Study of the impact of climate change on a winter wheat crop ......slows their development. In Ecotrons, significant differences are present at the end of the season between crops

http://lib.uliege.be https://matheo.uliege.be

Study of the impact of climate change on a winter wheat crop (Triticum

aestivum L.) by Ecotron simulation

Auteur : Antoine, Maurine

Promoteur(s) : Leemans, Vincent

Faculté : Gembloux Agro-Bio Tech (GxABT)

Diplôme : Master en bioingénieur : sciences et technologies de l'environnement, à finalité spécialisée

Année académique : 2018-2019

URI/URL : http://hdl.handle.net/2268.2/8434

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STUDY OF THE IMPACT OF CLIMATE

CHANGE ON A WINTER WHEAT CROP

(TRITICUM AESTIVUM L.) BY ECOTRON

SIMULATION

MAURINE ANTOINE

TRAVAIL DE FIN D’ETUDES PRESENTE EN VUE DE L’OBTENTION DU DIPLOME DE

MASTER BIOINGENIEUR EN SCIENCES ET TECHNOLOGIES DE L’ENVIRONNEMENT

ANNEE ACADEMIQUE 2018-2019

PROMOTEUR : VINCENT LEEMANS

Toute reproduction du présent document, par quelque procédé que ce soit, ne peut être

réalisée qu'avec l'autorisation de l'auteur et de l'autorité académique1 de Gembloux Agro-

Bio Tech.

Le présent document n'engage que son auteur.

1 Dans ce cas, l'autorité académique est représentée par le(s) promoteur(s) membre du personnel(s) enseignant de GxABT

STUDY OF THE IMPACT OF CLIMATE

CHANGE ON A WINTER WHEAT CROP

(TRITICUM AESTIVUM L.) BY ECOTRON

SIMULATION

MAURINE ANTOINE

TRAVAIL DE FIN D’ETUDES PRESENTE EN VUE DE L’OBTENTION DU DIPLOME DE

MASTER BIOINGENIEUR EN SCIENCES ET TECHNOLOGIES DE L’ENVIRONNEMENT

ANNEE ACADEMIQUE 2018-2019

PROMOTEUR: VINCENT LEEMANS

Acknowledgements

I would like to thank all the people who participated in this master thesis. I would

particularly like to thank:

Vincent Leemans, my promoter, for the time he gave me, the knowledge he shared with

me and the precious advice he gave me during this master thesis. I also wanted to thank

him for his passion and optimism during this first experience at Ecotron.

The professors and researchers who contributed to the development and smooth running

of this first experiment in Ecotron, P. Delaplace, C. Thonar, C. Fostier, A. Anckaert, B.

Dumont, B. Longdoz, J.T. Cornelis, P. Jacques, B. Heinesch, B. Mercatoris, A. Degré, S.

Garre… I would also like to thank Y. Brostaux for his statistical advice.

I also wanted to sincerely thank my parents, who allowed me to pursue these studies and

who supported me throughout these five years. I thank my family members who

encouraged me, especially my brother and sister, for their understanding. I also thank my

grandfather who, through his stories about his long career at the University of Liège, made

me want to spend a few years there aswell.

I thank my friends, who believed in me and listened to me talk about this master thesis all

the time for six months.

Finally, I thank all the people I met during my studies, who allowed me to evolve and

discover new ways of living and thinking.

Résumé

Dans un contexte de population grandissante, il est important de comprendre les effets

du changement climatiques sur les cultures, afin de pouvoir s’y préparer au mieux. Dans

cette étude, du froment d’hiver (Triticum aestivum L.) de la variété Sahara est cultivé en

Ecotron sous deux scénarios météorologiques belges : un scénario passé-récent (saison

de culture 2014-2015) et un scénario futur (saison de culture 2093-2094, selon les

prévisions de l’Institut Royal Météorologique). Des mesures agronomiques et de

fluorescence chlorophyllienne sont réalisées tout au long de la saison dans chaque

scénario météorologique. Pour la saison de culture 2014-2015, des mesures

agronomiques prises au champ sur la même variété de froment sont également

disponibles. L’ensemble de ces mesures permettent d’étudier la reproductibilité d’un

agroécosystème et de son scénario météorologique en Ecotron, le développement du

froment sous scénario météorologique passé-récent et futur, ainsi que la répétabilité

entre enceintes. Les résultats montrent que les scénarios météorologiques sont

globalement correctement reproduits en Ecotron, les principales différences concernent

le bilan radiatif de la culture et les conditions de vent. La culture en Ecotron se développe

mieux comparé à la même culture au champ. En Ecotron, les cultures sous scénario

météorologique futur développent dans un premier temps une plus grande biomasse et

une plus grande surface foliaire. Ensuite, elles font face à un stress hydrique qui ralenti

leur développement. Dans les Ecotrons, des différences significatives sont présentes à la

fin de la saison entre les cultures d’un même scénario météorologique.

Mots-clés : Changement climatique, Chambre de culture, Ecotron, Froment, Fluorescence

Chlorophyllienne.

Abstract

In the context of a growing population, it is important to understand the effects of climate

change on crops to be better prepared for it. In this study, winter wheat (Triticum

aestivum L.) of the Sahara variety is grown in Ecotron under two Belgian meteorological

scenarios: a past-recent scenario (2014-2015 growing season) and a future scenario

(2093-2094 growing season, according to the Royal Meteorological Institute's forecasts).

A set of meteorological variables are measured. Agronomic and chlorophyll fluorescence

measurements are performed throughout the season in each weather scenario. For the

2014-2015 growing season, agronomic measurements taken in the field on the same

wheat variety are also available. All these measurements allow studying the

reproducibility of an agroecosystem and its meteorological scenario in Ecotron, the

development of wheat under past-recent and future meteorological scenarios as well as

the repeatability between Ecotrons. The results show that the weather scenarios are

generally correctly reproduced in Ecotron, the main difference being that the radiation

balance of the crop is not the same as in the field. Ecotron crop is developing better

compared to the same crop in the field. In Ecotron, crops under the future meteorological

scenario initially develop a larger biomass and leaf area. Then, they face water stress that

slows their development. In Ecotrons, significant differences are present at the end of the

season between crops in the same weather scenario.

Keywords: Climate change, Chamber of culture, Ecotron, Wheat, Chlorophyll

Fluorescence, Ecotron.

TABLE OF CONTENT

Introduction ......................................................................................................................................................................... 1

State of art ............................................................................................................................................................................. 3

1 Climate change ...................................................................................................................................................... 3

1.1 Overview ........................................................................................................................................................ 3

1.2 Timescale concept ..................................................................................................................................... 4

2 Winter wheat ......................................................................................................................................................... 4

2.1 Overview ........................................................................................................................................................ 4

2.2 C3 Photosynthesis ..................................................................................................................................... 5

2.3 Crop response to climate change ........................................................................................................ 8

3 Chlorophyll fluorescence ............................................................................................................................... 11

4 Ecotron .................................................................................................................................................................. 14

Materials and method ................................................................................................................................................... 15

1 Seminal Experiment in the Ecotron (SEE) ............................................................................................. 15

2 Controlled Environment Room (CER)...................................................................................................... 16

2.1 Parameters controlled in the CERs and the wintering chamber ......................................... 16

2.2 Input data in CERs .................................................................................................................................. 17

2.3 Soil conditions .......................................................................................................................................... 18

3 Crop management ............................................................................................................................................ 19

4 Measures .............................................................................................................................................................. 20

4.1 Agronomic measures ............................................................................................................................. 20

4.2 Chlorophyll fluorescence ..................................................................................................................... 22

4.3 Additional measures : thermography ............................................................................................. 22

5 Statistical processing of collected data .................................................................................................... 23

5.1 Processing of raw weather data ........................................................................................................ 23

5.2 Statistical analysis of crop measures .............................................................................................. 23

Results ................................................................................................................................................................................. 25

1 Weather data analysis .................................................................................................................................... 25

1.1 Reproducibility of the Lonzée 2015 weather scenario in Ecotron ..................................... 25

1.2 Are 2015 and 2094 weather scenarios representative of their time horizons? ........... 28

1.3 Comparison of 2015 and 2094 weather scenarios ................................................................... 30

2 Crop measures ................................................................................................................................................... 34

2.1 Comparison of crops in Lonzée and Ecotron 2015 ................................................................... 34

2.2 Comparison of crops in Ecotron 2015 and 2094 ....................................................................... 39

3 Repeatability between CERs ........................................................................................................................ 49

3.1 Meteorological and soil conditions .................................................................................................. 49


3.3 Chlorophyll fluorescence measures ................................................................................................ 55

Discussion .......................................................................................................................................................................... 58

1 Reproducibility of weather conditions in Ecotron ............................................................................. 58

2 Are 2015 and 2094 representative of their time horizon? ............................................................. 59

3 Comparison of crops in Ecotron 2015 and Lonzée ............................................................................. 60

3.1 Phenology ................................................................................................................................................... 60


4 Comparison of crops in Ecotron 2015-2094 ......................................................................................... 61

4.1 Phenology ................................................................................................................................................... 61

4.2 Chlorophyll fluorescence measures ................................................................................................ 61


5 Repeatability between CERs ........................................................................................................................ 66

5.1 Weather conditions ................................................................................................................................ 66


5.3 Chlorophyll fluorescence measurements ..................................................................................... 67

6 Forecasting trials for field crops in 2094 ............................................................................................... 68

Conclusion and prospects ............................................................................................................................................ 69

Prospects ....................................................................................................................................................................... 70

Bibliography ................................................................................................................................................................. - 71 -

ANNEXES ................................................................................................................................................................................ I

1 ExtractData.m code .............................................................................................................................................. I

2 Statistical analysis : scripts for Rstudio .................................................................................................... IV

2.1 ANOVA on agronomic measures Lonzée-Ecotron 2015 .......................................................... IV

2.2 ANOVA on agronomic measures 2015-2094 ................................................................................. V

2.3 Statistical analysis of fluorescence measures .............................................................................. VI

3 Observation of Development Stages (BBCH) in Ecotrons and at Lonzée ................................. XII

4 Evolution of the Fv/Fm parameters measured on wheat throughout the season in the

three CERs under the 2014-2015 meteorological scenario and the three CERs under the 2093-

2094 meteorological scenario (full graph). ................................................................................................... XIII

5 SEE side I experiment ................................................................................................................................... XIV

6 SEE side II experiment.................................................................................................................................. XVI

List of abbreviations

ATP : Adenosine triphosphate

BBCH : scale used to identify the phenological development stages of plants, BBCH derives

from the names of the originally participating stakeholders : “Biologische Bundesanstalt,

Bundessortenamt und CHemische Industrie.”

CER : Controlled environment room

CRA-W : Centre de Recherche Agronomique de Wallonie

FACE : Free air CO2 Enrichment

HCPC : Hierarchical clustering on principal components

IPCC : International panel on climate change

IRM : Institut Royal Météorologique

LAI : Leaf area index (m²leaves/m²soil)

LHC : Light harvesting complex

NADP+ : Nicotinamide adenine dinucleotide phosphate

NADPH : Reduced form of nicotinamide adenine dinucleotide phosphate

PAR : Photosynthetically active radiation (µmol.m-².s-1)

PLSR : Partial least square regression

PQ : Plastoquinone

PQH2 : Plastohydroquinone

PSI : Photosystem I

PSII : Photosystem II

QA : Quinone A

RCP : Representative concentration pathway

RH : air relative humidity

RUBP : ribulose 1.5 biphosphate

SEE : Seminal experiment in the Ecotron

SWC : Soil water content

Tair : Air temperature (°C)

UTC : Universal coordinate time

https://en.wikipedia.org/wiki/Phenology

1

INTRODUCTION

According to the World Meteorological Organization, the climate is the synthesis of

meteorological conditions in a given region, characterized by long-term statistics of the

state variables of the atmosphere.

It is widely accepted that the Earth is currently undergoing a climate change that has not

experienced it for 800,000 years (IPCC, 2014). This climate change affects temperatures,

precipitation, radiation, the occurrence and intensity of extreme events and a lot of other

climatic factors. These changes would be induced by a significant increase in the

concentrations of certain greenhouse gases2. For example, the atmospheric concentration

of CO2 increased from 280 to 379 ppm between 1900 and 2005. It largely exceeds

variations of the last 650,000 years from 180 to 300 ppm (IPCC, 2007). That is why these

changes are most likely related to the increase in anthropogenic emissions of greenhouse

gases.

Considering the close link between climate and agroecosystem, the slightest change can

have disproportionate effects on crops. Indeed, the extreme heatwaves expected, coupled

with changes in precipitation may cause water stress. Furthermore, more frequent and

intense extreme events can physically damage crops. According to Porter et al. (2005),

changes in atmospheric gas composition will also have an impact on plant development

It is even more important to understand the impact that global warming will have on

crops given the global population increase. The forecasted increase of the world

population from 7,6 billion to 9,8 billion by 2050 (United Nations (UN), 2017), will lead

to an ever-greater demand for food. Increasing the cultivated area is not a sustainable

solution because the increase of the population will lead to a competition in land use.

Agriculture must, therefore, continue to improve yields while adapting to climate change.

A better understanding of the effects of climate change on crops is therefore essential.

This is the purpose of this master thesis, which is part of a project of the Faculty of

Gembloux Agro-Bio Tech concerning Ecotrons, which includes three closely related parts:

-study of the effect of agro-systems on climate change

-study of the effect of climate change on these agro-systems

-study of mitigation options

This work contributes to Part Two. Its objective is to study the impact of climate change

on a winter wheat crop (Triticum aestivum L.) by simulation in Ecotron. So, winter wheat

2 Greenhouse gases are defined as gases that absorb and emit radiations at specific wavelength in the emission spectrum of the Earth’s surface.

2

is grown in Ecotron under present (2014-2015) and future (2093-2094) weather

conditions in Belgium. The weather data come from the Ernage climate database

provided by the CRA-W (Centre de Recherches Agronomiques de Wallonie) and the IRM

(Institut Royal de Météorologie). Environmental conditions and the functioning of the

plant will be measured throughout the experiment. The impact of climate change on

wheat cultivation will be studied. The reproducibility of an agroecosystem in Ecotron, as

well as the repeatability between enclosures, will also be examined.

3

STATE OF ART

This section introduces concepts that will be necessary to understand the results and their

analysis. In the first part, the generalities about climate change are explained as well as

the concept of timescale. The second part deals with wheat. First, general information on

wheat and its importance is reported. Then, the C3 photosynthesis, realized by wheat, is

explained in detail in order to understand the processes put in place to counter abiotic

stresses. Finally, the effects of the different climatic variables on wheat, or on plants in

general, that have already been observed in other studies are reported. The third part

contains the basic concepts of chlorophyll fluorescence that will be required to

understand measurements made in this study. The fourth part concerns the Ecotron.

1 CLIMATE CHANGE

1.1 Overview As previously stated, the increase in CO2 content in the atmosphere is mainly due to

anthropogenic emissions such as agriculture, fossil fuel combustion, industrial activities

and land-use change (Le Quéré et al., 2016). This increase in the CO2 concentration

contributes to a change in net radiation flux density at troposis, called radiative forcing. It

enhances the energy absorption by the Earth and therefore, it increases its average

temperature (IPCC, 2014). This results in an increase in high-temperature peaks and a

decrease in cold temperatures.

In addition to the increase in temperatures, extreme events will be more frequent and will

be felt more strongly. For example, extreme rainfall events will be higher and more

frequent in mid-latitudes and tropical wetlands (IPCC, 2000). In general, wet areas will

become wetter and dry areas will become drier.

Four Representative Concentration Pathways (RCPs) have been developed by the climate

modeling community as a basis for the long term and near-term modeling experiments

(van Vuuren et al., 2011). In RCP, Representative means that each of the RCPs represents

a larger set of scenarios in literature and Concentration Pathways means that these RCPs

are not the final scenarios but constituting sets of coherent projections composing the

radiative forcing. Each RCP corresponds to a certain radiative forcing (Kirtman et al.,

2013) and presents a possible scenario of the future climate. They come from a

collaboration between climate modelers, terrestrial ecosystem modelers, and emission

inventory experts (van Vuuren et al., 2011). These four climate scenarios cover the period

1850-2100 and were determined for radiative forcing of 2.6, 4.5, 6.0, and 8.5 W / m².

Extensions have also been generated for the period from 2100 to 2300. RCPs consider

4

many parameters such as socio-economic and technological changes, changes in energy

use, land use, and changes in greenhouse gas emissions.

1.2 Timescale concept

The effect of any climatic variability depends on the temporal scale of such variability. For

wheat, which is an annual plant, the most relevant timescale is over years or even days.

Changes in daily climate variability and frequency of extreme events can be very

important for crop performance (Hulme, Harrison, and Arnell, 1999). Therefore, to assess

the impact of global warming, it is important to distinguish between natural climate

variations and anthropogenic climate change, but also to apply changes in climate

variability at appropriate time scales (Porter & Semenov, 1999). Climate change is a

phenomenon that must be studied in the long term because current emissions will

influence the climate for centuries to come.

2 WINTER WHEAT

2.1 Overview Winter wheat (Triticum aestivum L.) is an annual herb of the gramineous family. It is one

of the three most cultivated cereals over the world after rice and maize and the second

most consumed directly by man after rice. According to FAOSTAT (2014), world wheat

production amounted to 729 012 million tons in 2014.

Wheat is very common throughout the world because it has a better yield than other

cereals (Sramkova et al., 2009). Besides, wheat grain is rich in amino acids, minerals,

vitamins, and fiber that contribute to a good diet. This cereal is, therefore, very interesting

for emerging countries and helps to deal with malnutrition (Sramkova et al., 2009).

Many varieties of wheat are distinguished by their yield, their degree in resistance to

different diseases, extreme temperatures, and other stress generated by the environment.

Indeed, its large genetic diversity has spawned the development of many species adapted

to a wide range of climatic conditions (Reynolds, 2010). Durum wheat (Triticum durum

D.) is, therefore, more suited to hot, dry regions, while common wheat (Triticum aestivum

L.) is better adapted to temperate regions. In Belgium, two types of wheat are grown,

winter wheat, which is sown from October to January and is harvested in August, and

spring wheat, which is sown in mid-March and harvested at the end of August and mid-

September.

5

2.2 C3 Photosynthesis

The performance of photosynthesis depends greatly on environmental conditions and

therefore, climatic conditions. Indeed, certain physiological mechanisms are triggered in

response to stress; in other words, environmental changes cause physiological reactions

in plants. The following paragraphs, therefore, recall the basics of photosynthesis in C3 of

wheat, to understand further the impact of climate change on it.

Photosynthesis in higher plants is a process by which light energy is converted into

chemical energy (Taiz et al., 2015). This conversion comprises two synchronous phases

taking place in the chloroplast: the light phase (light reactions), which directly depends

on the light, and the dark phase (dark reactions), which does not. In the chloroplast, the

thylakoidal membranes form thylakoids, which, once stacked, form granas (Figure 1). On

one side of the membrane is the stroma, the former cytoplasm of the chloroplast, and on

the other the lumen, the inside of the thylakoid membrane.

Figure 1: Schematic picture of organization of the membranes in the chloroplast. Figure from Plant physiology and

development 6e © 2015 Sinauer Associates, Inc.

Light reactions

The light reactions take place in the thylakoid membrane where proton and electron

transfer is carried out by four protein complexes operating in series (Figure 2):

photosystem II (PSII), cytochrome, photosystem I (PSI) and ATPsynthase (Taiz et al.,

2015).

6

• Photosystems I and II are an association of chlorophylls. These molecules can

capture light and are, therefore, excitable. The photosystems are composed of an

antenna system and a reaction center. The antenna system consists of a series of

chlorophylls attached to the light-harvesting complex (LHC) proteins.

• The cytochrome is a complex called proton pump. It moves the protons against

their energy gradient, using redox reactions as an energy provider, and releases

them into the thylakoidal space (into the lumen).

• ATPsynthase produces ATP when protons diffuse through it, from the lumen to the

stroma. It is an enzymatic complex that consists of a hydrophobic membrane and

a portion that protrudes into the stroma.

Figure 2: Transfer of electrons and protons in the thylakoid membrane by the four protein complexes. Figure from Plant

physiology and development. © 2015 Sinauer Associates, Inc.

Chlorophyll present at the antennal complex of PSII is excited by light. It can de-energize

by transmitting energy to other chlorophylls present in the PSII reaction center. Thanks

to this energy, the chlorophyll of the reaction center becomes a strong reducer and

oxidizes water present in the lumen of the thylakoid into oxygen. This reaction releases

protons into the lumen and expels an electron into an electron transport chain (Figure 2).

The electron transport chain links the PSII to the PSI. The movement of electrons in the

membrane between the photosystems is provided by shuttles. One of these shuttles, the

plastoquinone (PQ) takes two electrons and two protons at the exit of the PSII and

becomes the plastohydroquinone (PQH2). The cytochrome then oxidizes the

plastohydroquinone and releases the protons in the thylakoidal space. The electrons from

this reaction are captured by plastocyanin, a protein that can accommodate an electron

7

between its two copper blades and gives it away when it is oxidized. It travels along the

membrane on the lumen side and will give up its electron to the PSI. At the exit of the PSI,

the electron is yielded in several stages to the ferredoxin. This then allows reducing NADP

+ to NADPH in the stroma. NADPH is a coenzyme carrying energy in the form of excited

electrons. It is a strong reducer needed for the assimilation of carbon. Ferredoxin can also

bring the electron back to the cytochrome complex to increase the proton gradient by

passing the same electron several times (cyclic operation). During the displacement of the

electrons, an electrochemical proton gradient is created. This potential difference on both

sides of the membrane has three origins: proton release, proton consumption in the

stroma and proton displacement by cytochromes. This gradient makes it possible to pass

the protons through the ATPsynthase complex. They, therefore, release energy that is

used for the synthesis of ATP.

Dark reactions

In the chloroplast stroma, ATP, and NADPH from the clear phase are consumed by the

Calvin-Benson cycle by a series of reactions driven by enzymes that reduce atmospheric

carbon dioxide to sugar (Figure 3). This cycle is the dark phase of photosynthesis (Taiz et

al., 2015). It is realized by C3 photosynthesizing plants, such as wheat.

Figure 3 : Calvin Benson cycle. Figure from Plant physiology and development 6e. © 2015 Sinauer Associates, Inc.

The cycle starts from a CO2 acceptor, RUBP, rubisco or ribulose 1,5-bisphosphate. Water

and carbon dioxide react with RUBP to form two phosphoglycerates, three-carbon

compounds that justify the name C3. It is carboxylation, a carbon fixation that does not

8

require energy. Then, the reducing phase uses the energy of ATP and NADPH, generated

during the light phase, reducing a phosphoglycerate to three-carbon sugar, called triose

phosphate. Part of the triose phosphate is used to regenerate the acceptor sugar. Only one

in six trios phosphate is available to export carbon to make sucrose. The different

reactions of the Calvin Benson cycle are catalyzed by enzymes. The enzyme ribulose 1,5

biphosphate (RUBP) has a dual activity. It is carboxylase and oxygenase. It has two

catalytic sites: a first binder RUBP and a second binder CO2 or O2. There is, therefore,

competition between CO2 and O2 to occupy the catalytic site. If RUBP is bound to a

molecule of oxygen, it is oxygenation. Lysis of the five-carbon compound will give a

phosphoglycerate and a phosphoglycolate which will have to be recycled. It is mainly the

ratio of concentrations of CO2 and O2, that drive the balance between carboxylation and

oxygenation (Raines, 2011).

2.3 Crop response to climate change Climate is one of the most important biotic factors in crop growth. Climatic factors such

as precipitation, temperature, CO2 and other gases concentration, depending on their

interactions, have very varied effects on yield (Asseng et al., 2009). Increased annual

variability in weather conditions also leads to increased variation in yields (Porter et al.,

2005). Also, variation in biotic factors influences protein content and grain composition,

affecting its nutritional and technical properties (Porter et al., 2005). The following

paragraphs present the effect of the main climatic factors on crops. These factors cannot

be considered independently because the global climate results from their interactions.

2.3.1 Effect of CO2 concentration

In C3 plants, an increase in atmospheric carbon concentration has a positive impact on

yield because it stimulates their photosynthesis (Porter et al., 2005). Indeed, at the site of

the Rubisco enzyme, a higher CO2 concentration promotes carboxylation that produces

sugar and so promotes the growth of the plant. It also induces an increase in intercellular

concentration, which increases photosynthesis (Asseng et al., 2009) and decreases

stomatal conductance (Farquhar, Dubbe, and Raschke, 1978). By reducing stomatal

conductance, elevated CO2 concentration decreases the evapotranspiration rate (-21%)

and increases instantaneous water-use-efficiency during early spring (Dijkstra, 1999;

Asseng et al., 2009). This could limit the effects of water stress related to rising

temperatures and decreasing precipitation. The experiments of Ainsworth and Long

(2004), using CO2 enrichment with the FACE (Free Air CO2 Enrichment) technique,

showed that doubling the CO2 concentration induced a gain of photosynthesis by 30 to

50% for C3 plants.

In the case of a crop limited in fertilizer inputs, a high CO2 concentration does not induce

a significant increase in yields (Mitchell et al., 1993). Naturally, a high CO2 level stimulates

9

the early growth of wheat (Bellia, 2003), which increases nitrogen uptake early in its

development and thus reduces the amount available for grain. Indeed, only a small

portion of the nitrogen that is sequestered in the structures can be remobilized and used

in the formation and filling of the grain (Keulen et al., 1987).

2.3.2 Effect of temperature

Cereals such as wheat have absolute temperature thresholds associated with particular

developmental stages beyond which yield is impacted (Porter et al., 2005). For example,

when temperatures increase during the growth period of wheat, an increase in yields can

be observed. However, some subsequent phenological stages, such as anthesis and grain

filling, are more sensitive to high temperatures that can have devastating effects (Wahid

et al., 2007; Asseng et al., 2009). The deleterious effect of increasing temperature on grain

fertility can be caused by exposure to high temperatures for periods as short as a day if it

is during a critical period (Mitchell et al., 1993). In general, while temperature remains

below these thresholds, an increase in average temperatures will induce a faster

development of the plant (Calderini et al., 2001). Sadras and Monzon (2006) showed via

the CERES-WHEAT culture model that the increase in average temperatures causes a

significant decrease in the time between sowing and maturity of winter wheat. The

flowering date would be advanced by seven days each time the average temperature

increases by one degree Celsius. Paradoxically, the reduction of phenological stages, and

thus the acceleration of the growth cycle, increases the risk of frost during the flowering

period and reduces the filling time of the grain (Mitchell et al., 1993).

Temperature changes also influence photosynthesis. At the scale of the whole plant, the

increase in temperature tends to reduce cell size, to close stomata, which reduces water

loss and thus lessen photosynthesis (Bañon et al., 2004). At the subcellular level, changes

in temperature cause changes in the chloroplasts, which induce significant changes in

photosynthesis. The photosynthesis of wheat is optimal at 25 ° C and declines below 15 °

C or above 30 ° C (Tashiro et al., 2006). High temperatures reduce photosynthesis by

altering the structural organization of thylakoids (Karim et al., 1997) or accelerating the

degradation of chlorophyll (Chung et al., 2006). Moderate thermal stress also decreases

Rubisco activity (Salvucci et al., 2001) and reduces electron transport between PSII and

PSI (Yan et al., 2013). The PSII is the most heat-sensitive component of the photosynthetic

apparatus. In conclusion, all these changes mainly due to high temperatures could result

in low plant growth and productivity (Porter & Gawith, 1999).

2.3.3 Effect of water stress

In the same way as for temperatures, the impact of water stress on yield depends on the

stage of development at which it occurs. For example, yields are more sensitive to the soil

10

water reserve when filling grain (Eitzinger et al., 2003; Connor, 1991). In general, water

stress reduces the efficiency of photosynthesis by reducing the diffusion of CO2 from the

atmosphere to the carboxylation site, Rubisco (Flexas et al., 2006). The decline in carbon

assimilation is fully explained by the reduction of stomatal conductance, which regulates

the intercellular CO2 concentration, and by the decline of mesophyll conductance, which

controls the chloroplastic CO2 concentration (Grassi et al., 2005). Wheat begins to close

its stomata and limit photosynthesis when the available soil water3 in the root zone falls

below 0.25 m³ / m³ of soil and leaf senescence is enhanced when available soil water falls

below 0.20 m³/m³ of soil (Keating et al., 2001).

Excess of water in soil reduces O2 availability for roots respiration. Flooding leads to a

decline in carbon assimilation due to the reduction of stomatal conductance possibly

promoted by a decrease in root hydraulic conductivity (Bertolde et al., 2012).

Moreover, change in rainfall variability would lead to longer periods of drought, which

would affect plant development. Besides, increased transpiration due to increased

temperatures could further increase water demand (Asseng et al., 2004). More extreme

storm events could cause irreversible crop damage from high winds and more intense

precipitation. Increasing precipitation intensity would also lead to nitrate leaching and

increased soil erosion (Sadras et al., 2007).

2.3.4 Effect of excessive radiation

Prolonged exposition and excess of light absorption can reduce the photosynthetic rate.

Indeed, excess of light induces the inactivation of the PSII by multiple chemical reactions

(Goh et al., 2012). Excess of light absorption can also result in irreversible damages to the

photosynthetic apparatus structure by the over-reduction of the electron transport chain

(Gururani et al., 2015). Indeed, in the case of over-excitation of the PSII, electron flow

might exceed the electron-accepting capacity of the PSI acceptor side.

To preserve the photosynthetic apparatus from an excess of light, the plant develops

different protective mechanisms (Ashraf et al., 2013). For example, it decreases the PSII

efficiency to dissipate the excess of light energy (C. Werner, R. J. Ryel, 2001). This

mechanism also contributes to regulate the electron transport chain and protect the PSI

from permanent damages. Another mechanism set up by the plant is the promotion of the

cyclic electron flow around the PSI (Takahashi et al., 2009).

3 The available soil water is the fraction of soil water that the plant can extract. It depends on the porosity of the soil and the amount of water in the soil.

11

2.3.5 Effect of ozone

Elevated ozone concentration induces H2O2 accumulation, which causes foliar symptoms

like the programmed death of palisade mesophyll cells (Desotgiu et al., 2010). Ozone

stress also induces the reduction of carbon assimilation rate by alteration of chloroplasts

and stomatal response (Bussotti et al., 2007). Moreover, upon prolonged exposition to O3

above 70ppb, the stomatal response becomes sluggish (Mcainsh et al., 2002; Desotgiu et

al., 2010). These responses to ozone stress are mechanisms of energy regulation to

prevent photo-oxidation damage (Bussotti et al., 2007).

2.3.6 Simultaneous impacts of different climatic factors

A combination of stresses is perceived by the plant as different stress and cannot be

directly extrapolated from the response to individual stressors (Rizhsky, L., Liang, H., and

Mittler, 2002). Combined heat and drought stress increase the reduction of carbon

assimilation rate of photosynthesis and induce more important oxidative damages

(Osório et al., 2011). A combination of high light and water stress reduces CO2 availability

by stomatal limitation and may induce over-excitation of the PSII (Georgieva et al., 2010).

The plant can down-regulate photosynthesis by increasing photorespiration to preserve

PSII and limit oxidative damage. Studies found that combined effect of an increase in

temperature and atmospheric CO2 would lead to a significant decrease in wheat yields

(Bellia, 2003). On the opposite, others found that higher CO2 concentration increases

photosynthesis less at low than at high temperatures (Bowes and Hall, 1991). Moreover,

elevated O3 sometimes reduced positive effects of elevated CO2 on yield, just like nitrogen

or water stress (Amthor, 2001).

2.3.7 Impact of climate change on crops in Belgium

More specifically, at the level of Belgium, climate change would increase winter cereal

yields but also the variability between yields (Gobin, 2010). Higher temperatures would

increase yields by up to 7% and shorten the length of the season. On the other hand,

changes in the seasonality of precipitation would lead to a decrease in the amount of

water available for cultivation and thus a decrease in yields of up to 12%.

3 CHLOROPHYLL FLUORESCENCE

Chlorophyll fluorescence gives a rapid and non-destructive diagnostic method

quantifying damages to the photosynthetic apparatus in response to environmental

stresses. Indeed, a small portion of the light energy absorbed by the leaves is dissipated

by the photosynthetic apparatus in the form of heat and fluorescence emission. This latter

can be measured. Therefore, the yield of chlorophyll fluorescence provides information

12

about changes in the efficiency of photochemistry and heat dissipation (Maxwell et al.,

2000).

Kautsky and Hirsch (1931) were the first to report a relationship between primary

reactions of photosynthesis and chlorophyll fluorescence. They found that, following

illumination of a dark-adapted photosynthetic sample, chlorophyll fluorescence emission

shows a fast rise to a maximum followed by a decline to a steady-state over some minutes.

The chlorophyll fluorescence measurements allow building a fluorescence emission

curve, on a linear or a logarithmic time scale (Figure 4), from which a lot of information

about the efficiency of the photosynthetic apparatus can be deduced. The fluorescence

rise during the fast phase is called the OJIP curve (O for origin, J and I are intermediary

steps and P for peak). The shape of the OJIP curve is universal for all photosystems

containing chlorophyll a but can change depending on the light intensity. The slow phase

is called the PSMT curve (P for peak, S for semi-steady state, M for maximum state and T

for terminal state).

Figure 4 : Chlorophyll a fluorescence emission of a dark-adapted pea leaf. The left graph is the fluorescence emission

curve plotted on a linear timescale and the right one on a logarithmic timescale. The fluorescence curves 1, 2, and 3 were

induced by a flashlight of 32, 320, and 3200 µmol m-2, respectively. Fluorescence is given in arbitrary units. Figure from

Stirbet and Govindjee (2011).

The rise from O to J is the photochemical phase and is strongly reliant on the exciting light

intensity (Buonasera et al., 2011). This phase is linked to the accumulation of

plastoquinone in its reduced form and thus to the closure of the PSII reaction center

(Stirbet et al., 2011). The fluorescence level at J is dependent on the availability of oxidized

plastoquinone (Schansker et al., 2005). The rise from J to I depend on the

reduction/oxidation of the plastoquinone pool (Petrouleas et al., 2005). As this phase is

sensitive to temperature, it is also called thermal phase (Stirbet et al., 2012). The rise

between I and P is related to the electron transfer through PSI (Schansker et al., 2005).

After reaching the maximum, fluorescence level falls during the slow phase because of

fluorescence quenching. It is due to the onset of carbon metabolism, called the

13

photochemical quenching, and energy dissipation by heat, called the non-photochemical

quenching.

The basal level (fluorescence at point O, noted Fo) represents emission by excited

chlorophyll in the antennae structure of PSII. It is an instantaneous rise to an original level

of fluorescence upon illumination. The true Fo level is only observed when the first stable

electron acceptor of PSII (QA) is fully oxidized. The maximum fluorescence level

(fluorescence at point P, noted FM) is measured when the light intensity is fully saturating

for the plant and when the electrons acceptor QA is fully reduced. The amount of variable

fluorescence (Fv = Fm – Fo) relates the photosynthetic apparatus potential to use photon

energy for photochemistry. The Fv/Fm ratio can also be calculated and indicates the

maximum quantum efficiency of PSII of a dark-adapted leaf. Fv/Fm is generally around

0.8 for higher plants. This ratio is one of the most studied fluorescence parameters.

Fv/Fm can be influenced by several phenomena. The physical separation of PSII and the

light-harvesting complex induces a decrease of Fv/Fm because of lower energy transfer.

Low temperatures induce dissipation of excess radiation energy within the light-

harvesting complex and so also the decrease of Fv/Fm (Adams III et al., 2008a; Jahns et

al., 2011). An increase in the energy transfer between PSII and PSI decreases the PSII

efficiency and so the Fv/Fm ratio (Adams III et al., 2013). As thermal stress reduces

electrons transport between PSII and PSI (Yan et al., 2013), it also reduces the Fv/Fm

ratio. Moreover, an excess of light reduces Fv/Fm by the inactivation of PSII (Goh et al.,

2012).

Another interesting parameter is Vi (Equation 2.1), which relates changes in the

amplitude of phase I-P of the fluorescence curve. Although its interpretation remains

controversial, its positive correlation with the effectiveness of the PSI is generally

accepted (Kalaji et al., 2017). Consequently, the high temperatures limiting electron

transport to the PSI and therefore, its efficiency, decrease the value of Vi (Yan et al., 2013).

𝑉𝑖 = 𝐹𝑀−𝐹𝐼

𝐹𝑀−𝐹𝑂 (Eq. 2.1)

with FI the fluorescence level at the intermediary step I.

A parameter related to the J step of the fluorescence emission curve, called (Eo), can be

calculated by Equation 2.2 below. It can be related to the limitation on the acceptor side

of PSI that decreases the oxidation rate of the plastoquinone pool and thus the electron

flow beyond the quinone B site (Tóth et al., 2007). Consequently, limitation on the

acceptor side of the PSI by thermal stress or excess of light reduces (Eo).

(𝐸𝑜) = 1 −𝐹𝐽−𝐹𝑂

𝐹𝑀−𝐹𝑂 (Eq. 2.2)

with FJ the fluorescence level at the intermediary step J.

14

The last parameter presented here is PIABS. It is an index that uses several parameters of

the OJIP curve (Equation 2.3). It provides information on the performance of certain

stages of the electron transport chain. It, therefore, gives an overview of the

photosynthetic system's tolerance to abiotic stresses (Stirbet et al., 2018). Indeed, PIABS

decreases with water stress (Jedmowski et al., 2015a) and high temperatures (Mathur et

al., 2011). It can also decrease when the temperature is too low.

𝑃𝐼𝐴𝐵𝑆 =𝑅𝐶

𝐴𝐵𝑆∗

𝐹𝑉𝐹𝑀

1−𝐹𝑉𝐹𝑀

∗𝐸𝑜

1−𝐸𝑜 (Eq. 2.3)

with 𝑅𝐶

𝐴𝐵𝑆=

1− 𝐹𝑜𝐹𝑀

𝑀𝑜𝑉𝑗

, Vj is the equivalent of Vi for the J-P phase.

4 ECOTRON

An Ecotron is an experimental device for reproducing ecosystems, including soils, plants,

animals (invertebrates) and micro-organisms, in a simplified way. Environmental

conditions, such as climatic variables, are controlled with a high accuracy. Daily variations

can be reproduced. Mater and energy fluxes can be measured in real-time and with

enough accuracy to be able to test and validate a model.

Such a system makes it possible to relate the growth dynamics of the plants to the

meteorological conditions. It allows to experiment crop models and to validate or not

their forecasts. In the case of this project, the Ecotron will be used to validate the STICS

(Simulateur mulTIdisciplinaire de Culture Standard) crop model (Leemans et al., 2017) as well

as its forecasts for crops under the future climate.

The Ecotron is part of a CARE (Cellule d’Appui à la Recherche et à l’Enseignement) named

"Environement Is Life". It is situated in the interfaculty research center of Gembloux Agro-

Bio Tech University, the TERRA.

15

MATERIALS AND METHOD

The materials and methods used for this study are described in this section. It first

contains a general description of the seminal experiment in the Ecotron. Then, it contains

a technical description of the Ecotron, weather data used, the soil and the crop, as well as

the various measurements carried out continuously or systematically. The last part

describes the statistical tools used in the results analysis.

1 SEMINAL EXPERIMENT IN THE ECOTRON (SEE) The Ecotron is composed of six Controlled Environment Rooms (CERs) which will be used

to meet the different objectives of SEE. First, this experiment will evaluate the

reproducibility of an agroecosystem and meteorological conditions in Ecotron. To do this,

three CERs will reproduce a recent-past climate, the weather scenario measured at the

ICOS (Integrated Carbon Observation System) station in Lonzée (Gembloux) during the

2014-2015 season. Agronomic measurements on winter wheat grown at this station are

also available and will be compared with measurements made in Ecotron. Then, the

experiment will assess the impact of climate change on a winter wheat crop, by comparing

the same crop grown under two weather scenarios. To this end, the three others CERs are

used for the simulation of a meteorological crop season representative of the climate

predicted for 2070-2100 by the Royal Meteorological Institute (IRM), according to the

radiative forcing scenario RCP 8.5. This scenario represents the least favorable scenario,

with the highest emission rate and its increase over time (van Vuuren et al., 2011). It

represents the case where the population would continue to increase without any change

in the use of energy resources. It would result in increasing amounts of greenhouse gases

released into the atmosphere (Riahi et al., 2011). According to the RCP 8.5, temperatures

of the 2071-2100 horizon are on average between 2.6 and 4.8 ° C higher than those for

the 1985-2005 horizon (IPCC, 2014). Wheat from CERs simulating the future climate will

be compared to wheat from CERs reproducing the past-recent climate. Finally, this

experiment will allow to study the repeatability between enclosures.

A wintering chamber is used to reproduce winter conditions for all CERs. The recent past

climate is attributed to odd chambers (1, 3 and 5), called “2015 CERs” or “Enclosures

2015”, and the future climate to even chambers (2, 4 and 6), called “2094 CERs" or

“Enclosures 2094”.

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2 CONTROLLED ENVIRONMENT ROOM (CER)

2.1 Parameters controlled in the CERs and the wintering chamber

The Ecotrons used in this study are controlled environmental rooms containing a

lysimeter of 1.63 m in diameter and 1.5 m deep (Figure 5). Controlled variables are

irradiation (spectrum, intensity, photoperiod), air temperature and humidity,

precipitation, wind, carbon dioxide, and ozone concentration, as well as temperature and

soil matrix potential at the bottom of the lysimeter. Table 1 bellow

shows the operating ranges of the different controlled variables and their sensitivity.

Table 1 : Controlled weather parameters in CERs.

Operating range Sensitivity

Photosynthetically active radiation (PAR) 0-1200 µmol.m-2.s-1 20 µmol.m-2.s-1

Air temperature 4-40 °C 1°C

Air humidity 7-98% 5%

Precipitations 0 ; 0.2-3.5 mm/5min 0.05mm/5min

Wind 0.5 m.s-1 (fixed) 0.1 m.s-1

CO2 Outside conc.- 800ppm 10ppm

O3 10-100 ppb 5 ppb

Figure 5 : Controlled environmental room with in the upper zone the system of simulation of the radiation and the system

of watering which simulates the precipitations. On the sides, the air passes through the walls at a speed of 0.5 m/s at the

determined temperature and more or less loaded with CO2, O3, and relative humidity.

17

The minimum temperature that can be simulated in CERs is 4°C. The lysimeters are

therefore moved to the wintering chamber to reproduce winter conditions as closely as

possible (Figure 6). In this chamber, the only parameters controlled are temperature

(which can go down to -7°C) and the photosynthetically active radiation spectrum (PAR)

which represents the main part of the energy supplied. Precipitations are reproduced

manually. The other parameters are not controlled, but given the temperature conditions,

their influence on the development of the plant is reduced. There is only one wintering

chamber for the six lysimeters from the six CERs. The temperature and the

photosynthetically active radiation are the same for all six lysimeters.

Figure 6 : Lysimeters in wintering chamber.

2.2 Input data in CERs

The selected weather conditions are typical of each climate, especially for the growth-

flowering-filling periods, from March to July. For the recent-past climate, the

meteorological scenario measured at the ICOS station in Lonzée from October 1st 2014 to

August 31th 2015 was chosen. In addition to be a typical meteorological year, agronomic

data about the winter wheat grown that year on this plot are available. For the future

climate, the meteorological scenario chosen is that of October 1st 2093 to August 31th

2094, predicted by the IRM' s climate prediction model, ALARO-0. Studies have shown

that ALARO-0 is well suited for regional climate modelling (De Troch et al., 2013, 2016).

The main differences between meteorological scenarios are a higher temperature for the

future climate (+2°C), more precipitation (+450 mm.year-1) and a less homogeneous

distribution of precipitation. Meteorological scenarios will be called meteorological

scenarios 2015 and 2094 regarding the year of harvest.

18

2.3 Soil conditions

The soil contained in the Ecotrons lysimeters is a disturbed soil from the Liroux site

(Gembloux) and more precisely from the Bordia 2 plot (Latitude: 50°33' North, Longitude:

4°42' East, Altitude: 165 m). It is part of the experimental farm fields belonging to the

faculty of Gembloux Agro Bio Tech. The soil installed in the lysimeters has three horizons:

a revised horizon above the plough base (0 to -27 cm), a brown silt horizon (-27 cm to -

100 cm) and a Bt horizon (-100cm to -150cm). The soil type is between Abp0 and Abp1.

Various sensors measure the soil conditions of the Ecotrons. The TEROS 21 (Meter

Environment) sensor measures soil water content and soil temperature using porous

discs. It is installed 5 cm below the ground surface. Two matrix potential sensors are

installed at -35 and -65 cm (SDEC SMS2040 + SKT850T). SM150T sensors (DeltaT) also

measure soil water content and temperature. Five of these sensors are installed at

different depths (Figure 7). Water extraction rods (SDEC 2440110) are installed in the

unsaturated zone of the soil. These rods extract some of the liquid solutions from the soil

by suction, which is then analyzed in the laboratory. Besides the sensors in the soil, the

Lysimeter mass is measured, which gives us the evolution of the soil water content (SWC)

and will be used further in the results and discussion sections. The leachate weights are

also monitored.

Figure 7 : Schematic representation of the sensors allowing the monitoring of the soil in the lysimeters of the CERs. Figure

from Leemans 2019. Seminal Experiment in the Ecotron – Protocol. Environment is Life (GxABT – Uliège).

19

3 CROP MANAGEMENT

The crop studied here is winter wheat (Triticum aestivum L.) and more precisely the

Sahara variety. It is the same variety that was cultivated in 2015 in the Lonzée field. Its

yield is close to the varieties of reference, and it presents good resistance to diseases.

The treatments applied to the crop in the Ecotron are carried out in such a way as to best

represent the agronomic reality. The CERs were commissioned in July 2018, and a catch

crop, Phacelia (Phacelia tanacetifolia B.) was installed in the lysimeters and remained

until the end of September. Before the start of the SEE experiment (Seminal Experiment

in the Ecotron), the phacelia is cut, chipped, and reincorporated into the soil. A first soil

sample is taken to determine the major content, and a second sample is used to determine

the amount of organic matter brought to the soil by the phacelia. The weather conditions

of 2014-2015 and 2093-2094 are reproduced in the CERs from October 1st. Winter wheat

seeds are sown on October 14 in 2014 and 2093, in a line, manually, in the same way as

with a cereal seed drill. The spacing is 14.7 cm. Eleven lines are, therefore, sown parallel

to the sidewalls of the Ecotron (Figure 8). The sowing density is 250 gr/m².

Figure 8 : Schematic representation of the sowing lines in the Ecotron, parallel to the side walls. The door is at the bottom

of the diagram. Figure from Leemans 2019. Seminal Experiment in the Ecotron – Protocol. Environment is Life (GxABT –

Uliège).

20

Winter conditions

Lysimeters are placed in wintering when the night temperature drops below 4°C. For the

return of lysimeters to the enclosures, neither temperature nor vegetation growth

provides a clear criterion. The lysimeters are repatriated to the enclosures at the end of

the frost period and before vegetation resumes. The transfer date is therefore different

for the two weather scenarios. In conclusion, the 2015 CERs were placed in wintering

from 31/12/2014 to 24/02/2015 and the 2094 CERs from 31/12/2093 to 06/01/2094.

Fertilization

Fertilization is carried out according to a three-part nitrogen supply scheme. The nitrogen

application dates in the 2015 CERs are based on the fertilization dates of the Lonzée plot

in 2015. For CER 2094, the application dates are calculated based on degree days and the

phenological stage (Table 2). Indeed, the degree days of the March 14th 2015 are

estimated, and the application of nitrogen will be made on the date corresponding to the

same number of degree days in 2094. The nitrogen is spread by hand.

Table 2 : Nitrogen application dates in the 2015 and 2094 CERs.

Application date - CER

2015

Application date - CER

2094

Fraction 1 : 60 kg N/ha 14-03-2015 04-03-2094

Fraction 2 : 40 kg N/ha 14-04-2015 08-04-2094

Fraction 3 : 80 kg N/ha 11-05-2015 26-04-2094

The harvest is carried out when the grain humidity decreases to 15%.

Harvest data will not be studied in this master thesis.

4 MEASURES

This section presents the measures taken on wheat in the 2015 and 2094 CERs to monitor

their development and the possible impacts of abiotic stresses.

4.1 Agronomic measures

The height of the wheat is accessed once a week, between March 1 and July 15. The total

height of the plant, from the soil to either vertically extended the top of the highest leave,

or the top of the ear is considered. During each measurement campaign, five replicates

are measured in each of the six CERs. At the same time, the number of tillers is observed,

also five times per CER. Plants are collected every two weeks and observed under a

microscope to follow the phenology of wheat.

21

Destructive samplings are organized at similar phenological stages (BBCH) in 2015 and

2094, and therefore at different dates, to compare similar physiological situations. Three

samplings are considered: at two tillers (BBCH 22 stage), at the end of tillering/starting

of elongation (BBCH 29) and the end of flowering (BBCH 69). Samples are taken in a

crown to avoid boundary effects and to leave the center intact until harvest. For each

sampling, at least nine plants are selected from three sections, based on a systematic

pattern, as shown in Figure 9 :

-BBCH 22 stage: Sections I, IV, and VII are sampled.

-BBCH 29 stage: Sections II, V, and VIII are sampled.

-BBCH 69 stage: Sections III, VI, and IX are sampled.

Figure 9 : Position of the sampling points. The lighter band is a restricted area to compensate boundary effects. The

dotted lines represent the crop rows. The blue circles represent the sampling position. Figure from Leemans 2019.

Seminal Experiment in the Ecotron – Protocol. Environment is Life (GxABT – Uliège).

The aerial part is harvested and weighted (total aerial biomass). During the last sampling,

aerial parts were divided into last leaves, before last leaves, lower green leaves, yellow-

reddish leaves (in the process of nitrogen reallocation), brown leaves (dead), stems and

ears. The different aerial parts are weighed separately for each plant.

22

The LAI (Leaf Area Index - Leaf Area (m²) per ground area (m²)) is also measured. The

leaves are spread on white sheets of paper. They are then scanned and transformed into

a TIFF (Tagged Image File Format) file. These images are segmented (leave/background)

based on the leave pixels’ color, the leaf area is measured using the "ImagePro" software.

The LAI is calculated using the following formula:

𝐿𝐴𝐼 = 𝐿𝑒𝑎𝑓 𝑎𝑟𝑒𝑎 ∗ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑡𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑙𝑦𝑠𝑖𝑚𝑒𝑡𝑒𝑟

𝐿𝑦𝑠𝑖𝑚𝑒𝑡𝑒𝑟 𝑎𝑟𝑒𝑎 ∗ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑡𝑠 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑒𝑑 [𝑚2/𝑚²]

The aerial parts of the plants are then dried in an oven at 60°C, and the dry mass is

measured one week later.

4.2 Chlorophyll fluorescence

Chlorophyll fluorescence is measured using a plant efficiency analyzer (PEA, Hansatec).

The surfaces of dark-adapted leaves are exposed to red light with a flux density of 3000

µmol.m-2 for 1 second. The induced subsequent fluorescence signals are recorded 50 µs,

100 µs, 300 µs, 2 ms, and 30 ms after illumination. Measurements are performed on non-

senescent mature leaves, 5cm from the top of leaves.

Before proceeding with systematic fluorescence measurements, the daily kinetics is

determined by measurements taken at 10 am, 12 pm, 2 pm and 4 pm on the same day for

each of the two weather scenarios. On this day, the average temperature of the air was

10°C in 2015 and 8°C in 2094, the air relative humidity was 4% higher in 2015, and the

photosynthetically active radiation was 112 µmol.m-².s-1 higher in 2015. CO2

concentration was 775 ppm in the 2094 CERs and 425 ppm in the 2015 CERs. The daily

kinetics curves allow determining the time of day when the differences of fluorescence

measurements between the two weather scenarios are the most pronounced. It is at this

time that systematic measures will be taken.

Systematic measurements are then taken in each of the chambers every Tuesday and

Friday at 2 p.m., with five repetitions per CER. When a measurement is performed

incorrectly or gives outliers, all the parameters related to that specific measurement are

deleted.

4.3 Additional measures : thermography

The walls of the Ecotron do not radiate in the same way as the sky. This impacts leaf

temperature and therefore, leaf development. To evaluate this effect, thermographic

measurements are carried out in the field and in CERs, day and night with an infrared

camera.

23

5 STATISTICAL PROCESSING OF COLLECTED DATA

This section presents the methodology set up to analyze the data collected during this

study. The first part explains how the raw meteorological data was processed. A second

part explains the statistical analyses carried out on the crop measures making it possible

to study three distinct points : the reproducibility of the crop in Ecotron with as reference

this same culture in the field, the effects of climate change on a winter wheat crop

and the repeatability between the Ecotrons of the same weather scenario. All statistical

analyses are performed with the Rstudio software (The R Foundation for Statistical

Computing). Assumptions are tested with a significance level of α = 0.05.

5.1 Processing of raw weather data

The sensors installed in the Ecotrons give measurements of atmospheric conditions

(temperature and relative humidity of the air, the concentration of CO2 and

photosynthetically active radiation) and the soil (weight of the lysimeter) every five

minutes. Data are extracted using a specific program for the Ecotron. Data available for

the reference crop at Lonzée in 2015 are also repeated every five minutes. It represents a

very large amount of data. All data are processed in the Octave software via the code

"ExtractData.m" (presented in Annex 1). The outputs of this code are, for each CER and

Lonzée, the daily averages of temperature and relative air humidity, CO2 concentration,

photosynthetically active radiation, and lysimeter weight. These data allow building

graphs to study the meteorological and soil conditions of each CER and Lonzée.

5.2 Statistical analysis of crop measures

5.2.1 Reproducibility in Ecotron

To evaluate the Ecotron's artificiality bias, i.e., to quantify the differences between wheat

grown in the field and the one grown in the Ecotron for the same climate, an analysis of

the variance (ANOVA) is performed. This ANOVA compares the measurements of LAI,

biomass, height and dry mass taken in the field, in Lonzée in 2015, and in Ecotron under

the 2015 climate scenario. The various measurements are also graphed using Microsoft

office Excel for a more visual representation.

5.2.2 Effects of climate change on a wheat crop

Agronomic measures

In order to quantify the impact of climate change on the wheat crop, a variance analysis

(ANOVA) is carried out on the agronomic measures (LAI, height, biomass, and dry mass)

taken under the 2015 and 2094 climate scenarios. Results are also set graphically using

Microsoft office Excel.

24

Chlorophyll fluorescence measures

Because chlorophyll fluorescence measurements produce a lot of data, a multivariate

analysis is necessary. Four parameters, described in the State of the Art, will be studied

here : Fv/Fm, Vi, (Eo) and PIABS.

Firstly, a clustering is carried out on the fluorescence parameters to separate chlorophyll

fluorescence measurements as a function of the ranges of Fv/Fm, Vi, (Eo) and PIABS

values and understand the weather conditions associated with these ranges. Groups of

comparable chlorophyll fluorescence measurements are defined by hierarchical

clustering on principal components (HCPC). The clustering is performed on the principal

components of a PCA where fluorescence parameters are entered as variables.

Then, on the clusters created, a partial least square regression (PLSR) is performed to

study the relationships between fluorescence parameters and meteorological conditions.

The relative contributions of meteorological parameters in the models explaining the

variability of chlorophyll fluorescence parameters are extracted from the PLSR. These

results are then used to explain the variability of chlorophyll fluorescence parameters

along the season.

Finally, the seasonal evolution of fluorescence parameters is compared between the two

weather scenario. An analysis of the variance between the two scenarios was also

performed on each fluorescence parameter.

5.2.3 Repeatability between enclosures

To study the repeatability between CERs under the same meteorological scenario, an

analysis of the variance (ANOVA) is performed on the crop measurements. Graphs are

also made with Microsoft office Excel to represent the evolution of the crop during the

growing season.

25

RESULTS

The raw results will be presented and quickly commented in this section. A global

interpretation, considering all the results, will be given in the following section

(5.Discussion). All meteorological data and measurements taken and used for this master

thesis are available on the "EcotronDB" database.

1 WEATHER DATA ANALYSIS

1.1 Reproducibility of the Lonzée 2015 weather scenario in Ecotron

The following graphs compare the meteorological conditions measured at Lonzée with

those reproduced in Ecotron in the three 2015 CERs. The parameters compared are air

temperature, air relative humidity, photosynthetically active radiation, CO2 concentration

in the air, and precipitations. These results will allow us to study the reproducibility of a

meteorological scenario in Ecotron.

The temperatures reproduced in the enclosures correspond almost exactly to those of

Lonzée up to 4°C (Figure 10). Lower temperatures are only reproduced correctly in the

wintering room (between days 78 and 134).

Figure 10 : Minimum daily temperature measured at Lonzée and in the three CERs under the 2014-2015 weather

scenario throughout the season.

The large variations in air relative humidity measured at Lonzée are not reproduced in

the 2015 CERs (Figure 11). The air relative humidity is on average higher in Ecotrons.

During the passage of lysimeters in the wintering room, humidity conditions are neither

controlled nor measured.

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Figure 11 : Daily average relative humidity of the air measured at Lonzée and in the three CERs under the 2014-2015

weather scenario throughout the season.

The average photosynthetically active radiation in enclosures corresponds perfectly to

that of Lonzée, except on certain days when it bites down (Figure 12). The daily evolution

of the radiation measured at Lonzée and in the enclosures describe the same curves, and

both reach 453 µmol.m-².s-1 at midday (Figure 13). A time lag of one hour is perceptible

between the curves because Lonzée measurements are made under the UTC (Universal

Coordinated Time) time system and Ecotron measurements under the UTC+1 system.

Figure 12 : Daily average photosynthetically active radiation (PAR) measured at Lonzée and in the three CERs under the

2014-2015 weather scenario throughout the season.

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27

Figure 13 : Daily evolution of photosynthetically active radiation in Lonzée and in the three 2015 CERs.

As the minimum reproducible CO2 concentration in Ecotrons is the external

concentration, it is not regulated in the 2015 CERs. The CO2 level in the air of the

enclosures is therefore equivalent to that of the outdoors in 2018-2019 (Figure 14). The

average CO2 concentration at Lonzée in 2015 was 389 ppm, and in CERs 1, 2 and 3 it was

418, 421 and 414ppm respectively. Early in the season, the CO2 concentration in the three

CERs is higher than the CO2 concentration measured at Lonzée.

Figure 14 : Daily average CO2 concentration measured at Lonzée and in the three CERs under 2014-2015 weather

scenario throughout the season.

Most of the precipitations measured at Lonzée was reproduced in the enclosures. Some

rains did not occur due to a malfunction of the regulating rain system and were, therefore,

reproduced manually.

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28

The wind is simulated in the Ecotrons with speed set at 0.3 m.s-1, which is not

representative of the reality in the field. Turbulences are not reproduced in the

enclosures.

Table 3 below summarizes the weather conditions encountered in Lonzée and the three

CERs in 2015. It includes the average of daily minimum (Tmin) and maximum (Tmax) air

temperatures (Tmin and Tmax), air relative humidity (RH), CO2 concentration in the air

and photosynthetically active radiation (PAR).

Table 3 : Average weather conditions over the entire growing season at Lonzée and in the three 2015 CERs.

Lonzée CER 1 CER 3 CER 5

Tmin (°C) 4.1 4.5 4.5 4.4

Tmax (°C) 11.4 10.9 10.7 10.9

RH (%) 84.59 94.81 93.77 91.47

CO2 concentration (ppm) 380 418.4 421.2 413.7

PAR (µmol.m-².s-1) 154.86 144.578 150.9383 148.702

1.2 Are 2015 and 2094 weather scenarios representative of their time

horizons?

The following two graphs provide an overview of the weather scenarios for the crop years

used in this study with their time horizon averages. The first plot (Figure 15) compares

rainfall and monthly average temperature for the 2014-2015 crop year with historical

averages over the past 30 years (1988-2018). Autumn 2014 is hot and dry. The winter is

generally hot and humid with particularly hot January and February. In spring, the

temperature fluctuates around historical averages and rainfall is low, especially in May

and June. The second plot (Figure 16) compares monthly rainfall and average

temperature in the 2093-2094 crop year with 30-year averages over the 2070-2100 time

horizon. Autumn 2093 is dry with a warm October, followed by a colder November.

Winter is hot. December and January are humid, and February is rather dry. Spring is

more mixed, March is particularly cold, April is wet, May is hot and dry, and June is more

moderate.

29

Figure 15 : Temperature and rainfall for the 2014-2015 season : deviation from historical monthly averages.

Figure 16 : Temperature and rainfall for the 2093-2094 season : deviation from historical monthly averages.

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30

1.3 Comparison of 2015 and 2094 weather scenarios

The following graphs compare different weather parameters for the 2014-2015 and

2093-2094 crop years. The parameters compared are the daily averages of air

temperature, air relative humidity, the CO2 concentration of the air, and

photosynthetically active radiation, as well as daily precipitation. The study of these

graphs will then allow to understand the effects of each weather scenario on crop

development.

The overall temperature is higher in the 2094 CERs (Figure 17). The average temperature

over the entire growing season is 10.6°C in the 2094 CERs while it is 7.8°C in the 2015

CERs. The graph clearly shows heat waves where the temperature is much higher in 2094

than in 2015, as well as cold waves in 2015 and not in 2094 (indicated by arrows in Figure

17).

Figure 17 : Average daily temperature in the three CERs reproducing the 2014-2015 weather scenario and in the three

CERs simulating the meteorological scenario 2093-2094.

The air relative humidity measured in the 2094 CERs is generally lower than that of the

2015 CERs, except during the wintering period when humidity conditions are equals

(Figure 18). Over the entire growing season, the average air relative humidity is 93% for

the 2015 CERs and 88% for the 2094 CERs.

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Figure 18 : Daily average air relative humidity in the three CERs reproducing the 2014-2015 weather scenario and in the

three CERs simulating the weather scenario 2093-2094.

The average daily photosynthetically active radiation is generally higher in 2015 than in

2094 from day 145 (Figure 19). The midday photosynthetically active radiation perceived

by the wheat of the 2015 CERs reaches 459 µmol.m-².s-1 whereas it is only 345 µmol.m-

².s-1 in the 2094 CERs (). Radiation measurements are to be related to the air relative

humidity measurements. A higher air relative humidity causes the formation of clouds,

and consequently, lower solar radiation reaches the crop.

Figure 19 : Daily average photosynthetically active radiation (PAR) in the three CERs reproducing the 2014-2015

weather scenario and in the three CERs simulating the weather scenario 2093-2094.

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Figure 20 : Daily evolution of photosynthetically active radiation (PAR) in the three CERs reproducing the 2014-2015

weather scenario and in the three CERs simulating the weather scenario 2093-2094.

Concerning the concentration of CO2 in the air, it is not regulated in the 2015 CERs. It is

the CO2 concentration in our atmosphere, which oscillates around 418ppm. In the 2094

CERs, the CO2 concentration of the air is 775ppm except during the wintering period when

it is not regulated and is therefore equivalent to the amount of CO2 in our atmosphere

(Figure 21). The CO2 concentration drops to 400 ppm twice (between days 91 and 122 as

well as between days 128 and 144) due to a technical problem in the CO2 injection system.

The CO2 concentration of the 2015 enclosures fluctuates continuously due to the

photosynthetic activity of the plants while the CO2 concentration of the 2094 enclosures

is continuously adjusted to remain at 775ppm and therefore does not show oscillations.

Figure 21 : Daily average CO2 concentration in the three CERs reproducing the 2014-2015 weather scenario and in the

three CERs reproducing the 2093-2094 weather scenario.

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The 2093-2094 season contains much heavier rains than the 2014-2015 season (Figure

22). Days 69 and 99 stand out with 48 and 57 mm of rain respectively on a single day in

2094 while the most intense rainfall day reaches only 26 mm in 2015 (day 87). The second

observation is that the 2093-2094 season contains longer periods without rain, including

a 19-day period (between days 225 and 244) when daily rainfall did not exceed 1mm.

Figure 22 : Daily precipitations in the three CERs reproducing the 2014-2015 weather scenario and in the three CERs

simulating the weather scenario 2093-2094.

Table 4 below shows the average weather conditions over the entire growing season.

Table 4 : Average weather conditions over the entire growing season in 2015 and 2094 CERs.

2014-2015 2093-2094

Daily average temperature (°C) 7.8 10.6

Daily average air relative humidity (%) 93 88

Average PAR (at midday) (µmol.m-².s-1) 454.831 321.950

Daily average CO2 (ppm) 417.76 686.43

Total rainfall (mm) 448.4 731.9

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2 CROP MEASURES

2.1 Comparison of crops in Lonzée and Ecotron 2015

This section compares the Ecotron and Lonzée (in the field) crops under the same

meteorological scenario, i.e., the 2015 meteorological scenario. This comparison aims to

quantify the impact of Ecotron itself on crops. In other words, the results that follow will

make it possible to understand the behavior of winter wheat in a new environment that

is Ecotron, by the study of phenology and agronomic measures (LAI, height and dry mass).

2.1.1 Phenology

Figure 23 compares the speed of the development of wheat (in BBCH code) in Ecotron

and in the field, with the same meteorological scenario. A table showing the stages of

development corresponding to the BBCH codes and the dates on which these stages were

observed is given in Annex 3. Wheat grows at the same rate until day 150, when Ecotron

wheat grows faster. Ecotron wheat reaches BBCH 39 (visible ligule) about a week before

wheat in the field and BBCH 75 around two weeks before the wheat in the field. Then, the

development of wheat in Ecotron slows down, and the wheat matures at the same time in

Ecotron and in the field.

Figure 23 : Evolution of wheat phenology in BBCH stages throughout the season in Lonzée and the three CERs under the

2014-2015 meteorological scenario.

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2.1.2 Agronomic measures

Since wheat in Ecotron and the field do not develop at the same speed, agronomic

measures (LAI, dry mass, and height) are compared according to stages and not as a

function of time. In this way, it will be possible to compare physiologically comparable

wheat. Leaf area and dry mass measurements should be considered with caution because

in Ecotron they were only measured three times with replicates during the three

destructive samples (stages 22, 29 and 69). The number of destructive samples was

limited to three to disrupt the crop a minimum.

Figure 24 below presents the LAI measurements made in Ecotron and the field in Lonzée

under the 2014-2015 weather scenario. The LAI in Ecotron is on average 20 to 40%

higher than that measured in the field. The analysis of variance shows that the difference

between the LAI measured at Lonzée and the LAI measured in Ecotron is significant (p-

value = 0.028).

Figure 24 : Evolution of the LAI of wheat throughout the season in Lonzée and the three CERs under the 2014-2015

meteorological scenario.

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The graph below (Figure 25) shows the dry matter of the wheat plants in Ecotron and

Lonzée. In samples taken at stages 29 and 69, Ecotron wheat has a higher dry weight than

Lonzée. But the analysis of variance does not show any significant difference between the

dry masses. However, given the limited number of measurement points over time, the

appearance of the curve in Ecotron does not represent the reality of the facts. It is

therefore difficult to know the exact difference between dry mass measured in Ecotron

and that measured in the field.

Figure 25 : Evolution of the dry matter of wheat throughout the season in Lonzée and the three CERs under the 2014-

2015 meteorological scenario.

Figure 26 shows the wheat heights measured in Ecotron and the field at Lonzée. Until

stage 32, the wheat in Ecotron is 40% higher than that in the field. After that, the

difference decreases. At stage 39, the wheat in the Ecotrons is only 10% higher than the

wheat in the field. At maturity, the wheats have the same height. The analysis of variance

does not show any significant difference between the wheat heights measured at Lonzée

and Ecotron.

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Figure 26 : Evolution of the height of wheat throughout the season in Lonzée and the three CERs under the 2014-2015


2.1.3 Thermography

Figure 27 below graphically shows the difference between the crop temperature and the

air temperature as a function of time. At night, on the field and by clear sky, the difference

between the crop temperature and the air temperature becomes negative, i.e. the crop

becomes colder than the air. On the other hand, at night in Ecotron and the field under a

cloud cover, the differences always remain positive, so the crop temperature never drops

below the air temperature. During the day, the difference is always positive.

Figure 27 : Difference between the air temperature and the crop temperature in Ecotron and the field. Measurements

were taken day and night.

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These thermographic measurements confirm that the temperature of Ecotron crops is

similar to the temperature of field crops by days (Figure 4.27). A difference is, however,

noticeable at night when there is no cloud cover. Indeed, during the day, the culture

receives heat from solar radiation in the field and artificial lighting in Ecotron. At night in

the field, without cloud cover, the plant emits heat by infrared radiation to the

atmosphere. As a result, the plant cools. At night in the Ecotron, the walls emit heat by

infrared radiation to the crop, which would prevent the crop from falling back to

temperature. Indeed, at the beginning of the night, the temperature of the culture

approaches 10 ° C in the Ecotron and 1° C in field. At the end of the night, the temperature

of the field crop is about -2°C while in the Ecotron it is about 5°C (Table 5).

Table 5 : Thermographic measures taken in the Ecotrons and in the field. Temperatures are average.

Ecotron Field Observation

Hour Tair(°C) Tcrop(°C) Tair(°C) Tcrop(°C)

05:40 4.2 5.2 1.1 -2 Clear night

10:40 9.5 13.2 14.3 18.4 Day

13:40 5.1 8.5 10.2 12.9 Day

14:10 8.7 12.7 12.6 16.5 Day

15:10 11.8 14.3 16.9 20.2 Day

16:00 10 14.9 10.2 11.9 Day

20:10 8.2 10.2 8.1 1.3 Clear night

22:25 . . 14.8 17.6 Cloudy night

39

2.2 Comparison of crops in Ecotron 2015 and 2094

This section compares wheat development under the 2015 and 2094 weather scenarios.

This comparison is based on phenological observations, LAI measurements, height, dry

mass (wheat state) as well as on chlorophyll fluorescence measurements (wheat process).

2.2.1 Phenology

Figure 28 shows the phenological stages (in BBCH code) reached by crops in the CERs

2015 and 2094 according to the days after sowing. A table showing the stages of

development corresponding to the BBCH codes and the dates on which these stages were

observed is given in Annex 3. The crops of the 2094 CERs are developing faster than the

crops of the 2015 CERs. Wheat from the 2094 CERs reaches the final tillering stage about

13 days before that of the 2015 CERs. Then, the wheat from the 2094 CERs flowers about

15 days before the wheat from the 2015 CERs.

Figure 28 : Evolution of wheat phenology in BBCH stages throughout the season in the three CERs under the 2014-2015

meteorological scenario and in the three CERs under the 2093-2094 meteorological scenario.

2.2.2 Agronomic measures

Since wheat under both meteorological scenarios does not develop at the same rate,

agronomic measures (LAI, height, and dry mass) are compared according to stage. In this

way, it will be possible to compare physiologically comparable wheat. Leaf area and dry

mass measurements should be considered with caution because, in Ecotron, they were

only measured three times with replicates, during the three destructive samples (stages

22, 29, and 69). The number of destructive samples was limited to three to disrupt the

crop a minimum.

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Figure 29 represents the wheat LAI measured at different stages of development in the

CERs 2015 and 2094. At tillering, the leaf area is 30% higher in 2094 CERs. The LAI is

significantly different under both weather scenarios (p-value = 0.0237). It then develops

more strongly in the CERs of 2094. The LAI measured in 2094 is twice as high as the LAI

measured in 2015. The difference is then very highly significant (p-value = 9.14 * 10-9). At

flowering, the LAI of the 2015 CERs has caught up with that of the 2094 CERs; the

difference is no longer significant (p-value = 0.864).

Figure 29 : Evolution of the LAI of wheat throughout the season in the three CERs under the 2014-2015 meteorological

scenario and in the three CERs under the 2093-2094 meteorological scenario.

Wheat under the two weather scenarios has significantly different dry mass differences

at stage 22 (p-value = 0.0198) (Figure 30). Then, at stage 29, the dry wheat mass becomes

91% higher in the 2094 CERs. The difference is very highly significant (p-value =

0.000339). At stage 69, the wheat of 2094 develops 169% more dry matter than the wheat

of 2015. The difference still very highly significant (p-value = 6.69*10-13).

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Figure 30 : Evolution of the dry mass of wheat throughout the season in the three CERs under the 2014-2015

meteorological scenario and the three CERs under the 2093-2094 meteorological scenario.

Figure 31 below shows the heights of wheat measured at different phenological stages

(BBCH). At stage 25, wheat from the CERs 2094 is 50% higher than that of the 2015 CERs.

Then the difference decreases. Wheat from 2094 is only 30% higher at stage 31 and 15%

higher from stage 33 to stage 69.The difference is very highly significant (p-value =

1.38*10-9).

Figure 31 : Evolution of the height of wheat throughout the season in the three CERs under the 2014-2015 meteorological


0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80

Dry

mat

ter

(g/p

lan

t)

BBCH stage


0

20

40

60

80

100

120

140

0 10 20 30 40 50 60 70 80

Hei

ght

of

wh

eat

(cm

)

BBCH stage


42

2.2.3 Chlorophyll fluorescence

A. Diurnal evolution of fluorescence parameters

In each of the meteorological scenarios, the diurnal evolution of the chlorophyll

fluorescence parameters of wheat was observed (Figure 32). For the Fv/Fm and (Eo)

parameters, the most significant differences between the two weather scenarios are

noticeable at 2 pm. Regarding Vi, differences are visible before midday and decrease

during the afternoon. Finally, for PIABS, the biggest differences are at 12 am and 2 pm. The

best compromise being 2 pm, it is at this time of the day that systematic measurements

will be taken.

Figure 32 : Diurnal evolution of chlorophyll parameters over the two meteorological scenarios. The average

values for each of the four sampled period are represented for Fv/Fm, Vi, (Eo) and PIABS for 2015 and 2094

CERs.

0,82

0,825

0,83

0,835

0,84

10:00 12:00 14:00 16:00

Fv/F

m (

-)

Hours (h)

CERs 2015 CERs 2094

0,6

0,62

0,64

0,66

0,68

0,7

10:00 12:00 14:00 16:00

Vi (

-)

Hours (h)

CERs 2015 CERs 2094

0,52

0,54

0,56

0,58

0,6

0,62

0,64

10:00 12:00 14:00 16:00

(E

o)

(-)

Hours (h)

CERs 2015 CERs 2094

0

2

4

6

8

10:00 12:00 14:00 16:00

PI A

BS

(-)

Hours (h)

CERs 2015 CERs 2094

43

B. Clustering

To understand the meteorological conditions associated with certain values of the

fluorescence parameters, a hierarchical clustering on principal components (HCPC) is

performed on the chlorophyll fluorescence measurements. The results of the clustering

are presented in Table 6 below. Since the sensors supposed to measure the soil water

content give aberrant values, it is the evolution of the weight of the lysimeter that is used

for the analysis of fluorescence measurements. As explained above, the change in the

weight of the lysimeter is proportional to the water content of the soil.

Table 6 : Results of the hierarchical clustering on principal components (HCPC) processed on the fluorescence measures

and average meteorological conditions associated with each cluster.

F1 F2 F3 F4 All data

Fv/Fm 0.42 0.76 0.83 0.83 0.82

Vi 0.06 0.13 0.3 0.35 0.3

(Eo) 0.23 0.45 0.59 0.67 0.60

PIABS 0.08 1.41 4.75 8.13 5.40

Tair (°C) 18.9 17.1 13.4 14.4 14.0

RH (%) 79.6 79.0 86.8 87.0 86.2

[CO2] (ppm) 772 729 565 562 579

PAR (µmol.m-².s-1) 553.7 581.5 519.1 547.2 532.2

Lysimeter weight

(Kg) 5543 5558 5683 5669 5614

Four chlorophyll fluorescence clusters are defined by HCPC. The two first clusters (F1 and

F2) are characterized by low Fv/Fm, Vi, (Eo) and PIABS values. These clusters are

associated with high temperature and PAR values and low lysimeter weight, so low soil

water content. The third and fourth clusters (F3 and F4) are characterized by high Fv/Fm

values. F3 has average Vi, (Eo) and PIABS values and F4 has high Vi, (Eo) and PIABS

values. The weather conditions corresponding to these clusters include average air

temperatures, air relative humidity, and photosynthetically active radiation as well as

above-average weight and so soil water content.

44

C. Partial least square regression (PLSR)

Considering the clustering results, two datasets are made for the PLSRs. The first dataset

includes F1 and F2 clusters data (limited photosynthesis) and the second clusters F3 and

F4 (optimal photosynthesis). Figure 33 and Figure 34 below represent the relative

contribution of the meteorological parameters in the models (PLSR) explaining the

variability of the chlorophyll fluorescence parameters. Photosynthetic active radiation is

removed from the models given its high correlation with the temperature. The CO2

concentration is also removed because it does not explain the fluorescence values

correctly. This decision is explained in the clustering analysis in the discussion section.

The soil water content (SWC) is related to the lysimeter's weight.

Figure 33 : Relative contribution (in %) of the meteorological parameters and their interactions in the models (PLSR)

explaining the variability of the fluorescence parameters for the clusters F1 and F2.

In the case of a limited photosynthetic function, most of the variability is explained by

interactions between meteorological parameters. Indeed, the interactions explain 70, 66,

64, and 64% of Fv/Fm, Vi, (Eo) and PIABS, respectively (Figure 33). Among these

interactions, the one that explains most of the variability of Vi, (Eo) and PIABS is the

interaction between air temperature and soil water content (19, 20 and 19%

respectively). For Fv/Fm, the one that explains most of the variability is the interaction

between the relative humidity of the air and the soil water content (30%). After the

interactions, the variability of Fv/Fm is explained at 22% by the air relative humidity. As

for Vi, (Eo) and PIABS, their variability is explained at 19, 20, and 19% respectively by

the temperature of the air.

1,32

19,3 19,68 19,1822,02

12,33 13,64 13,896,482,33 2,98 3,024

19,6717,68 14,84 14,86

9,5418,63 19,64 19,3

29,51 12,65 14,44 14,82

11,47 17,07 14,78 14,93

F V F M V I P S I ( E O ) P I A B S

Tair:SWC:RH

HR:SWC

Tair:SWC

Tair:RH

SWC

RH

Tair

45

Figure 34 : Relative contribution (in %) of the meteorological parameters and their interactions in the models (PLSR)

explaining the variability of the fluorescence parameters for the clusters F3 and F4.

In the case of optimal photosynthetic operation, most of the variability is also explained

by interactions between meteorological parameters. The interactions explain 79, 76, 67,

and 63% of the variability of Fv/Fm, Vi, (Eo) and PIABS, respectively (Figure 34). After

the interactions, most of the variability is explained by the air temperature. Indeed, the

temperature explains 15, 22, 20, and 21% of the variability of Fv / Fm, Vi, (Eo) and PIABS,

respectively.

14,7321,96 20,32 21

5,461,65 10,29 12,74

0,480,39

2,853,6627,01 25,69

17,23 14,35

16,7222,96 20,58 20,44

7,071,84

11,7 14,1728,53 25,53

17,03 13,64

F V F M V I P S I ( E O ) P I A B S

Tair:SWC:RH

HR:SWC

Tair:SWC

Tair:RH

SWC

RH

Tair

46

D. Seasonal evolution of fluorescence parameters

The following graphs show the fluorescence parameters calculated from chlorophyll

fluorescence measurements taken on CERs 2015 and 2094 wheat between March 9 and

June 25 (between days 147 and 255 after sowing). The analysis of these graphs will make

it possible to compare the seasonal evolution of the fluorescence parameters under the

two meteorological scenarios and thus, the evolution of the efficiency of photosynthesis.

These results will be related to agronomic measures in the discussion. On each graph, one

point represents a measurement, and a curve links the averages per day of measurement.

Figure 35 shows the values of the Fv/Fm parameters measured on wheat in the 2015 and

the 2094 Ecotrons. For better readability, the Fv/Fm axes have been cut to 0.5. As a result,

some measurement points in the CERs 2094 below 0.5 are no longer visible. The full graph

is given in Annex 4. In the 2015 CERs, Fv/Fm hovers around 0.82, the minimum and

maximum values reached by Fv/Fm in the 2015 CERs being 0.75 and 0.87 respectively. In

the CERs 2094, the Fv/Fm parameter oscillates around 0.83 until day 230. After that day,

the Fv/Fm values decrease to 0.6 on average. The minimum and maximum values reached

in the CERs 2094 are 0.1 and 0.86 respectively.

Figure 35 : Evolution of the Fv/Fm parameters measured on wheat throughout the season in the three CERs under the

2014-2015 meteorological scenario and the three CERs under the 2093-2094 meteorological scenario (cut graph).

0,5

0,55

0,6

0,65

0,7

0,75

0,8

0,85

0,9

140 160 180 200 220 240 260

Fv/F

m (

-)

Days after sowing

CERs 2015 Mean CERs 2015 CERs 2094 Mean CERs 2094

47

Figure 36 shows the values of Vi measured on wheat in 2015 and the 2094 CERs. Under

both weather scenarios, Vi decreases with the advance of the growing season. In the CERs

2015, Vi oscillates around an average value of 0.32. In the CERs 2094, Vi hovers around

0.3 and then falls from day 220.

Figure 36 : Evolution of the Vi parameters measured on wheat throughout the season in the three CERs under the 2014-

2015 meteorological scenario and the three CERs under the 2093-2094 meteorological scenario.

Figure 37 below shows the values of (Eo) in the 2015 and 2094 enclosures. In the 2015

CERs, it grows slightly from around 0.55 on day 145 up to about 0.65 on day 255. In the

CERs of 2094, it grows from around 0.55 on day 171 to 0.65 on day 231 and then falls to

0.3 on day 255.

Figure 37 : Evolution of the (Eo) parameters measured on wheat throughout the season in the three CERs under the

2014-2015 meteorological scenario and the three CERs under the 2093-2094 meteorological scenario.

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

140 160 180 200 220 240

Vi (

-)

Days after sowing


0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

140 160 180 200 220 240

(E

o)

(-)

Days after sowing


48

The PIABS values are shown in Figure 38. In the 2015 CERs, PIABS hovers around 6 except

on days 154 and 164 where its average value is only 3.99 and 2.91. In the CERs 2094, PIABS

also oscillates around 0.6 until day 231 when its value drops to 0.6 on day 255.

Figure 38 : Evolution of the PIABS parameters measured on wheat throughout the season in the three CERs under the

2014-2015 meteorological scenario and the three CERs under the 2093-2094 meteorological scenario.

In addition to the graphical analyses, an ANOVA was performed on each fluorescence

parameter to qualify the differences between the weather scenarios (Table 7).

Table 7 : Average fluorescence parameters over the entire growing season in the 2015 and the 2094 CERs and p-values

from the variance analyze.

Mean in

2015 CERs

Mean in

2094 CERs

p-value

(ANOVA)

Fv/Fm 0.825 0.804 1.45*10-5

Vi 0.323 0.264 2*10-16

(Eo) 0.616 0.576 8.57*10-10

PIABS 5.57 5.07 0.138

0

2

4

6

8

10

12

14

16

18

20

140 160 180 200 220 240

PI A

BS

(-)

Days after sowing


49

3 REPEATABILITY BETWEEN CERS

The study of the repeatability between CERs aims to complement the previous analyzes.

The weather conditions, as well as the agronomic and chlorophyll fluorescence

measurements, are compared between each CER in the same meteorological scenario. The

study of the repeatability of weather conditions might also be useful to optimize the

operation of Ecotrons in the future.

3.1 Meteorological and soil conditions

The simulated temperatures in the three 2015 enclosures are similar to within two-tenths

of a degree (Figure 39). The same applies to the three 2094 enclosures (Figure 40). The

temperature peaks visible around days 77 and 134 correspond to the displacement of the

lysimeters from the enclosures to the wintering room and vice versa.

Figure 39 : Daily average temperature measured in the three 2015 CERs.

Figure 40 : Daily average temperature measured in the three 2094 CERs.

-5,00

0,00

5,00

10,00

15,00

20,00

25,00

1 9

17

25

33

41

49

57

65

73

81

89

97

10

5

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1

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7

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5

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3

24

1

24

9

Dai

ly a

vera

ge t

emp

erat

ure

(°C

)

Days after sowing

CER 1 CER 3 CER 5

-5,00

0,00

5,00

10,00

15,00

20,00

25,00

1 9

17

25

33

41

49

57

65

73

81

89

97

10

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1

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5

19

3

20

1

20

9

21

7

22

5

23

3

24

1

24

9

Dai

ly a

vera

ge t

emp

erat

ure

(°C

)

Days after sowing

CER 2 CER 4 CER 6

50

The air relative humidity’s measured in 2015 CERs follows the same trend, and the

difference in their daily average never exceeds 6% (Figure 41) until day 135. Between

days 135 and 215, CERs 1 and 3 experienced a failure of the air humidity control system,

possibly linked to a general electrical break at TERRA. The air relative humidity measured

in the 2094 enclosures follows the same trend until day 135 (arrow) where the CER 4

suffered the same failure as CERs 1 and 3 (Figure 42). CER 2 has a higher humidity

between days 225 and 244.

Figure 41 : Daily average air relative humidity of the air measured in the three 2015 CERs.

Figure 42 : Daily average humidity of the air measured in the three 2094 CERs.

60

65

70

75

80

85

90

95

100

105

1 9

17

25

33

41

49

57

65

73

81

89

97

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1

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3

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1

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9

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7

22

5

23

3

24

1

24

9Dai

ly a

vera

ge r

elat

ive

hu

mid

ity

(%)

Days after sowing

CER 1 CER 3 CER 5

40

50

60

70

80

90

100

110

1 9

17

25

33

41

49

57

65

73

81

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97

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1

24

9Dai

ly a

vera

ge r

elat

ive

hu

mid

ity

(%)

Days after sowing

CER 2 CER 4 CER 6

51

Although CO2 concentrations in the 2015 CERs are not controlled, they follow the same

trend (Figure 43). The difference between the enclosures is an average of 5 ppm. The CO2

concentration in the 2094 enclosures perfectly describes the same variations except on

some days when the CO2 level in either enclosure decreases slightly (Figure 44). The

drops in CO2 concentration are linked to failures in the regulation system.

Figure 43 : Daily average CO2 concentration in the air measured in the three 2015 CERs.

Figure 44 : Daily average CO2 concentration in the air measured in the three 2094 CERs.

360

380

400

420

440

460

480

500

1 9

17

25

33

41

49

57

65

73

81

89

97

10

5

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1

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9

13

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3

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7

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5

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3

20

1

20

9

21

7

22

5

23

3

24

1

24

9

Dai

ly a

vera

ge C

O2

con

cen

trat

ion

(p

pm

)

Days after sowing

CER 1 CER 3 CER 5

360

410

460

510

560

610

660

710

760

810

1 9

17

25

33

41

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24

9

Dai

ly a

vera

ge C

O2

con

cen

trat

ion

(p

pm

)

Days after sowing

CER 2 CER 4 CER 6

52

Except for a few small stalls, the photosynthetically active radiation in the 2015

enclosures follows the same curve (Figure 45). The PAR measured at midday in CER 3 is

about 20 µmol.m-².s-1 higher than in the other two CERs (Figure 47). The

photosynthetically active radiation reproduced in the three 2094 enclosures follows

essentially the same curve (Figure 46). At midday, the average radiation perceived in

CERs 2 and 4 reaches 360 µmol.m-².s-1 while it reaches only 322 µmol.m-².s-1on average in

CER 6 (Figure 47).

Figure 45 : Daily average photosynthetically active radiation measured in the three 2015 CERs.

Figure 46 : Daily average photosynthetically active radiation measured in the three 2094 CERs.

-100

0

100

200

300

400

500

600

1 9

17

25

33

41

49

57

65

73

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PA

R (

µm

ol.m

- ².s

-1)

Days after sowing

CER 1 CER 3 CER 5

0

100

200

300

400

500

600

1 9

17

25

33

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1

24

9

PA

R (

µm

ol.m

- ².s

-1)

Days after sowing

CER 2 CER 4 CER 6

53

Figure 47 : Daily dynamics of photosynthetically active radiation in each of the 2015 and the 2094 CERs

The sensors installed in the soil of the lysimeters and supposed to provide information on

the soil's water content give aberrant values. Also, the sensors are placed on the edges of

the lysimeter while rain falls mainly in the center of the lysimeter. The measurements are,

therefore, not representative of the entire soil. These data will, therefore, not be used.

In addition to graphical analyses, Table 8 below shows the average meteorological

parameters measured in each of the CERs over the entire season.

Table 8 : Average weather conditions over the entire growing season in each of the 2015 and the 2094 CERs.

CER 1 CER 3 CER 5 CER 2 CER 4 CER 6

Air temperature (°C) 12.9 12.7 12.7 15.6 15.5 15.5

Air relative humidity (%) 93 92 91 82 84 76

CO2 concentration (ppm) 384 386 381 776 775 771

PAR (µmol.m-².s-1) 445 475 455 355 359 321

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21

PA

R (

µm

ol.m

2 .s-1

)

Hours (UTC+1)

CER 1 CER 3 CER 5

0

50

100

150

200

250

300

350

400

1 3 5 7 9 11 13 15 17 19 21 23

PA

R (

µm

ol.m

2 .s-1

)

Hours (UTC+1)

CER 2 CER 4 CER 6

54


At stages 22 and 29, the difference between the LAIs measured in the 2015 CERs is not

significant (Figure 48). At stage 69, the LAI measured in CER 1 is significantly different

from the LAIs measured in CERs 3 and 5 (p-value = 0.0434). In the 2094 CERs, the LAIs

are not significantly different either at stages 22 and 29. At stage 69, the LAI measured in

CER 4 is very significantly different from those measured in CERs 2 and 6 (p-value =

0.00158).

Figure 48 : Evolution of the LAI of wheat throughout the season in the three CERs under the 2014-2015 meteorological


The dry masses measured in the three CERs 2015 are significantly different only at stage

69 (p-value = 0.000474) (Figure 49). The dry masses measured in the three CERs 2094

are not significantly different, even at stage 69 (p-value = 0.428).

Figure 49 : Evolution of the dry matter of wheat throughout the season in the three CERs under the 2014-2015

meteorological scenario and the three CERs under the 2093-2094 meteorological scenario.

0

5

10

15

22 29 69

LAI (

m²

leaf

/m²s

oil)

BBCH stage

CER 1 CER 3 CER 5

0

5

10

15

22 29 69LA

I (m

² le

af/m

² so

il)BBCH stage

CER 2 CER 4 CER 6

0

5

10

15

20

22 29 69Dry

mat

ter

(g/p

lan

t)

BBCH stage

CER 1 CER 3 CER 5

0

10

20

30

40

22 29 69Dry

mat

ter

(g/p

lan

t)

BBCH stage

CER 2 CER 4 CER 6

55

None of the wheat heights measured in the three 2015 enclosures are significantly

different (p-value = 0.923) (Figure 50). The wheat heights are also not significantly

different in the three 2094 enclosures (p-value = 0.806).

Figure 50 : Evolution of the height of wheat throughout the season in the three CERs under the 2014-2015 meteorological


3.3 Chlorophyll fluorescence measures

Table 9 and Table 10 show the average values of the fluorescence parameters measured

in each of the CERs as well as the p-values that emerge from the variance analyses

between the chambers of the same meteorological scenario. Fluorescence parameters do

not differ significantly between the 2015 CERs. The same is true for the 2094 CERs.

Table 9 : Average values of the fluorescence parameters measured in the three 2015 CERs and p-values from their

variance analyses.

CER 1 CER 3 CER 5 p-value

Fv/Fm 0.822 0.825 0.827 0.181

Vi 0.327 0.318 0.322 0.404

(Eo) 0.381 0.391 0.381 0.198

PIABS 5.75 5.38 5.57 0.809

Table 10 : Average values of the fluorescence parameters measured in the three 2094 CERs and p-values from their

variance analyses.

CER 2 CER 4 CER 6 p-value

Fv/Fm 0.812 0.809 0.791 0.114

Vi 0.266 0.261 0.264 0.889

(Eo) 0.413 0.422 0.437 0.386

PIABS 5.33 4.95 4.92 0.426

0

20

40

60

80

100

120

25 31 33 39 58 61 69

Hea

igh

t o

of

wh

eat

(cm

)

BBCH stage

CER 1 CER 3 CER 5

0

50

100

150

25 31 32 33 41 57 68 69 75Hei

ght

of

wh

eat

(cm

)

BBCH stage

CER 2 CER 4 CER 6

56

In addition, observation of the graphs bellow confirms that variance equivalence is not

due to temporal variability (Figure 51, Figure 52). Nor do the graphs show clear

differences between the fluorescence parameters measured in the enclosures of the same

weather scenario.

Figure 51 : Evolution of the chlorophyll fluorescence parameters (Fv/Fm, Vi, (Eo) and PIABS) measured on wheat

throughout the season in the three CERs under the 2014-2015 meteorological scenario.

57

Figure 52 : Evolution of the chlorophyll fluorescence parameters (Fv/Fm, Vi, (Eo) and PIABS) measured on wheat

throughout the season in the three CERs under the 2093-2094 meteorological scenario.

58

DISCUSSION

In this section, all the results will be linked to study several points. First of all, the

reproducibility of the meteorological scenarios will be discussed. Then, the

representativeness of 2015 and 2094 with their respective time horizons will be

questioned. After that, the differences between the cultures of the 2015 CERs and Lonzée

as well as between the CERs 2015 and 2094 will be discussed. Afterward, the repeatability

between the chambers (reproduced weather conditions and wheat development) will be

discussed. Finally, an essay will be formulated to predict the behavior of the crop in a field

in 2094.

1 REPRODUCIBILITY OF WEATHER CONDITIONS IN ECOTRON

In general, the Ecotrons would reproduce the weather conditions correctly. Ecotrons can

simulate temperatures ranging from 4 to 40 ° C and the wintering room can simulate

temperatures ranging from -7 to 20 ° C. Despite these constraints, the main frost period

would have been correctly reproduced in the wintering room (Figure 10). One late frost

period was missed in 2094, but as it was limited in negative temperature it is unlikely that

the effect would have been important. On another hand, the cool period duration was

enough to ensure the vernalization process. Also, between 0 and 4 °C the plant goes on

growing but at a slow pace. The air relative humidity in Ecotron does not reproduce the

variations measured in the field (Figure 11). Also, three of the enclosures experienced a

failure of the air humidity control system for approximately 80 days. Moreover, a bias

would be induced by the fact that the phenomenon of dew is not reproduced in the

Ecotrons. Dew is a weather phenomenon that results in the condensation (liquefaction)

of moisture in the air on soil, plants, and objects surfaces. It is formed when the

temperature of these surfaces drops to the dew point, and the humidity is sufficient. It

would not be allowed in Ecotrons to protect the instrument from mould. Also, the

temperature of the crop does not come down at night as it does in the field because of

radiation exchanges (see 4.2.1.3 Thermography). The crop would therefore not be cold

enough to allow condensation of the humidity of the air on its surface. At the plant level,

a higher level of humidity in the air reduces transpiration and therefore, water loss. It

could reduce the risk of water stress but also increase the risk of thermal stress and

therefore, damage to the photosynthetic apparatus. For photosynthetically active

radiation, the measurements show that it is of the same intensity in Ecotrons and the field

(Figure 12). The CO2 concentration measured in the enclosures is higher than that

measured in the field in Lonzée by about 29 ppm. This difference could be responsible for

the better development of wheat in Ecotron (Li et al., 2004; Manderscheida, 2018). In

2094, the first break of the CO2 system occurred when the crops were then around BBCH

59

25 (Figure 21) and the second during the first stages of stem elongation (BCH 31). The

real impact of the few CO2 breakdowns is not known but, just after the breakdowns, wheat

from 2094 still has an LAI and a dry mass significantly higher than wheat from 2015,

which corresponds to expectations. However, since the temperature conditions were not

limiting at these times, without these failures, wheat might have developed further in the

2094 enclosures. Later, the short drop periods of limited time are unlikely to have any

observable effects. As explained in the results, the wind reproduced in the enclosures is

not representative of the field reality and turbulences are not reproduced. During the

night, the wind is calm in the field, so the wind conditions in Ecotron would have little

impact on wheat development. During the day, wind can have a greater impact on the

condition of the crop. In winter, the wind balances the crop temperature with the air

temperature, which cools the crop and would, therefore, reduce its development. In

summer, wind increases transpiration and therefore, water loss, which would increase

the risk and intensity of water stress.

As a whole, the Ecotrons reproduce correctly the conditions of temperature, the

precipitations, the photosynthetically active radiation as well as the CO2 concentration in

the air as long as it is higher than the concentration in CO2 from the outside air. The CO2

failures in 2094 may have slightly reduced wheat development, but this did not affect the

overall trend. On the other hand, the relative humidity of the air and the wind seem not

represent the reality of the field.

2 ARE 2015 AND 2094 REPRESENTATIVE OF THEIR TIME HORIZON?

The selection of years used to reproduce past and future meteorological scenarios was

based solely on periods of growth, flowering, and filling of wheat grain, which is from

March to July. In fact, winter conditions have little effect on yield. According to Figure 15,

the 2014-2015 crop year is warmer than the historical average of the past 30 years. It

makes sense given the fact that temperatures have been rising steadily since the 1980s

(IPCC, 2014). The year 2015 is probably the most representative of the current climate.

Moreover, the year 2015 is preferred because a lot of data concerning the weather

scenario and the development of the culture are available for the Lonzée site. These data

are necessary to study the reproducibility of a crop in Ecotron. For 2093-2094, the months

taken separately are not very representative of the averages of the time horizon, but the

year as a whole can be considered as representative of the time horizon 2070-2100.

60

3 COMPARISON OF CROPS IN ECOTRON 2015 AND LONZÉE

3.1 Phenology

There are two main differences, an advance in phenological stages during early spring and

a slower maturation during summer. The faster growth of wheat in the Ecotron (Figure

23) would be mainly due to a higher crop temperature (KIMBALL et al., 1995). Indeed, it

has been shown by thermography that the temperature of the crop in Ecotron does not

decrease at night as it does in the field (on clear nights) because the radiation balance of

the crop is not the same. In addition, since the wind is lighter in the Ecotron, the crop

temperature does not balance as easily with the air temperature (Pelton, 1966).

Therefore, during the cold periods (until spring) the Ecotron crop does not cool down as

in the field and would, therefore, develop more quickly (Brisson et al., 2003). During the

hot summer periods, the low wind in Ecotron would have inhibited the crop from cooling

down, causing heat stress that would then have slowed its development.

It should be noted that phenological observations in Ecotrons are only made every two

weeks. An error of a few days is therefore possible.


As for phenology, the difference between LAI and the dry mass measured in Ecotron and

the field at Lonzée (Figure 24 and Figure 25) would be due to the difference in

temperature of crop (KIMBALL et al., 1995). The best development of wheat in Ecotron

can also be linked to softer wind conditions (set at 0.5m.s-1). Low wind conditions would

lead to better crop development (Aslyng, 1958; Pelton, 1966). In addition, the higher CO2

concentration in the Ecotrons (Figure 14) would contribute to better development of

wheat by increasing the photosynthetic yield (Porter et al., 2005; Asseng et al., 2009).

Lamps reproducing solar radiation could also be held responsible for the larger LAI in

Ecotron. Indeed, the intensity of the wavelengths emitted by the far-infrared spectrum

(700-750nm) would influence leaf elongation. A side experiment (SEE side I) was carried

out to test this hypothesis, and no significant differences were observed between wheat

grown under different far-infrared spectrum intensities (Annex 5). So, lamps in the

Ecotron are not responsible for the larger foliar surface.

Given the low number of measurements in Ecotrons, the LAI and dry mass curves can be

considered not to represent the exact evolution of wheat during the season. However,

there are important differences in crop development at the end of the juvenile period. The

impact on these differences in the later stages remains to be seen. By the end of vegetative

period, differences are reduced.

61

Regarding the height of the wheat, the slight difference would be due to the higher crop

temperature in Ecotron and to the wind conditions, which are softer (Pelton, 1966). A side

experiment (SEE side II) shown that the wind has an effect on crop height at the beginning

of the growing season (Annex 6).

4 COMPARISON OF CROPS IN ECOTRON 2015-2094

The following paragraphs explain the differences in the development of wheat under the

past-recent and future meteorological scenarios, starting with phenology, then the

chlorophyll fluorescence measurements that reflect the efficiency of photosynthesis and

finally the agronomic measurements that will be linked to the efficiency of

photosynthesis.

4.1 Phenology

The faster development of CER 2094 wheat would be favored by softer temperatures in

winter (Calderini et al., 2001; Sadras et al., 2006). In fact, the wheat of the 2094 CERs no

longer undergoes temperatures lower than 4 ° C from day 85 while the wheat of the 2015

CERs must wait until day 130 (Figure 17). Moreover, precipitations are more abundant in

2094 (Figure 22), which would further promote the development of culture (Asseng et al.,

2004). The higher CO2 concentration would also be responsible for the rapid growth of

wheat (Bellia, 2003). In addition, in the event of water stress as encountered in the 2094

CERs at the end of the season, shortening the plant cycle would be an adaptive mechanism

that would allow the plant to produce seeds despite the limited water remaining in the

soil (Tardieu et al., 2006).

4.2 Chlorophyll fluorescence measures

The clustering and partial least square regression analysis will help to understand the

effects of meteorological variables on the measured fluorescence parameters. The results

of this analysis will then be used to understand the seasonal evolution of fluorescence

parameters.

4.2.1 Clustering

Clustering results suggest that low Fv / Fm, Vi, (Eo) and PIABS values would be induced

by stressful weather conditions, particularly heat, and drought stress. (Mathur et al.,

2011; Adams III et al., 2013; Jedmowski et al., 2015b). Indeed, this type of stress causes

the closure of the stomata and therefore the decrease in photosynthetic efficiency (Bañon

et al., 2004; Grassi et al., 2005; Flexas et al., 2006). The low values of the fluorescence

parameters can also be related to high photosynthetically active radiation. In fact, the

plant can dissipate energy to preserve the PSII. It is photoinhibition, a photoprotective

mechanism that inactivates the PSII (C. Werner, R. J. Ryel, 2001). Therefore, inactivation

62

of PSII decreases the photosynthetic capacity of the plant (Goh et al., 2012). Based on

cluster analysis, low Fv / Fm, Vi, (Eo) and PIABS values would also be associated with

elevated CO2 concentrations. However, a higher concentration of CO2 is normally

favorable to better photosynthetic yield in C3 plants such as wheat (Porter et al., 2005;

Asseng et al., 2009). This is because stressful conditions are only found in 2094 CERs,

where the CO2 concentration is higher. They, therefore, contain conditions of high CO2

concentrations, but these are not responsible for the stress caused to the plant and its

photosynthetic apparatus. Finally, the higher values of the fluorescence parameters

would translate into high photosynthetic activity in the case of optimal weather

conditions (average temperature and high soil water content).

4.2.2 PLSR

The PLS results show that the fluorescence parameters Fv/Fm and Vi have different

behaviors depending on the stress state of the plant (Figure 33, Figure 34). Indeed, when

the wheat is in optimal conditions, Fv/Fm would be mainly correlated with air

temperature. On the other hand, when the wheat is under stress, Fv/Fm is mainly

explained by the relative humidity of the air and the soil water content and no longer

depends on the air temperature. In the case of a non-stressed crop, Vi depends mainly on

air temperature (and its interaction with other meteorological parameters) while in the

case of a stressed crop, it also seems to depend on the soil water content and the relative

humidity of the air. The parameters (Eo) and PIABS depend on all meteorological

parameters, whether the plants are in stressed conditions or not. Indeed, PIABS is a

parameter that gives an overview of the photosynthetic system's tolerance to abiotic

stresses (see 2.3 Chlorophyll fluorescence). On the other hand, according to the literature,

(Eo) did depend on thermal stress and excess light (Tóth, Schansker and Strasser, 2007)

but no precise information is given on its behavior in the face of water stress. The results

of the PLS are in line with the evolution of fluorescence parameters throughout the

season. They are related in the following paragraph.

4.2.3 Seasonal evolution of fluorescence parameters

In general terms, until the day 230, Fv/Fm, (Eo) and PIABS are on average higher in 2094

CERs than in 2015 CERs (Table 7). Indeed, during this period, temperatures are on

average higher in 2094 (+3.4°C - Figure 17), approaching the optimum functioning of

photosynthesis (Tashiro Wardlaw 2006). Moreover, the CO2 concentration is 388ppm

higher, which also increases the photosynthetic yield of wheat by promoting

carboxylation at the site of the Rubisco enzyme (Porter et al., 2005; Asseng et al., 2009;

Manderscheida, 2018). Then, the abundant rainfall in 2094 (Figure 22) has further

favored the positive impacts of temperature and CO2 on photosynthesis (Li, Kang and

Zhang, 2004).

63

More specifically, until day 230, fluctuations in Fv/Fm are correlated with temperature

fluctuations. Indeed, when the temperature increases, the energy transfer between the

PSI and the PSII increases, and therefore, Fv/Fm increases (Adams III et al., 2013).

Moreover, since wheat is not under water stress, it transpires to regulate its temperature

and avoid oxidative damage. Vi also depends mainly on the air temperature. For example,

it decreases strongly on day 164 in the 2015 CERs due to a too low temperature (Adams

et al., 2008). The higher values of Vi in the 2015 CERs could also be related to the higher

values of photosynthetically active radiation. Wheat would develop a higher radiation

adaptation by rapidly delivering electrons to the final acceptors (ferredoxin and NADP +)

and then to the Calvin Benson cycle (Pollastrini et al 2016). (Eo) and PIABS are influenced

by all meteorological parameters.

Then, all fluorescence parameters fall from day 230 onwards in 2094 CERs. This fall has

to be related to the heat and drought stress experienced by the 2094 wheat. Indeed,

between days 225 and 244, the daily rainfall never exceeds 1mm (Figure 22). The soil is

drying up, and the wheat is running out of water. They, therefore, close their stomata to

limit transpiration and so water losses. The stomatal closure limits the assimilation of CO2

and therefore leads to a decrease in photosynthesis (Banon 2004, Flexas 2006). Also, heat

stress reduces electron transport between the PSII and the PSI (Yan et al., 2013), which is

consistent with the drop in the values of the fluorescence parameters. It should be noted

that during the last three measurements, the number of photosynthetically active leaves

in the 2094 CERs was limited to a maximum of ten leaves per enclosure. The last

fluorescence measurements would, therefore, be too "optimistic" compared to the general

state of the culture.

More specifically, from day 230, Fv/Fm falls in the CERs 2094 due to the decrease in

relative air humidity and available water (Yan et al., 2013). Fv/Fm is no longer positively

influenced by air temperatures close to optimal. The drop in Vi values in the CERs 2094

would be due to high temperatures (Yan et al., 2013). In addition, wheat is under water

stress and reduces its transpiration, which results in less cooling of the plant. Vi also

decreases in CERs 2015, probably due to higher temperatures and the decrease of the

amount of water in the soil at the end of the season. (Eo) and PIABS fall in 2094 due to

water stress on wheat (Jedmowski et al., 2015b).


Between stages 22 and 29, the better development of wheat in 2094 CERs, in terms of LAI,

dry matter and height, would be due to a combination of climatic conditions that would

promote photosynthesis. The fluorescence measurements allow to approve this

hypothesis in the previous point. Increasing the efficiency of photosynthesis results in an

64

acceleration of biomass and leaf area production (Li, 2004). The increase in leaf area

would, in turn, lead to an increase in photosynthesis. The main reason for the better

development of wheat in 2094, until stage 29, is, despite the breakdowns, the higher CO2

level (KIMBALL et al., 1995; D. J. Hunsaker et al., 1996; DERNER et al., 2003; Li, Kang and

Zhang, 2004) and higher temperatures (Calderini et al., 2001).

Between stages 29 and 69, the decrease in the average LAI in 2094 CERs (Figure 29)

reflects the drop in LAI from CER 4. Indeed, at stage 69, the average LAI measured in CERs

2 and 6 is 12.5 m² leaves/m² soil while in CER 4 it is 5.2 m² leaves/m² soil. These

differences will be discussed in the paragraph on enclosures repeatability. By removing

the LAI measurements from CER 4 (Figure 53), the average LAI measured in the 2094

enclosures remains higher than in the 2015 enclosures (10.4 and 12.4 m² leaf/m² ground

for the 2015 and 2094 enclosures respectively).

Figure 53 : Evolution of the LAI of wheat measured in the three 2015 CERs and two 2094 CERs, CER 2, and CER 6.

At the end of the vegetative period (BBCH stage 29), the LAI of wheat in 2094 CERs was

exceptionally high. Then, the leaf area of wheat 2094 has developed less intensively than

that of wheat 2015 (lower slope in 2094 between stages 29 and 69). Given the leaf area

developed by wheat in 2094, it is normal for the lower leaves to senesce since they are in

the shade, but in this case, the decrease in LAI between stages 29 and 69 is also explained

by other phenomena.

Just before reaching stage 69, temperatures are higher in 2094, which should optimize

photosynthesis. However, rainfall is lower (Figure 17, Figure 22). A first hypothesis could

be that the lack of water would push the plant to close these stomata to minimize

transpiration and therefore water losses (Grassi and Magnani, 2005) which would reduce

the efficiency of photosynthesis (Bañon et al., 2004 ; Flexas et al., 2006). However, this is

0

5

10

15

20

25

0 10 20 30 40 50 60 70 80

LAI (

m²

leaf

/m²s

oil)

BBCH stage

2015 2094 (without CER4)

Mean 2015 Mean 2094 (without CER4)

65

not in agreement with chlorophyll fluorescence measurements according to which the

photosynthetic yield of 2094 wheat is higher until day 230 (stage 69 arriving on day 225

for 2094 wheat).

So, between stages 29 and 69, the decrease in wheat leaf growth in 2094 can be explained

by a second hypothesis : it could be linked by early and intensive growth at the beginning

of development. As explained at the beginning of this section, wheat plants have

developed much more abundantly under the future meteorological scenario until stage

29. This intensive development would have reduced soil nitrogen resources. In addition,

limited rainfall has prevented some fertilizer grains from dissolving and thus nitrogen

from spreading into the soil. Moreover, wheat with a larger leaf area transpires more

abundantly, which would further deplete soil water resources (Asseng et al., 2004). As a

result, a little before stage 69, with less nitrogen and water available in the soil, wheat

from 2094 would have remobilized their resources to the upper leaves, leaving the lower

leaves dry, which would have reduced LAI. Indeed, reducing LAI is a process of adaptation

to water stress developed by the plant to reduce transpiration and therefore, water loss

(Tardieu et al., 2006). This second hypothesis cannot be directly related to fluorescence

measurements since they are taken on non-senescent leaves, which seem to continue to

photosynthesize effectively until day 230 (shortly after stage 69). However, the

proportions of senescent and remobilizing leaves measured at stage 69 confirm the

second hypothesis (Table 11). In fact, 2015 wheat has an average of 11% remobilized

leaves and 19% senescent leaves. Wheat from CERs 2094 contains 17% remobilized

leaves and 23% senescent leaves.

Table 11 : Proportion of photosynthetically active, remobilizing, or senescent leaves with the total weight of leaves

averaged in 2015 and 2094 CERs.

Photosynthetically

active leaves (%)

Remobilizing

leaves (%)

Senescent leaves

(%)

2015 69 11 19

2094 60 17 23

Between stages 29 and 69, dry weight and height continued to increase under both

meteorological scenarios. Wheat from 2094 would have continued to develop more

biomass until the drought stress. Then, the stress caused the drying of the lower leaves,

and thus the decrease of the LAI but not the dry mass and the height.

66

5 REPEATABILITY BETWEEN CERS

5.1 Weather conditions

The temperature conditions are correctly repeated in the enclosures of the same weather

scenario (Figure 39, Figure 40). Without considering the breakdowns, the air relative

humidity is also correctly repeated (Figure 41, Figure 42). The CO2 concentrations in the

2015 CERs follow the same variations (Figure 43). The same is true for the CO2

concentrations of the 2094 CERs (Figure 44). Regarding the average daily

photosynthetically active radiation, it follows the same variations in the CERs of the same

meteorological scenario (Figure 45, Figure 46). However, the average photosynthetically

active radiation measured at midday is not the same in the enclosures (Table 8). It could

be explained by broken bulbs.


Under the 2015 weather scenario, wheat would have developed in the same way in the

three enclosures up to stage 29 (Figure 48, Figure 49, Figure 50). Between stages 29 and

69, wheat has developed a larger leaf area in CER 1. However, wheat of CERs 3 and 5 have

a higher dry weight than the wheat of CER 1. Therefore, wheat of CERs 3 and 5 would

have, in a first time, developed more biomass than those of CER 1. Then, having developed

larger biomass and thus a larger leaf area, the wheat transpires more, which further

reduces soil water resources. Being in water stress, wheat of CERs 3 and 5 would be more

likely to close their stomata to limit water loss (Grassi et al., 2005) which would lead to a

decrease in photosynthesis (Bañon et al., 2004) and therefore a decrease in the growth of

wheat. Moreover, water stress would result in the remobilization of resources and thus

the drying of the lower leaves and therefore would reduce the photosynthetically active

leaf area. These hypotheses are confirmed by the relative mass of photosynthetically

active leaves measured at stage 69 (Table 12). Indeed, in the CERs 3 and 5, the

photosynthetically active leaves represent respectively 62 and 66% of the foliar mass

against 79% in the CER 1.

Under the meteorological scenario of 2094, at stage 69, the average LAI of CERs 2 and 6 is

12.5 m²leaves / m²soil whereas the LAI only reaches 5.2 m²leaves / m²soil in CER 4. This

leaf surface drop in CER 4 can be explained in the same way as for CERs 3 and 5. Indeed,

CER 4 wheat has a more developed leaf area at stage 29. Since CER 4 wheat has developed

a larger leaf area, they would have transpired more and therefore deplete the soil's water

and nutrient resources from the beginning of the season. At the end of the season, the CER

4 crop would, therefore, have had to face an earlier and more intense drought. During the

drought, the wheat would have closed their stomata to limit water losses (Grassi et al.,

2005) which would have resulted in a decrease in photosynthetic yield (Bañon et al.,

67

2004) and therefore the decrease of biomass production. The CER 4 crop would therefore

have developed less at the end of the season than the CER 2 and 6 crops. This hypothesis

is confirmed by the fact that, at stage 69, the wheat from CER 4 has a lower dry mass than

the wheat from the other two CERs. Since the plants in enclosure 4 were under stress,

they would also have mobilized their resources to the upper leaves, which would have

caused the senescence of the lower leaves and therefore the decrease in LAI (Figure 48).

The measurements taken during sampling at flowering confirm this hypothesis. Indeed,

at stage 69, photosynthetically active leaves represent only 43% of the leaf mass in CER 4

compared to 70 and 66% in CER 2 and 6, respectively (Table 12).

Table 12 : Proportion of photosynthetically active, remobilizing or senescent leaves with the total weight of leaves in each

of the 2015 CERs and each of the 2094 CERs.

Photosynthetically

active leaves (%)

Remobilizing

leaves (%)

Senescent leaves

(%)

2015

CER 1 79 8 13

CER 3 62 14 24

CER 5 66 12 21

2094

CER 2 70 12 18

CER 4 43 24 33

CER 6 66 15 19

5.3 Chlorophyll fluorescence measurements

Unlike LAI or dry mass measurements, the 2015 CERs do not show significant differences

in chlorophyll fluorescence. Fluorescence parameters fluctuate from enclosure to

enclosure and from day to day without showing a clear trend. The efficiency of

photosynthesis would, therefore, be similar in the three chambers under the 2015


In the 2094 CERs, the fluorescence parameters also do not show significant differences.

Even the early drought experienced by CER 4 is not reflected in fluorescence

measurements. According to the measurements, the photosynthesis efficiency would be

the same in all three chambers. This may be explained by the fact that the measurements

were taken on non-senescent leaves. Therefore, the measurements would rather

conclude that photosynthetically active leaves photosynthesize with the same efficiency

in all three chambers despite the differences in water stress, though there were fewer

active leaves.

68

6 FORECASTING TRIALS FOR FIELD CROPS IN 2094

Based on the differences observed between the crop in Ecotron 2015 and the field crop in

2015 and the differences observed between the crops in Ecotron 2015 and 2094, it is

possible to estimate the differences that would exist between a crop in the field in 2094

and the crop in the field in 2015.

In terms of phenology, the 2015 field and Ecotron crops mature about 290 days after

sowing. The 2094 field crop would therefore mature at the same time as the 2094 Ecotron

crop, i.e., 15 days earlier than in 2015.

At the end of the tillering process, the crop in Ecotron 2015 shows an LAI twice as high as

the crop in the field in 2015. At flowering, the LAI of Ecotron crop is only 30% higher than

that of field crop 2015. The crop in Ecotron 2094 has an average LAI of 10

m²leaves/m²soil. There are several possibilities. Field crop in 2094 could have an LAI of

5m²leaves/m²soil at the end of tillering and 7 m²leaves/m²soil at flowering. The LAI in

2094 in field would therefore initially be twice as high as the field LAI in 2015. Then, as in

Ecotron, the higher LAI would generate more transpiration and reduce the soil's water

reserves, which would lead to water stress. In addition, the higher wind in the field would

further increase transpiration and thus aggravate water stress. Or, since field conditions

are less favorable, the crop would not have an LAI as high as in Ecotron and would

therefore not transpire as much. Water stress at the end of the season and the decrease in

LAI could therefore be avoided. In the field, the LAI at the end of the season would thus

remain higher in 2094, which would surely favor yield.

Wheat in Ecotron 2015 has a dry mass of about 40% higher than wheat in the 2015 field.

Wheat in Ecotron 2094 has a dry mass of 32 g/plant at flowering. Field wheat in 2094

could, therefore, have a dry mass of about 19 g/plant, which is 10 grams more than field

wheat in 2015.

The crop in Ecotron 2015 is about 15% higher than the crop in the field in 2015. The

Ecotron 2094 crop reaches an average of 115cm at maturity, so field cultivation in 2094

could reach about 97cm. The height of wheat in the 2094 field would therefore not be

significantly different from that in the 2015 field.

These forecasts should be tempered by a combination of effects. For example, reducing

LAI might reduce drought. Or maybe, the wind would increase the drought. It does not

allow us to know whether wheat in the field in 2094 will have a higher yield than wheat

in the field in 2015. Initially, the biomass developed by wheat will indeed be higher in

2094, but the crop may lack resources during the grain filling phase (Eitzinger et al., 2003

; Connor, 1991).

69

CONCLUSION AND PROSPECTS

First of all, this study made it possible to evaluate the reproducibility of a meteorological

scenario and an agroecosystem (in this case a wheat crop) in Ecotron. Overall, weather

conditions are correctly reproduced as long as there are no failures in the various control

systems. However, the same crop under the same weather scenario does not develop in

the same way in Ecotron and in the field. Indeed, wheat in Ecotron has developed a greater

biomass and leaf area than wheat in the field, for the 2014-2015 weather scenario. The

main causes would be the radiation balance of the crop and wind conditions.

Then, this master thesis made it possible to study the effects of climate change on a wheat

crop by simulating a past and present weather scenario and a future weather scenario in

Ecotron. Crops under the 2094 weather scenario grow faster and mature about 15 days

before wheat grown under the 2015 weather scenario. Also, crops under the future

meteorological scenario initially develop a larger biomass and leaf area, mainly due to

higher temperatures and CO2 concentration and therefore to higher photosynthetic yield.

Then, they face drought and heat stress that slows their development and causes a

decrease in leaf area due to the senescence of the lower leaves. Fluorescence

measurements confirm a decrease in photosynthetic yield and therefore a decrease in

biomass production. These results do not allow us to draw any definite conclusions about

the crop's grain yield, but since photosynthesis is limited by heat and drought stress

during the grain filling phase, the yield will be impacted. However, models predicted good

yields (Gobin, 2010) but the drought period turned everything upside down. Within a few

days, the yields could have been quite different. Moreover, the impact of the enclosures

on performance is not precisely known. Ecotron culture remains only a simulation. In

addition, the 2094 weather scenario is based on a forecast model based on the worst

possible scenario in terms of radiative forcing. The future climate could be completely

different, and wheat could therefore evolve differently in the field in 2094.

Finally, irrigating crops in the future to maintain the positive effects of increased CO2 on

wheat yield and prevent water stress would not be a solution. Indeed, given the global

context, agriculture should rather tend towards a reduction in water and energy

consumption. It is therefore necessary to find alternative solutions in order to continue to

increase agricultural yields.

70

PROSPECTS In the Ecotrons, the first problem to be solved concerns the sensors installed in the soil of

the lysimeter and which should measure the water content of the soil (see Soil

conditions). Indeed, these sensors provide outliers. They should, therefore, be

recalibrated before launching an upcoming experiment in Ecotrons. Then, rain gauges

could be installed to know exactly how much rain fell in the enclosures. These

measurements could be related to the weight of the lysimeter and the water content of

the soil to better understand the behavior of water in the Ecotrons. Finally, the

temperature of Ecotrons could be biased at night to compensate for the radiation of the

walls. The temperature of the crop should be measured to verify the impact of

temperature bias.

At the level of culture, several things could improve its study and thus the understanding

of the effects of climate change on wheat. First, more agronomic measures could be

applied to the crop throughout the growth of wheat. Given the small size of the plot, non-

destructive measures are preferred. The LAI could be measured more frequently using

cameras, for example. Secondly, fluorescence measurements could be made over the

entire growing season to have an overview of the ranges of values of the fluorescence

parameters and thus on the operating ranges of the photosynthetic apparatus.

Finally, the STICS software and its calibration for the Ecotron could be used to test certain

hypotheses concerning the development of the crop in the field and the Ecotron as well as

under different weather scenarios.

- 71 -

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I

ANNEXES

1 EXTRACTDATA.M CODE

%Extract and synthesis of the meteorological data from Lonzée and Ecotrons(CERs) clear all run_dir="C:\Users\Maurine A\Documents\CoursMA2\TFE\SEE_Suivi\Monitoring_Ecot" working_dir='C:\Users\Maurine A\Documents\CoursMA2\TFE\SEE_Suivi\Monitoring_Ecot'; export_dir='C:\Users\Maurine A\Documents\CoursMA2\TFE\SEE_Suivi\Monitoring_Ecot'; %Lonzée Data load('StatsClimatesPeriod_SEE_2014-2015') load('StatsClimatesPeriod_SEE_2093-2094') Tmax_Lnz(1:305,1)=[1:305]; Tmax_Lnz(1:79,2) = tma(287:365,35); %data from 14/10/2014 to 31/12 Tmax_Lnz(80:305,2) = tma(1:226,36); %data from 1/1/2015 to 14/8 Tmin_Lnz(1:305,1)=[1:305]; Tmin_Lnz(1:79,2) = tmi(287:365,35); Tmin_Lnz(80:305,2) = tmi(1:226,36); Radbrut(:,1)=dayWeiss(:,1); Radbrut(:,2)=nPhotonPARWeiss(:,1); for i=1:305 Rad_Lnz(i,1)=i; %number of days after sowing Rad_Lnz(i,2)= mean(Radbrut(find(Radbrut(:,1)==i),2)); end CO2brut(:,1)=dayWeiss(:,1); CO2brut(:,2)=CO2Conc(:,1); for i=1:305 CO2_Lnz(i,1)=i; %number of days after sowing CO2_Lnz(i,2)= mean(CO2brut(find(CO2brut(:,1)==i),2)); end O3brut(:,1)=dayWeiss(:,1); O3brut(:,2)=OzoneConc(:,1); for i=1:305 O3_Lnz(i,1)=i; %number of days after sowing O3_Lnz(i,2)= mean(O3brut(find(O3brut(:,1)==i),2)); end HR_Lnz(1:305,1)=[1:305]; %number of days after sowing HR_Lnz(1:79,2) = hursHO(287:365,5); HR_Lnz(80:305,2) = hursHO(1:226,6); Pr_Lnz(1:305,1)=[1:305]; %number of days after sowing Pr_Lnz(1:79,2) = prHO(287:365,35); Pr_Lnz(80:305,2) = prHO(1:226,36); Pr_my_actu(1:305,1)=[1:305]; Pr_my_actu(1:79,2:31) = prHO(287:365,9:38); Pr_my_actu(80:305,2:31) = prHO(1:226,9:38); Pr_94(1:305,1)=[1:305]; %number of days after sowing Pr_94(1:305,2) = pr8585scExp(1:305,24); Pr_my_fut(1:305,1)=[1:305]; Pr_my_fut(1:305,2:31)=pr8585scExp(1:305,1:30); csvwrite('Pr_15.csv', Pr_Lnz); csvwrite('Pr_94.csv', Pr_94);

II

csvwrite('Pr_my_actu.csv', Pr_my_actu); csvwrite('Pr_my_fut.csv', Pr_my_fut); T_my_actu(1:305,1)=[1:305]; %number of days after sowing T_my_actu(1:79,2:31) = tasHO(287:365,9:38); T_my_actu(80:305,2:31) = tasHO(1:226,9:38); T_my_fut(1:365,1)=[1:365]; for i=1:365 T_my_fut(i,2:31)=mean(tas8585c(8*(i-1)+1:8*i,1:30)); end T_fut(1:305,1)=[1:305]; %number of days after sowing T_fut(1:79,2:31) = T_my_fut(287:365,2:31); T_fut(80:305,2:31) = T_my_fut(1:226,2:31); csvwrite('T_my_actu.csv', T_my_actu); csvwrite('T_my_fut.csv', T_fut); %CERs Data T_CER1 = dlmread ('CER1_nDay.csv'); T_CER2 = dlmread ('CER2_nDay.csv'); T_CER3 = dlmread ('CER3_nDay.csv'); T_CER4 = dlmread ('CER4_nDay.csv'); T_CER5 = dlmread ('CER5_nDay.csv'); T_CER6 = dlmread ('CER6_nDay.csv'); %1. Temperatures (mean, max and min) for i=1:255 Tmoy(i,1)=i; %number of days after sowing Tmoy(i,2)= mean(T_CER1(find(T_CER1(:,1)==i),2)); %Daily average temperature CER1 Tmoy(i,3)= mean(T_CER2(find(T_CER2(:,1)==i),2)); %Daily average temperature CER2 Tmoy(i,4)= mean(T_CER3(find(T_CER3(:,1)==i),2)); %Daily average temperature CER3 Tmoy(i,5)= mean(T_CER4(find(T_CER4(:,1)==i),2)); %Daily average temperature CER4 Tmoy(i,6)= mean(T_CER5(find(T_CER5(:,1)==i),2)); %Daily average temperature CER5 Tmoy(i,7)= mean(T_CER6(find(T_CER6(:,1)==i),2)); %Daily average temperature CER6 Tmax(i,1)=i; Tmax(i,2)= max(T_CER1(find(T_CER1(:,1)==i),2)); %Daily max temperature CER1 Tmax(i,3)= max(T_CER2(find(T_CER2(:,1)==i),2)); %Daily max temperature CER2 Tmax(i,4)= max(T_CER3(find(T_CER3(:,1)==i),2)); %Daily max temperature CER3 Tmax(i,5)= max(T_CER4(find(T_CER4(:,1)==i),2)); %Daily max temperature CER4 Tmax(i,6)= max(T_CER5(find(T_CER5(:,1)==i),2)); %Daily max temperature CER5 Tmax(i,7)= max(T_CER6(find(T_CER6(:,1)==i),2)); %Daily max temperature CER6 Tmax(i,8)=Tmax_Lnz(i,2); %Daily max temperature Lonzée Tmin(i,1)=i; Tmin(i,2)= min(T_CER1(find(T_CER1(:,1)==i),2)); %Daily min temperature CER1 Tmin(i,3)= min(T_CER2(find(T_CER2(:,1)==i),2)); %Daily min temperature CER2 Tmin(i,4)= min(T_CER3(find(T_CER3(:,1)==i),2)); %Daily min temperature CER3 Tmin(i,5)= min(T_CER4(find(T_CER4(:,1)==i),2)); %Daily min temperature CER4 Tmin(i,6)= min(T_CER5(find(T_CER5(:,1)==i),2)); %Daily min temperature CER5 Tmin(i,7)= min(T_CER6(find(T_CER6(:,1)==i),2)); %Daily min temperature CER6 Tmin(i,8)=Tmin_Lnz(i,2); %Daily min temperature Lonzée end csvwrite('Tmoy.csv', Tmoy); csvwrite('Tmax.csv', Tmax); csvwrite('Tmin.csv', Tmin); %2. Relative Humidity for i=1:255 HRmoy(i,1)=i; %days after sowing

III

HRmoy(i,2)= mean(T_CER1(find(T_CER1(:,1)==i),3)); %Daily average relative humidity CER1 HRmoy(i,3)= mean(T_CER2(find(T_CER2(:,1)==i),3)); %Daily average relative humidity CER2 HRmoy(i,4)= mean(T_CER3(find(T_CER3(:,1)==i),3)); %Daily average relative humidity CER3 HRmoy(i,5)= mean(T_CER4(find(T_CER4(:,1)==i),3)); %Daily average relative humidity CER4 HRmoy(i,6)= mean(T_CER5(find(T_CER5(:,1)==i),3)); %Daily average relative humidity CER5 HRmoy(i,7)= mean(T_CER6(find(T_CER6(:,1)==i),3)); %Daily average relative humidity CER6 HRmoy(i,8)=HR_Lnz(i,2); %Daily average relative humidity Lonzée end csvwrite('HRmoy.csv', HRmoy); %3. Daily average CO2 concentration for i=1:255 CO2moy(i,1)=i; %days after sowing CO2moy(i,2)= mean(T_CER1(find(T_CER1(:,1)==i),4)); %CER1 CO2moy(i,3)= mean(T_CER2(find(T_CER2(:,1)==i),4)); %CER2 CO2moy(i,4)= mean(T_CER3(find(T_CER3(:,1)==i),4)); %CER3 CO2moy(i,5)= mean(T_CER4(find(T_CER4(:,1)==i),4)); %CER4 CO2moy(i,6)= mean(T_CER5(find(T_CER5(:,1)==i),4)); %CER5 CO2moy(i,7)= mean(T_CER6(find(T_CER6(:,1)==i),4)); %CER6 CO2moy(i,8)=CO2_Lnz(i,2); %Lonzée end csvwrite('CO2moy.csv', CO2moy); %4. Daily average Radiation for i=1:255 Radmoy(i,1)=i; %1ere col = num des jours (1=semi) Radmoy(i,2)= mean(T_CER1(find(T_CER1(:,1)==i),6)); %CER1 Radmoy(i,3)= mean(T_CER2(find(T_CER2(:,1)==i),6)); %CER2 Radmoy(i,4)= mean(T_CER3(find(T_CER3(:,1)==i),6)); %CER3 Radmoy(i,5)= mean(T_CER4(find(T_CER4(:,1)==i),6)); %CER4 Radmoy(i,6)= mean(T_CER5(find(T_CER5(:,1)==i),6)); %CER5 Radmoy(i,7)= mean(T_CER6(find(T_CER6(:,1)==i),6)); %CER6 Radmoy(i,8)=Rad_Lnz(i,2); %Lonzée end csvwrite('Radmoy.csv', Radmoy); %5. Daily radiation dynamics CER1 = dlmread ('CER1.csv'); CER2 = dlmread ('CER2.csv'); CER3 = dlmread ('CER3.csv'); CER4 = dlmread ('CER4.csv'); CER5 = dlmread ('CER5.csv'); CER6 = dlmread ('CER6.csv'); Radbrut(:,3)=hrsWeiss(:,1) for i=1:23 Rad_LnzJ(i,1)=i; %1ere col = heure de 1 à 23 Rad_LnzJ(i,2)= mean(CER1(find(CER1(:,5)==i),10)); %CER 1 Rad_LnzJ(i,3)= mean(CER2(find(CER2(:,5)==i),10)); %CER 2 Rad_LnzJ(i,4)= mean(CER3(find(CER3(:,5)==i),10)); %CER 3 Rad_LnzJ(i,5)= mean(CER4(find(CER4(:,5)==i),10)); %CER 4 Rad_LnzJ(i,6)= mean(CER5(find(CER5(:,5)==i),10)); %CER 5 Rad_LnzJ(i,7)= mean(CER6(find(CER6(:,5)==i),10)); %CER 6 Rad_LnzJ(i,8)= mean(Radbrut(find(Radbrut(:,3)==i),2)); %Lonzée end csvwrite('RadJour.csv', Rad_LnzJ);

IV

2 STATISTICAL ANALYSIS : SCRIPTS FOR RSTUDIO

2.1 ANOVA on agronomic measures Lonzée-Ecotron 2015

#ANOVA to compare measures from Lonzée and Ecotron 2015 scenario setwd("~/CoursMA2/TFE/AnalyseData") #Data download install.packages("readxl") library(readxl) LAI_Lnz <- read_excel("LAI_Lnz.xlsx") View(LAI_Lnz) Hauteur_Lnz <- read_excel("Hauteur_Lnz.xlsx") View(Hauteur_Lnz) MS_Lnz <- read_excel("MS_Lnz.xlsx") View(MS_Lnz) #Check data install.packages("dplyr") str(LAI_Lnz) #check the structure str(Hauteur_Lnz) str(MS_Lnz) LAI_Lnz$Lieu <- factor(LAI_Lnz$Lieu) LAI_Lnz$CER <- factor(LAI_Lnz$CER) Hauteur_Lnz$Lieu <- factor(Hauteur_Lnz$Lieu) Hauteur_Lnz$CER <- factor(Hauteur_Lnz$CER) MS_Lnz$Lieu <- factor(MS_Lnz$Lieu) MS_Lnz$CER <- factor(MS_Lnz$CER) #Visualize data install.packages("ggpubr") library("ggpubr") ggline(LAI_Lnz, x = "Stade", y = "LAI", color = "Lieu", add = c("mean_se")) ggline(Hauteur_Lnz, x = "Stade", y = "Hauteur", color = "Lieu", add = c("mean_se")) ggline(MS_Lnz, x = "Stade", y = "MS", color = "Lieu", add = c("mean_se")) #TEST ANOVA LAI.aov2 <- aov(LAI ~ Stade+Lieu, data = LAI_Lnz) summary(LAI.aov2) Hauteur.aov2 <- aov(Hauteur ~ Stade+Lieu, data = Hauteur_Lnz) summary(Hauteur.aov2) MS.aov2 <- aov(MS ~ Stade+Lieu, data = MS_Lnz) summary(MS.aov2) #TEST homo and normality of variances install.packages("car") library(car) leveneTest(LAI ~ Lieu, data = LAI_Lnz) leveneTest(Hauteur ~ Lieu, data = Hauteur_Lnz) leveneTest(MS ~ Lieu, data = MS_Lnz) plot(LAI.aov2, 2) #OK plot(Hauteur.aov2, 2) #OK plot(MS.aov2, 2) #OK

V

2.2 ANOVA on agronomic measures 2015-2094

setwd("~/CoursMA2/TFE/AnalyseData") #Data download library(readxl) LAI_Stade <- read_excel("LAI_Stade.xlsx") View(LAI_Stade) Hauteur_Stade <- read_excel("Hauteur_Stade.xlsx") View(Hauteur_Stade) MS_Stade <- read_excel("MS_Stade.xlsx") View(MS_Stade) #Assumptions : no difference in the means of factor A, in the means of factor B # ~means of A and B are not equal OK # no interaction between factors A and B OK # observations normally distributed and have equal variances #Check data install.packages("dplyr") str(LAI_Stade) #check the structure str(Hauteur_Stade) str(MS_Stade) #Convert CER and climate into factors LAI_Stade$CER <- factor(LAI_Stade$CER) LAI_Stade$Climat <- factor(LAI_Stade$Climat) Hauteur_Stade$CER <- factor(Hauteur_Stade$CER) Hauteur_Stade$Climat <- factor(Hauteur_Stade$Climat) MS_Stade$CER <- factor(MS_Stade$CER) MS_Stade$Climat <- factor(MS_Stade$Climat) #Visualize data install.packages("ggpubr") library("ggpubr") #climates ggline(LAI_Stade, x = "Stade", y = "LAI", color = "Climat", add = c("mean_se")) ggline(Hauteur_Stade, x = "Stade", y = "Hauteur", color = "Climat", add = c("mean_se")) ggline(MS_Stade, x = "Stade", y = "MS", color = "Climat", add = c("mean_se")) #CERs ggline(LAI_Stade, x = "Stade", y = "LAI", color = "CER", add = c("mean_se")) ggline(Hauteur_Stade, x = "Stade", y = "Hauteur", color = "CER", add = c("mean_se")) ggline(MS_Stade, x = "Stade", y = "MS", color = "CER", add = c("mean_se")) #TEST ANOVA LAI.aov2 <- aov(LAI ~ Stade+Climat, data = LAI_Stade) summary(LAI.aov2) Hauteur.aov2 <- aov(Hauteur ~ Stade+Climat, data = Hauteur_Stade) summary(Hauteur.aov2) MS.aov2 <- aov(MS ~ Stade+Climat, data = MS_Stade) summary(MS.aov2) LAI.aov2 <- aov(LAI ~ CER, data = LAI_Stade) summary(LAI.aov2) Hauteur.aov2 <- aov(Hauteur ~ CER, data = Hauteur_Stade) summary(Hauteur.aov2) MS.aov2 <- aov(MS ~ CER, data = MS_Stade) summary(MS.aov2)

VI

#TEST homo library(car) leveneTest(LAI ~ CER, data = LAI_Stade) leveneTest(Hauteur ~ Climat*CER, data = Hauteur_Stade) leveneTest(MS ~ Climat*CER, data = MS_Stade) # Test Normality plot(LAI.aov2, 2) plot(Biomasse.aov2, 2) plot(Hauteur.aov2, 2) plot(MS.aov2, 2) #Stats resume per CER and Climate require("dplyr") group_by(LAI_Stade, Climat) %>% summarise(count = n(), mean = mean(LAI, na.rm = TRUE), sd = sd(LAI, na.rm = TRUE)) group_by(LAI_Stade, CER) %>% summarise(count = n(), mean = mean(LAI, na.rm = TRUE), sd = sd(LAI, na.rm = TRUE))

2.3 Statistical analysis of fluorescence measures

#Statisctical analysis Fluorescence measures #1.PCA #2.Descriptive statistics #3.Clustering on flurescence measures #4.PLS : partial least square regression on clusters #5.ANOVA : compare fluorescence measures 2015 - 2094 setwd("~/CoursMA2/TFE/AnalyseData") #Packages install.packages("FactoMineR") library(FactoMineR) install.packages("pander") library(pander) install.packages("pls") library(pls) library(glmnet) install.packages("ggpubr") library("ggpubr") #Import data EXCEL library(readxl) Data <- read_excel("data.xlsx") View(Data) Data$CER <- factor(Data$CER) Data$Climat <- factor(Data$Climat) #1.PCA PRINCIPAL COMPONENT ANALYSIS #test distribution unimodal symetric hist(Data$Tair,col='blue',xlab='T air') #ok hist(Data$HR,col='blue',xlab='HR') #OK hist(Data$CO2,col='blue',xlab='CO2') #comme deux distrubitions -> 2 climats hist(Data$Radiation,col='blue',xlab='Radiation') #distri OK hist(Data$HRVol20,col='blue',xlab='Weight') #OK

VII

hist(Data$FvFm,col='blue',xlab='FvFm') #OK hist(Data$Vi,col='blue',xlab='Vi') #ok hist(Data$psiEo,col='blue',xlab='psiEo') #ok hist(Data$PIabs,col='blue',xlab='PIabs') #ok #PCA PCA_Climat <- PCA(Data[5:9], scale.unit = TRUE) #PCA sur data climat originales PCA_Fluo <- PCA(Data[12:15], scale.unit = TRUE) #PCA sur Fluo oroginales PCA <- PCA(Data[5:15], scale.unit = TRUE) #PCA complete sur data orginales #2.DESCRIPTIVES STATISTICS pander(summary(Data)) #Mean Climate and CER apply(Data[,3:15],2,tapply, Data$Climat, mean) apply(Data[,3:15],2,tapply, Data$CER, mean) #3. Clustering on Fluorescence measures library(readxl) Data <- read_excel("data.xlsx") summary(Data) Fluo <- Data[,12:15] #3.1. ACP fluo ACP.Fluo <- PCA(Fluo, ncp=2, scale.unit = TRUE) #plot(ACP.Fluo$eig[,1], type="h", lwd=4, ylab="Eigenvalues") #abline(h=1, lty="dashed") #2 compos #3.2. HCPC fluo hcpc.Fluo <- HCPC(ACP.Fluo, graph = FALSE) #dendrogram fviz_dend(hcpc.Fluo, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8 ) #dendrogram : 4 groups -> see data per group fviz_cluster(hcpc.Fluo, repel = TRUE, # text ok show.clust.cent = TRUE, # center of clusters palette = "jco", # color, see ?ggpubr::ggpar ggtheme = theme_minimal(), main = "Factor map" ) #Output HCPC head(hcpc.Fluo$data.clust, 10) #data origine + number cluster hcpc.Fluo$desc.var$quanti #representative values for each cluster write.csv(hcpc.Fluo$data.clust, file = "ClusterFluo.csv") #Mean in clusters Cluster1Fluo <- read_excel("Cluster1Fluo.xlsx") summary(Cluster1Fluo) Cluster2Fluo <- read_excel("Cluster2Fluo.xlsx") summary(Cluster2Fluo) Cluster3Fluo <- read_excel("Cluster3Fluo.xlsx") summary(Cluster3Fluo)

VIII

Cluster4Fluo <- read_excel("Cluster4Fluo.xlsx") summary(Cluster4Fluo) #4. PLSR PARTIAL LEAST SQUARE REGRESSION #PLSR - FvFm plsFvFm <- plsr(FvFm ~ Tair*Weight*HR, data=Data[5:13],scale=TRUE, validation="CV",method="oscorespls") summary(plsFvFm) R2plsFvFm<-R2(plsFvFm,"all") RMSEPFvFm<-RMSEP(plsFvFm,"all") par(mfrow=c(1,1)) plot(R2plsFvFm$val[2,,]) #plot(RMSEPFvFm$val) R2plsFvFm$val #interpreter valeurs de R² best.dims = which.min(RMSEPFvFm$val[estimate = "adjCV", , ]) - 1 coefficientsFvFm = coef(plsFvFm) #coefficients de régression sum.coefFvFm = sum(sapply(coefficientsFvFm, abs)) coefficientsFvFm = coefficientsFvFm * 100 / sum.coefFvFm coefficientsFvFm = sort(coefficientsFvFm[, 1 , 1]) par(mfrow = c(1,1)) barplot(tail(coefficientsFvFm, 10),main="Coefficients FvFm PLS") coefficientsFvFm plsFvFm$scores #scores et loadings retourne au % de variance expliqué en tant qu'attribut plsFvFm$loadings #PLSR - Vi plsVi <- plsr(Vi ~ Tair*Weight*HR, data=Data[5:13],scale=TRUE,validation="CV",method="oscorespls") summary(plsVi) R2plsVi<-R2(plsVi,"all") RMSEPVi<-RMSEP(plsVi,"all") par(mfrow=c(1,1)) plot(R2plsVi$val[2,,]) #plot(RMSEPVi$val) R2plsVi$val #interpreter valeurs de R² best.dims = which.min(RMSEPVi$val[estimate = "adjCV", , ]) - 1 coefficientsVi = coef(plsVi) #coefficients de régression sum.coefVi = sum(sapply(coefficientsVi, abs)) coefficientsVi = coefficientsVi * 100 / sum.coefVi coefficientsVi = sort(coefficientsVi[, 1 , 1]) par(mfrow = c(1,1)) barplot(tail(coefficientsVi, 10),main="Coefficients Vi PLS") coefficientsVi plsVi$scores plsVi$loadings #PLSR - psiEo plspsiEo <- plsr(psiEo ~ Tair*Weight*HR, data=Data[5:13],scale=TRUE,validation="CV",method="oscorespls") summary(plspsiEo) R2plspsiEo<-R2(plspsiEo,"all") RMSEPpsiEo<-RMSEP(plspsiEo,"all") par(mfrow=c(1,1))

IX

plot(R2plspsiEo$val[2,,]) #plot(RMSEPpsiEo$val) R2plspsiEo$val #interpreter valeurs de R² best.dims = which.min(RMSEPpsiEo$val[estimate = "adjCV", , ]) - 1 coefficientspsiEo = coef(plspsiEo) #coefficients de régression sum.coefpsiEo = sum(sapply(coefficientspsiEo, abs)) coefficientspsiEo = coefficientspsiEo * 100 / sum.coefpsiEo coefficientspsiEo = sort(coefficientspsiEo[, 1 , 1]) par(mfrow = c(1,1)) barplot(tail(coefficientspsiEo, 10),main="Coefficients psiEo PLS") coefficientspsiEo plspsiEo$scores plspsiEo$loadings #PLSR - PIabs plsPIabs <- plsr(PIabs ~ Tair*Weight*HR, data=Data[5:14],scale=TRUE,validation="CV",method="oscorespls") summary(plsPIabs) R2plsPIabs<-R2(plsPIabs,"all") RMSEPPIabs<-RMSEP(plsPIabs,"all") par(mfrow=c(1,1)) plot(R2plsPIabs$val[2,,]) #plot(RMSEPPIabs$val) R2plsPIabs$val #interpreter valeurs de R² best.dims = which.min(RMSEPpsiEo$val[estimate = "adjCV", , ]) - 1 coefficientsPIabs = coef(plsPIabs) #coefficients de régression sum.coefPIabs = sum(sapply(coefficientsPIabs, abs)) coefficientsPIabs = coefficientsPIabs * 100 / sum.coefPIabs coefficientsPIabs = sort(coefficientsPIabs[, 1 , 1]) par(mfrow = c(1,1)) barplot(tail(coefficientsPIabs, 10),main="Coefficients PIabs PLS") coefficientsPIabs plsPIabs$scores plsPIabs$loadings #5.ANOVA fluorescence between 2015 and 2094 FvFm.aov2 <- aov(FvFm ~ Climat, data = Data) summary(FvFm.aov2) Vi.aov2 <- aov(Vi ~ Climat, data = Data) summary(Vi.aov2) psiEo.aov2 <- aov(psiEo ~ Climat, data = Data) summary(psiEo.aov2) PIabs.aov2 <- aov(PIabs ~ Climat, data = Data) summary(PIabs.aov2) #Anova fluorescence between CERs for 2015 and 2094 separetly data15 <- read_excel("data.xlsx", sheet = "Data15") data15$CER <- factor(data15$CER) #graphs p1 <- ggline(data15, x="Day", y="FvFm", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="Fv/Fm (-)", add=c("mean_sd")) p1 +

X

font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10) p2 <- ggline(data15, x="Day", y="Vi", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="Vi (-)", add=c("mean_sd")) p2 + font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10) p3 <- ggline(data15, x="Day", y="psiEo", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="psi(Eo) (-)", add=c("mean_sd")) p3 + font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10) p4 <- ggline(data15, x="Day", y="PIabs", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="PIabs (-)", add=c("mean_sd")) p4 + font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10) FvFm.aov2 <- aov(FvFm ~ CER, data = data15) summary(FvFm.aov2) Vi.aov2 <- aov(Vi ~ CER, data = data15) summary(Vi.aov2) psiEo.aov2 <- aov(psiEo ~ CER, data = data15) summary(psiEo.aov2) PIabs.aov2 <- aov(PIabs ~ CER, data = data15) summary(PIabs.aov2) data94 <- read_excel("data.xlsx", sheet = "Data94") data94$CER <- factor(data94$CER) #graphs p1 <- ggline(data94, x="Day", y="FvFm", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="Fv/Fm (-)", add=c("mean_sd")) p1 + font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10) p2 <- ggline(data94, x="Day", y="Vi", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="Vi (-)", add=c("mean_sd")) p2 + font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10) p3 <- ggline(data94, x="Day", y="psiEo", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="psi(Eo) (-)", add=c("mean_sd")) p3 + font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10) p4 <- ggline(data94, x="Day", y="PIabs", numeric.x.axis=TRUE, color="CER", plot_type=c("l"), size=0.5,xlab="Days after sowing",ylab="PIabs (-)", add=c("mean_sd")) p4 + font("xy",size=10)+ font("xy.text", size=10)+ font("legend.title", size=10)

XI

FvFm.aov2 <- aov(FvFm ~ CER, data = data94) summary(FvFm.aov2) Vi.aov2 <- aov(Vi ~ CER, data = data94) summary(Vi.aov2) psiEo.aov2 <- aov(psiEo ~ CER, data = data94) summary(psiEo.aov2) PIabs.aov2 <- aov(PIabs ~ CER, data = data94) summary(PIabs.aov2)

XII

3 OBSERVATION OF DEVELOPMENT STAGES (BBCH) IN ECOTRONS AND AT

LONZÉE

Developmental stage BBCH CER 2015 CER 2094 Lonzée

Sowing 00 14-10-14 14-10-93 14-10-14

Lifting 09 22-10-14 22-10-93 -

Tillering 2t. 22 01-12-14 - 27-11-14

Tillering 3t. 23 23-12-14 24-11-93 27-01-15

Tillering 4t. 24 29-01-15 - -

Tillering 5t. 25 23-02-15 29-01-94 -

Tillering 7t. 27 10-03-15 - 13-03-15

1st node 31 24-03-15 23-02-94 14-04-15

2nd node 32 - 10-03-94 28-04-15

3rd node 33 29-04-15 - 11-05-15

Last pointing leave 37 29-04-15 9-04-94 -

Last leaf spread out 39 7-05-15 15-04-94 26-05-15

Beginning of swelling 41 - 29-04-94 -

Epiaison 57 21-05-15 07-05-94 09-06-15

End of flowering 69 03-06-15 21-05-94 -

Milky stage grain 75 01-07-15 03-06-94 14-07-15

Complete maturation 89 28-07-15 1-07-94 31-07-15

XIII

4 EVOLUTION OF THE FV/FM PARAMETERS MEASURED ON WHEAT

THROUGHOUT THE SEASON IN THE THREE CERS UNDER THE 2014-2015

METEOROLOGICAL SCENARIO AND THE THREE CERS UNDER THE 2093-

2094 METEOROLOGICAL SCENARIO (FULL GRAPH).

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

140,00 160,00 180,00 200,00 220,00 240,00 260,00

Dai

ly a

vera

ge t

emp

erat

ure

(°C

)

Days after sowing


XIV

5 SEE SIDE I EXPERIMENT

A. The objective of the SEE side I experiment

The purpose of the SEE side I experiment is to understand why wheat in the Ecotron shows correct

physiological stages but develops longer leaves. The hypothesis is that the intensity of the

wavelengths emitted by the far-infrared spectrum between 700 and 750 nm influences the

elongation of the leaves.

The growth of wheat cultivation will be compared, specifically by comparing leaf blades, from

germination to BBCH 22 (until the return of lysimeters from wintering) under two radiations. The

crop will, therefore, be subject to identical conditions (2014-2015 weather conditions) except for

the far-infrared spectrum, the intensity of which has been halved. For example, in the graph below

(Figure A), the blue curve represents the sum of the spectra produced by the lamp (plasma,

infrared, and far-infrared). The second peak (between 700 and 750 nm) marks the far-infrared

spectrum and will, therefore, be halved in one of the two chambers and kept as it is in the other

chamber.

Figure A : Graph of the spectrum of lamps (blue) vs. solar spectrum (red) for stage 1 - Abscissa axis:

wavelength in nm, ordinate axis: relative intensity in W/m².

B. Experimentation protocol

Preparation of tanks and seedlings:

On January 22nd (S-14, S being the day of the semi) the tanks (dim 500 x 500 x 500 x 500 mm)

are filled with arable soil from the Terra site and left outside. On 29 January (S-7) the tanks

entered the building where the temperature is around 15°C. On 1 February (S-4), the tanks were

placed in the enclosures and subjected to the weather conditions of mid-October 2014 (rooms 3

and 5).

XV

On Tuesday, February 5 (S-0), the seeds are sown in lines by hand, parallel to the sidewalls of the

chamber, reproducing a seedling with a grain drill (~2cm deep). The spacing is 147 mm. Three

lines are sown with ~ 18 seeds per line. The density of semi is 250 gr/m². At the time of sowing,

the weather conditions of October 14, 2014 were in place.

Crop measures :

On February 8 (S+3), February 11 (S+6), February 13 (S+8), February 15 (S+10) and February 18

(S+13) the seedlings will be counted. Samples will be taken on 20 February (S+15), 27 February

(S+22), 6 March (S+29) and 13 March (S+36).

Five seedlings will be removed at each sampling, the first one will be randomly selected, and the

next four will follow the first with an interval of 30 cm of sowing line between each sample. For

each sampling, the seedlings will be displayed and photographed. The images will be analyzed to

determine leaf lengths and leaf area.

C. Summary of the results and conclusion

Days after sowing

Average height (mm) (CER with IR 50%)

Average height (mm) (CER with IR 100%)

3 0 0 6 23 22 8 52 47

10 53.6 63.4 13 91 102.6

Analysis of the leaf length and leaf area of wheat grown under both light conditions does not show

any significant difference. It would, therefore, seem that the intensity of the wavelengths emitted

by the far-infrared spectrum in Ecotron does not affect leaf elongation. The difference in leaf

growth would, therefore, come more from thermoperiodism, i.e. the sensitivity of wheat to

periodic temperature variations. This hypothesis will be verified by thermography

measurements.

XVI

6 SEE SIDE II EXPERIMENT A. The objective of the SEE side II experiment

The purpose of the SEE side II experiment is to evaluate the impact of reduced wind in Ecotron (set at 0.5 m.s-1). Indeed, the softer wind and the absence of turbulence in Ecotron would allow the wheat to grow higher.

Two small wheat crops will be exposed to different wind conditions : one will not be exposed to wind, and the other will be exposed to the wind reproduced by a ventilator at a speed of m.s-1 during the day. The height of the wheat will be compared, from germination to BBCH 22.

B. Experimentation protocol

Preparation of tanks and seedlings:

The tanks (size 500 x 500 x 500 x 500 mm) filled with arable soil are stored in the lysimeter hall at a temperature of 15-16°C. On March 27 (S-1, S being the day of sowing), the tanks are placed in the wintering room and subjected to the weather conditions 2014-2015, from mid-October.

On March 28 (S-0), the wheat grains are sown in line, by hand, by reproducing a seedling with a cereal sowing machine (~2cm deep). The spacing is 147 mm. Three lines are sown with ~18 seeds per line. The sowing rate is 250 gr/m². The 2014-2015 weather conditions are reproduced from October 14. Being in the wintering room, only the radiation and temperature conditions are controlled. The CO2 concentration and relative humidity of the air are equal to the outdoor conditions. Precipitation is reproduced manually.

Monitoring of the culture:

On April 1 (S+4), April 3 (S+6), April 5 (S+8), April 8 (S+11) and April 10 (S+13) the seedlings will be counted. Samples will be taken on 11 April (S+14), 18 April (S+22), 25 April (S+29), 2 May (S+36) and 7 May (S+41).

Five seedlings will be removed at each sampling, the first one will be randomly selected, and the next four will follow the first with an interval of 30 cm of semi line between each sample. For each sampling, the seedlings will be displayed and photographed. The images will be analyzed to determine leaf lengths and leaf area.

C. Summary of the results and conclusion

The absence of wind would be responsible for an increase in leaf length from 10 to 15%. Wind is therefore not the only cause of the best foliar development in Ecotron. The analysis of the heights of wheat grown under different wind conditions shows significant differences (p-value = 0.03).