On the Understanding of Climate Tolerance and Early Plant Stress...

On the Understanding of Climate Tolerance and Early

Plant Stress Detection in Greenhouse Cultivation

PhD Thesis

Eshetu Janka

December 2013

iii

On the Understanding of Climate Tolerance and Early Plant Stress

Detection in Greenhouse Cultivation

PhD Thesis

Eshetu Janka

December 2013

iv

Thesis main supervisor

Dr. Carl-Otto Ottosen Associate Professor, Plant Physiology Department of Food Science, Aarslev Aarhus University, Denmark

Co-supervisors

Dr. Oliver Körner AgroTech A/S, Institute for Agri Technology and Food Innovation, Denmark

Dr. Eva Rosenqvist Associate Professor, Plant Physiology Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Denmark

Assessment Committee

Dr. Marianne Bertelsen Department of Food Science, Aarhus University, Aarslev Denmark

Dr. ir. Kathy Steppe Professor –Head of plant Ecology research unit Laboratory of plant ecology Faculty of Bioscience engineering Ghent University, Belgium

Dr. Fulai Liu Associate Profesor , Department of Plant and Environmental Sciences Faculty of Science, University of Copenhagen, Denmark

v

On the Understanding of Climate Tolerance and Early Plant Stress

Detection in Greenhouse Cultivation

Eshetu Janka

Thesis

Submitted in fulfilment of the requirements of the degree of doctor

at Aarhus University, Faculty of Science and Technology, Department of Food Science

Approved by Thesis Committee appointed by the Academic Board

to be defended in public on March 18 2014

at Aarhus University, Aarslev.

vi

Eshetu Janka

On the Understanding of Climate Tolerance and Early Plant Stress Detection in

Greenhouse Cultivation

Thesis, Aarhus University, Aarslev, Denmark (2013)

With references, summaries in English

ISBN

vii

Abstract

Denmark is one of the leading world countries on renewable energy, energy efficiency, and climate

change policy. One of the country‟s projected goals is to decrease energy use and CO2 emissions from

the greenhouse horticulture industry. New greenhouse production methods and technologies to

minimise energy consumption, and ensure reduced CO2 emissions are under extensive research and

development. A dynamic greenhouse climate control regime is one of the new concepts, where

greenhouse climate control is based on plant physiological processes, outside solar irradiance, and crop

microclimate within the greenhouse. Hence, the control system increases carbon gain and reduces

energy consumption. However, tracking plant responses, which are promptly adjusted when plant

performance is affected by extreme microclimatic conditions, can serve to optimise the system.

Therefore, it is vital to understand plant responses under dynamic and potentially extreme greenhouse

microclimate conditions. Several experimental studies have been conducted under high temperature,

and high temperature and light conditions using the model plant chrysanthemum (Dendranthema

grandiflora Tzvelev) „Coral Charm‟. Chlorophyll fluorescence, fast chlorophyll a fluorescence transient,

and JIP-test parameters, respectively Fv/Fm, Fv/Fo, and PI revealed the PSII damage thermo-tolerance

and critical temperature limit. High temperature (> 38 °C) had a significant effect on PSII, and

temperature (T50) dose caused a 50 % reduction in Fv/Fm at 41 °C. Moreover, the high temperature

effect on Fv/Fm was substantial when combined with high light; Fv/Fm decreased notably at high

temperatures (> 32 °C). F'q/F'm was a useful indicator of the actual PSII operating efficiency under

illumination. Furthermore, the combined effect of high light and high temperature significantly

decreased F'q/F'm at temperatures > 28 °C. F'q/F'm and non-photochemical quenching (NPQ) were

impacted at lower temperatures than required for Fv/Fm. Under high irradiance and temperature,

changes in NPQ determined F'q/F'm, with no major change in the fraction of open PSII centres (qL)

(indicating a QA redox state). Moreover, thermal index (IG) showed a strong correlation with stomatal

conductance (gs), which enabled non-invasive estimates of gs using thermography. In addition, results

showed the coupled model can be applied to real-time predictions of leaf temperature, photosynthesis,

and stomatal conductance. The multilayer leaf model has potential to predict PSII operating efficiency

under different microclimate conditions. Overall, plant based monitoring techniques, with crop models

can be valuable in real-time stress detection.

Keywords: Greenhouse; Energy; CO2 emission; Climate control; Plant physiology; Extreme

microclimate; Stress, PSII efficiency; Chlorophyll fluorescence; JIP-test; Thermography; Thermal

index; coupled model; Multilayer leaf model

For the memory of my Father

xi

CONTENTS Chapter 1 General introduction 1

Chapter 2 Climate stress and physiological methods used to monitor plant responses 17

Chapter 2.1 High temperature stress monitoring and detection using chlorophyll a

fluorescence and infrared thermography in chrysanthemum (Dendranthema

grandiflora) 19

Chapter 2.2 Using the quantum yields of photosystem II and the rate of net

photosynthesis to monitor high light and temperature stress in chrysanthemum

(Dendranthema grandiflora) 35

Chapter 3 Crop models and monitoring plant stress 53

Chapter 3.1 Log-logistic model analysis of optimal and supra-optimal temperature

effect on photosystem II using chlorophyll a fluorescence in chrysanthemum


Chapter 3.2 A coupled model of leaf photosynthesis, stomatal conductance, and leaf

energy balance for chrysanthemum (Dendranthema grandiflora) 63

Chapter 3.3 PSII operating efficiency simulation from chlorophyll fluorescence in

response to light and temperature using a multilayer leaf model for chrysanthemum


Chapter 4 General discussion and Conclusion 99

Chapter 4.1 General discussion 101

Chapter 4.2 Conclusion 109

Chapter 4.3 Thesis contribution 111

Chapter 4.4 Possibilities for future research 113

References 115

Summary 139

Acknowledgements 143

List of publications 145

PhD certificates and Post graduate courses 147

Participation in international workshops and conferences 147

xiii

Abbreviations

b minimal stomatal conductance at light compensation point

Cs CO2 partial pressure at the leaf surface

Ci inter cellular CO2

Ci* CO2 compensation point in the absence of Rd

CO2 Carbon dioxide (µmol mol-1)

Cp specific heat capacity of air

D leaf dimension

DLI daylight integral

EJ activation energy maximum electron transport rate (kJ mol-1)

Ec activation energy Rubisco carboxylation (kJ mol-1)

Eo activation energy Rubisco oxygenation (kJ mol-1)

ERd activation energy dark respiration (kJ mol-1)

Evc activation energy carboxylation rate (kJ mol-1)

ETR electron transport rate

EPS expoxidation state of xanthophylls

F fluorescence emission from dark adapted leaf

Fo minimal fluorescence from dark adapted leaf

F' fluorescence emission from light adapted leaf

F'o minimal fluorescence from light adapted leaf

Fm maximal fluorescence from dark adapted leaf

F'm maximal fluorescence from light adapted leaf

Fv variable fluorescence form dark adapted leaf

F'v variable fluorescence from light adapted leaf

F'q difference in fluorescence between F'm and F'

Fv/Fm maximum photochemical efficiency of PSII

Fv/Fo conformation term for primary photochemistry

F'q/F'm PSII operating efficiency

gs stomatal conductance (mmol m-2 s-1)

GHGs greenhouse gases

Gt CO2-eq Gigaton CO2 equivalent

H constant for optimum curve temperature dependent

maximum electron transport rate

xiv

hs relative humidity at leaf surface (%)

IG thermal index

Jmax,25 maximum electron transport rate at 25 °C (µmol m-2 s-1)

J Electron transport of a leaf (µmol m-2 s-1)

Ko,25 michaelis-Menten constant Rubisco oxygenation (mbar)

Kc,25 michaelis-Menten constant Rubisco carboxylation (µbar)

K conversion factor from [m2 s mol-1] to [s m-1]

kDa kiloDalton

m empirical coefficient

MTOE million tonnes of oil equivalent

NPQ non-photochemical quenching

NDVI normalized difference vegetation index

OEC oxygen evolving complex

OJIP Fast fluorescence induction curve

PAR photosynthetically active radiation

PI performance index

PPFD photosynthetic photon flux density (µmol m-2 s-1)

Pn/( Pnl) net (leaf) photosynthesis (µmol m-2 s-1)

PRI photochemical reflectance index

PSII photosystem II

PSU photosynthetic unit

qL fraction of PSII centres that are open

QA primary electron acceptor quinine

R2 coefficient of determination

R gas constant (J mol-1 k-1)

RC reaction centre

RC/ABS density of active PSII reaction centres per chlorophyll

RH relative humidity (%)

RuBP ribulose bisphosphate

Rubisco ribulose-1,5-bisphosophate carboxylase/oxygenase

rb,Co2 boundary layer resistance for CO2 diffusion (s m-1)

Rd,25 dark respiration at 25 °C (µmol m-2 s-1)

rs,H2O stomatal resistance for H2O (s m-1)

rb, H2O boundary resistance for H2O (s m-1)

xv

rH total resistance to heat transfer (s m-1)

rv total resistance to latent heat transport (s m-1)

rb,H2O boundary resistance to water vapour transport

S constant for optimum curve temperature dependent maximum

electron transport rate (kJ mol-1 k-1)

s slop of the curve relation saturating water vapour pressure

to air temperature (Pa °C-1)

TJ terajoule (1012 joule)

T25 temperature in Kelvin at 25 °C (K)

Tl leaf temperature (°C)

Ta air temperature (°C)

Vc,max,25 maximum carboxylation rate at 25 °C (µmol m-2 s-1)

VPD vapour pressure deficit (kPa)

VPDa vapour pressure deficit of the ambient air (kPa)

Vo/c ration of oxygenation to carboxylation rate

YIC young information criterion

ΦPSII the quantum efficiency of PSII

ΦNPQ the yield for dissipation by down-regulation

ΦNO the yield of other non-photochemical losses

α Fraction of incident light absorbed by a leaf

α0 leaf photochemical efficiency in absence of oxygen

(mol CO2{mol photon}-1)

αforced empirical coefficient of forced convection

αfree empirical coefficient of free convection

αmixed empirical coefficient of mixed convection

θ degree of curvature of CO2 response of light saturated

net photosynthesis

β proportion of light absorbed by PSII

βmixed empirical coefficient of mixed convection

ρo2i O2 partial pressure inside stomata (mbar)

µ wind speed (m s-1)

γ psychometric constant (Pa K-1)

(Text in the bracket indicates measuring unit)

xvi

‘

1

General introduction

CHAPTER 1


Global warming is a worldwide concern (Solomon et al. 2007). Linear trend data

estimates global mean surface temperatures have risen 0.74 °C ± 0.18 °C over the last 100

years (1906–2005), with the warming rate over the last 50 years almost double that of the

last 100 years (0.13 °C ± 0.03 °C vs. 0.07 °C ± 0.02 °C per decade). Greenhouse gases

(GHGs), including carbon dioxide, methane, nitrous oxide, and fluorinated gases are the

major causes of global warming. GHG emissions covered by the Kyoto Protocol increased

by approximately 70% (from 28.7 to 49.0 Gt CO2-eq) from 1970–2004 (by 24% from

1990–2004), with carbon dioxide (CO2) the largest source, exhibiting an approximately

80% rise during the period (Barker et al. 2007).

Greenhouse horticulture contributions to global warming

One-third of GHG emissions are derived from agriculture (Gilbert 2012); and

greenhouse horticulture shares a major part of these emissions. The sector uses high-

energy amounts in burning fossil fuels for heat, and therefore contributes to high CO2

emissions. The European greenhouse horticulture sector is the most intensive in energy

use, and the European Environment Agency reported negative environmental

consequences of high CO2 emissions by the sector (EEA 2012). Greenhouses occupy an

estimated 200000 hectares in Spain, Italy, the Netherlands, and Greece; and consume not

less than 3.4 MTOE (million tonnes of oil equivalent) of energy, and 9.2 Gt CO2-eq

emissions (Campiotti et al. 2012). However, European countries have conducted several

studies to examine approaches to increase energy efficiency, and achieve economically and

environmentally sustainable greenhouse horticultural management and production

regimes (PASEGES 2008).

The Danish greenhouse industry

Agricultural GHG emissions are a significant contributor to overall Danish emissions

(Nielsen et al. 2011, Dalgaard et al. 2011). Greenhouse horticulture in Denmark is one of

the advanced agricultural sectors in northern Europe. Denmark operates approximately

400 greenhouse nurseries, with the average greenhouse area occupying 4.5 x 106 m-2

(Danish Energy Agency 2012, Statistics Denmark 2013). The greenhouse nurseries

consume high energy, with an estimated 5.267 TJ total energy consumption, of which 85%

2

Chapter 1

is for heating (Danish Energy Agency 2012), contributing substantial CO2 emissions.

Denmark environmental indicators, which footprint the CO2 amount emitted from various

industries, calculated the environmental energy indicator for the greenhouse industry was

2.6 x 106 GJ DKK-1, a larger amount compared to other industries (Aaslyng et al. 2003,

Danish Energy Agency 2012). Therefore, the Danish greenhouse horticultural industry

imposes high pressure to enforce CO2 emissions reduction policies. Denmark is one of the

leading world countries on renewable energy, energy efficiency, and climate change

policies, and has a targeted goal to convert all energy supplies to renewable energy by 2050

(IEA 2011). The overall new energy policy objective is to reduce national CO2 emissions by

20% in 2020 relative to 2005 (The Danish Government 2011). Therefore, in the last decade

several studies have been conducted to develop new greenhouse production methods,

climate control strategies and technologies to ensure reduced emissions, and minimise

energy consumption in the Danish greenhouse industry (Aaslyng et al. 2003, Aaslyng et al.

2005, Körner and Van Straten 2008, Danish Energy Agency 2012).

Several studies confirmed the potential to reduce energy consumption, and increase

greenhouse energy efficiency. Greenhouse climate control based on plant physiological

strategies have been implemented over the last three decades, including computer

controlled crop growth (Udink ten Cate and Challa 1984), optimal climate controlled crop

growth and flowering phenology (Fisher et al. 1997), process-based humidity controlled

greenhouse crops (Körner and Challa 2003), and climate control based on a leaf

photosynthesis model (Hansen et al. 1996a, b). For example, most Denmark greenhouse

nurseries use the dynamic model based greenhouse climate control strategy (IntelliGrow)

(Aaslyng et al. 2003). The dynamic climate control objective is to dynamically adjust the

greenhouse climate, so that optimal use of natural resources is achieved (Aaslyng et al.

2003, Markvart et al. 2008). An 8-40% greenhouse energy use savings can be gained by

IntelliGrow application, depending on the external climatic conditions, and crop species

(Aaslyng et al. 2003, Markvart et al. 2008). Therefore, among other measures,

improvements in climate control strategies in Danish greenhouses indicate energy

consumption can achieve approximately 90% reductions in the current usage (Danish

Energy Agency 2012).

Modern greenhouses with advanced climate control strategies and concepts, which

include increased insulation materials or a more closed greenhouse system, have the

potential to introduce new environmental conditions for most greenhouse crops. The

climatic conditions under controlled strategies differ considerably from those in

3


conventional greenhouses (Dieleman et al. 2010). Consequently, this can create unusual

and extreme microclimatic conditions (e.g. high temperature and light) in a greenhouse

exceeding crop requirements, resulting in short or long term plant stress. Monitoring plant

responses can optimise the control regime, and the approach can be promptly adjusted

when plant performance is influenced by extreme microclimatic conditions. Plant response

to these controlled conditions is not well understood; therefore it is vital to investigate crop

response under such microclimatic greenhouse conditions, and the physiological responses

plants elicit under dynamic micro environmental regimes. This requires testing different

physiological methods, and applying plant sensors while the plants are subjected to

extreme microclimate conditions. The defined sensor data can be used to monitor plant

conditions as well as an early warning for extreme climatic conditions, which occur in

greenhouses.

Abiotic plant stress

Plants are often subjected to unfavourable environmental conditions in the form of

biotic or abiotic stress (Boyer 1982). Physiological stress can be simply defined as a set of

conditions that cause an aberrant change in physiological processes, eventually resulting in

injury. A stressor can induce enough physiological change that growth or yield is reduced,

or evidence shows physiological acclimation or adaptation under extreme stress conditions

(Nilsen and Orcutt 1996). The following four main plant stress response phases have been

observed (Lichtenthaler 1988, Larcher 2003)

i) the alarm phase;

ii) the resistance phase;

iii) exhaustion phase; and

iv) recovery phase

The alarm phase begins with the so-called stress reaction, characterized by functional

declines due to the stress factor (Duque et al. 2013). However, abiotic stress rarely acts

individually on plants (e.g. heat-water deficit-high light stress) (Duque et al. 2013).

Greenhouse crops are susceptible to a number of environmental stress types, such as high

or low temperature, excess light, water shortage, and humidity, which may be too high or

low for optimum production (Peet 1999).

4

Chapter 1

High temperature stress

Heat stress is a rise in temperature sufficient to cause irreversible damage to plant

growth and development. It is a complex function of intensity, duration, and rate of

temperature increase (Wahid et al. 2007). High temperature stress can cause reductions in

yield and dry matter production (Krishnan et al. 2011). In higher plants, high temperatures

primarily affect photosynthetic capacity and photochemical efficiency, which are the

processes most sensitive to heat stress (Weis and Berry 1988, Percival 2005, Allakhverdiev

et al. 2008, Yamamoto et al. 2008). In fact, photosynthetic rates typically peak at

approximately 30 °C, with significant declines in assimilation for each additional degree

increase (Wise et al. 2004). The major stress-sensitive sites in the photosynthetic

apparatus are photosystem II (PSII), ATPase, and carbon assimilation (Allakhverdiev et al.

2008). However, studies have shown PSII inhibition might not occur until leaf

temperatures are quite high, often 40 °C and above (Havaux 1993a, Al-Khatib and Paulsen

1999). Two major mechanisms were proposed for a decrease in photosynthesis under high

temperature. Salvucci and Crafts-Brandner (2004) reported inhibition of the ribulose

bisphosphate (RuBP) carboxylation rate due to heat-induced decrease in activase activity,

and additional study demonstrated inhibition of photosynthetic electron transport under

heat stress conditions (Wise et al. 2004, Demirevska-Kepova and Feller 2004). Moreover,

several studies confirmed Calvin cycle activity was inhibited by more moderate

temperatures than electron transport inhibition, because ribulose-1,5-bisphosophate

carboxylase/oxygenase (Rubisco) was inhibited due to loss of Rubisco activase activity

(Feller et al. 1998, Salvucci et al. 2001, Salvucci and Crafts-Brandner 2004).

High light stress

The energy to drive photosynthetic reactions is derived from light energy, typically from

the sun, converted to chemical energy. Excessive light results in photoinhibition which is

light induced loss of PSII electron-transfer activity, resulting in potential photooxidative

damage to the photosynthetic apparatus (Demmig-Adams and Adams 1992, Long and

Humphries 1994, Foyer et al. 1994, Tyystjärvi 2013). Normally, photoinhibition is due to

an imbalance between the rate of photodamage to PSII and the rate of the repair of

damaged PSII (Niyogi 1999, Murata et al. 2007, Vass 2012) (Fig. 2). However, plants have

developed tolerance and/or acclimation mechanisms to avoid excess irradiance by

different physiological mechanisms, (e.g. non-photochemical quenching (NPQ),

xanthophyll cycle) (Holt et al. 2004, Horton et al. 2005, Walters 2005, Zhou et al. 2007).

5


Pho

t

Light Harvesting

Photochemistry

Generation of oxidizing

molecules

Targets of photooxidative

damage

Net photodamage

Photoinhibition

Adjsutement of Chl

antenna size

Thermal dissipation

CO2 fixation

PhotorespirationWater-water cycle

PSI cyclic e- transport

Antioxidant systems

Repair and new synthesis

Sun light

Fig. 1. Figure modified from Niyogi (1999) showing photoinhibition and photo-protective processes

occurring within chloroplasts.

Physiological mechanisms of photoinhibition (Fig. 2) are well established. Several

studies have provided evidence for photoinhibition processes within the chloroplast,

including non-radiative dissipation in the antenna, the xanthophyll cycle, and PSII

reaction centre inactivation and repair, which involves D1 protein turnover (Demming-

Adams 1990, Demming-Adams and Adams 1992, Aro et al. 1993, Niyogi 1999, Werner et

al. 2002). Photoinhibition can limit photosynthetic activity, growth, and productivity in a

sustained or transient nature, corresponding to chronic or dynamic photoinhibition,

respectively (Osmond 1994, Osmond and Grace 1995, Takahashi and Badger 2011, Adams

et al. 2013). Dynamic photoinhibition is a short-term reversible, regulatory process for

controlled dissipation of excessive light energy, and chronic photoinhibition is a slowly

reversible process following prolonged exposure to excessive photon fluxes and under

environmental stress conditions (Osmond 1994, Osmond and Grace 1995, Werner et al.

2002). Furthermore, photoinhibition can result from exposure to high light in the absence

or presence of other stressors, e.g. excess light and high temperature conditions (Adams et

al. 2013). Consequently, when excess light stress causes photoinhibition, high temperature

stress predisposes plants to photoinhibition and/or directly affects photosynthetic

6

Chapter 1

efficiency (Powles 1984, Havaux 1993b). In addition, high or moderate temperature levels

results in stress, which induces photoinhibition, and increases the extent of

photoinhibition in higher plants (Yang et al. 2007), due to repair of photo-damaged PSII

inhibition (Murata et al. 2007).

Methods and sensors used for plant stress detection

Several plant stress detection methods and sensors (Table 1) have been applied and

evaluated for different crop monitoring purposes. Each of the methods has documented

advantages and disadvantages, depending on scale, resolution, data acquisition, accuracy,

and ease of application. This thesis employed some of the plant sensors and physiological

methods (e.g. gas exchange, chlorophyll fluorimetry, and thermography) indicated to

measure photosynthesis, chlorophyll fluorescence, stomatal conductance, and leaf

temperature.

Infrared gas analysis (IRGA) system

Infrared gas analysis (IRGA) is the only current method of widespread importance for

measuring photosynthesis. These portable systems provide real-time measurement of CO2

uptake (A), transpiration (E), stomatal conductance (gs), and map intercellular CO2 mole

fraction (Ci) (Long et al. 1996, Long and Bernacchi 2003). In addition, modulated

chlorophyll fluorimetry, differential oxygen analysis, and higher resolution infrared gas

analysers have facilitated measurement of non -steady-state changes in CO2 fluxes (Long et

al. 1996, Long and Bernacchi 2003, Maxwell and Johnson 2000).

Photosynthesis varies based on crop type, and environmental parameters, including

irradiance, growth temperature, and CO2 concentration (Berry and Björkman 1980,

Björkman 1981). In fact, photosynthetic responses under various climatic conditions have

been used to follow plant responses (Ashraf and Harris 2013). For example, Ehler and

Hansen (1998) showed the plants-on-line-box method was an effective tool to monitor and

evaluate whole plant net photosynthesis and plant productivity, and stress in certain

greenhouse crops. Recently, a novel approach was applied using the FPGA-base (field

programmable gate array) wireless smart sensor to monitor real time photosynthesis

(Millan-Almaraz et al. 2013) and transpiration (Millan-Almaraz et al. 2010). The approach

uses sensors to measure temperature, relative humidity, solar radiation and CO2, which are

used to model net photosynthesis in real time. Rapid photosynthesis changes are identified

7


in relationship to net photosynthesis measurements, which can be utilized to detect

different crop stress conditions (Millan-Almaraz et al. 2013).

8

Chapter 1

Table 1. Physiological sensors/devices used to measure different plant parameters for stress monitoring and detection purposes.

Sensors/Device type

Measurements Spatial scale

Temporal scale

Advantage Disadvantage References

Thermocouples Leaf temperature Leaves Minute Inexpensive, easy, Prone to error due to radiation and heat

Tarnopolsky and Seginer 1999, Kirkham 2005

IR sensors Leaf temperature Leaves/ Canopy

Minute Simple, high accuracy, measure many leaves

Error, requires good focus on leaf or canopy

Graham 1989, Kirkham 2005, Chen et al. 2010

IRGA/Gas exchange Photosynthesis, stomatal conductance, transpiration

Leaves /Canopy

Minute/ Hour

Accuracy, Sensitivity Prone to Error, leaks, edge effects

Long et al. 1996, Long and Bernacchi 2003

MINI-PAM Chlorophyll fluorescence Leaves Second/ Minute

Sensitive, high accuracy

Short distance, electrical drifts

Ounis et al. 2001

MONI-PAM Chlorophyll fluorescence Leaves Second/ Minute

High accuracy, adequate resolution, continuous record

Cannot be applied at canopy level

Porcar-Castell et al. 2008

Handy PEA Chlorophyll fluorescence Leaves Seconds Simple, high accuracy, handy

Not continuous measurement, only dark adapted measurement

Strasser et al. 2000

Photochemical reflectance index (PRI)

Photosynthetic radiation use efficiency

Leaves /Canopy

Second/ Minute/ Hour

Well predicted photosynthetic efficiency

Affected by multiple factors

Garbulsky et al. 2011

Normalized difference vegetation index (NDVI)

Green biomass Leaves/ Canopy


Wider application/or multiple measurement

Sensitive parameter, less physiological application

Peñuelas and Filella 1998

Water index Leaf /Plant water content (PWC)

Leaves Second/ Minute/ Hour

Simple and fast Noisy Peñuelas and Filella 1998, Delalieux et al. 2009

Thermography Thermal index Leaves /Canopy

Seconds Minute

Accuracy, continuous measurement

Require reference leaf Jones 1999, Maes and Steppe 2012

Fluorescence imaging

Chlorophyll fluorescence Leaves /Canopy

Second Accuracy, visual Measuring disturbance (tissue, leaf hair, reflecting surface)

Buschmann and Lichtenthaler 1998, Gorbe and Calatayud 2012

Reflectance imaging Reflectance (chlorophyll content)

Leaves /Canopy


Broader application Image processing Peñuelas and Filella 1998,Chaerle et al. 2001

Multispectral fluorescence and reflectance imaging

Reflectance/fluorescence Leaves /Canopy


Image at different spectral band (wavelength), broad application

Image processing and analysis

Lenk et al. 2007

Sap Flow/stem diameter sensors

Sap flow/stem diameter variation

Plant/Stem Minute/ Hour

Continuous monitoring

Large data processing Steppe et al. 2008

C

ha

pte

r 1

8

9


Chlorophyll fluorescence measurements

Chlorophyll fluorescence is one of the most popular techniques in plant physiology due

to the ease with which the user can gain detailed information on the state of PSII at

relatively low cost (Murchie and Lawson 2013). It has been routinely used for many years

to monitor the photosynthetic performance of plants rapidly and non-invasively (Larcher

1994, Yamada et al. 1996, Maxwell and Johnson 2000, Willits and Peet 2001, Baker and

Rosenqvist 2004, Baker 2008, Murchie and Lawson 2013). The principle underling

chlorophyll fluorescence analysis is relatively straightforward. Light energy absorbed by

chlorophyll molecules can perform as follows: (i) drive photosynthesis (photochemistry);

(ii) be re-emitted as heat; or (iii) be re-emitted as light (fluorescence). These three

processes occur in competition, such that any increase in the efficiency of one will result in

a decrease in the yield of the other two. Hence, by measuring the yield of chlorophyll

fluorescence a wide variety of different fluorescence parameters are calculated (Fig. 2), and

information about changes in the efficiency of photochemistry and heat dissipation can be

found (Maxwell and Johnson 2000, Murchie and Lawson 2013).

Several types of fluorometers or measuring devices are used for fluorescence

measurement (e.g. MINI-PAM, MONI-PAM; Table 1). These devices are „modulated‟

(switched on and off at high frequency) fluorometers, which can measure fluorescence in

the presence of background illumination, and most importantly in the presence of full

sunlight in the field (Schreiber et al. 1986, Maxwell and Johnson 2000). The clear

advantage is that measurements can be recorded without darkening the leaf. Moreover,

non-modulated fluorescence measuring devices (e.g. Handy PEA, Table 1) have been

widely employed, since the measurement offers a number of parameters, with great

accuracy and speed in the dark (Murchie and Lawson 2013). For example, analysis of

transient fluorescence induction during a one second pulse application under dark

conditions has also been applied for early abiotic stress detection (Strasser et al. 2000,

Georgieva et al. 2000, Mathur et al. 2011, Christen et al. 2007). These approaches provide

detailed structural and functional information regarding PSII activity, antenna size, and

electron transport (Strasser et al. 2000, Georgieva et al. 2000, Mathur et al. 2011),

however more meaningful interpretations to elucidate more complex components and

underlying mechanisms are required (Maxwell and Johnson 2000, Murchie and Lawson

2013).

10

Chapter 1

Fo

Fm

Fv

Fm

F'o

F'q

F'm

F'F'v

Dark

Un-quenched

Dark

Un-quenched

Measuring

beam on

Saturating pulse Actinic light on

Saturating pulse

Light

Quenched

Dark adapted

20 -30 minute

Fig. 2. A typical fluorescence trace using a dark-adapted leaf to measure photochemical and non-

photochemical parameters (modified from Murchie and Lawson, 2013). A measuring light is switched

on (measuring beam on), and the zero fluorescence level is measured (Fo). Application of a saturating

flash of light (pulse) allows measurement of the maximum fluorescence level (Fm). A light to drive

photosynthesis (actinic light on) is subsequently applied. After a period of time, another saturating light

flash (pulse) allows maximum fluorescence in the light (F'm) to be measured. Turning off the actinic

light, typically in the presence of far-red light (i.e. to ensure all PSII reaction centres open rapidly after

illumination), allows the zero fluorescence level (F'o) in the light to be measured.

Generally, chlorophyll fluorescence is a widely accepted method to monitor the

photosynthetic performance of different plant species. In horticultural plants, chlorophyll

fluorescence has been used to monitor and detect high and low temperature stress on

tomato (Solanum lycopersicum) (Willits and Peet 2001, Camejo et al. 2005, Zushi et al.

2012, Ogweno et al. 2009), chrysanthemum (Dendranthema grandiflora) (Janka et al.

2012, Janka et al. 2013), high light stress on tomato (Janssen et al. 1992, Han et al. 2010),

lettuce (Lactuca sativa) (Fu et al. 2012), and water stress on grapevine (Vitis vinifera)

(Christen et al. 2007, Wright et al. 2009).

11


Temperature sensors (thermocouples and IR thermo-sensors) and thermograph

Leaf temperature is critical in plants, due to the subtle effects of small temperature

changes on the rates of key physiological processes (e.g. photosynthesis), and because of

the damaging effects of temperature extremes (Jones 2004). Consequently, leaf

temperature is a widely measured variable, which affects stomatal conductance,

transpiration, and leaf energy balance. However, a wide range of other plant (e.g. leaf size

and thickness), and environmental (e.g. light, air temperature, and wind speed) factors

(Jones 2004, Blonquist et al. 2009) also influence leaf temperature.

Prior to remote infrared sensing of leaf or canopy temperatures, leaf temperature

measurements were limited to the use of thermocouples (Jones 2004). In fact,

thermocouples have long been used to measure leaf temperature, although leaf

temperature measurements using thermocouples have always been subject to leaf-to-air

temperate effects if differences were large, and contact between thermocouples and leaf

were inadequate (Tarnopolsky and Seginer 1999). However, with the availability of new

infrared leaf temperature sensors used to sense canopy temperature, a rapid development

in infrared sensors was implemented to examine crop water stress (e.g. water stress index),

and estimate stomatal conductance, and transpiration (Idso 1982, Jones 1999, Jones

2004). Multiple indices have been proposed and applied to quantify plant water status

since the early use of thermocouples (Jones 2004).

Currently, imaging techniques such as thermography (thermal imaging) are used

extensively to visualise variation in surface temperature of stressed leaves, and to estimate

stomatal conductance (Jones 1999, Jones et al. 2002, Jones 2004, Bajons et al. 2005,

Maes et al. 2011). Jones (1999) proposed the thermal index (IG) as a new quantitative

measure specifically developed for thermography. IG is derived from overall leaf

temperature Tl; and wet (Twet) and dry (Tdry) leaf temperatures; where IG = (Tdry-Tl)/(Tl-

Twet) (Maes et al. 2011). IG is derived from dry and wet reference leaf surfaces, and does not

require detailed environmental information. When first introduced, these indicators made

it an attractive measure of stomatal conductance (Jones et al. 2002, Jones 2007, Cohen et

al. 2005, Maes et al. 2011). Most thermal imaging applications have been conducted in

relationship to monitoring and detection of water stress in horticultural crops, including

citrus (Rutaceae) (Ballester et al. 2013), cucumber (Cucumis sativus) (Kaukoranta et al.

2005), and other horticultural plants (López et al. 2012). Furthermore, thermography has

12

Chapter 1

also been applied to monitor plant diseases in cucumber (Wang et al. 2012, Wang et al.

2013) and apple (Malus domestica) (Belin et al. 2013).

Nevertheless, large variability in leaf temperature within the plant canopy, and variable

environmental conditions (e.g. in light intensity, temperature, relative humidity, and wind

speed) have resulted in systematic error in IG estimates, which limit the index applicability

(Jones et al. 2002, Grant et al. 2006, Grant et al. 2007, Jones et al. 2009, Leinonen et al.

2006). However, the index and imaging appear to have potential advantages (e.g. stomatal

function is not disturbed and rapid measure of large canopies) over the use of conventional

porometry or gas-exchange measurements in relationship to plant stress monitoring

(Jones et al. 2009).

Other physiological sensors/Methods used in plant stress monitoring

Other physiological sensors, which were not addressed in this thesis, but have shown

potential in plant monitoring at the leaf and canopy levels, include photochemical

reflectance index (PRI), normalized difference vegetation index (NDVI), fluorescence and

reflectance imaging, multispectral fluorescence and reflectance imaging, and sap flow

and/stem diameter sensors. NDVI is a standard reflectance index in the near infrared and

red regions of the spectrum, primarily used in assessment of green plant biomass and

green leaf area, especially at the ecosystem level. Moreover, NDVI measures potential but

not actual photosynthesis, consequently it can be a poor indicator of temporal variation in

CO2 fluxes, particularly when photosynthesis is down-regulated (Gamon et al. 1995,

Garbulsky et al. 2011). The NDVI index limitations are overcome by determination of the

Photochemical Reflectance Index (PRI), derived from two narrow wavelengths, 531 nm

and 570 nm, and calculated as follows: PRI = (R531-R570)/(R531+R570). Consequently,

together the two indices show promise as PRI exhibits a strong correlation with light use

efficiency (LUE). In addition, PRI provides an effective measure of relative photosynthetic

rates, the expoxidation state of xanthophylls (EPS) and non-photochemical quenching

(NPQ). PRI is increasingly applied as an index to monitor photosynthetic performance in

general, and LUE in particular (Sarlikioti et al. 2010, Garbulsky et al. 2011).

In addition, sap flow (SF) and stem diameter variation (SDV) are among the most useful

plant-based measurements to detect water stress, and to evaluate plant water consumption

(Bleyaert et al. 2012, Fernández et al. 2011). The SF and SDV measurements with

mathematical modelling can also be applied to irrigation scheduling (Steppe et al. 2008).

Although these measurements are very promising, the approaches are strongly dependent

13


on microclimatic changes, and require defined methodology to automatically distinguish

between drought stress and microclimatic effects (Baert et al. 2013).

Imagery techniques, such as fluorescence (Lazár et al. 2006, Calatayud et al. 2006) and

reflectance (Carter et al. 1996, Chaerle and Van Der Straeten 2000, Carter and Knapp

2001) have been extensively used to monitor and detect biotic and abiotic plant stress.

Chlorophyll fluorescence imaging presents an immediate overview of cell, leaf, or plant

fluorescence emission patterns, providing fast, intuitive, visual, and precise plant stress

information (Calatayud et al. 2006, Gorbe and Calatayud 2012). Moreover, chlorophyll

fluorescence imaging combined with thermal imaging has also been tested to diagnose

distinct diseases, and abiotic stressors (Chaerle et al. 2007). Nevertheless, as chlorophyll

fluorescence imaging is a useful and promising technique, imaging disturbance limitations

(e.g. non-flat tissues, reflecting surfaces, surface contamination with dust) should be

addressed in future applications of the approach as a stress indicator in greenhouse

cultivation (Gorbe and Calatayud 2012). Furthermore, reflectance imaging in the middle-

infrared region generates more data on plant conditions when combined with other

imaging techniques (Peñuelas and Filella 1998, Chaerle and Van Der Straeten 2001). To

this end, fluorescence and reflectance imaging in different spectral bands represents a

promising tool for non-destructive plant monitoring, and shows potential in a broad range

of identification tasks (Lenk et al. 2006). Fluorescence and reflectance imaging at

multispectral bands represents a potentially effective tool for non-destructive plant

monitoring, and exhibits possibilities in a broad range of spectral identification (Lenk et al.

2006)

However, these measurement techniques and monitoring tools are difficult to apply

directly under greenhouse cultivation conditions due to the following challenges ascribed

to measuring techniques: (i) most are considered “high-tech”; (ii) a substantial amount of

data is generated; (iii) some degree of discrepancy between sensor results, and plant

responses is evident; and (iv) skills are required in understanding the methodology, and

interpretation of results. However, these issues can be overcome by applying crop

modelling as a tool to explore the potential of these measurement techniques to elucidate

interrelated plant processes and plant responses to prevailing climatic conditions. The

pivotal step is to build mechanistic models with reduced complexity and ease to interpret

plant processes and responses. Ultimately, the measuring techniques, and mechanistic

models can be applied to assist in greenhouse production decisions (Steppe 2012).

14

Chapter 1

Crop modelling

Crop models are widely used in the horticultural industry (Gary et al. 1998, Lentz 1998,

Boote and Scholberg 2006). Photosynthesis, stomatal conductance, and leaf temperature

models are applied to simulate plant performance under various microclimatic conditions

in greenhouses. Greenhouse environments are optimised using photosynthesis models

(Nederhoff et al. 1989, Ehler 1991, Aaslyng et al. 2003, Sciortino et al. 2008) as well as

modelling temperature, light, and CO2 effects on crop growth and development (Seginer et

al. 1994, Johnson et al. 1996, Marcelis et al. 1998, Qian et al. 2012). Photosynthesis models

are also coupled with stomatal conductance (Kim and Lieth 2002, Kim and Lieth 2003,

Yin and Struik 2009, Li et al. 2012) and transpiration (Tuzet et al. 2003, Kim and Lieth

2003) models to examine plant responses under different climatic conditions and to assist

in climate control decisions. More recently, with the development of simple and easy plant

based measurement systems, crop models have been used in conjunction with sensors for

plant monitoring purposes, as well as in early plant stress detection applications (Helmer

et al. 2005, Dekock et al. 2006, Steppe et al. 2008, Villez et al. 2009, Sarlikioti et al. 2010,

Ehret et al. 2011, Vermeulen et al. 2012, Steppe 2012, Baert et al. 2013).

Aim of the thesis

This Ph.D. project has the following objectives: i) elucidate the major physiological, e.g.

photosynthesis and stomatal conductance plant responses to extreme microclimates, e.g.

high temperature and light: ii) identify the key physiological parameters that show the

early warning signs under such extreme conditions; ii) identify online measuring sensors;

and iv) build a mechanistic model that can be used in plant monitoring, and early stress

detection. Chrysanthemum (Dendranthema grandiflora Tzvelev) „Coral Charm‟ was

chosen as the study species. The species serves well as a model plant for the simple reason

that it is the most important species in greenhouse horticulture, and several physiological

and crop-modelling studies have been conducted on the taxon.

Thesis outline

Plant response under high temperature stress, and a combination of high temperature

and light, including the physiological methods applied to detect plant stress is presented in

CHAPTER 2.

CHAPTER 2.1 addresses chlorophyll fluorescence parameters, which can be used as

indicators of plant physiological performance under high temperature stress. In addition, a

15


discussion of thermal imaging to measure leaf surface temperature directly related to

stomatal conductance, which can be used as a non-invasive assessment of stomatal

conductance.

CHAPTER 2.2 provides a more in-depth examination of chlorophyll fluorescence and

photosynthesis to monitor plants under high temperature and irradiance stress. The

results presented in the study can be applied to monitoring continuous plant responses;

quantum yields of PSII and photosynthetic rates were obtained, which can be used to

predict short and long term stress resulting from extreme microclimatic conditions.

The application of crop models for early detection and understanding of plants under

stress is described in CHAPTER 3.

CHAPTER 3.1 describes log-logistic model analysis of optimal and supra-optimal

temperature effects on PSII, upper and lower temperature limits, and temperature dose

causing 50% reduction in key chlorophyll fluorescence parameters. This study indicated

that physiological parameters combined with model response curves indicated the PSII

high temperature tolerance.

In CHAPTER 3.2, the following three sub-models are presented: i) the C3

photosynthesis biochemical model; ii) the stomatal resistance model; and iii) the leaf

energy balance model. The models are combined to predict net leaf photosynthesis,

stomatal resistance, and leaf temperature under different microclimatic conditions. The

results presented in this study can be used to predict photosynthesis, stomatal resistance,

and leaf temperature under greenhouse microclimate conditions, which can also be used to

assist in decisions for climate control and plant stress monitoring.

CHAPTER 3.3 introduces the multi-layer leaf model, and a new way to approximate

PSII quantum yield to generate maximal fluorescence from light adapted leaves (F'm), and

fluorescence emissions from leaves adapted to actinic light (F'). The study describes a new

methodology to estimate fluorescence parameters with a simplified model, which used an

online measurement to monitor photosynthesis using chlorophyll fluorescence.

In CHAPTER 4, the results of the previous chapters are summarized, discussed, and

additional steps in the research are highlighted.

CHAPTER 4.1 frames the general discussion in broader perspective. The previous

chapters are combined, and discussed in-depth with emphasis given on the methods,

monitoring plant stress, and model application.

CHAPTER 4.2 summarises the general conclusions, and the thesis application results to

assist in greenhouse cultivation decision-making.

16

Chapter 1

In CHAPTER 4.3 the contribution of the thesis is highlighted in a more general context.

CHAPTER 4.4 extends the focus from the general view of the thesis to propose future

research.

CHAPTER 2

Climate stress and physiological methods used to monitor plant responses

2.1. High temperature stress monitoring and detection using chlorophyll a

fluorescence and infrared thermography in chrysanthemum

(Dendranthema grandiflora)

2.2. Using the quantum yields of photosystem II and the rate of net

photosynthesis to monitor high light and temperature stress in

chrysanthemum (Dendranthema grandiflora)

19


CHAPTER 2.1

High temperature stress monitoring and detection using chlorophyll a fluorescence

and infrared thermography in chrysanthemum (Dendranthema grandiflora)

Abstract

Modern highly insulated greenhouses are more energy efficient than conventional types. Furthermore

applying dynamic greenhouse climate control regimes will increase energy efficiency relatively more in

modern structures. However, this combination may result in higher air and crop temperatures. Too

high temperature affects the plant photosynthetic responses, resulting in a lower rate of photosynthesis.

To predict and analyse physiological responses as stress indicators, two independent experiments were

conducted, to detect the effect of high temperature on photosynthesis: analyzing photosystem II (PSII)

and stomatal conductance (gs). A combination of chlorophyll a fluorescence, gas exchange

measurements and infrared thermography was applied using Chrysanthemum (Dendranthema

grandiflora Tzvelev) „Coral Charm‟ as a model species. Increasing temperature had a highly significant

effect on PSII when the temperature exceeded 38 °C for a period of 7 (± 1.8) days. High temperature

decreased the maximum photochemical efficiency of PSII (Fv/Fm), the conformation term for primary

photochemistry (Fv/Fo) and performance index (PI), as well as increased minimal fluorescence (Fo).

However, at elevated CO2 of 1000 µmol mol-1 and with a photosynthetic photon flux density (PPFD) of

800 µmol m-2 s-1, net photosynthesis reached its maximum at 35 °C. The thermal index (IG), calculated

from the leaf temperature and the temperature of a dry and wet reference leaf, showed a strong

correlation with gs. We conclude that 1) chlorophyll a fluorescence and a combination of fluorescence

parameters can be used as early stress indicators as well as to detect the temperature limit of PSII

damage, and 2) the strong relation between gs and IG enables gs to be estimated non-invasively, which is

an important first step in modelling leaf temperature to predict unfavourable growing conditions in a

(dynamic) semi closed greenhouse.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) High temperature stress monitoring and detection

using chlorophyll a fluorescence and infrared thermography in chrysanthemum (Dendranthema

grandiflora). Plant physiology and Biochemistry 67: 87-94.

20

Chapter 2.1

Introduction

The climate generated by modern greenhouse climate control systems is often more

dynamic than standard rigid climate regimes, e.g. air temperature may vary considerably

in relation to the natural irradiance (Aaslyng et al. 2003, Ottosen et al. 2003, Aaslyng et al.

2005, Körner and Van Straten, 2008). These types of control strategies may result in

relatively high temperatures, potentially straining the crop microclimate conditions. High

temperature affects the photosynthetic apparatus of photosystem II (PSII) and thus net

photosynthesis directly and stomatal conductance (gs) indirectly, resulting in a lower rate

of photosynthesis which can even damage the photosynthetic apparatus (Havaux 1993a).

However, damage can be prevented and the plant regains full photosynthetic capacity if

the stress is detected in time (Crafts-Brander and Law 2000, Crafts-Brander and Salvucci

2000, Salvucci et al. 2001).

Besides increasing photorespiration, high temperatures (35-42 °C) can cause direct

injury to the photosynthetic apparatus (Havaux 1993a, Wise et al. 2004). Inactivation of

PSII, electron transport through PSII, and thylakoid disorganization are particularly

susceptible to high temperature, which might result in irreversible damage (Berry and

Björkman 1980, Havaux 1993b, Heckathorn et al. 1998, Baker and Rosenqvist 2004).

Chlorophyll a fluorescence has been extensively used as an indicator of high temperature

stress on photosynthetic performance (Willit 1994, Yamada et al. 1996, Willit and Peet

2001, Baker and Rosenqvist 2004, Baker 2008). Several studies indicate that the

maximum photochemical efficiency of PSII, Fv/Fm = (Fm - Fo)/ Fm, of dark-adapted leaves

is an excellent parameter to monitor temperature stress (Andrews et al. 1995, Fracheboud

et al. 1999, Baker and Rosenqvist 2004). Another indicator for different abiotic stress, the

JIP-test (a test applied to analyze a polyphasic rise of the chlorophyll a transient) has also

been investigated in some plant species (Strasser et al. 2000, Georgieva et al. 2000,

Christen et al. 2007, Mathur et al. 2011). The negative effects of high temperature on the

photorespiration and photosynthetic apparatus might be to some degree counteracted by

elevated CO2 (Long et al. 2006).

Leaf temperature is a function of the air temperature and transpiration rate and is thus

dependent on gs. The leaf temperature declines during transpiration, conversely leaf

temperature increases if the transpiration declines due to low gs. Using this basic principle

the thermal index (IG) has been developed as a method of estimating gs non-invasively by

infrared thermography (Jones 1999a, Jones 1999b, Maes et al. 2011). The IG has a linear

21


relation with gs and can be used to indicate relative gs from the leaf temperature and the

temperature of a dry and wet reference leaf (Jones et al. 2002, Jones 2007, Maes et al.

2011). Today, leaf temperature measurements using thermal infrared sensing is primarily

used to study plant water stress, and specifically stomatal reactions (Jones 1999, Leinonen

et al. 2006, Jones et al. 2009, Maes et al. 2011). Therefore, using the linear relation of IG

with gs a short and long term change in gs due to high temperature can be monitored at

different extreme microclimate conditions.

In order to use chlorophyll fluorescence or thermal imaging for early stress detection in

greenhouse crops, the measuring technique needs to be evaluated and combined with

explanatory models based on the biological early stress parameters. Hence, the aim of this

study was to find methods useful as early stress indicators of heat stress. As high

temperature initially affects the photosynthetic apparatus we hypothesized that continuous

monitoring of the microclimate and calculation of the quantum efficiency of PSII and net

photosynthesis together with explanatory simulation models would be useful tools for

detecting early reversible stress. By combining different non-invasive physiological

methods we expect to be able to understand the mechanisms of early stress indicators such

as declining Pn and stomatal closure. Both excised and intact leaves were analysed to

investigate whether the methods are useful in determining the effects of both short and

long term heat stress on chrysanthemum plants. Combining data from these

measurements with infrared thermography will form the basis for model-based early stress

detection in greenhouse crops.

Materials and Methods

Two experiments were performed on potted chrysanthemum (Dendranthema

grandiflora Tzvelv.) „Coral Charm‟. The first experiment focused on chlorophyll a

fluorescence and gas exchange measurements while the second experiment consisted of gs

measurements and infrared thermography. For both experiments identical plant material

was used either as a) regular greenhouse grown plants, b) leaf samples subjected to

temperature stress in a water bath in the laboratory or c) intact plants subjected to

temperature stress in growth chambers.

Plant material and cultivation

Cuttings of chrysanthemum were rooted in plastic pots (9.7 cm high, 11 cm diameter)

filled with a commercial peat, mixed with granulated clay (Pindstrup Mosebrug A/S,

22

Chapter 2.1

Ryomgaard, Denmark) in a greenhouse at Aarhus University (Aarslev, Denmark 55° 22' N)

in three different batches at 3 April, 25 April and 24 August 2011.

Three weeks after rooting shoot tips were pinched to avoid apical dominance and

stimulate side shoots. The plants in each batch were grown on a rolling growing bench in

the same greenhouse at a plant density of 40 plants m-2. The greenhouse climate was set at

temperature 20 °C/18 °C day/night. The mean daily light integral, for both natural and

supplement light combined, was on average 9.6 mol m-2 in April and 12.5 mol m-2 in

August measured at the top of the plant canopy with light period of 16h/8h light/dark,

respectively. The relative humidity (RH) was kept around 60% and CO2 concentration was

600 µmol mol-1. Nutrition (macronutrients: N 185 ppm, P 27 ppm, K 171 ppm and Mg 20

ppm; micronutrients: Ca, Na, Cl 18 ppm, SO4 27 ppm, Fe 0.9 ppm, Mn 1.17 ppm, B 0.25

ppm, Cu 0.1ppm, Zn 0.77 ppm and Mo 0.05 ppm) given was mixed with irrigation water

and automatically supplied twice a day as ebb-and flood irrigation (8:45 AM in the

morning and 4:15 PM in the afternoon). The electrical conductivity (EC) and pH of the

irrigation water were 1.88 µS cm-1 and 5.8, respectively. Biological pest controls Aphidius

Mix system and Phytoseiulus SD system (Biobest, Westerlo, Belgium) were used twice in

the growing period.

Excised leaf measurements

Three weeks after pinching heat stress was induced in a water bath in the laboratory on

excised leaves of six weeks old plants of the first batch. Eight temperatures from 24 to 45

°C with 3 °C step increase were used and for each temperature 15 leaf discs with 3.5 cm

diameter were cut from the third or the fourth youngest fully developed leaves of 15 plants

to ensure uniform physiological stage of the leaves. The leaf discs were incubated at the

respective temperatures for 30, 60 and 120 min by floating the leaves in de-ionized water

in a thermostatic water bath in darkness (digital immersion thermostat E 100 with a Pt

100 temperature probe for actual temperature control). The leaf discs were put in the

water when the water reached the desired temperature. The temperature of the water bath

was additionally controlled by two mini surface digital thermometers (Testo, no. 008,

Lenzkirch, Germany) with temperature resolution of 0.1 °C and measurement rate of 1 s.

Measurement of intact plants in growth chamber

Three weeks after pinching of the shoot tip, 15 plants of 2nd and 3rd batches were

transferred to four growth chambers and the temperature stress applied for five to ten

23


days. In three of the growth chambers (I, II, and II) the temperature was constant at the

given set point (Table 1) and in the fourth growth chamber (IV), the temperature was

allowed to increase over eleven hours (Fig. 7). The plants were irrigated and fertilized

frequently in the growth chamber to avoid water stress associated with the high

temperature and potentially higher vapour pressure deficit (VPD). The irrigation water

consisted of the nutrient formulation mentioned in section 4.1.

Table 1. The set point for climate in the growth chambers

Growth chambers Temperature

(°C, day/night)

PAR

(µmol m-2 s-1)

RH (%) CO2

(µmol mol-1)

Chamber I 32/28 235 60 600

Chamber II 38/32 235 60 600

Chamber III 40/36 235 60 600

Chamber IV (Fig.7) 235 60

600

Chlorophyll a fluorescence and gas exchange measurement

Chlorophyll fluorescence kinetics was measured before and after each heat stress period

on each dark-adapted leaf disc. Intact plants in the greenhouse (control) and in the growth

chambers (treatments) were measured with a plant efficiency analyzer (Hansatech

Instruments, Kings Lynn, UK) three times a day (morning, 9:00; noon, 12:00; afternoon,

17:00) for ten days after dark-adapting the leaves for 30 min using a leaf clip (Hansatech,

Instruments, Kings Lynn, UK). The maximum light intensity used was 3000 µmol m-2 s-1,

which was sufficient to generate maximal fluorescence (Fm) for all temperatures (Mathur et

al. 2011).

In the fourth (dynamic temperature) growth chamber, gas exchange was measured

using an infrared gas analyzer (CIRAS-2, PP-systems, Hitchin, UK). The Pn, gs, and

intercellular CO2 concentration (Ci) were measured at a PPFD of 800 µmol m-2 s-1 at three

CO2 levels of 400, 600 and 1000 µmol mol-1 at ambient air temperature. The quantum

yield of PSII, F'q/F'm = (F'm - F')/ F'm (Genty et al. 1989) were measured simultaneously

with a MINI-PAM (Walz, Effeltrich, Germany) at non-saturating moderate PPFD of 500

µmol m-2 s-1. A halogen lamp (Schott KL 1500, Göttingen, Germany) with mechanical light

control was used for the actinic light source. The light sources were fitted near to the leaf

clip holder so the required light level was achieved without heat transmission to the leaf. A

24

Chapter 2.1

thermo and micro quantum sensor on the leaf clip holder recorded the leaf temperature

and the incident PPFD.

Stomatal conductance measurement and infrared thermography

Stomatal conductance of plants in the greenhouse (control) and in growth chambers I,

II, and III was measured at the same time as chlorophyll a fluorescence three times a day

(morning, 9:00 noon, 12:00 afternoon, 17:00) for ten days using a steady state leaf

porometer (SC-1 porometer, Decagon, Pullman, WA, USA). The porometer was calibrated

daily and the conductance of the third or fourth youngest fully developed leaves was

measured in a measurement time of 30 s.

Thermographic images were made at the fourth day of stress on three plants per

treatments with a thermal camera (FLIR-A320 9Hz, Lens FOL18, FLIR systems, Oregon,

US). The background temperature was determined as the temperature of a crumpled sheet

of aluminium foil in a similar position to the leaves (Jones et al. 2002, Jones 2004). The

leaves were labelled as regular (average transpiring leaf), dry (non transpiring leaf) and

wet (highly transpiring leaf) leaves, respectively. The dry reference leaves were covered on

both sides with petroleum jelly (Vaseline) before the image was captured. The wet

references leaves were sprayed on both sides with water with a detergent to keep the leaves

consistently wet (Jones 1999, Jones 2004). Immediately after the images were taken, the

gs of five leaves from the same canopy and leaf position of a corresponding plant were

measured with a porometer.

The thermographic image was analyzed by ThermaCAM Researcher 2.10 software (FLIR

systems, Oregon, US) with input parameters distance set at 0.5 m, relative humidity 60%,

20 °C, 32 °C, 38 °C and 40 °C air temperature, respectively and emissivity (ε) of leaves

0.95 throughout the measurement (Jones 2004). Using the ThermaCAM image analysis

tool the temperature of normal, dry and wet leaves were measured by the line tool method,

which measures the minimum, maximum and average temperature of the leaves along a

straight line within the images. Five straight lines were made along each leaf and the

averages of the five lines were used as the temperature of the leaves. The IG was calculated

from the temperature of normal, dry and wet leaves, IG = (Tdry – Tnormal)/(Tnormal – Twet)

(Jones 1999a, Jones 1999b, Maes et al. 2011). Besides that the leaf temperature was

continuously measured in the greenhouse and in growth chambers with four

thermocouples. The thermocouples were attached to the abaxial leaf surface of the third

25


top leaf and the temperature of the leaf were measured at 5 min interval and recorded with

a data logger (DT605, CAS DataLoggers, Chillicothe OH, USA).

Data analysis

The JIP-test parameters for each experiment were calculated in the software PEA Plus

(Hansatech Instruments, Kings Lynn, UK). Analysis of the means of Fv/Fm, JIP-test

parameters and gas exchange between treatments were done with analysis of variance

(ANOVA) and linear mixed effect model of repeated measurement treating temperature as

a fixed effect. Linear regression analysis was done for gs to IG and the 95% confidence

intervals associated with prediction of gs. Goodness of fit was estimated by coefficient of

determination (R2). The significance of model terms was tested using the F-test at the P =

0.05 level of significance. The R statistical tool version 2.15.0 (www.r-project.org) was used

for the statistical analysis and graphics.

Results

Chlorophyll a fluorescence of heat stressed excised leaves and intact plants

The excised chrysanthemum leaves subjected to heat stress in a water bath at eight

different temperatures showed different degrees of change in chlorophyll a fluorescence

parameters and the fast fluorescence transient analysis (JIP-test). The Fv/Fm decreased

only slightly until the temperature reached 39 °C. At 39 °C and higher the linear mixed

effect analysis showed a significant decrease in Fv/Fm (P < 0.01) and an increase in the

minimal fluorescence (Fo) (P < 0.01) (Fig. 1A and B). At 39 °C Fv/Fm decreased by 14% and

Fo increased by 25%, compared to the control as a mean of all durations. The JIP

parameters, the conformation term for primary photochemistry (Fv/Fo) and performance

index (PI) decreased significantly (P < 0.05) when the temperature was 36 °C and higher

(Fig. 1C and D). The decrease at 36 °C was 20% and 30% for Fv/Fo and PI, respectively,

compared to the control, with larger fall at higher temperatures. The fluorescence

induction curves showing the complete fluorescence transient were plotted on a

logarithmic time scale for six temperatures (Fig. 2). The curves indicated the typical

polyphasic rise, called the OJIP until the temperature reached 39 °C. At temperatures

above 39 °C the final P step of the curve, which is equivalent to the maximum fluorescence

decreased. Moreover, at 45 °C an additional response to extremely high temperature stress

(K peak) was observed at 300 µs.

26

Chapter 2.1

Fig. 1. The fluorescence parameters Fv/Fm, Fo, Fv/Fo, and PI as a function of temperature (A, B, C and

D, respectively). The excised leaves were heated in water bath for 30, 60 and 120 min at each

temperature treatment respectively. Data at 20 °C is the control. The error bars represent the standard

error and n = 5.

When the intact chrysanthemum plants were subjected to high temperature in growth

chambers at 32 °C, 38 °C and 40 °C for ten days a slight decrease in Fv/Fm were observed,

while Fv/Fo and PI decreased to a large extent (Fig. 3C and D). The linear mixed effect

analysis showed a significant decrease in Fv/Fm, Fv/Fo and PI (P < 0.05) at 38 °C and 40 °C

after five and ten days of stress treatment, compared to the control at 20 °C. Plants

exposed to 38 °C in the growth chamber for five days decreased Fv/Fm, Fv/Fo and PI by 5%,

23% and 15%, respectively, compared to the control. Plants at 40 °C for ten days decreased

Fv/Fm, Fv/Fo and PI by 8%, 37% and 55%, respectively, compared to the control. The

minimal fluorescence increased significantly at 40 °C (P < 0.05); the increase was 15% and

34% after five and ten days of stress, respectively.

27


Time (ms)

0.01 0.1 1 10 100 1000

Flu

ore

sc

en

ce

in

ten

sit

y (

a.u

)

500

1000

1500

2000

2500

3000

3500 20 oC

30 oC

36 oC

39 oC

42 o

C

45 oC

O

J

I

P

K (300 µs)

Fig. 2. The fast chlorophyll fluorescence induction curves of excised chrysanthemum leaves incubated

at 30 °C, 36 °C, 39 °C, 42 °C and 45 °C for 60 min. The induction curves are compared with the full

fluorescence rise of the control at 20 °C.

Relation between measured stomatal conductance and linear thermal index

The stomatal conductance of intact plants subjected to high temperature decreased with

increasing treatment duration (Fig. 4). At 38 °C and 40 °C gs increased initially by 45% and

42%, respectively where after it decreased. After five days of stress treatment the gs of

stress treated plants decreased by 20% and 36% at 38 °C and 40 °C, respectively,

compared to the control. Except on day six, where the control plants showed a high gs, the

gs at 32 °C was slightly higher than control plants. The gs was significantly reduced on day

eight at 38 °C and on day four at 40 °C.

28

Chapter 2.1

Fig. 3. The fluorescence parameters Fv/Fm, Fo, Fv/Fo and PI (A, B, C and D, respectively) as a function

of air temperature. Plants were subjected to high temperature in climate chambers for five and ten days.

Data at 20 °C is the control in the greenhouse and the error bars indicate the standard error and n = 5.

Fig. 4. The stomatal conductance measured at early morning for ten days. The gs at 20 °C is the control

in the greenhouse. The error bars indicate the standard error and n = 5.

The linear correlation between gs and IG were observed for each temperature (Fig. 5).

The linear correlations of gs and IG were strong for the control and at 40 °C (R2 = 0.71 and

0.78, respectively). It indicated high gs and IG for the control whereas lower gs and IG at 40

°C. However, at 38 °C IG was relatively lower than at 20 °C but gs was high. The 95%

confidence intervals drawn below and above the regression line showed better prediction

29


of gs from IG except that at 38 °C and 32 °C few points, which fell outside the 95%

confidence limits of the fitted values.

Fig. 5. The relationship between gs and the index thermal IG derived from infrared thermographic

images of leaves at four temperatures. The solid line is the linear regression and the dashed lines are the

95% confidence intervals associated with prediction of stomatal conductance at different values of IG

and n = 15.

Net photosynthesis, operating quantum efficiency and electron transport

The net photosynthesis increased with temperature and reached different temperature

optimum at the three CO2 levels (Fig. 6A). At 400 and 600 µmol mol-1, Pn reached

maximum at 30 °C, while at 1000 µmol mol-1 the maximum was at 35 °C. After the

optimum, Pn declined when the temperature increased. The mixed effect model analysis

showed a significant difference in Pn (P < 0.05) between the CO2 levels. The Pn decreased

by 11% at 40 °C at both 400 and 600 µmol mol-1 and by 6% at 40 °C in 1000 µmol mol-1.

Irrespective of the CO2 levels gs increased with increasing temperature except at 400 µmol

mol-1, where gs reached a plateau at 35 °C (Fig. 6B). The increase in gs was only significant

(P < 0.05) from 20 °C to 30 °C.

30

Chapter 2.1

Fig. 6. The temperature response of Pn, gs, ETR at three CO2 concentrations and the error bars indicate

the standard error and n = 3. Different letters indicate statistically significant values (P < 0.05) of Pn

between the CO2 levels at different temperature (A) and gs and ETR difference between the different

temperatures (B and C).

Time (hour)

02:00 06:00 10:00 14:00 18:00

Tem

pera

ture

(o

C)

15

20

25

30

35

40

45

Fig. 7. The temperature settings in growth chamber IV. The temperature was increased over 11 h from

20 °C at 7:00 AM to 40 °C late in the day (17:00).

31


The PSII operating efficiency (F'q/F'm) and electron transport rate increased

significantly with broad temperature optimum of 30 °C – 35 °C and declined at higher

temperature (Fig. 6C). At 30 °C the F'q/F'm increased by 29% compared to F'q/F'm at 20 °C

and declined by 22% at 40 °C (data not shown). Similarly ETR increased by 21% at 30 °C

compared to ETR at 20 °C and declined by 24% at 40 °C.

Discussion

Chlorophyll a fluorescence of heat stressed excised leaves and intact plants

We have investigated the effect of high temperature on chrysanthemum plants at three

levels; the efficiency of PSII, gs and Pn. Several studies have indicated that Fv/Fm is an

excellent parameter to monitor temperature stress since it is a rapid indication of changes

in the maximum photochemical efficiency of PSII (Andrews et al. 1995, Fracheboud et al.

1999, Baker Rosenqvist 2004). In our experiments heat treatment of excised leaves and

exposure of intact chrysanthemum plants to high temperature for an extended period of

time significantly decreased Fv/Fm when the temperature exceeded 38 °C. The effect was 6-

9% greater in heat stressed excised leaves treated in dark conditions, compared to leaves of

intact plants in light. One would expect that heating the leaves of intact plants would be

milder compared to a water bath since the intact plant can cool its leaves by transpiration.

On the other hand heating the leaves in the light on intact plants increases the heat stress

effect on PSII, compared to darkness for the excised leaves in the water bath.

However, the PSII in chrysanthemum leaves has high thermo-tolerance since the

integrity and efficiency of the system was only slightly affected at temperatures below 38

°C. Previous studies have shown that high temperature stress decrease the quantum

efficiency of PSII through a decrease in the rate of primary charge separation, a reduction

in the stabilization of charge separation and the disconnection of some minor antenna

from PSII (Armond et al. 1980, Sundby et al. 1986, Havaux 1993a, Briantais et al. 1996,

Mathur et al. 2011). In both excised chrysanthemum leaves and intact plants it was

observed that the sharp decrease in Fv/Fm was accompanied by a fast rise in the minimal

fluorescence (Fo). An increase in Fo is caused by the physical separation of the PSII reaction

centres from their associated pigment antennae or light harvesting complex II resulting in

blocked energy transfer to the PSII reaction centre (Armond et al. 1980, Sundby et al.

1986, Havaux 1993b, Briantais et al. 1996, Mathur et al. 2011). In this study for

chrysanthemum the critical temperature at which we see a sharp fluorescence rise was

around 38 °C, calculated by the intersection point of the two linear parts of fluorescence

32

Chapter 2.1

rise from the fluorescence induction curve (Havaux 1993a, Lazár and Ilík 1997). The

critical temperature gives information on the relative thermo-tolerance of PSII in

chrysanthemum leaves and it was found that temperature higher than 38 °C significantly

decreased the PSII function, for instance at 39 °C Fv/Fm decreased by 14% (Fig. 1A).

The Fv/Fo, called the conformation term for primary photochemistry, which has been

interpreted as the structural alterations on the donor side of the PSII (Strasser et al. 2000,

Georgieva et al. 2000, Christen et al. 2007, Mathur et al. 2011) decreased with an increase

in temperature (Fig. 1C and 3C). In both excised leaves and intact plants Fv/Fo started to

decrease at 2-3 °C lower temperature than Fv/Fm. Fv/Fm and Fv/Fo are mathematically

correlated in a close to exponential way and Fv/Fo starts to drop before any significant

effect was seen in Fv/Fm i.e. before any effect on the maximal photochemical efficiency of

PSII.

The „vitality‟ index known as photosynthetic performance index on an equal chlorophyll

basis was the most sensitive JIP-test parameter where an effect was occasionally seen at 24

°C and 27 °C. Therefore it may be a too early warning for practical use in dynamic

greenhouse climate control. The more precise physiological meaning of the photosynthetic

performance index is also unclear (Fig. 1D and 3D). The effect of high temperature stress

was clearly reflected in the change in the OJIP curve compared to unstressed plant (Fig. 2).

The final P step decreased significantly at temperatures above 39 °C, which was reflected

in the decrease in Fv/Fm. As reported in various studies (Lazár and Ilík 1997, Srivastava et

al. 1997, Mathur et al. 2011) a high fluorescence peak called K step was observed at 45 °C

(at 300 µs). This additional K step is a specific response to high temperature stress and it is

believed to be caused by inhibition of the oxygen evolving complex (OEC) and change in

the structure of the light harvesting complex of PSII (Lazár and Ilík 1997, Srivastava et al.

1997, Mathur et al. 2011).

Relation between measured stomatal conductance and linear thermal index

Several factors affect gs (light, vapour pressure deficit (VPD), CO2 etc.) but in this

experiment the differences in gs were associated with leaf temperature and VPD. Large

variations in gs were seen in all temperature treatments and the variation of the control

plants in the greenhouse could be due to the higher probability of light and temperature

fluctuation in the greenhouse than in the growth chambers. The increase in gs at 38 °C and

40 °C (Fig. 4) on the first day of treatment could be due to high transpiration demand (as a

result of increased VPD) in order to decrease leaf temperature. Because the relative

33


humidity was identical in all treatments, the VPD was different and increased with air

temperature though we tried to avoid water stress by frequent irrigation. However, the

decreases in gs on day two and onwards at 38 °C and 40 °C could be a response to maintain

leaf water status (Fisher et al. 2006, Peak and Mott 2011). Irrespective of the CO2 levels gs

increased with increasing temperature (Fig. 6B), though studies have shown that elevated

CO2 reduces stomatal conductance (Morison 1998, Wheeler et al. 1999, Bunce 2004,

Ainsworth and Rogers 2007). However an increase in temperature might reverse the

decrease in gs with elevated CO2, which might be due to a short term regulation of leaf

temperature by increasing transpiration rate.

The stomatal conductance strongly correlated with IG for each temperature treatment

(Fig. 5). The IG was developed as an alternative approach for estimating gs using the leaf

temperature (Jones 1999a, b, Jones et al. 2002). The relation between IG and gs observed

in this experiment corresponds with previous studies (Jones 1999a, b, Jones et al. 2002,

Leinonen et al. 2006). The higher gs and IG values for the control plants indicated that the

leaves were cooler compared to the stressed plants at higher temperature; for instance at

40 °C the gs and IG were low which indicates that the plants had high leaf temperature.

Moreover, the gs and IG of plants at 38 °C also showed the leaves were warm and stressed

even though gs was high for some leaves. The over all relationship between gs and IG for all

temperatures showed the correlation was moderately consistent. The strong correlation

between gs and IG means that IG can be used in models to estimate the gs of plants non-

invasively in different temperature conditions. However, using IG to estimate gs requires

additional environmental variables, which can be easily found from greenhouse climate

data. Estimating gs from IG has at least three advantages. Firstly, there is no physical

contact with the leaves and no disturbance of stomatal function. Secondly, IG can be

measured continuously and thirdly, a large canopy area can be measured much more

rapidly than when using porometry (Jones et al. 2002, Leinonen et al. 2006).

Net photosynthesis, operating quantum efficiency and electron transport

The light saturated rate of net photosynthesis increased with temperature until the

optimum temperature was reached, depending on the CO2 level (Fig. 6A). The rate of

photosynthesis and temperature optimum was higher at elevated CO2. Several studies have

documented that elevated CO2 can shift the thermal optimum and increase the

assimilation rates in response to increased growth temperature (Berry and Björkman 1980,

Sage and Kubien 2007). At elevated CO2 the optimum temperature tended to increase by 5

34

Chapter 2.1

°C and Pn increased by 42% compared to the lower CO2 levels. It was obvious that the

higher CO2 level reduced the negative temperature effect on photosynthesis and shifted the

thermal optimum, presumably by reducing photorespiration (Sage and Kubien 2007, von

Caemmerer and Farquhar 1981, Sharkey 1985). However, regardless of CO2 level

photosynthesis gradually decreased at temperatures higher than the temperature optimum

since increases in temperature reduces photosynthetic efficiency and stimulates

photorespiration (Brooks and Farquhar 1985, Schrader et al. 2004). Moreover, the gradual

decrease in Pn at temperature higher than the optimum (in all three CO2 levels) can be

related to the decline in electron transport capacity (Fig. 6C). Above the temperature

optimum the decrease in photosynthesis could be significant under longer-term high

temperature stress. It has been documented that the decrease in photosynthesis is

pronounced with increase in temperature following decline in photosynthetic electron

transport and ribulose 1,5-bisphosphate (RuBP) regeneration capacity (Wise et al. 2004,

Sage and Kubien 2007).

Comparing the temperature effect on Pn with the fluorescence measurements it was

found that photosynthesis was affected at lower temperature than PSII (Fig. 1A, 3A and

6A). Thus the PSII was intact at temperature that affected photosynthetic enzymes.

Previous studies have indicated that chlorophyll fluorescence signals from PSII may not be

affected by temperatures that cause deactivation of Rubisco (Crafts-Brandner and Salvucci

2000a). The effect of high temperature stress on PSII was highly significant when the

temperature exceeded 38 °C. The net photosynthesis can increase up to a temperature

optimum of 35 °C by elevating the CO2 level. The extra CO2 alleviates a functional

limitation of enzymes and effect of photorespiration at high temperature as short-term

remedy. Temperature above 38 °C for an extended period of time may cause structural

damage due to the effect on the PSII complex which can be reversible or irreversible

damage.

In conclusion, physiological information from chlorophyll a fluorescence, gas exchange

and infrared thermography are useful tools for monitoring the response of chrysanthemum

plants to high temperature and predict stressful situations before damage occurs. Infrared

thermography together with information from chlorophyll a fluorescence can be used for

monitoring and early detection of temperature stress. However, the limitations in

estimating IG (e.g. use of dry and wet reference leaf) need to be addressed and improved if

infrared thermography is to be applied extensively in greenhouse production. Explanatory

models are needed to optimize real-time stress detection.

35

35 High temperature and high light stress

CHAPTER 2.2

Using the quantum yields of photosystem II and the rate of net photosynthesis to

monitor high light and temperature stress in chrysanthemum


Abstract

Under a dynamic greenhouse climate control regime, temperature is adjusted to optimise plant

physiological responses to prevailing light levels; thus, both temperature and light are used by the plant

to maximise the rate of photosynthesis, assuming other factors are not limiting. The control regime may

be optimised by monitoring plant responses, and may be promptly adjusted when plant performance is

affected by extreme microclimatic conditions, such as high light or temperature. To determine the stress

indicators of plants based on their physiological responses, net photosynthesis (Pn) and four

chlorophyll-a fluorescence parameters: maximum photochemical efficiency of PSII [Fv/Fm], electron

transport rate [ETR], PSII operating efficiency [F'q/F'm], and non-photochemical quenching [NPQ]

were assessed for potted chrysanthemum (Dendranthema grandiflora Tzvelev) „Coral Charm‟ under

different temperature (20, 24, 28, 32, 36 °C) and daily light integrals (DLI; 11, 20, 31, and 43 mol m-2

created by a PAR of 171, 311, 485 and 667 µmol m-2 s-1 for 16 h). High light (667 µmol m-2 s-1) combined

with high temperature (>32 °C) significantly (p < 0.05) decreased Fv/Fm. Under high light, the

maximum Pn and ETR were reached at 24 °C. Increased light decreased the PSII operating efficiency

and increased NPQ, while both high light and temperature had a significant effect on the PSII operating

efficiency at temperatures >28 °C. Under high light and temperature, changes in the NPQ determined

the PSII operating efficiency, with no major change in the fraction of open PSII centres (qL) (indicating

a QA redox state). We conclude that 1) chrysanthemum plants cope with excess light by non-radiative

dissipation or a reversible stress response, with the effect on the Pn and quantum yield of PSII

remaining low until the temperature reaches 28 °C and 2) the integration of online measurements to

monitor photosynthesis and PSII operating efficiency may be used to optimise dynamic greenhouse

control regimes and to detect plant stress caused by extreme microclimatic conditions.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) Using the quantum yields of photosystem II and

the rate of net photosynthesis to monitor high irradiance and temperature stress in chrysanthemum

(Dendranthema grandiflora).(submitted)

36

36 Chapter 2.2

Introduction

A dynamic greenhouse climate control regime is based on plant physiology, outside

solar irradiance and the microclimate of the crop within the greenhouse (Aaslyng et al.

1999, Aaslyng et al. 2003, Körner et al. 2007). Dynamic climate conditions facilitate

greater precision in the regulation of temperature and humidity inside the greenhouse,

thereby improving energy efficiency by reducing unnecessary heating or ventilation

(Aaslyng et al. 2003, Körner and Challa 2004, Körner and Straten 2008). The temperature

fluctuates more with solar irradiance under a dynamic control system compared to a

traditional control system. This phenomenon allows the plants to utilise both temperature

and light to maximise the rate of photosynthesis, provided CO2 is not limiting. The system

optimises carbon gain at high light, and reduces energy consumption at low light (Aaslyng

et al. 1999, Ottosen et al. 2003).

On sunny days, a dynamic greenhouse climate regime in a regular greenhouse may be

compared to a semi-closed greenhouse type, because greenhouse air temperature is high

due to a higher temperature set point and delayed screen folding, while vent opening is

minimised via a higher ventilation set point. In addition, on a sunny day, plants may

absorb more light than needed for photosynthesis (Long and Humphries 1994, Wilhelm

and Selmar 2011). With increasing greenhouse air temperature, plant tissue temperature

may increase rapidly (i.e. within seconds; Jones 1992), due to low stomatal conductance,

because stomata respond comparatively slowly (i.e. within minutes; Chamont et al. 1995).

This phenomenon may create both temporary and long-term stress reactions in the plants.

Photosynthesis has a temperature optimum, depending on the light, the growth

temperature, CO2 concentration and plant species (Berry and Björkman 1980, Björkman

1980). When the temperature rises above optimum, photosynthesis declines, at first

gradually and reversibly, but, at a certain critical temperature level, the photosynthesis

apparatus may be irreversibly damaged (Melis 1999, Takahashi and Murata 2008,

Takahashi and Badger 2011). In most plants species, the light-saturated rates of

photosynthesis decline as a direct response to extremely high temperatures, and operate at

an optimum at intermediate temperatures (Hikosaka et al. 2006).

Photoinhibition is one of the basic responses when plants are subjected to excess light,

representing the photo-inactivation of the photosynthetic apparatus (Powles 1984, Long

and Humphries 1994, Tyystjärvi 2013). Most plants have developed tolerance and/or

acclimation mechanisms to avoid excess light by different physiological mechanisms (Holt

37


et al. 2004, Horton et al. 2005, Yanhong et al. 2007). For instance, an increase in non-

radiative dissipation (NPQ: non-photochemical quenching of chlorophyll fluorescence) is a

feedback regulatory mechanism induced upon exposure to high light exceeding that which

may be used at maximum quantum yield by photosystem II (PSII) (Horton et al. 1996,

Niyogi 1999, Horton et al. 2005, Deming-Adams and Adams 2006). Previous studies have

shown that low light protects the photosynthetic apparatus from the adverse effects of high

temperature, while photoinhibition protects against both high light and high temperature

stress (Long and Humphries 1994, Murata et al. 2007, Adams et al. 2013). Moreover,

photoinhibitory and photooxidative damage to the photosynthetic apparatus represent

plant responses to high light and high temperature stress (Powles 1984, Vass 2012,

Tyystjärvi 2013).

However, to advance the dynamic climate control regime based on photosynthesis, it is

vital to understand plant responses under dynamic and potentially extreme greenhouse

microclimate conditions. Therefore, in this study, we aimed to determine the stress

indicators of plants based on their physiological responses by testing two hypotheses. First,

it was hypothesised that an optimum physiological response may be provided for the early

adjustment of a climate control system, especially when plant performance is affected by

extreme microclimate conditions, such as excess light and high temperature. Second, it

was hypothesised that integrating online measurements of physiological processes may

assist climate control decisions under a dynamic climate control regime. Both high light

and high temperature conditions were applied in a growth chamber, while both

chlorophyll fluorescence and gas exchange were continuously measured under high light

and temperature conditions in a greenhouse. The results of this study are anticipated to

contribute towards enhancing dynamic climate control regime based on photosynthesis to

maximise plant growth and, hence, the economic benefits of crop production.


Plant material

Cuttings of chrysanthemum were rooted in plastic pots (9.7 cm high, 11 cm diameter)

and filled with a commercial peat mixed containing granulated clay (Pindstrup 2,

Pindstrup A/S, Ryomgaard, Denmark) in a greenhouse at Aarhus University (Aarslev,

Denmark 55° 22' N) in three different batches: (1) spring (06/04–30/04/2012); (2)

spring/summer (30/04–16/06/2012); and (3) summer/fall (10/08–10/09/2012).

38

38 Chapter 2.2

The plants were grown on a growing bench in the greenhouse at a plant density of 40

plants per m2. The greenhouse climate data for the three batches is in Table 1. Nutrients

(macronutrients: N, 185 ppm; P, 27 ppm; K, 171 ppm; and Mg, 20 ppm; micronutrients:

Ca, Na, Cl, 18 ppm; SO4, 27 ppm; Fe, 0.9 ppm; Mn, 1.17 ppm; B, 0.25 ppm; Cu, 0.1ppm;

Zn, 0.77 ppm; and Mo, 0.05 ppm) were incorporated into the irrigation water, and

automatically supplied twice a day as ebb and flood irrigation (08:45 and 16:15). The

electrical conductivity (EC) of the irrigation water was 1.88 µS cm-1 and the pH was 5.8.

Biological controls against insects were used twice during the growing period.

Table 1. The climate set point and measured climate data in the greenhouse for each experimental

period. Climatic parameters were collected by respective climatic sensors at 10 min intervals, with the

data being recorded on a climate computer. Values are means ± SE, n = 4.

Exp. Date Set points Measured climatic parameters

Temp. (°C,

day/night)

RH (%)

VPD (kPa)

CO2 (µmol mol-1)

Temp. (°C,

day/night)

RH (%)

VPD (kPa)

CO2 (µmol mol-1)

DLI (mol m-2)

I 06/04–30/04 2012

24/18 60 0.82 600 24/21 (± 0.1)

48.0 (±0.2)

1.65 (±0.09)

587 (±4.9)

9.6

II 30/04–16/06 2012

24/24 60 0.82 600 26/26 (± 0.1)

45.8 (±0.2)

1.98 (±0.09)

461.2 (±3.8)

11.5

III 10/08–10/09 2012

20/20 60 0.82 600 24/24 (± 0.1)

50.7 (±0.2)

1.75 (±0.18)

536 (±5.7)

12.9

Temperature and light treatments

The first two experiments were conducted in a growth chamber (MB-teknik, Brøndby,

Denmark). The two experiments included two combinations of three different

temperatures (Experiment 1: 20, 24 and 28 °C; Experiment 2: 20, 32 and 36 °C; Table 2).

In each experiment, a total of 240 six week old uniformly sized plants (plant height of 0.12

± 0.01 m) were transferred from the greenhouse to three growth chambers. For the higher

temperature settings (28, 32 and 36 °C), the temperature was increased stepwise (1–2 °C

every 2 h) in the climate chamber on the first day, to avoid temperature shock.

39


Table 2. The five irradiance and temperature treatment combinations in the growth chambers.

Irradiance was measured at maximum plant height (n = 5). The VPD was set to a constant level by

varying the RH. The CO2 concentration was kept the same in all treatments.

Treatments Temperature (°C, day/night)

DLI (mol m-2)

VPD (kPa) RH (%) CO2 (µmol mol-1)

I 20/20 11 (± 1.03) 0.82 65 600 20 (± 1.22) 31 (± 2.51) 43 (± 0.72)

II 24/22 11 (± 1.03) 0.82 72 600 20 (± 1.22) 31 (± 2.51) 43 (± 0.72)

III 28/26 11 (± 1.03) 0.82 78 600 20 (± 1.22) 31 (± 2.51) 43 (± 0.72)

IV 32/30 11 (± 1.03) 0.82 83 600 20 (± 1.22) 31 (± 2.51) 43 (± 0.72)

V 36/34 11 (± 1.03) 0.82 86 600 20 (± 1.22) 31 (± 2.51) 43 (± 0.72)

In each growth chamber, four light levels were created by combining shading screens

with different transmissions (F-80 Extra, Fibertex Nonwovens A/S, Aalborg, Denmark and

P19 Utrasil, Lundhede Planteskole, Feldborg, Denmark). Three rectangular aluminium

frames (width x length x height = 0.78 x 1.13 x 0.83 m) were constructed for each chamber,

and the frames were covered with the screen material, which covered two-thirds of the

frame height from the top, to ensure that the minimum light was reflected from the side, in

addition to supplying sufficient air movement under the screens. The frames were placed

above the bench, leaving an open space for full light. The light in the open space and inside

each frame with the screens was measured at pot height (0.11 m), 0.25 m and 0.35 m high

(maximum plant height) above the bench using a quantum sensor (LI-250 light meter, LI-

COR, Lincoln, Nebraska, USA). At each level of light, 20 plants were used, with a total of

80 plants per chamber. The treatments were a factorial combination of five temperatures

and four light levels.

The light source was metal halide lamps (HQI, 400W, Osram, Munich, Germany),

operated at a 16 h/8 h light/dark photoperiod. The CO2 level in the chambers was

maintained at 600 µmol mol-1. The vapour pressure deficit (VPD) was kept constant at

40

40 Chapter 2.2

0.82 (± 0.004) kPa for each temperature using different relative humidity levels (Table 2).

Light (LI-190SA quantum sensor, Lincoln, USA), air temperature (Pt 100 DIN 43760B,

Helsinki, Finland) and air humidity (Humitter 50U, Helsinki, Finland) were recorded at 5-

min intervals with a data logger (dataTaker DT605, CAS DataLoggers, Chillicothe OH,

USA).

Chlorophyll a fluorescence measurements

Chlorophyll a fluorescence was measured before the plants were transferred to the

growth chambers and during the treatment period when it was placed alternately in each

batch for three days. The measurement was done in the morning (2 h after the light was

switched on) and in the afternoon (3 h before the light was switched off) using a plant

efficiency analyser (PEA) (Hansatech Instruments, Kings Lynn, UK) after dark-adapting

the leaves for 30 min using a leaf clip (Hansatech, Instruments, Kings Lynn, UK), and then

subsequently exposing the leaves to 3000 µmol m-2 s-1 measuring light to generate

maximal fluorescence (Fm) (Mathur et al., 2011; Strasser et al., 2000), to measure the

maximum photochemical efficiency Fv/Fm = (Fm - Fo)/Fm of dark adapted leaves. In

parallel, a MINI-PAM (Walz, Effeltrich, Germany) was used to measure the PSII operating

efficiency F'q/F'm = (F'm - F')/F'm (Baker and Rosenqvist 2004) and linear electron

transport rate (ETR), which were calculated as described by Genty et al. (1989), at the

ambient light in the treatments.

Gas exchange measurement

Net photosynthesis (Pn) and stomatal conductance (gs) were measured on three

randomly selected plants from each treatment on the third or fourth fully developed and

illuminated leaves for three subsequent days during the treatment period. The IRGA

system (CIRAS-2, PP-systems, MA, USA) was used for this measurement. The light,

temperature and relative humidity of the leaf cuvette was set according to treatment, and

recorded when Pn was at a steady state.

Long term measurements of fluorescence

Diurnal changes and acclimation (e.g. short or long term) of PSII was monitored for the

same leaf in each treatment. One measuring head (compact/robust metal tube of 3 cm

diameter and 22 cm length with a complete PAM chlorophyll fluorometer) was used per

light treatment and a total of four measuring heads were used in the Monitoring-PAM

(Walz, Eifeltrich, Germany), which were connected to a Moni-Bus (Field bus, RS485) and

41


computer controlled by the WinControl-3 software (Version 2.xx). The Moni-PAM

continuously measured the fluorescence emission from the light adapted leaf (F'), the

maximum fluorescence during saturating pulses (F'm), light and leaf temperature were

measured every 30 min, from which the PSII operating efficiency was calculated, F'q/F'm.

The mean measurements of F'm at night was used as maximal fluorescence from dark

adapted leaves (Fm), and was used to calculate the fraction of open PSII based on the lake

model for the photosynthetic unit (qL) and heat dissipation through non-photochemical

quenching (NPQ) (Kramer et al. 2004). Furthermore, in addition to the PSII operating

efficiency (F'q/F'm), which, in the nomenclature of Kramer et al. (2004), is the quantum

efficiency of PSII, ΦII, the quantum yield of the down-regulatory non-photochemical

process (ΦNPQ) and the quantum yield for other energy losses (ΦNO) were also calculated,

where ΦII + ΦNPQ + ΦNO = 1 (Kramer et al. 2004) (Table 3).

The Moni-PAM was also used to obtain long-term measurements in the greenhouse on

sunny days, where the average daily light integral exceeded 12 mol m-2 day-1. Microclimate

data, such as air temperature, leaf temperature and humidity, were measured using the

methods described in Section 2.2.

42

42 Chapter 2.2

Table 3. Chlorophyll fluorescence parameters used, descriptions of how they are used to analyse irradiance and their temperature effect on PSII, in addition

to the equations used to calculate respective parameters.

Parameter Description Formula Reference

F′o Minimum fluorescence from light adapted leaf F'o = Fo/(Fv/Fm + Fo/F'm) Baker and Rosenqvist 2004

ΦPSII/ F'q/F'm Quantum efficiency or operating efficiency of

PSII

ΦPSII = F'q/F'm = F'm - F'/ F'm Kramer et al. 2004, Baker and Rosenqvist 2004

ETR Linear electron transport ETR = F'q/F'm * 0.5 *0.84 Genty et al. 1989

NPQ Non-photochemical quenching NPQ = (Fm/F'm) - 1 Baker and Rosenqvist 2004

ΦNPQ Yield for dissipation by down-regulation ΦNPQ = 1- F'q/F'm - 1/(NPQ + 1 +

qL*(Fm/Fo - 1)

Kramer et al. 2004

ΦNO Yield of other non-photochemical losses ΦNO = 1/(NPQ + 1 + qL*(Fm/Fo -

1)

Kramer et al. 2004

qL Fraction of open PSII centres (lake model for

PSU)

qL = (F'q/F'v)*(F'o/F') Baker and Rosenqvist 2004

1

Ch

ap

ter 2.2

42

43


Data analysis

To avoid the outlier effect, a function mvoutlier (R-package, version 2.15.0) was applied

to identify any outliers. A univariate normality test was applied to test the normality, and

the Bartlett test function was used to test the equality of variance of the data. The means of

the Fv/Fm, F'q/F'm , qL, NPQ and Pn were used for analysis of variance (ANOVA). The

experimental design was a split plot, where temperature was the main factor and light was

the split factor. A nonlinear mixed effect model for repeated measurement was used to test

interactions between factors. Treatment effects were tested at the 5% probability level. R

version 2.15.0 (www.r-project.org) was used for ANOVA and regression analysis, while

SigmaPlot 11.0 (Systa software, Inc. Washington USA) was used for graphics.

Results

Maximum photochemical efficiency of PSII (Fv/Fm) and the electron transport rate (ETR)

The statistical analysis showed that light and temperature had a significant interaction

effect on Fv/Fm by the third day of the treatment (P < 0.05). At a low temperature (20 °C)

Fv/Fm was 4% lower at high light compared to low light; however, this value changed to

12% when the temperature was high (36 °C) (Fig. 1A). The decrease in Fv/Fm was highly

correlated with increased light and temperature. For instance, an increase in light from 171

to 667 µmol m-2 s-1 decreased Fv/Fm by 4 to 10% at the high temperature of 36 °C. The

effect of light was consistent during the treatment period and, on day six of the treatment,

the light and temperature showed significant interaction (P < 0.01) effect on Fv/Fm.

However, after day five, the difference in Fv/Fm between the light levels (high and low) at

each temperature setting reduced until the temperature exceeded 32 °C, while the effect of

high light and high temperature showed a significant decline of Fv/Fm at 36 °C (Fig. 1B).

Light and temperature had a significant interaction effect on the ETR on the third and

sixth day of the treatment (Fig. 1C, D). The ETR was high at 20 °C and 24 °C under high

light conditions, exhibiting the most pronounced temperature optimum at 24 °C under

high light conditions on the sixth day of the treatment. However, at lower lights, there was

no significant change in ETR with increasing temperature. At the two high light levels, ETR

overlapped at temperatures ≥ 28 °C, indicating that light saturation had already been

reached at around 485 µmol m-2 s-1 under these conditions.

44

44 Chapter 2.2

Temperature (oC)

20 25 30 35

ET

R

0

20

40

60

Temperature (oC)

20 25 30 35

Fv/F

m

0.0

0.6

0.7

0.8

0.9

1.0

667 µmol m-2 s-1

485 µmol m-2

s-1

311 µmol m-2

s-1

171 µmol m-2

s-1

A B

C D

Fig. 1. The fluorescence parameters as a function of temperature at the four light levels. The maximum

photochemical efficiency (Fv/Fm) measured from a dark-adapted leaf for 30 min on the third (A) and

sixth (B) day of the stress treatment. The electron transport rate (ETR) on the third (C) and sixth day

(D) of the stress treatment. The standard was 20 °C with a PAR of 171 µmol m-2 s-1. The error bar

represents the standard error and n = 5.

Gas exchange (Pn) and stomatal conductance (gs)

The photosynthesis measured at the ambient light showed that Pn was significantly

different (P < 0.01) for the light levels under the respective temperature treatments, with

the temperature optimum being 24 °C for all light levels (Fig. 2A). The Pn slowly declined

above the temperature optimum and the difference between the light levels decreased with

increasing temperature above the temperature optimum. There was no significant

difference in Pn among the temperatures at the low light level (171 µmol m-2 s-1). The gs

showed no significant difference for the various light and temperature combinations,

except at 24 °C, where gs was high at the two lowest lights (Fig. 2B). The same pattern was

observed for the measurement of intercellular CO2 (Ci) (Fig. 2C).

45


Pn

(µ

mo

l m

-2 s

-1)

-5

0

5

10

15

20

43 mol m-2

31 mol m-2

20 mol m-2

11 mol m-2

gs (

mm

ol

m-2

s-1

)

0

200

400

600

800

A

B

Temperature (oC)

0 20 25 30 35

Ci

(µm

ol

mo

l-1)

0

300

400

500

667 µmol m-2

s-1

485 µmol m-2

s-1

311 µmol m-2

s-1

171 µmol m-2

s-1

C

Fig. 2. Net leaf photosynthesis (A), stomatal conductance (B) and inter cellular CO2 as a function of

temperature at the four light levels on the sixth day of the stress treatment. The error bar represents the

standard error and n = 5.

PSII operating efficiency (F'q/F'm), fraction of open PSII (qL) and non-photochemical

quenching (NPQ) (long term measurements of fluorescence)

Monitoring F'q/F'm, qL and NPQ in the controlled climates based on dark-adapted Fm

values from the previous night showed that light had a significant effect (P < 0.01), in

addition to a clear interactive effect of light and temperature (Fig. 3). F'q/F'm decreased

with increasing light, with the rate of decrease being dependent on temperature. At 24 °C,

the decrease in F'q/F'm stopped at 485 µmol m-2 s-1 (Fig. 3A), whereas it continued to

46

46 Chapter 2.2

decrease to 667 µmol m-2 s-1 at 32 °C (Fig. 3B). qL primarily decreased to 485 µmol m-2 s-1,

and was generally unaffected by temperature (Fig. 3B, E). At the two lowest lights, NPQ

was not affected by temperature; however, at the two highest lights, NPQ increased with

increasing temperature, exhibiting more fluctuations through the day as light increased

(Fig. 3C, F).

Time of day (h)

0:00 4:00 8:00 12:00 16:00 20:00 0:00

F' q

/F' m

0.0

0.2

0.4

0.6

0.8

1.0

667 µmol m-2

s-1

485 µmol m-2

s-1

311 µmol m-2

s-1

171 µmol m-2

s-1

Time of day (h)

0:00 4:00 8:00 12:00 16:00 20:00 0:00

NP

Q

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

24o

C

qL

0.0

0.2

0.4

0.6

0.8

A

B

C

32o

CD

E

F

Fig. 3. The diurnal change of PSII operating efficiency, fraction of PSII centres that were open (qL) and

non-photochemical quenching (NPQ) at the four light levels and at two temperatures, 24 °C (A, B and

C) and 32 °C (D, E and F), respectively. Fluorescence parameters were measured every 30 min, and the

data was averaged using 2.5 h intervals. The error bar represents the standard error and n = 5.

47


The quantum yield of the competing pathways for de-excitation; ΦPSII, ΦNPQ and ΦNO

(long term measurements of fluorescence)

The Moni-PAM measurements were used to calculate the quantum yield of the

competing pathways for de-excitation based on the equations of Kramer et al. (2004) (Fig.

4). The quantum efficiency of photosystem II (ΦPSII, = F'q/F'm, is PSII operating efficiency

based on Baker and Rosenqvist 2004) decreased with increasing light, but was not affected

by temperature (Fig. 4A–C), except under the highest light (32 °C and 36 °C), where it

decreased (Fig. 4D). This effect was balanced by a slight increase in both ΦNPQ and ΦNO

with increasing light (Fig. 4A–C), except under the highest light (32 °C and 36 °C), where

ΦNPQ increased in a similar pattern to ΦPSII (Fig. 4D).

0.0

0.2

0.4

0.6

0.8

1.0

PSII

NPQ

PAR (µmol m-2 s

-1)

20 25 30 35

Qu

an

tum

yie

lds

0.0

0.2

0.4

0.6

0.8

PAR (µmol m-2

s-1)

20 25 30 35

PAR = 117 µmol m-2 s

-1PAR = 311 µmol m

-2 s

-1

PAR = 485 µmol m-2 s

-1 PAR = 667 µmol m-2 s

-1

A B

C D

Fig. 4. Effects of temperature on the quantum efficiency of PSII (ΦPSII), the yield for dissipation by

down-regulation (ΦNPQ) and the yield of other non-photochemical losses (ΦNO) at the four light levels.

The error bar represents the standard error and n = 5.

The continuous measurement of F'q/F'm in the greenhouse showed a direct relationship

with light and leaf temperature during the course of the day, where a significant decrease

was observed during the middle of the day with an increase in light and leaf temperature

(Fig. 5). F'q/F'm and NPQ were more dynamic with fluctuating light conditions in the

48

48 Chapter 2.2

greenhouse. The NPQ showed an increase with increasing light and leaf temperature,

which resulted in a decrease in F'q/F'm (Fig 5D), while qL never dropped below 0.5,

indicating that PSII was ≥50% open at all times (Fig. 5E).

PA

R (

µm

ol

m-2

s-1

)

0

100

200

300

400

500

F' q

/F' m

0.0

0.2

0.4

0.6

0.8

1.0

Time of day (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

Le

af

tem

pera

ture

(o

C)

15

20

25

30

35

40N

PQ

0.0

0.2

0.4

0.6

0.8

CA

B D

Time of day (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

qL

0.0

0.2

0.4

0.6

0.8

E

Fig. 5. Light on typical sunny days during August 2012 in a greenhouse (A), leaf temperature (B), the

diurnal course of PSII operating efficiency (C), non-photochemical quenching (D) and fraction of PSII

centres that are open (E). The error bar represents the standard error and n = 4.

Discussion

Maximum photochemical efficiency of PSII (Fv/Fm), electron transport rate (ETR) and gas

exchange (Pn)

The current study demonstrated that the combination of high light and high

temperature caused the photoinhibition (i.e. decrease in Fv/Fm; Adams et al. 2013) of

chrysanthemum. Specifically, the highest level of light had a significant negative effect on

Fv/Fm at high temperatures. Furthermore, as light increased, Fv/Fm decreased at each

49


temperature, with temperature having a limited effect during the first three days of the

treatment (Fig. 1A). The short term stress that caused the Fv/Fm to decrease at higher lights

was attributed to partial photoinhibition (Rosenqvist et al. 1991). The Fv/Fm slightly

increased after the third day of the treatment, except under temperature conditions

exceeding 32 °C, with Fv/Fm significantly declining under high light (Fig. 1B). This result

shows that, during the final days of the treatment, acclimation to high light might have

alleviated the effect of high light on Fv/Fm at temperatures below 32 °C. In contrast, at

temperatures above 32 °C, temperature mediated photoinhibition might have been

occurred (Štroch et al. 2010, Kornyeyev 2003). In general, the decrease in Fv/Fm occurs as

a result of the inactivation of PSII photochemistry and/or the increase in thermal energy

dissipation from PSII associated chlorophyll antennae (Adams et al. 2013). The

acclimation of photosynthesis to high light may arise due to an increase in PSII and a

concomitant decrease in light harvesting complex II (LHCII); in other words, reduced

antenna size is matched by a corresponding increase in the number of PSII units (Walters

2005).

The acclimation of Fv/Fm over time for plants under high light and low temperature

(below 28 °C) conditions possibly indicates that the PSII is protected by a mechanism that

dissipates excess energy (NPQ) to prevent the photosynthetic apparatus from becoming

damaged. Plants grown under high light often have substantially increased capacities for

∆pH-dependent protective energy dissipation (Walters 2005). However, when high light

was combined with high temperature in the current experiment, the Fv/Fm decreased

significantly. This phenomenon might be associated with the effect of high temperature on

the PSII reaction centre (Mathur et al. 2011, Janka et al. 2013). The current study indicated

that the PSII reaction centre might be damaged by temperatures above 28 °C combined

with high light. Even though it is extremely important to dissipate excess light to avoid

possible photo-damage to the PSII, the current study demonstrated that this response

would cause a major reduction in the net gain of CO2 when temperature stress was

imposed under high light conditions (Fig. 2A).

At all light levels, ETR and Pn reached an optimum at 24 °C (Fig. 1C, D, Fig. 2A),

whereas ETR noticeably changed above 28 °C under high light conditions. Hence, this

study demonstrates that ETR, gs and Ci limitation (Fig. 2B and C) do not cause a decline in

Pn with increasing temperature (Fig. 2B and C). Rather, we found that this phenomenon is

caused by to photorespiration, supporting previous studies (Osmond and Grace 1995,

50

50 Chapter 2.2

Muraoka et al. 2000). A decrease in PSII operating efficiency is always accompanied by an

increase in NPQ (Demmig-Adams and Adams 1992, Demmig-Adams and Adams 2006), as

a reversible down regulation of PSII under high light conditions (Tyystjärvi 2013). As

Chrysanthemum are able to cope with excess light at temperatures below 28 °C, we suggest

that the process involved in acclimating the photosynthetic apparatus to high light and

temperature stress might be due to changes in the efficiency of the open PSII reaction

centre and the dissipation of excess absorbed energy (NPQ) (Figueroa et al. 1997, Song et

al. 2010).

PSII operating efficiency (F'q/F'm), fraction of open PSII centres (qL) and non-

photochemical quenching (NPQ)

F'q/F'm is determined by the concentration of open PSII reaction centres and the

efficiency of excitation energy capture by the open PSII centres (Genty et al. 1989).

However, in the current study, F'q/F'm declined under high light and high temperature

conditions (Fig. 3D), because temperature stress enhances the extent of photoinhibition

(Yang et al. 2007, Murata et al. 2007). The decrease in F'q/F'm was accompanied by a

decrease in qL, which is an indicator of the QA redox state (Kramer et al. 2004). However,

as more than 50% of PSII centres were open (Fig. 3B, E) we concluded that the PSII

operating efficiency was primarily determined by changes in NPQ (Fig. 3C, F).

Furthermore, Kramer et al. (2004) showed that a large increase in NPQ induces a large

decrease in PSII operating efficiency, with little change in qL.

The quantum efficiency of PSII (ΦPSII), yield for dissipation by down-regulation (ΦNPQ)

and yield of other non-photochemical losses (ΦNO)

The exciton fraction dissipated via photochemistry (ΦPSII) and via the two competing

non-productive pathways (ΦNPQ and ΦNO) (Kramer et al. 2004), based on estimates for the

different temperature and light combinations (Fig. 4). Our data showed that high

temperature (i.e. above 28 °C) combined with high light increased the extent of PSII

photoinactivation through increased ΦNPQ and decreased ΦPSII. It has been previously

shown that PSII photoinactivation is indirectly dependent on the level of thermal energy

dissipation (Demming-Adams and Adams 1992, Niyogi 1999). Supporting previous

studies, the current study demonstrated that ΦNO was relatively stable for all temperature

and light combinations, as a result of compensatory changes in ΦPSII and ΦNPQ (Kramer et

51


al. 2004). We suggest that high light and temperature above 28 °C might limit the capacity

of NPQ to regulate light capture by Chrysanthemum. In comparison, certain stress tolerant

plant species are able to cope with high light and high temperature by an effective

regulating mechanism in energy partitioning of PSII complexes (Song et al. 2010).

Under greenhouse conditions, Chrysanthemum plants tend to respond to high light and

high leaf temperature (Fig. 5A, B) by decreasing the PSII operating efficiency and

increasing the NPQ, but with minimal change in qL (Fig. 5C–E). The increase in leaf

temperature might be associated with the possible closure of stomata at midday (Zweifel et

al. 2002). Therefore, by down-regulating the PSII operating efficiency through increasing

NPQ might cause an increase in photorespiration when CO2 is a limiting factor. Moreover,

under high light, increased capacities for NPQ and photorespiration are essential to avoid

photoinhibitory damage and to tolerate high temperature stress under excess light

(Muraoka et al. 2000).

Conclusions

A dynamic climate control regime facilitates the precise regulation of high temperature

and light conditions, under which a plant may utilise both temperature and light to

maximise the rate of photosynthesis. However, we also observed that excess light and high

temperature (>28 °C) creates temperature mediated photoinhibition and photorespiration,

which may cause temporary or long-term stress on Chrysanthemum plants. Yet, the effect

of photorespiration may be alleviated by elevating CO2 concentrations, which is a regular

practice in greenhouse cultivation. Therefore, the continuous monitoring of plant

responses, based on the quantum yields of PSII and photosynthetic rates, provides a useful

tool for predicting both short-and long-term stress resulting from extreme microclimate

conditions. In conclusion, continuous monitoring systems could be up-scaled from the

leaf-to the crop-level, with crop models being used to assist with real-time stress detection.

CHAPTER 3

Crop models and monitoring plant stress

3.1 Log-logistic model analysis of optimal and supra-optimal temperature

effect on photosystem II using chlorophyll a fluorescence in

chrysanthemum (Dendranthema grandiflora)

3.2 A coupled model of leaf photosynthesis, stomatal conductance, and leaf

energy balance for chrysanthemum (Dendranthema grandiflora)

3.3 PSII operating efficiency simulation from chlorophyll fluorescence in

response to light and temperature in chrysanthemum (Dendranthema

grandiflora) using a multilayer leaf model

55

55 Crop models and monitoring plant stress

CHAPTER 3.1

Log-logistic model analysis of optimal and supra-optimal temperature effect on

photosystem II using chlorophyll a fluorescence in chrysanthemum


Abstract

Air temperature with modern greenhouse climate control often allows more freedom for dynamic

behaviour than standard rigid greenhouse climate regimes. These types of climate control strategies

potentially result in severe microclimate conditions such as high crop temperature, which affects

photosynthesis and the major sensitive sites in the photosynthetic apparatus of photosystem II (PSII).

In two independent experiments i) excised chrysanthemum leaves were treated with optimal and supra-

optimal temperatures from 24 to 45 °C and ii) intact plants were subjected to high temperature of 32

°C/28 °C, 38 °C/32 °C and 40 °C/36 °C day/night, respectively for ten successive days. The initial

fluorescence kinetics (OJIP curve) was used to characterize high temperature effect on PSII on four

selected parameters. The log-logistic model of the dose response curve was used to model the maximum

quantum efficiency of PSII (Fv/Fm), density of active PSII reaction centers per chlorophyll (RC/ABS),

the conformation term for primary photochemistry (Fv/Fo) and performance index (PI), as temperature

was the dose function. The model estimated the upper and lower limit and the temperature dose

causing 50% reduction in Fv/Fm and Fv/Fo was 41 °C and 39 °C, respectively. The critical temperature

limit of thermo-tolerance of PSII in chrysanthemum leaves was estimated to be 38 °C. This study

suggests that physiological information combined with modelled response curves originating from

chlorophyll a fluorescence can be used as early detection of high temperature stress in chrysanthemum,

and probably in other ornamental plants in greenhouse production.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2012) Log-logistic model analysis of optimal and supra-

optimal temperature effect on photosystem II using chlrophyll a fluorescence in chrysanthemum

(Dendranthema grandiflora). Acta Horticulturae 957: 297-302.

56

56 Chapter 3.1

Introduction

With dynamic greenhouse climate control temperature is allowed to vary considerably

more than with standard climate regimes due to e.g. delayed vent opening and limited use

of screens (Aaslyng et al. 2003, Körner and Van Straten 2008). However, these climate

conditions and control strategies may result in potentially high temperature stress that

may decrease the photosynthetic efficiency of the plant.

The maximum quantum efficiency of photosystem II (PSII) defined as the ratio of

variable to maximal fluorescence (Fv/Fm) of dark-adapted leaves has been widely used as

an indication of temperature stress because it determines rapidly changes in the maximum

quantum efficiency of PSII photochemistry (Andrews et al. 1995, Fracheboud et al. 1999,

Baker et al. 2004). Moreover, analysis of fluorescence induction kinetics has been

proposed as a useful tool for early detection of temperature stress in many plant species,

and parameters have been derived from the OJIP curves (Christen et al. 2007, Mathur et

al. 2011b).

However, there is a need for more additional information to characterise the major

fluorescence parameters and the fluorescence induction kinetics if we want to apply it as a

fast and reliable tool for early detection of high temperature stress. System approach and

models are means to understand the fundamental physiological meanings of fluorescence

parameters. Adjusted models can be applied as components in greenhouse decision

support systems (DSS) and climate control systems. Most chlorophyll fluorescence models

that have been developed have a complex high ordered model structure and hence are

difficult to apply (Zhu et al. 2005). Therefore, simple and robust models are required for

early stress detection. Hence, the aim of these experiments was to identify simple models

and system approaches that use chlorophyll fluorescence for use in a DSS for early stress

detection.


Chrysanthemum cuttings were rooted in plastic pots and grown in a greenhouse at a

plant density of 40 plants m-2. The greenhouse climate was set at temperatures of 20 °C/18

°C day/night, a light level of 250 µmol m-2 s-1 photosynthetic photon flux density (PPFD)

measured at the top of the plant canopy, light period of 16h/8h light/dark, relative

humidity (RH) of 60% and a CO2 concentration of 600 µmol mol-1. When the plants were

six weeks old, a heat stress was induced either in the laboratory (experiment 1) or growth

chambers (experiment 2). In the laboratory, leaf discs (3.5 cm diameter) were cut from

57


third or fourth fully grown leaves of 15 uniform plants. The leaf discs were incubated in

eight temperatures from 24 to 45 °C with a 3 °C step increase at different durations, i.e. 30,

60 and 120 min by floating the discs in de-ionized water in thermostatic water bath in

darkness. The growth chambers temperatures were set at 32 °C, 38 °C and 40 °C. Fifteen

plants in each growth chambers were subjected to temperature stress treatments for ten

successive days.

Chlorophyll (Chl) a fluorescence measurements

Chlorophyll a fluorescence induction kinetics was measured before and after each heat

stress on the dark-adapted leaf discs. Intact plants in the growth chambers were measured

three times a day (morning, 9:00; noon, 12:00; afternoon, 17:00) for ten days after dark-

adapting the leaves for 30 min using a plant efficiency analyser (PEA; Hansatech

Instruments, Kings Lynn, UK).

Data analysis

Linear mixed effect of repeated measurement and ANOVA was used for dose-response

model fits. The significance of model terms was tested using the F-test (P < 0.05). The R

version 2.15.0 (www.r-project.org) was used.

Model theory

As dose response the log-logistic curve and the mathematical expression relating the

response y to the dose x was used (Equation 1).

The upper limit d corresponds to the mean response of the control and the lower limit c

is the mean response at every high dose. The parameter b describes the slope of the curve

around the inflection point (e), which is 50% of the dose response (Steven et al. 1995,

Stevan et al. 2007).

Results and Discussion

Fv/Fm, density of active PSII reaction centres per chlorophyll (RC/ABS), the

conformation term for primary photochemistry (Fv/Fo) and performance index (PI)

decreased with increasing temperature (Fig. 1). The linear mixed effect analysis showed

that the effect of temperature on Fv/Fm was minimal until the temperature was above 39 °C

(P = 0.01). Similarly, RC/ABS was significantly affected by temperature at and above 39 °C

58

58 Chapter 3.1

(Table 1). This indicates that the PSII in chrysanthemum leaves was intact and undamaged

until the leaf temperature increased to 39 °C and above. Studies have indicated that PSII

inhibition does not occur until leaf temperature are quite high, usually above 40 °C

(Havaux, 1993a), in agreement with our results. However, the Fv/Fo and PI decreased

significantly at 36 °C and above.

Fv/F

m

0.0

0.4

0.6

0.8

Excised leaf

Intact plant

Temperature (oC)

0 20 25 30 35 40 45

Fv/F

o

0

2

4

6

RC

/AB

S

0.0

0.4

0.8

1.2

Excised leaf

Intact plant

Temperature (oC)

0 20 25 30 35 40 45

PI

0

1

2

3

4

A B

C D

Fig.1. Fv/Fm, RC/ABS, Fv/Fo, and PI of excised leaves treated with heat in water bath for 60 min and

intact plants subjected to high temperature in climate chambers for ten days as a function of

temperature (A, B, C and D, respectively), the error bars represents the standard error (n = 5).

From the fluorescence induction curve analysis, the critical temperature was the leaf

temperature at which a sharp fluorescence rise was observed and approximated 38 °C. The

critical temperature gives information on the relative thermo-tolerance of PSII in

chrysanthemum leaves. It was found that temperatures higher than 38 °C significantly

decreased the PSII function. For instance, at 39 °C Fv/Fm decreased by 14% (Table 1).

59


Table 1. Fv/Fm, RC/ABS, Fv/Fo and PI of leaf discs treated with heat in water bath for 60 min at

different temperature treatments.

Temperature Fv/Fm RC/ABS Fv/Fo PI

20 (control) 0.87(0.00)a 0.93(0.21)a 6.41(0.42)a 3.55(0.12)a

24 0.86(0.00)a 0.83(0.02)b 6.05(0.09)ab 3.02(0.10)b

27 0.85(0.00)a 0.79(0.02)b 5.83(0.07)bc 2.64(0.08)bc

30 0.86(0.00)a 0.82(0.01)b 6.09(0.02)ab 2.92(0.09)c

33 0.84(0.00)a 0.80(0.01)b 5.40(0.04)cd 2.55(0.07)cd

36 0.84(0.00)a 0.81(0.02)b 5.13(0.11)d 2.49(0.10)d

39 0.75(0.01)b 0.62(0.03)c 3.20(0.23)e 1.27(0.13)e

42 0.47(0.04)c 0.25(0.04)d 1.12(0.19)f 0.31(0.10)f

45 0.36(0.02)d 0.11(0.01)e 0.59(0.05)g 0.02(0.01)f

Different letters indicate statistically significant values (P < 0.05) by Kruskal and Wallis-Duncan test

The effect of optimal and supra-optimal leaf temperature on Fv/Fm, RC/ABS, Fv/Fo and

PI were modeled by the dose response curve using the four parametric log-logistic

equation (Equation 1). The Fv/Fm, RC/ABS, PI and Fv/Fo data of heat treated excised leaf

discs and intact plant were fitted to the model and parameters were estimated as shown in

Fig. 2, Fig. 3 and Table 2. It was estimated that the lower limit of Fv/Fm at the temperature

of 45 °C was 0.34 (Table 2). The slope of the log-logistic curve around the inflection point

(the temperature point at which 50% of Fv/Fm decreased) was high, which showed a sharp

and fast decline in Fv/Fm at temperatures above 40 °C. The model estimated that the lower

limit of Fv/Fo at a temperature of 45 °C was 0.18, and the temperature causing a 50%

decrease in Fv/Fo was 39 °C.

60

60 Chapter 3.1

Fig. 2. The log logistic model (dose response curve) fitted on Fv/Fm, RC/ABS, Fv/Fo and PI data of

excised leaves treated with heat in water bath for 60 min (A, B, C and D, respectively). The vertical dash

line is the temperature dose at which 50% reduction as estimated by the model.

Table 2. The parameter estimates and the standard error from the log-logistic model fitted on Fv/Fm, RC/ABS, Fv/Fo and PI data.

Parameters Parameter estimates Fv/Fm RC/ABS Fv/Fo PI

b 31.61(±4.32) 27.64(±4.31) 19.95(±2.14) 17.61(±4.03)

c 0.34(±0.02) 0.07(±0.04) 0.18(±0.21) -0.25(±0.24)

d 0.86(±0.01) 0.83(±0.01) 6.04(±0.06) 3.01(±0.66)

e 40.68(0.24) 40.33(±0.29) 39.01(±0.22) 38.76(±0.44)

(b is the slope, c is lower limit, d is upper limit and e is dose giving 50% response)

The PI and RC/ABS were highly variable in both excised leaves and intact plants. The

Fv/Fm appeared to be insensitive and late to indicate early stress compared to Fv/Fo.

61


Fig. 3. The log logistic model (dose response curve) fitted on Fv/Fm, RC/ABS, Fv/Fo and PI data of

intact plants subjected to high temperature in climate chambers for ten days (A, B, C and D,

respectively). The vertical dash line is the temperature dose at which 50% reduction as estimated by the

model. The estimated temperature dose for 50% reduction of Fv/Fm, RC/ABS and PI is above the given

temperature dose.

Conclusions

The fast chlorophyll transient and some of the derived parameters can be used for early

detection of temperature stress. The temperature response of Fv/Fm, RC/ABS, PI and Fv/Fo

can be fitted on the log-logistic model, which produced a dose response curve, as

temperature was the dose function. The model estimates the upper and lower limit of the

response variable, the slope and the temperature dose that causes 50% reduction. The

model parameters explained biological relations associated with the stress factor and key

fluorescence parameters. This implies that the model can be adjusted and validated with

supplement data to be implemented as DSS component in greenhouse production.

63


CHAPTER 3.2

A coupled model of leaf photosynthesis, stomatal conductance, and leaf energy

balance for chrysanthemum (Dendranthema grandiflora)

Abstract

While dynamic greenhouse climatic regimes are often applied to achieve energy efficiency, dynamic

mechanistic models can assist in climate control decisions, and to elucidate plant stress under extreme

microclimatic conditions. The present study developed a model system with three integrated sub-models to

predict net leaf photosynthesis (Pnl), stomatal conductance (gs), and leaf temperature under different

microclimatic conditions: (1) a C3 photosynthesis biochemical model; (2) a stomatal conductance model; and

(3) a leaf energy balance model. Leaf photochemical efficiency and maximum gross photosynthesis using a

negative exponential light response curve were modelled with different leaf temperatures, light levels, and

CO2 concentrations. The stomatal conductance and leaf energy balance models were calibrated

independently. Pnl, gs, and leaf temperature model predictions were validated with independent

measurements and climate input data. Model performance was evaluated by a linear regression of predicted

values relative to observed values. The coupled model estimated Pnl with a 2-12% mean difference between

the observed and the model, and a 1.82 οC maximum leaf temperature difference between the observed and

the model. The coefficient of determination (R2) for Pnl and leaf temperature were 0.98 and 0.97,

respectively, while the gs estimate was R2 = 0.78. An additional stomatal model was implemented for

comparison, and tested against the model system. Our model showed a better fit to Pnl, leaf temperature, and

stomatal conductance validation data. The adjusted model R2 for Pnl, leaf temperature, and gs were 0.83,

0.87, and 0.58, respectively. The coupled model was therefore a good predictor for crop growth and

microclimate. We suggest the use of the model to assist in decisions optimising light, temperature, and CO2

for maximum photosynthetic rates for climatic conditions applied in the model (i.e. high light, temperature,

and CO2 concentration). Furthermore, the model leaf temperature prediction could be used for leaf

temperature monitoring under unfavourable microclimatic conditions. However, the model system needs to

be extensively validated under different climatic conditions, and potentially with different cultivars to verify

the model‟s capacity to function as a plant stress monitoring tool designed for dynamic greenhouse climate

control regimes.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) A coupled model of leaf photosynthesis, stomatal

conductance, and leaf energy balance for chrysanthemum (Dendranthema grandiflora). (to be

submitted)

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64 Chapter 3.2

Introduction

A dynamic greenhouse climate regime is energy efficient, but has the potential to result

in relatively extreme crop microclimatic conditions (Aaslyng et al. 1999, Körner et al.

2007). The effects of such extreme and potentially stressful microclimates can be

minimised by integrated crop models, which use microclimate input parameters to predict

leaf temperature, photosynthesis, and stomatal conductance, and show promise in

monitoring plant conditions, and assisting in climate control decisions (Kim and Lieth

2003, Vermeulen et al. 2012).

Leaf temperature is a function of air temperature and natural light, and is the most

important plant characteristic used to monitor plant conditions in controlled climates

(Jones 1992, Ehret et al. 2001). Leaf temperature at any point in time can be determined

by the major energy fluxes between a leaf and its surroundings (Jones 1992). The leaf

energy balance model comprises several environmental input variables (e.g. net radiation,

air temperature, wind speed, and relative humidity), and plant parameters (e.g. leaf

dimension, boundary layer, and stomatal conductance). Stomatal conductance is one of the

key plant parameters that controls leaf temperature, together with other plant-

environment variables. Furthermore, stomatal conductance plays an integral role in

regulating the balance between transpiration and net CO2 uptake in photosynthesis

(Collatz et al. 1991).

Stomatal conductance serves a dual role; first, CO2 diffusion in photosynthesis, and

second, control of transpiration. Consequently, stomatal conductance and transpiration

have been applied in a coupled approach to model photosynthesis (Collatz et al. 1991,

Harley et al. 1992, Leuning et al. 1995, Nikolov et al. 1995, Tuzet et al. 2003). In most

cases, the models were aimed at linking the photosynthesis biochemical model (Farquhar

et al. 1980) with stomatal conductance models (Ball et al. 1987) and leaf energy balance

(Stanghellini 1987, Jones 1999), with the applied objective to assist in greenhouse

environmental control decisions for growers (Kim and Lieth 2003). In addition, the

coupled model had the potential for monitoring plant responses associated with

unfavourable microclimate conditions. For example, Vermeulen et al. (2012) developed a

method using only the leaf energy balance model to monitor leaf temperature of a

glasshouse tomato crop under drought stress. In most studies, either the parameter values

in the leaf energy balance models were not calibrated (e.g. Kim and Lieth 2003, Wang et al.

65


2006), or the leaf energy balance models were not coupled with photosynthesis models

(e.g. Vermeulen et al. 2012).

As such, the coupled model of photosynthesis, stomatal conductance, and leaf energy

are not applied as frequently to crops grown in dynamic greenhouse climate control

regimes. Therefore, the objectives of this study were as follows: i) calibrate and validate a

coupled model of photosynthesis, stomatal conductance, and leaf energy balance for a

dynamic greenhouse climate regime; and ii) verify the sub-models in assisting climate

control decisions through monitoring leaf temperature, photosynthesis, and stomatal

conductance of greenhouse crops. In the study, a coupled biochemical model of

photosynthesis (Farquhar et al. 1980), stomatal conductance model (BWB model) (Ball et

al. 1987), and leaf energy balance model (Stanghellini 1987, Jones 1992) were calibrated

and validated for chrysanthemum leaves. Simulations showed our model had good

predictive power for leaf microclimatic conditions under the temperature (20-40 οC), light

(200-1000 µmol m-2 s-1), and CO2 (400-1200 µmol mol-1) ranges tested. In addition,

updated BWB model versions were implemented and compared, i.e. BWB-Leuning-Yin

model (Li et al. 2012). Results showed the BWB model system exhibited more robust

predictive power.

Model Description

Photosynthesis, stomatal conductance, and energy balance models

The three sub-models and parameter descriptions within each model are summarised in

Tables A1 and A2. The C3 photosynthesis biochemical model (Farquhar et al. 1980,

Farquhar and von Caemmerer 1982), and the Gijzen (1995) approach as applied by Körner

(2004) were followed as the photosynthesis models. Leaf photochemical efficiency (αl, mol

CO2 {mol photons}-1) and maximum gross photosynthesis rate (Pg, max, µmol m-2 s-1) fit to a

negative exponential light response curve (Spitters 1986, Gijzen 1995), and were modelled

with different leaf temperatures, light levels, and CO2 concentrations.

The BWB model (Ball et al. 1987) was calibrated and tested for chrysanthemum leaves.

Leaf temperature was calculated from the basic leaf energy balance equation modified for

greenhouse crops as a function of the total resistance to heat transfer (rH, s m-1), net

irradiance absorbed by a leaf (Rn, W m-2), and total resistance to latent heat transport (rv, s

m-1). Leaf temperature estimates were iterative procedures previously reported by Gates

(1980) and Jones (1999), and applied by Vermeulen et al. (2012). First, forced convection

was assumed; subsequently, leaf temperature, net leaf photosynthesis (Pnl), and stomatal

66

66 Chapter 3.2

conductance (gs) were estimated. Later, calculated rH for mixed convection, and gs were

used to recalculate the new leaf temperature. Finally, the new leaf temperature was used to

calculate net leaf photosynthesis and gs. Therefore, the three sub-models were

interconnected and interdependent.


Experiments

Plant material and growth conditions

Cuttings of chrysanthemum (Dendranthema grandiflora Tzvelev) „Coral Charm‟ were

rooted in plastic pots (9.7 cm high x 11 cm diameter) filled with a commercial peat mixed

with granulated clay (Pindstrup. 2, Ryomgaard, Denmark) under greenhouse conditions

(see below) at Aarhus University (Aarslev, Denmark, 55° 22' N) in two different groups, in

spring (30 April to 16 June 2012) and summer (10 August to 10 September 2012). Three

weeks after rooting, shoot tips were pinched to avoid apical dominance, and stimulate side

shoots.

Plants in each group were grown on a rolling growing bench in the greenhouse at a plant

density of 40 plants m-2. The greenhouse climate set point and measured climate data for

the three experimental groups are provided in Table 1. Nutrition (macronutrients: N 185

ppm, P 27 ppm, K 171 ppm, and Mg 20 ppm; micronutrients: Ca, Na, Cl 18 ppm, SO4 27

ppm, Fe 0.9 ppm, Mn 1.17 ppm, B 0.25 ppm, Cu 0.1ppm, Zn 0.77 ppm and Mo 0.05 ppm)

was provided mixed with irrigation water, and automatically supplied twice a day as ebb-

and flood irrigation (08:45 and 16:15). Irrigation water electrical conductivity (EC) and pH

were 1.88 µS cm-1 and 5.8, respectively. Biological controls Aphidius Mix system and

Phytoseiulus SD system (Biobest, Westerlo, Belgium) were used twice during the growing

period.

CO2 gas exchange measurement

On pinched three-week-old plants, leaf gas exchange was measured for each treatment

on randomly selected plants from the third or fourth fully developed and illuminated

leaves. The IRGA system (CIRAS-2, PP-systems, zzm MA, US) was used for the gas

exchange measurements.

The leaf light responses were measured from 9:30 to 12:00 for all treatments at different

leaf temperatures (20, 25, 28, 32, 36, and 40 °C). Light levels were programmed at 0, 50,

150, 300, 500, 700, 900, 1200, 1500, and 2000 µmol m-2 s-1 using the CIRAS-2 response

67


curve control method, which records the responses automatically. The program control

setting was adjusted to record intervals and settling times. The light source was a blue and

red light emitting diode (LED, PLC6 (U) automatic universal light unit, CIRAS-2, PP-

systems, Hitchin, UK). The light response curve measurements were initiated using a leaf

equilibrated to high light, and the light level was gradually decreased (Kim and Lieth,

2003). The light response curves were measured at three CO2 concentrations, i.e. 400,

800, and 1000 µmol mol-1. For each leaf temperature and CO2 concentration, five leaves

were randomly selected from five plants and five light response curves were generated per

treatment. A total of 90 response curves were obtained for all leaf temperature and CO2

combinations.

Table 1. Greenhouse climate set points for the two experimental groups in spring (30 April – 16 June

2012) and summer (10 August – 10 September 2012). Climate parameters were recorded with the

respective climate sensors at five minute intervals, and recorded with a climate computer.

Climate type Sensor type Greenhouse climate Set point

Exp. I Exp. 2

PAR LI-190SA Quantum sensors (Lincoln, USA)

Air temperature (°C, day/night)

24/24 20/20

Leaf temperature IRt/C.01 Exergen infrared sensor (Massachusetts, USA)

Light (DLI*, mol m-2) 11.5 12.9

Air temperature Pt 100 Air temperature sensors (Helsinki, Finland)

RH (%) 60 60

Humidity Humitter 50U (Helsinki, Finland)

VPD (kPa) 0.82 0.82

Wind speed Anemometric hot wire probe (Minnesota, USA)

CO2 (µmol mol-1) 600 600

* DLI = day light integral

Leaf CO2 responses were measured from 9:30 to 12:00 for all treatments at different

temperatures (20, 25, 28, 32, 36, and 40 °C). The CO2 concentrations were set at 0, 50,

100, 200, 300, 400, 600, 800, 1200, and 1500 µmol mol-1 using the CIRAS-2 response

curve control method. The CO2 response curves were measured at four light levels, i.e. 400,

600, 800, and 1000 µmol m-2 s-1. For each leaf temperature and light level, five leaves were

randomly selected from five plants, and a total of 120 response curves were generated for

all leaf temperature and light combinations.

For light and CO2 response measurements, vapour pressure deficit of air (VPDa) in the

leaf chamber was controlled below 1.5 kPa. For temperatures above 28 °C, and when VPDa

exceeded 1.5 kPa, VPDa was regulated by maintaining a wet cloth close to the CIRAS-2

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68 Chapter 3.2

water vapour equilibrator, and air around the measurement area was humidified using an

ionizer humidifier (EE-8064, NJ, USA).

Greenhouse microclimate data

Microclimate measurements on greenhouse experimental plants were performed using

four quantum sensors (LI-190SA, Lincoln, USA), four Exergen infrared sensors (IRt/C.01,

Massachusetts, USA), four air temperature sensors (Pt 100 DIN 43760B), four humidity

sensors (Humitter 50U, Helsinki, Finland), three thermocouples, and a wind speed sensor

(Anemometric hot wire probe with normalised output for air ducts) to measure light, leaf

temperature, air temperature, relative humidity, and wind speed, respectively. The

quantum, air temperature, and humidity sensors were placed close to the third or fourth

fully developed plant leaves. The infrared thermometer was mounted on the adaxial leaf

surface at a fixed distance of 2 - 3 cm with respect to the field-of-view [1:1 (60 °C),

approximately] (Vermeulen et al. 2012). The sensors were frequently checked and

adjusted, with plant elongation, changes in leaf position, and sensor errors taken into

consideration. The thermocouples were attached to the abaxial surface of the top third

leaves. All measurements were conducted at five-minute intervals, and recorded with a

data logger (DT605, CAS DataLoggers, Chillicothe OH, USA).

Model

Model calibration

Leaf photochemical efficiency (αl) and leaf gross photosynthesis (Pg, max) were determined

by fit to the negative-exponential response curve (Spitters 1986, Gijzen 1995) to the

measured light response at each temperature and CO2 level (Figs. 1 and 2). The potential

photochemical efficiency in the absence of oxygen (α0, mol CO2 {mol photons}-1) was

calibrated from estimated values from model-fitting, and values calculated from the CO2

molar mass (Goudriann and Van Laar 1994, Heuvelink 2005). The stomatal conductance

model parameter values were determined from the same data at different CO2

concentrations, light, temperatures, and humidity using nonlinear (weighted) least-

squares estimates of nonlinear model parameters (R version 2.15.0, www.r-project.org).

The leaf energy balance model was calibrated using parameter values related to total

resistance to heat transfer (rH, s m-1) in the leaf energy balance equation. The parameters

were estimated based on a randomised search for parameter values with increased

predictive value for measured leaf temperature data (i.e. step wise calibration of each

69


parameter). The parameter alpha (αforced) in the forced convection, which ranged between

88 and 150, the parameter alpha (αmixed) in the mixed convection, which ranged between

330 and 670, and the parameter beta (βmixed) in the mixed convection, which ranged

between 24 and 3360 (Vermeulen et al. 2012) were used to determine separate values by

several iterations.

Chrysanthemum leaf dimension was estimated as follows: leaf width was 0.036 m (w =

0.05 m ± 0.25 m), and using the formula (d = 72 x w), leaflet leaf dimension in relationship

to leaf width was calculated (Kim and Lieth 2003) assuming leaf dimension was dependent

on leaf width. In fact, leaf dimension varied with crop type, and ranged from 0.001- 0.3

(i.e. 0.001 m for narrow and 0.3 m for large leaves) (Jones 1992). Vermeulen et al. (2012)

determined mean tomato leaflet dimension was 0.07 m following Thorpe and Butler

(1977).

The combined model was written on computer programming language for mathematical

computing and simulation, MATLAB (version 7.11.0, MathWorks, Natick, MA, USA). The

nonlinear (weighted) least-squares estimates of the model parameters were completed in

the programming language R (R version 2.15.0, www.r-project.org).

Model validation

The coupled model was tested with different validation data sets. The following were

examined: light response measurements at three CO2 levels (400, 600, and 1000 µmol

mol-1); CO2 response at two light levels (400 and 1000 µmol m-2 s-1); and temperature

response at three light levels (400, 800, and 1000 µmol m-2 s-1) and three CO2 levels (400,

800, and 1200 µmol mol-1). In addition, greenhouse microclimate data at different growing

periods were used for leaf temperature prediction and model validation.

Model comparison

The BWB-model was selected due to its ease of use in practical applications, however it

has been modified several times in different studies (Leuning 1995, Yin and Struik 2009).

Consequently, the BWB-Leuning-Yin model was incorporated to compare the BWB-model

implemented in the coupled model (i.e. a modified version of the BWB-model), which was

calibrated and tested for chrysanthemum (Li et al. 2012). In the BWP-Leuning-Yin model,

the BWB model was modified by replacing hs in the BWB-model with a factor that

considers the VPDa effect on gs (Leuning 1995, Yin and Struik 2009).

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70 Chapter 3.2

Data handling and statistical methods

Measurement outlier effects were avoided by applying a function mvoutlier (R-package,

version 2.15.0). A linear regression analysis was conducted to evaluate the model

prediction performance to the observed values (Retta et al. 1991). Goodness of fit was

estimated by a coefficient of determination (R2). Model term significance was examined by

an F-test at the P < 0.05 level of significance. The R statistical tool version 2.15.0 (www.r-

project.org) was employed for statistical analyses and graphics.

Results

Model calibration

The light response curve at different temperatures and CO2 levels showed varied

responses for maximum leaf photosynthesis and leaf photochemical efficiency (Fig. 1A–D).

The maximum net leaf photosynthesis estimated by fitting the observed data to the

negative exponential light response curve ranged from 21.6 to 49.0 µmol m-2 s-1, and the

leaf photochemical efficiency ranged from 0.03 to 0.05 µmol CO2 {mol photons}-1. The

combined model closely predicted the net leaf photosynthesis and leaf photochemical

efficiency in the range of the measured data range. In all temperature and CO2

combinations, the model predicted leaf photochemical efficiency relatively well.

Leaf photochemical efficiency at different CO2 levels showed a decrease with increasing

temperature (Fig. 2). The decrease was significant (P < 0.05) at high temperature and

lower CO2 levels; however no significant differences were detected at higher CO2 levels.

Model validation

Measured leaf temperature with model leaf temperature comparisons showed the

combined model was a successful predictor of leaf temperature (Fig. 3). Leaf temperature

was directly affected by net radiation absorbed by leaves; and maximum leaf temperature

was observed around midday. However, occasionally the model overestimated midday leaf

temperature (Fig. 3B). The model also successfully predicted leaf temperature for different

successive days during the experimental period (Fig. 3C, D). The leaf temperature

difference between the measured and model prediction was ± 0.62 ° C (Fig. 3E), except

model predictions of higher night leaf temperatures were observed (Fig. 3F).

71


Fig. 1. Net leaf photosynthesis (Pnl) light responses. Symbols are calibration data, and solid lines

represent the model prediction of net photosynthesis at respective temperatures and CO2

concentrations. A) at 20 °C and 400 µmol mol-1 CO2; B) at 28 °C and 800 µmol mol-1 CO2; C) at 32 °C

and 800 µmol mol-1 CO2; and D) at 36 °C and 1000 µmol mol-1 CO2. RH was maintained at 60%.

Fig. 2. Temperature and CO2 dependence of leaf photochemical efficiency as a function of leaf

temperature at three CO2 levels.

72

72 Chapter 3.2

Moreover, the light response at 28 °C and 600 µmol mol-1 CO2 under prevailing light

conditions was congruent with the model prediction (Fig. 4A). The model also successfully

predicted the CO2 response to Pnl at 25 °C at 400 and 1000 µmol m-2 s-1 (Fig. 4B). However

at lower CO2 levels, the model prediction was higher compared to observations. The

combined model was a successful predictor of observed Pnl at different temperatures under

high light (1200 µmol m-2 s-1), and three CO2 levels (Fig. 5C, F, and I). The model also

predicted the observed Pnl at 800 µmol m-2 s-1 light at all three CO2 levels relatively well

(Fig. 5B, E, and H). However, the model predicted high Pnl values at low CO2 and low and

high light levels (Fig. 5A, D, and G).

Furthermore, the combined model performance was evaluated using a linear regression

analysis of the model prediction on observed values (Table 2). The combined model was

consistent with the Pnl and leaf temperature observations, R2 = 0.98 and 0.97, respectively.

The model also estimated gs within a moderate R2 = 0.78 range. The coupled model was

also tested with the BWB-Leuning-Yin model, and compared with the BWB-model,

however the BWB-Leuning-Yin model was not a successful predictor of the observed data

(R2 = 0.58) relative to the BWB-model (Fig. 6). Furthermore, incorporation of BWB-

Leuning-Yin into the coupled model predicted observed Pnl and leaf temperature with

respective R2 = 0.83 and 0.87 values.

73


Measured leaf temperatrue (oC)

15 20 25 30 35 40 45 50

Mo

del

leaf

tem

pera

ture

(o

C)

20

30

40

50

A

r2

= 0.97

Solar time (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

Leaf

tem

pera

ture

(o

C)

20

30

40

50

Observed

Model

B

Day of year

216 218 220 222 224

Leaf

tem

pera

ture

(o

C)

20

30

40

50

Observed

Model

C

Day of year

244 246 248 250 252 254 256 258

Leaf

tem

pera

ture

(o

C)

20

30

40

50

Observed

ModelD

Solar time (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

Tem

pera

ture

dif

fere

nce

(ob

serv

ed

-mo

del)

(oC

)

-6

-4

-2

0

2

4

6T

diff

Standard error

Solar time (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

Tem

pera

ture

dif

fere

nce

(ob

serv

ed

-mo

del)

(oC

)

-6

-4

-2

0

2

4

6T

diff

Standard error

E F

Fig. 3. Comparison of measured versus modelled leaf temperatures. Correlation of simulated and

measured leaf temperatures (A); The simulated and measured leaf temperatures for a sunny day in

August 2012, the shaded region indicates the mean standard error for measured leaf temperatures (B);

the simulated and measured leaf temperatures for six consecutive days in August, 2012 (C); September,

2012 (D); mean temperature differences between measured and modelled in August, 2012 (E)

September, 2012 (F)

74

74 Chapter 3.2

Fig. 4. Model validation of net leaf photosynthesis. Light response at 600 µmol mol-1 at 28 °C (A); and

CO2 response at 25 °C at two light levels (B). Lines represent the combined model prediction, and

symbols are validation data observations.

Model behaviour and simulation study

Pnl was simulated with the combined model as a function of temperature at different

light levels and CO2 concentrations (Fig. 7). The model simulated different optimum

temperatures depending on light levels and CO2 concentrations. The combined model

simulated a change in temperature optimum at increased CO2 and higher light levels (Fig.

7B, C). Pnl increased substantially at higher light and CO2 levels with increased

temperature until the temperature optimum was reached at approximately 32 °C.

The combined model prediction of net leaf photosynthesis from the greenhouse climate

data [temperature, light, CO2, and relative humidity (RH)] showed Pnl was reached at a

temperature maximum of 32 °C for all CO2 concentrations (Table 2). The photosynthetic

rate with increased CO2 concentration was significantly higher at 32 °C compared to other

temperatures. The simulation results indicated that Pnl at 32 °C increased by respective

29% and 38% at 700 and 1000 µmol mol-1 CO2 compared to the temperature optimum of

20 °C at 400 µmol mol-1 CO2 (Fig. 8). The model predicted a significant decrease in Pnl at

temperatures exceeding 36 °C, and the decrease was more significant at 40 °C, where Pnl

decreased by 17% and 22% at 700 and 1000 µmol mol-1 CO2, respectively. Leaf

photochemical efficiency decreased with increased temperature, but increased with a rise

in CO2 levels at each temperature (Table 2).

75


Table 2. Fitted values of Pnl (µmol m-2 s-1), and leaf photochemical efficiency (α, mol CO2 {mol photon}-1), for

the combined model simulation. Leaf temperature, light, and RH were 5 min measurement values at three

CO2 concentrations, and six air temperatures.

Parameter CO2 400 µmol mol-1

Temperature (° C) 20 24 28 32 36 40

α 0.049 0.048 0.045 0.044 0.042 0.039

Pnl 19.48 21.10 22.37 23.16 23.20 21.73

CO2 700 µmol mol-1 Temperature (° C)

20 24 28 32 36 40 α 0.056 0.054 0.053 0.052 0.050 0.049

Pnl 27.10 29.83 31.86 32.76 31.66 26.99

CO2 1000 µmol mol-1 Temperature (° C)

20 24 28 32 36 40 α 0.058 0.057 0.056 0.055 0.054 0.053

Pnl 30.70 34.20 36.79 37.75 35.70 29.09

Discussion

Model calibration and validation

Goudriaan et al. (1985) modelled the effects of temperature and CO2 concentration

on leaf photochemical efficiency and maximum gross photosynthesis. Observations and

the combined model simulation of leaf photochemical efficiency were largely consistent

with other reports (Ehleringer and Björkman 1977, Ehleringer and Pearcy 1983). In the

present study, the model showed a significant effect of temperature and CO2 on leaf

photochemical efficiency, and a linear decrease with increased temperature (Fig. 2 and

Table 3). Leaf photochemical efficiency in the model was calculated as a function of

maximum CO2 concentration, and temperature dependence on CO2 compensation

concentration (Goudriaan and Van Laar 1994). Peri et al. (2005) reported that under

field conditions cocksfoot leaves (Dactylis glomerata L.), leaf photochemical efficiency

decreased linearly for temperatures > 24 °C, and a linear function for modelling was

applied to examine leaf photochemical efficiency.

The leaf energy balance model was a successful predictor of observed leaf temperatures

(Fig. 3A-F). The model showed leaf temperature was primarily affected by net radiation

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76 Chapter 3.2

absorbed by leaf and air temperature (Fig. 3B). Furthermore, total heat transfer

parameters in the leaf energy balance model also affected estimates of leaf temperature.

Moreover, in our model we found the alpha mixed (αmixed) parameter was relatively

sensitive and estimated first, while the default value was beta mixed (βmixed) parameter

(Vermeulen et al. 2012). However, with the maximum βmixed default value, the estimated

parameter αmixed value achieved the best leaf temperature simulation, but the alpha mixed

(αmixed) parameter was lower than expected. In addition to these parameters, we also found

leaf temperature was critically influenced by input variables, primarily air temperature.

Vermeulen et al. (2012) noted input variables, including air temperature and vapour

pressure exhibited marked effects in leaf temperature simulations compared to model

parameters.

The combined model was validated with independent observations, and the model

performance was evaluated by linear regression, i.e. the model‟s predicted values were

tested against observed values (Retta et al. 1991) (Table 3). The combined model estimated

Pnl and leaf temperatures with high accuracy, however gs was moderately estimated. Kim

and Lieth (2003) criticised the absence of a mechanistic basis for using hs in the stomatal

conductance model, consequently we also tested a coupled model that incorporated the

BWB-Leuning-Yin model calibrated for chrysanthemum leaves (Li et al. 2012). However,

the BWB-Leuning-Yin model did not yield better gs estimates than the BWB model. Li et al.

(2012) reported the BWB-Leuning-Yin model was suitable for gs estimates under low light

and low CO2 conditions (i.e. PAR < 400 µmol m-2 s-1, CO2 < 300 µmol mol-1) (Li et al.

2012), which is a viable explanation for our results. Future research will focus on elaborate

sub-model implementation, and comparisons of different sub-model versions and methods

to calculate Pnl, leaf temperature, or stomatal conductance inside the coupled model. We

suggest a multi-model approach with self-selective sub-models. This, however, was not

part of the current study.

77


Pn

l (µ

mo

l m

-2 s

-1)

0

10

20

30

40Observed

Model

Pn

l (µ

mo

l m

-2 s

-1)

0

10

20

30

40

Leaf temperature (oC)

15 20 25 30 35 40

Pn

l (µ

mo

l m

-2 s

-1)

0

10

20

30

40

A

B

C


15 20 25 30 35 40


15 20 25 30 35 40

D

E

F

G

H

I

400 µmol m-2

s-1

400 µmol mol-1

400 µmol m-2

s-1

800 µmol mol-1

400 µmol m-2

s-1

1200 µmol mol-1

800 µmol m-2

s-1

400 µmol mol-1

800 µmol m-2

s-1

800 µmol mol-1

800 µmol m-2

s-1

1200 µmol mol-1

1000 µmol m-2

s-1

400 µmol mol-1

1000 µmol m-2

s-1

800 µmol mol-1

1000 µmol m-2

s-1

1200 µmol mol-1

Fig. 5. Model validation of net leaf photosynthesis. Temperature response of net leaf photosynthesis at

400 µmol mol-1 CO2 level, and three light levels (A, D, G); at 800 µmol mol-1 CO2 level, and three light

levels (B, E, H); and 1200 µmol mol-1 CO2 level, and three light levels (C, F, I). The bar represents the

standard error; n = 5.

Table 3. Model performance evaluation for linear regression of the model‟s predictive power on

observed net leaf photosynthesis (Pnl), stomatal conductance (gs), and leaf temperature (Tl) values.

Evaluation was performed on intercepts, slopes, R2, bias, and root mean square error (RMSE).

Variable Intercept Slope R2 Bias RMSE

Pnl 3.63** 0.94** 0.98 2.88 9.10

gs 78.87** 0.63** 0.78 9.28 29.35

Tl 0.93** 0.97** 0.97 0.097 1.31

Number of observations = 50 ** Significantly different from intercept = 0, and slope = 1 (P < 0.01)

78

78 Chapter 3.2

Measured gs (mmol m

-2 s

-1)

0 200 400 600 800

Esti

mate

d g

s (m

mo

l m

-2 s

-1)

0

200

400

600

800

BWB model (r2 = 0.78, rRME = 0.29)

BWB-Leuning-Yin model

r2 = 0.58, rRME = 0.43)

Fig. 6. Model comparison of measured and estimated gs using the BWB and BWB-Leuning-Yin models.

Solid line indicates a one to one relationship; n = 69.

Simulation study

Pnl simulated under the combined model as a function of temperature showed the

optimum Pnl temperature was dependent on light levels and CO2 concentrations. A rise

in CO2 levels resulted in increased light saturated leaf photosynthesis consistent with an

increase in temperature (Long 1991) by reducing photorespiration (Berry and

Björkaman 1980). However, Sage and Kubien (2007) reported declines in Pnl above the

temperature optimum at higher CO2 levels was due to limitations in electron transport

capacity (Sage and Kubien 2007). In our combined model simulation, the sharp decline

in Pnl above the temperature optimum was also associated with decrease stomatal

conductance caused by confounding VPDa effects. In fact, increased temperatures result

in decreased stomatal conductance to minimise transpiration by a simple feedback

mechanism (Peak and Mott 2011).

The Pnl light response simulated from the greenhouse microclimate data increased

with temperature and CO2. The model simulation predicted a 32 °C temperature

optimum at all CO2 levels (Table 2). The C3 photosynthesis biochemical model

(Farquhar et al. 1980) predicts that at high CO2 levels, the photorespiration rate is

reduced, increasing the temperature optimum with increasing CO2 or light levels (Yin

and Struik 2009). Our model strongly supported Farquhar et al. (1980) with increasing

light and CO2 levels (Fig. 7).

Furthermore, the decreased leaf photochemical efficiency observed with increased

temperature at all CO2 levels was not large enough to have an effect on maximum Pnl.

Congruent with our model simulations, Peri et al. (2005) reported that leaf

79


photochemical efficiency decreased by 0.001 µmol CO2 {µmol photon}-1 up to 31 °C. In

the present study, the model showed increased light, temperature, and CO2

concentration exhibited a marked influence on maximum Pnl until photosynthesis was

saturated at maximum light (Fig. 8). Greer and Weedon (2012) reported that maximum

Pnl was highly temperature dependent, and temperature over the growing season

showed a marked impact on the photosynthetic response to light.

P

nl

(µm

ol

m-2

s-1

)

0

10

20

30

40400 µmol mol-1

700 µmol mol-1

1000 µmol mol-1

Pn

l (µ

mo

l m

-2 s

-1)

0

10

20

30

40 200 µmol m-2 s-1

500 µmol m-2 s-1

1000 µmol m-2 s-1

Temperature (oC)

0 10 20 30 40

Pn

l (µ

mo

l m

-2 s

-1)

0

10

20

30

40

400 µmol mol-1

700 µmol mol-1

1000 µmol mol-1

A

B

C

Fig. 7. Simulated net leaf photosynthesis (Pnl) temperature response. Modelled at 500 µmol m-2 s-1, and

three CO2 levels (A); 1000 µmol mol-1, and three light levels (B); and 1000 µmol m-2 s-1, and three CO2

levels (C).

The dynamic greenhouse climate regime concept is to optimise light use efficiency to

achieve higher photosynthetic rates (Aaslyng et al. 1999). Therefore, our model can

serve as a valuable tool to determine optimum temperatures and CO2 levels to achieve

maximum photosynthetic rates under greenhouse growing conditions. Furthermore,

80

80 Chapter 3.2

leaf temperature predictions with high accuracy facilitate plant stress monitoring (e.g.

heat damage to leaves) in support of climate control decisions. For example, canopy

temperature is the parameter applied to control ventilation windows and shading

screens (Aaslyng et al. 2003).

Pn

l (µ

mo

l m

-2 s

-1)

0

10

20

30

40

PAR (µmol m-2 s-1)

0 500 1000 1500 2000

Pn

l (µ

mo

l m

-2 s

-1)

0

10

20

30

40

400 µmol mol-1

700 µmol mol-1

1000 µmol mol-1

A

B

Fig. 8. Simulated net leaf photosynthesis (Pnl) from greenhouse climate data. Modelled at three CO2

concentrations, 400, 700, and 100 µml mol-1, and two temperatures, 20 °C (A); and 32 °C (B) as a

function of light. The light and relative humidity were a 5 min input value in the simulation. The

negative-exponential response curve was fit to the data, and leaf photochemical efficiency (α) and

maximum leaf photosynthesis were determined.

In conclusion, the coupled model was a reliable modelling approach to predict Pnl,

leaf temperature, and gs from air temperature, light, ambient CO2, and relative

humidity greenhouse microclimate parameters. The model can be applied as a primary

decision-making tool in dynamic greenhouse climate control, and for plant stress

monitoring under extreme microclimate conditions. However, the coupled model must

be validated under dynamic climatic conditions, and potentially with different cultivars

to verify the model‟s capacity as a reliable plant stress-monitoring tool for plants under

dynamic greenhouse climate control conditions.

81


Table A1. Equations of photosynthesis, stomatal conductance, and energy balance models

82

82 Chapter 3.2

Table A2. Variables, parameters, their descriptions used in the model

Symbol Description Units value

Photosynthesis model EJ Activation energy maximum electron transport

rate kJ mol-1 37

Ec Activation energy Rubisco carboxylation kJ mol-1 59.356

Eo Activation energy Rubisco oxygenation kJ mol-1 35.948

ERd Activation energy dark respiration kJ mol-1 66.405

Evc Activation energy carboxylation rate kJ mol-1 58.520

rb,Co2 Boundary layer resistance for CO2 diffusion s m-1 136

S Constant I. for optimum curve temperature dependent maximum electron transport rate

kJ mol-1 k-1 0.71

H Constant II. For optimum curve temperature dependent maximum electron transport rate

kJ mol-1 220

Rd,25 Dark respiration at 25 oC µmol CO2 m-2 1.1

θ Degree of curvature of CO2 response of light saturated net photosynthesis

--- 0.8

R Gas constant J mol-1 k-1 8.314

α0 Leaf photochemical efficiency in absence of oxygen mol Co2 {mol photon}-1 0.065

Vc,max,25 Maximum carboxylation rate at 25 oC µmol CO2 m-2 s-1 97.875

Jmax,25 Maximum electron transport rate at 25 oC µmol m-2 s-1 210

Ko,25 Michaelis-Menten constant Rubisco oxygenation mbar 155

Kc,25 Michaelis-Menten constant Rubisco carboxylation µbar 310

rs,H2O Stomatal resistance for H2O s m-1 250

rb, H2O Boundary resistance for H2O s m-1 150

ρo2i O2 partial pressure inside stomata mbar 210

σ Scattering coefficient --- 0.15

T25 Temperature in Kelvin at 25 oC K 298.15

Vo/c Vo,max/Vc,max --- 0.21

83



Stomatal conductance model m Empirical coefficient --- 4.75

Pnl Net leaf photosynthesis µmol m-2 s-1 ---

hs Relative humidity at leaf surface --- ---

Cs CO2 partial pressure µmol mol-1 ---

b Minimal stomatal conductance at light compensation point in the BWB model

mol m-1 s-1 0.1

g0 Residual gs when PAR approaches zero in BWB-Leuning-Yin model

mol m-2 s-1 0.01

Ci Intercellular CO2 concentration µmol mol-1 ----

Ci* CO2 compensation point in the absence of Rd µmol mol-1 ----

fVPD The impact factor of VPDa on gs kPa 0.03(0.09)

k Conversion factor from [m2 s mol-1] to [s m-1] --- 0.025

Leaf energy balance model Tl Leaf temperature °C ---

Ta Air temperature °C ---

VPDa Vapour pressure deficit of the ambient air kPa ---

d Leaf dimension m 0.036

µ Wind speed m s-1 0.1

rH Total resistance to heat transfer s m-1 ---

rv Total resistance to latent heat transport s m-1 ---

rb,H2O Boundary resistance to water vapour transport s m-1 ---

γ Psychometric constant Pa K-1 67.2

cp Specific heat capacity of air J kg-1 k-1 1012

s Slop of the curve relation saturating water vapour pressure to air temperature

Pa °C-1 ---

αforced Empirical coefficient of forced convection --- 150

αfree Empirical coefficient of free convection --- 330

αmixed Empirical coefficient of mixed convection --- 1.2

βmixed Empirical coefficient of mixed convection --- 3360

85


CHAPTER 3.3

PSII operating efficiency simulation from chlorophyll fluorescence in response to

light and temperature in chrysanthemum (Dendranthema grandiflora) using a

multilayer leaf model

Abstract

Chlorophyll fluorescence is an accessible, non-invasive tool to monitor plant photosynthetic

performance. The direct relationship between PSII quantum yield and photosynthesis enables the

method to serve as a proxy photosynthesis measure under different climatic conditions. The objective of

this study was to predict PSII quantum yield using greenhouse microclimate data to monitor plant

conditions under various climates. This objective in place, the multilayer leaf model was applied to

model fluorescence emission from actinic light (F') adapted leaves, maximum fluorescence from light-

adapted (F'm) leaves, PSII operating efficiency (F'q/F'm), and electron transport rate (ETR). A linear

function was used to approximate F' from several measurements under constant and variable light

conditions. Model performance was evaluated by comparing the differences between the root mean

square error (RMSE) and mean square error (MSE) of observed and predicted values. The model

exhibited predictive success for F'q/F'm and ETR under different temperature and light conditions with

lower RMSE and MSE. However, prediction of F' and F'm was poor due to a weak relationship under

constant (R2 = 0.48) and variable (R2 = 0.35) light. We concluded that better estimates of fluorescence

parameters might improve the model. Furthermore, the model‟s simplicity facilitated implementation

with online microclimate data measurements to monitor photosynthesis using chlorophyll fluorescence.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) PSII of operating efficiency simulation from

chlorophyll fluorescence in response to light and temperature in chrysanthemum (Dendranthema

grandiflora) using a multilayer leaf model. (to be submitted)

86

86 Chapter 3.3

Introduction

Chlorophyll fluorescence is a sensitive and relatively simple non-invasive approach

applied under a range of controlled and field conditions to monitor plant photosynthetic

performance (Baker and Rosenqvist 2004). The PSII operating quantum efficiency in

leaves is linearly related with CO2 assimilation (Genty et al. 1989, Harbinson et al. 1990),

which resulted in wider applications of chlorophyll fluorescence as a plant monitoring tool

(Baker and Rosenqvist 2004). Photosynthetic rate is the product of absorbed irradiance

and quantum yield, and quantum yield depends on absorbed irradiance and

photosynthetic capacity (Evans 1995).

Vogelmann and Han (2000) resolved light absorption and carbon fixation profiles in

leaves using chlorophyll imaging to measure chlorophyll fluorescence profiles in spinach,

which is used as a valid measure of light absorption and carbon fixation (Evans and

Vogelmann 2003). A measured chlorophyll fluorescence profile was applied, and the light

absorption and CO2 fixation under a range of conditions was successfully estimated, and

tested against the prediction using a multilayer leaf model. The model was congruent with

the gas exchange data, and was largely consistent with conventional chlorophyll

fluorescence data (Vogelmann and Evans 2002, Evans and Vogelmann 2003, Evans

2009).

Evans (2009) applied the multilayer leaf model, and revealed potential errors in

calculating electron transport rates from chlorophyll fluorescence measurements.

Therefore, the model was revised and a new approximation of PSII quantum yield was

calculated, and used to generate maximum fluorescence from light adapted leaves (F'm),

and fluorescence emission from actinic light (F') adapted leaves. The revised model

assumes F' is constant and proportional to the light absorbed, which was validated by

measurment from several plant speceis (Evans 2009).

The new approximation of PSII quantum yield is simple and straightforward to simulate

under different light and temperature conditions. In our simulation model, we used a

linear function for estimating F' from measured data, and electron transport as a function

of irradiance with the temperature function of maximum electron transport (Farquhar et

al. 1980, Yin and Struik 2009) implemented within the multilayer leaf model. Our primary

objective was to compare the model simulation with the actual measurements derived

from chlorophyll fluorescence parameters. Congruence between model simulation and

empirical data can contribute substantially to our progress in combining model prediction

87


and online measurement of chlorophyll fluorescence parameters to monitor plant

conditions under different microclimatic conditions.

The models

The multilayer model (Evans and Vogelmann 2003, Evans 2009) was used to model

PSII operating quantum efficiency. The multilayer model is based on paradermal leaf

sections (leaf cross-sections), and for each section the fraction of light absorbed is defined

as:

where I is incident irradiance, α is leaf absorbance, β is the fraction of light absorbed by

PSII, and a1 is the fraction of light absorbed in layer one. However, the model assumes

uniform light intensity and constant biochemical composition through a given leaf

(Farquhar et al. 1980), consequently the model considers a single paradermal leaf layer.

The electron transport rate for layer one is calculated as a non-rectangular hyperbolic

function of irradiance (Evans 2009, Yin and Struik 2009). In the model, θ is assumed to be

0.85. The modelled rate of electron transport for the entire leaf is calculated as follows:

Maximum electron transport (Jmax,1) temperature dependency is calculated using the

biochemical model temperature function for C3 photosynthesis (Farquhar et al. 1980), and

following Gijzen (1994):

Therefore, the quantum yield of layer one is calculated as:

88

88 Chapter 3.3

The PSII operating efficiency of a leaf is derived from the fluorescence of actinic light

(F') adapted leaves, and maximum fluorescence from light adapted (F'm) leaves, using the

following equation (Genty et al. 1989, Baker and Rosenqvist 2004):

Evans (2009) used a new approximation method to calculate fluorescence of an actinic

light adapted leaf (F'), where a relationship is developed between F' and the light absorbed

by PSII. In the model, the linear relationship between F' and irradiance was provided as

follows:

where c is the slope and k is the intercept, which were estimated from the linear

relationship between irradiance absorbed by PSII and measured F'. The calculated F' is

used with the following equation to derive F'm:

The total calculated quantum yield for the leaf is the sum of F' and F'm for each layer, and

the PSII operating efficiency is derived as follows:

Following Gentry et al (1989), the leaf ETR is calculated from the fluorescence

measurement:

The calculated ETR from the calculated leaf quantum yield is calculated as:

89



Plant material

Chrysanthemum (Dendranthema grandiflora) cuttings were rooted in plastic pots (9.7

cm high x 11 cm diameter) filled with a commercial peat mixed with granulated clay

(Pindstrup 2, Pindstrup A/S, Ryomgaard, Denmark) at Aarhus University (Aarslev,

Denmark 55°22' N). Plants were grown in a growth chamber (MB-teknik, Brøndby,

Denmark) during spring/summer (6 April 2012 to 16 June 2012), and in a greenhouse

during summer/fall (10 August 2012 to 10 September 2012), and used for continuous

fluorescence measurements for calibration and validation of the model.

Plants were grown in the growth chamber under four temperature regimes (20, 24, 28,

32, 36 °C), and four constant irradiance levels (117, 311, 485, 667 µmol m-2 s-1). Plants were

grown at a density of 40 plants m2, and the air temperature set-point was 20/20 °C.

Supplemental nutrition (macronutrients: N 185 ppm, P 27 ppm, K 171 ppm and Mg 20

ppm; micronutrients: Ca, Na, Cl 18 ppm, SO4 27 ppm, Fe 0.9 ppm, Mn 1.17 ppm, B 0.25

ppm, Cu 0.1ppm, Zn 0.77 ppm and Mo 0.05 ppm) was provided mixed with irrigation

water, and automatically supplied twice a day as ebb-and-flood irrigation (08:45 and

16:15). Irrigation water electrical conductivity (EC) and pH were 1.88 µS cm-1 and 5.8,

respectively. Biological controls against insects were used twice during the growing period.

Chlorophyll measurements

Chlorophyll fluorescence was measured continuously for three days in the growth

chamber, and five consecutive days in the greenhouse using four Monitoring-PAM (Walz,

Eifeltrich, Germany) measuring heads. The Moni-PAM heads were connected using the

Moni-Bus (Field bus, RS485) to a computer controlled by software (WinControl-3, Version

2.xx). The Moni-PAM measured fluorescence of actinic light (F') adapted leaves ,

maximum fluorescence from light adapted (F'm) leaves , PSII operating efficiency (F'q/F'm),

electron transport rate (ETR), PAR, and leaf temperature. The saturating pulse for

fluorescence measurements was recorded every 30 min to avoid potential photoinhibition.

Statistical analysis

The multilayer leaf model was written on computer programming language for

mathematical computing and simulation, MATLAB (version 7.11.0, MathWorks, Natick,

MA, USA). The nonlinear (weighted) least-squares estimates of the model parameters were

completed in the programming language R (R version 2.15.0, www.r-project.org). Model

90

90 Chapter 3.3

performance was evaluated by linear regression to test the model prediction against the

observed values (Retta et al. 1991). SigmaPlot 11.0 (Systa software, Inc. Washington USA)

was employed to generate graphics. Model performance was evaluated by comparing the

differences between the root mean square error (RMSE) and mean square error (MSE) of

observed and predicted values.

Results

Regression analyses were performed between several fluorescence measurements,

including actinic light (F') adapted leaves and maximal fluorescence (F'm), and light

adapted leaves under constant (i.e. growth chamber) and variable (i.e. greenhouse) light

conditions. A weak and positive relationship between F' and irradiance in both uniform

and variable irradiance conditions were detected (R2 = 0.48 and 0.35, respectively) (Fig. 1A

and B). The slope of the linear relationship showed the rate of increase in F' with increased

irradiance was higher under variable than uniform irradiance conditions. The F'm

decreased under increased irradiance in both uniform and variable light conditions (Fig.

1C and D). However, the decrease in F'm was more rapid under constant irradiance than

variable irradiance conditions.

F'q/F'm, ETR, F', and F'm were predicted at different temperatures using the model, and

compared with measured data. The model successfully predicted F'q/F'm and ETR (Fig. 2A

and B) with lower RMSE between predicted and observed. However, the model was

moderate in predicting F' and F'm with a relatively high residual MSE between predicted

and observed values (Fig. 2C, D, and Table 1). At 28 °C, the model more was successful in

predicting F'q/F'm than F'q/F'm at 24 °C, and ETR (Fig. 3A, B and Table 1). At 28 °C, the

model showed increased accuracy at predicting F'm than F'm at 24 °C, with lower RMSE

(Fig. 3C and D).

91


F'

0

200

400

600

800

PAR (µmol m-2 s-1)

100 200 300 400 500

F' m

0

500

1000

1500

2000

2500

PAR (µmol m-2 s-1)

0 100 200 300 400 500 600

y = 343 + 0.35 * xy = 447 + 0.51 * x

R2 = 0.48

R2 = 0.35

y = 2139 - 2.77 * x + 0.003 * x2

y = 1969 - 5.89*x + 0.008 * x2

R2 = 0.28

R2 = 0.59

A B

C D

Fig.1. Fluorescence emissions from actinic light (F') adapted leaves measured in a growth chamber (A),

and greenhouse (B), and maximum fluorescence (F'm) from growth chamber light adapted leaves (C)

and greenhouse (D) as a function of light. The growth temperature was 20 °C in the growth chamber,

and 22 °C (± 1.31) in the greenhouse, and CO2 was 600 µmol mol-1. Fluorescence was measured

continuously every 30 min using a monitoring PAM. A linear function was fitted for the relationship

between F' and PAR, and a second order polynomial function was fitted for the relationship between F'm

and PAR.

Table 1. Model performance evaluation of observed and predicted values of each variable using the

Mean Bias Error (MBE) and the Root Mean Square Error (RMSE) at five temperatures.

variable Temperature (° C )

20 24 28 32 36 MBE RMSE MBE RMSE MBE RMSE MBE RMSE MBE RMSE

F' 36.03 71.21 -8.28 66.98 19.57 67.85 -58.12 108.96

-83.52 124.56

F'm -79.59 358.06 -50.87 236.05 -44.86 219.46 -88.44 551.74 -171.39 396.36

F'q/F'm -0.04 0.06 0.00 0.06 -0.03 0.05 0.07 0.11 0.04 0.05

ETR -3.83 7.65 -0.43 6.25 -1.82 6.73 12.01 18.29 6.07 9.37

The model behaviour was investigated by simulating three different temperature

regimes (Fig. 4). The simulation showed F'q/F'm, ETR, and F'm were different among

temperatures, but not F'. F'q/F'm ranged between 0.72-0.18, which decreased with

92

92 Chapter 3.3

increased light, and the minimum was 20 °C with irradiance of 1500 µmol m-2 s-1 (Fig. 4A).

The model simulated high ETR at 28 °C and lower at 20 °C. F' increased with light ranging

between a minimum of 410 and maximum of 668; no change was observed with

temperature. However, F'm showed temperature differences; the prediction range was 1398

at the lower irradiance and all temperatures, and 794 at the higher irradiance and 20 °C

(Fig. 4D). In the simulation, F'm showed a maximum difference among temperatures

within the 200-800 µmol m-2 s-1 light range.

F' q

/F' m

0.0

0.2

0.4

0.6

0.8

1.0

Observed

Model

PAR (µmol m-2 s-1)

0 200 400 600 800

ET

R

0

50

100

150

F'

0

200

400

600

800 Observed

Model

PAR (µmol m-2 s-1)

0 200 400 600 800

F' m

0

500

1000

1500

2000

2500

A C

B D

Fig. 2. Measured and predicted PSII operating efficiency (A), electron transport rate (ETR) (B),

fluorescence emissions from actinic light (F') adapted leaves (C) and maximum fluorescence (F'm) (D) as

a function of light. Growing temperature was 24 °C, and CO2 concentration was 600 µmol mol-1.

Fluorescence was measured continuously every 30 min using a monitoring PAM. Lines represent the

model predictions, and symbols are observational data.

F'q/F'm, ETR, F', and F'm simulations were performed using irradiance and temperature

as input variables over the course of each day (Figs. 5 and 6). The F'q/F'm model simulation

was accurate relative to data observations throughout day three treatments (Fig. 5A), and

fair on day six treatments (Fig. 6A), however simulations were poor following observations

for early morning and late afternoon. ETR was successfully simulated for both treatment

days following the daily course of variation in light. However, the model simulated F' and

93


F'm with less accuracy on both treatment days (Fig. 5C and D, Fig. 6C and D). The

simulated F' was nearly constant on treatment day three compared to observational data

(Fig. 5C), with little variation over the course of the day, while marked variation was

detected in F'. Similarly, simulated F'm was not congruent with observations on treatment

day three, and notable variation between simulated and observational data was found for

early morning and late afternoon (Fig. 5D). F' and F'm model simulations were more

accurate compared to measurement data on treatment day six, particularly near midday

(Fig 6C and D), however greater variation between simulated and measured data was

observed in early morning and late the afternoon for both fluorescence parameters.

F' q

/F' m

0.0

0.2

0.4

0.6

0.8

1.0

Observed

Model

PAR (µmol m-2 s-1)

0 200 400 600 800

ET

R

0

50

100

150

F'

0

200

400

600

800 Observed

Model

PAR (µmol m-2 s-1)

0 200 400 600 800

F' m

0

500

1000

1500

2000

2500

A C

B D

Fig. 3 Measured and predicted PSII operating efficiency (A), electron transport rate (ETR) (B),

fluorescence emissions from actinic light (F') adapted leaves (C) and maximum fluorescence (F'm) as a

function of light. Growing temperature was 28 °C, and CO2 concentration was 600 µmol mol-1.

Fluorescence was measured continuously every 30 min using a monitoring PAM. Lines represent the

model prediction and symbols are observational data.

94

94 Chapter 3.3

F' q

/F' m

0.0

0.2

0.4

0.6

0.8

1.0

20oC

24oC

28oC

PAR (µmol m-2

s-1)

0 200 400 600 800 1000 1200 1400 1600

ET

R

0

50

100

150

F'

0

200

400

600

800

PAR (µmol m-2

s-1)

0 200 400 600 800 1000 1200 1400 1600

F' m

0

500

1000

1500

2000

250020

oC

24oC

28oC

A

B

C

D

Fig. 4. Simulated PSII operating efficiency (A), electron transport rate (ETR) (B), fluorescence

emission from actinic light (F') adapted leaves (C), and maximum fluorescence (F'm) (D) as a function of

light at three temperatures.

95


F' q

/F' m

0.0

0.2

0.4

0.6

0.8

1.0

Observed

Model

Time of day (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

ET

R

0

50

100

150

F'

0

200

400

600

800Observed

Model

Time of day (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

F' m

0

500

1000

1500

2000

2500

A

B

C

D

Fig. 5. Simulated PSII operating efficiency (A), electron transport rate (ETR) (B), fluorescence

emissions from actinic light (F') adapted leaves (C), and maximum fluorescence (F'm) from light

adapted leaves (D) on observation day three. The irradiance and temperature were 30 min input values

during the simulation. The growth temperature was 20 °C in a growth chamber, 22 °C (± 1.31) in a

greenhouse, and CO2 was 600 µmol mol-1. Fluorescence was measured continuously every 30 min using

a monitoring PAM.

Discussion

The positive linear increase in F' (Fig. 1A and B) and decrease in F'm (Fig. 1C and D),

with increased irradiance is a common fluorescence trend associated with PSII reaction

centres. Maxwell and Johnson (2000) showed during the first irradiance illumination, an

increase chlorophyll fluorescence yield was observed, similar to F', however the F'm

generated by the saturating irradiance pulse decreased with increased irradiance due to

fluorescence quenching, also known as non-photochemical quenching processes (Maxwell

and Johnson 2000, Baker and Rosenqvist 2004). In the present study, greenhouse

irradiance notably fluctuated F' and F'm values, which were more variable relative to

uniform irradiance conditions in the growth chamber (Fig. 1). Consequently, the irradiance

relationship with F' and F'm was weak compared with the growth chamber.

96

96 Chapter 3.3

The model predicted F'q/F'm relatively well at 24 °C (Fig. 2A), and 28 °C (Fig. 3A), however

over F' or under F'm estimates affected an accurate prediction of F'q/F'm. An opposite

relationship with F' and F'm, in addition to a low R2 value with irradiance (Fig. 1)

F' q

/F' m

0.0

0.2

0.4

0.6

0.8

1.0

Observed

Model

Time of day (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

ET

R

0

50

100

150F

'

0

200

400

600

800 Observed

Model

Time of day (h)

00:00 04:00 08:00 12:00 16:00 20:00 00:00

F' m

0

500

1000

1500

2000

2500

A

B

C

D

Fig. 6 Simulated PSII operating efficiency (A), electron transport rate (ETR) (B), fluorescence

emissions from actinic light (F') adapted leaves (C), and maximum fluorescence (F'm) from light (D)

adapted leaves on day six of the observations. The irradiance and temperature were 30 min input values

during the simulation. The CO2 and humidity were 600 µmol mol-1 and 60%, respectively. Fluorescence

was measured continuously every 30 min using a monitoring PAM.

might also affect an accurate prediction of the individual parameters, and F'q/F'm. Evans

(2009) suggested F' was a function of irradiance, however we used a positive linear

relationship derived from F' and irradiance from measured empirical data to calculate F'

and F'm. Model performance was evaluated by comparing observed with predicted data,

rather than using the coefficient of determination (R2); we assessed the performance of the

model using RMSE and MSE. R2 was not appropriate to compare the observed and

predicted values of our model. Previous studies have shown (Willmott 1982, Retta et al.

1991) R2 and significance tests in general are often inappropriate or misleading when

applied to compare model predicted and observed variables. RMSE and MSE indicated the

model was weak in predicting F'm with large RMSE and more negative MSE (Table 1).

97


Similarly, F' comparisons between observed and modelled results were not adequate, with

large RMSE and negative MSE, particularly at 32 and 36 °C.

However, provided the F' and F'm predictions were not robust, the model predicted

F'q/F'm and ETR reasonably well. RMSE and MSE for observed and predicted values

showed F'q/F'm and ETR were predicted reasonably well for all temperatures with the

exception of 32 °C (Table 1). Moreover, the F'q/F'm simulation under different temperature

regimes showed a sharp decline with increased irradiance, which was directly associated

with ETR under different temperatures (Fig. 4A and B). Genty et al. (1989) reported ETR

was a function of F'q/F'm and irradiance. In our simulation, F'm exhibited a temperature

response related with F'q/F'm and ETR. F'm showed increased differences among

temperatures within a 300 to 800 µmol m-2 s-1 light range, and the difference between

temperatures was minimised with increased light afterwards (Fig. 4D). This might be

explained by ETR reaching an optimum, and stabilising following 800 µmol m-2 s-1 for

nearly all temperatures.

The F'q/F'm diurnal course simulation exhibited more reliable predictions near midday

(Fig. 5A). ETR is directly related to irradiance, and therefore showed enhanced simulations

throughout the day (Genty et al. 1989). However, F' and F'm model simulations were lower

than observations in most cases. Based on our analysis, the model inaccuracy in predicting

F' and F'm might result from the weaker light relationship on the upper and lower leaf

surfaces, but results differed among plant species. However, in our model little variation in

F' with increased light, but large differences in daytime observations were observed.

Results detected a strong relationship between F'm and light, therefore the simulation

showed a more consistent trend between the observations and decreased light during

midday (Fig. 5D). Evans (2009) reported the F'm response to irradiance differed among

species, as well as on the upper and lower leaf surfaces.

In conclusion, enhanced F' and F'm approximation and prediction facilitated PSII

operating efficiency predictions under different microclimate conditions. The

approximation was simple, but required accurate fluorescence parameter estimations,

which considered all factors that affected the parameters, rather than applying a simple

linear equation to estimate F'. However, results indicated the approximation of

fluorescence parameters can be much improved by testing different plant species. In doing

so, the prediction capacity of the model will be strengthened; and the model‟s simplicity

enables it to be implemented with online microclimate measurement data to monitor

chlorophyll fluorescence and photosynthesis under extreme microclimatic conditions.

98

98 Chapter 3.3

Table A1. Variables, parameters, and descriptions applied in the model.


EJ Activation energy maximum electron transport rate

kJ mol-1 37

S Constant I. for optimum curve temperature dependent maximum electron transport rate

kJ mol-1 k-1 0.71

H Constant II. for optimum curve temperature dependent maximum electron transport rate

kJ mol-1 220

θ Convexity factor for response of J to irradiance --- 0.85

R Gas constant J mol-1 k-1 8.314

Jmax,25 Maximum electron transport rate at 25oC µmol m-2 s-1 210

J Electron transport rate of a leaf µmol m-2 s-1

T25 Temperature in Kelvin at 25oC K 298.15

c Slope --- 0.43

k Constant --- 395

β Proportion of light absorbed by PSII --- 0.5

α Fraction of incident light absorbed by a leaf --- 0.84

a1 Fraction light absorbed in layer one --- 1

F'q/F'm PSII operating efficiency ---

F'q/F'm,1 PSII operating efficiency calculated ---

I Incident light µmol m-2 s-1

F' Fluorescence emission from leaf adapted to actinic light

---

F'm Maximal fluorescence from light-adapted leaf ---

CHAPTER 4

General discussion and Conclusion

4.1 General discussion

4.2 Conclusion

4.3 Contribution of the thesis

4.4 Possibilities of future research

101

101 General discussion

CHAPTER 4.1

General discussion

High temperature effect on PSII and photosynthesis (chlorophyll fluorescence

measurements)

Photosynthesis has long been recognized as one of the most high temperature

sensitive processes in plants (Berry and Björkman 1980, Sharkey and Schrader 2008,

Zhang and Sharkey 2009). High temperature affects the PSII photosynthetic apparatus,

and consequently net photosynthesis (Havaux 1993a). Because PSII is a multi-subunit

complex comprised of several different types of chlorophyll binding components, it is

one of the major high temperature-sensitive sites in the photosynthetic apparatus

(Allakhverdiev et al. 2008, Mathur et al. 2011b). Among partial PSII reactions, the

oxygen-evolving complex (OEC) shows particularly high temperature sensitivity

(Georgieva et al. 2000, Mathur et al. 2011a). High temperature can also induce

dissociation of the manganese-stabilizing 33 kDa protein from the PSII reaction centre

complex, followed by a release of manganese atoms (Enami et al. 1994, Yamane et al.

1998, Mathur et al. 2011a). It is reported that high temperature PSII inactivation might

be accompanied by the aggregation and subsequent dissociation of the light harvesting

complex II (LHCII) (Li et al. 2009, Mathur et al. 2011a).

Chlorophyll fluorescence is a non-destructive intrinsic probe of photosynthesis

widely used to investigate the inactivation of PSII and photosynthetic performance as a

result of high temperature (Baker and Rosenqvist 2004, Mathur et al. 2011a).

Therefore, one of the objectives of this thesis was to use chlorophyll fluorescence

methods to investigate the effects of high temperature on the PSII photosynthetic

apparatus in chrysanthemum (Chapter 2.1). High temperature was imposed on both

excised and intact chrysanthemum leaves. Among the fluorescence measurements,

Fv/Fm was used as one parameter to analyse the damage to PSII resulting from high

temperature. Results showed heat treatment to excised leaves, and exposure of intact

chrysanthemum plants under high temperatures for an extended time period

significantly decreased Fv/Fm when the temperature exceeded 38 °C. Heat stressed

excised leaves under dark conditions showed 6-9% more decrease in Fv/Fm compared

to intact plant leaves under light conditions. Transpiration served to cool intact plant

102

102 Chapter 4.1

leaves; however the heat stress effect on PSII was increased due to light compared to

dark conditions on excised leaves.

In addition, results showed PSII in chrysanthemum leaves exhibited high thermo-

tolerance, since Fv/Fm was slightly affected at temperatures below 38 °C. Consistent

with these results, previous studies reported PSII inhibition did not occur until leaf

temperatures were quite high, typically 40 °C and above (Havaux 1993a, b, Al-Khatib

and Paulsen 1999, Mathur et al. 2011b). Lu and Zhang (2000) indicated two distinct

temperature domains characterised PSII heat stress: moderately elevated temperatures

(30-38 °C), and severely elevated temperatures (> 38 °C), and it is only severely

elevated temperatures that affect the maximum PSII photochemistry efficiency (Lu and

Zhang 2000). In the experiment (Chapter 3.1) that examined the temperature dose

causing a 50% reduction in Fv/Fm using the temperature dose function model, results

showed a 50% reduction in Fv/Fm when the temperature (T50) was 41 °C. Likewise, Law

and Crafts-Brandner (1999) manually determined T50 values from plotted data, and

found respective 42.5 °C and 45 °C for wheat and cotton.

Decreased Fv/Fm was due to reduced excitation energy capture by open PSII reaction

centres, and damage to the OEC and acceptor side of PSII (Lu and Zhang 2000,

Allakhverdiev et al. 2008, Mathur et al. 2011a). Moreover, decreased Fv/Fm was

accompanied by a rapid rise in the minimal fluorescence (Fo) (Chapter 2.1), which was

associated with physical separation of the PSII reaction centres from LHCII (Briantais

et al. 1996, Mathur et al. 2011a). The rise in Fo is typically used to determine plant

critical limits to high temperatures (Havaux et al. 1988, Willits and Peet 2001). In this

study, a sharp rise in fluorescence was observed at a critical temperature of

approximately 38 °C, calculated by the intersection point of two linear components

from a rise in fluorescence derived from the fluorescence induction curve (Havaux

1993a, Lazár et al. 1997). However, some studies reported Fo increased only slightly

with temperatures below 40 °C (Schreiber and Bilger 1987, Schreiber et al. 1994,

Yamane et al. 1997).

Alternatively, the shape of the fast rise of chlorophyll a fluorescence transient, which

reveals the steps O-J-I-P (OJIP curve) has been used to probe photosynthesis under

high temperature stress (Srivastava et al. 1997, Strasser et al. 2000, Mathur et al.

2011b). To create the OJIP curve (Chapter 2.1), saturating light was used to illuminate a

dark-adapted leaf, and the shape of the curve depicts the plant physiological state

(Strasser et al. 2000). The O-step reflects the minimum fluorescence when the primary

103


quinine electron acceptor (QA) is oxidized. The P-step corresponds to the QA reduction

state. The rise from step O to step J reflects QA reduction, and is associated with

primary photochemical reactions of PSII. Intermediate step I, and the final step P

reflect the fast and slow reducing plastoquinone (PQ) centres, in addition to the

different redox states of the reaction centre (RC) complex (Strasser et al. 1995,

Srivastava et al. 1997, Chen and Cheng 2009, Guo and Tan 2011). Congruent with other

studies (Srivastava et al. 1997, Mathur et al. 2011a), we also found a high fluorescence

peak termed the K step at 45 °C (at 300 ms) (Chapter 2.1). This additional K step is a

specific response to high temperature stress, and is believed to be the result of OEC

inhibition, and change in the light harvesting complex structure of PSII (Lazár et al.

1997, Srivastava et al. 1997, Mathur et al. 2011a). OEC damage makes its electron

donation to the PSII reaction centre limiting to total electron transport (Srivastava et

al. 1997, Strasser et al. 2000, Chen et al. 2008).

Based on OJIP fluorescence transient analysis, a test was developed, and called the

JIP-test following the transient steps. The JIP test has several parameters extracted

from the fast chlorophyll a fluorescence transient based on the PSII energy flux model

(Strasser et al. 2000). These JIP test parameters have been applied to one fluorescence

parameter, which is often the case for Fv/Fm (Strasser et al. 2000, Force et al. 2003). In

the present study, two of the JIP-test parameters, including Fv/Fo and PI were

examined (Chapter 2.1). Fv/Fo, called the conformation term for primary

photochemistry, started to decrease at a 2-3 °C lower temperature than Fv/Fm (Chapter

2.1), whereas PI, which is the product of an antenna, reaction centre, and electron

transport dependent parameter (Strasser et al. 2000, Oukarroum et al. 2007, Stirbet

and Govindjee 2011) was highly temperature sensitive. Fv/Fo and PI have been used as

early stress indicators by several studies (Force 2002, Christen et al. 2007, Kalaji et al.

2012). These parameters are relatively sensitive indicators for stress compared to

Fv/Fm; therefore some studies have reported Fv/Fm is less suitable for early detection of

certain stressors under specific conditions (Law and Crafts-Brandner 1999, Christen et

al. 2007, Li et al. 2009, Kalaji et al. 2012).

High temperature and high light effects on PSII and photosynthesis (chlorophyll

fluorescence measurements)

Air temperatures in greenhouses vary considerably in relationship to natural

irradiance. For example, sunny days might result in high temperature and high light

104

104 Chapter 4.1

conditions for greenhouse crops. Hence, to understand the early responses of the

photosynthetic apparatus under the effects of natural environmental conditions,

additional chlorophyll florescence parameters were used in this study, while plants

were exposed to different light and temperature combinations (Chapter 2.2). The study

showed the effects of high temperature on Fv/Fm were more severe when combined

with high light, i.e. Fv/Fm significantly decreased under high temperature (exceeding 32

°C) and high light conditions. Results showed Fv/Fm was impacted at lower

temperatures under high light relative to the effects on Fv/Fm at high temperature and

low light (Chapter 2.1). Similarly, Georgieva et al. (2000) reported that under low light,

PSII displayed high thermo-stability due to the absence of excess light. Chen et al.

(2008) showed high light combined with high temperature damage effected the

acceptor side (i.e. QA) of the electron transport compared to high temperature alone.

Moreover, decreased Fv/Fm can also be associated with photoinhibition (Long et al.

1994, Adams et al. 2013).

Nevertheless, Fv/Fm results indicated PSII maximum efficiency for only dark-

adapted leaves. Hence, PSII operating efficiency (F'q/F'm) generated the actual PSII

efficiency under illumination (Maxwell and Johnson 2000, Baker and Rosenqvist

2004, Murchie and Lawson 2013), and therefore should be used (Chapter 2.2). In this

study, the combined effect of high light and high temperature were investigated using

F'q/F'm, and other fluorescence parameters. The study showed increased light decreased

PSII operating efficiency, while increasing non-photochemical quenching (NPQ).

Moreover, the combined effects of high light and high temperature significantly

decreased F'q/F'm at temperatures above 28 °C. These results emphasised that a

significant change in F'q/F'm and NPQ were observed at lower temperatures than

required for Fv/Fm. Previous evidence suggested NPQ is also an indicator of Calvin cycle

activity, and Law and Crafts-Brandner (1999) showed Calvin cycle activity was more

sensitive than Fv/Fm, consistent with former studies. Moreover, NPQ change

determined alterations in F'q/F'm, with no substantial difference in the open fraction of

PSII centres (qL), indicating the QA redox state. Consequently, under high light, PSII

was protected by NPQ through dissipation of excess irradiance, and F'q/F'm decrease

was always accompanied by NPQ increase (Demmig-Adams and Adams 1992, Demmig-

Adams and Adams 2006). The damaged PSII repair mechanism exhibited equally

paramount importance in the protection of the photosynthetic apparatus from high

light and high temperature stress (Vass 2012, Nath et al. 2013, Tyystjärvi 2013).

105


Unlike Fv/Fm, F'q/F'm measurements can be used for on-line photosynthesis

monitoring purposes. In this thesis (Chapter 2.2), continuous monitoring of F'q/F'm to

predict short or long-term stress resulted from high temperature and high irradiance

using a PAM fluorometer (MONI-PAM) (Porcar-Castell et al. 2008). However, the

primary limitation of the PAM fluorometer is its lack of practicality for canopy scale

measurements. Despite this, monitoring photosynthesis using F'q/F'm can be applied at

the canopy level with the advance and application of chlorophyll fluorescence on-line

monitoring technology (Ji et al. 2010). Pieruschka et al. (2010) used laser-induced

fluorescence transient (LIFT) for remotely monitoring photosynthetic efficiency due to

stress effects at the canopy level and/or selected leaves at up to a 50 m distance.

Furthermore, a continuous, automatic, and remote monitoring tool offers an

opportunity to relate real-time plant status to current microclimate conditions in a

greenhouse (Ehret et al. 2011). In addition, continuous monitoring provides real-time

information, which can be applied to chlorophyll fluorescence and photosynthesis

models. In the study presented in Chapter 3.3, the relationship among different light

levels with fluorescence emissions from leaves adapted to light was used in a multi-

layer leaf model for predicting PSII quantum yield. The model uses a new method of

approximating PSII quantum yield from maximal fluorescence in light-adapted leaves

(F'm), and fluorescence emissions from leaves adapted to actinic light (F') (Evans

2009). Results showed suitable model prediction for F'q/F'm and ETR under different

temperature and light conditions (Chapter 3.3). The model is simple and

straightforward to simulate PSII quantum yield, and used for monitoring

photosynthesis under various climatic conditions. The multi-layer leaf model was

congruent with the gas exchange data, and was largely consistent with conventional

chlorophyll fluorescence data (Vogelmann and Evans 2002, Evans and Vogelmann

2003). Similarly, the chlorophyll fluorescence model showed variations in chlorophyll

fluorescence correlated well with variations in actual photosynthesis for plant

monitoring purposes (Van der Tol et al. 2009).

Using thermography to monitor leaf temperature and estimate stomatal conductance

(gs)

Thermography provides a very powerful tool to study spatial variation in plant and

canopy temperatures with many potential applications in plant physiology (Jones

2004). Energy balance considerations have clearly established that leaf temperature

106

106 Chapter 4.1

varies with leaf evaporation, and is therefore a function of stomatal conductance (Jones

1999). This basic energy balance equation (Chapter 3.2) has been used more or less to

derive explicit estimates of stomatal conductance from infrared thermography (Jones

1999, Jones et al. 2002, Leinonen and Jones 2004, Leinonen et al. 2006). Jones (1999)

derived the thermal index (IG) as a new approach from measured leaf temperature, and

wet and dry reference leaf temperatures. For most gs values, IG is linearly proportional

to gs, as demonstrated under a wide range of conditions (Maes and Steppe 2012, Costa

et al. 2013). In this thesis, the method was applied (Chapter 2.1) to investigate

thermography to monitor leaf temperature, and estimate gs. The relation between

thermal index and gs observed in this study was congruent with previous reports (Jones

1999, Leinonen et al. 2006, Maes et al. 2011) and the overall relationships between

thermal index and gs can be used to model non-invasive gs estimates.

The majority of thermal imaging applications are estimates of spatial and temporal

gs variation in relationship to water stress (Jones 1999, Jones 2004, Fuentes et al.

2005, Bloom-Zandstra and Metselaar 2006, Wang et al. 2010, Maes et al. 2011,

Òshaughnessy et al. 2011). Kaukoranta et al. (2005) applied thermography in

greenhouse cucumber to detect water deficiency prior to any long-term crop damage. In

addition, the method shows promise in monitoring photosynthetic efficiency through

NPQ based on light-induced leaf heating (Kaňa and Vass 2008). In a very recent study,

a novel approach was applied that combined thermal imaging with chlorophyll

fluorescence to determine gs images from thermography at the whole-plant scale

(McAusland et al. 2013).

Nevertheless, Costa et al. (2013) reported environmental variability (e.g. in light

intensity, temperature, relative humidity, and wind speed) affected the accuracy of

thermal imaging measurements. In addition, Maes et al. (2011) emphasized the

limitations in IG estimates (e.g. use of dry and wet reference leaves), and IG application

under humid cool and low light conditions needs to be addressed and improved if

infrared thermography is to be applied extensively in its current form. Furthermore, the

capacity of thermal imaging depends on leaf angle, and crop species with increased

stomatal control over tissue water loss, such as species exhibiting isohydric behaviour,

(i.e. maintain leaf water potential almost unchanged by rapid stomatal closure) (Jones

et al. 2009, Grant et al. 2006, Costa et al. 2013, Gallé et al. 2013).

107


Modelling photosynthesis, leaf temperature, and stomatal conductance

C3 photosynthesis over a range of plant growth conditions led to accurate

photosynthesis predictions (Farquhar et al. 1980, Bernacchi et al. 2013). A complete C3

net leaf photosynthesis (Pnl) description is presented in Chapter 3.2 following Farquhar

et al. (1980), and approaches of (Gijzen 1995) as used by Körner (2004). In this model,

the primary environmental determinants of leaf photosynthesis include air temperature

and irradiance. Vapour pressure deficits (VPD) do not directly influence

photosynthesis, however VPD does have a strong influence on stomatal conductance

(Bernacchi et al. 2013). In fact, stomatal conductance directly affects leaf temperature

because it controls leaf evaporative cooling (Blonquist et al. 2009). In principle,

stomatal conductance regulates CO2 exchange, and subsequently limits Pnl (Kusumi et

al. 2012). The interrelationships and interdependence among photosynthesis, stomatal

conductance, and leaf temperature can be respectively analysed using coupled model

photosynthesis, stomatal conductance, and leaf energy balance (Collatz et al. 1991,

Leuning et al. 1995, Tuzet et al. 2003, Kim and Lieth 2003).

The Chapter 3.2 study interconnected the three sub-models to predict net leaf

photosynthesis (Pnl), leaf temperature, and stomatal conductance from a greenhouse

microclimate (e.g. air temperature, light, ambient CO2, and relative humidity). Results

showed the coupled model effectively predicted the Pnl temperature optimum at

different CO2 concentrations, and leaf photochemical efficiencies at varied

temperatures and CO2 concentrations. The coupled model simulated increased light,

temperature, and CO2 concentration, which exhibited a large Pnl influence until

photosynthesis was saturated at maximum light. Furthermore, model simulations

showed limits in photosynthesis that occurred with increased leaf temperatures

resulting from stomatal conductance due to increased VPD.

Strong evidence for the biochemical model of C3 photosynthesis show temperatures

above optimum increased photorespiration and decreased Pnl (Farquhar et al. 1980, Yin

and Struik 2009). In fact, the rate and temperature optimum of photosynthesis was

dependent on growth temperature and CO2 concentration (Berry and Björkaman 1980,

Hikosaka et al. 2006). Studies indicated Pnl declines above optimum temperatures at

higher CO2 levels could also result from limitations in electron transport capacity (Sage

and Kubien 2007, Wise et al. 2004). Essentially, increased leaf net photosynthetic rate

was reported with concurrent, stomatal conductance (Jarvis and Davies 1989, Kusumi

108

108 Chapter 4.1

et al. 2012). Hence, the coupled model simulation predicted lower photosynthesis

under high leaf temperature due to lower stomatal conductance (Chapter 3.2).

Subsequently, the VPD rise with temperature decreased stomatal conductance and

decreased photosynthesis.

Alternatively, the leaf energy balance sub-model successfully predicted observed leaf

temperature (Chapter 3.2). In greenhouse crops, Vermeulen et al. (2012) showed leaf

temperature was largely influenced by natural irradiance absorbed by leaves, because it

drives many energy fluxes. Leaves normally exchange absorbed radiation with the

surrounding environment, either as latent or sensible heat (Leuning et al. 1989, Jones

1992). The leaf energy balance model (Chapter 3.2) described in this study, and

modified for greenhouse crops, comprised all these factors (Stanghellini 1987, Jones

1992, Vermeulen et al. 2012). The model determined the total resistance to heat

transfer (rH), and total resistance to water vapour transfer (rv) by the respective

biophysical expressions and parameters. The mixed convection regime is valid under

greenhouse conditions; therefore total resistance to heat was calculated following

Vermeulen et al. (2012). In fact, Vermeulen et al. (2012) used the coefficient of

determination (R2) and Young Information Criterion (YIC) as selection criteria for

different resistance to heat transfer equations, and identifiability analysis (De Pauw et

al. 2008) on the leaf temperature algorithm. In this thesis, the leaf temperature

simulation was highly sensitive to the alpha mixed (αmixed) parameter in the total

resistance to heat transfer equations compared to other parameters. Nevertheless, leaf

temperature simulations were critically influenced by input variables, primarily air

temperature. Similarly, Vermeulen et al. (2012) confirmed more than 90% of the total

was input uncertainty, and consequently data input quality is of paramount importance

to monitor leaf temperature of greenhouse crops.

In short, leaf temperature is the most important plant characteristic used for

temperate-based plant stress monitoring (Ehret 2001, Blonquist et al. 2009, Vermeulen

et al. 2012). Accordingly, the coupled model in this thesis demonstrated the potential

for accurate leaf temperature prediction from a greenhouse microclimate for real-time

monitoring of stomatal conductance and photosynthesis in greenhouse crops.

109

109 Conclusion

CHAPTER 4.2

Conclusion

A dynamic climate control regime facilitates the precise regulation of temperature and

irradiance conditions based on plant physiology. However, to advance the dynamic climate

control regime based plant physiology, it is vital to understand plant responses under

dynamic and potentially extreme greenhouse microclimate conditions. This thesis

confirmed physiological methods, including gas exchange, chlorophyll fluorescence, and

infrared thermography are useful tools to monitor plant response, and predict stressful

conditions prior to plant damage.

Chlorophyll fluorescence is a non-destructive intrinsic probe to evaluate plant stress,

and Fv/Fm is a useful parameter to monitor maximum photochemical efficiency in leaves,

and damage on PSII caused by high temperature and light stressors. In addition, fast

chlorophyll transient, and some derived JIP parameters are alternative early indicators of

physiological damage caused by high temperature and light stress. Nevertheless, Fv/Fm and

JIP parameters only indicated PSII efficiency in dark-adapted leaves. Consequently, an

alternative chlorophyll fluorescence parameter under illuminated leaves, which is the PSII

operating efficiency (F'q/F'm) is proposed. Therefore, continuous plant response

monitoring, based on F'q/F'm provides a useful tool for predicting both short and long-term

stress resulting from extreme microclimate conditions.

Moreover, infrared thermography together with information from chlorophyll

fluorescence showed notable potential for monitoring and early detection of temperature

and light stress, in addition to other greenhouse environmental stressors. Nevertheless, the

limitations in estimating IG must be addressed and improved if infrared thermography is

extensively applied in greenhouse plant production.

Finally, crop models in conjunction with plant monitoring sensors are advancements for

plant monitoring purposes, as well as for real-time stress detection. The multi-leaf layer

model enables estimates of F' and F'm to predict PSII operating efficiency, therefore

together with online microclimate data measurements it can be used to monitor

chlorophyll fluorescence and photosynthesis. Moreover, the coupled model can be applied

for real-time prediction of leaf temperature, photosynthesis, and stomatal conductance, as

well as a decision-making tool for dynamic greenhouse climate control.

111

111 Thesis contribution

CHAPTER 4.3

Thesis contribution

This thesis generated substantial information that addressed the underlining plant

physiology under climate stress, and potential physiological methods/sensors/models

used to detect plant stress.

1) Chlorophyll a fluorescence and fast chlorophyll transient with JIP-test fluorescence

parameters, including Fv/Fm, Fv/Fo, and PI were used to detect the critical temperature

limit of PSII damage. Therefore, high temperature might cause a significant effect on

PSII in chrysanthemum when the temperature exceeded 38 °C for a period over seven

days.

2) The critical temperature limit of PSII thermo-tolerance in chrysanthemum leaves,

and the temperature dose causing 50% reduction in chlorophyll fluorescence

parameters was estimated using the log-logistic model of the temperature dose

response curve. This study confirmed in chrysanthemum the temperature dose causing

a 50% reduction (T50) in Fv/Fm was 41 °C. Furthermore, the temperature dose response

curve log-logistic model can be used to determine T50 for any greenhouse crop treated

with high temperature stress.

3) The high temperature on Fv/Fm was severe when combined with high light, and

Fv/Fm decreased significantly at high temperatures (>32 °C), and light. This thesis

supported studies indicating lower temperatures under high light influenced Fv/Fm,

more than Fv/Fm at high temperatures and low light.

4) Results for Fv/Fm indicated only maximal PSII efficiency in dark-adapted leaves. This

thesis proposed F'q/F'm as an effective indicator of the actual PSII efficiency under

illumination. Furthermore, the combined effects of high temperature and light

significantly decreased F'q/F'm at temperatures exceeding 28 °C. A significant change in

F'q/F'm and NPQ at lower temperatures than required for Fv/Fm were elucidated by this

thesis. Results also showed Calvin cycle activity was more sensitive than Fv/Fm. Thus,

F'q/F'm measurements are a promising parameter that may be used for photosynthesis

online monitoring.

112

112 Chapter 4.3

5) In addition to chlorophyll fluorescence, thermography shows wider potential to

monitor plant stress in greenhouse horticulture. Results from this thesis confirmed

thermography potential for monitoring leaf temperature and estimating gs. The

relationship between thermal index and gs observed in this study corresponded with

previous reports, and the overall relationship between thermal index and gs can be used

in models to non-invasively estimate gs.

6) Continuous plant monitoring tools, with crop models can be used to assist with real-

time stress detection. Results from this thesis proposed a multi-layer leaf model to predict

PSII operating efficiency under different microclimate conditions. Moreover, the coupled

model can be applied for real-time prediction of leaf temperature, photosynthesis, and

stomatal conductance, as well as a viable tool for decision-making in dynamic greenhouse

climate control.

113

113 Possibilities for future research

CHAPTER 4.4

Possibilities for future research

This thesis extensively documented the seminal and current literature, and empirical

research results on basic plant physiology, online monitoring methods used to detect

climate stress, and the potential crop models to assist with real-time stress detection.

However, this work has also revealed potential research gaps requiring further research to

implement online monitoring tools with crop models to excel decision support systems in

greenhouse cultivation.

1) Application of different plant based sensors from leaf to crop levels, depending on the

crop type, physiology, and greenhouse climate and cultivation.

2) Identification of simple, reliable, and continuous monitoring systems for greenhouse

purposes that consider greenhouse cultivation complexities (i.e. greenhouse structure,

climate sensors and management, among others)

3) Building generic and reliable mechanistic models with reduced complexity, data

requirements, and output interpretation.

4) Characterisation and identification of sensor limitations, failure, and degrees of freedom

for error, during on-line plant stress monitoring and detection.

5) Identification and subsequent combination of different types of continuous monitoring

sensors for multi-sensor stress-identification purposes.

115

115 References

References

Aaslyng JM, Ehler N, Karlsen P, Rosenqvist E (1999) IntelliGrow: A component based

climate control system for decreasing the greenhouse energy consumption. Acta

Horticulturae 507: 35–41.

Aaslyng JM, Lund JB, Ehler N, Rosenqvist E (2003) IntelliGrow: a greenhouse

component- based climate control system. Environmental Modelling and Software 18:

657–666.

Aaslyng JM, Ehler N, Jakobsen L (2005) Climate control software integration with a

greenhouse environmental control computer. Environmental Modelling and Software

20: 521–527.

Adams III WW, Muller O, Cohu CM, Demming-Adams B (2013) May photoinhibition be a

consequence, rather than a cause of limited plant productivity. Photosynthesis Research

doi: 10.1007/s11120–013–9849–7.

Ainsworth EA, Rogers A (2007) The response of photosynthesis and stomatal conductance

to rising CO2: mechanisms and environmental interactions. Plant, Cell and

Environment 30: 258–270.

Al-Khatib K, Paulsen GM (1999) High-temperature effects on photosynthetic processes in

temperate and tropical cereals. Crop Science 39: 119–125.

Allakhverdiev SI, Kreslavski VD, Klimov VV, Los DA, Carpentier R, Mohanty P (2008)

Heat stress: an overview of molecular responses in photosynthesis. Photosynthesis

Research 98: 541–550.

Andrews JR, Fryer MJ, Baker NR (1995) Characterization of chilling effects on

photosynthetic performance of maize crops during early season growth using

chlorophyll fluorescence. Journal of Experimental Botany 46: 1195–1203.

Armond PA, Björkman O, Staehelin LA (1980) Dissociation of supramolecular complexes

in chloroplast membranes. A manifestation of heat damage to the photosynthetic

apparatus. Biochimica et Biophysica Acta (BBA) Biomembranes 601: 433–442.

Aro EM, Virgin I, Andersson B (1993) Photoinhibition of photosystem II Inactivation,

protein damage and turnover. Biochimica et Biophysica Acta 1143: 113–134.

Ashraf M, Harris PJC (2013) Photosynthesis under stressful environments: An overview.

Photosynthetica 51: 163–190.

Baert A, Villez K, Steppe K (2013) Automatic drought stress detection in grapevines

without using conventional threshold values. Plant and Soil 369: 439–452.

116

116 References

Bajons P, Klinger G, Schlosser V (2005) Determination of stomatal conductance by means

of infrared thermography. Infrared Physics and Technology 46: 429–439.

Baker NR (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annual

review of plant biology 59: 89–113.

Baker NR, Rosenqvist E (2004) Applications of chlorophyll fluorescence can improve crop

production strategies: an examination of future possibilities. Journal of Experimental

Botany 55: 1607–1621.

Ball JT, Woodrow IE, Berry JA (1987) A model predicting stomatal conductance and its

contribution to the control of phtosynthesis under different environmental conditions.

In: Biggins J (eds) Progress in photosynthesis research. Martinus Nijhoff Publishers,

The Netherlands.

Ballester C, Jiménez-Bello MA, Castel JR, Intrigliolo DS (2013) Usefulness of

thermography for plant water stress detection in citrus and persimmon trees.

Agricultural and Forest Meteorology 168: 120–129.

Barker T, Bashmakov I, Bernstein L, Bogner JE, Bosch PR, Dave R, Davidson OR, Fisher

BS, Gupta S, Halsnæs K, Heij GJ, Kahn Ribeiro S, Kobayashi S, Levine MD, Martino DL,

Masera O, Metz B, Meyer LA, Nabuurs GJ, Najam A, Nakicenovic N, Rogner HH, Roy J,

Sathaye J, Schock R, Shukla P, Sims REH, Smith P, Tirpak DA, Urge-Vorsatz D, Zhou D

(2007) Technical Summary. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA

(eds) Climate Change 2007: Mitigation. Contribution of Working Group III to the

Fourth Assessment Report of the Intergovernmental Panel on Climate Change.

Cambridge University Press, Cambridge, UK.

Belin Ĕ, Rousseau D, Boureau T, Caffier V (2013) Thermography versus chlorophyll

fluorescence imaging for detection and quantification of apple scab. Computers and

Electronics in Agriculture 90: 159–163.

Bernacchi CJ, Bagley JE, Serbin SP, Ruiz-Vera UM, Rosenthal DM, Vanloocke A (2013)

Modelling C3 photosynthesis from the chloroplast to the ecosystem. Plant, Cell and


Berry J, Bjorkman O (1980) Photosynthetic response and adaptation to temperature in

higher plants. Annual Review of Plant Physiology 31: 491–543.

Björkman O (1981)The responses of photosynthesis to temperature. In: Grace J, Ford ED,

Jarvis PG (eds) Plants and Their Atmospheric Environment. Edinburgh: Oxford Black-

well, London, UK, pp 273–301.

117

117 References

Bleyaert P, Vermeulen K, Steppe K, Dekock J (2012) Evaluation of a sensor for online

measurement of stem diameter variation of leafy vegetables. Acta Horticulturae 927:

571–579

Blonquist Jr JM, Norman JM, Bugbee B (2009) Automated measurement of canopy

stomatal conductance based on infrared temperature. Agricultural and Forest

Meteorology 149: 1931–1945.

Bloom-Zandstra M, Metselaar K (2006) Infrared thermometry for early detection of

drought stress in Chrysanthemum. Hortscience 41: 136–142.

Boote KJ, Scholberg JMS (2006) Developing, parameterizing and testing of dynamic crop

growth models for horticultural crops. Acta Horticulturae 718: 23–34.

Boyer JS (1982) Plant productivity and Environment. Science 218: 443-448.

Briantais JM, Dacosta J, Goulas Y, Ducruet JM, Moya I (1996) Heat stress induced in

leaves an increase of the minimum level of chlorophyll fluorescence, Fo: a time-resolved

analysis. Photosynthesis Research 48: 189-196.

Brooks A, Farquhar GD (1985) Effect of temperature on the CO2/O2 specificity of ribulose-

1,5-bisphosphate carboxylase/oxygenase and the rate of respiration in the light. Planta

165: 397–406.

Bunce J (2004) Carbon dioxide effects on stomatal responses to the environment and

water use by crops under field conditions. Oecologia: 140 1–10.

Buschmann C, Lichtenthaler HK (1998) Principles and characteristics of multi-colour

fluorescence imaging of plants. Journal of Plant Physiology 152: 297–314.

Calatayud A, Roca D Martínez PF (2006) Spatial-temporal variations in rose leaves under

water stress conditions studied by chlorophyll fluorescence imaging. Plant Physiology

and Biochemistry 44: 564–573.

Camejo D, Rodríguez P, Morales MA, Amico JMD, Torrecillas A, Alarcón JJ (2005) High

temperature effects on photosynthetic activity of two tomato cultivars with different

heat succeptibility. Journal of plant physiology 162: 281–289.

Campiotti C, Viola C, Alonzo G, Bibbiani C, Giagnacovo G, Scoccianti M, Tumminelli G

(2012) Sustainable greenhouse horticulture in Europe. Journal of sustainable energy 3:

pp 1–50.

Carter GA, Cibula WG, Miller RL (1996) Narrow-band reflectance imagery compared with

thermalimagery for early detection of plant stress. Journal of plant physiology 148: 515–

522.

118

118 References

Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: Linking spectral

characteristics to stress and chlorophyll concentration. American Journal of Botany 88:

677–684.

Chaerle L, Leinonen I, Jones HG, Van Der Straeten D (2007) Monitoring and screening

plant populations with combined thermal and chlorophyll fluorescence imaging.

Journal of Experimental Botany 58: 773–784.

Chaerle L, Van Der Straeten (2000) Imaging techniques and the early detection of plant

stress Trends in plant Science 5: 495–501.

Chaerle L, Van Der Straeten (2001) Seeing is believing: imaging techniques to monitor

plant health. Biochimica et Biophysica Acta (BBA)-Gene Structure and Expression 1519:

153–166.

Chamont S, Strainchamps D, Thunot S (1995) Short- and long-term stomatal responses to

fluctuations in environment in southern European greenhouses. Annals of Botany

London 75: 39–47.

Chen C, Weng YK, Shen TC (2010) Performance evaluation of an infrared thermocouple.

Sensors 10: 10081–10094.

Chen LS, Cheng L (2009) Photosystem II is more tolerant to high temperature in apple

(Malus domestica Borkh.) leaves than in fruit peal. Photosynthetica 47: 112–120.

Chen LS, Li P, Cheng L (2008) Effects of high temperature with high light on the balance

between photooxidation and photoprotection in the sun-exposed peel of apple. Planta

228: 745–756.

Christen D, Schönmann S, Jermini M, Strasser RJ, Dèfago GV (2007) Characterization and

early detection of grapevine (Vitis vinifera) stress responses to esca disease by in situ

chlorophyll fluorescence and comparison with drought stress. Environmental and

Experimental Botany 60: 504–514.

Cohen Y, Alchanatis V, Meron M, Saranga Y, Tsipris J (2005) Estimation of leaf water

potential by thermal imagery and spatial analysis. Journal of Experimental Botany 56:

1843–1852.

Collatz GJ, Ball JT, Grivet C, Berry JA (1991) Physiological and environmental regulation

of stomatal conductance, photosynthesis and transpiration: a model that includes a

laminar boundary layer. Agricultural and Forest Meteorology 54: 107–136.

Costa JM, Grnat OM, Chaves MM (2013) Thermography to explor plant-environment

interactions. Journal of Experimental Botany doi:10.1093/jxb/ert029.

119

119 References

Crafts-Brandner SJ, Law RD (2000a) Effect of heat stress on the inhibition and recovery of

the ribulose-1,5-bisphosphate carboxylase/oxygenase activation state. Planta 212: 67–

74.

Crafts-Brandner SJ, Salvucci ME (2000b) Rubisco activase constrains the photosynthetic

potential of leaves at high temperature and CO2. Proceedings of the National Academy

of Sciences of the United States of America 97: 13430–13435.

Dalgaard T, Olesen JE, Petersen SO, Petersen BM, Jørgensen U, Kristensen T, Hutchings

NJ, Gyldenkærne S, Hermansen JE (2011) Developments in greenhouse gas emissions

and net energy use in Danish agriculture-How to achieve substantial CO2 reductions?

Environmental Pollution 159: 3193–3203.

Danish Energy Agency (2012) Energy statistics 2011, The Danish Energy Agency,

Copenhagen, Denmark.

De Pauw DJ, Steppe K, De Baets B (2008) Identifiability analysis and improvement of a

tree water flow and storage model. Mathematical Biosciences 211: 314–332.

Dekock J, Aerts JM, Berckmans D, Vermeulen K, Steppe K, Lemeur R, Janssen K, Bleyaert

P, Westra J, Rieswijk T (2006) Crop monitoring by means of an on-line early warning

system. Acta Horticulturae 718: 183–188.

Demirevska-Kepova K, Feller U (2004) Heat sensitivity of Rubisco, Rubisco activase and

Rubisco binding protein in higher plants. Acta Physiologiae Plantarum 26: 103–114.

Demmig-Adams B, Adams III WW (1992) Photoprotection and other responses of plants

to high light stress. Annual Review of Plant Physiology and Plant Molecular Biology 43:

599–626.

Demmig-Adams B, Adams III WW (2006) Photoprotection in an ecological context: the

remarkable complexity of thermal energy dissipation. New Phytologist 172: 11–21.

Demming-Adams B (1990) Carotenoids and Photoprotection in plants: a role for the

xanthophyll zeaxanthin. Biochimica et Biophysica Acta (BBA)-Bioenergetics 1020: 1–24.

Dieleman JA, Qian T, Elings A, Marcelis LFM (2010) Crop physiology in (Semi-) closed

greenhouses. Wageningen UR Greenhouse Horticulture, Wageningen, The Netherlands

pp 1–17.

Duque AS, de Almeida AM, da Silva AB, da Silva JM, Farinha AP, Santos D, Fevereiro P, de

Sousa Araújo (2013) Abiotic Stress Responses in Plants: Unraveling the complexity of

genes and networks to survive In: Abiotic Stress-Plant Responses and Applications in

Agriculture. Vahdati K, Leslie C (eds) INTECH, pp 50–101.

120

120 References

EEA (2012) Greenhouse gas emission trends and projections in Europe, publications office

of the European Union, Luxembourg. ISBN 978–92–9213–331–3.

Ehler N (1991) An autocalibrating model for simulating and measuring net canopy

photosynthesis using a standard greenhouse climate computer. Computer and

Electronics in Agriculture 6: 1–20.

Ehler N, Hansen MJ (1998) The Plants-on-line-box: monitoring whole plant net

photosynthesis as a tool to evaluate plant productivity and stress in Begonia Elatior

„ILONA‟. Acta Horticulturae 421: 145–154.

Ehleringer J, Björkman O (1977) Quantum yields of for CO2 uptake in C3 and C4 plants.

Dependence on temperature, CO2 and O2 concentration. Plant Physiology 59: 86–90.

Ehleringer J, Pearcy RW (1983) Variation in quantum yield for CO2 uptake among C3 and

C4 plants. Plant Physiology 73: 555–559.

Ehret DL, Hill BD, Helmer T, Edwards DR (2011) Neural network modeling of greenhouse

tomato yield, growth and water use from automated crop monitoring data. Computer

and electronics in agriculture 79: 82–89.

Ehret DL, Lau A, Bittman S, Lin W, Shelford T (2001) Automated monitoring of

greenhouse crops. Agronomie 21: 403–414.

Enami I, Kitamura M, Tomo T, Isokawa Y, Ohta H, Katoh S (1994) Is the primary cause of

thermal inactivation of oxygen evolution in spinach PSII membranes release of the

extrinsic 33 kDa protein or of Mn? Biochimica et Biophysica Acta (BBA)-Bioenergetics

1186: 52–58.

Evans JR (1995) Carbon fication profiles do refelect light absorption profiles in leaves.

Australian Journal of Plant Physiology 22: 865–873.

Evans JR (2009) Potential errors in electron transport rates calculated from chlorophyll

fluorescence as revealed by a multilayer leaf model. Plant and Cell Physiology 50: 698–

706.

Evans JR, Vogelmann TC (2003) Profiles of 14C fixation through spinach leaves in relation

to light absorption and photosynthetic capacity. Plant, Cell & Environment 26: 547–

560.

Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic

CO2 assimilation in leaves of C3 species. Planta 149: 78–90.

Farquhar GD, von Caemmerer S (1982) Modelling of photosynthetic response to

environmental conditions. In: Lange OL, Nobel CB, Osmond ZH (eds) Water relations

and carbon assimilation. Springer-Verlag, Berlin, Germany.

121

121 References

Feller U, Crafts-Brandner SJ, Salvucci ME (1998) Moderately high temperature inhibit

Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase (Rubisco) activase-mediated

activation of Rubisco. Plant Physiology 116: 539–546.

Fernández JE, Moreno F, Martĭn-Palomo MJ, Cuevas MV, Torres-Ruiz JM, Moriana A

(2011) Combining sap flow and trunk diameter measurements to assess water needs in

mature olive orchards. Environmental and Experimental Botany 72: 330–338.

Figueroa FL, Jiménez C, Lubián LM, Montero O, Lebert M, Häder DP (1997) Effects of

high light and temperature on photosynthesis and photoinhibition in Nannochloropsis

gaditana Lubián (Eustigmatophyceae). Journal of Plant Physiology 151: 6–15.

Fisher PR, Heins RD, Ehler N, Lieth JH (1997) A decision-support system for real-time

management of Easter lily (Lilium longiflorum Thunb.) scheduling and height-I: system

description. Agricultural Systems 54: 23–37.

Fisher RA, Williams M, Do Vale RL, Da Costa AL, Meir P (2006) Evidence from

Amazonian forests is consistent with isohydric control of leaf water potential. Plant Cell

and Environment 29: 151–165.

Force L (2002) Applications of the JIP-test of chlorophyll fluorescence PhD Thesis, School

of Biological Sciences, The University of Queensland, Australia.

Force L, Critchley C, Van Rensen JSJ (2003) New fluorescence parameters for monitoring

photosynthesis in plants. Photosynthesis Research 78: 17–33.

Foyer CH, Lelandais M, Kunert KJ (1994) Photooxidative stress in plants. Physiologia

plantarum 92: 696–717.

Fracheboud Y, Haldimann P, Leipner J Stamp P (1999) Chlorophyll fluorescence as a

selection tool for cold tolerance of photosynthesis in maize (Zea mays L.). Journal of


Fu W, Li P, Wu Y (2012) Effects of different light intensities on chlorophyll fluorescence

characteristics and yield in lettuce. Scientia Horticulturae 135: 45–51.

Fuentes S, Conroy JP, Kelley G, Rogers G, Collins M (2005) Use of infrared thermography

to asses spatial and temporal variability of stomatal conductance of grapevines under

partial root-zone drying: an irrigation scheduling application. Acta Horticulturae 689:

309–316.

Gallé Á, Csiszár J, Benyó D, Laskay G, Leviczky T, Erdei L, Tari I (2013) Isohydric and

anisohydric strategies of wheat genotypes under osmotic stress: Biosynthesis and

function of ABA in stress. Journal of Plant Physiology 170: 1389–1399.

http://espace.library.uq.edu.au/list/author_id/18155/

http://espace.library.uq.edu.au/view/UQ:105842

122

122 References

Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Penuelas J, Valentini R

(1995) Relationships between NDVI, canopy structure, and photosynthesis in three

Californian vegetation types. Ecological Applications 5: 28–41.

Garbulsky MF, Peñuelas J, Gamon J, Inoue Y, Filella I (2011) The photochemical

reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation

use efficiencies: A review and meta-analysis. Remote sensing of Environment: 115: 281–

297.

Gary C, Jones JW, Tchamitchian M (1998) Crop modeling in horticulture: state of the art.

Scientia Horticulturae 74: 3–20.

Gates DM (1980) Biophysical Ecology. Springer, New York, NY, USA.

Genty B, Briantais JM, Baker NR (1989) The relationship between the quantum yield of

photosynthetic electron transport and quenching of chlorophyll fluorescence.

Biochimica et Biophysica Acta (BBA) General Subjects 990: 87–92.

Georgieva K, Tsonev T, Velikova V, Yordanov I (2000) Photosynthetic activity during high

temperature treatment of pea plants. Journal of Plant Physiology 157: 169–176.

Gijzen H (1995) Short-term crop responses In: Bakker JC, Bot GPA, Challa H, Van de

Braak NJ (eds) Greenhouse climate control an integrated approach. Wageningen Press,

Wageningen, The Netherlands, pp 16–44.

Gilbert N (2012) One-third of our greenhouse gas emissions come from agriculture. Nature

doi: 10.1038/11708.

Gorbe E, Calatayud A (2012) Application of chlorophyll fluorescence imaging technique in

horticultural research: A review. Scientia Horticulturae 138: 24–35.

Goudriaan J, Van Laar HH (1994) Modeling potential crop growth processes. Kluwer

academic publishers, Dordrecht, The Netherlands, pp 121–167.

Goudriaan J, Van Laar HH, Van Keulen H, Louwerse W (1985) Photosynthesis, CO2 and

plant production. In: Day W, Aikin RK (eds) Wheat Growth and Modelling, NATO ASI

Series, Series A: Life Sciences, Plenum Press New York, USA, pp 107–122.

Graham MED (1989) Calibration of a small infrared sensor for measuring leaf temperature

in the field: non-steady state conditions. Agricultural and Forest Meteorology 44: 295–

305.

Grant OM, Chaves MM, Jones HG (2006) Optimizing thermal imaging as a technique for

detecting stomatal closure induced by drought stress under greenhouse conditions.

Physiologia Plantarum 127: 507–518.

123

123 References

Grant OM, Tronina L, Jones HG, Chaves MM (2007) Exploring thermal imaging variables

for the detection of stress responses in grapevine under different irrigation regimes.


Greer DH, Weedon MM (2012) Photosynthetic light responses in relation to leaf

temperature in sun and shade leaves of grapevines. Acta Horticulturae 956: 149 –156.

Guo Y, Tan J (2011) Modeling and simulation of the initial phases of chlorophyll

fluorescence from photosystem II. Biosystems 103: 152–157.

Han H, Gao S, Li B, Dong CX., Feng HL, Meng QW (2010) Overexpression of violaxanthin

de-epoxidase gene alleviates photoinhibition PSII and PSI in tomato during high light

and chilling stress. Journal of Plant Physiology 167: 176–183.

Hansen JM, Høgh-Schmidt K (1996a) A computer controlled chamber system designed for

greenhouse microclimatic modelling and control. Acta Horticulturae 440: 310–315.

Hansen JM, Ehler N, Karlsen P, Høgh-Schmidt K, Rosenqvist E (1996b) Decreasing the

environmental load by a photosynthetic based system for greenhouse climate control.

Acta Horticulturae 440: 105–110.

Harbinson J, Genty B, Baker N (1990) The relationship between CO2 assimilation and

electron transport in leaves. Photosynthesis Research 25: 213–224.

Harley PC, Thomas RB, Reynolds JF, Strain BR (1992) Modelling photosynthesis of cotton

grown in elevated CO2. Plant, Cell & Environment 15: 271–282.

Havaux M (1993a) Rapid photosynthetic adaptation to heat stress triggered in potato

leaves by moderately elevated temperatures. Plant, Cell & Environment 16: 461–467.

Havaux M (1993b) Characterization of thermal damage to the photosynthetic electron

transport system in potato leaves. Plant Science 94: 19–33.

Havaux M, Ernez M, Lannoye R (1988) Tolerance of poplar (Populus sp.) to environmental

stresses: I. Comparative study of poplar clones using the in vivo chlorophyll

fluorescence method. Acta Oecologia/Oecologia Plant 9: 161–172.

Heckathorn SA, Downs CA, Sharkey TD, Coleman JS (1998) The small, methionine-rich

chloroplast heat-shock protein protects photosystem II electron transport during heat

stress. Plant physiology 116: 439–444.

Helmer T, Ehret DL, Bittman S (2005) Crop Assist, an automated system for direct

measurement of greenhouse tomato growth and water use. Computers and Electronics

in Agriculture 48: 198–215.

Heuvelink E (2005) Tomatoes: Crop Production Scinece in Horticulture. CABI publishing,

Wallingford, Oxfordshire, UK, pp 352.

124

124 References

Hikosaka K, Ishikawa K, Borjigidai A, Muller O, Onoda Y (2006) Temperature acclimation

of photosynthesis: mechanisms involved in the changes in temperature dependence of

photosynthetic rate. Journal of Expermental Botany 291: 291–302.

Holt NE, Fleming GR, Niyogi KK (2004) Toward an understanding of the mechanism of

the nonphotochemical quenching in green plants. Biochemistry 43: 8281–8289.

Horton P, Ruban AV, Walters RG (1996) Regulation of light harvesting in green plants.

Annual Reveview of Plant Physiology and Plant Molecular Biology. 47: 655–84.

Horton P, Wentworth M, Ruban A (2005) Control of the irradiance harvesting function of

chloroplast membranes: The LHCII-aggregation model for non-photochemical

quenching. FEBS Letters 579: 4201–4206.

Idso SB (1982) Non-water-stressed baselines: A key to measuring and interpreting plant

water stress. Agricultural Meteorology 27: 59–70.

IEA (2011) Energy policies of IEA countries Denmark review, pp 1–154.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2012) Log-logistic model analysis of

optimal and supra-optimal temperature effect on photosystem II using chlorophyll a

fluorescence in chrysanthemum (Dendranthema grandiflora). Acta Horticulturae 957:

297–302.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) High temperature stress monitoring

and detection using chlorophyll a fluorescence and infrared thermography in

chrysanthemum (Dendranthema grandiflora). Plant physiology and Biochemistry 67:

87–94.

Janssen LHJ, Wams EH, van Hasselt PR (1992) Temperature dependence of chlorophyll

fluorescence induction and photosynthesis in tomato as affected by temperature and

light conditions during growth. Journal of Plant Physiology 139: 549–554.

Ji J, Xu M, Li Z (2010) Research and application on chlorophyll fluorescence on-line

monitoring technology. Advanced Materials Research 139-141: 2550–2555.

Johnson IR, Riha SJ, Wilks DS (1996) Modelling daily net canopy photosynthesis and its

adaptation to irradiance and atmospheric CO2 concentration. Agricultural Systems 50:

1–35.

Jones HG (1992) Plants and Microclimate, a Quantitative Approach to Environmental

Plant Phsiology. Cambridge University Press, Cambridge, UK.

Jones HG (1999) Use of infrared thermometry for estimation of stomatal conductance as a

possible aid to irrigation scheduling. Agricultural and Forest Meteorology 95: 139–149.

125

125 References

Jones HG 1999) Use of thermography for quantitative studies of spatial and temporal

variation of stomatal conductance over leaf surfaces, Plant, Cell and Environment. 22:

1043–1055.

Jones HG, Stoll M, Santos T, de Sousa C, Chaves MM, Grant OM (2002) Use of infrared

thermography for monitoring stomatal closure in the field: application to grapevine.


Jones HG (2004) Application of thermal imaging and infrared sensing in plant physiology

and ecophysiology. Advances in Botanical Research 41: 107–163.

Jones HG (2004) Irrigation scheduling: Advantages and pitfalls of plant-based methods.


Jones HG (2007) Monitoring plant and soil water status: established and novel methods

revisited and their relevance to studies of drought tolerance. Journal of Experimental

Botany 58: 119–130.

Jones HG, Serraj R, Loveys BR, Xiong L, Ashley W, Price AH (2009) Thermal infrared

imaging of crop canopies for the remote diagnosis and quantification of plant responses

to water stress in the field. Functional Plant Biology 36: 978–989.

Kalaji MH, Carpentier R, Allakhverdiev IS, Bosa K (2012) Fluorescence parameters as

early indicators of light stress in barley. Journal of Photochemistry and Photobiology B:

Biology 112: 1–6.

Kaňa R, Vass I (2008) Thermoimaging as a tool for studying light-induced heating of

leaves: Correlation of heat dissipation with the efficiency of photosystem II

photochemistry and non-photochemical quenching. Environmental and Experimental

Botany 64: 90–96.

Kaukoranta T, Murto J, Takala J, Tahvonen R (2005) Detection of water deficit in

greenhouse cucumber by infrared thermography and reference surfaces. Scientia


Kaukoranta T, Murto J, Takala J, Tahvonen R (2005) Detection of water deficit in

greenhouse cucumber by infrared thermography and reference surfaces. Scientia


Kim SH, Lieth JH (2002) Parameterization and testing of coupled model of

photosynthesis-stomatal conductance for greenhouse rose crop. Acta Horticulturae 593:

113–120.

Kim SH, Lieth JH (2003) A coupled model of photosynthesis, stomatal conductance and

transpiration for a rose leaf (Rosa hybrida L.) Annals of Botany 91: 771–781.

126

126 References

Kirkham MB (2005) Measurement of canopy temperature with infrared thermometers. In:

Principles of Soil and PlantWater Relations, Academic press Kansas State University,

USA, pp 425–435.

Knezevic SZ, Streibig JC, Christian, R (2007) Utilizing R software package for dose-

response studies: the concept and data Analysis. Weed Technology 21: 840–848.

Körner O (2004) Evaluation of crop photosynthesis models for dynamic climate control.


Körner O, Aaslyng JM, Andreassen AU, Holst N (2007) Microclimate prediction for

dynamic greenhouse climate control. HortScience 42: 272–279.

Körner O, Chall H (2003) Process-based humidity control regime for greenhouse crops.

Computers and Electronics in Agriculture 39: 173–192

Körner O, Challa H (2004) Temperature integration and process-based humidity control

in chrysanthemum. Computer and Electronics in Agriculture 43:1–21.

Körner O, Van Straten G (2008) Decision support for dynamic greenhouse climate control

strategies. Computers and Electronics in Agriculture 60: 18–30.

Kornyeyev D, Holaday S, Logan B (2003) Predicting the extent of photosystem II

photoinactivation using chlorophyll a fluorescence parameters measured during

illumination. Plant, Cell and Physiology 44: 1064–70.

Kramer DM, Johnson G, Kiirats O, Edwards GE (2004) New fluorescence parametrs for

the determination of QA redox state and excitation energy fluxes. Photosynthesis

Research 77: 209–18.

Krishnan P, Ramakrishnan B, Reddy RK, Reddy VR (2011) High temperature effects on

rice growth, yield, and grain quality. Advances in agronomy 111: 87–206.

Kusumi K, Hirotsuka S, Kumamaru T, Iba K (2012) Increased leaf photosynthesis caused

by elevated stomatal conductance in a rice mutant deficient in SLAC1, a guard cell anion

channel protein. Journal of Experimental Botany 63: 5635–5644.

Larcher W (1994) Photosynthesis as a tool for indicating temperature stress events. In:.

Schulze ED, Caldwell MM (eds) Ecophysiology of photosynthesis ecological studies,

Springer Velag, Berlin, Germany, pp 261–277.

Larcher W (2003) Physiological Plant Ecology: Ecophysiology and Stress Physiology of

Functional Groups. Springer-Velag, Berlin, Germany, 345–427.

Law RD, Crafts-Brandner SJ (1999) Inhibition and acclimation of photosynthesis to Heat

stress is closely correlated with activation of Ribulose-1,5-Bisphosphate

Carboxylase/Oxygenase. Plant Physiology 120: 173–181.

127

127 References

Lazár D L, Sušila P, Nauš J (2006) Early detection of plant stress from changes in

distributions of chlorophyll a fluorescence parameters measured with fluorescence

imaging. Journal of Fluorescence 16: 173–176.

Lazár D, Ilík P, Nauš J (1997) High-temperature induced chlorophyll fluorescence changes

in barley leaves comparison of the critical temperatures determined from fluorescence

induction and from fluorescence temperature curve. Plant Science 124: 159–164.

Lazár D, Ilík P, NauŠ J (1997) An appearance of K-peak in fluorescence induction depends

on the acclimation of barley leaves to higher temperatures. Journal of Luminescence

72–74: 595–596.

Leinonen I, Grant OM, Tagliavia CP, Chaves MM, Jones HG (2006) Estimating stomatal

conductance with thermal imagery. Plant, Cell & Environment 29:1508–1518.

Leinonen I, Jones HG (2004) Combining thermal and visible imagery for estimating

canopy temperature and identifying plant stress. Journal of Experimental Botany 44:

1423–1431.

Lenk S, Chaerle L, Pfündel EE, Langsdorf G, Hagenbeek D, Lichtenthaler HK, Van Der

Straeten D, Buschmann C (2006) Multispectral fluorescence and refelectance imaging

at the leaf level and its possible applications. Journal of Experimental Botany 58: 807–

814.

Lentz W (1998) Model applications in horticulture: a review. Scientia Horticulturae 74:

151–174.

Leuning R, Grace J Monteith JL, Milford JR, Unsworth MH, Fowler D (1989) Leaf energy

balances: Developments and application [and Discussion]. Philosophical Transactions

the Royal Society 324: 191–206.

Leuning R, Kelliher FM, De Pury DGG, Schulze ED (1995) Leaf nitrogen, photosynthesis,

conductance and transpiration: scaling from leaves to canopies. Plant, Cell &


Li G, Lin L, Dong Y, An D, Li Y, Luo W, Yin X, Li W, Shao J, Zhou Y, Dai J, Chen W, Zhao C

(2012) Testing two models for the estimation of leaf stomatal conductance in four

greenhouse crops cuccumber, chrysanthemum, tulip and lilium. Agricultural and Forest

Meteorology 165: 92–103.

Li P, Cheng L, Gao H, Jiang C, Peng T (2009) Heterogeneous behavior of PSII in Soybean

(Glycine max) leaves with identical PSII photochemistry efficiency under different high

temperature treatments. Journal of Plant Physiology 166: 1607–1615.

128

128 References

Lichtenthaler HK (1988) In vivo chlorophyll fluorescence as a tool for stress detection in

Plants. In: Lichtenthaler HK (eds) application of chlorophyll fluorescence in

photosynthesis research, stress physiology, physiology, hydrobiology and remote

sensing. Kluwer Academic Publisher, Dordrecht, The Netherlands, pp 129–142.

Long SP (1991) Modification of the response of photosynthetic productivity to rising

temperature by atmospheric CO2 concentrations: has its importance been

underestimated? Plant, Cell and Environment 14: 729–739.

Long SP, Ainsworth EA, Leakey AD, Nösbsrger J, Ort DR (2006) Food for thought: Lower-

than-expected crop yield stimulation with rising CO2 concentrations. Science 312: 1918–

1921.

Long SP, Bernacchi CJ (2003) Gas exchange measurements, what can they tell us about

the underlying limitations to photosynthesis? Procedures and sources of error. Journal

of Experimental Botany 392: 2393–2401.

Long SP, Farage PK, Garcia RL (1996) Measurement of leaf and canopy photosynthetic

CO2 exchange in the field. Journal of Experimental Botany 47: 1629–1642.

Long SP, Humphries S (1994) Photoinhibition of photosynthesis in nature. Annual Review

of Plant Physiology and Plant Molecular Biology 45: 633–662.

López A, Molina-Aiz FD, Valera DL, Peňa A (2012) Determining the emissivity of the

leaves of nine horticultural crops by means of infrared thermography. Scientia


Lu MC, Zhang HJ (2000) Heat induced multiple effects on PSII in Wheat plants. Journal

of Plant Physiology 156: 259-265.

Maes WH, Achten WMJ, Reubens B, Muys B (2011) Monitoring stomatal conductance of

Jatropha curcas seedlings under different levels of water shortage with infrared

thermography. Agricultural and Forest Meteorology 151: 554–564.

Maes WH, Steppe K (2012) Estimating evaportranspiration and drought stress with

ground-based thermal remote sensing in agriculture: a review. Journal of Experimental

Botany 63: 4671–4712.

Marcelis LFM, Heuvelink E, Gourdriann J (1998). Modelling biomass production and yield

of horticultural crops: a review. Scientia Horticulturae 74: 83–111.

Markvart J, Kalita S, Jørgensen BN, Aaslyng JM, Ottosen CO (2008). Intelligrow 2.0-A

greenhouse component-based climate control system. Acta Horticulturae 801: 507–514.

Mathur S, Allakhverdiev SI, Jajoo A (2011a) Analysis of high temperature stress on the

dynamics of antenna size and reducing side heterogeneity of Photosystem II in wheat

129

129 References

leaves (Triticum aestivum). Biochimica et Biophysica Acta (BBA) - Bioenergetics 1807:

22–29.

Mathur S, Jajoo A, Mehta P, Bharti S (2011b) Analysis of elevated temperature-induced

inhibition of photosystem II using chlorophyll a fluorescence induction kinetics in

wheat leaves (Triticum aestivum). Plant Biology 13: 1–6.

Maxwell K, Johnson GN (2000) Chlorophyll fluorescence-a practical guide. Journal of


McAusland L, Davey PA, Kanwa N, Baker NR, Lawson T (2013) A novel system for spatial

and temporal imaging of intrinsic plant water use efficiency. Journal of Experimental

Botany dio:10.1093/jxb/ert288.

Melis A (1999) Photosystem-II damage and repair cycle in chloroplasts: what modulates

the rate of photodamage in vivo? Trends in Plant Science 4: 130–135.

Millan-Almaraz JR, Torres-Pacheco I, Duarte-Galvan C, Guevara-Gonzalez RG, Contreras-

Medina LM, Romero-Troncoso R de J, Rivera-Guillen JR (2013) FPGA-based wireless

smart sensor for real-time photosynthesis monitoring. Computers and Electronics in

Agriculture 95: 58–69.

Millan-Almaraz JR, Romero-Troncoso RdeJ, Guevara-Gonzalez RG, Contreras-Medina

LM, Carrillo-Serrano RV, Osornio-Rios RA, Duarte-Galvan C, Rios-Alcaraz MA, Torres-

Pacheco I (2010) FPGA-based fused smart sensor for real-time plant transpiration

dynamic estimation. Sensors 10: 8316–8331.

Morison JIL (1998) Stomatal response to increased CO2 concentration. Journal of


Muraoka H, Tang Y, Terashima I, Koizumi H, Washitani I (2000) Contributions of

diffusional limitation, photoinhibition and photorespiration to midday depression of

photosyntheis in Arisaema heterophyllum in natural high light. Plant, Cell and

Environment 23:235–50.

Murata N, Takahashi S, Nishiyama Y, Allakhverdiev SI (2007) Photoinhibition of

photosystem II under environmental stress. Biochimica et Biophysica Acta (BBA)-

Bioenergetics 1767: 414–421.

Murchie EH, Lawson T (2013) Chlorophyll fluorescence analysis: a guide to good practice

and understanding some new applications. Journal of Experimental Botany 64: 3983–

3998.

130

130 References

Nath K, Jajoo A, Poudyal SR, Timilsina R, Park SY, Aro ME, Nam GH, Lee HC (2013)

Towards a critical understanding of the photosystem II repair mechanism and its

regulation during stress conditions. FEEBS Letters, In Press.

Nederhoff EM, Gijzen H, Vegter JG, Rijsdijk AA (1989) Dynamic model for greenhouse

crop photosynthesis: Validation by measurements and application for CO2 optimization.


Nielsen OK, Winther M, Nielsen M, Mikkelsen MH, Albrektsen R, Gyldenkærne S,

Plejdrup M, Hoffmann L, Thomsen M, Hjelgaard K, Fauser P (2011) Projection of

greenhouse gas emissions 2010 to 2030. NERI Technical Report no. 841, Aarhus

University, National Environmental Research Institute, Roskilde, Denmark.

Nikolov NT, MassmanWJ, Schoettle AW (1995) Coupling biochemical and biophysical

processes at the leaf level: an equilibrium photosynthesis model for leaves of C3 plants.

Ecological Modelling 80: 205–235.

Nilsen ET, Orcutt DM (1996) Physiology of plants under stress: Abiotic factors. John wiley

and sons, New York, USA, pp 8–15.

Niyogi KK (1999) Photoprotection revisited: Genetic and Molecular Approaches. Annual

Review of Plant Physiolology and Plant Molecular Biology 50: 333–359.

Ogweno JO, Song XS, Hu WH, Shi K, Zhou YH, Yu JQ (2009) Detached leaves of tomato

differ in their photosynthetic physiological response to moderate high and low

temperature stress. Scientia Horticulturae 123: 17–22.

ÒShaughnessy SA, Evett SR, Colaizzi PD, Howell TA (2011) Using radiation thermography

and thermometry to evaluate crop water stress in soybean and cotton. Agricultural

Water Management 98: 1523–1535.

Osmond CB (1994) What is photoinhibition? Some insights from comparison of shade and

sun plants. In: Baker NR, Bowyer JR (eds) Photoinhibition of photosynthesis: From

molecular mechanisms to the field. Bio Scientific Publishers, Oxford, UK, pp 1–24.

Osmond CB, Grace SC (1995) Perspective on photoinhibition and photorespiration in the

field: quintessential inefficiencies of the light and dark reactions of photosyntheis?.

Journal of Experimantal Botany 46: 1351–1362.

Ottosen CO, Rosenqvist E, Sørensen L (2003) Effect of a dynamic climate control on

energy saving, yield and shelf life of spring production of bell peppers (Capsicum

annuum L.). European Jornal of Horticultural Science 68: 26–31.

131

131 References

Oukarroum A, Madidi ES, Schansker G, Strasser JR (2007) Probing the responses of

barley cultivars (Hordeum vulgare L.) by chlrorophyll a fluorescence OLKJIP under

drought stress and re-watering. Environmental and Experimental Botany 60: 438–446.

Ounis A, Evain S, Flexas J, Tosti S, Moya I (2001) Adaptation of a PAM-fluorometer for

remote sensing of chlrophyll fluorescence. Photosynthetic Research 68: 113–120.

PASEGES (2008) Greenergy: Energy optimisation in European greenhouse. Final activity

report, Coll-CT-2005-012566, Athens, Greece.

Peak D, Mott KA (2011) A new, vapour-phase mechanism for stomatal responses to

humidity and temperature. Plant, cell and environment 34: 162–178.

Peet MM (1999) Greenhouse crop stress management. Acta Horticulturae 481: 643–655.

Peňuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing

plant physiological status. Trends in Plant Science 3: 151–156.

Percival GC (2005) The use of chlorophyll fluorescence to identify chemcial and

environmental stress in leaf tissue of three oak (Quercus) species. Journal of

Arboriculture 31: 215–227.

Peri LP, Moot JD, McNeil LD (2005) Modelling photosynthetic efficiency (α) for the light -

response curve of cocksfoot leaves grown under temperatue field conditions. Europian

Journal of Agronomy 22: 277–291.

Pieruschka R, Klimov D, Kolber ZS, Berry JA (2010) Monitoring of cold and light stress

impact on photosynthesis by using the laser induced fluorescence transient (LIFT)

approach. Fuctional Plant Biology 37: 395–402.

Porcar-Castell A, Pfündel E, Korhonen JFJ, Juurola E (2008) A new monitoring PAM

fluorometer (MONI-PAM) to study the short-and long-term acclimation of photosytem

II in field conditions. Photosynthesis Research 96: 173–179.

Powles SB (1984) Photoinhibition of photosynthesis induced by visible light. Annual

Review of Plant Physiology 35: 15–44.

Qian T, Elings A, Dieleman JA, Gort G, Marcelis LFM (2012) Estimation of photosynthesis

parameters for a modified Farquhar-von Caemmerer-Berry model using simultaneous

estimation method and nonlinear mixed effects model. Environmental and


Retta A, Vanderlip RL, Higgins RA, Moshier LJ, Feyerherm AM (1991) Suitability of corn

grwoth model for incorporation of weed and insect stresses. Agronomy Journal 83: 757–

765.

132

132 References

Rosenqvist E, Wingsle G, Ögren E (1991) Photoinhibition of photosynthesis in intact

willow leaves in response to moderate changes in irradiance and temperature.

Physiologia Plantarum 83: 390–96.

Sage RF, Kubien DS (2007) The temperatue response of C3 and C4 photosynthesis. Plant,

cell and environment 30: 1086–1106.

Salvucci ME, Osteryoung KW, Crafts-Brandner SJ, Vierling E (2001) Exceptional

sensitivity of Rubisco activase to thermal denaturation in vitro and in vivo. Plant

Physiology 127: 1053–1064.

Salvucci ME, Crafts-Brandner SJ (2004) Inhibition of photosynthesis by heat stress: the

activation state of Rubisco as a limiting factor in photosynthesis. Physiologia Plantarum

120: 179–186.

Sarlikioti V, Driever SM, Marcelis LFM (2010) Photochemical reflectance index as a mean

of monitory early water stress: Annals of Applied Biology 157: 81–89.

Schrader SM, Wise RR, Wacholtz WF, Ort DR, Sharkey TD (2004) Thylakoid membrane

responses to moderately high leaf temperature in Pima cotton. Plant Cell and


Schreiber U, Schliwa U, Bilger W (1986) Continuous recording of photochemical and non-

photochemical chlorophyll fluorescence quenching with a new type of modulation

fluorometer. Photosynthesis Research 10: 51–62.

Schreiber U, Bilger W (1987) Rapid assessment of stress effects on plant leaves by

chlorophyll fluorescence measurements. In: Tenhunen JD, Catarino EM (eds) Plant

response to stress. Springer-Verlag, Berlin, Germany, pp 27–53.

Schreiber U, Bilger W, Neubauer C (1994) Chlorophyll fluorescence as a non-destructive

indicator for rapid assessment of in vivo photosynthesis. In: Schulze ED, Caldwell MM

(eds) Ecophysiology of photosynthesis. Springer-Verlag, Berlin, Germany, pp 49–70.

Sciortino M, Mimmo T, Vitali G, Gianquinto G, Aaslying JM (2008) Application of a

photosynthesis model to optimize greenhouse climate management. Acta Horticulturae

801: 1035–1040.

Seefeldt SS, Jens JE, Fuerst EP (1995) Log-logistic analysis of herbicide dose-response

relationships. Weed Technology 9: 218–227.

Seginer I, Gary C, Tchamitchian M (1994) Optimal temperature regimes for a greenhouse

crop with a carbohydrate pool: A modelling study. Scientia Horticulturae 60: 55–80.

133

133 References

Sharkey TD, Schrader SM (2008) High temperature stress. In: Rao KVM, Raghavendra AS,

Reddy KJ (eds) Physiology and molecular biology of stress tolerance in plants. Springer,

Dordrecht, The Netherlands, pp 101–130.

Solomon S, Qin D, Manning M, Alley RB, Berntsen T, Bindoff NL, Chen Z, Chidthaisong A,

Gregory JM, Hegerl GC, Heimann M, Hewitson B, Hoskins BJ, Joos F, Jouzel J, Kattsov

V, Lohmann U, Matsuno T, Molina M, Nicholls N, Overpeck J, Raga G, Ramaswamy V,

Ren J, Rusticucci M, Somerville R, Stocker TF, Whetton P, Wood RA, Wratt D (2007)

Technical summary. In: Solomon S, Qin D,.Manning M, Chen Z, Marquis M, Averyt KB,

Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment

Report of the Intergovernmental Panel on Climate Change Climate Change: The

Physical Science Basis. Cambridge University Press, Cambridge, UK.

Song L, Chow SW, Sun L, Li C, Peng C (2010) Acclimation of phtosystem II to high

temperature in two Wedelia species from different geographical origins: implications for

biological invasions upon global warming. Journal of Experimental Botany 61: 4087–

4096.

Spitters CJT (1986) Separating the diffuse and direct component of global radiation and its

implicaions for modeling canopy photosyntheis part II. Calculation of canopy

photosynthesis. Agricultural and Forest Meteorology 38: 231–242.

Srivastava A, Guissé B, Greppin H, Strasser RJ (1997) Regulation of antenna structure and

electron transport in PSII of Pisum sativum under elevated temperature probed by the

fast polyphasic chlorophyll a fluorescence transient: OKJIP. Biochimica et Biophysica

Acta (BBA)-Bioenergetics 1320: 95–106.

Stanghellini C (1987) Transpiration of Grenhouse Crops: an aid to climate management.

PhD Dissertation, Wageningen, The Netherlands.

Statistics Denmark (2013) National Statistics Database (Statistikbanken), Statistics

Denmark, Copenhagen, Denmark.

Steppe K, De Pauw DJ, Lemeur R, Vanrolleghem PA (2006) A mathematical model linking

tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth. Tree

Physiology 26: 257–273.

Steppe K, De Pauw DJ, Lemeur Raoul (2008) A step towards new irrigation scheduling

strategies using plant-based measurements and mathematical modeling. Irrigation

Science 26: 505–517.

Steppe K (2012) Plant sensors and Models: Getting the dialogue started. Acta


134

134 References

Stirbet A, Govindjee (2011) On the relation between the Kautsky effect (chlorophyll a

fluorescence induction) and Photosystem II: Basics and applications of the OJIP

fluorescence transient. Journal of Photochemistry and Photobiology B: Biology 104:

236–257.

Strasser RJ, Srivastava A, Govindjee (1995) Polyphasic chlorophyll a fluorescence transient

in plants and cyanobacteria. Photochemistry and Photobiology 61: 32–42.

Strasser RJ, Srivastava A, Tsimilli MM (2000) The fluorescence trransient as a tool to

chracterize and screen photosynthetic samples In: Mohammad Y, Uday P, Prasanna M

(eds) Probing photosynthesis: Mechanism, regulation and Adaptation. Taylor and

Francis, London, pp 443–480.

Štroch M, Vráble D, Podolinská J, Kalina J, Urban O, Špunda V (2010) Acclimation of

Norway spruce photosynthetic apparatus to the combined effect of high irradiance and

tempeature. Journal of Plant Physiology 167: 597–605.

Sundby C, Melis A, Mäenpää P, Andersson B (1986) Temperature-dependent changes in

the antenna size of Photosystem II. Reversible conversion of Photosystem IIα to

Photosystem IIβ. Biochimica et Biophysica Acta (BBA) - Bioenergetics 851: 475–483.

Sharkey T (1985) Photosynthesis in intact leaves of C3 plants: Physics, physiology and rate

limitations, The Botanical Review 51: 53–105.

Takahashi S, Badger MR (2011) Photoprotection in plants: a new light on photosystme II

damage. Trends in Plant Science 16: 53–60.

Takahashi S, Murata MR (2008) How do environmental stresses accelerate

photoinhibition? Trends in Plant Science 13:178–82.

Tarnopolsky M, Seginer I (1999) Leaf temperature error from heat conduction along

thermocoouple wires. Agricultural and Forest Meteorology 93: 185–194.

The Danish government (2011) Energy strategy 2050 from coal, oil and gas to green

energy, pp 1–29.

Thorpe MR, Butler DR (1977) Heat transfer coefficients for leaves on orchard apples trees.

Boundary-Layer Meteorology 12: 61–73.

Tuzet A, Perrier A, Leuning R (2003) A coupled model of stomatal conductance,

photosynthesis and transpiration. Plant, cell and Environement 26: 1097–1116.

Tyystjärvi E (2013) Photoinhibition of Photosystem II. Internatinal Review of Cell and

Molecular Biology 300: 243–303.

Udink ten Cate, AJ, Challa H (1984) On optimal computer control of the crop growth

system. Acta Horticulturae 148: 267–276.

135

135 References

Van Der Tol C, Verhoef W, Rosema A (2009) A model for chlorophyll fluorescence and

photosynthesis at leaf scale. Agricultural and Forest Meteorology 149: 96–105.

Vass I (2012) Molecular mechanisms of photodamage in the photosystem II complex.

Biochimica et Biophysica Acta (BBA)-Bioenergetics 1817: 209–217.

Vermeulen K, Aerts JM, Dekock J, Bleyaert P, Berckmans D, Steppe K (2012) Automated

leaf temperature monitoring of glasshouse tomato plants by using a leaf energy balance

model. Computers and Electronics in Agriculture 87: 19–31.

Villez K, Steppe K, De Pauw DJ (2009) Use of unfold PCA for on-line plant stress

monitoring and sensor failure detection. Biosystems Engineering 103: 23–34.

Vogelmann TC, Evans, JR (2002) Profiles of light absorption and chlorophyll within

spinach leaves from chlorophyll fluorescence. Plant, Cell & Environment 25: 1313–1323.

Von Caemmerer S, Farquhar GD (1981) Some relationships between the biochemistry of

photosynthesis and the gas exchange of leaves. Planta 153: 376–387.

Wahid A, Gelani S, Ashraf M, Foolad MR (2007) Heat tolerance in plants: An overview.

Environmental and Experimental Botany 61: 199–223.

Walters RG (2005) Towards and understanding of photosynthetic acclimation. Journal of


Wang X, Yang W, Wheaton A, Cooley N, Moran B (2010) Automated canopy temperature

estimation via infrared thermography: A first step towards automated plant water stress

monitoring. Computers and Electronics in Agriculture 73: 74–83.

Wagn J, Yu Q, Li J, Li LH, Li XG, Yu GR, Sun XM (2006) Simulation of diurnal variations

of CO2, water and heat fluxes over winter wheat with a model coupled photosynthesis

and tranpirstion. Agriculture and Forest Meteorolgoy 137: 194–219.

Wang M, Ling N, Dong X, Zhu Y, Shen Q, Guo S (2012) Thermographic visualizaiton of leaf

response in cucumber plants infected with th soil-borne pathogen Fusarium oxysporum

f.sp. cucumerinum. Plant Physiology and Biochemistry 61: 153–161.

Wang M, Xiong Y, Ling N, Feng X, Zhong Z, Shen Q, Guo S (2013) Detection of the

dynamic response of cucumber leaves to fusaric acid using thermal imaging. Plant

Physiology and Biochemistry 66: 68–76.

Weis E, Berry JA (1988) Plants and high temperature stress. Symposia of the Society of

Experimental Biology 42: 329–346.

Werner C, Correia O, Beyschlag W (2002) Characteristic patterns of chronic and dynamic

photoinhibition of different functional groups in a Mediterranean ecosystem. Functional

Plant Biology 29: 999–1011.

136

136 References

Wheeler RM, Mackowiak CL, Yorio NC, Sager JC (1999) Effects of CO2 on stomatal

conductance: Do stomata open at very high CO2 concentrations? Annals of Botany 83:

243–251.

Wilhelm C, Selmar D (2011) Energy dissipation is an essential mechanism to sustain the

viability of plants: The physiological limits of improved photosynthesis. Journal of Plant

Physiology 168:79–87.

Willits DH, Peet MM (2001) Measurement of chlorophyll fluorescence as a heat stress

indicator in tomato: Laboratory and greenhouse comparisons. Journal of the American

Society for Horticultural Science 126: 188–194.

Wise RR, Olson AJ, Schrader SM, Sharkey TD (2004) Electron transport is the funtional

limitation of photosynthesis in field-grwon Pima cotton plants at high temperature.

Plant, Cell and Environment 27: 717–724.

Wright H, DeLong J, Lada R, Prange R (2009) The relationship between water status and

chlorophyll a fluorescence in grapes (Vitis spp.). Postharvest Biology and Technology 51:

193–199.

Yamada M, Hidaka T, Fukamachi H (1996) Heat tolerance in leaves of tropical fruit crops

as measured by chlorophyll fluorescence. Scientia Horticulturae 67: 39–48.

Yamamoto Y, Aminaka R, Yoshioka M, Khatoon M, Komayama K, Takenaka D, Yamashita

A, Nijo N, Inagawa K, Morita N, Sasaki T, Yamamoto Y (2008) Quality control of

photosystem II: impact of light and heat stresses. Photosynthesis Research 98: 589–

608.

Yamane Y, Kashino Y, Koike H, Satoh K (1997) Increases in the fluorescence Fo level and

reversible inhibition of Photosystem II reaction centre by high-temperature treatments

in higher plants. Photosynthesis Research 52: 57–64.

Yamane Y, Kashino Y, Koike H, Satoh K (1998) Effects of high temperatures on the

photosynthetic systems in spinach: oxygen-evolving activities fluorescence

characteristics and the denaturation process. Photosynthesis Research 57: 51–59.

Yang X, Wen X, Gong H, Lu Q, Yang Z, Tang Y, Liang Z, Lu C (2007) Genetic engineering

of the biosynthesis of glycinebetaine enhances thermotolerance of photosystem II in

tobacco plants. Planta 225: 719–733.

Yanhong Z, Lam HM, Zhang J (2007) Inhibition of photosynthesis and energy dissipation

induced by water and high light stresses in rice. Journal of Experimental Botany 58:

1207–17.

137

137 References

Yin X, Struik PC (2009) C3 and C4 photosynthesis models: An overview from the

perspective of crop modelling. NJAS-Wageningen Journal of Life Sciences 57: 27–38.

Zhang R, Sharkey TD (2009) Photosynthetic electron transport and proton flux under

moderate heat stress. Photosynthesis Research 100: 29–43.

Zhou Y, Lam HM, Zhang J (2007) Inhibition of photosynthesis and energy dissipation

induced by water and high light strsses in rice. Journal of Experimental Botany 58:

1207–1217.

Zhu X, Govindjee, Baker NR (2005) Chlorophyll a fluorescence induction kinetics in leaves

predicted from a model describing each discrete step of excitation energy and electron

transfer associated with Photosystem II. Planta 223: 114–133.

Zushi K, Kajiwara S, Matsuzoe N (2012) Chlorophyll a fluorescence OJIP transient as a tool

to characterize and evaluate response to heat and chilling stress in tomato leaf and fruit.

Scientia Horticulturae 148: 39–46.

Zweifel R, Böhm JP, Häsler R (2002) Midday stomatal closure in Norway spruce–

reactions in the upper and lower crown. Tree Physiology 22: 1125–1136.

139

139 Summary

Summary

A dynamic greenhouse climate control regime is based on plant physiology, outside

solar irradiance, and greenhouse crop microclimate. Solar irradiance under a dynamic

control system showed increased temperature fluctuations compared to a traditional

control system. Consequently, plants utilized both temperature and irradiance to maximize

the photosynthetic rate, provided other resources were not limiting. The system optimized

carbon gain at high irradiance, and reduced energy consumption at low irradiance.

However, this type of climate control regime might create potentially extreme greenhouse

microclimatic conditions (e.g. high temperature and light). High temperature affects the

photosynthetic apparatus of photosystem II (PSII), and therefore net photosynthesis (Pn)

directly, and stomatal conductance (gs) indirectly, resulting in a lower photosynthetic rate.

In fact, photosynthesis exhibited a temperature optimum, dependent on irradiance and

CO2 concentration. Excess light can result in photoinhibition, i.e. photo-inactivation of the

photosynthetic apparatus. Some plants are acclimated to tolerate excess irradiance using

different physiological mechanisms; however the ultimate result of high temperature and

light stress is photoinhibitory and photooxidative damage to the photosynthetic apparatus.

Moreover, with the advance in physiological methods (e.g. gas exchange, chlorophyll

fluorescence, and thermography), it might be possible to determine and monitor

physiological plant responses to prevailing stressors. Thus, this project focused on

understanding the following: i) the optimum physiological response, and reliable

physiological indicators in chrysanthemum to high temperature and light stress; ii)

potential physiological methods and online monitoring tools useful as early stress

indicators; and iii) continuous monitoring systems applications with crop models for real-

time stress detection.

In Chapter 2.1, high temperature effects on photosynthesis were investigated by

analysing photosystem II (PSII), and stomatal conductance (gs). In this study, high

temperature was imposed on detached leaves and intact plants, and a combination of

chlorophyll a fluorescence, gas exchange, and infrared thermography was applied to

chrysanthemum (Dendranthema grandiflora Tzvelev) „Coral Charm‟. High temperature

decreased PSII maximum photochemical efficiency (Fv/Fm), the conformation term for

primary photochemistry (Fv/Fo) and performance index (PI), as well as increased minimal

fluorescence (Fo). High temperature effects were significant on PSII when the temperature

exceeded 38 °C, showing the critical temperature limit of PSII. The effect was 6-9% greater

140

140 Summary

in heat stressed detached leaves than intact plants. The fluorescence induction curves

showing the complete fluorescence transient indicated the typical polyphasic rise (OJIP)

until the temperature reached 39 °C. At high temperatures (> 39 °C) the final P step of the

curve, equivalent to maximum fluorescence, decreased. Moreover, at 45 °C an additional

response to extremely high temperature stress (K peak) was observed at 300 ms. The net

photosynthesis (Pn) reached a maximum at 35 °C, elevated CO2 of 1000 µmol mol-1, and a

photosynthetic photon flux density (PPFD) of 800 µmol m-2 s-1. Thermography was

applied, and the thermal index (IG) showed a strong correlation with gs. These results

indicated chlorophyll a fluorescence, and a combination of fluorescence parameters can be

employed as early stress indicators, as well as to detect the temperature limit for PSII

damage. Furthermore, the strong relationship between gs and IG enabled non-invasive gs

estimates.

In Chapter 2.2, the combined high temperature and high light effects were investigated

on net photosynthesis (Pn), and the following four Chlorophyll a fluorescence parameters:

Fv/Fm, electron transport rate (ETR), PSII operating efficiency (F'q/F'm), and non-

photochemical quenching (NPQ) in chrysanthemum under different temperatures (20, 24,

28, 32, 36 °C), and daily light integrals (DLI; 11, 20, 31, and 43 mol m-2 created by a PAR of

171, 311, 485, and 667 µmol m-2 s-1 for 16 h). The highest light level had a significant

negative effect on Fv/Fm at high temperatures (> 32 °C), and at the highest light level, the

maximum Pn and ETR were reached at 24 °C. In addition, increased light decreased PSII

operating efficiency (F'q/F'm), and increased NPQ, while both high light and temperature

had a significant effect on PSII operating efficiency at temperatures exceeding 28 °C. PSII

maximum efficiency (Fv/Fm) acclimation over time for plants under high light and low

temperature (below 28 °C) conditions potentially indicates that PSII is protected by a

mechanism that dissipates excess energy (NPQ). Moreover, under high irradiance and

temperature, NPQ changes determined PSII operating efficiency, with no major change in

the fraction of open PSII centres (qL) (indicating a QA redox state). This indicated that

chrysanthemum plants tolerated excess irradiance by non-radiative dissipation or a

reversible stress response, with the effect on Pn and PSII quantum yield remaining low

until the temperature reached 28 °C.

In Chapter 3.1, the temperature dose causing a 50% reduction (T50) in chlorophyll

fluorescence parameters was estimated using the log-logistic model of the temperature

dose response curve. Chrysanthemum leaves were treated with temperatures from 24 to 45

°C. Initial fluorescence kinetics (OJIP curve) was applied to characterise high temperature

141

141 Summary

effects on PSII for four selected parameters (Fv/Fm, RC/ABS, Fv/Fo, and PI). The model

estimated upper and lower limits, and T50 caused 50% reduction in Fv/Fm and Fv/Fo was 41

°C and 39 °C, respectively. The PSII thermo-tolerance critical temperature limit in

chrysanthemum leaves was estimated at 38 °C. This study suggested the physiological

information combined with model response curves of chlorophyll a fluorescence

parameters can be used to estimate the PSII critical temperature limits. Furthermore, the

log-logistic model temperature dose response curve can be used to determine T50 for any

greenhouse crop treated with high temperature stress.

In Chapter 3.2, climate sensors and crop models were used. The following three sub-

models were tested: the biochemical C3 photosynthesis model, the stomatal conductance

model, and the leaf energy balance model. The respective models were calibrated and

tested to predict net leaf photosynthesis (Pnl), stomatal conductance (gs), and leaf

temperature at different microclimatic conditions. Pnl, gs, and leaf temperature predictions

were validated with independent data. The model showed significant temperature and CO2

effects on leaf photochemical efficiency, and a linear decrease with increase temperature.

The coupled model estimated Pnl and leaf temperature, resulting in R2 = 0.98 and R2 =

0.97, respective values, while gs was estimated with a R2 = 0.78 value. Observed leaf

temperatures showed leaf temperature was primarily affected by net radiation absorbed by

leaf and air temperatures using the leaf energy balance sub-model. Furthermore, the total

heat transfer in the leaf energy balance model influenced most leaf temperature estimates.

Results indicated the model will be valuable in assisting decisions to optimize light,

temperature, and CO2 for maximum photosynthetic rates. In addition, the model has

potential use as a plant stress monitoring tool, and for real-time stress detection in

dynamic greenhouse climate control regimes.

In Chapter 3.3, the multilayer leaf model was applied to model fluorescence emissions

from actinic light (F') adapted leaves, maximal fluorescence from light-adapted leaves

(F'm), F'q/F'm, and ETR. A linear function was used to approximate F' from several

measurements under constant and variable light conditions (growth chamber and

greenhouse). Model performance was evaluated using the root mean square error (RMSE)

and mean square error (MSE) of observed and predicted values. The model exhibited high

predictive values for F'q/F'm and ETR under different temperature and light conditions

with low RMSE and MSE. The model predictive values for F'q/F'm were relatively high at 24

°C and 28 °C. The model predictive values for F' and F'm were low due to the weak F'

correlation under constant (R2 = 0.48) and variable (R2 = 0.35) light. However, with better

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142 Summary

fluorescence parameter estimates, considering all factors that affected chlorophyll

fluorescence, the model prediction capacity might be improved. In addition, the model

implementation is simple using the online microclimate data measurements to monitor

photosynthesis using chlorophyll fluorescence.

Finally, this thesis provided in depth information on high temperature and high light

effects on photosynthesis, chlorophyll fluorescence, and stomatal conductance. Moreover,

the thesis addressed potential physiological methods and online monitoring tools with

simple and mechanistic models for real-time stress detection. Finally, the general

discussion, conclusion, thesis contribution, and future research opportunities were

discussed in Chapter 4.1, 4.2, 4.3, and 4.4, respectively.

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143 Acknowledgements

Acknowledgements

First and above all, I praise God, the almighty for providing me this opportunity and strength to

complete my PhD successfully. This thesis appears in its current form due to the assistance and

guidance of several people. I would therefore like to offer my sincere thanks to all of them.

Foremost, I want to express my deep thanks to my supervisors, Assoc. Prof. Carl-Otto Ottosen,

Dr. Oliver Kröner and Assoc Prof. Eva Rosenqvist for their timely guidance and continuous

sport throughout my PhD study. I have highly benefited from your follow-up and constructive

criticism which has improved my research work and accomplish my project according to my PhD

plan.

I have special thanks to all science technicians of our research group Kaj Ole Dideriksen, Ruth

Nielsen, Helle Kjærsgaard Sørensen and Connie Damgaard. I have completed all my

experiments without any problem because of your kind help. I always appreciate your positive mind

and answers to my entire request about my experiments with no delay. A special thanks goes to Dr.

Katrine Heinsving Kjær for her kind advice and encouragement during my difficult times.

Indeed, I was so fortunate to be in Aarhus University, Aarslev filled with a positive working

environment and a good sense of team spirit. Therefore, I must thank to all staff at Aarslev. The coffee

break, social club, Christmas party, PhD dinner, paper writing weeks, Friday seminar, group

meetings, journal club are few among many which I always remember about Arslev. Allow me to

forward my special thanks to Camilla Fjord, Tina Lillelund Magaard and Dianne Solvang for

their kind help I have received during my PhD time in matters related to administration, travel and

finance.

My three year PhD has given me a chance to know many fellow PhD students at Aarslev which I

would like to thank all for the best time I enjoyed with them during the journal club, PhD lunch and

PhD parties. Special thanks also goes to PhD student at our group Habtamu, Sabibul, Natasa,

Theoharis, Tek and Azad for all good time we had together and issues we discussed and food

parties we enjoyed. I may lift up my special friend, I consider him my younger brother Habtamu

and I give my special thanks for all his brotherhood and accompany during our stay together for

three years. I would say the three year long journey became shorter because you were beside me in

the same office and sharing the same living house. I never forget all those best time we had together. I

also wish to thank Yuxiaqing and Rong for their kind help and welcoming me during my short stay

in China. I enjoyed China because you were with me and indeed the Chinese food while I was in China

as well as during your research visit at Aarslev.

Go beyond all, my special thank is to my love and my wife Lili. I succeeded this PhD because you

were behind me. No one can imagine that you took responsibility for two children for three years and

let me to finish my PhD with extraordinary freedom. I thank you that you are a great wife and I am

happy to be with you forever. Indeed, I must thank also my boys Barkot and Filimon though they

were little to know what PhD is but they tolerated those years missing me every day at home.

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144 Acknowledgements

At this moment, I wish to thank Finn Apesland, and Karin Apesland for their special help and

spiritual support to my family. Similarly I will like to forward my thanks to all members of Porsgrunn

Frikirke (Church) in Norway and Jubilee Centre International Church of Odense, Denmark for their

spiritual support and brotherhood.

I dedicated this thesis for the memory of my father Janka Wakjera who passed away while I was

doing my PhD. I never forget his love, care and blessing in all my life. I forward my warm love and

thanks to my mother Tsegaye Weldeyohanes and to the rest of the big family. I was so blessed and

I thank God for placing me in that loving and great family. Here, a special thanks goes to my elder

brother Girma Janka who is always my inspiration in life.

Last but not least I would like to extend my sincer thanks to all my wife family specially to my

mother in law Askalech Tegegn who always reminds me not to forget my job while I spend more

time with my kinds during my visits. I would like also to extend my sincere thanks to Dr. Joanna

Schultz for her excellent English proofreading and editing my thesis.

Finally, I would like to acknowledge the financial support from the Danish high technology

foundation for the project itGrows and for the additional support from the European regional

development fund (ERDF) and EU project GreenGrowing.

145

145 List of publications

List of publications

Papers published/submitted/to be submitted in refereed journals

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) High temperature stress monitoring and

detection using chlorophyll a fluorescence and infrared thermography in chrysanthemum

(Dendranthema grandiflora). Plant physiology and Biochemistry 67: 87–94.

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) Using the quantum yields of

photosystem II and the rate of net photosynthesis to monitor high irradiance and

temperature stress in chrysanthemum (Dendranthema grandiflora) (submitted)

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) A coupled model of leaf photosynthesis,

stomatal conductance, and leaf energy balance for chrysanthemum (Dendranthema

grandiflora) (to be submitted)

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013) PSII operating efficiency simulation

from chlorophyll fluorescence in response to light and temperature in chrysanthemum

(Dendranthema grandiflora) using a multilayer leaf model (to be submitted).

Conference proceedings

Janka E, Körner O, Rosenqvist E, Ottosen CO (2012) Log-logistic model analysis of optimal

and supra-optimal temperature effect on photosystem II using chlrophyll a fluorescence in

chrysanthemum (Dendranthema grandiflora). Acta Horticulturae 957: 297–302.

147

147 Certificate and post graduate courses

PhD certificates and Post graduate courses

Introduction course for PhD students at DJF (2 ETC)

Applied methods in crop physiology (5 ETS)

Applied statistics with R for the agricultural, life and veterinary (6 ECTS)

Increasing photosynthesis in plants (2 ECTS)

Photosynthesis, from metabolic regulation to gas exchange in intact leaves (4 ECTS)

Visual display of quantitive information in applied plant science (2 ECTS)

Writing scientific paper in English (5 ECTS)

Introductory MATLAB (basic statistics and programming) (3 ECTS)

Food PhD seminar (1 ECTS)

Modeling climate effects on crops and cropping systems (5 ECTS)

Participation in international workshops and conferences

The fourth international symposium on models for plant growth, environment

control and farm management in protected cultivation November 4th - 8th, 2012,

Nanjing, China.

Cover illustration: A leaf cliped with MONI-PAM (Photo: Helle Kjærsgaard Sørensen)

On the Understanding of Climate Tolerance and Early Plant Stress...

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