# Brown Rot Disease in Stored Nectarines: Modeling the Combined Effects of Preharvest and Storage Conditions

Enrico Casagrande, Michel Génard, Sébastien Lurol, Florence Charles, Danièle Bevacqua, Davide Martinetti, Françoise Lescourret

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Enrico Casagrande, Michel Génard, Sébastien Lurol, Florence Charles, Danièle Bevacqua, et al. Brown Rot Disease in Stored Nectarines: Modeling the Combined Effects of Preharvest and Storage Conditions. Phytopathology, 2022, 112 (7), pp.1575-1583. 10.1094/PHYTO-12-21-0499-R. hal-03843304

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# Brown Rot Disease in Stored Nectarines: Modeling the Combined Effects of Preharvest and Storage Conditions

Enrico Casagrande,<sup>1,2,†</sup> Michel Génard,<sup>2</sup> Sébastien Lurol,<sup>1</sup> Florence Charles,<sup>3</sup> Daniele Bevacqua,<sup>2</sup> Davide Martinetti,<sup>4</sup> and Françoise Lescourret<sup>2</sup>

## ABSTRACT

Brown rot in stored stone fruits, caused by *Monilinia* spp., may be due to preharvest and storage factors, but the combined effect of these factors has yet to be investigated. We set up two experiments to monitor the progression of brown rot during the storage of nectarines subjected to various preharvest and storage conditions. We assessed the effects of different agricultural practices (irrigation regimen × fruit load) and harvest dates on brown rot progress during storage in 2018 and the effect of different storage temperatures in 2019. We found that the cumulative incidence of brown rot during storage increased with individual fruit mass, which was influenced by agricultural practices, and for later harvest dates. It also increased with storage temperature. We observed that during storage no secondary infections developed in nectarines not in direct contact with fruits infected with *Monilinia laxa*. These findings led to the

identification of candidate variables describing the brown rot risk on nectarines during storage, such as individual fruit mass, meteorological conditions before fruit harvest, prevalence of brown rot at harvest, and storage temperature. We used these variables to build a mathematical model for estimating the time-to-appearance of brown rot symptoms in stored nectarines. This model fitted the experimental data well, highlighting the need to pay greater attention to the interaction between preharvest and storage conditions. This model could be used to evaluate management strategies for reducing the impact of brown rot in nectarines during storage.

**Keywords:** modelling, disease control and pest management, epidemiology, postharvest pathology and mycotoxins

Brown rot is a major fungal disease of stone fruit. It is caused by *Monilinia* spp., especially *Monilinia laxa* (Aderhold & Ruhland) Honey and *M. fructicola* (G. Wint.) Honey in France (Labeyrie and Chambeyron 2020). This disease is challenging for both farmers and people who store and sell the fruit postharvest, causing economic losses that can reach $1.7 million per year worldwide (Martini and Mari 2014).

The probability of a fruit being infected depends on both the susceptibility of the fruit, which is influenced by fruit growth rate and maturity stage (Garcia-Benitez et al. 2017; Xu et al. 2007), and exposure to the pathogen, which is determined by the density of *Monilinia* spp. spores on the surface of the fruit (Gibert et al. 2009; Villarino et al. 2010). In the field, agricultural practices, such as thinning and irrigation, may have a large effect on fruit susceptibility by promoting the appearance of microcracks in the cuticle (Bellingeri et al. 2018; Gibert et al. 2007), which act as entry sites for the spores of *Monilinia* spp. (Gibert et al. 2009). By contrast, spore germination, reproduction, and dispersal in the orchard depend heavily on

environmental variables such as temperature, RH, and rainfall (Bannon et al. 2009; Biggs and Northover 1988; Holb 2008). Moreover, longer periods of wetness and higher temperatures also favor latent infection that may develop after harvest (Gell et al. 2008; Luo and Michailides 2003). *Monilinia* spp. infections can, indeed, develop and become latent in fruit that are not ripe, subsequently being reactivated during the harvest season when environmental conditions become favorable and the fruit are approaching maturity (Emery et al. 2000; Garcia-Benitez et al. 2016; Gell et al. 2008).

Most disease-related losses occur at postharvest stages (Hong et al. 1997). Peaches are generally transported from the field to the packing house, where they are cooled to prevent deterioration. The fruit can be stored at low temperature for several days before being sold (Bernat et al. 2017a). Inadequate storage conditions, such as high temperatures, can lead to reactivation of latent infections and favor the development of new infections (Bernat et al. 2017c; Casals et al. 2009; Tian and Bertolini 1999; Xu et al. 2001). Moreover, secondary infections during storage and handling operations (Bernat et al. 2017a; Michailides and Manganaris 2009) may have a considerable epidemiological importance, because sources of *Monilinia* spp. are rarely found on packing house surfaces (Bernat et al. 2017b). Tian and Bertolini (1999) and Bernat et al. (2017c) observed that fruit infected with *Monilinia* spp. could be able to sporulate (generating conidia) during storage and suggested that this could lead to infection of surrounding fruit. However, the issue of brown rot secondary infections during the storage of fruit that are not in physical contact has been little studied. Finally, the incidence of brown rot during storage can be influenced by multiple factors, including susceptibility of fruit to *Monilinia* spp. infections at harvest (which are affected by agricultural practices), preharvest environmental conditions, and storage conditions.

Several mathematical models have been developed to describe and simulate brown rot infections dynamics, both during the growing

<footnote n="1"><sup>1</sup>Centre Technique Interprofessionnel des Fruits et Légumes, 13210 Saint-Rémy-de-Provence, France</footnote>

<footnote n="2"><sup>2</sup>UR 1115 Plantes et Systèmes de Culture Horticoles, INRAE, 84914 Avignon Cedex, France</footnote>

<footnote n="3"><sup>3</sup>UMR Qualisud, Université de Montpellier, Avignon Université, CIRAD, Institut Agro, Université de La Réunion, Montpellier, France</footnote>

<footnote n="4"><sup>4</sup>UR 546 Biostatistique et Processus Spatiaux, INRAE, 84914 Avignon Cedex, France</footnote>

<footnote>Accepted for publication 28 January 2022.</footnote>

<footnote><sup>†</sup>Corresponding author: E. Casagrande; enricom.casagrande@gmail.com</footnote>

<footnote>**Funding:** The study was funded by an industrial training agreement through a CIFRE research fellowship (2017/1788) from the Centre Technique Interprofessionnel des Fruits et Légumes (CTIFL) and the Association Nationale de la Recherche et de la Technologie (ANRT) on behalf of the French Ministry of Higher Education and Research.</footnote>

<footnote>*The e-Xtra logo stands for “electronic extra” and indicates there are three supplementary material files published online.</footnote>

<footnote>The author(s) declare no conflict of interest.</footnote>

<footnote>This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. The American Phytopathological Society, 2022.</footnote>
season (Bevacqua et al. 2018) and during the postharvest stage. Post-harvest studies have focused on the incidence of latent infection under different meteorological conditions during the growing season and on the influence of handling practices on disease development (García-Benitez et al. 2020; Gell et al. 2009; Villarino et al. 2012). The effects of temperature and RH on the development of brown rot have also been studied both in vitro and directly on fruit (Bernat et al. 2017c, 2018). However, none of the models developed to date take into account the combined effects of preharvest and storage conditions on the incidence of brown rot after harvest. In this situation, survival analysis can be used to assess the influence of several variables on the time to appearance of brown rot on nectarines during storage. Modeling techniques of this type have been used in plant pathology (Humplík et al. 2020; Pethybridge et al. 2010; Scherm and Ojiambo 2004), but only a few studies have applied these methods to postharvest diseases such as citrus black rot (Frare et al. 2019) and brown rot (Marquenie et al. 2002; Nesi et al. 2015). Survival analysis tolerates the use of censored observations (i.e., individuals for whom the event of interest does not occur before the end of the study) and repeated measurements of the epidemiological state of the same individual in studies considering the effects of several covariates (Bradburn et al. 2003; Muenchow 1986).

In this context, the specific objectives of this study were to (i) test experimentally whether the inoculum produced from nectarines (*Prunus persica* var. *nucipersica*) infected with *Monilinia* spp. can result in secondary infections during storage, (ii) investigate the effect of preharvest and storage conditions on the development of brown rot during storage of nectarines, and (iii) identify a set of variables related to the susceptibility of the fruit to infection with *Monilinia* spp., preharvest environmental conditions, and storage conditions for quantitative assessment of the risk of brown rot appearing during nectarine storage.

## MATERIALS AND METHODS

**Experimental design to assess secondary infections from *Monilinia* spp. during storage.** *Nectarine fruits.* The experiment was performed on two midseason nectarine cultivars, Honey Fire and Magique. For each cultivar, fruits of uniform size were collected at commercial maturity. Honey Fire nectarines were retrieved from a commercial producer located in the south of France on 28 June 2019, whereas Magique nectarines were harvested at the INRAE station in Avignon (France) on 19 July 2019.

**Inoculum preparation and fruit inoculation.** An isolate of *M. laxa* (FlAbBerga2014) from the Microbiology Laboratory of the University of Montpellier (France) was collected before the start of the experiment. A culture of this pathogen was plated on potato dextrose agar medium and incubated for 3 days at 25°C in the dark. After collection of nectarines, we selected three fruits at random and disinfected their surfaces with a 0.5% chlorine solution for 3 min. Disinfected fruit were wounded on one cheek with a sterile box cutter (wound size: 1 cm long, 0.5 cm deep). A plug of agar (1 cm) containing fungal mycelia and conidia was cut from the potato dextrose agar medium and placed against the wound on each fruit. A water-soaked cotton pad was taped to the fruits, which were placed in a plastic box. The boxes were incubated at 22°C and 100% RH in the dark for 3 days to allow fungal colonization and the production of conidia to occur. During this period, the remaining nectarines (noninoculated) were kept in a cold-storage room at 2°C.

**Experimental device setup and disease progress.** Before the experiment was set up, we disinfected the noninoculated fruits by soaking them in 0.5% chlorine solution for 3 min. The disinfected nectarines were then placed on paper towels and allowed to dry at room temperature (~25°C). Two different arrangements of fruit were set up. In the first (I, infected treatment), an inoculated fruit (displaying signs of sporulation) was surrounded by disinfected fruit arranged in a square (five rows with five fruit per row), with care taken to ensure uniform separation distance of 1 cm between all fruits to prevent contact. In the second arrangement (C, control group), the

same layout was used, but only noninoculated fruit were included. We set up three replicates for each nectarine cultivar and group.

Once arranged as described above, the fruits were placed in transparent Perspex cells (60 × 44 × 60 cm) to make it possible to monitor disease progression without the need to manipulate the fruit (Fig. 1). A low-power fan (0.426 m³/min; Orion Fans) was placed in each Perspex cell to simulate air turbulence and, potentially, to facilitate the dispersion of conidia. The temperature in the storage room was maintained at 25°C to ensure that conditions were optimal for *M. laxa* sporulation (Bernat et al. 2017c). A small plastic container filled with water was placed in each cell to keep the humidity high (~95% RH). Temperature and RH were measured continuously with a HOBO data logger (Onset). A 12-h light/12-h dark photoperiod regimen was established, corresponding to the preferred conditions of *M. laxa* (Byrde and Willetts 1977). For each group, the number of fruits displaying brown rot was assessed over a period of 7 days.

**Assessment and modeling of the effects of preharvest and storage conditions on brown rot development during storage.**

*Experimental orchard and preharvest conditions.* Two experiments were performed on nectarines harvested from an orchard planted in 2013 at the INRAE center in Avignon (cultivar Nectarlove, grafted onto the GF667 rootstock, 43°60 N, 4°49 E), with a spacing of 3.5 m between trees and 5 m between rows. A first experiment was performed in 2018 and a second in 2019. Full bloom occurred on 17 March in 2018 and on 10 March in 2019. Manual hand-thinning was performed on 22 May (66 days after full bloom [DAFB]) and on 6 May (57 DAFB), respectively. Routine horticultural care practices and uniform pruning for commercial fruit production were performed on the trees in the two growing seasons. Crop protection treatments were applied in the orchard but were not directed to *Monilinia* spp.

In 2018, the experiment was performed on fruit from 24 trees. We assessed the effects of two levels of fruit load (low fruit load = 200 fruit per tree versus high fruit load = 400 fruit per tree) and two levels of water supply: a well-irrigated regimen meeting 100% of the crop water requirement (CWR) versus a moderate water-stressed regimen of 50% CWR. The CWR was calculated as the difference between potential evapotranspiration and rainfall over the irrigation period (Allen et al. 1998) and was used as an indicator for irrigation management. Each treatment consisted of six trees. In 2019, a commercial thinning procedure was performed for all trees (from 400 to 500 fruits per tree). The trees were split into two groups of 12 trees, with one group subjected to a well-irrigated regimen (70% CWR) and the other subjected to water stress (30% CWR).

**Assessment of the brown rot progress during storage.** Fruit were harvested on three dates in 2018 (12 to 27 July; 117, 124, and

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Experimental set-up for the brown rot secondary infection experiment for the nectarine 'Magique' cultivar. The three upper cells contained a central inoculated fruit (I), whereas the lower cells contained the control group (C). Each cell contained one experimental replicate.</figcaption></figure>
132 DAFB) and two dates in 2019 (22 and 29 July; 134 and 141 DAFB). At each harvest date in the 2 years, we calculated the prevalence of brown rot at harvest ($I_H$, %) as the ratio of the number of infected fruits divided by the total number of fruits harvested (from 24 trees in 2018 and 12 in 2019). Only fruits without symptoms of *Monilinia* spp. infection were selected. These fruits were weighed individually before the start of the experiment.

In the first experiment (2018), 60 fruits per treatment (irrigation regimens × fruit loads) were selected at random on each harvest date and placed in four plastic crates (15 fruits per crate). Plastic alveoli were used to prevent contact between fruits. The fruits were stored in a cool-storage chamber for 48 h at 2°C and then in a storage chamber at 20°C for 10 to 12 days. In the second experiment (2019), 240 fruits were randomly selected at each harvest date and for each irrigation treatment. The fruits were placed into crates, each containing 20 fruits, and stored at 2°C for 2 days. The crates were then placed in different storage conditions for 15 to 19 days (four crates per irrigation treatment and temperature level, 80 fruits) in storage rooms at 2, 15, or 25°C.

In both experiments, the number of fruits in each crate displaying brown rot symptoms was determined by counting at various time points. The date on which brown rot first appeared on each fruit was also noted. Fruits displaying brown rot disease were removed from the boxes to prevent other infections (e.g., *Rhizopus stolonifera*; Baggio et al. 2017). The increase in the cumulative incidence of brown rot during storage was then calculated for each experimental treatment.

**Modeling framework for parametric survival analysis of brown rot caused by *Monilinia* spp. on fruit during storage.** For each individual fruit ($i$), we considered the time at which infection was detected by visual examination ($t_i$) (in days) or at which the follow-up period finished. Fruits not yet infected at the end of each experiment were considered censored, and a censoring indicator ($c_i$) was therefore introduced. Moreover, each fruit $i$ was associated with a vector of P covariates $\mathbf{x}_i = (1, x_{i,1}, ..., x_{i,p})$. The individual time until brown rot became visually detectable during storage (in days) was used as a random response variable. The probability of a fruit $i$ displaying visual symptoms of *Monilinia* spp. infection before time $t$ is expressed as a cumulative density function $F_i(t)$. We considered the positive effect of fruit ripening and senescence on the probability of fruit infection (Díaz Ricci et al. 2020; Flaishman and Kolattukudy 1994; Xu et al. 2007), testing a Gompertz and a Weibull functional shape for the survival time probability distribution (Juckett and Rosenberg 1993; Wilson 1994). For the Gompertz functional shape, the effect of increasing time on the hazard rate is exponential, which could result from an increase in the vulnerability of individuals to extrinsic sources, compared with the Weibull one, which is a power function of time, resulting in a predominance of intrinsic sources (Ricklefs and Scheuerlein 2002). The individual probability of infection can be expressed as the following equation:

$$F_{i,G}(t) = 1 - \exp\left(-\frac{b_i}{a}(e^{a \cdot t} - 1)\right) \quad (1)$$

where $b_i$ is an individual rate parameter (controlling the magnitude of the hazard), dependent on the individual covariate vector, and $a$ is a shape parameter (controlling the changes in the hazard over time, with the hazard increasing over time if $a > 0$) that is constant in fruits. The dependence of the rate parameter on covariates can be described in equation 2:

$$\log(b_i) = \mathbf{x}_i^T \cdot \beta \quad (2)$$

where the log-link is used to ensure that the parameter is positive and $\beta = \beta_0, \beta_1, ..., \beta_p$ is the vector of the corresponding regression coefficients. For the Weibull case, individual infection probability function was described in equation 3 as follows:

$$F_{i,W}(t) = 1 - e^{-c_i \cdot t} \quad (3)$$

where $c_i$ is the individual scale parameter and it depends on covariates as $b_i$.

**Candidate variables for survival analysis.** The cohort of candidate covariates was selected on the basis of a literature survey. We considered six variables in total, five related to the preharvest stage and one related to the postharvest stage.

* The individual fruit mass $w$ (g). We used this variable as a proxy for the area of the cuticular crack on the fruit, which is directly related to the probability of infection (Gibert et al. 2009). This variable is influenced by agricultural practices in the orchard (Gibert et al. 2007; Minas et al. 2018).

* The growing degree-days calculated at harvest $GDD_H$ (°C-days). This indicator is calculated by summing the degree-days for the growing season (from the date of full blooming until harvest). Degree-days were calculated based on daily orchard temperatures using the single-sine horizontal cutoff method (critical temperatures: 7 and 35°C) as in DeJong and Goudriaan (1989). $GDD_H$ is an indicator of fruit development and maturity. The stage of development influences the susceptibility of the fruit to brown rot infections, with this susceptibility increasing as the fruit approaches maturity (Biggs and Northover 1988; Garcia-Benitez et al. 2017; Keske et al. 2011; Luo and Michailides 2001).

* The prevalence of brown rot at harvest $I_H$ (%). This variable may be correlated to the development of brown rot during storage (Emery et al. 2000; Luo and Michailides 2003; Gell et al. 2008).

* The meteorological variables in the 7 days preceding each harvest date: average daily temperature ($T_H$, °C) and cumulative wetness duration $WD_H$ (h). These meteorological variables can influence the density of conidia in the air and on the fruit surface (Bannon et al. 2009; Gell et al. 2009; Holb 2008) and may favor the development of infection and affect the incidence of latent infection (Emery et al. 2000; Gell et al. 2008; Luo and Michailides 2003).

* The mean temperature during storage $T_S$ (°C), due to its contribution to the rate of rot developmental rate (Bernat et al. 2017c; Tamm and Fluckiger 1993).

Meteorological data were retrieved from the INRAE CLIMATIK database (a project managed by the Agroclim unit). Mean daily temperature (°C) and daily wetness duration (hours) were obtained from a stationary weather station located next to the experimental orchard (43°92 N, 4°88 E). Wetness duration was measured by a leaf wetness sensor, which is fixed to the weather station. Cumulative wetness duration and mean daily temperature over the week before harvest dates were then calculated.

**Model fitting procedure and model selection process.** All the possible combinations of covariates for each functional shape (Gompertz and Weibull) were tested, with a total of 126 alternative model formulations. The models were fitted to data from the 2018 and 2019 experiments, by maximum likelihood estimation in R with the *flexsurvreg* package (Jackson 2016). The exhaustive model search was used to find the single best-fitting model. In this method, the Bayesian information criterion (BIC) of every possible model (i.e., all subsets of predictors) estimated with the observed data and the model with the lowest value of this criterion was selected as the best model (Burnham and Anderson 2004). BIC was chosen because it penalizes more model complexity than Akaike information criterion (AIC) does, being better at avoiding overfitting.

The predictive quality of each model formulation was also checked via a k-fold cross-validation procedure (k = 6). The goal of this procedure was to test the model's ability to predict new data that were not considered during the estimation. The statistic used for cross-validation was the Somers' $D_{XY}$ statistic for censored data (Chen et al. 2012; Newson 2006) calculated with the *rcorr.cens* function (from the *Hmisc* package of R). $D_{XY}$ measures the association
between two ordinal variables, in this case, the mean predicted and observed rates of appearance of brown rot. Its values range from -1 (for a perfect disagreement) to 1 (for a perfect agreement) (Newson 2006). K-fold cross-validation statistic for each model formulation was calculated by averaging the value of $D_{XY}$ obtained across the k-partitioning.

Finally, we used nonparametric Kaplan Meier curves to compare the model estimates to the observed data. Using this technique, we were able to calculate the observed survival probability (the probability of a nectarine not displaying brown rot) from the observed survival times and censoring times (Clark et al. 2003).

## RESULTS

**No symptoms of secondary brown rot infection were observed during storage.** During storage for 7 days at 25°C and 95% RH, no infection was observed in the control group (C), for either Honey Fire or Magique nectarines. In the cells containing a central inoculated fruit (I), the nectarines showed no symptoms of secondary infection from *M. laxa*. However, fruits that had accidentally come into contact with the infected fruit presented brown rot disease. Moreover, when juice from the central inoculated fruit came into contact with the noninoculated fruit, it induced symptoms of secondary infection. Spore dissemination thus occurred via the juice dripping from the fruit infected with *M. laxa* and through direct physical contact between fruits, leading to a spread of infection (Supplementary Material A).

**Cumulative incidence of brown rot during storage under different preharvest and storage conditions.** During the 2018 experiment, the cumulative incidence of brown rot disease at the end of storage was higher for later harvest dates (Fig. 2). Indeed, a cumulative incidence of 10.4% (mean value for all experimental treatments at the end of storage) was observed after the first harvest, and this value increased to 52.5% after the third harvest (Supplementary Material B). The disease progression curve was steeper after the third harvest than after the other harvests, with fruits from trees that had been well watered (100% CWR) being the most contaminated, with a mean loss of 70% at the end of storage, versus only 35% for fruits from trees grown under moderate water stress (50% CWR). For all the experimental treatments, fruits from the well-irrigated trees had a higher cumulative incidence of brown rot than those from water-stressed trees, whereas fruit load had no marked effect on disease incidence. The greatest fruit losses (83.3%) were observed after the third harvest for the well-irrigated and low-load treatment, whereas only 3.3% of the fruits collected after the first harvest from water-stressed and high-load treatment trees displayed symptoms at the end of the storage period.

In the 2019 experiment, the cumulative incidence of brown rot increased with the harvest date but also with storage temperature (Fig. 3). Irrigation level also affected disease development, but this effect was not uniform over storage temperatures and harvest dates (Supplementary Material B). Fruits stored at 2°C displayed no sign of infection during storage (for either the first or second harvest date), whereas the incidence was higher for storage at 15°C (28% on average at the end of the storage) and 25°C (62%). Losses at the end of storage were maximal after the second harvest for fruits from 70% CWR treatment stored at 25°C (95%).

In both experiments, fruits from well-irrigated trees (100% CWR in 2018 and 70% CWR in 2019) were significantly larger than those from trees growing under water stress (70% CWR in 2018 and 30% CWR in 2019) (Supplementary Material B).

**Modeling the influence of pre- and postharvest covariates on the time to appearance of brown rot on nectarines during storage.** The horticultural practices considered had no direct effect on disease during storage, but they did affect host susceptibility during the preharvest stage by affecting the degree of cuticular cracking. Fruit mass served as a proxy for this phenomenon. Both irrigation and fruit load significantly influenced the fruit mass at

harvest, but irrigation level had a larger effect on fruit mass than fruit load (Supplementary Material B). Similarly, harvest date had no direct influence on brown rot during storage, but it did have effects via meteorological, physiological, and epidemiological variables considered as covariates in the survival analysis (Table 1). During 2018 and 2019, 433 of the 1,560 fruits on which brown rot disease was assessed during storage were infected at the end of the experiment (72% right-censoring). The best five models in terms of BIC are presented in Supplementary Material C. The Gompertz's functional shape seemed to be the most suitable to explain the observed brown rot in nectarines. The model selection procedure retained as best model the one with the following covariates: $x = \{1, w, I_H, WD_H, T_S\}$. The fitting of the best model identified six significant parameters (Table 2, $P \le 0.05$). The hazard ratios for these parameters were greater than that for all the selected variables. Thus, increases in storage temperature, fruit mass, brown rot prevalence at harvest, or mean wetness duration in the week before harvest increased the risk of brown rot during storage. The model suggested that the risk of infection increased by a factor of >60 for an increase in temperature from 2 to 25°C, whereas the hazard was almost three times greater for a fruit weighing 200 g than for a fruit weighing 150 g. The predictive quality of this model was good in the sixfold cross-validation procedure ($D_{XY} = 0.68$). For calculation of the survival probability with the calibrated model, we used the mean fruit mass ($w$) and the values of other selected covariates ($I_H, WD_H, T_S$) at each harvest date and storage temperature (Table 1). In 2018, the model estimates of the probability of brown-rot free survival were satisfactory for the various irrigation treatments and harvest dates, despite an overestimation for nectarines harvested at the third harvest date (Fig. 4). In 2019, the model accurately captured the steeper decrease in survival after the second harvest than after the first harvest (Fig. 5). The model also described the temperature effect well, especially for the minimum (2°C) and maximum (25°C) temperatures.

## DISCUSSION

This study provides experimental evidence that secondary infections due to *M. laxa* conidia do not occur during storage for the evaluation time (7 days) if the noninfected fruit does not come into contact with fruit displaying sporulation. We also found that the secondary inoculum could be spread via the juice dripping from the infected fruit. These results contrast with the assumptions made in several previous studies (Bernat et al. 2017c; Garcia-Benitez et al. 2020; Tian and Bertolini 1999), according to which the conidia produced by *Monilinia* spp. during storage were thought to be potentially important for the spread of secondary infections via turbulent air movements. However, Bernat et al. (2017b) found that sources of *Monilinia* spp. were rarely found on the surfaces and in the environment of packing houses.

Attention should thus be paid to controlling spore dispersal on harvested fruits during handling practices at harvest and during post-harvest stages (Amorim et al. 2008; Bernat et al. 2017a), such as fruit grading and transport. Moreover, the use of separating alveoli in crates may prevent secondary infections by limiting fruit-to-fruit contact. Practices that can decrease the number of conidia present on the fruit surface at harvest, such as immersion in hot water (Karabulut et al. 2010), are also of potential interest. No secondary infections were observed during storage for 7 days, but observations over longer periods are required to confirm the results obtained here because the expression of symptoms of the secondary infections could take more than 7 days (Bernat et al. 2017c). However, stone fruit are generally not stored for long periods (Camelo 2004). Moreover, although *M. laxa* is the most common species in French orchards (Labeyrie and Chambeyron 2020; Ruesch et al. 2021), *M. fructicola* may also be present and may sporulate more abundantly (De Cal and Melgar-ejo 1999; Villarino et al. 2010). These experiments could thus be extended to other *Monilinia* species and to other peach/nectarine cultivars.
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Cumulative brown rot incidence during storage for 'Nectarlove' nectarines harvested on three different dates in 2018. The fruits came from trees subjected to two preharvest irrigation treatments: A, 100% crop water requirement (CWR) and B, 50% CWR, where CWR is the crop water requirement (the percentage is referred to the fraction of crop water requirement satisfied by irrigation). Fruits were stored for 2 days at 2°C and then for the rest of the experiment at 20°C. The points indicate the mean disease progression in crates, and the bars indicate the standard error of the mean.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Cumulative brown rot incidence at different storage temperatures for 'Nectarlove' nectarines harvested at A and B, 134 days after full bloom (DAFB) and C and D, 141 DAFB. The fruits came from trees subjected to two preharvest irrigation treatments: 70% crop water requirement (CWR) (A to C) and 30% CWR (B to D), where CWR is the crop water requirement (the percentage is referred to the fraction of crop water requirement satisfied by irrigation). The points indicate the mean disease progression in crates, and the bars indicate the standard error of the mean.</figcaption></figure>
The result of the secondary infection experiment justified the choice of survival analysis at the individual fruit level to explain the pattern of brown rot observed during storage. The censoring rate was high, which potentially indicated that the experiments ended too soon. The model could be improved by adding data from additional longer experiments in a broader range of conditions. The model selection process retained the Gompertz as the most accurate functional shape for the survival function, probably for its ability to capture the increase in the vulnerability to extrinsic causes (such as infectious disease) with senescence (Ricklefs and Scheuerlein 2002). The selected variables, which influenced the fruit susceptibility to the disease during storage, were related to fruit morphology and maturity (fruit mass), epidemiology (brown rot prevalence at harvest), and environmental conditions (mean daily duration of wetness in the week before harvest and storage temperature).

Fruit mass was considered a meaningful variable because it is a useful proxy for the area of cuticular cracking on the surface of the fruit (Børve et al. 2000; Gibert et al. 2007). However, cuticular development during the growing season depends on fruit cultivar (Lara et al. 2014), so the relationship between cuticular crack size and fruit mass may differ between cultivars. In future studies, it may be of interest to investigate the relationship between fruit cuticular crack size and brown rot during storage directly. Fruit mass is also an indicator of the state of maturity of the fruit (Kader 1999), which is also of importance in the context of brown rot. During maturation and ripening, the fruit cuticle undergoes biochemical and structural changes, promoting the fungal sporulation and the germination of fungal spores (Oliveira Lino et al. 2016).

The prevalence of brown rot at harvest was also retained by the model selection process. The observed values of this variable were higher for later harvest dates in 2018 and 2019 because the fruits were more mature at harvest and had been exposed to the fungal inoculum present in the air for longer. The incidence of brown rot during postharvest has been shown to be positively correlated with the prevalence of brown rot at harvest in various climatic and crop conditions (Emery et al. 2000; Gell et al. 2009; Luo and Michailides 2003; Villarino et al. 2012; Xu et al. 2007). This variable is positively related to the density of *Monilinia* spp. conidia on the fruit surface (Bannon et al. 2009; Holb 2008), which is predictive of the incidence of brown rot infections after harvest (Gibert et al. 2009). However, alternative variables, such as the incidence of latent infection and the density of conidia at different stages of fruit development, could be measured in the field and included as candidate variables for predicting losses due to postharvest brown rot (Gell et al. 2008, 2009; Keske et al. 2011; Villarino et al. 2012).

Mean daily wetness duration in the week before harvest was the only meteorological variable found to be significant and retained as a variable in the best survival model. This variable was already considered to make an important contribution to penetration and the incidence of latent infection due to *Monilinia* spp. in stone fruit

(Gell et al. 2008; Koball et al. 1997; Luo and Michailides 2003). However, alternative parameterizations of wetness duration are possible, considering different time lags before the harvest or considering different aggregation methods. Moreover, other meteorological variables, such as rainfall and temperature in the field, have also been reported to be important for spore dispersal and for the development of latent infections (Biggs and Northover 1988; Tamm and Fluckiger 1993), which could reactivate during storage. The possibility of such variables being included in the best model should therefore be checked by considering a wider range of meteorological and epidemiological conditions.

Mean storage temperature was also retained in the best compromise model. An effect of increasing temperature was observed in the 2 to 25°C range, as already reported by Bernat et al. (2017c) on peach, and Tamm and Fluckiger (1993) and Xu et al. (2001) in Petri dishes. At 2°C, no visual signs of brown rot infection were observed during storage, but other studies have reported a phase of exponential increase in brown rot infections after a longer storage period at a similar temperature (Bernat et al. 2017c; Garcia-Benitez et al. 2020). It would be interesting to consider storage temperature as a covariate changing over time in the survival model (Petersen 1986) to evaluate the effect of different temperature scenarios on the incidence of brown rot during the nectarine postharvest supply chain (e.g., during transport, sale, and household storage conditions) (Garcia-Benitez et al. 2020). RH during storage was not considered in this analysis because this variable was measured only during the 2019 experiment. However, Gibert et al. (2009) found no effect of RH on the rates of brown rot on stored nectarines. By contrast, Bernat et al. (2018) found that the combination of high RH and low temperature could extend the viability of conidia during storage. Several studies have highlighted the importance of this variable for spore germination, but these studies were performed in vitro, and significant results were obtained only at near-saturation conditions (RH ≥ 97%) (Byrde and Willetts 1977; Tamm et al. 1995; Xu et al. 2001).

Finally, the model provided was of satisfactory explanatory and predictive value, as shown by model cross-validation. The selected

<table><caption>TABLE 2. Coefficient estimates and associated test statistics for the best model (Gompertz functional shape) in terms of Bayesian information criterion to explain the time-to-infection of brown rot on nectarine during storage</caption><thead><tr><th>Parameter</th><th>Coefficient estimate</th><th>95% CI</th><th>P &gt; -χ<sup>2</sup></th><th>exp(coefficient)</th></tr></thead><tbody><tr><td>Shape, a</td><td>0.295</td><td>[0.272, 0.318]</td><td>&lt;0.0001</td><td></td></tr><tr><td>Rate, b</td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept, β<sub>0</sub></td><td>-15.482</td><td>[-16.38, -14.57]</td><td>&lt;0.0001</td><td>1.89·10<sup>-7</sup></td></tr><tr><td>w (g)</td><td>0.021</td><td>[0.019, 0.024]</td><td>&lt;0.0001</td><td>1.02</td></tr><tr><td>I<sub>H</sub> (%)</td><td>0.637</td><td>[0.456, 0.818]</td><td>&lt;0.0001</td><td>1.58</td></tr><tr><td>WD<sub>H</sub> (h)</td><td>0.064</td><td>[0.049, 0.079]</td><td>&lt;0.0001</td><td>1.07</td></tr><tr><td>T<sub>S</sub> (°C)</td><td>0.172</td><td>[0.151, 0.193]</td><td>&lt;0.0001</td><td>1.19</td></tr></tbody></table>

<table><caption>TABLE 1. Covariate values measured for the harvest dates in the 2018 and 2019 storage experiments<sup>a</sup></caption><thead><tr><th rowspan="3">Covariate name</th><th rowspan="3">Symbol</th><th colspan="5">Value</th></tr><tr><th colspan="3">2018</th><th colspan="2">2019</th></tr><tr><th>117 DAFB</th><th>124 DAFB</th><th>132 DAFB</th><th>134 DAFB</th><th>141 DAFB</th></tr></thead><tbody><tr><td>Conditions at harvest</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fruit mass<sup>b</sup> (g)</td><td>w</td><td>181 ± 33</td><td>162 ± 33</td><td>180 ± 38</td><td>161 ± 27</td><td>136 ± 25</td></tr><tr><td>Growing degree-days (°C-days)</td><td>GDD<sub>H</sub></td><td>1,357</td><td>1,495</td><td>1,652</td><td>1,548</td><td>1,685</td></tr><tr><td>Brown rot prevalence (%)</td><td>I<sub>H</sub></td><td>0.16</td><td>0.65</td><td>2.41</td><td>0.77</td><td>1.18</td></tr><tr><td>Meteorological conditions (in the 7 days before harvest)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Average daily temperature (°C)</td><td>T<sub>H</sub></td><td>26.4</td><td>25.9</td><td>26.4</td><td>25.7</td><td>26.5</td></tr><tr><td>Cumulative wetness duration (h)</td><td>WD<sub>H</sub></td><td>3.9</td><td>22.8</td><td>21.6</td><td>0.9</td><td>14.3</td></tr><tr><td>Storage condition</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean temperature (°C)</td><td>T<sub>S</sub></td><td>20</td><td>20</td><td>20</td><td>2/15/25</td><td>2/15/25</td></tr></tbody></table>

<footnote><sup>a</sup> In 2019, three different levels of average storage temperature *T*<sub>S</sub> were tested. Harvest dates are expressed in days after full bloom (DAFB).</footnote>

<footnote><sup>b</sup> Average value ± standard deviation.</footnote>
covariates highlighted the greater susceptibility of high-grade fruit
than of smaller fruit to the appearance of brown rot symptoms during
storage (Gibert et al. 2009), suggesting that these fruits should be
consumed more rapidly or stored at a lower temperature. Moreover,
the model simulation suggested that if epidemiological and meteoro-
logical conditions favorable for brown rot development during stor-
age occur before harvest, the fruit should be stored at low
temperature to prevent massive fruit losses during postharvest stages.

The model could potentially be used to calculate the expected time
to appearance of brown rot disease for individual fruits, which would
make it possible to determine the expected fruit loss to brown rot dur-
ing storage. Because temperature management is also important for
the maintenance of nectarine quality during storage (Aubert et al.
2014; Paull 1999), this model could also be used in combination
with models describing changes in nectarine quality during storage
(Casagrande et al. 2021). This would make it possible to determine

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Parametric (continuous) and Kaplan Meier (dotted) estimates of brown rot-free survival during storage in 2018, for nectarines grown under **A**, a 100% crop water requirement (CWR) and **B**, a 50% CWR irrigation treatment for different harvest dates. CWR is the crop water requirement (the percentage is referred to the fraction of crop water requirement satisfied by irrigation). In this case, survival probability is the probability of a nectarine presenting no brown rot during storage.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Parametric (continuous) and Kaplan Meier (dotted) estimates of brown rot-free survival during storage in 2019 for nectarines harvested at the **A**, first (134 days after full bloom [DAFB]) and **B**, the second (141 DAFB) harvest dates and stored at different storage temperatures. In this case, survival probability is the probability of a nectarine presenting no brown rot during storage.</figcaption></figure>
the optimal storage temperature for preventing fruit loss and maximizing fruit quality.

## ACKNOWLEDGMENTS

We thank Sabrina Azhaf and Maurane Damoiseaux for their dedication in performing these experiments. They would also like to thank Sophie Brunel-Muguet, Jean-Luc Regnard, and Didier Andrivon, who made valuable suggestions for improving this article. The authors also thank Daniel Plenet for providing fruit for the experiments.

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