for the given date. Then these 
calculated values of AUDPC
S
, Y
f
 and b should be compared 
with the given maximum values inserted into the simulation 
program at the beginning. If the calculated values reach the 
given values, then three threshold values of the brown rot 
development can be calculated: i) the first threshold value is 
the time when disease incidence reaches 1.5% (T
1.5
), ii) the 
second threshold value is the inflection point (M), and iii) 
the third threshold value is the final disease incidence (Y
f
). 
Each threshold value corresponds to an epidemic intensity 
level which ranges between lower asymptote (Y
0
) and T
1.5
 for 
the first level (beginning of disease development), between 
T
1.5
 and M for the second level (increasing phase of disease 
Bloom Inoculum Temperature Wetness duration 
(h)
 
stage potential (°C) 4 8 12 16 20 24
popcorn lowx 10       
to full  15       
bloom  20       
  25       
 high 10       
  15       
  20       
  25       
late full low 10       
bloom to  15       
petal fall  20       
  25       
 high 10       
  15       
  20       
  25       
 No risk
Low risk
 Moderate risk
 High risk
Figure 1 Relative risks of blossom blight of prunes caused by Monilinia 
fructicola in relation to different bloom stages, inoculum potentials, 
temperatures, and wetness durations (adapted from Luo et al., 2001b)
E
st
im
at
ed
 p
er
ce
nt
-
ag
e 
of
 b
ra
nc
he
s 
w
ith
 
fr
ui
t r
ot
 Relative probability of leading latent infection to 
fruit rot
<50 50-80 81-90 >90
<1.0     
1.0-5.0     
5.1-10.0     
>10.0     
 
Safe, no fungicide application is needed
Wait, continue to investigate latent infections
 Refer to historical weather to determine 
necessity of fungicide application
 
Spray fungicide immediately
Figure 2 Diagram of decision process used in the decision support model for 
fungicide application to reduce risk of prune fruit rot caused by Monilinia 
fructicola (adapted from Luo & Michailides, 2003)
21Disease warning models for brown rot fungi of fruit crops
development), and between M and Y
f
 for the third level (final 
phase of disease development). 
An essential threshold and epidemic intensity part of 
the BRFMS is the timing of first spray against fruit rot by 
predicting the occurrence of first infected fruit on the tree (Fig. 
4). In the first step, assessment of first infected fruit on the 
orchard floor need to be inserted into the pathogen submodel 
then according to the method of Holb and Scherm (2007) 
on lag period of AUDPC between fruit rot incidence on the 
orchard floor and on the tree need to be applied to predict first 
symptom appearance (T
0
) on the tree. The predicted T
0
 can 
be used in the first level of epidemic intensity and time the 
first fungicide spray against brown rot. With this approach, 
5-10 days prediction of appearance of first symptoms can be 
achieved.
The success of BRFMS in the three epidemic intensity 
levels is largely dependent upon the prediction of insect 
injury (Fig. 3). In our model, counts of codling moth adults 
from pheromone trap are suggested to collect as basic data 
set. From this data insect injured fruit can be predicted by 
regression equations express relationship between injured 
fruit and trapped moth and then actual brown rot incidence 
Computer-based simulation program: 
pathogen submodel
Computer calculated 
AUDPC S , Y f ,  
Given AUDPC S , Y f ,  
(max.)!
level 1: 
between Y 0  and T 1.5 
level 2: 
between T 1.5  and M 
Fit three-parameter logistic function
level 3: 
between M  and Y f
Detection of first fruit infection 
on the orchard floor
Application of lag period AUDPC 
(Holb and Scherm, 2007)
5-10 days earlier prediction of 
T 0 on the tree 
7-14 days earlier prediction of 
brown rot infected fruit
Number of injured fruit
Monitoring of codling moth by 
pheromone trap
Relationship calculated between 
injured fruit and trapped moth
(Holb and Scherm, 2008)
BROWN ROT SPECIFIC ANALYSES METHODS:
i) AR(6) model for spore dispersal,
ii) Weather-spore dispersal correlation coefficients,
iii) Gompertz-function for spore-symptom relationship,
iv) AUDPC S , Y f ,  (max.).
BASIC DATA:
i) weekly assessed fruit brown rot incidence, 
ii) weather parametersassessed. 
Threshold value
Epidemic intensity
Decision modul with disease management practices
level 1 (lag phase): 
- timing of first fungicide spray
- insect control based on injury prediction
- removal of dropped fruit according to 
  Holb and Scherm (2007)
level 2 (log phase): 
- insect control based on injury prediction
- fungicide spray based on prediction
- weekly removal of infected fruit from the
  tree and orchard floor
level 3 (terminal phase): 
- insect control based on injury prediction
- intensive fungicide spray to reduce
  postharvest rot
- removal of infected fruit
Prediction of occurrence 
of first fruit rot symptom
Prediction of insect
 injury on fruit
Relationship calculated between 
injured fruit and rotted fruit
(Holb and Scherm, 2008)
M Y fT 1.5 
Figure 3 A brown rot forecasting and management strategy (BRFMS) against Monilinia fructigena for organic apple orchards. Explanations: AR6 
model: autoregressive model with six autoregressive parameters, AUDPC: area under the disease progress curve (%-days) AUDPC
S
: standardized 
area under the disease progress curve (%-days), Y
f
: is the final disease incidence (%), b: is the relative rate variable (day-1), T
0
: is the time when the fruit 
first symptoms occurs on the tree (day), T
1.5
: is the time when disease incidence reaches 1.5% (day), and M: is the inflection point, measured in days. 
(Adopted from Holb et al., 2011)
22 Holb, I.J.
can achieved by applying regression equations of Holb and 
Scherm (2008) for the relationship between injured fruit and 
brown rot incidence. With this approach, 7-14 days earlier 
prediction of a newly infected, injury related fruit can be 
achieved in all epidemic intensity levels.
Then, in the decision modul, brown rot management 
practices can be suggested throughout the three of epidemic 
intensity levels (Fig. 3). In the period of the first epidemic 
intensity level, timing of first fungicide spray, preventive insect 
spray programs based on injury prediction can be applied as 
well as removal of dropped fruits according to the study of 
Holb and Scherm (2008). In the period of the second epidemic 
intensity level, fungicide spray based on model prediction, 
insect spray programs based on injury prediction and weekly 
removal of infected fruits from the tree and from the orchard 
floor is needed. In the period of the third epidemic intensity 
level, continuous insect spray programs followed based on 
injury prediction, intensive brown rot spray programs applied 
to reduce postharvest rot and removal of infected fruits can be 
sufficient for brown rot management.
In a three-year field evaluation, season-long application 
of BRFMS treatments reduced the number of sprays against 
brown rot by 22.2-33.3% compared with the treatments of 
general spray schedules against brown rot.
Acknowledgements
This research was partly supported by grants of the Hungarian 
Scientific Research Fund (K78399) and the NKTH-
OM-00227/2008 as well as by a János Bolyai Research 
Fellowship awarded to Imre J. Holb..
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Disease warning models for brown rot fungi of fruit crops

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Disease warning models for brown rot fungi of fruit crops

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