Modelling the effect of cuticular crack surface area and inoculum density on the probability of...

Plant Pathology (2009) 58, 1021–1031 Doi: 10.1111/j.1365-3059.2009.02121.x

Modelling the effect of cuticular crack surface area andinoculum density on the probability of nectarine fruitinfection by Monilinia laxa

C. Giberta, J. Chadœufb, P. Nicotc, G. Vercambrea, M. Genarda and F. Lescourreta*aINRA, Unite Plantes et Systemes de Culture Horticoles, UR 1115, Domaine Saint-Paul, Site Agroparc, Avignon Cedex 9, F-84914;bINRA, Unite de Biometrie, UR 546, Domaine Saint-Paul, Site Agroparc, Avignon Cedex 9, F-84914; and cINRA, Unite de Pathologie

Vegetale, UR 407, Domaine Saint-Maurice, Montfavet Cedex, BP 94, F-84143, France

The effects of cuticular crack surface area and inoculum density on the infection of nectarine fruits by conidia of Monilinia

laxa were studied using artificial inoculations with conidial suspensions and dry airborne conidia during the 2004 and 2005

seasons, respectively. Additionally, the effect of ambient humidity on fruit infection was evaluated in the 2005 experiment.

An exploratory analysis indicated that (i) ambient humidity did not significantly explain the observed variability of data, but

that (ii) the incidence of fruit infection increased both with increasing inoculum density and increasing surface area of cuticu-

lar cracks. The product of these two variables represented the inoculum dose in the cracks, and was used as a predictor of

fruit infection in the model. Natural infection in the orchard was observed to increase throughout the season in both 2004

and 2005. The relationship between the probability of fruit infection by M. laxa and the artificially inoculated dose in the

cuticular cracks was well described by a logistic regression model once natural inoculum density was taken into account

(pseudo R2 = 65%). This function could be helpful for estimating the risk of fruit infection at harvest based on fruit size and

natural inoculum density.

Keywords: cuticular cracks, inoculum density, logistic regression, Monilinia laxa, peach

Introduction

Brown rot, one of the main polycyclic diseases in stonefruits, can cause peach crop losses of as much as 30–40%(Lichou et al., 2002). This disease occurs both in the orch-ard and after harvest. Three species of Monilinia are rec-ognized as causal agents of brown rot on stone fruits (Gellet al., 2007): Monilinia fructigena, which occurs fre-quently in European stone and pome orchards, M. fructi-cola, which is mainly distributed on the American, Asianand Oceanian continents, and M. laxa, which is the mostcommon species in European and South African peachorchards. Studying the interactions between M. laxa andpeach fruits under the influence of various cultural prac-tices could lead to a reduction in fungicide use and thus toeconomical and environmental benefits.

The life cycle of M. laxa is composed of three mainphases (germination, sporulation and dissemination)which are governed by climatic conditions and the sus-ceptibility of the host (Byrde & Willetts, 1977; Gradzielet al., 1994; Mari et al., 2003). The fungus overwinters

*E-mail: [email protected]

Published online 20 July 2009

ª 2009 The Authors

Journal compilation ª 2009 BSPP

in mummies and cankers, and when climatic conditionsare favorable, it sporulates on these infected organs,producing primary inoculum for the infection of blos-soms. The secondary inoculum results from the infectedfruits of the current season. The disease is transmittedfrom infective to susceptible organs by conidia. Conidiaof Monilinia sp. are mainly dispersed by air currents(Corbin et al., 1968), but insects have also been shownto play a significant role in conidial dispersal of M. vac-cinii-corymbosi in blueberry fields (Cox & Scherm,2001). Splash dispersal of conidia by water droplets isone of the other mechanisms of conidial dispersal(Byrde & Willetts, 1977; van Leeuwen et al., 2000).Conidia are carried to the fruits either in clusters or assingle cells (Fourie & Holz, 2003a). Inoculation ofpeach fruits by M. laxa can thus be simulated by spray-ing conidial suspension or dusting airborne conidia ontothem.

Direct penetration of M. laxa conidia through an intactcuticle has never been observed [Nguyen-The, 1991; Fou-rie & Holz, 2003a (nectarine), 2003b (plum)]. Conidialinfection has been described occurring through stomata,lenticels, wounds (mainly caused by insects and birds) orcuticular cracks (Byrde & Willetts, 1977; Xu & Robin-son, 2000; Xu et al., 2001b). Cuticular cracks are likely

1021

1022 C. Gibert et al.

to play a major role in fungal infection (Nguyen-The,1991; Fourie & Holz, 2003a). Cuticular cracks occurwhen the fruit growth rate is maximal, a few weeks beforeharvest, although they can also result from fruit-to-fruit,fruit-to-leaf or fruit-to-twig contact (Michailides &Morgan, 1993, 1997). Cuticular crack density wasshown to mainly vary with the intensity of fruit growth,itself varying with fruit crop load and irrigation regimes(Milad & Shackel, 1992; Peet & Willits, 1995; Knoche etal., 2004; Peschel & Knoche, 2005; Knoche & Peschel,2006; Gibert et al., 2007). Cuticular cracks can representmore than 10% of the fruit surface area (Gibert et al.,2007), providing great opportunity for fungal infection.

Many studies have been conducted to evaluate theimpact of environmental factors (air temperature and rel-ative humidity) on the various steps of the life cycle of M.laxa. Some models were developed to describe germina-tion rate as a function of time and temperature (Tamm &Fluckiger, 1993; Tamm et al., 1995), or sporulation inrelation to temperature and relative humidity (Luo et al.,2001; Lalancette et al., 2003). Conidia of M. laxa wereshown to germinate optimally in vitro when the ambientrelative humidity was above 97% and the temperaturebetween 22 and 25�C (Byrde & Willetts, 1977; Tamm &Fluckiger, 1993; Xu et al., 2001a). Few studies have beenconducted on the development of M. laxa in relation to itshost. However, information on direct interactionsbetween the pathogen and the host surface is needed, asgermination of M. laxa conidia on fruits occurs only ifthey reach susceptible host tissue (cuticular cracks). Fur-thermore, modelling such interactions provides a toolwith which to explore disease dynamics and optimizemanagement strategies (Kropff et al., 1995). This newapproach has begun to be adopted in studies on the epide-miology of M. fructigena on apple fruits (Xu & Robin-son, 2000), on fruit scab incidence (Xu & Robinson,2005) and on powdery mildew in grapevine with themodel of Calonnec et al. (2008). In these models, infec-tion was driven by climatic parameters (temperature,wetness duration) and ⁄ or temporal plant variables (fruitmaturity, wound age, leaf age, decay rate of susceptibil-ity, fruit age). Concerning the interaction betweenM. laxa and peach fruits, the risk of contamination byM. laxa was first formulated by Arnoux (1979) as theproduct of the inoculum density of the pathogen and theentry surface available on its host for given climatic con-ditions. In his mathematical model describing theoreti-cally the spatiotemporal development of M. laxa in apeach orchard, Huang (2003) also suggested that thedriving force of infectivity was the set of conidia likely togerminate, i.e. in a first simplification, those presentinside the cuticular cracks.

Based on this assumption, the main objective of thepresent work was to study the occurrence of M. laxa onnectarine fruits, grown under various crop loads, in rela-tion to inoculum dose in cuticular cracks. The first aimwas to determine whether both cuticular crack surfacearea and inoculum density on the fruit positively and sep-arately influenced brown rot incidence. To this end were

set up: (i) field experiments in 2004 and in 2005 with vari-ous crop loads to obtain contrasting fruit growth curvesand different amounts of cuticular cracking and (ii) artifi-cial-inoculation experiments performed with conidialsuspensions in 2004. A second objective was to assess theinfluence of the microclimate of the cuticular cracks onthe occurrence of M. laxa infections on fruits. This wasstudied in 2005 by combining artificial inoculations (dryairborne conidia) with exposure of the fruits to differentambient relative humidities by means of salt solutions.The data were used to model the probability of fruitinfection.

Materials and methods

Plant material and experimental design

Experiments were conducted in 2004 and 2005 on orch-ard-grown cv. Zephir (late-maturing) nectarine trees,grafted on Grande-Ferrade 305 (GF305) rootstock andplanted in 1998. They were performed at the NationalInstitute of Agronomical Research (INRA) in Avignon(south-eastern France 43.9�N, 4.8�E). Trees were goblet-trained and received routine horticultural care, includingwinter pruning and daily irrigation by microjet sprin-klers. Crop protection measures were performed, but notagainst Monilinia sp. so that the fruits would remain sus-ceptible for use in artificial-inoculation experiments.Fruit growing conditions varied as a result of differentcrop loads, yielding a total of four treatments.

Time was expressed in degree days after full bloom(dd) so that data from the 2004 and 2005 seasons couldbe considered on the same basis. Degree days were calcu-lated from daily minimal and maximal temperatures,extracted from climatic databases, according to the singlesine method with lower and upper temperature thresh-olds at 7�C and 35�C, respectively, for peaches (Zalomet al., 1983; DeJong & Goudriaan, 1989). Degree-daysdata were cumulated from full bloom. Full bloomoccurred on 22 March in 2004 and on 28 March in 2005.

The first experiment was performed on fruits grown on20 trees (trunk cross-sectional area = 38 cm2) in 2004,where a high (HC-04) and a low crop load (LC-04) wereapplied at 239 dd (3 May 2004) with 400 and 120 fruitsper tree, respectively. Each treatment was composed of10 trees. This experiment was repeated in 2005, with twolevels of crop load applied at 267 dd (4 May 2005) on 10trees per treatment. For the high (HC-05) and low crop-load (LC-05) treatments, 1000 and 120 fruits per treewere left on, respectively.

Fruit characterization and artificial inoculation

In 2004, sets of 20 symptomless fruits per field treatment(two per tree) were harvested three times, at 1587, 1720and 1845 dd (harvest), for artificial-inoculation experi-ments. In 2005, sets of 80 symptomless fruits per fieldtreatment (eight per tree) were harvested three times,at 1563, 1677 and 1802 dd (harvest), for artificial

Plant Pathology (2009) 58, 1021–1031

Modelling nectarine brown rot 1023

inoculation under various ambient relative humidities.The cuticular crack density per fruit was evaluated usingthe relationship developed by Gibert et al. (2007) betweennectarine fresh mass and crack density (see below) afterweighing fruits individually with a Mettler PM 4600balance, 10)2 g; Mettler-Toledo S.A. To evaluate fruitsurface area, diameters (cheek, suture and height) weremeasured with a digital calliper gauge (Mitutoyo 500-181 U, 10)5 m). Assumed to be an ellipsoid, fruit surfacearea was computed with MAPLE V

� release 4 (Maplesoft).In 2004, the day following harvest, fruits were surface-

sterilized by rinsing (i) in a 2% sodium hypochlorite solu-tion for 2 min and (ii) with sterile water, to optimize theeffect of further artificial inoculation. Fruits were driedwith blotting paper and packed on a table sterilized withethanol. A stock suspension of conidia was prepared bydipping conidia sampled with a paintbrush from a freshlysporulating mummy into distilled water, containing glyc-erine at 0.29%, to disperse conidia. The mummies werekindly provided by Vincent Mercier (INRA Gotheron)and prepared by inoculating fruits in a preliminary step.The concentration of conidia in the stock suspension wasadjusted to 4 · 105 conidia mL)1 using counts from ahaemocytometer. This stock suspension was then dilutedto obtain suspensions of 4 · 104 and 4 · 103 conidiamL)1. The control treatment consisted of distilled watercontaining glycerine at 0.29%. For each spore concentra-tion, including the control treatment, five fruits weresprayed one by one. For each fruit, the sprayer was main-tained at a constant distance for which no spore suspen-sion losses were noticed. To estimate the amount ofinoculum deposited on the surface of each fruit (no. ofspores cm)2), the beaker containing the spore suspensionwas weighed after each spray. The fruits were then placedin cradle divider trays. Each tray was covered with cello-phane to foster conidial germination and to avoid exter-nal contaminations. The trays were then incubated in aroom where relative humidity and air temperature wererecorded (Sefram Log 1520; Radiospares) and rangedfrom 50 to 80% and 20 to 30�C, respectively, during theexperiment.

In 2005, the experiment was repeated with sets of 80symptomless fruits per field treatment. The postharvesttreatment of the fruits was modified from that of 2004 toallow incubation in a controlled-humidity environmentafter inoculation. The three differences were as follows:(i) the step of surface-sterilization was not carried out toavoid adding humidity to the micro-environment of thecuticular cracks; (ii) the spore suspension was replaced bydry inoculum, and (iii) following inoculation, the fruitswere placed inside plastic boxes, where relative humiditywas controlled using salt-saturated solutions of K2CO3,NaCl and K2SO4 to maintain the relative humidity at43%, 75% and 98%, respectively (OIML, 1996). Thefruits collected from the orchard were inoculated inbatches of 10 (five fruits each from plots with high andlow crop loads). Each batch was arranged on a tray (sur-face-disinfested with ethanol) placed under a spore-settling tower (1 m high and 0.25 m in diameter).

Plant Pathology (2009) 58, 1021–1031

Conidia were blown into the tower from a 3.1-cm2 disc offreshly sporulating mummified fruit for 2 min with airfrom a pressure pump (Rena 301, 325 L h)1) and the lidin the ceiling closed. The conidia were then allowed to set-tle for 5 min. Different levels of inoculum pressure wereapplied, by using one, two or three discs of mummifiedfruit. The amount of inoculum actually deposited per unitof surface was estimated by placing a Petri dish contain-ing potato dextrose agar medium among the fruits on thetray and by counting the number of conidia per cm2 atseveral locations (five to 11) on the Petri dish in an areacorresponding to a glass side (18 · 18 mm) for eachbatch of inoculum density. The number and percentageof germinated conidia were determined after 24 h incu-bation at 22�C. Fruits for the control treatment were notsprayed. Following inoculation, the fruits were placed inplastic boxes on mesh wire grids above saturated saltsolutions, and the lid of the box closed. All the boxes wereincubated at 22�C under a 12 ⁄ 12-h light ⁄ dark regime.

The artificially inoculated conidial density rangedbetween 35 and 13 737 conidia cm)2 in 2004 andbetween 9 and 3088 conidia cm)2 in 2005.

For both years, disease incidence was rated for eachfruit as 0 for symptomless fruits and as 1 for fruitsinfected by M. laxa (visible brown rot lesions) 3–4 daysafter incubation. When no conidia were found on thePetri dish, it was assumed that the artificial inoculationfailed and the fruits from the same inoculation batch werenot taken into account.

Estimation of cuticular crack density

In 2004 and 2005, the cuticular crack density per fruitwas not assessed from direct observation on fruits whichwere inoculated, lest this step would disturb the host–par-asite interaction. Rather, it was estimated for each fruitaccording to the relationship (Eqn 1) established betweenthe fruit fresh mass (X, g) and the cuticular crack density(Y, %):

either Y ¼ 0 if X < a1k

or Y ¼ b1k � ðXk � aÞ

� �1k

if X � a1k ð1Þ

with k = 0.41, a = 5.396 (± 0.086 [gk]) and b = 0.635(± 0.024 [(% ⁄ g)k]); details in Gibert et al. 2007). Foreach fruit, the cuticular crack surface area was calcu-lated by multiplying the calculated surface area by theestimated cuticular crack density.

Data analysis

The data analysis was two-tiered. First, an exploratoryanalysis was performed to identify, year by year, whichfactors had the largest influence on infection, among thepossible explanatory variables. The following factorswere tested: ambient relative humidity (only for 2005);and the product of cuticular crack surface area and inocu-lum density, i.e. the inoculum dose in the cuticular cracks,in order to select the most relevant for inclusion in a


model. It needed to be established that each variable ofthe product had a separate influence on infection. Theexplanatory variables were either discrete (e.g. ambientrelative humidity for the 2005 dataset) or continuous(e.g. inoculum dose in cuticular cracks, cuticular cracksurface and artificially inoculated conidial density). In thecase of discrete variables, the proportions of symptomlessand infected fruits were represented by histograms inrelation to the levels of the variable. Theoretical andobserved frequencies of symptomless and infected fruitsamong these levels were compared using the v2-test. Inthe case of continuous variables, symptomless andinfected data were graphically represented by the proba-bility density function of the tested variable. The corre-sponding means of the variable were compared by using aStudent’s test. The following notations N.S., *, ** and ***

were used to designate no significance, P £ 0Æ05,P £ 0Æ01 and P £ 0Æ001, respectively. The data wereanalysed using the R-language (version 2.2.1) statisticspackage (http: ⁄ ⁄ www.R-project.org).

In a second phase, a model was developed of the pro-portion of infected fruits driven by the variables selectedat the previous step. Logistic regression, a generalized lin-ear model well adapted to binary data (Chambers &Hastie, 1991) and based on the logit transformation ofthe studied response, was used.

As several model functions were tested, the AkaikeInformation Criterion (AIC) was used to compare themodels which were not nested. The models were alsoevaluated by comparing their pseudo R2 (also calledMcFadden R2; McFadden, 1974) coefficient values; thiscoefficient quantifies the improvement of fit for a pro-posed model over a reference model which only includesa constant. Pseudo R2 was calculated as:

Pseudo R2 ¼ 1��2LL1

�2LLo

where LLo corresponded to the log-likelihood of thereference model and LL1 to the log-likelihood of themodel constructed with the predictors. The deviancewas defined by the positive number )2LL. LL1 isequal to 0 for a perfectly explicative model, i.e. onethat accurately fits the data, and pseudo R2 is equal to1 in this case.

The likelihood-ratio test was used to compare hierar-chically nested models. The formula of the likelihood-ratio test is RV = 2(LVNC – LVC), where LVNC and LVC

are the logarithmic value of likelihood function whichmaximizes the reference model and the model con-structed with the predictors, respectively. This statistichas a v2-distribution with degrees of freedom correspond-ing to the difference between the degrees of freedom ofthe nested models. If the calculated statistical value ishigher than the v2-critical value, the restraints of themodel are rejected.

A sensitivity analysis was performed in order to assessthe model’s response to variations of (i) input variables(inoculum density and fruit fresh mass) and (ii) parame-ters a and b in the equation predicting the cuticular crack

density according to fruit fresh mass (Eqn 1). First, theinoculum density and the cuticular crack surface areawere varied and the corresponding probabilities of infec-tion calculated. Five inoculum densities were tested: 250,500, 750, 1000 and 1500 conidia cm)2. Seven fresh fruitmasses were tested, corresponding to the seven Europeancommercial grades (4A, 3A, 2A, A, B, C and D; EuropeanUnion, 2004). Fruit surface areas and cuticular crack den-sities were calculated from fresh fruit mass with the helpof an allometric relationship (Gibert et al., 2007) and Eqn1, respectively. The cuticular crack surface area was theproduct of fruit surface area and cuticular crack density.

Secondly, a and b had a low variability, so a localapproach was adopted. The method consisted of varyinga and b (Eqn 1), one at a time, by )10% and +10%around the estimated parameter and observing the effectof each modification on the model intermediate variable(cuticular crack density) and output (probability of infec-tion in this case). The effect was quantified by means of acoefficient of elasticity (CE; Link & Doherty, 2002)defined as:

CExv¼

pv�pi

pi

�xv�xi

xi

where pv and pi are outputs corresponding to the basicparameter xi, and to the basic input plus or minus a10% variation, xv, respectively. A value of CE higherthan one indicates that the variation of the model out-put is higher than the variation of the tested modelparameter. A positive value of CE indicates that theyvary in the same way. The symbols ap, am, bp andbm stand for parameter a plus and minus 10%, andfor parameter b plus and minus 10%, respectively.

Results

Exploratory analysis

Some fruits were found infected in the non-inoculated(control) treatments in both 2004 and 2005. The proba-bility that non-inoculated fruits were infected by M. laxaincreased from less than 0.1 at about 1600 dd to 0.5 ormore at about 1800 dd (Fig. 1). There was a higher vari-ability in natural infection in 2004 than in 2005. Naturalinfection by M. laxa increased with fruit maturity forboth years, which could be explained by the parallelincrease in cuticular crack surface area. This could also beattributed to the increasing natural inoculum concentra-tion in the orchard.

For artificial-inoculation and control treatments in2005, ambient humidity did not significantly influenceobserved disease incidence (v2-test, P > 0.05; Fig. 2). Thisvariable was thus not further considered for modellingthe probability of fruit infection.

The synthetic (and biologically meaningful) explana-tory variable for the model was the product of the artifi-cially inoculated conidial density and the cuticular cracksurface area, i.e. the dose of artificial inoculum in thecuticular cracks. The mean values of the product were

Plant Pathology (2009) 58, 1021–1031

43% 75% 98%

Control

Rel

ativ

e fr

eque

ncy

Ambient relative humidity43% 75% 98%

Artificial inoculation

Rel

ativ

e fr

eque

ncy

0

20

40

60

80

0

20

40

60

80

Figure 2 Histograms of the relative frequency of Monilinia laxa-

infected (dark grey) and symptomless (light grey) nectarine fruits

in relation to the ambient humidity observed in 2005 for control (top)

and artificial inoculation (bottom) treatments.

1500 1600 1700 1800 1900

0

0·2

0·4

0·6

0·8

1·0

Degree-days after full bloom

Pro

port

ion

of fr

uits

infe

cted

by

Mon

ilini

a la

xa 20042005

Figure 1 Mean evolution of natural infection by Monilinia laxa on

non-inoculated necatrine fruits in 2004 and 2005. Points joined by

solid lines represent the proportion of infected fruits at a given date

and segments represent the standard errors.


2626Æ2 (±735Æ1) in 2004 and 321 (±60Æ1) in 2005 forsound fruits, and 29 144 (±6212Æ6) in 2004 and 7968(±1316Æ4) in 2005 for infected fruits, i.e. much higher for

Plant Pathology (2009) 58, 1021–1031

infected fruits (Student’s test, P £ 0Æ001 in 2004 and2005).

An important step was to ensure that each explanatoryvariable of this product was involved in brown rot inci-dence, i.e. that there was no bias from using the inoculumdose in the cracks. The inoculum density per unit fruitsurface area was different between sound and infectedfruits for the artificial inoculation treatments both in2004 and 2005. Mean values were 1425Æ4 (±357.7) and3094Æ2 (±554.3) conidia cm)2, for sound and infectedfruits, respectively, in 2004, and 510.2 (±49.0) and 815.2(±90.8) conidia cm)2, respectively, in 2005. The differ-ences between sound and infected fruits were significantboth in 2004 and 2005 (Student’s test, P £ 0.01 in 2004and 2005).

The cuticular crack surface area differed betweensound and infected fruits whatever the season and treat-ment (Fig. 3). Mean values were 3Æ7 (±0.8) and 2Æ2 (±0.4)cm2 (control and artificial inoculation treatments, respec-tively) for symptomless fruits vs. 16Æ2 (±2.2) and 11Æ8(±0Æ9) cm2, respectively, for infected fruits in 2004; and 1(±0Æ2) and 1 (±0Æ1) cm2 vs. 9Æ4 (±0Æ9) and 8.1 (±0Æ6) cm2 in2005. The differences between symptomless and infectedfruits were all significant (Student’s test, P £ 0Æ001 forcontrol and artificial-inoculation treatments in 2004 and2005). It was noticed that, regardless of year or treat-ment, as long as the cuticular crack surface area waslower than 5 cm2, fruits were much more frequentlysymptomless than infected.

As cuticular crack surface area and inoculum densityseparately influenced brown rot incidence, the synthetic(and biologically meaningful) explanatory variabledefined as the inoculum dose in the cuticular cracks wasretained for the model.

A model describing the relationship between fruitinfection and inoculum dose in cuticular cracks

A first logistic regression model was fitted separately tothe data for each of 2004 and 2005 seasons, using asan independent variable the estimated inoculum dosein the cuticular cracks. The estimate was computed asthe product of the artificially inoculated inoculumdensity per unit of surface (dco.ai; conidia cm)2), thecuticular crack density (dck.f; cm2 cm)2) and the fruitsurface area (Sf; cm2):

log itðPÞ ¼ a� dco:ai � dck:f � Sf þ j ð2Þ

with P the probability that a fruit was infected byM. laxa, and a and j model parameters differing forthe 2004 and 2005 datasets.

This simple model improved the reference model by27.03% only (AIC = 502.87). A probable explanation ofthis result could be that the variable natural infection(Fig. 1) was not taken into account at this stage.

The first logistic model (Eqn 2) was thus modified toinclude an effect of the natural infection of fruits in theorchard as a function of time, in accordance with the

0·00

0·05

0·10

0·15Control − 2004

Pro

babi

lity

dens

ity

0·00

0·05

0·10

0·15

Artificial inoculation − 2004

Pro

babi

lity

dens

ity

0·00

0·10

0·20Control − 2005

Pro

babi

lity

dens

ity

5 10 20 30

5 10 20 30

5 10 20 30

5 10 20 30

0·00

0·10

Artificial inoculation − 2005

Cuticular crack surface area (cm2)Cuticular crack surface area (cm2)

Cuticular crack surface area (cm2) Cuticular crack surface area (cm2)

Pro

babi

lity

dens

ity

Figure 3 Probability density of cuticular

crack surface area (cm2) for Monilinia laxa-

infected (solid line) and symptomless

nectarine fruits (dotted line) for control and

artificial inoculation treatments in 2004 and

2005.


observed increase of natural infection with fruit develop-ment (Fig. 1):

log itðPÞ ¼ a� ðdco:ai þ dco:niðtÞÞ � dck:f � Sf þ j ð3Þ

with dco.ni, the unknown natural inoculum densitypresent in the orchard (conidia cm)2) which must beestimated. The model could also be written as:

log itðPÞ ¼ a�Nco:ck þ bi � Sck:f ðiÞ þ j ð4Þ

with Nco.ck the inoculum dose in the cuticular cracksof the fruit, i the date of artificial inoculation (1, 2 or

Table 1 Mean values of the coefficients (± standard error) of a logistic regression

as a function of the inoculum dose in the cuticular cracks of the fruit and of the cut

coefficients j, b1, b2, b3 and a were estimated on data combining artificially inocul

coefficient j for 2005, i.e. j’ = j + s’), b’1, b’2, b’3 and a’ were estimated on data c

Model B: coefficients b1, b2, and b3 were estimated on data combining artificially i

and b’3 were estimated on data combining artificially inoculated and control fruits

Model A

Coefficient Estimate (± SE) P

j -4Æ557 (± 0Æ985) ***

s’ 1Æ440 (± 1Æ030) N.S.

b1 0Æ407 (± 0Æ114) ***

b2 0Æ593 (± 0Æ143) ***

b3 1Æ447 (± 0Æ374) ***

b’1 0Æ412 (± 9Æ078 · 10-2) **

b’2 0Æ649 (± 0Æ103) ***

b’3 1Æ234 (± 0Æ270) ***

a 2Æ141 · 10-4 (± 7Æ621 · 10-5) ***

a’ 6Æ198 · 10-5 (± 1Æ524 · 10-4) N.S.

n = 514.

3 depending on the fruit), bi the product of a by theunknown natural inoculum density dco.ni(i) in the orch-ard at date i, and Sck.f(i) the cuticular crack surfacearea at date i. Data from the 2004 and 2005 seasonswere pooled for model adjustment with coefficientsspecific to experiment year (model A): a, bi and j for2004; a’, b’i and s’ for 2005, s’ being the correction toj to obtain j’ = j + s’ (Table 1). The artificial inocu-lation in 2005 seemed to have a negligible effect sincethe corresponding coefficient a’ was not significant;s’ was also not significantly different from zero. All

model describing the probability of nectarine fruit infection by Monilinia laxa

icular crack surface area at each date i of inoculation (i = 1 to 3). Model A:

ated and control fruits from the 2004 experiment; coefficients s’ (correction to

ombining artificially inoculated and control fruits from the 2005 experiment.

noculated and control fruits from the 2004 experiment; coefficients b’1, b’2,

from the 2005 experiment; j and a were estimated on whole datasets

Model B

Coefficient Estimate (± SE) P

j -3Æ295 (± 0.291) ***

b1 0Æ230 (± 6.817 · 10-2) ***

b2 0Æ461 (± 9.440 · 10-2) ***

b3 1Æ094 (± 0.234) ***

b’1 0Æ431 (± 8.661 · 10-2) ***

b’2 0Æ651 (± 8.621 · 10-2) ***

b’3 1Æ241 (± 0.264) ***

a 1Æ481 · 10-4 (± 5.007 · 10-5) **

Plant Pathology (2009) 58, 1021–1031


the coefficients bi were significant at each date of inoc-ulation and for both seasons, thus strengthening thehypothesis of natural contamination by M. laxa in theorchard. The model was found to be satisfactory sinceit reduced the deviance by 65Æ28% (AIC = 255Æ48).

As the object of this study was to devise a genericmodel, a model was tested with one common set of coeffi-cients, a and j, for both seasons and its fit compared withthat of the more complex one (Table 1, model B). No sig-nificant difference between the two models was revealedsince the likelihood ratio was inferior to the critical value(P = 0.05). In consequence, the model with a unique setof coefficients, a and j, was retained.

This simpler model reduced the deviance by 64Æ87%with an AIC of 254Æ27. Using this model, the natural inoc-ulum densities (conidia cm)2) in the orchard at each dateof inoculation (dco.ni = bi ⁄ a, Eqns 2 and 3) were estimatedto amount to 1975 at 1587 dd, 3113 at 1720 dd and 7387at 1845 dd for 2004 and 2911 at 1563 dd, 4398 at 1685dd and 8379 at 1802 dd for 2005. Finally, the modelledprobability of fruit infection by M. laxa increased sharplywith increasing inoculum dose (natural + artificial inocu-lum) in the cuticular cracks (Fig. 4). It reached 1 at about50 000 conidia.

Simulations with the infection model in the frame-work of the sensitivity analysis showed that fruitgrowth patterns resulting in high commercial grades(e.g. 2A) can also result in a high risk of infection(>0Æ2) as soon as the inoculum density is high (>750conidia cm)2; Fig. 5). The model did not respond lin-early to its inputs. At inoculum densities lower orequal to 750 conidia cm)2, the inoculum density

Pro

babi

lity

Figure 4 Probability of nectarine fruit infection by Monilinia laxa as a

function of inoculum dose in the cuticular cracks. Star points

represent observed symptomless (0) and infected (1) fruits. The

solid line represents the fitted function of infectivity and the dashed

lines represent the 95% confidence intervals.

Plant Pathology (2009) 58, 1021–1031

effect increased with increasing fruit size. At inocu-lum densities higher than 750 conidia cm)2, the inoc-ulum density effect tended to decrease withincreasing fruit size. By lowering the objective of fruitsize and thus limiting cuticular crack occurrence, therisk of fruit infection could be significantly reduced.For a lower commercial grade, but still remunerativefor growers (A or B), the probability of infection waslow (<0.2) for all the tested inoculum densities.

The response of both cuticular crack density andprobability of infection to a variation of + ⁄ ) 10% inparameters a and b involved in the estimation of cuticu-lar crack density from fruit fresh mass is presented inTable 2 for various fruit sizes and inoculum densities.First, the coefficient of elasticity was always negativefor a, meaning that outputs varied in the opposite man-ner to a; it was the contrary for b. Secondly, there was avery large impact of a on the estimation of cuticularcrack density (elasticity varied from 2Æ3 to 18Æ8 in abso-lute value according to fruit size). Here, the smaller thefruit, the more sensitive the crack density. Thirdly, forthe probability of infection the elasticity varied in thesame way for a and b. The response of the probabilityof infection differed according to fruit size and inocu-lum density. For low and medium inoculum densities,the response was moderate for small fruits (D: at most0.6 in absolute value) and larger for medium and bigfruits (A and 3A: up to 4Æ1 in absolute value). For thehigh inoculum density, the response was moderate forsmall and big fruits (less than 1Æ2 in absolute value), butlarger for medium fruits (2Æ7–8Æ2 in absolute value).

Figure 5 Sensitivity analysis of the model of necatrine fruit infection

by Monilinia laxa to the input variables inoculum density and mean

fruit fresh mass. The latter variable corresponds to mean

commercial grades (indicated by letters D, C, B, A, 2A, 3A and 4A)

inducing variable fruit and cuticular crack surface areas. The

cuticular crack density is deduced from the fruit fresh mass by

means of this equation: Y ¼ b1k � ðXk � aÞ� �1

kifX � a1k with k = 0.41,

a = 5.396 (± 0.086 [gk]) and b = 0.635 (± 0.024 [(% ⁄ g)k].

Table 2 Values of the coefficient of elasticity

(CE)a calculated by varying the parameters a and

b of Eqn 1 by + ⁄ )10% around their estimated

values and observing the effect of each

modification for three commercial grades (D, A

and 3A) of nectarine fruit, first, on the estimation

of cuticular cracks density and, secondly, on the

probability of infection by Monilinia laxa for three

inoculum densities (250, 750 and 1500 conidia

cm)2)

Variable of interest Commercial grade CEap CEam CEbp CEbm

Cuticular crack density D )8Æ4 )18Æ8 2Æ5 2Æ5

A )3Æ7 )4Æ7 2Æ6 2Æ3

3A )2Æ4 )2Æ8 2Æ6 2Æ3

Probability of infection

for inoculum density of 250 conidia cm)2

D )0Æ1 )0Æ2 0 0

A )0Æ9 )1Æ3 0Æ7 0Æ6

3A )2Æ3 )3Æ4 3Æ2 2Æ2

Probability of infection for

inoculum density of 750 conidia cm)2

D )0Æ2 )0Æ6 0Æ1 0Æ1

A )2Æ4 )4Æ1 2Æ1 1

3A )3Æ6 )3Æ6 3Æ4 3Æ4

Probability of infection for

inoculum density of 1500 conidia cm)2

D )0Æ5 )1Æ2 0Æ2 0Æ1

A )4Æ1 )8Æ2 4Æ1 2Æ7

3A )0Æ8 )0Æ2 0Æ2 0Æ7

aSubscripts ap, am, bp and bm represent parameter a plus and minus 10% and parameter b

plus and minus 10%, respectively.


Discussion

In this study, the density of artificial inoculum had asignificant effect on fruit infection for both seasons.The increase of brown rot fruit infection with inoculumdensity was previously observed on sour cherry (Wilcox,1989) and on nectarine, peach and plum fruits forM. fructicola (Hong et al., 1998). In parallel, althoughthe fruits were surface-sterilized in 2004, natural infec-tion for both seasons was detected on the control fruits.The surface-sterilization of cherry fruits with sodiumhypochlorite solution failed to reduce the amount of resi-dent fungi (Dugan & Roberts, 1994). Natural infectionwas observed to increase with fruit maturity in this exper-iment. This could result from increasing inoculum con-centration in the orchard, but also from an increase inhost susceptibility to M. laxa. In orchards, conidial den-sity of M. fructicola on fruit surfaces was shown to buildup as the season progressed (Hong et al., 1997; Hong &Michailides, 1998) and to reach extremely high levelstoward harvest (Phillips, 1971). Further examinations inthe present study led to the estimation of the density ofnatural inoculum on fruits grown in a study orchardthat had been fungicide-free against Monilinia sp. for atleast 2 years. The estimated natural inoculum densityincreased from about 2000 to 8000 conidia cm)2 at3 weeks and 1 week before harvest, respectively. Byspreading washings from stone fruits (plums and nectar-ines) on plates of selective media, Phillips (1971) detected0.22 to 1.7 · 105 conidia per fruit; whilst densities of0.3 to 2.3 · 106 conidia per fruit were recorded on fruitsin Australia (Jerome, 1958). Hong et al. (1998) demon-strated that for a given stone fruit at maturity, with asurface area of 200 cm2 for the biggest fruit size, suchresults would amount to a conidial density ranging from100 to 11 600 conidia cm)2 of fruit surface. The range ofnatural inoculum densities in the present study was of thesame order of magnitude.

Increasing cuticular crack surface area influenced fruitinfection. This result confirms that cuticular cracks con-stitute a main entry type for fungal penetration into thepericarp (Fogle & Faust, 1975; Nguyen-The et al., 1989)

and promote disease infection on nectarine fruits, asobserved on sweet cherries (Børve et al., 2000), prunes(Michailides & Morgan, 1997) and litchis (Underhill &Simons, 1993). The cuticular crack surface area over thefruit increases with fruit maturity (Gibert et al., 2007).This could result from cultural practices (intensive irriga-tion) that can enhance the occurrence of cuticular cracks(Claypool et al., 1972; Peet, 1992; Ehret et al., 1993;Gibert et al., 2007) as well as from the reduction in cohe-sion of epidermal cells that occurs during ripening (Brum-mel et al., 2004). Moreover, the wound-healing process,leading to the formation of boundary-zone tissue andwound periderm (Skene, 1981), is less efficient at harvest,so that more susceptible tissue is exposed to the fungus atthis time. For levels of cuticular crack surface area lowerthan 5 cm2, the observed disease incidence was highlyvariable whatever the treatment, whereas disease wasmore frequently observed once the cuticular crack surfacearea was higher than 5 cm2.

Ambient relative humidity failed to explain a part ofthe observed variability in the data from the 2005 experi-ment. This is inconsistent with all the studies that put for-ward the importance of climatic conditions (relativehumidity and temperature) during the germination stage.Conidia were shown to germinate in vitro only whenhumidity was near saturation (‡97%) and little germina-tion was observed below this level (Byrde & Willetts,1977; Tamm & Fluckiger, 1993; Tamm et al., 1995; Xuet al., 2001a). Indeed, definitive conclusions cannot bedrawn on the lack of effect of ambient relative humidityon brown rot infection since this experiment was onlyperformed in one year. Moreover, in this experiment, therelative humidity of the environment in the box was con-trolled with salt solutions, but as the fruit transpired andas no movement occurred in the box, the boundary layeraround the fruit was likely to be at 100% humidity. Nev-ertheless, this result deserves further analysis, since Xu &Robinson (2000) and Xu et al. (2001a) also concludedthat in vivo infection of wounded fruits by M. fructigenadid not need wetness. These results suggest that the mois-ture inside the unhealed cuticular cracks may be sufficientto allow conidial germination.

Plant Pathology (2009) 58, 1021–1031


The inoculum dose in the cracks, which was the prod-uct of the inoculum density and the cuticular crack sur-face area, was a good predictor of fruit infection. Indeed,no infection could occur without inoculum or cuticularcracks in this work. The probability of fruit infection byM. laxa according to the inoculum dose in the cuticularcracks was fitted using a logistic regression model. Theadjustment quality of the model was very good. Model-ling fruit infection in relation to variable specific to thehost (cuticular crack surface density and fruit surfacearea) and the pathogen (inoculum density) is originalcompared to most existing models (Tamm & Fluckiger,1993; Xu & Robinson, 2000) that are based only on cli-matic factors (temperature, wetness duration) or tempo-ral variables (fruit maturity and wound age). It appearsthat the model is highly sensitive to inoculum density andto cuticular crack density through commercial grade.Inoculum density has an increasing effect with fruit sizeuntil a threshold above which fruit size effect, and thuscuticular crack density, is dominant. By lowering theobjective of commercial grade and thus decreasing cutic-ular crack occurrence, the risk of fruit infection could besignificantly reduced provided that inoculum density iscontrolled by prophylactic measures. Moreover, themodel is sensitive to parameters of a relationship estimat-ing cuticular crack density from fruit fresh mass, whenfruit size is medium (A) to big (3A) and inoculum densityis low to intermediate. This means that these parametershave to be precisely estimated, which was the case for thisapplication (Gibert et al., 2007).

In order to be used to describe what could happen inorchards, the model requires the characterization ofinoculum density. Dynamics of conidial densities couldbe obtained either by regularly washing fruits on selectivemedia and ⁄ or counting M. laxa spores on petroleumjelly slides accurately placed in the orchard, as suggestedfor monitoring Oidium mangifera in mango orchards(Schoeman et al., 1995), or on Petri plates containingMonilinia-selective medium in a similar way to thatused by Schmale et al. (2005) to describe deposition ofGibberella zeae in wheat fields, or by using a Hirst auto-matic volumetric spore trap (Hirst, 1952). Since it consti-tutes a key variable in epidemiological studies, a methodto estimate natural inoculum density of Monilinia sp.quickly and reliably is necessary. Since the introductionof PCR (Lievens & Thomma, 2005), advances in molecu-lar diagnostics (West et al., 2008) have now made it possi-ble to detect, identify (Gell et al., 2007) and accuratelyquantify airborne inoculum of Monilinia sp. rapidly.

Finally, the importance of cuticular crack surface areain the infection process suggests that cultural practicesshould be considered to avoid preharvest cuticularcracks, all the more because they promote postharvestdecay (Sekse, 1998; Børve et al., 2000). A better controlof irrigation and crop load, as well as techniques limitingthe formation of fruit clusters or leafy shoots clusters,could limit cuticular crack occurrence for fungal develop-ment (Mercier et al., 2005; Gibert et al., 2007). A betterunderstanding of the influence of such cultural practices

Plant Pathology (2009) 58, 1021–1031

on the development of both the host and the pathogen isnecessary to reduce the use of pesticides, the efficacy ofwhich is restricted to some specific stages of host develop-ment.

Acknowledgements

We gratefully acknowledge P. Rouet for his assistance inthe field experiments, C. Vigne for his technical help, C.Troulet for her technical assistance in the laboratory andV. Mercier for his regular supply of freshly sporulatingmummies. This research was supported by grants fromthe French Ministry of Ecology and Sustainable Develop-ment, programme ‘Evaluation and reduction of risksrelated to pesticide use’ (# 12-E ⁄ 2003 CV 300099) andfrom an ‘Irriqual’ programme, co-funded by the Euro-pean Commission, DG Research, within the 6th Frame-work Program of RTD, Priority 5-Food Quality andSafety (# FP6-FOOD-CT-2006-023120).

References

Arnoux M, 1979. Maladie de conservation de la peche In: Les

Maladies des Plantes. IIIes Journees Francaises d’Etudes

et d’Informations. 123–6.

Børve J, Sekse L, Stensvand A, 2000. Cuticular fractures promote

postharvest fruit rot in sweet cherries. Plant Disease 84,

1180–4.

Brummel DA, Dal Cin V, Crisosto CH, Labavitch JM, 2004. Cell

wall metabolism during maturation, ripening and senescence of

peach fruit. Journal of Experimental Botany 55, 2029–39.

Byrde RJW, Willetts HJ, 1977. The Brown Rot Fungi. Their

Biology and Control. Oxford, UK: Pergamon Press Ltd.

Calonnec A, Cartolaro P, Naulin J-M, Bailey D, Langlais M, 2008.

A host-pathogen simulation model: powdery mildew of

grapevine. Plant Pathology 57, 493–508.

Chambers JM, Hastie TJ, 1991. Statistical Models in S. Boca

Raton, FL, USA: Chapman & Hall ⁄ CRC.

Claypool LL, Uriu K, Lasker PF, 1972. Split-pit of ‘Dixon’ cling

peaches in relation to cultural factors. Journal of the American

Society for Horticultural Science 97, 181–5.

Corbin JB, Ogawa JM, Schultz HB, 1968. Fluctuations in numbers

of Monilinia laxa in an apricot orchard during the 1966 season.

Phytopathology 58, 1387–94.

Cox KD, Scherm H, 2001. Gradients of primary and secondary

infection by Monilinia vaccinii-corymbosi from point sources of

ascospores and conidia. Plant Disease 85, 955–9.

DeJong TM, Goudriaan J, 1989. Modeling peach fruit growth and

carbohydrate requirements: reevaluation of the double-sigmoid

growth pattern. Journal of the American Society for

Horticultural Science 114, 800–4.

Dugan FM, Roberts RG, 1994. Etiology of preharvest colonization

of Bing cherry fruit by fungi. Journal of Phytopathology 84,

1031–6.

European Union, 2004. Reglement (CE) n�1861 ⁄ 2004 de la

commission du 26 octobre 2004 fixant la norme de

commercialisation applicable aux peches et aux nectarines.

Journal Officiel de l’Union Europeenne 47, L325, 12–16.

Ehret DL, Helmer T, Hall JW, 1993. Cuticle cracking in tomato

fruit. Journal of Horticultural Science 68, 195–201.


Fogle HW, Faust M, 1975. Ultrastructure of nectarine fruit

surfaces. Journal of the American Society for Horticultural

Science 100, 74–7.

Fourie PH, Holz G, 2003a. Germination of dry, airborne conidia of

Monilinia laxa and disease expression on nectarine fruit.

Australasian Plant Pathology 32, 9–18.

Fourie PH, Holz G, 2003b. Germination of dry, airborne conidia

of Monilinia laxa and disease expression on plum fruit.

Australasian Plant Pathology 32, 19–25.

Gell I, Cubero J, Melgarejo P, 2007. Two different PCR

approaches for universal diagnosis of brown rot and

identification of Monilinia spp. in stone fruit trees. Journal of

Applied Microbiology 103, 2629–37.

Gibert C, Chadœuf J, Vercambre G, Genard M, Lescourret F,

2007. Cuticular cracking on nectarine fruit surface: spatial

distribution and development in relation to irrigation and

thinning. Journal of the American Society for Horticultural

Science 132, 583–91.

Gradziel TM, 1994. Changes in susceptibility to brown

rot with ripening in three clingstone peach genotypes.

Journal of the American Society for Horticultural Science

119, 101–5.

Hirst JM, 1952. An automatic volumetric trap spore. Annals of

Applied Biology 39, 257–65.

Hong C, Michailides TJ, 1998. Effect of temperature on the

discharge and germination of ascospores by apothecia of

Monilinia fructicola. Plant Disease 82, 195–202.

Hong C, Holtz BA, Michailides TJ, 1997. Significance of thinned

fruit as a source of the secondary inoculum of Monilinia

fructicola in California nectarine orchards. Plant Disease 81,

519–24.

Hong C, Michailides TJ, Holtz BA, 1998. Effects of wounding,

inoculum density, and biological control agents on postharvest

brown rot of stone fruits. Plant Disease 82, 1210–6.

Huang Y, 2003. The Interplay of Migration and Population

Dynamics in a Patchy World. Utrecht, the Netherlands: Utrecht

University, PhD thesis.

Jerome SMR, 1958. Brown rot of stone fruits: latent contamination

in relation to spread of the disease. Journal of the Australian

Institute of Agricultural Science 24, 132–40.

Knoche M, Peschel S, 2006. Water on the surface aggravates

microscopic cracking of the sweet cherry fruit cuticle. Journal

of the American Society for Horticultural Science 131,

192–200.

Knoche M, Beyer M, Peschel S, Oparlakov B, Bukovac MJ, 2004.

Changes in strain and deposition of cuticle in developing sweet

cherry fruit. Physiologia Plantarum 120, 667–77.

Kropff MI, Teng PS, Rabbing R, 1995. The challenge of

linking pest and crop models. Agricultural Systems 49,

413–34.

Lalancette N, Foster KA, Robison JD, 2003. Quantitative

models for describing temperature and moisture effects on

sporulation of Phomopsis amygdali on peach.


van Leeuwen GCM, Stein A, Holb I, Jeger MJ, 2000. Yield loss in

apple caused by Monilinia fructigena (Aderh. & Ruhl.) Honey,

and spatio-temporal dynamics of disease development.

European Journal of Plant Pathology 106, 519–28.

Lichou J, Mandrin JF, Breniaux D, et al., 2002. Une nouvelle

moniliose: Monilia fructicola s’attaque aux arbres fruitiers a

noyaux. PHYTOMA La Defense des Vegetaux 547, 22–5.

Lievens B, Thomma BPHL, 2005. Recent developments in

pathogen detection arrays: implications for fungal plant

pathogens and use in practice. Phytopathology 95, 1374–80.

Link WA, Doherty Jr PF, 2002. Scaling in sensitivity analysis.

Ecology 83, 3299–305.

Luo Y, Ma Z, Michailides TJ, 2001. Analysis of factors affecting

latent infection and sporulation of Monilinia fructicola on prune

fruit. Plant Disease 85, 999–1003.

McFadden, 1974. Conditional logit analysis of qualitative choice

behaviour. In: Zarembka P, ed. Frontiers of Econometrics. New

York, USA: Academic Press, 105–42.

Mari M, Casalini L, Baraldi E, Bertolini P, Pratella GC, 2003.

Susceptibility of apricot and peach fruit to Monilinia laxa during

phenological stages. Postharvest Biology and Technology 30,

105–9.

Mercier V, Gueldry H, Neraudeau E, Chauffour D, 2005. Taille,

irrigation et monilioses sur pecher. PHYTOMA La Defense des

Vegetaux 581, 40–1.

Michailides TJ, Morgan DP, 1993. Wind scab of French prune:

symptomatology and predisposition to preharvest and

postharvest fungal decay. Plant Disease 77, 90–5.

Michailides TJ, Morgan DP, 1997. Influence of fruit-to-fruit

contact on the susceptibility of French prune to infection by

Monilinia fructicola. Plant Disease 81, 1416–24.

Milad RE, Shackel KA, 1992. Water relations of fruit end

cracking in ‘French’ prune (Prunus domestica L. cv. French).

Journal of the American Society for Horticultural Science

117, 824–8.

Nguyen-The C, 1991. Structure of epidermis wall, cuticle and

cuticular microcracks in nectarine fruit. Agronomie 11, 909–

20.

Nguyen-The C, Hugueney R, Arnoux M, 1989. Contribution a

l’etude des voies de penetration de parasites fongiques des

nectarines Monilia laxa (Ascomycete-Discomycete) et

Rhizopus stolonifer (Zygomycete-Mucorale). Agronomie 9,

271–6.

OIML, 1996. Echelle d’Humidite Relative de l’Air Certifiee par

Rapport a des Solutions Saturees de Sels. Paris, France:

Organisation Internationale de Metrologie Legale (OIML):

Recommandation Internationale R 121.

Peet MM, 1992. Fruit cracking in tomato. HortTechnology 2,

216–23.

Peet MM, Willits DH, 1995. Role of excess water in tomato fruit

cracking. HortScience 30, 65–8.

Peschel S, Knoche M, 2005. Characterization of microcracks

in the cuticle of developing sweet cherry fruit. Journal of

the American Society for Horticultural Science 130,

487–95.

Phillips DJ, 1971. A medium for the selective growth of Monilinia

species. Phytopathology 61, 906.

Schmale DG, Shah DA, Bergstrom GC, 2005. Spatial patterns of

viable spore deposition of Gibberella zeae in wheat fields.


Schoeman MH, Manicom BQ, Wingfield MJ, 1995. Evaluation of

a petroleum jelly slide technique for monitoring Oidium

mangiferae conidia in mango orchards. African Plant

Protection 1, 51–4.

Sekse L, 1998. Cuticular fractures in fruits of sweet cherry (Prunus

avium L.) affect fruit quality negatively and their development is

influenced by cultivar and rootstock. Acta Horticulturae 468,

671–6.

Plant Pathology (2009) 58, 1021–1031


Skene DS, 1981. Wound healing in apple fruit: the anatomical

response of Cox’s Orange Pippin at different stages of

development. Journal of Horticultural Science 56, 145–53.

Tamm L, Fluckiger W, 1993. Influence of temperature and

moisture on growth, spore production and conidial germination

of Monilinia laxa. Phytopathology 83, 1321–6.

Tamm L, Minder CE, Fluckiger W, 1995. Phenological analysis of

brown rot blossom blight of sweet cherry caused by Monilinia

laxa. Phytopathology 85, 401–8.

Underhill SJR, Simons DH, 1993. Lychee (Litchi chinensis Sonn.)

pericarp dessication and the importance of postharvest micro-

cracking. Scientia Horticulturae 54, 287–94.

West JS, Atkins SD, Emberlin J, Fitt BDL, 2008. PCR to

predict risk of airborne disease. Trends in Microbiology 16,

380–7.

Wilcox WF, 1989. Influence of environment and inoculum density

on the incidence of brown rot blossom blight of sour cherry.


Plant Pathology (2009) 58, 1021–1031

Xu XM, Robinson JD, 2000. Epidemiology of brown rot

(Monilinia fructigena) on apple: infection of fruits by conidia.

Plant Pathology 49, 201–6.

Xu XM, Robinson J, 2005. Modelling the effects of wetness

duration and fruit maturity on infection of apple fruits of Cox’s

Orange Pippin and two clones of Gala by Venturia inaequalis.

Plant Pathology 54, 347–56.

Xu XM, Guerin L, Robinson JD, 2001a. Effects of temperature

and relative humidity on conidial germination and viability,

colonization and sporulation of Monilinia fructigena. Plant

Pathology 50, 561–8.

Xu XM, Robinson JD, Berrie AM, Harris DC, 2001b. Spatio-

temporal dynamics of brown rot (Monilinia fructigena) on apple

and pear. Plant Pathology 50, 569–78.

Zalom FG, Goodell LT, Wilson LT, Barnett WW, Bentley WJ,

1983. Degree-Days: the Calculation and Use of Heat Units in

Pest Management. Berkeley, CA. USA: University of California,

leaflet 21373.

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