# Dare to be resilient: the key to future pesticide-free orchards?

Marie Serrie, Fabienne Ribeyre, Laurent Brun, Jean-Marc Audergon,
Bénédicte Quilot, Morgane Roth

<figure><img src="image_1.png" /></figure>

## To cite this version:

Marie Serrie, Fabienne Ribeyre, Laurent Brun, Jean-Marc Audergon, Bénédicte Quilot, et al. Dare to be resilient: the key to future pesticide-free orchards?. Journal of Experimental Botany, 2024, erae150, 10.1093/jxb/erae150. hal-04560076

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Submitted on 30 Apr 2024

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# Dare to be resilient: the key to future pesticide-free orchards?

Marie Serrie, Fabienne Ribeyre, Laurent Brun, Jean-Marc Audergon,
Bénédicte Quilot, Morgane Roth

<figure><img src="image_1.png" /></figure>

## To cite this version:

Marie Serrie, Fabienne Ribeyre, Laurent Brun, Jean-Marc Audergon, Bénédicte Quilot, et al. Dare to be resilient: the key to future pesticide-free orchards?. Journal of Experimental Botany, 2024, 75 (13), pp.3835-3848. 10.1093/jxb/erae150. hal-04560076v2

HAL Id: hal-04560076

https://hal.inrae.fr/hal-04560076v2

Submitted on 5 Jul 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
Dare to be resilient: the key to future pesticide-free orchards?

Marie Serrie¹, Fabienne Ribeyre², Laurent Brun³, Jean-Marc Audergon¹, Bénédicte Quilot¹, Morgane Roth¹*

¹INRAE, UR GAFL, Avignon, France

²CIRAD, UMR PHIM, Montpellier, France

³INRAE, UERI Gotheron, Saint-Marcel-Lès-Valence, France

*Correspondence:

Morgane Roth

INRAE GAFL

67 Allée des chênes, 84140 Avignon, France

email: morgane.roth@inrae.fr

Marie Serrie – marie.serrie@inrae.fr

Fabienne Ribeyre – fabienne.ribeyre@cirad.fr

Laurent Brun – laurent.brun@inrae.fr

Jean-Marc Audergon – jean-marc.audergon@inrae.fr

Bénédicte Quilot – benedicte.quilot-turion@inrae.fr

Morgane Roth – morgane.roth@inrae.fr
REVIEW PAPER

# Dare to be resilient: the key to future pesticide-free orchards?

Marie Serrie<fnref n="1" />, ID, Fabienne Ribeyre<fnref n="2" />, ID, Laurent Brun<fnref n="3" />, ID, Jean-Marc Audergon<fnref n="1" />, ID, Bénédicte Quilot<fnref n="1" />, ID
and Morgane Roth<fnref n="1" />,* ID

Received 3 October 2023; Editorial decision 5 April 2024; Accepted 15 April 2024

Editor: Monica Höfte, University of Ghent, Belgium

## Abstract

Considering the urgent need for more sustainable fruit tree production, it is high time to find durable alternatives to the systematic use of phytosanitary products in orchards. To this end, resilience can deliver a number of benefits. Relying on a combination of tolerance, resistance, and recovery traits, disease resilience appears as a cornerstone to cope with the multiple pest and disease challenges over an orchard's lifetime. Here, we describe resilience as the capacity of a tree to be minimally affected by external disturbances or to rapidly bounce back to normal functioning after being exposed to these disturbances. Based on a literature survey largely inspired from research on livestock, we highlight different approaches for dissecting phenotypic and genotypic components of resilience. In particular, multisite experimental designs and longitudinal measures of so-called 'resilience biomarkers' are required. We identified a list of promising biomarkers relying on ecophysiological and digital measurements. Recent advances in high-throughput phenotyping and genomics tools will likely facilitate fine scale temporal monitoring of tree health, allowing identification of resilient genotypes with the calculation of specific resilience indicators. Although resilience could be considered as a 'black box' trait, we demonstrate how it could become a realistic breeding goal.

**Keywords:** Biomarkers, breeding, disease resilience, fruit tree, pesticide reduction, pests and diseases, sustainability.

## Introduction

Plant pests and diseases are responsible for massive yield and economic losses and are a global threat for food safety and security (Savary et al., 2019). The diversity of endemic organisms that threaten agricultural crops includes a large range of viruses, bacteria, fungi, phytoplasma, nematodes, and pests. Since these biotic stresses can occur simultaneously throughout the season, this means that plants must face a multi-disease challenge, where possible co-infections have the potential

to considerably affect growth and productivity (Savary et al., 2017). On the other hand, due to the large spread of high-yield and genetically homogeneous cultivars for most crops, some major genetic resistances have already been overcome (Gessler et al., 2006; Gibson and Nguyen, 2021). This has led to devastating epidemics in the last decades both for annual and perennial crops, as shown by viral epidemics of cereal yellow dwarf or citrus tristeza (Jones, 2021). Moreover, the current

<footnote n="1">¹ INRAE, UR GAFL, Avignon, France</footnote>

<footnote n="2">² CIRAD, UMR PHIM, Montpellier, France</footnote>

<footnote n="3">³ INRAE, UERI Gotheron, Saint-Marcel-Lès-Valence, France</footnote>

<footnote>* Correspondence: morgane.roth@inrae.fr</footnote>
## Highlight

Based on a multidisciplinary literature review, we contextualize, reflect on and propose a novel breeding perspective focused on disease resilience to deal with the massive pesticide restrictions in fruit production.

<figure><img src="image_3.png" /></figure>
global warming strongly favours the emergence and the re-
emergence of pests and pathogens, and can amplify their im-
pact on crops by modifying their biological reactions (Ladányi
and Horváth, 2010; Das et al., 2011; Skendžić et al., 2021).
On aphids for example, more species are being observed in
orchards as well as a higher number of reproduction cycles per
year (Hullé et al., 2010; Devi et al., 2019). This unpredictability
means that although we know that biotic pressures on crops are
likely to intensify in the next few years, they might be difficult
to forecast accurately.

Since fruit trees are perennials, pathogen attacks in a single
year are likely to affect tree health for the following years. In
addition, long rotation time favours the settlement of plant
pests and diseases within orchards. The cumulative effect of
the different pests and diseases over the years is therefore much
more pronounced in fruit trees when compared with annual
crops. For producers, this implies that it is not enough to sus-
tain tree health in a single year, but the entire lifetime of the
orchard has to be accounted for. Nowadays this multiyear chal-
lenge is largely addressed by phytosanitary treatments, and ef-
ficient alternatives remain very limited (Lamine et al., 2017).
For comparison, the treatment frequency index (TFI) was
29.5 and 18.4, respectively, for apple and peach in 2018 in
France (Desprat et al., 2021), which is two to six times higher
than values reported in grain (TFI=4.6) and vegetable crops
(TFI=7.5, Crisan, 2019; Chapelle, 2023). It should also be high-
lighted that a significant proportion of phytosanitary products
used in orchards are dictated by fresh-market demands, which
require flawless fruit with long-conservation ability. All these
facts explain why the fruit tree sector is highly dependent on
phytosanitary products, and this raises several concerns.

Firstly, not every disease or pest can be cured by pesticides,
such as sharka disease caused by *Plum pox virus* (García and
Cambra, 2007) or European stone fruit yellows caused by
the bacterium *Candidatus Phytoplasma prunorum* (*Sauvion
et al.*, 2012). Besides, the massive use of phytosanitary prod-
ucts accelerates the emergence of resistant pests and patho-
gens (Kole *et al.*, 2019). In any case, the negative impact of
pesticides on environmental and human health is largely ac-
knowledged (Wilson and Tisdell, 2001) and society is urging a
drastic reduction of pesticides (Du *et al.*, 2017). To address this
concern, many active compounds have already been banned
(Donley, 2019), and the trend is intensifying in Europe with
the ‘Farm to Fork’ strategy (European Commission, 2020). In
addition, stricter application procedures are being imposed on
farmers: for instance, in France restrictions aimed at protect-
ing bees and other pollinators over flowering periods (Ferreira
et al., 2021). Finally, if many biocontrol products (such as es-
sential oils, antimicrobial products, or chemical mediators
like pheromones and kairomones) have been developed for
orchards, they are still often less efficient and costlier than syn-
thetic products (Nicot *et al.*, 2012). All these points clearly
highlight the long-term dead end of phytosanitary products in
durably fighting pests and diseases as well as the urgent need

to find alternative and sustainable solutions to these products
in orchards.

Among available alternatives, breeding trees for pest and
disease resistance is an efficient strategy to cope with biotic
pressures. For instance some resistance mechanisms have al-
ready been identified for sharka and bacterial canker in ap-
ricot (Lambert et al., 2009; Rubio et al., 2014; Omrani et al.,
2019), and for aphids and powdery mildew in peach (Sauge
et al., 2012; Lambert et al., 2016; Pascal et al., 2017; Duval
et al., 2022). Likewise, in apple, the pyramiding of function-
ally different major resistance genes has shown great effi-
ciency in fighting individually scab, powdery mildew, or fire
blight (Baumgartner et al., 2015). We should also acknowl-
edge the emergence in the last decade of a few breeding
programmes targeting multiple disease resistance (Wiesner-
Hanks and Nelson, 2016). In grapevine, new varieties com-
bining polygenic resistance to both grapevine downy mildew
and powdery mildew have been successfully created within
the programme ‘ResDur’ (Schneider et al., 2019). However,
so far in fruit trees, breeding for low susceptibility to di-
verse pests and diseases has often been overlooked. Breeding
programmes are mostly based on a restricted elite material,
targeting varieties suitable to conventional management in
the widest possible range of locations. As a result, we still
lack elite materials that combine multiple resistances and that
would be adapted to low phytosanitary protection. To truly
tackle the agroecological transition, a paradigm shift is nec-
essary in fruit tree breeding to prioritize natural defences of
trees against pests and diseases (Fig. 1). Unfortunately, the
sources of genetic resistance are rare, often come from wild or
closely related species, and are difficult to precisely detect and
introgress, especially if they have a polygenic basis. Moreover,
it has been recognized that the explicit breeding for mul-
tiple resistance requires systematic and exhaustive recording
of pathogen burden at individual level throughout the time
of infection (Mulder and Rashidi, 2017). Unfortunately, these
data are not currently recorded in orchards, either because it
is particularly laborious and costly, or because adequate inoc-
ulation protocols are lacking. If we push this reflection fur-
ther, considering that new pests or diseases and alternative
strains will continue to emerge or re-emerge through the
years in a way that is difficult to predict in the context of
climate change, and considering the possible bypass of plant
resistances, targeting multiple resistance may actually not be
sufficient. In addition, it has been shown that a drastic reduc-
tion of the phytosanitary umbrella could lead to the reap-
pearance of forgotten diseases, which raises serious concerns
for the future. Considering all these constraints, and the fact
that in traditional breeding it takes 20 years on average to
register a new fruit variety, during the time needed to com-
bine multiple diseases resistances one after the other, the
obtained varieties might be already outdated. We thus claim
that targeting multi-resistance might not be compatible with
the need to rapidly find alternative solutions to pesticides and
**Abstract**

In a context of urgent need for a more sustainable fruit tree production, it's high time to find durable alternatives to the systematic use of phytosanitary products in orchards. To this aim, resilience can deliver a number of benefits. Relying on a combination of tolerance, resistance and recovery traits, disease resilience appears as a corner stone to cope with the multiple pest and disease challenge over the orchard's lifetime. Here, we propose to describe resilience as the capacity of a tree to be minimally affected by external disturbances or to rapidly bounce back to normal functioning after being exposed to these disturbances. Based on a literature survey largely inspired from research on livestock, we highlight different approaches for dissecting resilience phenotypic and genotypic components. In particular, multisite experimental designs and longitudinal measures of so-called 'resilience biomarkers' are required. We identified a list of promising biomarkers relying on eco-physiological and digital measurements. Recent advances in high-throughput phenotyping and genomics tools will likely facilitate the fine and temporal monitoring of tree health, allowing to identify resilient genotypes with the calculation of specific resilience indicators. Although resilience can appear as 'black box' trait, we demonstrate how it could become a realistic breeding goal.

**Key words:** biomarkers, breeding, disease resilience, fruit tree, pesticide reduction, pests and diseases, sustainability
that, consequently, new breeding targets must be envisioned
that go beyond the idea of multiple resistances.

Searching for the right breeding target:
disease resilience as a promising approach

Disease resilience: resistance, tolerance, and recovery interaction

As illustrated by the multiple, sometimes antagonistic defini-
tions that have emerged over the years, resilience is an attractive
concept but it remains difficult to define. This ambiguity orig-
inates from a widespread use of the term ‘resilience’ to describe
phenomena in multiple disciplines across biology, physics, and
social sciences (Martin-Breen and Anderies, 2011). Besides, the
fact that resilience is used at multiple scales and can describe
properties of an isolated object, an individual, or even of com-
plex multi-organism or multi-actor socio-economic systems
adds to the complexity and plasticity of this term. As recom-
mended by Martin-Breen and Anderies (2011), we need to
clarify the nature of what is being considered in our context. In
our geneticists’ view, we focus on the scale of individual trees or
genotypes. A broad definition of resilience, also called general
environment resilience, has emerged in the last years and could
be relevant here. General environment resilience is defined as
the capacity of an organism to be minimally affected by a dis-
turbance or to rapidly return to the physiological, behavioural,
cognitive, health, affective, and production states that pertained
before exposure to a disturbance (Colditz and Hine, 2016).
Interestingly, this concept is also often referred to as ‘robust-
ness’, which is more commonly used to describe the combina-
tion of a high production potential with high resilience (Knap
and Doeschl-Wilson, 2020). More specifically, general envi-
ronment resilience represents the capacity of an organism to
maintain its productivity in a wide range of environments, in-
cluding stressful conditions, without compromising reproduc-
tion, health, and wellbeing (Knap, 2005; Urruty et al., 2016). It
is thus a composite trait encompassing a variety of profiles in
response to different kinds of perturbations (e.g. disease, heat
stress, drought).

Bearing in mind the above-described context, and for the
sake of clarity, in the present work the focus will be on ‘di-
sease resilience’, as derived from the definition of general en-
vironment resilience (Albers et al., 1987; Bisset and Morris,
1996; Bishop, 2012; Doeschl-Wilson et al., 2021). It is often
admitted that disease resilience captures two complementary
host defence mechanisms, resistance and tolerance, which are
defined as follows:

* Disease resistance is the ability of the individual to inhibit or limit within-host pathogen load either by preventing infection in the first place or by inhibiting within-host pathogen replication once infected (Agrios, 2005; Råberg et al., 2008; Bishop, 2012; Knap and Doeschl-Wilson, 2020).

* Disease tolerance is the ability of an infected host to reduce the impact of this infection on performance and health, i.e. maintaining high health or production performance at a given within-host pathogen load without necessarily reducing this pathogen load (Agrios, 2005; Knap and Doeschl-Wilson, 2020). A tolerant organism maintains its performance despite the pathogen burden (Mulder and Rashidi, 2017).

In summary, disease resistance is the ability to reduce and con-
trol pathogen load whereas disease tolerance is the ability of an
infected host to limit the damage caused by a given within-
host pathogen load without necessarily reducing this pathogen
load.

In the context of fruit tree breeding, we define disease re-
silience as the capacity of a given individual (i.e. a genotype)
to be minimally affected by one or multiple attacks of pests
or diseases, and thus to reduce their impact via resistance or
tolerance mechanisms, but also to bounce back to normal
functioning after being exposed to these disturbances. This
ability to return to pre-disturbance levels is achieved through
recovery mechanisms reflected in a dynamic manner by high
elasticity and a low return time (Fig. 2, see also Lloret et al.,
2011; Hodgson et al., 2015; DeSoto et al., 2020). Therefore,
we recommend describing disease resilience by quantifying to-
gether *resistance*, *tolerance*, and *recovery*. This definition accounts
for the fact that disease resilience can follow multiple trajec-
tories that could therefore be governed by different genetic
mechanisms. We think that this comprehensive view is nec-
essary for setting up durable fruit tree production under low
phytosanitary protection.

Beyond multi-resistance: the added value and the
opportunity of disease resilience

In practice, disease resilience can be used as a ‘black box trait’
that can be targeted without an individual monitoring of path-
ogen load and without dissecting components of resistance,
tolerance, and recovery (Mulder and Rashidi, 2017; Knap and
Doeschl-Wilson, 2020). Until now, most studies on fruit trees
have focused on the search for resistance genes for a specific
pathogen (Fig. 1). The multi-disease context has not been
considered as such, thus ignoring interactions between pests
and diseases as well as cumulative effects of multiple attacks
and overlooking possible recovery capacity of trees (Khan and
Korban, 2022). Paying more attention to trees’ ability to re-
cover would bring a more integrative and long-term vision
of plant health. Thus, breeding for disease resilience appears to
be a pragmatic and complementary way for fruit tree breed-
ers to cope with the complex, fluctuating, and unpredictable
multi disease and pest challenge. The question remaining is
how to measure disease resilience in practice. In perennial spe-
cies, much work has been carried out over the last decades on
so-called forest resilience (Thompson et al., 2009; Nikinmaa
et al., 2020). If these studies were to be particularly relevant
**Introduction: the challenge of pests and diseases for a durable fruit production and the need to develop new breeding targets**

Plant pests and diseases are responsible for massive yield and economic losses and are a global threat for food safety and security (Savary et al., 2019). The diversity of endemic organisms which threaten agricultural crops includes a large range of viruses, bacteria, fungi, phytoplasma, nematodes and pests. Since these biotic stresses can occur simultaneously throughout the season, this means that plants must face a multi-disease challenge where possible co-infections have the potential to considerably affect growth and productivity (Savary et al., 2017). On the other hand, due to the large spread of high-yield and genetically homogeneous cultivars for most crops, some major genetic resistances have already been overcome (Gessler et al., 2006; Gibson and Nguyen, 2021). This has led to devastating epidemics in the last decades both for annual and perennial crops as shown by virus disease epidemics of cereal yellow dwarf or citrus tristeza (Jones, 2021). Moreover, the current global warming strongly favours the emergence and the re-emergence of pests and pathogens, and can amplify their impact on crops by modifying their biological reactions (Ladányi and Horváth, 2010; Das et al., 2011; Skendžić et al., 2021). On aphids for example, more species are being observed in orchards as well as a higher number of reproduction cycles per year (Hullé et al., 2010; Devi et al., 2019). This unpredictability means that although we know that biotic pressures on crops are likely to intensify in the next few years, they might be difficult to forecast accurately.

Since fruit trees are perennials, pathogen attacks in a single year are likely to affect tree health for the following years. In addition, long rotation time favours the settlement of plant pests and diseases within the orchards. The cumulative effect of the different pests and diseases over the years is therefore much more pronounced in fruit trees when compared to annual crops. For producers, this implies that it is not enough to sustain tree health in a single year, but the entire lifetime of the orchard has to be accounted for. Nowadays this multiyear challenge is largely addressed by phytosanitary treatments and efficient alternatives remain very limited (Lamine et al., 2017). For comparison, the treatment frequency indexes (TFI) amounted to 29.5 and 18.4 respectively for apple and peach in 2018 in France (Desprat et al., 2021) which is two to six times higher than values reported in grain (TFI = 4.6) and vegetable crops (TFI = 7.5, Chapelle, 2020; Crisan, 2019). It should also be highlighted that a significant proportion of phytosanitary products used in orchards is dictated by the fresh market demands, which require flawless fruit with long-conservation ability. All these facts explain why the fruit tree sector is highly depending on phytosanitary products, which raises several concerns.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Description of the major shifts recommended for a transition towards a more sustainable breeding strategy accounting for the current environmental and societal constraints.</figcaption></figure>

for searching metrics of resilience (Lloret *et al.*, 2011; DeSoto *et al.*, 2020), due to several considerations, they cannot be directly translated into our study system. Chiefly, resilience is there considered at the level of forests, regarded as systems where interspecific interactions are at play, whereas in our case the focus is made up at the genotypic level within monospecific orchards. Besides, another important difference between forestry and fruit production is that cultural practices, such as pruning and fertilization, strongly determine the expression of traits in fruit trees. Caution is therefore required before transferring knowledge on forest resilience to the case of disease resilience for fruit tree breeding.

## Breeding for disease resilience: a source of inspiration in livestock

Although the methodology for disease resilience breeding does not yet exist for fruit trees, the animal field provides promising sources of inspiration when considering the recent flourishing literature related to disease resilience in livestock breeding. While searching for solutions to fruit tree-oriented matters in the field of livestock breeding may seem surprising at first sight, it turns out that many useful parallels can be drawn between these two worlds. Firstly, in fruit tree breeding as in animal breeding, the focus is on the individual scale where

each tree/animal is considered as a unit with a respective genotype. Secondly, fruit trees and livestock must both face the multi-disease challenge and their health must be managed on a multi-year basis, over and after a long juvenile phase. Thirdly, until today vaccines and antimicrobials were massively used to control livestock infection levels, which poses similar problems to phytosanitary products in fruit production in terms of long-term efficiency (microbial/pest resistances, new pathogen emergence) and societal acceptance (antibiotics/vaccines/pesticide residues in our food and in the environment). In both sectors, reducing the dependency on synthetic products is thus perceived as an urgent need.

Over the last few years, more and more studies have specifically addressed the question of breeding for disease resilience in livestock (Colditz and Hine, 2016; Mulder and Rashidi, 2017; Berghof *et al.*, 2019b; Knap and Doeschl-Wilson, 2020; Poppe *et al.*, 2020, 2022; Bai and Plastow, 2022). Among the many benefits, they highlight the fact that resilient animals require less attention, thereby decreasing labour and health costs, which represents important economic gains (Knap and Doeschl-Wilson, 2020). More resilient animals are expected to show minor deviations in performance compared with susceptible ones because they are less impacted by infections or have the ability to rapidly recover from diseases (Mulder and Rashidi, 2017; Berghof *et al.*, 2019b; Knap and Doeschl-Wilson, 2020).
Firstly, not every disease or pest can be curated by pesticides, such as Sharka disease caused by *Plum pox virus* (García and Cambra, 2007), or European stone fruit yellows (ESFY) caused by the bacteria *Candidatus Phytoplasma prunorum* (Sauvion *et al.*, 2012). Besides, the massive use of phytosanitary products accelerates the emergence of resistant pests and pathogens (Kole *et al.*, 2019). In any case, the negative impact of pesticides on environmental and human health is largely acknowledged (Wilson and Tisdell, 2001) and the society is urging for a drastic reduction of pesticides (Du *et al.*, 2017). To address this concern, many active compounds have already been banned (Donley, 2019) and the trend is intensifying in Europe with the 'Farm to Fork' strategy (European commission, 2020). In addition, stricter application procedures are imposed to farmers: for instance, in France restrictions aimed at protecting bees and other pollinators over flowering periods (Ferreira *et al.*, 2021). Last, if many biocontrol products (such as essential oils, antimicrobial products, or chemical mediators like pheromones and kairomones) have been developed for orchards, they are still often less efficient and costlier than synthetic products (Nicot *et al.*, 2012). All these points clearly highlight the long-term dead end of phytosanitary products to durably fight pests and diseases as well as the urgent need to find alternative and sustainable solutions to these products in orchards.

Among available alternatives, breeding for pest and disease resistance tree response to pest and disease attacks is an efficient strategy to cope with biotic pressures. For instance some resistance mechanisms have already been identified for sharka and bacterial canker in apricot (Lambert *et al.*, 2009; Rubio *et al.*, 2014; Omrani *et al.*, 2019), and for aphids and powdery mildew in peach (Sauge *et al.*, 2012; Lambert *et al.*, 2016; Pascal *et al.*, 2017; Duval *et al.*, 2022). Likewise, in apple, the pyramiding of functionally different major resistance genes have shown great efficiency to fight individually scab, powdery mildew or fire blight (Baumgartner *et al.*, 2015). In the last decade, we must also acknowledge the emergence of a few breeding programs targeting multiple disease resistance (Wiesner-Hanks and Nelson, 2016). In grapevine, new varieties combining polygenic resistance to both grapevine downy mildew and powdery mildew have been successfully created within the program 'ResDur' (Schneider *et al.*, 2018). However so far in fruit trees, breeding for low susceptibility to diverse pests and diseases has often been overlooked. Breeding programs are mostly based on a restricted elite material, targeting varieties suitable to conventional management in the widest possible range of locations. As a result, we still lack elite materials which combine multiple resistances and which would be adapted to low phytosanitary protection. To truly tackle the agroecological transition, a paradigm shift is necessary in fruit tree breeding to prioritise natural defences of trees against pests and diseases (Fig. 1). Unfortunately, the sources of genetic resistance are rare, often coming from wild or closely related species, and difficult to be precisely detected and introgressed, especially if they have a polygenic basis. Moreover, it has been recognised that the
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Conceptual illustration of disease resilience expression over time in fruit trees by distinction of (A) the multiple trajectories leading to disease resilience and (B) the decay of non-disease-resilient trees in a multi-disease context.</figcaption></figure>

Estimating the ability of an animal to resist or to recover quickly from a disturbance requires a completely novel phenotyping approach based on longitudinal measurements of animal performance before, during, and after a disturbance to provide dynamic trajectories of resilience. As such, multiple traits derived from kinetic production and fitness performance indicators have been explored for an operational measurement of disease resilience (Sandberg et al., 2006; Elgersma et al., 2018; Berghof et al., 2019a, b; Poppe et al., 2020, 2022; Bedere et al., 2022). In these examples, the identification of resilient animals is achieved by measuring the rate of deviation and the speed of return of these key variables to the normal or previous state. Studies might differ in the exact way dynamic resilience indicators are derived but the common underlying assumption is that resilience can be quantified by the scale, pattern, and duration of deviations.

A major advantage is that disease resilience indicators can be quantified in many practical ways based on routine measurements of animal productivity and performance. Ongoing work focuses on improving the heritability of resilience indicators,

mainly by better adapting experimental designs to the context of natural disease challenge (Bai and Plastow, 2022). An important question still remains on the effect of breeding for disease resilience on the infection itself, including disease transmission and pathogen evolution. Since resilient animals are able to be productive regardless of the pathogen burden, disease-resilient individuals could increase the spread and persistence of infections (Doeschl-Wilson et al., 2021). Nevertheless, given the contribution of resistance, tolerance, and recovery to disease resilience, one could also expect a reduction of pathogen pressure, hence curbing the arms race between hosts and pathogens.

## Towards an operational measurement of disease resilience in orchards

### Choosing a suitable experimental design for studying disease resilience in fruit trees

A few key elements need to be considered to quantify disease resilience experimentally in orchards. Firstly, orchards must be
explicit breeding for multiple resistance is requiring systematic and exhaustive recording of pathogen burden at individual level during all the time of infection (Mulder and Rashidi, 2017). Unfortunately, these data are not currently recorded in orchards, because it is particularly laborious and costly, or because we lack adequate inoculation protocols. If we push the reflection even further, considering that new pests or diseases and alternative strains will continue to emerge or re-emerge through years in a way that is difficult to predict in the context of climate change, and considering the possible bypass of plant resistances, targeting multiple resistance may actually not be sufficient. In addition, it has been shown that a drastic reduction of the phytosanitary umbrella could lead to the reappearance of forgotten diseases which raises serious concerns for the future. Considering all these constraints, and the fact that in traditional breeding it takes twenty years on average to register a new fruit variety, during the time needed to combine multiple diseases resistances one after the other, the obtained varieties might be already outdated. We thus claim that targeting multi-resistance might not be compatible with the need to rapidly find alternative solutions to pesticides and that, consequently, new breeding targets must be envisioned to go beyond the idea of multiple resistances.

## Searching for the right breeding target: disease resilience as a promising approach

### Disease resilience: resistance, tolerance and recovery interaction

As illustrated by the multiple, sometimes antagonistic definitions that have emerged over the years, resilience is an attractive concept but it remains difficult to define. This ambiguity originates from a widespread use of the term 'resilience' to describe phenomena in multiple disciplines across biology, physics and social sciences (Martin-Breen and Anderies, 2011). Besides, the fact that resilience is used at multiple scales and can describe properties of an isolated object, an individual or even of complex multi-organism or multi-actor socio-economic systems adds to the complexity and plasticity of this term. As recommended by Martin-Breen and Anderies (2011), we need to clarify the nature of what is being considered in our context. In our geneticists' view, we focus on the scale of individual trees or genotypes. A broad definition of resilience, also called general environment resilience, has emerged in the last years and could be relevant here. General environment resilience is defined as the capacity of an organism to be minimally affected by a disturbance or to rapidly return to the physiological, behavioural, cognitive, health, affective and production states that pertained before exposure to a disturbance (Colditz and Hine, 2016). Interestingly, this concept is also often referred to as 'robustness' which is more commonly used to describe the combination of a high production potential with high resilience (Knap and Doeschl-Wilson, 2020). More specifically, general environment resilience represents the capacity of an organism to maintain its productivity in a wide
managed under low phytosanitary protection to allow natural infections to (co-)occur, and because resilience is a dynamic process, monitoring these orchards over several consecutive years is essential. Secondly, experimental orchards must comprise genetically highly diversified accessions, for example by designing a core collection, to capture the phenotypic diversity underlying disease resilience (Frankel and Brown, 1984). In this process, closely related wild species, landraces, and elite cultivars of contrasting geographical origins should be represented and genetically characterized via genotyping or sequencing. Thanks to statistical analyses linking genomic and phenotypic information, such as genome wide association studies (Visscher et al., 2017), it will be possible to decipher the genetic architecture of disease resilience, including the number and position of quantitative trait loci and/or specific genes underlying this trait. The first objective is thus to screen a wide range of genetic diversity to characterize disease resilience mechanisms and their heritability. At a later stage, it may be necessary to investigate the behaviour of resilient candidate varieties in monovarietal orchards.

Finally, given that disease resilience also encompasses the ability of a tree to maintain itself when confronted to a different range of pathogen pressures, studying the response of the same accessions across contrasting environments is also of crucial importance. Of note, for studies focusing on resilience mechanisms *per se*, it can be relevant to provide a fine characterization

of the environment in which trees are growing through epidemiological monitoring or envirotyping methods (Xu, 2016). This requires specific instrumentation in the orchards and in this sense, new tools coming from digital agriculture, such as intelligent sensors or remote sensing images, offer attractive perspectives (Lee *et al.*, 2010; Whelan and Taylor, 2013).

To summarize, we propose that setting up a genetically highly diversified, multi-year and multi-site orchard design forms a strong basis for understanding the mechanisms of disease resilience throughout the life of a tree.

## How to monitor disease resilience in orchards: identification of resilience biomarkers

Disease resilience is a dynamic trait relying on multiple biological functions that might be difficult to assess accurately in the orchard. Although the classical visual monitoring of disease incidence and severity caused by the diverse source of pathogens (Madden *et al.*, 2017) is an essential step towards characterizing disease resilience, we should search for additional variables reflecting the impacts of infections on tree health over time. Proxy traits of tree performance or health, also called 'resilience biomarkers', need to be identified, and importantly, they should not be significantly invasive or destructive. Since disease resilience has not yet been explored in fruit trees, multiple possibilities should be tested and complementary variables might

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Illustration of resilience biomarkers that could be considered to study the impacts of pests and disease infection on fruit trees under low phytosanitary protection.</figcaption></figure>
range of environments, including stressful conditions, without compromising reproduction, health and wellbeing (Knap, 2005; Urruty et al., 2016). It is thus a composite trait encompassing a variety of profiles in response to different kinds of perturbations (ex. disease, heat stress, drought).

Reminding the above-described context, and for the sake of clarity, in the present work the focus will be made on 'disease resilience', as derived from the definition of general environment resilience (Albers et al., 1987; Bisset and Morris, 1996; Bishop, 2012; Doeschl-Wilson et al., 2021). It is often admitted that disease resilience captures two complementary host defence mechanisms, resistance and tolerance, which are defined as follows:

* Disease resistance: Ability of the individual to inhibit or limit within-host pathogen load either by preventing infection in the first place or by inhibiting within-host pathogen replication once infected (Agrios, 2005; Råberg et al., 2008; Bishop, 2012; Knap and Doeschl-Wilson, 2020).

* Disease tolerance: Ability of an infected host to reduce the impact of this infection on performance and health, i.e. maintaining high health or production performance at a given within-host pathogen load without necessarily reducing this pathogen load (Agrios, 2005; Knap and Doeschl-Wilson, 2020). A tolerant organism maintains its performance despite the pathogen burden (Mulder and Rashidi, 2017).

In summary, disease resistance is the ability to reduce and control pathogen load whereas disease tolerance is the ability of an infected host to limit the damage caused by a given within-host pathogen load without necessarily reducing this pathogen load.

In the context of fruit tree breeding, we define disease resilience as the capacity of a given individual (i.e. a genotype) to be minimally affected by one or multiple attacks of pests or diseases, and thus to reduce their impact via resistance or tolerance mechanisms, but also or to bounce back to normal functioning after being exposed to these disturbances. This ability to return to pre-disturbance levels is achieved through recovery mechanisms reflected in a dynamic manner by high elasticity and a low return time (Fig. 2) (Lloret et al., 2011; Hodgson et al., 2015; DeSoto et al., 2020). Therefore, we recommend to describe disease resilience by quantifying together *resistance*, *tolerance* and *recovery*. This definition accounts for the fact that disease resilience can follow multiple trajectories which could therefore be governed by different genetic mechanisms. We think that this comprehensive view is necessary for setting up a durable fruit tree production under low phytosanitary protection.
be retained. In the work carried out in livestock, multiple
traits derived from kinetic production and fitness performance
indicators, such as deviation of body weights of layer chickens
over time (Berghof et al., 2019a), fluctuation of milk yield of
an individual cow per lactation (Elgersma et al., 2018; Poppe
et al., 2020), variation of feed intake and feed duration of grow-
finishing pigs (Sandberg et al., 2006; Putz et al., 2019), egg
production (Bedere et al., 2022), or daily step count of cows
(Poppe et al., 2022), have been identified to illustrate disease re-
silience. In forestry studies, vegetation indices like normalized
difference vegetation index, leaf area index, canopy reflectance,
but also defoliation and discoloration are often used in con-
junction as tree health indicators (Bannari et al., 1995; Fang
and Liang, 2014). Chlorophyll content has also been studied
as a possible index of tree health (Talebzadeh and Valeo, 2022).
Therefore, many biomarkers of resilience can be considered,
such as fruit productivity; vegetative growth through measure-
ments of above-ground or root biomass based on height, leaf
or canopy volume, basal area, and branch or trunk diameters
(Dobbertin, 2005); ecophysiological parameters such as pho-
tosynthetic activity, evapotranspiration, water use efficiency, or
canopy temperature (Méthy, 2000; Oerke et al., 2011; Klein
et al., 2013); and metabolomic profiles of leaves and sap
throughout the entire time of infestation (Castro-Moretti et al.,
2021) (Fig. 3). Since the different pests and diseases do not
attack the same organs and cause different types of damages,
a cumulative measurement of different biomarkers could be
relevant to properly illustrate their impact on tree condition.
Finally, given that these potential biomarkers are highly linked
to environmental conditions, it may be appropriate to correct
them by specific cofactors, in particular those linked to climate
or to epidemiological knowledge. In apricot, for example, a
climatic index of cumulative blossom blight risk has been de-
veloped (Tresson et al., 2020) and is already used to prevent
misinterpretations of low susceptibility due to the avoidance
of contamination related to phenological and meteorological
factors.

Once relevant biomarkers are identified and measured over
the time, the last step is to derive adequate resilience indicators.

Proposed indicators to be calculated from dynamic
measurements of resilience biomarkers

Multiple approaches have emerged over the last decades to cal-
culate resilience indicators (Ingrisch and Bahn, 2018). Two of
them coming from livestock and forestry research seem to be
well-suited and offer complementary perspectives to study re-
silience in fruit trees.

The first one was proposed by Berghof et al. (2019b) and
sees resilience of livestock in terms of deviations between the-
oretical and observed performance over a period of time (Fig.
4A). Three resilience indicators can be calculated to illustrate
and quantify these deviations: (i) the variance, (ii) the autocor-
relation, and (iii) the skewness of deviations over a period of

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Illustration of resilience indicators proposed by (A) Berghof et al. (2019b) to compare disease resilience between livestock animals, and (B) Lloret et al. (2011), Thurm et al. (2016), and Schwarz et al. (2020) to assess the response of forest trees to drought episodes. These indicators are well-suited to study fruit tree disease resilience.</figcaption></figure>

time (Table 1). According to Berghof et al. (2019b) the var-
iance of deviations quantifies the impact of disturbances, the
autocorrelation gives the length of the impact of disturbances,
and skewness is an indication of the direction of disturbances.
This method describes particularly well the temporal evolu-
tion of resilience biomarkers. We suggest defining a new global
resilience indicator based on a combination of variance, auto-
correlation, and skewness. The authors further proposed using
the slope of reaction norms as an indicator of resilience. A re-
action norm describes the pattern of phenotypic expression of
a genotype across a range of environmental conditions, which
corresponds in our case to a range of different quantities of
pathogen disturbance (Cheng et al., 2022). Reaction norm
models based on measurement of pathogen load could be
helpful for studying the relationship between resilience and its
component traits (Knap and Doeschl-Wilson, 2020). However,
calculating reaction norms would require the quantification of
pathogen load, which is in practice ill-suited for orchards with
multiple natural infections (and re-infections). Given that each
disturbance has its own slope, it is also almost impossible to
estimate multiple slopes for multiple disturbances occurring at
the same time (Berghof et al., 2019b).

The second approach was first proposed by Lloret et al. (2011) and later completed by Thurm et al. (2016) and Schwarz et al. (2020). These authors decompose resilience into several indicators to assess the response of forest trees to drought episodes based on tree ring measurements. Illustrated in Fig. 4B and Table 1, these indicators are the following: 'Resistance'
Beyond multi-resistance: the added value and the opportunity of disease resilience

In practice, disease resilience can be used as a ‘black box trait’ which can be targeted without an
individual monitoring of pathogen load and without dissecting components of resistance, tolerance
and recovery (Mulder and Rashidi, 2017; Knap and Doeschl-Wilson, 2020). Until now, most studies
on fruit trees have focused on the search for resistance genes for a specific pathogen (Fig. 1). The
multi-disease context has not been considered as such, thus ignoring interactions between pests and
diseases as well as cumulative effects of multiple attacks and overlooking possible recovery capacity
of trees (Khan and Korban, 2022). Paying more attention to trees’ ability to recover would bring a
more integrative and long-term vision of plant health. Thus, breeding for disease resilience appears
to be a pragmatic and complementary way for fruit tree breeders to cope with the complex,
fluctuating and unpredictable multi disease and pest challenge. The question remaining to be
addressed is how to measure disease resilience in practice. In perennial species, numerous works
were carried out since the last decades on so-called forest resilience (Thompson et al., 2009;
Nikinmaa et al., 2020). If these works may be particularly relevant for searching metrics of resilience
(Lloret et al., 2011; DeSoto et al., 2020), in practice it might be delicate to relate them directly to our
case of study for many reasons. Chiefly, resilience is there considered at the level of forests, regarded
as systems where interspecific interactions are at play, whereas in our case the focus is made at the
genotypic level within mono-species orchards. Besides, another important difference between
forestry and fruit production, is that cultural practices, such as pruning and fertilization, strongly
determine the expression of traits in fruit trees. Caution is therefore required before transferring
knowledge on forest resilience to the case of disease resilience for fruit tree breeding.

Breeding for disease resilience: a source of inspiration in livestock

Although the methodology for disease resilience breeding does not exist yet for fruit trees, the
animal field provides promising sources of inspiration when considering the recent flourishing
literature related to disease resilience in livestock breeding. While searching for solutions to fruit
tree-oriented matters in the field of livestock breeding can be surprising at the first sight, it turns out
that many useful parallels can be drawn between these two worlds. Firstly, in fruit tree breeding as in
animal breeding, the focus is made on the individual scale where each tree/animal is considered as a
unit with a respective genotype. Secondly, fruit trees and livestock must both face the multi-disease
challenge and their health must be managed on a multi-year basis, over and after a long juvenile
phase. Thirdly, until today vaccines and antimicrobials were massively used to control livestock’s
infection levels, which poses similar problems than phytosanitary products do in fruit production in
terms of long-term efficiency (microbial/pest resistances, new pathogen emergence) and societal
<table><caption>**Table 1.** Summary of useful indicators of resilience identified in literature</caption><thead><tr><th>Indicators</th><th>Description</th><th>Calculation</th><th>Authors</th></tr></thead><tbody><tr><td>Variance of deviations (σ²)</td><td>Indication of the impact of disturbances. Captures the severity and duration of environmental perturbations experienced by an individual</td><td>ln(σ<sub>j</sub><sup>2</sup>) = ln(Σ<sub>i=1</sub><sup>η<sub>j</sub></sup>(x<sub>ij</sub> - x̄<sub>j</sub>)<sup>2</sup>)<br>Where x<sub>ij</sub> is deviation i of the j<sup>th</sup> individual, x̄<sub>j</sub> is the mean of deviations of the j<sup>th</sup> individual, and n<sub>j</sub> is the number of deviation observations of the j<sup>th</sup> individual</td><td>Berghof et al. (2019a, b)</td></tr><tr><td>Skewness of deviations</td><td>Indication of the direction of disturbances. Captures the severity of environmental perturbations experienced by an individual</td><td>skewness<sub>j</sub> = (n<sub>j</sub> / (n<sub>j</sub> - 1)(n<sub>j</sub> - 2)) Σ<sub>i=1</sub><sup>n<sub>j</sub></sup> (x<sub>ij</sub> - x̄<sub>j</sub>)<sup>3</sup> / (√σ<sub>j</sub><sup>2</sup>)</td><td>Berghof et al. (2019a, b)</td></tr><tr><td>(Lag-one) Autocorrelation of deviations</td><td>Indication of the length of the impact of disturbances. Captures the duration (i.e. rate of recovery) of environmental perturbations experienced by an individual</td><td>autocorrelation<sub>j</sub> = (Σ<sub>i=1</sub><sup>η<sub>j</sub> - 1</sup>(x<sub>ij</sub> - x̄<sub>j</sub>)(x<sub>i(i+1)</sub> - x̄<sub>i</sub>)) / (Σ<sub>i=1</sub><sup>η<sub>j</sub></sup>(x<sub>ij</sub> - x̄<sub>j</sub>)<sup>2</sup>)</td><td>Berghof et al. (2019a, b)</td></tr><tr><td>Slope of a reaction norm</td><td>Indication of resilience in the face of a macro-environmental disturbance. Captures the severity of a macro-environmental perturbation experienced by an individual</td><td>The slope of a reaction norm a is estimated based on the trait value of an individual given the level of a disturbance, with a=0 for animals not influenced by the disturbance, a<0 for animals negatively influenced by the disturbance, and the |a| value quantifying the impact of the disturbance on the trait</td><td>Berghof et al. (2019a, b)</td></tr><tr><td>Impact</td><td>Inverse of performance reduction during the episode (at the time of the peak impact)</td><td>Performance during disturbance<br>Performance before disturbance<br>Performance after disturbance</td><td>'Resistance' in Lloret et al. (2011)</td></tr><tr><td>Recovery rate</td><td>Ability to recover relative to the damage experienced during disturbance</td><td>Performance during disturbance<br>Performance after disturbance</td><td>'Recovery' in Lloret et al. (2011)</td></tr><tr><td>Net change</td><td>Capacity to reach pre-disturbance performance levels</td><td>Performance before disturbance<br>= Impact × Recovery rate</td><td>'Resilience per se' in Lloret et al. (2011)</td></tr><tr><td>Relative resilience</td><td>Ability to achieve the levels of pre-disturbance performance with respect to the impact during the disturbance</td><td>Performance after – Performance during<br>Performance before disturbance</td><td>Lloret et al. (2011)</td></tr><tr><td>Recovery time</td><td>The time a system needs to return to an equilibrium following disturbance</td><td>Time to reach pre-disturbance levels</td><td>Thurm et al. (2016)</td></tr><tr><td>Total performance reduction</td><td>Accumulated loss of performance due to the perturbation during the perturbation period plus all the time in the recovery period</td><td>End of the recovery period<br>Beginning of the perturbation<br>Loss of performance at a given time</td><td>'Increment loss due to disturbance' in Thurm et al. (2016)</td></tr><tr><td>Average performance reduction</td><td>Quantification of the average annual, monthly, etc. perturbation impact</td><td>Total performance reduction<br>Recovery time</td><td>Schwarz et al. (2020)</td></tr><tr><td>Average recovery rate</td><td>Quantification of how much of the performance reduction could be recovered within one year/month etc.</td><td>(1 – Impact) / (Recovery time × (1 – Impact)) × 100</td><td>Schwarz et al. (2020)</td></tr></tbody></table>

(subsequently called *Impact*); ‘Recovery’ (subsequently called *Recovery rate*); ‘Resilience per se’ (subsequently called *Net change*); *Relative resilience*; *Recovery time*; ‘Increment loss’ due to disturbance (subsequently called *Total performance reduction*); *Average performance reduction*; and *Average recovery rate*. The first four indicators initially proposed by Lloret et al. (2011) were improved by Thurm et al. (2016) and Schwarz et al. (2020) in order to avoid bias and incorrect interpretations

arising from heterogeneity across studies regarding the growth variable types and the lengths of reference periods considered. Based on the measurement of tree-ring width, these indicators have been used in other studies, and have become of great interest in particular for establishing a link between the risk of drought mortality and low resilience to past drought events (DeSoto et al., 2020) or for assessing the influence on trees of successive or prolonged droughts (Schwarz et al., 2020). In
acceptance (antibiotics/vaccines/pesticide residues in our food and in the environment). In both sectors, reducing the dependency on synthetic products is thus perceived as an urgent need.

Since a few years, more and more studies specifically address the question of breeding for disease resilience in livestock (Bai and Plastow, 2022; Berghof et al., 2019b; Colditz and Hine, 2016; Knap and Doeschl-Wilson, 2020; Mulder and Rashidi, 2017; Poppe et al., 2020, 2022). Among the many benefits, they highlight the fact that resilient animals require less attention time, thereby decreasing labour and health costs which represents important economic gains (Knap and Doeschl-Wilson, 2020). More resilient animals are expected to show minor deviations in performance compared to susceptible ones because they are less impacted by infections or have the ability to rapidly recover from diseases (Mulder and Rashidi, 2017; Berghof et al., 2019b; Knap and Doeschl-Wilson, 2020). Estimating the ability of an animal to resist or to recover quickly from a disturbance requires a completely novel phenotyping approach based on longitudinal measurements of animal performance before, during, and after disturbance to provide dynamic trajectories of resilience. As such, multiple traits derived from kinetic production and fitness performance indicators have been explored for an operational measurement of disease resilience (Sandberg et al., 2006; Elgersma et al., 2018; Berghof et al., 2019a,b; Poppe et al., 2020, 2022; Bedere et al., 2022). In these examples, the identification of resilient animals is achieved by measuring the rate of deviation and the speed of return of these key variables to the normal or previous state. Studies might differ in the exact way dynamic resilience indicators are derived but the common underlying assumption is that resilience can be quantified by the scale, the pattern and the duration of deviations.

A major advantage is that disease resilience indicators can be quantified in many practical ways based on routine measurements of animal productivity and performance. Ongoing works focus on improving the heritability of resilience indicators, mainly by better adapting experimental designs to the context of natural disease-challenge (Bai and Plastow, 2022). An important question still remains on the effect of breeding for disease resilience on the infection itself, including disease transmission and pathogen evolution. Since resilient animals are able to be productive regardless of the pathogen burden, disease resilient individuals could increase the spread and persistence of infections (Doeschl-Wilson et al., 2021). Nevertheless, given the contribution of both resistance, tolerance and recovery to disease resilience, one could also expect a reduction of pathogen pressure, hence curbing the arms race between hosts and pathogens.
Bivariate representation of resilience

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Illustration of the bivariate representation of resilience proposed by Ingrisch and Bahn (2018) by plotting the normalized recovery rate against normalized impact of disturbance in order to define the bivariate space. Recovery time results from the combination of coordinates in the bivariate space. Four examples (a–d) are detailed to describe possible trajectories when searching for resilience. Impact of disturbance is the difference between states at pre-disturbance and maximum impact time; recovery rate is the change of system state per unit time after a disturbance.</figcaption></figure>

their study, and inspired by the work of Nimmo et al. (2015), Cantarello et al. (2017) used rather similar indicators for assessing forest resilience to different types of disturbances (pulse or pulse+press scenarios). Based on the measurements of different ecosystem services and biodiversity measurements (such as timber volume, aboveground biomass, and total carbon stock), the authors were able to identify thresholds of response according to the type of disturbance. Therefore, these indicators seem to be well-suited to explore disease resilience based on temporal biomarker measurements, and could be applied on pests and diseases damage.

To make the most of these indicators, a few authors suggest a bivariate map of resilience based on a joint consideration of resistance and recovery in the system (Hodgson et al., 2015; Ingrisch and Bahn, 2018). This proposal came about in response to the emergence of multiple ways of calculating a metric of resilience leading to diverging conclusions when comparing response trajectories from different ecosystems, different types of disturbances, or different ecosystem state variables. The goal was to obtain a quantitative and comparable assessment of resilience that integrates the major components underlying resilience into a single framework. Thus, Hodgson et al. (2015) represent return time in relation to change in state whereas Ingrisch and Bahn (2018) use the normalized impact of disturbance and the normalized recovery rate to define the bivariate space. This vision, illustrated in Fig. 5, could be particularly relevant for a consistent comparison of disease resilience across trees and to account for the different types of pathogen attacks. Besides, this bivariate framework could be useful for assigning the respective roles of resistance, tolerance, and recovery to disease resilience.

Implementing disease resilience mechanisms into an effective breeding strategy

Breeding for disease resilience means balancing objectives for the simultaneous improvement of tree health in parallel with fruit yield and quality under low phytosanitary input, which calls for the definition of new ideotypes (Debaeke and Quilot-Turion, 2014). Series of resilient ideotypes should be designed to obtain varieties able to cope with the most problematic diseases occurring in the target production area while accounting for further environmental, production, and market requirements (Fig. 6). Considering phenotypic trade-offs, via the inclusion of resilience indicators in the breeding value, is particularly relevant in this context.

For a successful quantification of disease resilience, trees must be observed in challenging and contrasted environments on a multi-year basis. As mentioned previously, given that fruit yield and quality are the highest priorities for breeders, most breeding programmes occur in high-health environments. Therefore, breeding environments need to be completely rethought to allow the spread of pests and diseases and the scoring of resilience indices. An entry step could be the implementation of a 'multi-disease index' based on a weighted sum of individual pests and diseases damages. A more advanced step would require temporal and large-scale measurements of relevant biomarkers of resilience from which heritable indicators of resilience could be derived. More research is still required to identify the most suitable biomarkers and to develop easy, non-destructive and inexpensive tools to ensure a regular and precise monitoring of tree health. Specific instrumentation for digital sensing could be
# Towards an operational measurement of disease resilience in orchards

## Choosing a suitable experimental design for studying disease resilience in fruit trees

A few key elements need to be considered to quantify disease resilience experimentally in orchards. Firstly, orchards must be managed under a low phytosanitary protection to allow natural infections to (co-)occur, and because resilience is a dynamic process, monitoring these orchards over several, consecutive years is essential. Secondly, experimental orchards must comprise genetically highly diversified accessions, for example by designing a core collection, to capture the phenotypic diversity underlying disease resilience (Frankel and Brown, 1984). In this process, closely related wild species, landraces and elite cultivars from contrasted geographical origins should be represented and genetically characterized via genotyping or sequencing. Thanks to statistical analyses linking genomic and phenotypic information, such as genome wide association studies (GWAS, Visscher et al., 2017), it will be possible to decipher the genetic architecture of disease resilience, including the number and position of quantitative trait loci and/or specific genes underlying this trait. The first objective is thus to screen a wide range of genetic diversity to characterize disease resilience mechanisms and their heritability. At a later stage, it may be necessary to investigate the behavior of resilient variety candidates in monovarietal orchards.

Finally, given that disease resilience also encompasses the ability of a tree to maintain itself when confronted to a different range of pathogen pressures, studying the response of the same accessions across contrasting environments is also of crucial importance. Of note, for studies focussing on resilience mechanisms *per se*, it can be relevant to provide a fine characterisation of the environment in which trees are growing through epidemiological monitoring or envirotyping methods (Xu, 2016). This requires specific instrumentation in the orchards and in this sense, new tools coming from digital agriculture, such as intelligent sensors or remote sensing images offer attractive perspectives (Lee *et al.*, 2010; Whelan and Taylor, 2013).

To summarize, we propose that setting up a genetically highly diversified, multi-year and multi-site orchard design is a strong basis to understand the mechanisms of disease resilience throughout the life of a tree.

## How to monitor disease resilience in orchards: identification of resilience biomarkers

Disease resilience is a dynamic trait relying on multiple biological functions which might be difficult to assess accurately in the orchard. Although the classical visual monitoring of disease incidence and severity caused by the diverse source of pathogens (Madden *et al.*, 20017) is an essential step towards characterizing disease resilience, we should search for additional variables reflecting the
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Proposal for future breeding programmes with a description of the major shifts recommended for a transition towards a more sustainable breeding based on resilience breeding.</figcaption></figure>

implemented to monitor plant stress and growth in a simple way with fixed sensors such as dendrometers (Lamacque *et al.*, 2020). A more sophisticated approach called digital phenotyping, referring to the combination of sensors measuring specific spectral areas (such as visual, infrared, radar), vectors (such as unmanned aerial vehicles [UAVs], robots, satellites) and artificial intelligence (such as deep learning) for the estimation of quantitative phenotypes should also be considered given the recent advances in that field for monitoring diseases (Mahlein *et al.*, 2019). Recent research in urban or forest contexts have shown how to deploy these tools to assess tree health (e.g. Sampson *et al.*, 2003; Degerickx *et al.*, 2018; Vidal and Pitarma, 2019; Sudakova *et al.*, 2021). With the development of new biological sensors and high-throughput phenotyping technologies, temporal data related to tree health might soon make it possible to decipher the genetic and phenotypic architecture of disease resilience. These technologies will likely remain difficult to access for breeders in the short-term. Yet we suggest that flights with standard UAVs equipped with simple RGB cameras could be easily carried out for a regular monitoring of tree health parameters such as chlorophyll or vegetation indices (Zhang *et al.*, 2021). From these measurements, the indicators proposed by Berghof *et al.*, (2019b) can be derived to rank genotypes according to their ability in disease resilience.

It is important to remember that a drastic increase in disease breeding efficiency should occur once adequate and robust genetic diagnostics are established. With this approach, the breeder can shorten the breeding cycle and/or re-allocate phenotyping efforts towards the screening of more hybrid populations or different traits. To this end, public research is needed to screen large collections representing a vast genetic diversity to identify potential genitors and to develop relevant predictive methods (marker assisted selection, genomic and phenomic selection). These tools are needed to assist breeders in achieving disease resilience goals. Due to the expected genetic complexity of disease resistance traits, genomic selection appears to be highly promising. This method is still underexploited in fruit trees but looks attractive to increase genetic gains for complex traits (McClure *et al.*, 2014). Likewise, phenomic selection—an alternative to genomic prediction based on spectral instead of genetic information—has been developing rapidly in recent years and could be used as a complementary tool (Robert *et al.*, 2022). We emphasize that achieving resilience goals in the near future will not be possible without a close collaboration between public research and private companies enabling the development of adapted phenotypic and genetic tools.
impacts of infections on tree health over the time. Proxy traits of tree performance or health, also called 'resilience biomarkers', need to be identified and importantly, they should not be significantly invasive or destructive. Since disease resilience has not yet been explored in fruit trees, multiple possibilities should be tested and complementary variables might be retained. In the works carried out in livestock, multiple traits derived from kinetic production and fitness performance indicators, such as deviation of body weights of layer chickens over time (Berghof et al., 2019a), fluctuation of milk yield of an individual cow per lactation (Elgersma et al., 2018; Poppe et al., 2020), variation of feed intake and feed duration of grow-finishing pigs (Sandberg et al., 2006; Putz et al., 2019), egg production (Bedere et al., 2022) or daily step count of cows (Poppe et al., 2022), have been identified to illustrate disease resilience. In forestry studies, vegetation indices like Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), canopy reflectance, but also defoliation and discoloration are often used in conjunction as tree health indicators (Bannari et al., 1995; Fang and Liang, 2014). Chlorophyll content has also been studied as possible index of tree health (Talebzadeh and Valeo, 2022). Therefore, many biomarkers of resilience can be considered, such as fruit productivity; vegetative growth through measurements of above-ground or root biomass based on height, leaf or canopy volume, basal area, branch or trunk diameters (Dobbertin, 2005); ecophysiological parameters such as photosynthetic activity, evapotranspiration, water use efficiency or canopy temperature (Méthy, 2000; Oerke et al., 2011; Klein et al., 2013); metabolomic profiles of leaves and sap throughout the entire time of infestation (Castro-Moretti et al., 2021) (Fig. 3). Since the different pests and diseases do not attack the same organs and cause different types of damages, a cumulative measurement of different biomarkers could be relevant to properly illustrate their impact on tree condition. Finally, given that these potential biomarkers are highly linked to environmental conditions, it may be appropriate to correct them by specific cofactors, in particular those linked to climate or to epidemiological knowledge. In apricot for example, a climatic index of cumulated blossom blight risk has been developed (Tresson et al., 2020) and is already used to prevent misinterpretations of low susceptibility due to the avoidance of contamination related to phenological and meteorological factors.

Once relevant biomarkers are identified and measured over the time, the last step is to derive adequate resilience indicators.

**Proposed indicators to be calculated from dynamic measurements of resilience biomarkers**

Multiple approaches have emerged over the last decades to calculate resilience indicators (Ingrisch and Bahn, 2018). Two of them coming from livestock and forestry research seem to be well-suited and offer complementary perspectives to study resilience in fruit trees.
Conclusion and perspectives: towards a
broader consideration of resilience

By being managed in open fields, fruit trees are simultaneously confronted with a large number of biotic stresses over their lifetime, often combined with various types of abiotic stress. This co-occurrence of many potential challenges and stressors has been shown to be particularly destructive (Pandey et al., 2017). As Bai and Plastow (2022) proposed in their review, it could be relevant in the long term to extend our breeding goal from disease resilience to ‘general resilience’ defined as high resilience to different types of perturbations. As general resilience accounts for broad environmental effects, it is particularly adapted for fruit trees affected by fluctuating environments within and between years, but also in the context of global warming where perturbations of different kinds are likely to intensify on broad scales. Therefore, while we describe disease resilience as the first priority for the field today, breeding for generally resilient trees might be the next target in a more distant future.

From the farmer's perspective, it should not be overlooked that a loss of productivity in a given year may be difficult to tolerate even if production is maximized over the years. To limit the potential inter-annual production variations of resilient varieties and ensure sustainable fruit production, combining resilient varieties with several agro-technical levers will be essential. Among them, we draw attention to cultural practices designed to reduce the incidence of pests and diseases, and to biocontrol (Shaw et al., 2021). In particular, we can mention prophylactic measures to limit entry points and inoculum spread or the use of physical barriers such as rain shelters or insect netting (Gomez et al., 2007; Brun et al., 2023). Biocontrol for its part is based on the use of natural active products, chemical mediators (e.g. sexual confusion or plant defence stimulators) and natural micro- or macro-organism auxiliary (Fauvergue et al., 2020; Shaw et al., 2021; Stenberg et al., 2021). More generally, it is urgent to go beyond highly artificial systems in order to make the most of biological regulation, take advantage of the stimulating and protective effects of the microbiota, and fully express the potential of resilient varieties in order to move towards resilient orchards. This also means mobilizing the different principles of agroecology (Thomas and Kevan, 1993), diversifying cultivated populations, carefully considering the spread of varieties in space and time, using service plants, and if necessary, supplementing these approaches with synthetic products. While it has been demonstrated in some low input trials that agroecology does not systematically have a negative impact on yields (Dittmer et al., 2023), progress is still needed to adapt these practices to each context and to preserve yields within given time and space requirements. We should keep in mind that cultural practices and their interaction with variety choice can impact global orchard resilience, which should be studied in the future in the frame of systemic experiments.

To face multiple biotic and abiotic challenges posed by low input management in fruit tree production, more multidisciplinary work and increased exchanges between stakeholders are needed. Combining a diversity of levers and means of control has great potential to maintain productivity while reducing phytosanitary treatments, making way for environmentally and economically sustainable orchards.

Acknowledgements

We sincerely thank members of the Prunus team from the INRAE GAFL research unit for enlightening discussions which contributed to enrich this work. We also thank two anonymous reviewers for their helpful feedback.

Conflict of interest

The authors declare no conflict of interest.

Funding

This work was supported by funding through LabEx AGRO 2011-LABX-002 (under I-Site Muse framework) coordinated by Agropolis Fundation (project ID: 2002-030), the INRAE Department for Plant Genetics and Breeding and the France AgriMer CASDAR Project 'RésiDiv' (project ID: 6846752).

References

Agrios GN. 2005. Plant pathology. Elsevier.

Albers GAA, Gray GD, Piper LR, Barker JSF, Jambre LFL, Barger IA. 1987. The genetics of resistance and resilience to *Haemonchus contortus* infection in young merino sheep. International Journal for Parasitology **17**, 1355-1363.

Bai X, Plastow GS. 2022. Breeding for disease resilience: opportunities to manage polymicrobial challenge and improve commercial performance in the pig industry. CABI Agriculture and Bioscience **3**, 6.

Bannari A, Morin D, Bonn F, Huete AR. 1995. A review of vegetation indices. Remote Sensing Reviews **13**, 95-120.

Baumgartner IO, Patocchi A, Frey JE, Peil A, Kellerhals M. 2015. Breeding elite lines of apple carrying pyramided homozygous resistance genes against apple scab and resistance against powdery mildew and fire blight. Plant Molecular Biology Reporter **33**, 1573-1583.

Bedere N, Berghof TV, Peeters K, Pinard-van Der Laan MH, Visscher J, David I, Mulder HA. 2022. Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens. Genetics, Selection, Evolution **54**, 26.

Berghof TV, Bovenhuis H, Mulder HA. 2019a. Body weight deviations as indicator for resilience in layer chickens. Frontiers in Genetics **10**, 1216.

Berghof TV, Poppe M, Mulder HA. 2019b. Opportunities to improve resilience in animal breeding programs. Frontiers in Genetics **9**, 692.

Bishop SC. 2012. A consideration of resistance and tolerance for ruminant nematode infections. Frontiers in Genetics **3**, 168.

Bisset SA, Morris CA. 1996. Feasibility and implications of breeding sheep for resilience to nematode challenge. International Journal for Parasitology **26**, 857-868.

Brun L, Combe F, Gros C, Walser P, Saudreau M. 2023. Protecting apricot orchards with rain shelters reduces twig blight damage caused by
The first one is proposed by Berghof et al. (2019b) which sees resilience of livestock in terms of
deviations between theoretical and observed performance over a period of time (Fig. 4.A). Three
resilience indicators can be calculated to illustrate and quantify these deviations: (i) the variance, (ii)
the autocorrelation and (iii) the skewness of deviations over a period of time (Table 1). According to
Berghof et al. (2019b) the variance of deviations quantifies the impact of disturbances, the
autocorrelation gives the length of the impact of disturbances and skewness is an indication of the
direction of disturbances. This method describes particularly well the temporal evolution of resilience
biomarkers. We suggest to define a new global resilience indicator based on a combination of
variance, autocorrelation and skewness. The authors further proposed using the slope of reaction
norms as an indicator of resilience. A reaction norm describes the pattern of phenotypic expression
of a genotype across a range of environmental conditions, which corresponds in our case to a range
of different quantities of pathogen disturbance (Cheng et al., 2022). Reaction norm models based on
measurement of pathogen load could be helpful for studying the relationship between resilience and
its component traits (Knap and Doeschl-Wilson, 2020). However, calculating reaction norms would
require the quantification of pathogen load which is in practice ill-suited for orchards with multiple
natural infections (and re-infections). Given that each disturbance has its own slope, it is also almost
impossible to estimate multiple slopes for multiple disturbances occurring at the same time (Berghof
et al., 2019b).

The second approach was first proposed by Lloret et al. (2011) and later completed by Thurm et al. (2016) and Schwarz et al. (2020). These authors decompose resilience into several indicators to assess the response of forest trees to drought episodes based on tree ring measurements. Illustrated in figure 4.B and table 1, these indicators are the following ones: ‘Resistance’ (subsequently called *Impact*); ‘Recovery’ (subsequently called *Recovery Rate*); ‘Resilience per se’ (subsequently called *Net Change*); *Relative resilience*; *Recovery time*; ‘Increment loss’ due to disturbance (subsequently called *Total Performance Reduction*); *Average performance reduction* and *Average recovery rate*. The first four indicators initially proposed by Lloret et al. (2011) were improved by Thurm et al. (2016) and Schwarz et al. (2020) in order to avoid bias and incorrect interpretations arising from heterogeneity across studies regarding the growth variable types and the lengths of reference periods considered. Based on the measurement of tree-ring width, these indicators have been used in other studies, and have shown great interest in particular for establishing a link between the risk of drought mortality and low resilience to past drought events (DeSoto et al., 2020) or to assess the influence on trees of successive or prolonged droughts (Schwarz et al., 2020). In their study, and inspired by the work of (Nimmo et al., 2015), Cantarello et al. (2017) used rather similar indicators for assessing forest resilience to different types of disturbances (pulse or pulse + press scenarios). Based on the
*Monilinia* spp. and makes it possible to reduce fungicide use. Agronomy **13**, 1338.

**Cantarello E, Newton AC, Martin PA, Evans PM, Gosal A, Lucash MS.** 2017. Quantifying resilience of multiple ecosystem services and biodiversity in a temperate forest landscape. Ecology and Evolution **7**, 9661–9675.

**Castro-Moretti FR, Cocuron J-C, Cia MC, Cataldi TR, Labate CA, Alonso AP, Camargo LEA.** 2021. Targeted metabolic profiles of the leaves and xylem sap of two sugarcane genotypes infected with the vascular bacterial pathogen *Leifsonia xyli* subsp. *xyli*. Metabolites **11**, 234.

**Chapelle P.** 2023. Pratiques phytosanitaires en production légumière en 2018 – IFT et nombre de traitement. Agreste – Chiffres et Données 2023-9. Paris: Ministère de l'Agriculture et de l'Alimentation Secrétariat general.

**Cheng J, Lim K, Putz AM, Wolc A, Harding JC, Dyck MK, Fortin F, Plastow GS, PigGen C, Dekkers JC.** 2022. Genetic analysis of disease resilience of wean-to-finish pigs under a natural disease challenge model using reaction norms. Genetics, Selection, Evolution **54**, 11.

**Colditz IG, Hine BC.** 2016. Resilience in farm animals: biology, management, breeding and implications for animal welfare. Animal Production Science **56**, 1961.

**Crisan M.** 2019. Pratiques culturales en grandes cultures 2017 – IFT et nombre de traitements. Agreste – Chiffres et données 2019-3. Paris: Ministère de l'Agriculture et de l'Alimentation Secrétariat general.

**Das DK, Singh J, Vennila S.** 2011. Emerging crop pest scenario under the impact of climate change – a brief review. Journal of Agricultural Physics **11**, 13–20.

**Debaeke P, Quilot-Turion B.** 2014. Conception d'idéotypes de plantes pour une agriculture durable. In: Ecole chercheur INRA, Oct 2012, Seillac, France. FormaScience.

**Degerickx J, Roberts DA, McFadden JP, Hermy M, Somers B.** 2018. Urban tree health assessment using airborne hyperspectral and LiDAR imagery. International Journal of Applied Earth Observation and Geoinformation **73**, 26–38.

**DeSoto L, Cailleret M, Sterck F, et al.** 2020. Low growth resilience to drought is related to future mortality risk in trees. Nature Communications **11**, 545.

**Desprat MJ, Cretin L, Espinosa M.** 2021. Enquête Pratiques phytosanitaires en arboriculture en 2018 IFT et nombre de traitements. Agreste – Chiffres et Données 2021-8. Paris: Ministère de l'Agriculture et de l'Alimentation Secrétariat general.

**Devi D, Verma SC, Sharma PL, Sharma HK, Gupta N, Thakur P.** 2019. Effect of climate change on insect pests of fruit crops and adaptation and mitigation strategies: a review. Journal of Entomology and Zoology Studies **7**, 507–512.

**Dittmer KM, Rose S, Snapp SS, Kebede Y, Brickman S, Shelton S, Egler C, Stier M, Wollenberg E.** 2023. Agroecology can promote climate change adaptation outcomes without compromising yield in smallholder systems. Environmental Management **72**, 333–342.

**Dobbertin M.** 2005. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. European Journal of Forest Research **125**, 89–89.

**Doeschl-Wilson A, Knap PW, Opriessnig T, More SJ.** 2021. Review: Livestock disease resilience: from individual to herd level. Animal **15**, 100286.

**Donley N.** 2019. The USA lags behind other agricultural nations in banning harmful pesticides. Environmental Health **18**, 44.

**Du S, Bartels J, Reinders M, Sen S.** 2017. Organic consumption behavior: a social identification perspective. Food Quality and Preference **62**, 190–198.

**Duval H, Heurtevin L, Dlalah N, Callot C, Lagnel J.** 2022. The *Rm1* and *Rm2* resistance genes to green peach aphid (*Myzus persicae*) encode the same TNL proteins in peach (*Prunus persica* L.). *Genes* **13**, 1489.

**Elgersma GG, de Jong G, van der Linde R, Mulder HA.** 2018. Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows. Journal of Dairy Science **101**, 1240–1250.

**European Commission.** 2020. A Farm to Fork Strategy for a fair, healthy and environmentally-friendly food system. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Brussels: European Commission.

**Fang H, Liang S.** 2014. Leaf area index models. Encyclopedia of Ecology. Academic Press, 2139–2148.

**Fauvergue X, Rusch A, Barret M, Bardin M, Jacquin-Joly E.** 2020. Biocontrôle: Éléments pour une protection agroécologique des cultures. Versailles, France: Quae.

**Ferreira B, Bourillet C, Beaumeunier V, Salmon J.** 2021. Arrêté du 20 novembre 2021 relatif à la protection des abeilles et des autres insectes pollinisateurs et à la réservation des services de pollinisation lors de l'utilisation des produits phytopharmaceutiques. Journal Officiel de la République Française, No. 0271, 17. https://www.legifrance.gouv.fr/eli/arrete/2021/11/20/AGRG2134356A/jo/texte

**Frankel OH, Brown AHD.** 1984. Current plant genetic resources a critical appraisal. In: Chopra VL, Joshi BC, Sharma RP, Bansal HC, eds. Genetics: New frontiers. Proceedings of the XV International Congress of Genetics. New Delhi: IBH Publishing Co., 3–13.

**García JA, Cambra M.** 2007. Plum Pox virus and Sharka disease. Plant Viruses **1**, 69–79.

**Gessler C, Patocchi A, Sansavini S, Tartarini S, Gianfranceschi L.** 2006. *Venturia inaequalis* resistance in apple. Critical Reviews in Plant Sciences **25**, 473–503.

**Gibson AK, Nguyen AE.** 2021. Does genetic diversity protect host populations from parasites? A meta-analysis across natural and agricultural systems. Evolution Letters **5**, 16–32.

**Gomez C, Brun L, Chauffour D, Vallée DDL.** 2007. Effect of leaf litter management on scab development in an organic apple orchard. Agriculture, Ecosystems & Environment **118**, 249–255.

**Hodgson D, McDonald JL, Hosken DJ.** 2015. What do you mean, 'resilient'? Trends in Ecology & Evolution **30**, 503–506.

**Hullé M, Cœur d'Acier A, Bankhead-Dronnet S, Harrington R.** 2010. Aphids in the face of global changes. Comptes Rendus Biologies **333**, 497–503.

**Ingrisch J, Bahn M.** 2018. Towards a comparable quantification of resilience. Trends in Ecology & Evolution **33**, 251–259.

**Jones RA.** 2021. Global plant virus disease pandemics and epidemics. Plants **10**, 233.

**Khan A, Korban SS.** 2022. Breeding and genetics of disease resistance in temperate fruit trees: challenges and new opportunities. Theoretical and Applied Genetics **135**, 3961–3985.

**Klein T, Di Matteo G, Rotenberg E, Cohen S, Yakir D.** 2013. Differential ecophysiological response of a major Mediterranean pine species across a climatic gradient. Tree Physiology **33**, 26–36.

**Knap PW.** 2005. Breeding robust pigs. Australian Journal of Experimental Agriculture **45**, 763–773.

**Knap PW, Doeschl-Wilson A.** 2020. Why breed disease-resilient livestock, and how? Genetics, Selection, Evolution **52**, 60.

**Kole RK, Roy K, Panja BN, Sankarganesh E, Manda T, Worede RE.** 2019. Use of pesticides in agriculture and emergence of resistant pests. Indian Journal of Animal Health **58**, 53–70.

**Ladányi M, Horváth L.** 2010. A review of the potential climate change impact on insect populations – general and agricultural aspects. Applied Ecology and Environmental Research **8**, 143–151.

**Lamacque L, Charrier G, Farnese F dos S, Lemaire B, Améglio T, Herbette S.** 2020. Drought-induced mortality: branch diameter variation reveals a point of no recovery in lavender species. Plant Physiology **183**, 1638–1649.

**Lambert P, Karayiannis I, Poëssel JL, et al.** 2009. Mapping the resistance to *Plum pox virus* (PPV) carried by the apricot 'Stark Early Orange' using different environmental conditions and genetic backgrounds; comparison with *P. davidiana*. Acta Horticulturae **814**, 727–732.

**Lambert P, Campoy JA, Pacheco I, et al.** 2016. Identifying SNP markers tightly associated with six major genes in peach [*Prunus persica* (L.) Batsch] using a high-density SNP array with an objective of marker-assisted selection (MAS). Tree Genetics & Genomes **12**, 121.
measurements of different ecosystem services and biodiversity measurements (such as timber volume, aboveground biomass, or total carbon stock), the authors were able to identify thresholds of response according to the type of disturbance. Therefore, these indicators seem to be well-suited to explore disease resilience based on temporal biomarkers measurements, and could be applied on pests and diseases damage.

To make the most of these indicators, a few authors suggest a bivariate map of resilience based on a joint consideration of resistance and recovery in the system (Hodgson et al., 2015; Ingrisch and Bahn, 2018). This proposal came out in response to the emergence of multiple ways of calculating a metric of resilience leading to diverging conclusions when comparing response trajectories from different ecosystems, different types of disturbances, or different ecosystem state variables. The goal was to obtain a quantitative and comparable assessment of resilience that integrates the major components underlying resilience into a single framework. Thus, Hodgson et al. (2015) represent return time in relation to change in state whereas Ingrisch and Bahn (2018) use the normalized impact of disturbance and the normalized **recovery rate** to define the bivariate space. This vision illustrated in figure 5 could be particularly relevant for a consistent comparison of disease resilience across trees and to account for the different types of pathogen attacks. Besides, this bivariate framework could be useful for assigning the respective roles of resistance, tolerance and recovery to disease resilience.

## Implementing disease resilience mechanisms into an effective breeding strategy

Breeding for disease resilience means balancing out objectives for the simultaneous improvement of tree health in parallel with fruit yield and quality under low phytosanitary input, which calls for the definition of new ideotypes (Debaeke and Quilot-Turion, 2014). Series of resilient ideotypes should be designed to obtain varieties able to cope with the most problematic diseases occurring in the target production area while accounting for further environmental, production and market requirements (Fig. 6). Considering phenotypic trade-offs, via the inclusion of resilience indicators in the breeding value, is particularly relevant in this context.

For a successful quantification of disease resilience, trees must be observed in challenging and contrasted environments over a multi-year basis. As mentioned before, given that fruit yield and quality are the highest priorities for breeders, most breeding programs occurs in high-health environments. Therefore, breeding environments need to be completely rethought to allow the spread of pests and diseases and the scoring of resilience indices. An entry step can be the implementation of a 'multi-disease index' based on a weighted sum of individual pests and diseases damages. A more advanced step would require temporal and large-scale measurements of relevant
Lamine C, Simon S, Audergon J-M, Penvern S, Clauzel G, Garçon L, Bui S, Pluvinage J. 2017. Réalités et perspectives de l'écologisation en arboriculture fruitière - Pour une approche intégrée à partir du cas des vergers de pêchers et d'abricotiers en Rhône-Alpes. Innovations Agronomiques 59, 103-118.

Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C. 2010. Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture 74, 2-33.

Lloret F, Keeling EG, Sala A. 2011. Components of tree resilience: effects of successive low-growth episodes in old ponderosa pine forests. Oikos 120, 1909-1920.

Madden LV, Hughes G, van den Bosch F. 2017. The study of plant disease epidemics. St Paul: The American Phytopathological Society.

Mahlein A-K, Kuska MT, Thomas S, Wahabzada M, Behmann J, Rascher U, Kersting K. 2019. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! Current Opinion in Plant Biology 50, 156-162.

Martin-Breen P, Anderies JM. 2011. Background paper-resilience: a literature review. The Bellagio Initiative = The Future of Philanthropy and Development in the Pursuit of Human Wellbeing 1, 67.

McClure KA, Sawler J, Gardner KM, Money D, Myles S. 2014. Genomics: a potential panacea for the perennial problem. American Journal of Botany 101, 1780-1790.

Méthy M. 2000. Analysis of photosynthetic activity at the leaf and canopy levels from reflectance measurements: a case study. Photosynthetica 38, 505-512.

Mulder HA, Rashidi H. 2017. Selection on resilience improves disease resistance and tolerance to infections. Journal of Animal Science 95, 3346-3358.

Nicot PC, Alabouvette CC, Bardin M, Blum B, Kohl J, Ruocco M. 2012. Review of factors influencing the success or failure of biocontrol: technical, industrial and socio-economic perspectives. IOBC WPRS Bulletin 78, 95-98.

Nikinmaa L, Lindner M, Cantarello E, Jump AS, Seidl R, Winkel G, Muys B. 2020. Reviewing the use of resilience concepts in forest sciences. Current Forestry Reports 6, 61-80.

Nimmo DG, Mac Nally R, Cunningham SC, Haslem A, Bennett AF. 2015. Vive la résistance: reviving resistance for 21st century conservation. Trends in Ecology & Evolution 30, 516-523.

Oerke E-C, Fröhling P, Steiner U. 2011. Thermographic assessment of scab disease on apple leaves. Precision Agriculture 12, 699-715.

Omrani M, Roth M, Roch G, Blanc A, Morris CE, Audergon J-M. 2019. Genome-wide association multi-locus and multi-variate linear mixed models reveal two linked loci with major effects on partial resistance of apricot to bacterial canker. BMC Plant Biology 19, 31.

Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M. 2017. Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Frontiers in Plant Science 8, 537.

Pascal T, Aberlenc R, Confolent C, Hoerter M, Lecerf E, Tuéro C, Lambert P. 2017. Mapping of new resistance (Vr2, Rm1) and ornamental (Di2, pl) Mendelian trait loci in peach. Euphytica 213, 132.

Poppe M, Veerkamp RF, van Pelt ML, Mulder HA. 2020. Exploration of variance, autocorrelation, and skewness of deviations from lactation curves as resilience indicators for breeding. Journal of Dairy Science 103, 1667-1684.

Poppe M, Mulder HA, van Pelt ML, Mullaart E, Hogeveen H, Veerkamp RF. 2022. Development of resilience indicator traits based on daily step count data for dairy cattle breeding. Genetics, Selection, Evolution 54, 21.

Putz AM, Harding JCS, Dyck MK, Fortin F, Plastow GS, Dekkers JCM, PigGen C. 2019. Novel resilience phenotypes using feed intake data from a natural disease challenge model in wean-to-finish pigs. Frontiers in Genetics 9, 660.

Råberg L, Graham AL, Read AF. 2008. Decomposing health: tolerance and resistance to parasites in animals. Philosophical Transactions of the Royal Society B: Biological Sciences 364, 37-49.

Robert P, Brault C, Rincent R, Segura V. 2022. Phenomic selection: a new and efficient alternative to genomic selection genomic selection (GS). In: Ahmadi N, Bartholomé J, eds. Genomic prediction of complex traits: methods and protocols. New York: Springer, 397-420.

Rubio M, Ruiz D, Egea J, Martínez-Gómez P, Dicenta F. 2014. Opportunities of marker-assisted selection for *Plum pox virus* resistance in apricot breeding programs. Tree Genetics & Genomes 10, 513-525.

Sampson PH, Zarco-Tejada PJ, Mohammed GH, Miller JR, Noland TL. 2003. Hyperspectral remote sensing of forest condition: estimating chlorophyll content in tolerant hardwoods. Forest Science 49, 381-391.

Sandberg FB, Emmans GC, Kyriazakis I. 2006. A model for predicting feed intake of growing animals during exposure to pathogens. Journal of Animal Science 84, 1552-1566.

Sauge M-H, Lambert P, Pascal T. 2012. Co-localisation of host plant resistance QTLs affecting the performance and feeding behaviour of the aphid *Myzus persicae* in the peach tree. Heredity 108, 292-301.

Sauvion N, Thébaud G, Marie-Jeanne V, Peyre J, Brun L. 2012. Enroulement chlorotique de l'abricotier (ECA). Phytoma 654, 28-32.

Savary S, Bregaglio S, Willocquet L, et al. 2017. Crop health and its global impacts on the components of food security. Food Security 9, 311-327.

Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A. 2019. The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution 3, 430-439.

Schneider C, Onimus C, Prado E, et al. 2019. INRA-ResDur: the French grapevine breeding programme for durable resistance to downy and powdery mildew. Acta Horticulturae 1248, 207-214.

Schwarz J, Skiadaresis G, Kohler M, Kunz J, Schnabel F, Vitali V, Bauhus J. 2020. Quantifying growth responses of trees to drought—a critique of commonly used resilience indices and recommendations for future studies. Current Forestry Reports 6, 185-200.

Shaw B, Nagy C, Fountain MT. 2021. Organic control strategies for use in IPM of invertebrate pests in apple and pear orchards. Insects 12, 1106.

Skendžić S, Zovko M, Živković IP, Lešić V, Lemić D. 2021. Effect of climate change on introduced and native agricultural invasive insect pests in Europe. Insects 12, 985.

Stenberg JA, Sundh I, Becher PG, et al. 2021. When is it biological control? A framework of definitions, mechanisms, and classifications. Journal of Pest Science 94, 665-676.

Sudakova M, Terentyeva E, Kalashnikov A. 2021. Assessment of health status of tree trunks using ground penetrating radar tomography. AIMS Geosciences 7, 162-179.

Talebzadeh F, Valeo C. 2022. Evaluating the effects of environmental stress on leaf chlorophyll content as an index for tree health. IOP Conference Series: Earth and Environmental Science 1006, 012007.

Thomas VG, Kevan PG. 1993. Basic principles of agroecology and sustainable agriculture. Journal of Agricultural and Environmental Ethics 6, 1-19.

Thompson I, Mackey B, McNulty S, Mosseler A. 2009. Forest resilience, biodiversity, and climate change. A synthesis of the biodiversity/resilience/stability relationship in forest ecosystems. Technical Series no. 43. Montreal: Secretariat of the Convention on Biological Diversity.

Thurm EA, Uhl E, Pretzsch H. 2016. Mixture reduces climate sensitivity of Douglas-fir stem growth. Forest Ecology and Management 376, 205-220.

Tresson P, Brun L, García de Cortázar-Atauri I, et al. 2020. Future development of apricot blossom blight under climate change in Southern France. European Journal of Agronomy 112, 125960.

Urruty N, Tailliez-Lefebvre D, Huyghe C. 2016. Stability, robustness, vulnerability and resilience of agricultural systems. A review. Agronomy for Sustainable Development 36, 15.

Vidal D, Pitarma R. 2019. Infrared thermography applied to tree health assessment: a review. Agriculture 9, 156.
biomarkers of resilience from which heritable indicators of resilience could be derived. More research is still required to identify the most suitable biomarkers and to develop easy, non-destructive and inexpensive tools to ensure a regular and precise monitoring of tree health. Specific instrumentation for digital sensing could be implemented to monitor plant stress and growth in a simple way with fixed sensors such as dendrometers (Lamacque et al., 2020). A more sophisticated approach called digital phenotyping, referring to the combination of sensors measuring specific spectral areas (such as visual, infrared, radar), vectors (such as unmanned aerial vehicles or UAV, robots, satellites) and artificial intelligence (such as deep learning) for the estimation of quantitative phenotypes should also be considered given the recent advances in that field for monitoring diseases (Mahlein et al., 2019). Recent research in urban or forest contexts have shown how to deploy these tools to assess tree health (e.g. Sampson et al., 2003; Degerickx et al., 2018; Vidal and Pitarma, 2019; Sudakova et al., 2021). With the development of new biological sensors and high-throughput phenotyping technologies, temporal data related to tree health might soon make it possible to decipher the genetic and phenotypic architecture of disease resilience. These technologies will likely remain difficult to access for breeders in the short-term. Yet we suggest that flights with standard UAVs equipped with simple Red-Green-Blue cameras could be easily carried out for a regular monitoring of tree health parameters such as chlorophyll or vegetation indices (Zhang et al., 2021). From these measurements, the indicators proposed by Berghof et al., (2019b) can be derived to rank genotypes according to their ability for disease resilience.

It is important to remind that a drastic increase in resilience breeding efficiency should occur once adequate and robust genetic diagnostics will be established. With this approach, the breeder can shorten the breeding cycle and/or re-allocate phenotyping efforts towards the screening of more hybrid populations or different traits. To this aim, public research is needed to screen large collections representing a vast genetic diversity to identify potential genitors and to develop relevant predictive methods (marker assisted selection, genomic selection, phenomic prediction). These tools are needed to assist breeders in achieving disease resilience goals. Due to the expected genetic complexity of disease resilience traits, genomic selection appears to be highly promising. This method is still under exploited in fruit trees but looks attractive to increase genetic gains for complex traits (McClure et al., 2014). Likewise, phenomic prediction - an alternative to genomic prediction based on spectral instead of genetic information - has been developing rapidly in recent years and could be used as a complementary tool (Robert et al., 2022). We emphasize that achieving resilience goals in a near future will not be possible without a close collaboration between public research and private companies enabling the development of adapted phenotypic and genetic tools.
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 2017. 10 years of GWAS discovery: biology, function, and translation. American Journal of Human Genetics **101**, 5–22.

Whelan B, Taylor J. 2013. Precision agriculture for grain production systems. Clayton, Australia: CSIRO Publishing.

Wiesner-Hanks T, Nelson R. 2016. Multiple disease resistance in plants. Annual Review of Phytopathology **54**, 229–252.

Wilson C, Tisdell C. 2001. Why farmers continue to use pesticides despite environmental, health and sustainability costs. Ecological Economics **39**, 449–462.

Xu Y. 2016. Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics **129**, 653–673.

Zhang C, Valente J, Kooistra L, Guo L, Wang W. 2021. Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches. Precision Agriculture **22**, 2007–2052.
Conclusion and perspectives: towards a broader consideration of resilience

By being managed in open field, fruit trees are simultaneously confronted with a large number of biotic stresses over their lifetime, often combined with various types of abiotic stress. This co-occurrence of many potential challenges and stressors have shown to be particularly destructive (Pandey et al., 2017). As Bai and Plastow (2022) proposed in their review, it could be relevant in the long term to extend our breeding goal from disease resilience to ‘general resilience’ defined as high resilience to different types of perturbations. As general resilience accounts for environmental effects at broad, it is particularly adapted for fruit trees affected by fluctuating environments within and between the years, but also to the context of global warming where perturbations of different kinds are likely to intensify over broad scales. Therefore, while we describe disease resilience as the first priority for the field today, breeding for general resilient trees might be the next target in a farer future.

From the producer's perspective, it should not be overlooked that a loss of productivity in a given year may be difficult to tolerate even if production is maximized over the years. To limit the potential inter-annual production variations of resilient varieties and ensure a sustainable fruit production, combining resilient varieties with several agro-technical levers will be essential. Among them, we draw attention on cultural practices designed to reduce the incidence of pests and diseases, and biocontrol (Shaw et al., 2021). In particular, we can mention prophylactic measures to limit entry points and inoculum spread or the use of physical barriers such as rain shelters or insect netting (Gomez et al., 2007; Brun et al., 2023). Biocontrol for its part is based on the use of natural active products, chemical mediators (e.g. sexual confusion or plant defence stimulators) and natural micro- or macro-organism auxiliary (Fauvergue et al., 2020; Shaw et al., 2021; Stenberg et al., 2021). More generally, it is urgent to go beyond highly artificial systems in order to make the most of biological regulation, take advantage of the stimulating and protective effects of the microbiota, and fully express the potential of resilient varieties to move towards resilient orchards. This also means mobilising the different principles of agroecology (Thomas and Kevan, 1993), diversifying cultivated populations, carefully considering the widespread of varieties in space and time, using service plants and, if necessary, supplementing these approaches synthetic products. While it has been demonstrated in some low input trials that, agroecology does not systematically have a negative impact on yields (Dittmer et al., 2023), progress is still needed to adapt these practices to each context and to preserve yields within given time and space requirements. We should keep in mind that cultural practices and their interaction with variety choice can impact global orchard resilience, which should be studied in the future in the frame of systemic experiments.
To face the multiple biotic and abiotic challenges posed by low input management in fruit tree production, more multidisciplinary works and increased exchanges between stakeholders are needed. Combining a diversity of levers and means of control has great potential to maintain productivity while reducing phytosanitary treatments, making way for environmentally and economically sustainable orchards.

<figure><img src="image_4.png" /></figure>
Acknowledgements

We sincerely thank the different members of the Prunus team from GAFL unit for all the enlightening discussions and exchanges we had on this subject, which helped us to construct and nourish this article. We also thank two anonymous reviewers for their helpful feedback.

Conflict of interest

The authors declare no conflict of interest.

Funding

This work was supported by funding from Agropolis Fundation (project ID: 2002-030), the INRAE Department for Plant Genetics and Breeding and the France AgriMer CASDAR Project ‘RésiDiv’.
## References

**Agrios GN.** 2005. *Plant pathology*. Elsevier.

**Albers GAA, Gray GD, Piper LR, Barker JSF, Jambre LFL, Barger IA.** 1987. The genetics of resistance and resilience to Haemonchus contortus infection in young merino sheep. *International Journal for Parasitology* **17**, 1355–1363.

**Bai X, Plastow GS.** 2022. Breeding for disease resilience: opportunities to manage polymicrobial challenge and improve commercial performance in the pig industry. *CABI Agriculture and Bioscience* **3**, 1–17.

**Bannari A, Morin D, Bonn F, Huete AR.** 1995. A review of vegetation indices. *Remote Sensing Reviews* **13**, 95–120.

**Baumgartner IO, Patocchi A, Frey JE, Peil A, Kellerhals M.** 2015. Breeding Elite Lines of Apple Carrying Pyramided Homozygous Resistance Genes Against Apple Scab and Resistance Against Powdery Mildew and Fire Blight. *Plant Molecular Biology Reporter* **33**, 1573–1583.

**Bedere N, Berghof TV, Peeters K, Pinard-van Der Laan MH, Visscher J, David I, Mulder HA.** 2022. Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens. *Genetics Selection Evolution* **54**, 26.

**Berghof TV, Bovenhuis H, Mulder HA.** 2019a. Body Weight Deviations as Indicator for Resilience in Layer Chickens. *Frontiers in Genetics* **10**, 1216.

**Berghof TV, Poppe M, Mulder HA.** 2019b. Opportunities to Improve Resilience in Animal Breeding Programs. *Frontiers in Genetics* **9**, 692.

**Bishop SC.** 2012. A consideration of resistance and tolerance for ruminant nematode infections. *Frontiers in Genetics* **3**.

**Bisset SA, Morris CA.** 1996. Feasibility and implications of breeding sheep for resilience to nematode challenge. *International Journal for Parasitology* **26**, 857–868.

**Brun L, Combe F, Gros C, Walser P, Saudreau M.** 2023. Protecting Apricot Orchards with Rain Shelters Reduces Twig Blight Damage Caused by Monilinia spp. and Makes It Possible to Reduce Fungicide Use. *Agronomy* **13**, 1338.

**Cantarello E, Newton AC, Martin PA, Evans PM, Gosal A, Lucash MS.** 2017. Quantifying resilience of multiple ecosystem services and biodiversity in a temperate forest landscape. *Ecology and Evolution* **7**, 9661–9675.

**Castro-Moretti FR, Cocuron J-C, Cia MC, Cataldi TR, Labate CA, Alonso AP, Camargo LEA.** 2021. Targeted Metabolic Profiles of the Leaves and Xylem Sap of Two
Sugarcane Genotypes Infected with the Vascular Bacterial Pathogen Leifsonia xyli subsp. xyli. *Metabolites* **11**, 234.

**Chapelle P.** 2020. Pratiques phytosanitaires en production légumière en 2018 - IFT et nombre de traitement. Agreste - Chiffres et Données *in press*.

Cheng J, Lim K, Putz AM, Wolc A, Harding JC, Dyck MK, Fortin F, Plastow GS, PigGen Canada, Dekkers JC. 2022. Genetic analysis of disease resilience of wean-to-finish pigs under a natural disease challenge model using reaction norms. Genetics Selection Evolution **54**, 11.

Colditz IG, Hine BC. 2016. Resilience in farm animals: biology, management, breeding and implications for animal welfare. Animal Production Science **56**, 1961.

Crisan M. 2019. Pratiques culturales en grandes cultures 2017 - IFT et nombre de traitements. Agreste - Chiffres et données *in press*.

Das DK, Singh J, Vennila S. 2011. Emerging Crop Pest Scenario under the Impact of Climate Change – A Brief Review. Journal of Agricultural Physics **11**, 13–20.

Debaeke P, Quilot-Turion B. 2014. Conception d'idéotypes de plantes pour une agriculture durable. FormaScience, 254 p.

Degerickx J, Roberts DA, McFadden JP, Hermy M, Somers B. 2018. Urban tree health assessment using airborne hyperspectral and LiDAR imagery. International Journal of Applied Earth Observation and Geoinformation **73**, 26–38.

DeSoto L, Cailleret M, Sterck F, et al. 2020. Low growth resilience to drought is related to future mortality risk in trees. Nature Communications **11**, 545.

Desprat MJ, Cretin L, Espinosa M. 2021. Enquête Pratiques phytosanitaires en arboriculture en 2018 IFT et nombre de traitements. Agreste - Chiffres et Données **8**.

Devi D, Verma SC, Sharma PL, Sharma HK, Gupta N, Thakur P. 2019. Effect of climate change on insect pests of fruit crops and adaptation and mitigation strategies: A review. Journal of Entomology and Zoology Studies **7**, 507–512.

Dittmer KM, Rose S, Snapp SS, Kebede Y, Brickman S, Shelton S, Egler C, Stier M, Wollenberg E. 2023. Agroecology Can Promote Climate Change Adaptation Outcomes Without Compromising Yield In Smallholder Systems. Environmental Management **72**, 333–342.

Dobbertin M. 2005. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. European Journal of Forest Research **124**, 319–333.

Doeschl-Wilson A, Knap PW, Opriessnig T, More SJ. 2021. Review: Livestock disease resilience: from individual to herd level. Animal **15**, 100286.
**Donley N.** 2019. The USA lags behind other agricultural nations in banning harmful pesticides. *Environmental Health* **18**, 1–12.

**Du S, Bartels J, Reinders M, Sen S.** 2017. Organic consumption behavior: A social identification perspective. *Food Quality and Preference* **62**, 190–198.

**Duval H, Heurtevin L, Dlalah N, Callot C, Lagnel J.** 2022. The Rm1 and Rm2 Resistance Genes to Green Peach Aphid (Myzus persicae) Encode the Same TNL Proteins in Peach (Prunus persica L.). *Genes* **13**, 1489.

**Elgersma GG, de Jong G, van der Linde R, Mulder HA.** 2018. Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows. *Journal of Dairy Science* **101**, 1240–1250.

**European commission.** 2020. *A Farm to Fork Strategy for a fair, healthy and environmentally-friendly food system*. Brussels: Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions.

**Fang H, Liang S.** 2014. Leaf Area Index Models☆. Reference Module in Earth Systems and Environmental Sciences. Elsevier.

**Fauvergue X, Rusch A, Barret M, Bardin M, Jacquin-Joly E.** 2020. *Biocontrôle: Éléments pour une protection agroécologique des cultures*. Quae.

**Ferreira B, Bourillet C, Beaumeunier V, Salmon J.** 2021. Arrêté du 20 novembre 2021 relatif à la protection des abeilles et des autres insectes pollinisateurs et à la réservation des services de pollinisation lors de l'utilisation des produits phytopharmaceutiques. *Journal officiel de la république française in press*.

**Frankel OH, Brown AHD.** 1984. Current plant genetic resources a critical appraisal. *Genetics: new frontiers - proceedings of the XV International Congress of Genetics in press*.

**García JA, Cambra M.** 2007. Plum Pox Virus and Sharka Disease. *Plant Viruses* **1**, 69–79.

**Gessler C, Patocchi A, Sansavini S, Tartarini S, Gianfranceschi L.** 2006. Venturia inaequalis Resistance in Apple. *Critical Reviews in Plant Sciences* **25**, 473–503.

**Gibson AK, Nguyen AE.** 2021. Does genetic diversity protect host populations from parasites? *A meta-analysis across natural and agricultural systems*. *Evolution Letters* **5**, 16–32.

**Gomez C, Brun L, Chauffour D, Vallée DDL.** 2007. Effect of leaf litter management on scab development in an organic apple orchard. *Agriculture, Ecosystems & Environment* **118**, 249–255.

**Hodgson D, McDonald JL, Hosken DJ.** 2015. What do you mean, ‘resilient’? *Trends in Ecology & Evolution* **30**, 503–506.
Hullé M, Cœur d'Acier A, Bankhead-Dronnet S, Harrington R. 2010. Aphids in the face of global changes. Comptes Rendus Biologies **333**, 497–503.

Ingrisch J, Bahn M. 2018. Towards a Comparable Quantification of Resilience. Trends in Ecology & Evolution **33**, 251–259.

Jones RA. 2021. Global Plant Virus Disease Pandemics and Epidemics. Plants **10**, 233.

Khan A, Korban SS. 2022. Breeding and genetics of disease resistance in temperate fruit trees: challenges and new opportunities. Theoretical and Applied Genetics **135**, 3961–3985.

Klein T, Di Matteo G, Rotenberg E, Cohen S, Yakir D. 2013. Differential ecophysiological response of a major Mediterranean pine species across a climatic gradient. Tree Physiology **33**, 26–36.

Knap PW. 2005. Breeding robust pigs. Australian Journal of Experimental Agriculture **45**, 763–773.

Knap PW, Doeschl- Wilson A. 2020. Why breed disease- resilient livestock, and how? Genetics Selection Evolution, **52**, 1–18.

Kole RK, Roy K, Panja BN, Sankarganesh E, Manda T, Worede RE. 2019. Use of pesticides in agriculture and emergence of resistant pests. Indian J. Anim. Hlth **58**, 53–70.

Ladányi M, Horváth L. 2010. A review of the potential climate change impact on insect populations – general and agricultural aspects. Applied Ecology and Environmental Research **8**, 143–151.

Lamacque L, Charrier G, Farnese F dos S, Lemaire B, Améglio T, Herbette S. 2020. Drought-Induced Mortality: Branch Diameter Variation Reveals a Point of No Recovery in Lavender Species. Plant Physiology **183**, 1638–1649.

Lambert P, Campoy JA, Pacheco I, et al. 2016. Identifying SNP markers tightly associated with six major genes in peach [Prunus persica (L.) Batsch] using a high-density SNP array with an objective of marker-assisted selection (MAS). Tree Genetics & Genomes **12**, 1–21.

Lambert P, Karayiannis I, Poëssel JL, et al. 2009. Mapping the resistance to Plum pox virus (PPV) carried by the apricot ‘Stark Early Orange’ using different environmental conditions and genetic backgrounds; comparison with P. davidiana. Acta Horticulturae **814** (VOL 2), 727–732.

Lamine C, Simon S, Audergon J-M., Penvern S, Clauzel G, Garçon L, Bui S, Pluvinage J. 2017. Réalités et perspectives de l’écologisation en arboriculture fruitière - Pour une approche intégrée à partir du cas des vergers de pêchers et d’abricotiers en Rhône-Alpes. Innovations Agronomiques **59**, 103–118.

Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C. 2010. Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture **74**, 2–33.
Lloret F, Keeling EG, Sala A. 2011. Components of tree resilience: effects of successive low-growth episodes in old ponderosa pine forests. Oikos **120**, 1909–1920.

Madden LV, Hughes G, Bosch F van den. 20017. *The study of plant disease epidemics*. The American Phytopathological Society.

Mahlein A-K, Kuska MT, Thomas S, Wahabzada M, Behmann J, Rascher U, Kersting K. 2019. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! Current Opinion in Plant Biology **50**, 156–162.

Martin-Breen P, Anderies JM. 2011. Background Paper-resilience: a literature review. Bellagio Initiat= The Futur Philanthr Dev Purs Hum Wellbeing **1**, 67.

McClure KA, Sawler J, Gardner KM, Money D, Myles S. 2014. Genomics: A potential panacea for the perennial problem. American Journal of Botany **101**, 1780–1790.

Méthy M. 2000. Analysis of Photosynthetic Activity at the Leaf and Canopy Levels from Reflectance Measurements: A Case Study. Photosynthetica **38**, 505–512.

Mulder HA, Rashidi H. 2017. Selection on resilience improves disease resistance and tolerance to infections. Journal of Animal Science **95**, 3346–3358.

Nicot PC, Alabouvette CC, Bardin M, Blum B, Kohl J, Ruocco M. 2012. Review of factors influencing the success or failure of biocontrol: technical, industrial and socio-economic perspectives. IOBC WPRS Bulletin **78**, 95–98.

Nikinmaa L, Lindner M, Cantarello E, Jump AS, Seidl R, Winkel G, Muys B. 2020. Reviewing the Use of Resilience Concepts in Forest Sciences. Current Forestry Reports **6**, 61–80.

Nimmo DG, Mac Nally R, Cunningham SC, Haslem A, Bennett AF. 2015. Vive la résistance: reviving resistance for 21st century conservation. Trends in Ecology & Evolution **30**, 516–523.

Oerke E-C, Fröhling P, Steiner U. 2011. Thermographic assessment of scab disease on apple leaves. Precision Agriculture **12**, 699–715.

Omrani M, Roth M, Roch G, Blanc A, Morris CE, Audergon J-M. 2019. Genome-wide association multi-locus and multi-variate linear mixed models reveal two linked loci with major effects on partial resistance of apricot to bacterial canker. BMC Plant Biology **19**, 1–18.

Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M. 2017. Impact of Combined Abiotic and Biotic Stresses on Plant Growth and Avenues for Crop Improvement by Exploiting Physio-morphological Traits. Frontiers in Plant Science **8**, 537.

Pascal T, Aberlenc R, Confolent C, Hoerter M, Lecerf E, Tuéro C, Lambert P. 2017. Mapping of new resistance (Vr2, Rm1) and ornamental (Di2, pl) Mendelian trait loci in peach. Euphytica **213**, 1–32.
**Poppe M, Mulder HA, van Pelt ML, Mullaart E, Hogeveen H, Veerkamp RF.** 2022. Development of resilience indicator traits based on daily step count data for dairy cattle breeding. *Genetics Selection Evolution* **54**, 1–16.

**Poppe M, Veerkamp RF, van Pelt ML, Mulder HA.** 2020. Exploration of variance, autocorrelation, and skewness of deviations from lactation curves as resilience indicators for breeding. *Journal of Dairy Science* **103**, 1667–1684.

**Putz AM, Harding JCS, Dyck MK, Fortin F, Plastow GS, Dekkers JCM, PigGen Canada.** 2019. Novel Resilience Phenotypes Using Feed Intake Data From a Natural Disease Challenge Model in Wean-to-Finish Pigs. *Frontiers in Genetics* **9**, 660.

**Råberg L, Graham AL, Read AF.** 2008. Decomposing health: tolerance and resistance to parasites in animals. *Philosophical Transactions of the Royal Society B: Biological Sciences* **364**, 37–49.

**Robert P, Brault C, Rincent R, Segura V.** 2022. Phenomic Selection: A New and Efficient Alternative to Genomic Selection. *Genomic selection (GS). In: Ahmadi N, Bartholomé J, eds. Genomic Prediction of Complex Traits: Methods and Protocols. New York, NY: Springer US, 397–420.

**Rubio M, Ruiz D, Egea J, Martínez-Gómez P, Dicenta F.** 2014. Opportunities of marker-assisted selection for Plum pox virus resistance in apricot breeding programs. *Tree Genetics & Genomes* **10**, 513–525.

**Sampson PH, Zarco-Tejada PJ, Mohammed GH, Miller JR, Noland TL.** 2003. Hyperspectral Remote Sensing of Forest Condition: Estimating Chlorophyll Content in Tolerant Hardwoods. *Forest Science* **49**, 381–391.

**Sandberg FB, Emmans GC, Kyriazakis I.** 2006. A model for predicting feed intake of growing animals during exposure to pathogens. *Journal of Animal Science* **84**, 1552–1566.

**Sauge M-H, Lambert P, Pascal T.** 2012. Co-localisation of host plant resistance QTLs affecting the performance and feeding behaviour of the aphid Myzus persicae in the peach tree. *Heredity* **108**, 292–301.

**Sauvion N, Thébaud G, Marie-Jeanne V, Peyre J, Brun L.** 2012. Enroulement chlorotique de l'abricotier (ECA). *Phytoma* **654**, 28–32.

**Savary S, Bregaglio S, Willocquet L, et al.** 2017. Crop health and its global impacts on the components of food security. *Food Security* **9**, 311–327.

**Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A.** 2019. The global burden of pathogens and pests on major food crops. *Nature Ecology & Evolution* **3**, 430–439.
**Schneider C, Onimus C, Prado E, et al.** 2018. INRA-ResDur: the French grapevine breeding programme for durable resistance to downy and powdery mildew. in XII International Conference on Grapevine Breeding and Genetics, 207–214.

**Schwarz J, Skiadaresis G, Kohler M, Kunz J, Schnabel F, Vitali V, Bauhus J.** 2020. Quantifying Growth Responses of Trees to Drought—a Critique of Commonly Used Resilience Indices and Recommendations for Future Studies. Current Forestry Reports **6**, 185–200.

**Shaw B, Nagy C, Fountain MT.** 2021. Organic Control Strategies for Use in IPM of Invertebrate Pests in Apple and Pear Orchards. Insects **12**, 1106.

**Skendžić S, Zovko M, Živković IP, Lešić V, Lemić D.** 2021. Effect of Climate Change on Introduced and Native Agricultural Invasive Insect Pests in Europe. Insects **12**, 985.

**Stenberg JA, Sundh I, Becher PG, et al.** 2021. When is it biological control? A framework of definitions, mechanisms, and classifications. Journal of Pest Science **94**, 665–676.

**Sudakova M, Terentyeva E, Kalashnikov A.** 2021. Assessment of health status of tree trunks using ground penetrating radar tomography. AIMS Geosciences **7**, 162–179.

**Talebzadeh F, Valeo C.** 2022. Evaluating the Effects of Environmental Stress on Leaf Chlorophyll Content as an Index for Tree Health. In IOP Conference Series: Earth and Environmental Science. IOP Publishing. **1006**, 012007.

**Thomas VG, Kevan PG.** 1993. Basic principles of agroecology and sustainable agriculture. Journal of Agricultural and Environmental Ethics **6**, 1–19.

**Thompson I, Mackey B, McNulty S, Mosseler A.** 2009. Forest resilience, biodiversity, and climate change. In A synthesis of the biodiversity/resilience/stability relationship in forest ecosystems. Secretariat of the Convention on Biological Diversity, Montreal. Technical Series **43**, 1–67.

**Thurm EA, Uhl E, Pretzsch H.** 2016. Mixture reduces climate sensitivity of Douglas-fir stem growth. Forest Ecology and Management **376**, 205–220.

**Tresson P, Brun L, García de Cortázar-Atauri I, et al.** 2020. Future development of apricot blossom blight under climate change in Southern France. European Journal of Agronomy **112**, 125960.

**Urruty N, Tailliez-Lefebvre D, Huyghe C.** 2016. Stability, robustness, vulnerability and resilience of agricultural systems. A review. Agronomy for Sustainable Development **36**, 1–15.

**Vidal D, Pitarma R.** 2019. Infrared thermography applied to tree health assessment: a review. Agriculture **9**, 156.
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 2017. 10 Years of GWAS discovery: biology, function, and translation. The American Journal of Human Genetics **101**, 5–22.

Whelan B, Taylor J. 2013. *precision agriculture for grain production systems*.

Wiesner-Hanks T, Nelson R. 2016. Multiple disease resistance in plants. Annual Review of Phytopathology **54**, 229–252.

Wilson C, Tisdell C. 2001. Why farmers continue to use pesticides despite environmental, health and sustainability costs. Ecological Economics **39**, 449–462.

Xu Y. 2016. Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics **129**, 653–673.

Zhang C, Valente J, Kooistra L, Guo L, Wang W. 2021. Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches. Precision Agriculture **22**, 2007–2052.
Tables

<table><caption>**Table 1.** Summary of useful indicators of resilience identified in literature</caption><thead><tr><th>Indicators</th><th>Description</th><th>Calculation</th><th>Authors</th></tr></thead><tbody><tr><td>Variance of deviations (σ²)</td><td>Indication of the impact of disturbances. Captures the severity and duration of environmental perturbations experienced by an individual</td><td>ln(σ<sub>j</sub><sup>2</sup>) = ln(Σ<sub>i=1</sub><sup>n<sub>j</sub></sup> (x<sub>ij</sub> - x&#773;<sub>j</sub>)<sup>2</sup> / n<sub>j</sub> - 1)<br>Where x<sub>ij</sub> is deviation i of the j<sup>th</sup> individual, x&#773;<sub>j</sub> is the mean of deviations of the j<sup>th</sup> individual, and n<sub>j</sub> is the number of deviation observations of the j<sup>th</sup> individual</td><td>Berghof et al. (2019a,b)</td></tr><tr><td>Skewness of deviations</td><td>Indication of the direction of disturbances. Captures the severity of environmental perturbations experienced by an individual</td><td>skewness<sub>j</sub> = (n<sub>j</sub> / (n<sub>j</sub> - 1)(n<sub>j</sub> - 2)) Σ<sub>i=1</sub><sup>n<sub>j</sub></sup> (x<sub>ij</sub> - x&#773;<sub>j</sub>)<sup>3</sup> / √Σ<sub>j</sub><sup>2</sup></td><td>Berghof et al. (2019a,b)</td></tr><tr><td>(Lag-one) Autocorrelation of deviations</td><td>Indication of the length of the impact of disturbances. Captures the duration (i.e., rate of recovery) of environmental perturbations experienced by an individual</td><td>autocorrelation<sub>j</sub> = (Σ<sub>i=1</sub><sup>n<sub>j</sub>-1</sup> (x<sub>ij</sub> - x&#773;<sub>j</sub>)(x<sub>(i+1)j</sub> - x&#773;<sub>j</sub>) / Σ<sub>i=1</sub><sup>n<sub>j</sub></sup> (x<sub>ij</sub> - x&#773;<sub>j</sub>)<sup>2</sup></td><td>(Berghof et al., 2019a,b)</td></tr><tr><td>Slope of a reaction norm</td><td>Indication of resilience in the face of a macro-environmental disturbance. Captures the severity of a macro-environmental perturbation experienced by an individual</td><td>The slope of a reaction norm a is estimated based on the trait value of an individual given the level of a disturbance. with:<br>- a = 0 for animals not influenced by the disturbance<br>- a < 0 for animals negatively influenced by the disturbance<br>- the |a| value quantifying the impact of the disturbance on the trait</td><td>(Berghof et al., 2019a,b)</td></tr><tr><td>Impact</td><td>Inverse of performance reduction during the episode (at the time of the peak impact)</td><td>Performance during disturbance / Performance before disturbance</td><td>'Resistance' in Lloret et al. (2011)</td></tr></tbody></table>
<table><tr><td>Recovery rate</td><td>Ability to recover relative to the damage experienced during disturbance</td><td>Performance after disturbance / Performance during disturbance</td><td>'Recovery' in Lloret et al. (2011)</td></tr><tr><td>Net change</td><td>Capacity to reach pre-disturbance performance levels</td><td>Performance after disturbance / Performance before disturbance = Impact × Recovery rate</td><td>'Resilience per se' in Lloret et al. (2011)</td></tr><tr><td>Relative resilience</td><td>Ability to achieve the levels of pre-disturbance performance with respect to the impact during the disturbance.</td><td>Performance after – performance during / Performance before disturbance</td><td>Lloret et al. (2011)</td></tr><tr><td>Recovery time</td><td>The duration a system needs to return to an equilibrium following disturbance</td><td>Time to reach pre-disturbance levels</td><td>Thurm et al. (2016)</td></tr><tr><td>Total performance reduction</td><td>Accumulated loss of performance due to the perturbation during the perturbed period plus all the time in the recovery period</td><td>End of the recovery period ∑ Beginning of the perturbation Loss of performance at a given time</td><td>'Increment loss due to disturbance' in Thurm et al. (2016)</td></tr><tr><td>Average performance reduction</td><td>Quantification of the average annual/month etc. perturbation impact</td><td>Total performance reduction / Recovery time</td><td>Schwarz et al. (2020)</td></tr><tr><td>Average recovery rate</td><td>Quantification of how much of the performance reduction could be recovered within one year/month etc.</td><td>(1 – Impact) / Recovery time × (1 – Impact) × 100</td><td>Schwarz et al. (2020)</td></tr></table>
## Figure Legends

**Fig. 1.** Description of the major shifts recommended for a transition towards a more sustainable breeding strategy accounting for the current environmental and societal constraints.

**Fig. 2.** Conceptual illustration of disease resilience expression over time in fruit trees by distinction of (A) the multiple trajectories leading to disease resilience and (B) the decay of non disease resilient trees in a multi-disease context.

**Fig. 3.** Illustration of resilience biomarkers which could be considered to study the impacts of pests and diseases infection on fruit trees under low phytosanitary protection.

**Fig. 4.** Illustration of resilience indicators proposed by (A) Berghof et al. (2019) to compare disease resilience between livestock animals, (B) Llorret et al. (2011), Thurm et al. (2016), and Schwarz et al. (2020) to assess the response of forest trees to drought episodes. These indicators are well-suited to study fruit tree disease resilience.

**Fig. 5.** Illustration of the bivariate representation of resilience proposed by Ingrisch and Bahn (2018) by plotting the normalized recovery rate against normalized impact of disturbance in order to define the bivariate space. Recovery time results from the combination of coordinates in the bivariate space. Four examples (a-d) are detailed to describe possible trajectories when searching for resilience. Impact of disturbance, difference between states at pre-disturbance and maximum impact time; Recovery rate, change of system state per unit of time after a disturbance.

**Fig. 6.** Proposal for future breeding programs with a description of the major shifts recommended for a transition towards a more sustainable breeding based on resilience breeding.
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Description of the major shifts recommended for a transition towards a more sustainable breeding strategy accounting for the current environmental and societal constraints.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Conceptual illustration of disease resilience expression over time in fruit trees by distinction of (A) the multiple trajectories leading to disease resilience and (B) the decay of non disease resilient trees in a multi-disease context.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Illustration of resilience biomarkers which could be considered to study the impacts of pests and diseases infection on fruit trees under low phytosanitary protection.</figcaption></figure>
A

<figure><img src="image_8.png" /></figure>

B

<figure><img src="image_9.png" /><figcaption>**Fig. 4.** Illustration of resilience indicators proposed by (A) Berghof et al. (2019) to compare disease resilience between livestock animals, (B) Llorret et al. (2011), Thurm et al. (2016), and Schwarz et al. (2020) to assess the response of forest trees to drought episodes. These indicators are well-suited to study fruit tree disease resilience.</figcaption></figure>
Bivariate representation of resilience

<figure><img src="image_10.png" /><figcaption>**Fig. 5.** Illustration of the bivariate representation of resilience proposed by Ingrisch and Bahn (2018) by plotting the normalized recovery rate against normalized impact of disturbance in order to define the bivariate space. Recovery time results from the combination of coordinates in the bivariate space. Four examples (a-d) are detailed to describe possible trajectories when searching for resilience. Impact of disturbance, difference between states at pre-disturbance and maximum impact time; Recovery rate, change of system state per unit of time after a disturbance.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Proposal for future breeding programs with a description of the major shifts recommended for a transition towards a more sustainable breeding based on resilience breeding.</figcaption></figure>