On the Understanding of Climate Tolerance and Early Plant Stress...
Transcript of 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
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On the Understanding of Climate Tolerance and Early Plant Stress
Detection in Greenhouse Cultivation
PhD Thesis
Eshetu Janka
December 2013
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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
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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.
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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
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For the memory of my Father
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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
(Dendranthema grandiflora) 55
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
(Dendranthema grandiflora) 85
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
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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
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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)
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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)
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1
General introduction
CHAPTER 1
General introduction
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%
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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
General introduction
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).
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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).
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General introduction
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
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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
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General introduction
in relationship to net photosynthesis measurements, which can be utilized to detect
different crop stress conditions (Millan-Almaraz et al. 2013).
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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
Second/ Minute/ Hour
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
Second/ Minute/ Hour
Broader application Image processing Peñuelas and Filella 1998,Chaerle et al. 2001
Multispectral fluorescence and reflectance imaging
Reflectance/fluorescence Leaves /Canopy
Second/ Minute/ Hour
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
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General introduction
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).
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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).
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General introduction
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
General introduction
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
General introduction
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
High temperature stress
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
High temperature stress
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
High temperature stress
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
High temperature stress
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
High temperature stress
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
High temperature stress
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
High temperature stress
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
High temperature stress
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
(Dendranthema grandiflora)
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
37 High temperature and high light stress
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.
Materials and Methods
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
39 High temperature and high light stress
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
41 High temperature and high light stress
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
43 High temperature and high light stress
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
45 High temperature and high light stress
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
47 High temperature and high light stress
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
49 High temperature and high light stress
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
51 High temperature and high light stress
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
(Dendranthema grandiflora)
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.
Materials and Methods
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
57 Crop models and monitoring plant stress
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
59 Crop models and monitoring plant stress
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.
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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.
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61 Crop models and monitoring plant stress
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.
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63 Crop models and monitoring plant stress
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.
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65 Crop models and monitoring plant stress
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.
Materials and Methods
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
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67 Crop models and monitoring plant stress
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
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69 Crop models and monitoring plant stress
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).
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71 Crop models and monitoring plant stress
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.
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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.
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73 Crop models and monitoring plant stress
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)
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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).
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75 Crop models and monitoring plant stress
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
77 Crop models and monitoring plant stress
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
Leaf temperature (oC)
15 20 25 30 35 40
Leaf temperature (oC)
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
79 Crop models and monitoring plant stress
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.
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81 Crop models and monitoring plant stress
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
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83 Crop models and monitoring plant stress
Symbol Description Units value
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
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85 Crop models and monitoring plant stress
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
87 Crop models and monitoring plant stress
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
89 Crop models and monitoring plant stress
Materials and Methods
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
91 Crop models and monitoring plant stress
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
93 Crop models and monitoring plant stress
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
95 Crop models and monitoring plant stress
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
97 Crop models and monitoring plant stress
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.
Symbol Description Units value
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
103 General discussion
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
105 General discussion
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
107 General discussion
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
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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
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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
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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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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.
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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.
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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)