# Impact of climate change on fungal diseases of agroecosystems

Laurent Huber, Marie-Odile Bancal, Marie Launay

► To cite this version:

Laurent Huber, Marie-Odile Bancal, Marie Launay. Impact of climate change on fungal diseases of agroecosystems. International conference ANSES The impact of global change on the emergence of plant diseases and pests in Europe, Apr 2018, Paris, France. hal-01799549

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# Impact of climate change on fungal diseases of agroecosystems

L. Huber¹, M.-O. Bancal² and M. Launay³

¹INRA, Joint Research Unit EcoSys, Thiverval-Grignon, France

²AgroParisTech, Joint Research Unit EcoSys, Thiverval-Grignon, France

³INRA, AgroClim Unit, Avignon, France
Past and recent climate : temporal and spatial variability

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# Fungal growth response to temperature

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Experiments under controlled conditions of **constant** or **fluctuating** temperature and **global warming T2-T1(→)**

For $T_{mean} > T_{opt}$:

1. At T1 under fluctuating T → Growth rate↓

2. From T1 to T2 at Constant T → Growth rate ↓

3. At T2 under fluctuating T → Growth rate ↓↓
# Fungal growth response to the leaf temperature fluctuating around optimal temperature

## Fungal pathogen :

Zymoseptoria tritici with 3 isolates

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Experiments under controlled conditions on adult wheat plants
Toptimal = Tmean = 18 °C with fluctuations of temperature

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# Climate change in the near and far future

## 1. Solutions for continuous variables such as temperature

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

## 2. What about intermittent variables such as rainfall and leaf wetness duration ?

(IPCC, 2014)
**Temperature-dependent response
of fungal infection to leaf wetness duration**

<figure><img src="image_9.png" /></figure>

Experiments on infection efficiency under
controlled conditions of temperature and
Surface Wetness Duration with
**virtual global warming of + 3 °C (→)**

For T < Toptimal :

$$
T \uparrow \Rightarrow \text{SWD} \downarrow
$$

=> Potential opportunity
for the pathogen

For T > Toptimal :

$$
T \uparrow \Rightarrow \text{SWD} \downarrow
$$

=> Potential threat
for the pathogen

Criteria for predicting apple scab infection periods
Leca et al, Agricultural and Forest Meteorology, 2011
McHardy and Gadoury, Phytopathology, 1989

Brown rust and yellow rust on wheat seedlings
de Vallavieille-Pope et al, Phytopathology, 1995, 2002
# Effects of climate change on pathogens, host plants and aerial diseases

## Direct effects of climatic factors on epidemiological processes

* effects of UV-B, CO₂, O₃, temperature, heat/water stress, rainfall, drought, ...

## Indirect effects of warming on pathosystems

* shift in host phenology and spatial distribution

* pathogen adaptation to host plant resistance

<figure><img src="image_10.png" /></figure>

## Consequences for foliar plant diseases

* allowing emergence of infectious diseases in new territories

* varying incidence and severity of native diseases

* filtering (or generating new) pathogens, pathotypes, races, ...

<figure><img src="image_11.png" /></figure>

STYLVES DAVIDAL POUR SCIENCESET Avenir

Future distribution of vineyards in Europe ?

## Protection and prevention against diseases

* no technological rupture in near-future tactical measures

* manipulating biodiversity : possible long-term lever
# Effects of CC on crop diseases and pathosystems

## Contribution of French research institutions and R&D organisations

### CLIMATOR project
2007-2010 / ANR funding

* Mostly on healthy crop and forest ecosystems

* Biophysical modelling

* Preliminary work on plant diseases and pathosystems :

* brown rust and Septoria on wheat

* botrytis on vineyard

Notice early research work on forest diseases in France

### CLIF project
2014 – 2017 / INRA-ACCAF MP funding

WP1

Scientific objectives
and

stakeholder expectations

<figure><img src="image_12.png" /></figure>

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WP2 Modeling the impact
of climate change on pathosystems

<figure><img src="image_15.png" /></figure>

WP4

Taking into account
pathogen diversity

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

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WP3

Adaptation strategies
to improve plant health

<figure><img src="image_21.png" /></figure>

WP5 Innovation and transfer of results

<figure><img src="image_22.png" /></figure>
# Simulating Leaf Wetness Duration (LWD) to predict future fungal infection events

Huard F et al, AGU Fall Meeting, 2014
(Huber, Bancal, Launay, 2017. INRA-Accaf CLIF project, final report)

## 3 models to estimate LWD

<figure><img src="image_23.png" /></figure>

### DPD

Vapour pressure deficit

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<figure><img src="image_25.png" /></figure>

### LWD variation (%) relative to the period 1976-2005

Future climate data obtained using Aladin model, RCP 8.5, and QQ method

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### CART

Decision Tree

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### MEDHI

Statistical

2021-2050

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2051-2070

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2071-2100

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# Estimating disease trends and uncertainties
*in relation to modeling assumptions*

## Case study : wheat infection by brown rust

### Simulation of disease infection risks based on :

- 2 LWD models

- 2 infection models

Infection model
de Vallavieille-Pope

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

## Results

Predicted infection values differ significantly depending on both LWD and infection models but temporal trends are similar

Infection model
ClimInfeR

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<figure><img src="image_36.png" /></figure>

Disease infection risks :

- increasing in the fall and winter

- decreasing in summer
WP2

# Understanding disease trends according to climate and microclimate evolution

## Case study : wheat infection by brown rust

- Simulation of the relative number of days with infection efficiency > 5%

## Results

Whatever the site (northern or southern France) and whatever the scenario (RCP 4.5 or 8.5):

- The number of infectious days is

- Significantly increased in winter by the expected warming

- Significantly reduced in summer by the predicted drought

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

Huber, Bancal, Launay 2017
INRA-Accaf CLIF project, final report

Relative Thermal index

Relative Leaf Wetness Duration

<figure><img src="image_39.png" /></figure>
# Impact of Climate Change scenario on wheat crop growth and *Puccinia tritici* development

<figure><img src="image_40.png" /></figure>

5% of disease severity is reached sooner in wheat under future climate

<figure><img src="image_41.png" /></figure>
# Manipulating the microclimate using cultivar mixtures

*Crop heterogeneity as a lever to lower infection (a) and limit splash-dispersal (b)*

<figure><img src="image_42.png" /></figure>

PUR-Sho : short pure stand
Resistant (R) or Susceptible (S)

<figure><img src="image_43.png" /></figure>

HOM : homogeneous mixture = Short S + Short R

HET : heterogeneous mixture = Short S + Tall R

In the HET mixture, upper leaves of tall R cultivar protect bottom leaves of short S cultivar from :

- radiative loss and dew deposits (a)

- incident rainfall drops and splash dispersal (b)
# Thermal impact on Infection Efficiency (IE) & Latent Period (LP)

(Vallavieille-Pope et al, Plant Pathology, 2018)

## Controlled-conditions experiments on 16 *Puccini striiformis* isolates

<figure><img src="image_44.png" /></figure>

### 2 susceptible wheat varieties: cv-Cartago, cv-Victo

<figure><img src="image_45.png" /></figure>

40 leaves per experiment, 2 biological replicates
# Combining isolate-response of infection efficiency and temperature scenario in the near and far future

<figure><img src="image_46.png" /></figure>

Comparing Northern (N) and Southern (S) reference isolates to invasive PstS and W in terms of response to temperature assessed using 2 major epidemiological processes : Infection Efficiency (IE) and Latent Period (LP)

**Does the response to temperature provide an advantage to S or W isolates ?**

Vallavieille-Pope et al, 2002, 2018

## Approach including both experiments and thermal response models

<figure><img src="image_47.png" /></figure>

PCA of actual thermal behaviour of Pst isolates according to LP and IE

<figure><img src="image_48.png" /></figure>

Mean monthly infection efficiencies in January and May for two French sites

$$ \text{January : } IE(\text{FF}) > IE(\text{NF}) $$

$$ \text{May : } IE(\text{FF}) < IE(\text{NF}) $$
Conclusions and perspectives

❖ Pathogens are sensitive to climate, likely more than their host plants, and particularly to persistence and motion of liquid water in the canopy

➤ modelling intermittent variables such as leaf Wetness duration

➤ simulating diseases in relation to climate scenarios and knowledge gaps

➤ controlled conditions experiments to anticipate climate-disease relationships

➤ field experiments to increase disease prevention and ecosystem resilience

<figure><img src="image_49.png" /></figure>

methods to assess the CC effects on native diseases of existing crops

❖ What about disease emergence risks ? primarily linked to crop area expansion, pathogen introduction, technological tools, globalized trade, human migrations, ... rather than direct CC effects (von Tiedemann et al., 2017)

❖ What could be our contribution to the understanding of disease emergence ?

➤ incorporating the human behaviour : sensitivity analysis to modifications of agricultural practices, use of technological innovation

➤ assessing the impact of CC adaptation to abiotic stresses on plant disease risks

➤ applying system dynamics in a wider approach to emerging risks