Institut für Pflanzenernährung
der Rheinischen Friedrich-Wilhelms-Universität
zu Bonn
Modelling concepts for sodium and potassium uptake in rice plants
as a function of transpiration
Wissenschaftliche Arbeit
für die
Magisterprüfung
zur
Erlangung des Grades
Magister der Agrarwissenschaften (M.Agr.)
der
Landwirtschaftlichen Fakultät
der
Rheinischen Friedrich-Wilhelms-Universität zu Bonn
Vorgelegt am 19-03-2004
von
Uday Sankar Das
aus
Bangladesh
Institut für Pflanzenernährung
der Rheinischen Friedrich-Wilhelms-Universität
zu Bonn
Modelling concepts for sodium and potassium uptake in rice plants
as a function of transpiration
Thesis submitted
in
Partial fulfillment of the requirements
for the Masters of Agriculture (M.Agr.)
of the
Faculty of Agriculture
University of Bonn
Submitted on 19-03-2004
by
Uday Sankar Das
from
Bangladesh
i
ERKLÄRUNG Ich versichere, dass ich diese Arbeit selbstständig verfasst habe, keine
anderen Quellen und Hilfsmittel als die angegebenen benutzt und die Stellen
der Arbeit, die anderen Werken dem Wortlaut oder dem Sinn nach
entnommen sind, kenntlich gemacht habe.
Die Arbeit hat in gleicher oder ähnlicher Form keiner anderen
Prüfungsbehörde vorgelegen.
Bonn, den 19. März 2004
Uday Sankar Das
ii
1. Supervisor :Dr. Folkard Asch
2. Supervisor :Professor Dr. Mathias Becker
3. Chairman :Professor Dr. Karin Holm-Müller
iii
ACKNOWLEDGEMENT
I would like to express my thanks to all who helped me directly and indirectly
to complete my work. I am very grateful to Dr. Folkard Asch, my first
supervisor, for his encouragement and suggestion to carry out this study and
his continuous guidance, advice throughout the study period. I am indebted to
my second supervisor Professor Dr. Mathias Becker for his suggestions, and
inspiration to carry out the research work.
I wish to express my gratitude to Dr. Monika Wimmer for her suggestions and
for proof reading at short notice. I wish to express my deep sense of gratitude
to all of my teachers in ARTS for their benevolent cooperation and
encouragement during the course of study. I would like to express my
gratitude to all the members of the Institute of Plant Nutrition, University of
Bonn. I am also thankful to Keshav Prasad Dahal and Lili Wittmaeir for helping
me with the laboratory and greenhouse work.
This study was supported by the German Academic Exchange Service
(DAAD) who is gratefully acknowledged. Finally, my sincere gratitude to my
family for their patience, help and support during my two years study period.
iv
DEDICATION
I dedicate this work
to my Parents
Mr. Purna Chandra Das and Mrs. Bakul Rani Das
v
ABSTRACT Models can be used to study and understand plant physiological processes. In
order to understand the traits enabling resistance to salt models are needed to
simulate sodium uptake and distribution in the rice plant. Sodium uptake in
rice is transpiration driven, therefore this model must be able to simulate the
transpirational behaviour of the plant. Transpiration occurs on the leaf blades,
therefore the model must be able to predict the number and sizes of leaves,
particularly leaf blades. Any changes in this, due to a stress the model should
reflect in order to predict the actual amount of sodium that can be taken up to
the plant. This study was conducted to develop: (1) a mathematical
description of leaf appearance in irrigated rice as affected by salinity (2) a
concept to describe leaf development and leaf senescence level for different
leaves (3) calculate water loss from individual leaves (4) a mathematical
description of the physiological activity level of different leaves during their
entire life span (5) concept for sodium and potassium uptake and distribution
as related to the transpiration of the particular leaf. Leaf appearance pattern
was hastened under salt stress compared to control conditions but in the
same environment genotypes did not differ. Leaf development stages were
defined, ranging from –1 (leaf initiation) over 0 (full extension) to +1 (100 %
senescence). Under salinity leaf development differed from control conditions
and among genotypes. The concept of leaf senescence as a driving force for
leaf appearance seemed to be present independent of treatment and
genotypic. Sodium uptake was linearly related to water loss. The tolerant
genotype accumulated less sodium per unit of water that was lost from the
leaves than the sensitive genotype. Tissue level tolerance to and the
distribution of sodium in the plant seemed to be depending of the respective
potassium concentration of the tissue. Leaf sheaths retained more sodium
when more potassium was present and leaf senescence rate was less when
potassium concentrations in the leaf blades were high. A concept of
transpiration driven sodium and active potassium uptake and distribution is
included in the model structure.
Table of contents
vi
Table of contents Page No
Erklärung i
Acknowledgement iii
Dedication iv
Abstract v
Table of Contents vi
List of Photos viii
List of Tables viii
List of Figures ix
1. Introduction 1
1.1 Introduction 1
1.2 Hypotheses 2
1.3 Objectives of the study 2
2. Literature review 4
2.1 Leaf appearance 4
2.2 Temperature effects on the appearance of leaves 5
2.3 Leaf senescence 6
2.4 Tillering 7
2.5 Sodium and potassium distribution 7
2.6 Leaf transpiration 9
2.7 Existing models 9
3. Materials and methods 13
3.1 Experimental site and plant material 13
3.2 Seed germination and transplantation 14
3.3 Hydroponic systems 15
3.3.1 Hydroponic system I 15
3.3.2 Hydroponic system II 15
3.4 Growth conditions 16
3.5 Leaf /tiller nomenclature 16
3.6 Experimental set up 17 3.6.1 Experiment-I: Tiller and leaf number, leaf appearance rate,
leaf elongation, senescence and photosynthesis 17 3.6.2 Experiment II: Photosynthesis of individual leaves of the main culm 18
Table of contents
vii
3.6.3 Experiment III: Response of transpiration of irrigated rice genotypes to air humidity and salinity 19
3.7 Analysis of Na+ and K+ 19
3.8 Data analysis 19
4. Results 20
4.1 Leaf development, appearance and senescence 20
4.1.1 Leaf development 20
4.1.2 Leaf appearance 21
4.1.3 Leaf senescence 22
4.2 Transpiration and humidity 26
4.2.1 Leaf area 26
4.2.2 Transpiration rate 27
4.2.3 Water loss 28
4.2.4 Humidity effects on transpiration 30
4.3 Sodium and potassium uptake into individual leaves 33
4.3.1 Tiller number 33
4.3.2 Leaf number 34
4.3.3 Leaf area of individual leaves 35
4.3.4 Transpiration rates of individual leaves 36
4.3.5 Leaf K/Na ratio 38
4.3.6 Sodium and potassium uptake into individual leaves 40
5. Discussion 45
5.1 The “root filter” concept 45
5.2 Leaf development, leaf appearance and senescence 46
5.3 Water loss 49
5.4 Leaf level transpiration 50
5.5 Humidity effects on transpiration 51
5.6 Sodium and potassium distribution into individual leaves 52
5.7 Tillering pattern 53
5.8 Model concepts 54
5.9 Conclusion 58
6. References 59
Appendix
List of photos and tables
ix
LISTS OF PHOTOS
PLATES Page No
Plate 1 Hydroponic system I: culture pots are connected by
plastic tubes 15
Plate 2 Hydroponic system II: Individual culture pots are not connected 15
Plate 3 Different colours threads used to identify leaf and
tiller number 17
Plate 4 Photosynthesis measuring by ADC-LCA4 porometer
and infra-red gas analyzer 18
LISTS OF TABLES
TABLES
Table 1 Composition of Yoshida culture solution 16
Table 2 Overview of the experimental set up 17
Table 3 Leaf area of individual leaves of the two rice genotypes 35
Table 4 Leaf transpiration rate for individual leaves of two genotypes 37
List of Figures
x
LISTS OF FIGURES
FIGURES Page No
Fig.1 Schematic representation of the model ORYZA2000 in the situation of potential production 11 Fig.2 Schematic representation of the model ORYZA2000 (gray area)
and its links to the water balance subroutines, under the situation of water-limited production 12
Fig.3 Schematic representation of the model ORYZA2000 (gray area) and its links to the nitrogen balance subroutines under for the situation of nitrogen-limited production 12
Fig.4 Daily minimum and maximum temperatures and minimum
and average relative humidity recorded in the greenhouse 13
Fig.5 Development stages of different leaves of IR 31785 and IR 4630 20
Fig.6 Leaf duration of two genotypes under control and saline condition 21
Fig.7 Leaf appearance patterns of IR 31785 and IR 4630 under saline
and non-saline conditions 22
Fig.8 Time course versus canopy senescence level at leaf initiation 23
Fig.9 Relationship between leaf initiation and leaf appearance of the
two rice genotypes under both control and saline condition 24
Fig.10 Relationship between leaf initiation and leaf fully extended of the
two rice genotypes under both control and saline condition 24
Fig.11 Relationship between leaf initiation and onset of senescence of the
two rice genotypes under both control and saline condition 25
Fig.12 Relationship between leaf initiation and 75% senescence of the
two rice genotypes under both control and saline condition 25
Fig.13 Relationship between leaf initiation and 100% senescence of the
two rice genotypes under both control and saline condition 26
Fig.14 Leaf area for two rice genotypes from greenhouse experiment I 27
Fig.15 Relationship between transpiration rates of individual leaves
and time of two rice varieties in control and salt conditions 28
Fig.16 Water loss calculated for different leaf positions 29
Fig.17 Observed versus simulated combined water loss of the two
genotypes under saline and non-saline conditions 30
Fig.18 Transpiration rate versus median relative humidity of weekly of
8 genotypes from greenhouse experiment III 31
List of Figures
xi
Fig.19 Transpiration rates of different leaf layers versus daily minimum
relative humidity of two varieties from experiment I 32
Fig.20 Tiller number of the two rice genotypes 33
Fig.21 Distribution of leaf numbers on leaf layers for two rice genotypes
from experiment I. 34
Fig.22 Relationship between leaf level transpiration rates and leaf
development stages of two rice genotypes in saline and
non-saline conditions 36
Fig.23 K / Na ratio of leaf blades and sheaths of IR 31785
under both saline and non-saline conditions 38
Fig.24 K / Na ratio of leaf blades and sheaths of IR 4630 under
both saline and non-saline conditions 39
Fig.25 Relationship between leaf sodium content and water loss
under saline condition 40
Fig.26 Relationship between leaf sodium content and water loss
under control condition 41
Fig.27 Relationship between leaf potassium content and water loss of
individual leaves under saline condition 42
Fig.28 Relationship between leaf potassium content and water loss of
individual leaves under control condition 43
Fig.29 (A) Leaf blade sodium versus leaf blade potassium content from two rice genotypes (B) Leaf sheath sodium versus leaf potassium content from same genotypes 44
Fig.30 Relationship between leaf numbers and degree-days in the
greenhouse 47
Fig.31 Diurnal trend of canopy CO2 assimilation 50
Fig.32 Relationship between relative tillering rate and relative growth
rate of two rice genotypes under control and saline conditions. 54
Fig.33 Relational diagram representing tiller senescence level as well as
stomatal conductance 56
Fig.34 A conceptual diagram for sodium and potassium uptake and
distribution 57
Introduction
1
1. INTRODUCTION
1.1 Introduction
Salinity is one of the major problems in irrigated rice cropping systems,
decreasing rice production worldwide. Rice tolerates growing in submerged
soils, making it a well-suited crop to control and reduce salt concentration
levels in the soil by proper irrigation and drainage techniques (Asch et al.,
1997). In areas where some degree of salinity cannot be avoided and in the
costly process of soil regeneration, rice varieties tolerant to moderate levels of
salinity can be grown to provide some immediate economic return for the
farmer. However, rice is a salt susceptible crop and yield losses due to salinity
can be substantial ( Asch et al., 1997)
Salt stress affects the entire rice plant at all developmental stages, but the
sensitivity to salt varies between different growth stages (Fageria, 1985). Rice
is relatively tolerant during germination, in the vegetative growth phase and at
maturity. Leaves will suffer from excess sodium taken up into the blade
impeding photosynthesis and their growth.
Rice genotypes subjected to salinity employ different strategies to cope with
excess sodium accumulated in the leaves. The most prominent strategies are
avoidance or tolerance of critical salt levels in active tissues. Salt resistance in
rice is probably based on a combination of both avoidance and tolerance
traits. Sodium uptake into the rice plant is thought to be passive and related to
the transpirational volume (Yeo et al., 1987). Sodium uptake into the plant and
distribution within the plant seems a priori to be regulated by root properties
and transpiration (Yeo et al., 1984, 1985, 1987). Transpiration rates depend
on the leaf's physiological status, e.g. leaf nitrogen content (Dingkuhn et al.,
1992) and water status (Dingkuhn et al., 1989). Transpiration is driven by
environmental conditions e.g. temperature, relative humidity, wind speed and
solar radiation.
Models provide a means of integrating physiological knowledge, variety traits
and environmental data to generate new information. However, at present no
models are available to simulate differential responses of varieties to a given
environment and research is required to address this shortcoming (Zhov,
Introduction
2
2002). The ability of simulation models to predict growth and development as
affected by soil and weather conditions, agronomic practice and cultivar traits
may make models attractive tools for cropping systems research (White,
1998).
Models to date simulate at best a canopy with three layers differing in
physiological activity. This is problematic when dealing with accumulative
stresses such as salinity as the stress affects the individual leaf at all
development stages. In order to predict the efficiency of traits related to
immobilisation of sodium and tissue tolerance to salinity, models need to
simulate individual leaves and their development independently of the
biophysical environment, thereby, robust tools to predict the senescence level
of a rice canopy grown under e.g. saline conditions.
1.2 Hypotheses
To date, no model exists for single rice plants. It is assumed that:
1. Na uptake is passively driven by leaf transpiration.
2. The concentration of sodium in any tissue is directly related to the
amount of water that passed through the tissue and knowing, that the
root is no sink for sodium but the leaf blades and sheaths are.
3. Individual leaf level transpiration rates depend on the genotype, the
sodium stress level of the leaf and the leaf’s development stage.
4. Canopy senescence level is the same for all stages of leaf
development.
5. Leaf appearance is slowed down and leaves die-off earlier under
salinity.
6. The control of water loss under salinity is a trait to resist salinity.
1.3 Objectives of the study
Based on these hypotheses, the following objectives of the study were
formulated:
Introduction
3
1. Develop a concept to describe leaf development and leaf
senescence level for different leaves.
2. Develop a mathematical description of leaf appearance patterns in
irrigated rice as affected by salinity.
3. Calculate water loss from individual leaves.
4. Develop a mathematical description of the physiological activity
level of different leaves during their entire life span.
5. Develop a concept for sodium and potassium uptake and
distribution as related to the transpiration of the particular leaf.
Literature review
4
2. LITERATURE REVIEW
2.1 Leaf appearance
Yoshida (1981) mentioned that a leaf of rice plants emerge approximately
every 4-5 days in early stages of development and approximately every 7-8
days in later stages. The rate of leaf emergence is affected by temperature.
When the rice plant is grown at 20ºC, leaves emerge every 5 days; when it is
grown at 25ºC, they emerge every 4 days before panicle primordial initiation.
In terms of temperature summation index, the development of one leaf
requires about 100 degree-days before the panicle primordial initiation and
approximately 170 degree-days thereafter. The concept of temperature
summation presupposes that the growth or development of a plant is linearly
related to temperature or the total amount of heat to which it is exposed.
Leaves elongate quickly after emergence, complete their elongation, and start
functioning. The life span of individual leaves after elongation differs widely
among leaf positions.
Sie et al. (1998) describe a simulation model for leaf appearance based on
several phenological assumptions and empirical relationships between organ
initiation or appearance rates and external conditions (water and air
temperatures, photoperiod). The basic assumptions are (i) a non-inductive
Basic Vegetative Phase (BVP) exists, which is best described not by a fixed
period of time but by a fixed number of leaves that appear during that period,
(ii) after BVP, an inductive period (IP) begins during which leaves continue to
appear until the panicle has been initiated (PI event); (iii) after PI, no further
leaves are initiated, thereby limiting subsequent leaf appearances to the few
leaves that had already been initiated at PI. This model generates a
succession of leaves, onto which development events such as “end of BVP /
beginning of IP” and “panicle initiation” are overlaid, thereby terminating leaf
production at a specific date. The model has the surprising consequence that
crop duration varies on the basis of quantum steps, each quantum being
equivalent to the time elapsing from one leaf initiation to the next
(approximately 1 week). Because the panicle is initiated in the same growing
Literature review
5
point that produced the leaves, leaf and panicle initiation are not regarded as
independent events.
2. 2 Temperature effects on the appearance of leaves
According to Sie et al. (1998) the duration from seed soaking to the
emergence of the first leaf (henceforth termed germination) range from 4 to 13
days. The development rate was linearly correlated with the water
temperature. The extrapolated base temperature for germination was 8°C,
with the 5% confidence interval ranging from 6°C to 9.5°C. Within the
observed range (15-27°C), the germination rate responded linearly to an
increase in temperature indicating that the optimum temperature for
germination was greater than 27°C. According to Yoshida (1981), the
germination of rice tolerates a particularly wide range of temperatures (10-
45°C), with the optimum temperature between 20 °C and 35°C.
Sie et al. (1998) reported that the appearance rate of the first to the fourth
leaves of different rice varieties followed a parabolic temperature response
with a distinct optimum temperature. For example IR 13240 had a distinct
optimum temperature at 22°C, associated with a pronounced decrease in leaf
appearance rate below and above that value.
Yin et al. (1995) described a nonlinear model for crop development as a
function of temperature. The Beta function used as a skewed probability
density function in statistics was introduced to describe the effect of
temperature on the rate of crop development. The framework was set by three
cardinal temperatures, namely the base (Tb), the optimum (To) and the ceiling
(Tc) temperature.
Birch et al. (1998) also reported that temperature controls the rate of leaf
appearance. A critical variable for modelling plant development and growth is
the phyllochron, which represents the thermal interval between the
appearance of successive leaf tips. The phyllochron varies under different
environmental conditions, but is generally constant for a given species grown
in specific environments. For example, the phyllochron of maize is lower in
temperate as compared to tropical and subtropical climates. Birch et al. (1998)
Literature review
6
showed that the phyllochron in maize is related to light intensity. They also
suggested that the phyllochron depends on the adequacy of current
photosynthesis (source) to meet the demands of the plant for growth (sink),
one aspect of which is the production of new leaves. Birch et al. also report on
concepts and early progress in relating the phyllochron to both irradiance and
thermal time. So far existing data have only been evaluated under a narrow
range of environmental conditions and the underlying mechanisms have not
been adequately examined. Models generally use constant values of
phyllochron that have been determined for a particular environment.
2. 3 Leaf senescence
Salinity applied at the seedling stage frequently induces premature
senescence of leaves (Sahu and Mishra, 1987; Yeo et al.,1991). Leaf
senescence is most often quantified by decreases in protein or chlorophyll
concentrations (Kurra-Hotta et al., 1987 and Hashimoto et al.,1989) and by
increases in membrane permeability (Dhindsa et al.,1981). Specific effects of
salt stress on leaf senescence have been related to the accumulation of toxic
ions (Na+ and Cl¯) or to K+ and Ca++ depletion (Yeo and Flowers, 1983; Yeo et
al.,1991).
Lutts et al. (1996) studied the NaCl induced senescence in leaves of rice
cultivars differing in salinity resistance. Senescence is a normal process
during leaf ontogeny. NaCl hastened the occurrence of senescence in rice
leaves. It decreased chlorophyll and protein concentrations and increased
membrane permeability and malondialdehyde synthesis. This acceleration of
deteriorative processes affected all leaves in salt-sensitive cultivars, while it
was more pronounced in older as composed to younger leaves in salt-
resistant genotypes.
Lizaso et al. (2003) presented a leaf area model to simulate cultivar-specific
expansion and senescence of maize leaves. The model describes three
processes within the life cycle of leaves: leaf expansion, leaf longevity and leaf
senescence. Leaf expansion refers to the increase in surface area of the leaf
blade during leaf growth from leaf appearance to full size. Leaf longevity can
Literature review
7
be described as the period that a leaf requires to grow from 50% of its full size
to the stage where 50% of the leaf surface area is senescent. A leaf is
senescent when its surface area exhibits a loss of green colour (chlorosis) and
cell death (necrosis). Lizaso´s model simulates leaf expansion and leaf
senescence independently for each leaf. Calculations are performed on a per
leaf basis because current environmental conditions and stresses are known
to affect specific processes on individual leaves. Three cultivars-specific
inputs are used to distinguish between genotypes. These are the area of the
largest leaf (cm2), the longevity of the longest living leaf (growing degree days,
base temperature 8°C) and the final leaf number.
Plaut et al. (2000) studied leaf development, transpiration and ion uptake and
distribution in sugarcane cultivars grown under salinity. Salinity reduced leaf
dry weight and leaf area and to a lesser extent, transpiration. Genotypes
responded differently to saline conditions. In a tolerant cultivar, salinity hardly
reduced the average leaf area, while the number of leaves declined sharply.
This decline was caused by an enhanced senescence of mature leaves and
not by a decreased rate of leaf initiation. In a sensitive cultivar, salinity strongly
reduced leaf area and leaf initiation, while leaf senescence was less affected.
2. 4 Tillering
Tivet et al. (2000) showed that the tillering capacity of rice plants depends on
the genotype, available resources for growth and the level of physiological
stress. The tiller number depends on overall biomass growth rather than on
the number of buds. Relative tillering and tiller formation is a function of
relative growth rate (Schnier et al.,1990a). Tillering is followed by a tiller
abortion phase which is particularly pronounced in direct seeded rice
(Dingkuhn et al.,1991).
2. 5 Sodium and potassium distribution
Tester and Davenport (2002) described that leaves of higher plants were more
vulnerable to damage by Na+ than roots, simply because Na+ accumulates to
higher concentrations in shoots than in roots. Roots tend to maintain fairly
Literature review
8
constant levels of NaCl over time, and can regulate NaCl levels by export
either to the soil or to the shoot. Na+ is transported to shoots in the rapidly
moving transpiration stream in the xylem, but can only be returned to roots via
the phloem. There is only limited evidence of recirculation of Na+ within the
plant, suggesting that Na+ transport is largely unidirectional and results in
progressive accumulation of Na+ as leaves age. It has been shown for wheat
that salt tolerant genotypes have the ability to partly exclude Na+ from the
shoot, or at least the leaf blades, and concurrently maintain high levels of K+
(Munns et al., 2000b)
According to Sultana et al. (1999) salinity affects plant water relations and
ionic relations. During initial exposure to salinity, plants experience water
stress, which in turn reduces leaf expansion. During long-term exposure to
salinity, plants also experience ionic stress, which can lead to premature
senescence of adult leaves, and thus a reduction in the photosynthetic area
available to support continued growth (Cramer & Nowak, 1992). Reduced
photosynthesis due to increasing salinity can be attributed to either stomatal
closure, leading to a reduction in intracellular CO2 partial pressure, or non-
stomatal factors (Bethke & Drew, 1992). There is increasing evidence that
salinity changes photosynthetic parameters, including osmotic and leaf water
potential, transpiration rate, leaf temperature and relative leaf water content.
Salt also affects photosynthetic components such as enzymes, chlorophylls
and carotenoids. Salt stress decreases photosynthetic activity and inhibits
foliar growth (Hu and Schmidhalter, 1998). The degree of damage was
positively correlated to the external Na+ concentration. In several crops,
limitation of Na+ uptake is a trait related to salinity tolerance (Gorham et al.,
1985). The photosynthetic rate (Yeo et al. 1985) and the growth rate (Akita &
Cabuslay, 1990) decreased as the Na+ content increased in the shoots.
Mitsuya et al. (2002) has studied the relationship between the distribution of
Na+ and the damages caused by salinity in the leaves of rice seedlings grown
under saline conditions. In each leaf, the Na+ content was higher in the leaf
sheaths as compared to the leaf blades. Na+ content was higher in older
leaves than in younger leaves in salt treated rice plants (Mitsuya et al.,2002).
Literature review
9
Na+ is not distributed uniformly in the plant but accumulated in the older
leaves (Yeo & Flowers, 1983). Many studies of closely related varieties have
shown that low Na+ concentrations in leaves correlated with salt tolerance
(Yeo and Flowers.,1983). Genotypic differences in Na+ uptake rates as well as
cellular tolerance to Na+ may cause different patterns of Na+ accumulation and
distribution with time. Due to the uneven distribution of Na+ within the plants,
analysis of whole shoot Na+ concentrations is not suitable to detect genotypic
differences in Na+ concentrations of single leaf blades (Schachtman et al.,
1992)
2. 6 Leaf transpiration
Transpiration behaviour may change in the presence of salt and in response
to relative humidity. Without stomatal control, salt uptake to the leaves
strongly dependent on relative humidity (Asch et al., 2003). Theoretically, the
amount of sodium accumulated in a rice leaf should be directly correlated to
the amount of water lost from that leaf’s surface. Therefore, total Na+ uptake
should be related to the total amount of water transpired by the plant.
Yeo (1985) reported that the youngest leaf blades of rice plants showed the
highest transpiration rates but the lowest Na+ accumulation rates under saline
conditions. Conversely, the older leaves showed lower transpiration rates and
a greater accumulation of Na+. This observation might be related to the fact
that the apparent concentration of Na+ was 44 times lower in the xylem stream
reaching the younger as compared to the older leaves. When rice seedlings
grow in relatively low external salinities different leaves accumulate different
concentrations of Na+. Pronounced leaf to leaf gradients developed rapidly as,
the older leaves quickly reach toxic sodium concentrations at first while, the
youngest leaves remain practically free of Na+ (Yeo and Flowers, 1983).
2. 7 Existing Models
Bastiaans (1993) developed a model for the calculation of the diurnal course
of instantaneous canopy photosynthesis in rice. The model comprises routines
for the calculation of photosynthesis and respiration for crop growth (Vries et
al., 1989) and routines to generate diurnal trends of radiation and temperature
Literature review
10
on the basis of daily weather data. The procedure of calculation of the total
canopy photosynthesis was basically identical to the procedure described by
Spitters et al., (1986). Subsequently daily global irradiance was used to
generate diurnal trends of total, direct and diffuse radiation reaching the top of
the canopy (Spitters et al.,1986). After accounting for reflection, the light
profile within the canopy was determined by calculating the decrease in
radiation flux with canopy depth for both diffuse and direct radiation
(Goudriaan, 1988). On the basis of both light profiles, absorption can be
calculated for any depth within the canopy. Substitution into the
photosynthesis-light response of single leaves gives the assimilation rates per
unit leaf area at the concerning depth within the canopy. In this model five
canopy layers were distinguished and the instantaneous photosynthesis per
canopy layer was calculated using a three point Gaussian integration
(Goudriaan, 1988). Total instantaneous canopy photosynthesis was obtained
by adding the photosynthesis rates of the five layers. A negative exponential
function, comprising the total assimilation rate at light saturation and the initial
light use efficiency was used to describe the CO2 assimilation light response
of individual leaves. Light saturation was related to the leaf N content (g N
m¯²), according to the relationship derived by Vries et al. (1989). To date, no
model exists that is based on individual plants.
Asch et al. (1997) developed a conceptual model for sodium uptake and
distribution in rice on the basis of the following observations:
1) sodium uptake into the plant was driven by transpiration;
2) varieties differed in the way they regulated their stomata in relation to
realtive humidity and salt stress;
3) sodium uptake was modulated at the root level by a ´root filter´; and
4) sodium was taken out of the transpiration stream and retained in the
sheaths;
The model describes the passive uptake of sodium into and its distribution
within the plant as a function of several varietal constants and of transpiration.
Bouman et al. (2001) developed ORYZA 2000. The growth and development
of rice can be simulated with the model ORYZA 2000. Figure 1 shows the
Literature review
11
general structure of the model. Under conditions of potential production, light,
temperature, and varietal characteristics for phenological, morphological, and
physiological processes determine the growth of the crop (Figure1). The
model follows the daily calculation scheme for the rates of dry matter
production of the plant organs and for the rate of phenological development.
By integrating these rates over time, dry matter production of the crop is
simulated throughout the growing season.
The daily rate of total canopy CO2 assimilation is calculated from the daily
incoming radiation, temperature, and the leaf area index. The model contains
a set of subroutines that calculate the daily rate by integrating instantaneous
rates of leaf CO2 assimilation over time and depth within the canopy. The
calculation is based on an assumed daily sinusoidal time course of radiation
and on an exponential light profile within the canopy. On the basis of the
photosynthesis characteristics of single leaves, which depend on the N
concentration, the photosynthesis profile in the canopy is obtained. Integration
over the leaf area index of the canopy gives the daily CO2 assimilation rate.
After subtraction of respiration requirements, the net daily growth rate in kg
dry matter per ha per day is obtained. The dry matter produced is partitioned
among the various plant organs.
Figure 1: Schematic representation of the model ORYZA2000 in the situation of potential production. Boxes represent state variables, valves represent rate variables, and circles represent intermediate variables. Flow of material is symbolised by solid lines, flow of information by dotted lines
Literature review
12
Figure 2: Schematic representation of the model ORYZA2000 (gray area) and its links
to the water balance subroutines, under the situation of water-limited production.
Figure 3: Schematic representation of the model ORYZA2000 (gray area) and its links
to the nitrogen balance subroutines under for the situation of nitrogen-limited
production.
Materials and methods
13
3. MATERIALS AND METHODS
In order to achieve the above mentioned objectives, different rice genotypes
were grown under varying environmental conditions in a greenhouse in Bonn.
3.1 Experimental site and plant material
All experiments were conducted with hydroponic culture from March to May
2003 in a temperature-controlled greenhouse of the Institute of Plant Nutrition
at the University of Bonn, Germany. Climatic data were recorded automatically
by a Tiny tag Plus (TGP-1500, Gemini Data Loggers) located in the
greenhouse. In general, the average air humidity ranged between 14% and
54% and the minimum temperature was constantly at around 20ºC. Climatic
data from March to May 2003 are given in Figure 4.
Julian Date (JD)
78 88 98 108 118 128
Rel
ativ
e h
um
idit
y %
0
10
20
30
40
50rH min rH avg
Tem
per
atu
re o
C
15
20
25
30
35
40
T min T max
Figure 4: Daily minimum and maximum temperatures and minimum and average
relative humidity recorded in the greenhouse.
Materials and methods
14
The following rice genotypes were used for the experiments:
1. IR 4630-22-2 (henceforth IR 4630) medium duration, salt
tolerant, origin Philippines.
2. IR 31785-58-1-2-3-3 (henceforth IR 31785), improved semi-
dwarf, salt susceptible, short duration supplied by WARDA /
Senegal.
3. I Kong Pao (IKP), short duration salt tolerant, origin Taiwan.
4. IR 13240-108-2-3-3 (IR13240), semi dwarf, high yielding, salt-
susceptible, origin Philippines.
5. ITA 306, salt susceptible, origin Nigeria.
6. WAS 44-B-B-68-2.
7. WAS 30-11-1-4-6-1-2 .
8. WAS 30-11-1-4-6-1-1-3.
The genotype number 6, 7 and 8 are breeding lines developed at WARDA for
salt tolerance. Seeds were supplied by WARDA (Côte d'Ivoire) and IRRI (Los
Baños, Philippines). Seeds were soaked, pre-germinated in petri dishes for 48
hours and sown in plastic seed trays containing water and sand. The seeds
were kept for 21 days in the seedbed. Afterwards the seedlings were
transplanted to culture pots and grown in hydroponic solution.
3. 2 Seed germination and transplanting
Rice seedlings were grown in two different hydroponic systems. In all
experiments the seedlings were transplanted into plastic pots of 1.2 liter
volume each containing one litre of Yoshida solution (Table 1). The
preparation of stock solutions is described in the Appendix 1. The pots were
covered with aluminium foil to prevent algae growth. At transplanting the
seedlings were supported with a sponge just above the root to hold the
transplants in the culture pots.
Materials and methods
15
3. 3 Hydroponic systems
In the following section, the two hydroponic systems will be described in detail.
3.3.1 Hydroponic system I
Individually valve-regulated pots were connected to a system (Plate1)
supplying nutrient solution (circulated daily for about 1.5 hours) directly from a
buffer tank containing 60L of the respective treatment solution. The overflow
was drained back into the tanks by a separate drainage pipe in the system.
3. 3.2 Hydroponic system II
The seedlings were transplanted to individual pots not connected to a system
(Plate 2). All pots were filled/refilled individually. This system was chosen
because it allowed to measure the total water loss for each pot separately.
Plate 1: Hydroponic system I culture pots are connected by plastic tubes
. .
Plate 2: Hydroponic system II: Individual culture pots are not connected
Materials and methods
16
3.4 Growth conditions
In experiment II and III (see Table 2), the transplanted plants were grown for 3
weeks in pots containing 50 % Yoshida solution (hydroponic system II) Then,
they were allowed to grow for a further 3 weeks in pots containing 80 %
Yoshida solution. Afterwards, they were grown in 100 % Yoshida solution. In
experiment I, the transplanted plants were grown for 3 weeks in hydroponic
system I containing 50 % Yoshida solution. Afterwards, they were allowed to
grow in 100% Yoshida solution when treatments started. The solution in the
system was replaced once a week. The transpired volume was refilled with 25
% Yoshida solution in the system once a week. The pH was measured every
3 days and maintained at pH 5. Three weeks after transplanting, the plants
were subjected to the different treatments.
Table 1: Composition of Yoshida culture solution
Element Milliliters of stock solution per 1 liter of culture solution
Concentration of element nutrient solution (ppm)
N P K
Ca Mg Mn Mo B Zn Cu Fe
1.25 1.25 1.25 1.25 1.25
Combined micronutrients 1.25
40 10 40 40 40
0.50 0.05 0.20 0.01 0.01
2
3.5 Leaf / tiller nomenclature
The leaves on the main culm, primary tillers, secondary tillers and tertiary
tillers were marked with threads of different colours representing different
leaves and the observation dates were recorded. The nth leaf on the main culm
was described as MCn, on primary tiller as PTn, on secondary tiller as STn and
on tertiary tiller as TTn where MC, PT, ST and TT stand for main culm, primary
tiller, secondary tiller and tertiary tiller respectively. Thus MC1 refers to the first
leaf on the main culm, PT1; the first leaf on the primary tiller; ST1; the first leaf
on the secondary tiller and TT1; the first leaf on the tertiary tiller respectively.
Materials and methods
17
Plate 3: Different colour threads used to identify leaf and tiller number
3.6 Experimental set up
In the following section, the set up of the three experiments will be described
in detail. An overview is given in Table 2.
Table 2: Overview of the experimental set up
Experiment Genotypes Hydroponic system
Treatments Replications
I IR 4630 IR 31785
I 0 mmol and 60 mmol NaCl
Three
II IR 4630 IR 31785
II 0 mmol and 60 mmol NaCl
Three
III IR 4630 IR 31785 IKP IR 13240 ITA 306 WAS 44-B-B-68-2 WAS 30-11-1-4-6-1-2 WAS 30-11-1-4-6-1-1-3
II 0 mmol and 60 mmol NaCl
Three
3.6.1 Experiment-I: tiller and leaf number, leaf appearance rate, leaf
elongation, leaf duration, senescence and photosynthesis.
In experiment 1, two varieties were treated with two levels of salinity (0 and 60
mmol NaCl). Two varieties IR 4630 and IR 31785 were chosen because they
differ in their response to salt stress. The hydroponic system I was used for
this experiment. This system consisted of 24 pots per treatment holding one
Materials and methods
18
litre of nutrient solution each. Throughout the experiment leaf appearance, leaf
length, duration of existence and senescence were recorded for individual leaf
positions. Water loss from the buffer tank was also recorded at each change
of culture solution. Samplings were conducted at 10 day intervals starting 31
days after transplanting (10 days after the onset of treatments). Each
sampling consisted of non-destructive measurements of photosynthesis for
each leaf position and a destructive sampling of all plant organs. The plants
were separated into roots, dead leaves, leaf blades and sheaths by leaf
position. The leaf blade area was determined using a LiCor 3600 leaf area
meter. Tiller number and leaf number were recorded. All plant parts were
sampled into cellophane bags, oven dried at 70ºC until constant weight and
dry weights determined using a precision balance.
3.6.2 Experiment II:Photosynthesis of individual leaves of the main culm.
This experiment was conducted with two varieties two treatments (0 and 60
mmol NaCl) and three replications (Table 1). Hydroponic system II was used
for this experiment. Photosynthesis measurements started when sixth leaf
was developing. Photosynthesis rates were recorded for each individual leaf
level of the main culm at 7-day intervals between 11 am and 13 pm.
Throughout the experiment, leaf appearance and existence was also
observed.
Plate 4: Photosynthesis measuring by ADC-LCA4 porometer and infrared gas
analyser
Materials and methods
19
3.6.3 Experiment III: response of transpiration of rice genotypes to air
humidity and salinity.
This experiment was conducted with eight varieties (Table 2), two treatments
(0 and 60 mmol NaCl ) and three replications using the hydroponic system II.
Evapotranspirational water loss was determined by weighing each pot at 7-
day intervals. Evaporation was monitored using three pots containing no
plants. The total leaf area of each plant was determined at the onset and at
the end of the treatment period.
3.7 Analyses of sodium and potassium (Na+, K+)
Oven-dried plant tissue (roots, dead leaves, leaf blades and sheaths) from
experiment I was cut into fine pieces. Sub samples of less than 0.1 g were
weighed into centrifuge tubes (10 ml volume) and 7 ml of purified water added
and the tubes covered by aluminium foil. Tubes were then autoclaved under
pressure at 121 degree Celsius for 60 minutes. After cooling the samples
were centrifuged for 20 minutes at 4300 rpm and room temperature to
precipitate the solid particles. Samples from the centrifuge tube were either
directly or after filtering transferred into 100 ml volumetric flasks, made up to
volume with H2O and analysed directly for Na+ and K+ using a Flame
photometer (ATS 200, Advanced Technical Services GmbH).
3.8 Data analysis
For analysis of canopy senescence level, development stages were defined
as leaf initiation as (-1), leaf appearance as (-0.5), fully extended as 0, onset
of leaf senescence as 0.5 and leaf death as 1. The equations (y=
a+bx+cx²+dx³ and lny=a+bx+cx0.5) were used for the simulation of individual
leaf transpiration rates and areas. TableCurve 2D 5.01 and Ms Excel 2000
were used for the calculation of non-linear and linear functions. Graphical
representations were done using SigmaPlot 2001. Correlation matrix was
done by Statistica 5.1.
Results
20
4. RESULTS
In the following, the results of the different experiments will be shown
according to the objectives of the individual investigations.
4.1 Leaf development, leaf appearance and leaf senescence
4.1.1 Leaf development
Observations were made for all leaves on several dates at 7-day intervals on
main culm and defined as leaf initiation as (-1), leaf emergence (0.5), fully
extended as (0), onset of leaf senescence as (0.5) and leaf death (1). Based
on recorded data daily extrapolation were done of all leaves. Figure 5 shows
that the tolerant genotype (IR 4630) had developed 7 leaves at 40 days after
sowing where as the same genotype had developed 8 leaves under saline
Days after sowing
10 20 30 40 50 60 70 80
Leaf development stage
-1.0
-0.5
0.0
0.5
1.0
10 20 30 40 50 60 70 80
-1.0
-0.5
0.0
0.5
1.0
Days after sowing
10 20 30 40 50 60 70 80
-1.0
-0.5
0.0
0.5
1.0
Days after sowing
10 20 30 40 50 60 70 80
-1.0
-0.5
0.0
0.5
1.0
Tolerant - IR 4630-22-2 Susceptible - IR 31785-58-1-2-3
+ Salt + Salt
Control Control
end source deathEmergenceonset source
fully extended
onset senescence 75 % senescenceInitiation
Figure 5: Development stages of different leaves of IR 31785-58-1-2-3-3 and IR
4630-22-2
Results
21
Conditions. Under control conditions, the sensitive genotype (IR31785) had
developed 6 leaves 40 days after sowing, however, in the same thermal
environment the same genotype developed 8 leaves under saline conditions.
In both genotypes and treatments, linear functions described the development
of the leaf from initiation to leaf fully extension and from full extension to
death. Under salinity leaf development differed from control and among
genotypes. In the sensitive genotype, leaves developed and died-off faster
under salinity, whereas in the tolerant genotype leaves appeared earlier, but
lasted about as long as under control conditions (shown in Figure 6) thus the
exchange in the canopy with fresh leaf material was faster.
Total leaf duration in days (Control)
15 20 25 30 35 40 45
To
tal l
eaf
du
ratio
n in
day
s (S
alin
ity)
15
20
25
30
35
40
45
L1
L2L3L4
L5
L6L7
L8
L1
L2L3
L4 L5L6
L7 L8
IR4630-22-2
IR31785-58-1-2-3-3
Figure 6: Leaf duration of two genotypes under control and saline condition
4.1.2 Leaf appearance
Figure 7 shows the time course for leaf appearance under control and
stressed conditions. Leaf initiation and appearance were hastened under salt
stress for both genotypes, however, in the same thermal environment
genotypes did not differ in leaf appearance pattern under control conditions
Results
22
Days after sowing
0 10 20 30 40 50 60 70
Lea
f p
osi
tio
n
0
2
4
6
8
10
12
14
susceptible control
susceptible + salttolerant control
tolerant + saltpredicted controlpredicted susecptiblepredicted tolerant
Leaf appearance patterns
function used:y = a+bx+clnxother functions possible
Figure 7: Leaf appearance patterns for IR 31785-58-1-2-3-3 and IR 4630-22-2 under
saline and non-saline conditions.
4.1.3 Leaf senescence
Figure 8 shows the overall senescence level for the main culm and different
leaf development stages. Development stages were defined as leaf initiation
as (-1), leaf emergence (0.5), fully extended as (0), onset of leaf senescence
as (0.5) and leaf death (1). Daily Individual leaf development stages were
calculated (shown in Figure 5) and then existing leaves on main culm at leaf
initiation were added to calculate canopy senescence level. The canopy
senescence level at leaf initiation excluding dead leaves was on average
about two for both genotypes, when the first one and a half leaves had passed
(Figure 8). There seemed to be a genotype independent concept for leaf
initiation under both salinity and normal conditions based on the overall
canopy senescence level. Canopy senescence level at leaf initiation followed
up to 20 days after sowing parabolic function in both genotypes.
Results
23
Figure 9 shows the relationship between leaf initiation and leaf appearance of
the two rice genotypes under both control and saline condition. Figure 9
showed that correlation between leaf initiation and leaf appearance was high
in both genotypes under both control and saline condition. Figure 10 shows
the relationship between leaf initiation and leaf fully extended. Relationship
was weaker compare to leaf appearance. Figure 11 shows the relationship
between leaf initiation and onset of senescence of the two rice genotypes
under both control and saline condition. Sensitive genotype showed 45 days
after sowing (DAS) at leaf initiation, onset of senescence was approximately
60 DAS under salinity but onset of senescence was approximately 82 DAS
under control condition. However, in tolerant genotype did not differ. Figure 12
shows relationship between leaf initiation and 75 percent senescence.
Sensitive genotype showed 25 DAS at leaf initiation, 75 percent senescence
was approx. 50 DAS under salinity but 68 DAS was under control condition.
Tolerant genotype showed 25 DAS at leaf initiation, 75 percent senescence
was approx. 68 DAS under salinity but 80 DAS was under control condition.
Figure 13 shows the relationship between leaf initiation and 100 percent
senescence. Figure 13 showed correlation was better under saline condition
compared to control condition in both genotypes.
0 10 20 30 40 50 60 70 80
Days after sowing
0 10 20 30 40 50 60 70 80
Can
op
y se
nes
cen
ce le
vel a
t le
af in
itia
tio
n
-2
0
2
4
6
8
10
12
tolerantIR 4630
susceptibleIR 31785
CSL - control(including dead leaves)CSL - controlwithout dead leavesCSL - salinity(including dead leaves)CSL - salinity(without dead leaves)
Figure 8: Time course versus canopy senescence level at leaf initiation
Results
24
Leaf
app
eara
nce
(DA
S)
Leaf initiation (DAS) Figure 9: Relationship between leaf initiation and leaf appearance of the two rice
genotypes under both control and saline conditions.
Figure 10: Relationship between leaf initiation and leaf fully extended of the two rice
genotypes under both control and saline conditions.
Results
25
Figure 11: Relationship between leaf initiation and onset of senescence of the two rice
genotypes under both control and saline conditions.
Figure 12: Relationship between leaf initiation and 75 percent senescence of the two
rice genotypes under both control and saline conditions.
Results
26
Figure 13: Relationship between leaf initiation and 100 percent senescence of the two
rice genotypes under both control and saline conditions.
4.2 Transpiration and humidity
4.2.1 Leaf area
Leaf areas were measured for all leaf positions at 4 dates in ten-day intervals
starting 31 days after transplanting for two rice genotypes (tolerant IR 4630-
22-2 and susceptible IR 31785-58-1-2-3-3). In Figure 14 leaf area over time is
illustrated. Salinity significantly reduced both total and individual leaf area
already at the first date and increasingly so at the later dates. Reductions in
leaf area were less severe in the tolerant genotypes (shown in Figure 14) as
compared to the susceptible one where total leaf area was reduced by more
than 50%.
Results
27
IR 31758-58-1-2-3-3
0
100
200
300
400
LB1 LB2LB3LB4Total leaf
Control
IR 4630-22-2
To
tal l
eaf
area
(cm
2 )
0
200
400
600
800
1000
1200Control
Days after transplanting
25 30 35 40 45 50 55 60
Lea
f are
a (c
m2 )
0
100
200
300
400Salt
25 30 35 40 45 50 55 600
200
400
600
800
1000
1200
Salt
Figure 14: Leaf area for two rice genotypes from greenhouse experiment I. Control
treatment was 0 mmol NaCl and salt treatment was 60 mmol NaCl. Error bar =
standard error of mean. LB1, LB2, LB3 represent topmost leaf 1, 2 and 3. LB4 =
oldest leaf.
4.2.2 Transpiration rate
The transpiration rates of different leaves were measured from greenhouse
experiment I for two genotypes at the fifth leaf stage namely L0 (youngest not
yet fully developed leaf), L1, L2, L3 and L4 from the top level during the
period. In Figure 15 transpiration rates of leaf positions over time are
illustrated. Figure 15 shows that the transpiration rates of the young leaves
were greater than of the older leaves in both control and salt conditions. But in
case of leaf zero it was difficult to measure because L0 leaf was too small for
accurate transpiration rate measurements. Figure 15 also shows that leaf 1 of
IR 31785 had the highest transpiration rate after 70 days of transplanting in
control treatments but in IR 4630, leaf 1 had highest transpiration rate after 65
Results
28
days of transplanting but leaf one of IR 4630 also had the highest transpiration
rate in control treatment.
IR 31785-58-1-2-3-3
Tra
nsp
irat
ion
rat
e (m
mo
l m-2
S-1
)
1
2
3
4
5
6
7
L1
L2 L3 L4
Control
Days after transplanting
50 60 70 801
2
3
4
5
6
7
Salt
IR 4630-22-2
Control
50 60 70 80
Salt
Figure 15: Relationship between transpiration rates of individual leaves and time of
two rice varieties in control and salt conditions. L1, L2, L3 represent topmost leaf 1, 2
and 3. L4 = oldest leaf.
4.2.3 Water loss
Total water loss from the system (experiment I) was known. The system
comprised of two genotypes differing in transpiration rates and leaf area. In
order to know which of the genotypes was used how much water, it was
necessary to determine leaf area and transpiration rates for individual leaf
levels on a daily basis to estimate maximum leaf transpiration accumulating
from existing leaves over time.
In order to calculate the water loss from the individual leaf blades, non-linear
functions were made for daily leaf area (see chapter 3.8). Similarly the
transpiration rates of individual leaves were also interpolated using non-linear
functions (see chapter 3.8) of the data in Figure 15.Then the daily increments
Results
29
of leaf area and transpiration rates were multiplied to calculate the water loss
for each variety and treatment. Figure 16 shows that in all cases leaf position I
had the highest water loss than the other leaf positions followed by leaf
position 2. In the experiment-I, water loss was measured daily for both
genotypes and treatments. IR 31785 showed higher transpiration rates than
IR 4630 under both treatments whereas consequently water loss was greater
from IR 31785 under control conditions. Under saline conditions water loss
from leaf surfaces was greatly reduced in IR 31785 due to the substantial
decrease in leaf area. Water loss in IR 4630 was little affected by salinity.
Figure 17 shows results of the regression between observed combined water
loss and simulated combined water loss for both treatments under saline and
non-saline conditions. Whereas for the control treatment simulated water loss
accurately described the observed data. The transpirational water loss under
saline conditions was underestimated by about 30%.
IR 31785-58-1-2-3-3
50 60 70 80
Wat
er lo
ss (
Lit
re)
0
10
20
30
40
IR 4630-22-2
Days after transplanting
50 60 70 800
10
20
30
40 L4 L3 L2 L1 L0
Control Salinity
Figure 16: Water loss calculated for different leaf positions. L0 = youngest not yet
fully extended leaf; L1 = youngest fully developed leaf; L2-L4 the next older leaves in
succession
.
Results
30
20 40 60 80
Sim
ulat
ed c
ombi
ned
wat
er lo
ss (
L)
0
20
40
60
80
Observed combined water loss (L)
20 40 60 80
r2 =0.99
r2 =0.981:1
1:1
Non-saline Salinity
Figure 17. Observed versus simulated combined water loss of the two genotypes
under saline and non-saline conditions.
4.2.4 Humidity effects on transpiration
For this trial, in order to determine air humidity effects on transpiration, water
loss was measured weekly by different weighing over the entire period for
eight rice genotypes grown in Yoshida culture solution. Initial and final leaf
area was measured and daily increments of leaf area were extrapolated
assuming a linear relationship for leaf area and time in both treatments. A
decrease in air humidity is associated with an increase in evaporative
demand. Therefore, it was expected that transpiration rates would increase
with decreasing relative air humidity, unless the genotype counteracted this
process through stomatal regulation. For the eight genotypes tested, no
influence of relative humidity was observed for the range of humidity during
the experimental period. Figure 18 salinity reduced transpiration rates in IR
31785, IKP and ITA 306 whereas in the others salinity did not affect
transpiration rates. The results indicate that IR4630, IR13240, ITA306, WAS
44-B-B-68-2 and WAS 30-11-1-4-6-1-2 stomata must have responded to air
humidity under both saline and non-saline conditions but other genotypes did
not respond.
Results
31
Tra
nsp
irat
ion
rat
e (
litre
( m
2 leaf
are
a)-1
d-1
)
WAS 30-11-1-4-6-1-2
Median relative humidity %
5 10 15 20 25 300
1
2
3
4
IR 4630-22-2
0
1
2
3
4
IR 31785-58-1-2-3-3
I Kong Pao
0
1
2
3
4IR 13240-108-2-3-3
ITA 306
0
1
2
3
4WAS 44-B-B-68-2
WAS 30-11-1-4-6-1-1-3
5 10 15 20 25 30
ControlSalt
Figure 18: Transpiration rate versus median relative humidity of weekly of 8
genotypes from greenhouse experiment III. The lines represent the regression.
.
Results
32
Figure 19 shows leaf layer transpiration rates versus daily minimum relative
humidity. In general transpiration rate decreased with increasing relative
humidity up to 10 to12% in both genotypes under control condition.
Transpiration rate was highest when minimum relative humidity was at 22 %
under salinity in IR 31785. Older leaves had lowest transpiration rates under
control condition for both genotypes and linear relationship was found except
for youngest leaves under control condition but no relationship with relative
humidity under salinity. Older leaves of susceptible genotype had highest
transpiration rates but no relationship was established under salinity. Older
leaves of tolerant genotype had lower transpiration rates and an almost linear
relationship was found between relative humidity and transpiration rate.
Control
0 5 10 150
2
4
6
LoL1L2L3L4
0 5 10 15
Minimum Relative humidity %
0 10 20 30
Tra
nsp
irat
ion
Rat
e (m
mo
l m-2
S-1
)
0
2
4
6
Salt
0 10 20 30
Control
Salt
IR 31785-58-1-2-3-3 IR 4630-22-2
Figure 19: Transpiration rates of different leaf layers versus daily minimum relative humidity of two varieties from experiment I. L0 = youngest not yet fully extended leaf; L1 = youngest fully developed leaf; L2-L4 the next older leaves in succession.
Results
33
4.3 Sodium and potassium uptake into individual leaves
4.3.1 Tiller number
Figure 20 shows the tiller number of the two rice genotypes. Tiller number was
determined at four dates in ten-day intervals starting 31 day after
transplanting. Most tillers were tertiary in both genotypes and treatments.
Total tiller number was higher under control treatment and total tiller number
increased over time in both treatments. IR 31785 was highest tiller number
compared to IR 4630 under control condition. Salinity reduced tiller numbers
and sensitive genotype showed largest reduction.
IR 31785-58-1-2-3-3
Till
er N
o
0
10
20
30
40Primary TillerSecondary TillerTertiary TillerTotal Tiller
Control
Salt
Days after transplanting
20 30 40 50 600
10
20
30
40
IR 4630-22-2
To
tal
Till
er N
o
0
20
40
60
80
100
Salt
20 30 40 50 600
20
40
60
80
100
Control
Figure 20: Tiller number of the two rice genotypes. Control treatment was 0 mmol
NaCl and salt treatment was 60 mmol NaCl. Vertical bar represents standard error.
Results
34
4.3.2 Leaf number
Figure 21 shows the distribution of leaf numbers on leaf layers for two rice
genotypes. Leaf number was determined at each sampling. Leaf number was
severely reduced under salinity compared to control conditions. IR 31785 had
more leaves number than IR 4630. The youngest leaf layers were composed
of the major share of the leaves whereas older leaf layer had less leaves. The
unequal distribution of leaves over the layers is a result of the distribution of
primary, secondary and tertiary tillers.
IR 31785-58-1-2-3-3
Lea
f nu
mb
er
0
10
20
30
Lo L1 L2 L3 L4 Total
30 40 50 600
10
20
30
IR 4630-22-2
0
20
40
60
80
100
120
Days after transplanting
30 40 50 60
Tot
al l
eaf n
umbe
r
0
20
40
60
80
100
120
Control Control
SaltSalt
Figure 21: Distribution of leaf numbers on leaf layers for two rice genotypes from
experiment I. Control treatment was 0 mmol NaCl and salt treatment was 60 mmol
NaCl. L0 = youngest not yet fully extended leaf; L1 = youngest fully developed leaf;
L2-L4 the next older leaves in succession. Vertical bar represents standard error.
Results
35
4.3.3 Leaf area of individual leaves
Leaf area of individual leaves of the two rice genotypes is shown in Table 3.
Table 3 shows that leaf area increased over time. As leaf number increased,
leaf area was also increased. Increasing trend of leaf area was highest under
Table 3: Leaf area (cm²) of individual leaves of the two rice genotypes. The value
represents standard error in the parenthesis. DAT means days after transplanting
Geno type
Treat ment
Leaf no
DAT 31
DAT 44
DAT 51
DAT 61
IR31785 Control 6 7 8 9
10 11 12 13
6.80 (0.03) 8.38 (0.07) 9.86 (0.11) 10.69(0.07) 8.22 (0.09)
8.58 (0.07) 10.21 (0.07) 10.89 (0.01) 11.68 (0.07) 6.86 (0.09)
10.41 (0.07) 11.40 (0.04) 12.69 (0.09) 12.97 (0.09) 8.33 (0.08)
12.64 (0.04) 12.86 (0.05) 13.22 (0.10) 13.81 (0.03) 6.07 (0.04)
IR31785 Salt 6 7 8 9
10 11 12 13
5.76 (0.04) 6.98 (0.21) 8.38 (0.34) 9.28 (0.07) 5.02 (0.13)
7.58 (0.11) 8.46 (0.15)
9.39 (0.20) 9.24 (0.32) 4.10 (0.33)
8.97 (0.42) 9.55 (0.05) 9.81 (0.07)
10.09 (0.31) 4.60 (0.07)
10.01 (0.03) 10.03 (0.02) 10.18 (0.14) 10.69 (0.03)
IR4630 Control 6 7 8 9
10 11 12 13
6.08 (0.07) 8.18 (0.02) 9.15 (0.13)
10.25(0.09) 8.04 (0.22)
8.69 (0.26)
10.03(0.28) 10.86(0.05) 11.59(0.33) 5.66 (0.37)
10.45(0.33) 11.12(0.08) 12.62(0.14) 12.57(0.08) 5.13 (0.33)
12.55 (0.15) 12.89 (0.03) 12.79 (0.11) 13.64 (0.13) 6.68 (0.10)
IR 4630 Salt 6 7 8 9
10 11 12 13
5.86 (0.14) 7.58 (0.31) 8.38 (0.34) 9.28 (0.17) 5.12 (0.23)
7.88 (0.10) 8.56 (0.16)
9.59 (0.80) 10.24(0.52) 4.50 (0.39)
9.27 (0.52) 10.25(0.15) 10.81(0.77) 11.09(0.41) 4.60 (0.09)
10.11 (0.23) 10.93 (0.32) 11.18 (0.34) 11.69 (0.13)
control conditions compared to saline conditions in both genotypes. Table 3
shows that at 61 days after transplanting, leaf area was highest in both
genotypes and treatments. At 44 days after transplanting, the sensitive
genotype had more leaf area compared to the tolerant genotype under control
Results
36
conditions but at same time (44 DAT) the tolerant genotype had more leaf
area compared to the sensitive genotype under saline conditions.
4.3.4 Transpiration rates of individual leaves
Transpiration rates of individual leaves on the main tillers of the two varieties
were measured in 7-day intervals starting 21 days after sowing, ending 77
days after sowing the first leaves to be measured was leaf number 6 in the
order of appearance and the last was leaf number 13 in IR 4630 which had
only recently emerged. Transpiration rates are shown in Table 4.
Leaf development stage
-0.2 0.0 0.2 0.4 0.62
3
4
5
6
7
IR 31785-58-1-2-3-3
Tra
nsp
irat
ion
rat
e (m
mo
l m-2
s-1
)
2
3
4
5
6
7
-0.2 0.0 0.2 0.4 0.6
IR 4630-22-2
Control Control
SaltSalt
L8 L9 L10 L11
Figure 22: Relationship between leaf level transpiration rates and leaf development
stages of two rice genotypes in saline and non-saline conditions. Where L8, L9, L10,
L11 represents leaf eight, nine, ten and eleven respectively. Red and blue lines
represent regression and confidence intervals respectively.
Results
37
Table 4: Leaf transpiration rate (mmol m¯²s¯¹) for individual leaves (counted from below) of two genotypes. The value represents standard error in the parenthesis. DAT means days after transplanting. Genotype Treatment Leaf no DAT
31
DAT
44
DAT
51
DAT
61
IR 31785 Control 6
7
8
9
10
11
12
13
3.25 (0.90)
3.03 (0.26)
3.67 (0.43)
4.20 (0.16)
3.21 (0.8)
5.71 (0.65)
5.34 (0.29)
5.54 (0.19)
5.74 (0.16)
5.85 (0.38)
5.98 (0.49)
6.18 (0.97)
7.20 (0.14)
4.74 (0.91)
4.14 (0.30)
5.47 (0.29)
6.10 (0.70)
6.56 (0.58)
6.50 (0.20)
IR 31785 Salt 6
7
8
9
10
11
12
13
3.15 (0.39)
3.35 (0.80))
3.20 (0.37)
4.15 (0.52)
3.62 (0.23)
3.57 (0.29)
4.36 (0.37)
6.09 (0.19)
3.52 (0.46)
4.52 (0.81)
6.11 (0.37)
4.51 (0.64)
3.40 (0.39)
4.69 (0.68)
4.59 (0.33)
IR4630 Control 6
7
8
9
10
11
12
13
3.25 (0.34)
6.14 (0.91)
6.27 (0.35)
6.14 (0.52)
5.16 (0.28)
5.83 (0.37)
6.12 (0.47)
6.05 (0.31)
7.36 (0.32)
5.62 (0.19)
6.88 (0.24)
6.27 (0.36)
6.85 (0.21)
7.67 (0.26)
6.61 (0.18)
5.46 (0.31)
5.39 (0.28)
6.29 (0.21)
6.23 (0.64)
6.72 (0.32)
IR 4630 Salt 6
7
8
9
10
11
12
13
5.22 (0.29)
6.76 (0.37)
6.35 (0.28)
6.69 (0.36)
4.33 (0.94)
4.21 (0.71)
4.22 (0.34)
4.32 (0.62)
3.93 (0.21)
3.95 (0.37)
6.03 (0.31)
5.48 (0.27)
5.56 (0.23)
5.11 (0.42)
4.45 (0.41)
3.50 (0.23)
3.93 (0.20)
3.06 (0.19)
3.98 (0.18)
Results
38
By measuring the same leaf several times during its existence, the
relationship between leaf transpiration rate and leaf development stage could
be established (shown in Figure 22). Transpiration rates are high in both
genotypes under control conditions. Under control condition, transpiration rate
was between 5 to 7 mmol m¯² s¯¹ in both genotypes but transpiration rate was
between 3 to 6 mmol m¯² s¯¹ in IR31785 and 4 to 6.5 mmol m¯² s¯¹ in IR 4630.
4.3.5 K/ Na ratio
The molar K/Na ratio of leaf blades and sheaths of IR 31785 and IR 4630 are
expressed in Figures 23 and 24 respectively, under both saline and non-saline
conditions. In overall it showed that both genotypes, K/Na ratio was higher
under control conditions than under saline conditions.
20 30 40 50 600.00.20.40.60.81.01.21.41.6
Leaf blades
0
2
4
6
8
10
12
14
16
18
L1 L2 L3 L4
Leaf Sheaths
20 30 40 50 600
2
4
6
8
10
12
14
16
18
Control
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6 Salt
IR 31785-58-1-2-3-3
Leaf blades
Leaf Sheaths
Days after transplanting Figure 23: K / Na ratio of leaf blades and sheaths of IR 31785-58-1-2-3 under both
saline and non-saline conditions. L1 = youngest fully developed leaf; L2-L4 the next
older leaves in succession.
Results
39
Under salinity K/Na ratios of leaf blades and sheaths were reduced in both
genotypes Salinity substantially reduced the K/Na ratios of leaf blades. Leaf
blades and sheaths of IR 4630 had higher K/Na ratio than IR 31785 but leaf
blade and sheath K/ Na ratios followed the same pattern in both genotypes
and treatments. Under control conditions, leaf blade K/Na ratio was increased.
Under salinity, leaf blades and sheaths K/ Na ratios decreased in the order of
canopy layers.
20 30 40 50 600
1
2
3
4
Leaf blades
Lea
f K/N
a
0
2
4
6
8
10
12
14
L1L2L3L4
Leaf Sheaths
20 30 40 50 600
2
4
6
8
10
12
14
Control
0
1
2
3
4
Salt
IR 4630-22-2
Leaf blades
Leaf Sheaths
Days after transplanting Figure 24: K / Na ratio of leaf blades and sheaths of IR 4630-22-2 under both saline
and non-saline conditions. L1 = youngest fully developed leaf; L2-L4 the next older
leaves in succession.
Results
40
4.3.6 Sodium and potassium uptake and distribution into individual
leaves
Figure 25 shows the relationship between leaf sodium content and water loss
under saline condition. Sensitive genotype showed that leaf sodium increased
with water loss whereas tolerant genotype did not show much sodium content.
IR 31785 was higher uptake of sodium than IR 4630. IR 31785 showed that
sodium content is linearly correlated with water loss In the leaves of salt
treated plant, sodium content was highest in leaf nine and ten and was lowest
in the 7th and 8th leaf. In both cases sodium uptake was linearly related with
water loss. IR4630 accumulated less sodium per unit of water than IR31785
indicating a more efficient root selectivity for sodium.
IR 4630-22-2
Water loss (ml)
0 2 4 6 8 100.00
0.05
0.10
0.15
0.20
IR 31785 58-1-2-3-3
Lea
f N
a+ c
onte
nt (
mg)
0.0
0.1
0.2
0.3
0.4
L6 L7L8L9L10L11
r2 = 0.52*
r2 = 0.72**
Salinity
Figure 25: Relationship between leaf sodium content and water loss under saline
condition. (* = significant p<0.05 and ** = highly significant p< 0.01)
Results
41
Figure 26 shows the relationship between leaf sodium content and water loss
under control condition. In both genotypes showed sodium was low under
control condition. In both cases sodium uptake was linearly related with water
loss.
IR 4630-22-2
Water loss (ml)
0 2 4 6 8 10 12 140.00
0.01
0.02
0.03
0.04
IR 31785 58-1-2-3-3
Lea
f N
a+ c
on
ten
t (
mg
)
0.00
0.01
0.02
0.03
0.04
L6 L7L8L9L10L11
r2 = 0.48*
r2 = 0.67**
Control
Figure 26: Relationship between leaf sodium content and water loss under control condition. (* = significant p<0.05 and ** = highly significant p< 0.01)
Figures 27 and 28 shows relationship between leaf potassium content and
water loss of individual leaves under saline and control conditions
respectively. In salt treated plants, leaf potassium content was lower in both
genotypes as compared to control conditions. Leaf potassium content
Results
42
increased with increasing water loss but correlation was weak. Figure 27 also
shows that the relationship between water loss and leaf potassium content of
different leaves were different. Younger leaves content highest potassium
than older leaves in both genotypes.
IR 31785 58-1-2-3-3
Lea
f K
+ co
nte
nt
(mg
)
0.00
0.05
0.10
0.15
0.20
0.25 L6L7L8L9L10L11
Water loss (ml)
0 2 4 6 8 100.00
0.05
0.10
0.15
0.20
0.25
IR 4630-22-2
r2 = 0.29*
r2 = 0.19 *
Salinity
Figure 27: Relationship between leaf potassium content and water loss of individual
leaves under saline condition.(* = significant p<0.05)
Results
43
IR 4630-22-2
Water loss (ml)
0 2 4 6 8 10 12 140.00
0.05
0.10
0.15
0.20
0.25
IR 31785 58-1-2-3-3
Lea
f K
+ co
nte
nt
( m
g)
0.00
0.05
0.10
0.15
0.20
0.25
L6 L7L8L9L10L11
r2 = 0.90*
r2 = 0.70**
Control
Figure 28: Relationship between leaf potassium content and water loss of individual
leaves under control condition. (* = significant p<0.05)
Figure 29 (A) shows the relationship between leaf blade sodium versus leaf
blade potassium and Figure 29 (B) leaf sheath sodium versus leaf sheath
potassium. In salt treated plants, leaf sheaths contained more sodium than the
leaf blades. In both varieties leaf blade sodium content was lower than leaf
sheath sodium content. Oldest leaves had the highest ion contents. Leaf
sheath sodium content was high in both genotypes and increased with
increasing potassium content, per unit potassium IR31785 accumulated more
sodium in the leaf sheaths and blades than IR4630 leading to a decrease in
Results
44
the K/Na ratios in IR31785 in both tissues and thus a higher tissue level
stress.
A
r 2 = 0.92**
r 2 = 0.91**
0.0 0.1 0.2 0.3
LS
Na+
(m
g)
0
2
4
6
8
10
12
14
B
LS K+ (mg)
r 2 = 0.91**
r 2 = 0.91**
IR 31785
LB K+ (mg)
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
LB
Na+ (
mg)
0.0
0.1
0.2
0.3
0.4
0.5IR 4630
L8
L9
L10
L11
Figure 29: (A) Leaf blade sodium versus leaf blade potassium content from two rice
genotypes (B) Leaf sheath sodium versus leaf potassium content from same
genotypes. ** = highly significant p<0.01
Discussion
45
5. DISCUSSION
The experimental part of this work addressed a complex fabric of interlinked
processes ultimately describing sodium and potassium uptake into and
distribution in an irrigated rice plant. The assumed driving force for sodium
uptake is hereby transpiration, respectively water loss from plant surfaces,
whereas for potassium active transport across membranes against a
concentration gradient and the possibility of re-circulation of potassium from
older tissues was assumed. The basis for the theoretical considerations of this
work were based on a conceptual model developed by Asch et al. (1997) and
describing the main factors for sodium uptake and distribution in the rice plant
with some indications on the role of potassium. This model, however, was
based on some assumptions that merited further research into the underlying
processes and with the present work an attempt was made to fill some of
those gaps and expand the existing model concept. In the following the results
of the present study will be discussed in view of the additions that can possibly
made to the concept of Asch et al. (1997) and in view of findings of other
research groups working in a similar field.
5.1 The “root filter” concept
The selectivity of the rice root for sodium and potassium has been
investigated before (Yeo et al., 1987, Tsuchiya et al., 1992, Asch et al., 1997).
It has been established, that in rice two morphological barriers reduce the
sodium concentration, so that the resulting concentration in the xylem is in the
order of 10 percent of the external medium. In the present study we calculated
the selectivity for the root by first calculating the potential sodium uptake – that
is: external sodium concentration multiplied by the total amount of water lost
from the plant–assuming no regulatory processes. By subtracting the actual
sodium content found in the plant from the potential accumulation we
determined the percentage selectivity of the root. With 60 mmol NaCl applied,
genotypes differed in their root xylem sodium content. Only 8% of the external
sodium content was found in the tolerant genotype, 12 % in the sensitive
Discussion
46
genotype and therefore the “root filter” concept for sodium at least seems to
be valid and functioning also under the greenhouse conditions of the present
study. In case of potassium, the reverse was true for tolerant and sensitive
genotype.
5.2 Leaf development, leaf appearance and leaf senescence
In the present study, leaf appearance pattern was hastened under salt stress
compared to control conditions but in the same environment genotypes did not
differ (Figure 7). So far, phyllochron was used for modelling plant growth and
development. Phyllochron varies under different environmental conditions, but
is generally constant in specific environment. Birch (1998) reported that
phyllochron depends on the accuracy of photosynthesis (source) to meet the
demands of the plant for growth (sink).
Organs should be simulated on a supply and demand basis or source and
sink. That means the productive unit of a tiller (leaves) could also be defined
via supply (source) and demand (sink) status.
Thus, if the overall senescence level of a tiller exceeds a certain value, a new
leaf is initiated to ensure the maintenance of a functioning canopy structure to
supply the plant with the required carbohydrates.
The concept of a threshold value seems to be present independent of
treatment and genotypic (Figure 8). However, the actual value of this
threshold needs validation through an independent data set as far now leaf
development stages are based on arbitrary values and may not proof to be
enough over a wider range of genotypes or stress levels. Among the
problematic parts of the original model was the handling of the initiation and
duration of the individual leaves. The leaf biomass is the sink for sodium and
as such needs to be accurately predicted. Possible stress influences on leaf
biomass development need to be reflected in the model. The existing model
already had a routine to increase leaf senescence as a function of the leaf
sodium concentration, however, leaf initiation rate was static and not
influenced by the environmental conditions. One objective of the present study
was to investigate if there is an influence of the salt stress on leaf initiation
Discussion
47
rate, if genotypes differ their response and if there is a way to describe leaf
appearance mathematically that would optimally be independent of the
environmental conditions and of the genotype. Crop development is primarily
affected by temperature and can be modified by other factors such as
photoperiod (Hodges, 1991). The effect of temperature on development is
often described by using a thermal concept usually degree-days. When, in the
present study, the degree days were calculated that described either the
thermal interval required to initiate a new leaf or the thermal interval between
leaf initiation and leaf death it became apparent, that less degree days were
needed under salinity to initiate a new leaf than under control conditions (see
Figure 30). Since the plants were subjected to the treatments in the same
thermal environment, salt stress obviously changed the plant’s responses to
temperature. However, when leaf appearance was plotted against time
(Figure 7) it became apparent, that under salt stress in both genotypes leaves
were initiated earlier.
IR 31785-58-1-2-3-3
123456789
1011121314
IR 4630-22-2
degree-days
0 500 1000 1500 2000
Leaf
no
123456789
1011121314
Leaf initiation (Control)Leaf appearance (Control)Leaf initiation (salt)Leaf appearance (salt)
Figure 30: Relationship between leaf numbers and degree-days in the greenhouse.
Discussion
48
Since it obviously was not the temperature here affecting leaf initiation, a
temperature based phyllochron (as proposed by Birch, 1998) approach to leaf
initiation did not deem the proper concept to use in the model. Additionally,
leaf duration was strongly affected by salinity at least in the susceptible
genotype. Again, trying to link temperature and phyllochron did not seem a
promising concept for leaf duration in the model.
In the present study, leaf development was observed for individual leaves up
to leaf 13 on the main tiller. From these observations leaf development stages
were defined, ranging from –1 (leaf initiation) over 0 (full extension) to +1
(100% senescence or death). Since leaves appeared at almost regular
intervals (Figure 5), between 5 and 6 leaves of different development stages
were found on the main tiller at any given time (see also Figure 5). If for all
those active leaves present on the tiller, the individual leaf development
stages were added up, the result would describe the relative senescence level
of that particular tiller with a dimensionless value. The kinetic of this tiller
senescence level (TSL) is shown for both genotypes and the two treatments in
Figure 8. At the beginning TSL decreased as leaves appeared faster than
individual leaves developed. At the six-leaf stage TSL began to increase as a
more permanent leaf area developed on the tiller and at approximately 15
days after sowing TSL leveled out. This pattern was observed for both
genotypes and treatments. After the curve leveled out TSL spikes appeared at
values of 2.0 to 2.4. At almost each of the spikes a new leaf was initiated. The
values differed a little between treatments and genotypes, but since the
calculation of leaf senescence levels depended on day-to-day observations on
individual leaves, some variation was to be expected. It needs to be seen if
this concept of TSL turns out to be applicable in the actual model, however,
after the initial establishment of the tiller, TSL yielded consistent results under
the experimental conditions.
For the first 3 to 5 leaves TSL cannot be used to predict leaf initiation because
until the first leaf dies TSL becomes more and more negative with each newly
initiated leaf. It may well be, that the initiation of the first few leaves is
temperature controlled and only the later leaves are initiated as a function of
Discussion
49
TSL. Sie et al. (1998) described in their model of temperature driven leaf
appearance that only in the first 1 to 3 leaves of field grown irrigated rice
appearance was a function of temperature.
5.3 Water loss
Salt stress is a function of sodium taken up to and distributed in the plant.
Transpiration is the main driving force for sodium uptake to the rice plants.
Theoretically, if there were no regulatory mechanisms, the amount of sodium
accumulating in a rice leaf should be directly proportional to the amount of
water lost from the leaf surface.
In the present study, due to technical constraints, water loss from the plants
subjected to the different treatments was measured as bulk water loss. This
bulk water loss included all plants participating in the respective treatment. In
order to calculate the actual water loss per genotype and leaf position, leaf
transpiration rate and leaf area were measured at regular intervals (Figure 15
and Figure 14). The calculations based on non-linear interpolations between
the data points. Transpiration rates were measured once per leaf layer,
treatment, genotype, replication and sampling between 11.00h and 13.00 h.
Diurnal transpiration profiles were not established. In order to correctly
estimate water loss from the leaves, corrections for diurnal influences of
transpiration rates were necessary (see Figure 31). Lösch et al. (1992) studied
on diurnal courses and factorial dependencies of leaf conductance and
transpiration of differently potassium fertilized and watered field grown barley
plants. Lösch et al. (1992) showed that leaf photosynthesis was highest at
13.00h and at 10.00h 40 % lower before noon than in the afternoon. Ingram et
al. (1991) showed that diurnal courses of net CO2 assimilation of rice peaked
at midday. Daytime patterns were bell shaped and did not exhibit a midday
depression. Net CO2 assimilation (AC) was lower before noon than in the
early afternoon.
Discussion
50
Time of day (hour)
8 10 12 14 16 18 20
Tra
nspi
ratio
n ra
te (
mm
ol m
-2 s
-1)
0
2
4
6
8
Figure 31: Diurnal trend of canopy CO2 assimilation
Based on these reports transpiration rates were adapted to reflect a diurnal
curve as observed by Ingram et al., (1991) and Lösch et al., (1992) assuming
transpiration rate was 100% at 13.00h, 60% at 11.00h and 30% at 9.00h
before midday and mirrored in the afternoon. This simulated water loss was
plotted against observed water loss in Figure 17. Water loss was accurately
estimated for the control treatment. However, under salinity water loss was
underestimated. One reason could be that the diurnal patterns of transpiration
differed between saline and non-saline conditions. Which would not be
surprising, because stomatal conductance at least in some genotypes also
differed between saline and non-saline conditions. Some efforts should be
devoted to investigate salinity influence on diurnal transpiration patterns.
5.4 Leaf level transpiration
Transpiration rates of rice leaves by position have been investigated before
(Yeo et al., 1985 and Asch et al., 1997). It has been established that younger
leaves showed the highest transpiration rates and the older leaves showed
lower transpiration rates. But for the model we need to know, if all leaves do
Discussion
51
behave in the same way or not. The present study shows that individual leaf
level transpiration rates had similar patterns (shown in Figure 22) in non-saline
condition for both genotypes (IR31785-58-1-2-3-3 and IR 4630-22-2). But the
pattern was little bit different under salt conditions. One of the reasons might
be a humidity effect on transpiration rates or due to potassium. Senescence
could be the other reason for different behavior of leaf level transpiration rates
in salt condition.
5.5 Humidity effects on transpiration
Environmental conditions (relative humidity, temperature) influence the
stomatal conductance. Stomatal conductance influences the overall
transpiration. For the concept model we need to know humidity effects on
transpiration.
Asch et al., (1997) reported that for IR 31785, transpiration rates increased
with decreasing air humidity under both saline and non-saline conditions,
although transpiration rates were generally lower under salinity. Tolerant
genotype IR4630 controlled its transpiration under both saline and control
conditions, I Kong Pao controlled its transpiration specifically under salinity
and the susceptible IR31785 controlled its transpiration least. In the present
study, Figure 18 shows that no influence of relative humidity on transpiration
rate was observed within the humidity range of the experimental period.
Because water loss from individual pots by difference weighing was measured
only once a week. But minimum relative humidity was recorded every 30
minutes per day. From the recorded data floating averages were calculated
for relative humidity for every 2 hours and then the median relative humidity
value was computed. Figure 4 showed that minimum relative humidity was
very low between 95 to 105 Julian dates. These days were also very warm
and dry, that is unexpected in greenhouse study.
Figure 19 showed leaf layer transpiration rates versus minimum relative
humidity. A linear relationship was found under control conditions for the
sensitive genotype and under salt conditions for the tolerant genotype. No
linear relationship was found under control conditions for IR4630 or under salt
Discussion
52
conditions for IR 31785. The humidity range in the experimental period was
between 14% and 64%. Transpiration values at humidity values of 65-80% or
more were not available. It may well be, that the relationship between
transpiration and relative humidity would be different if such values were
included.
5.6 Sodium and potassium distribution into individual leaves
Leaves are the major sink for sodium and it consists of a leaf blade and a
sheath. Sodium enters into the leaf via transpiration. So, we need to know the
distribution of sodium into the leaf (blade and sheath). Asch et al (1987)
mentioned that the highest sodium and potassium concentrations were found
in the stems, which consisted mainly of leaf sheaths. Dahal (2004) also
showed that the highest sodium and potassium concentrations were found in
the leaf sheaths. It indicates that sheaths store more cations. For the concept
model, we need to know sheath retention because this retention protects the
photosynthetically active leaf blades, maintains a low sodium concentration
and sustains their productivity. Sheath retention was calculated as the leaf
level maximum potential sodium content minus the actual leaf content of
sodium. Dahal (2004) described youngest leaf had lowest sodium
concentrations and highest potassium concentrations. Sodium and Potassium
concentrations were particularly higher in leaf sheaths compared to leaf
blades, this effect being particularly pronounced under saline condition for
sodium and non-saline condition for potassium. Potassium concentrations
were generally higher in IR4630, whereas sodium concentrations were
generally higher in IR31785. Highest sodium concentrations were found in
dead leaves followed by leaf sheaths, roots and leaf blades in all stages in
both varieties under saline condition. Under control conditions sodium
concentrations were generally low, with concentrations being higher in leaf
sheaths than blades. Potassium concentration was observed highest in leaf
sheaths followed by leaf blades and root in both varieties under both control
and saline condition.
Discussion
53
From experiment I, organs of rice plants were analyzed for sodium and
potassium content (see 3.7). These were composition of all tillers at a
sampling day. But the present study focused on mainly on the main tillers.
Existing leaves by position and tiller no (shown in Figure nos 21 and 20
respectively) and their existence was known. Based on this data, average
individual leaf area and dry weight were calculated and similarly average
sodium and potassium concentrations of individual leaves were calculated.
Study shows that sodium ions accumulated in larger amounts in older leaves
of sheath than in young leaves. These results indicate that, at the whole plant
levels, the old leaves that accumulate a large amount of Na would die earlier
than the young leaves. Munns and Termaat (1986) proposed that the death of
the leaf treated with salt is induced when the amount of sodium accumulated
in the leaf exceeds a threshold value. Greenway and Munns (1980) reported
that, in salt stressed non-halophytes, the fully expanded leaves are damaged
long before the young leaves were damaged, simply because the salt content
during the period of exposure to salinity is always higher in old leaves.
5.7 Tillering pattern
Relative tillering and tiller abortion are a function of relative growth rate
(Schnier et al.1990a). Tillering was followed by a tiller abortion phase which
was particularly pronounced in direct-seeded rice and linear relationship was
identical among the genotypes (Dingkuhn, 1991). Figure 32 shows
relationship between relative tillering rate (RTR) and relative growth rate
(RGR) of two rice genotypes under control and saline conditions. The present
study shows a direct non-linear relationship between tillering rate and
biomass. Under salinity tillering was reduced proportionally to the decrease in
biomass accumulation. It can be assumed that the daily increase in biomass
needed to sustain tillering, also holds for the experimental conditions of this
study, however the data base was not sufficient to calculate the minimum
growth rates for tillering to occur directly.
Discussion
54
IR 31785-58-1-2-3-3
0
5
10
15
20
25
30
salinity
RGR ( % increase day )
0 5 10 15 20 25
RT
R (
% in
crea
se d
ay )
0
5
10
15
20
25
30
IR 4630-22-2
0 5 10 15 20 25
salinity
control control
Figure 32: Relationship between relative tillering rate (RTR) and relative growth rate
(RGR) of two rice genotypes under control and saline conditions.
5.8 Model concepts
Based on the discussion above and the ideas development from extensive
literature research on advanced new conceptual model (Figure 34) is
presented improving the one presented by Asch et al., (1997). This concept is
based on individual tillers receiving their biomass from partitioning out of the
photo assimilate pool (Figure 33). Figure 33 indicates the morphological
processes. Stomatal conductance is influenced by leaf N (Dingkuhn et al.,
1992). Photosynthesis characteristics of single leaves, which depend upon the
Leaf N, light and stomatal conductance and Figure 34 indicates physiological
processes related to transpiration and sodium, potassium uptake and
distribution. Tillering rate depends on growth rate here (Figure 33). On the
individual tillers, individual leaves develop which appear in the beginning as a
function of temperature (Sie et al., 1998) and later as a function of tiller
Discussion
55
senescence level (TSL, Figure 34). Leaf senescence level is influenced by the
leaves potassium concentration and the leaves sodium concentration, the
latter being a function of the leaf blade transpiration. Stomatal conductance
influences the overall transpiration and environment conditions influences the
stomatal conductance. Overall transpiration influences the actual uptake of
sodium and potassium by the root where the “root filter” reduces the sodium
concentration with two morphological barriers.
Sodium is taken up via the transpiration stream; distributed over the existing
tillers and moves towards the individual leaves, passing through the leaf
sheaths. The maximum daily absorption of sodium by the leaves sheath is a
varietal constant, (Figure 34) reflecting the potential rate of sodium
immobilization in the leaves sheath tissue. The fraction of the sodium that
exceeds the varietal leaf concentration limit, is translocated into the leaf
blades and increases the sodium concentration in the leaf blades over time.
When the leaf’s K/Na ratio drops below a genotypic constant, this leads to
accelerated leaf development and thus influences individual leaf level
transpiration.
This conceptual model assumes an active uptake of potassium by the root,
distribution in the plant via the xylem and relocation via the phloem. Potassium
is actively mobilized from senescent leaves and fed into an incremental pool.
Each Plant organ has a demand function for potassium, which is modified by
the amount of sodium it has accumulated. The present study supports that a
high potassium concentration in the sheaths seems to be coupled with a high
sodium concentration and salt tolerant variety is highly potassium efficient
genotype.
Discussion
56
Photosynthesis
rS
Light
Leaf N
CF
Atmospheric CO2
Assimilates
Root growth
Shoot growth
Partitioning
Root biomass
Tillering rate
LAPR
Tiller biomass
LSh LBl
Tillers..
Senescence Level
State variables
Rate variables
Intermediate variables
Material flow
Information flow
Figure 33: Relational diagram representing tiller senescence level as well
as stomatal conductance. rS= stomatal conductance , CF= conversion
factor, Lsh= leaf sheath, LBl= leaf blade, LAPR= leaf appearance rate
Discussion
57
Figure 34: A relational diagram of sodium and potassium uptake and distribution. (Abbreviations see appendix 2)
Environmental conditions (rH, WS, SR, T)
Stomatal Conductance
Daily stem Na Retention capacity
Root Na
Soil Na
upt.
State variables
Rate variables
Intermediate variables
Passive
Active
Varietal constants
Material flow
Information flow
Italic Plant Organs
Upt.
L2 L1 L2 L3........
Root Na Filter
Tillers.. Tillers..
Soil K
Root K
Transport
Upt..
K/ Na
Transp.
Sheath K
Blade
K
Transp.
Sheath K
Blade
K
See Fig33
See Fig.33
L1 L3.....
Blade Na
Sheath Na
Reten
Blade Na
Sheath Na
Reten
LDR LDR
LDS LDS
Tiller Senescence Level
L1 L2
Transpiration
LLTR
Varietal Constant
Tiller 1 Tiller 2.......
Leaf N
Shoo
t R
oot
Tiller 1 Tiller 2
Recircul.
?
Discussion
58
5.9 Conclusion
The main objectives were to describe leaf development, leaf appearance and
leaf senescence level for different leaves, to calculate water loss from
individual leaves and finally to establish a model concept for sodium and
potassium uptake and distribution as related to the transpiration of the
particular leaf. The present study showed the leaf development stages of
individual leaves and their appearance patterns. A new concept was
developed to describe the overall senescence level for tillers and different leaf
development stages. Through sequential transpiration measurements on the
same leaf, we are now able to estimate the water use of individual leaves
throughout their life cycle and through sequential sampling throughout the life
cycle of the plant, we know the accumulation of sodium in the leaf sheaths
and blades of any particular leaf and how much water passed through any leaf
sheath and blade of the particular rice plant. Since water is the carrier for
sodium in transpiration-driven xylem transport, the amount of sodium passing
through those tissues can also be known. These findings have been
transferred into a model concept that in the near future needs to be validated
with independed data set from different genotypes and different climatic
conditions.
References
59
6. REFERENCES
Akita, S., Cabuslay, G. S., 1990. Physiological basis of differential response to salinity in rice cultivars. Plant and Soil 123: 277-294.
Angus, J. F., Mackenzie, D. H., Schafer, R., C. A., 1981. Phasic development in field crops. II Thermal and photoperiodic responses of spring wheat. Field Crops Research 4: 269-283.
Asch, F., Dahal, K. P., Das, U. S., 2003. Is the transpiration history of leaves indicative for the salt load of individual leaves? Deutscher Tropentag 2003, 8-10 October 2003, Göttingen, Germany.
Asch, F., Dingkuhn, M., Dörffling, K., 1997. Physiological stresses of irrigated rice caused by soil salinity in the Sahel. In: Irrigated rice in the Sahel: Prospects for sustainable development. Eds. K. M. Miezan, K. M., Wopereis, M.C.S., Dingkuhn, M. J. Deckers, Randolph, T. F.West Africa Rice Development Association, BP 2551, Bouake 01, Côte d'Ivore. 247-273.
Asch, F., Dingkuhn, M., Miezan, K., Dörffling, K., 2000. Leaf K/Na ratio predicts salinity induced yield loss in irrigated rice. Euphytica 113: 109-118.
Asch, F., Dingkuhn, M., Wopereis, M., Dörffling, K., Miezan, K. M., 1997, A conceptual model for sodium uptake and distribution in irrigated rice, In: Applications of systems Approaches at the Field Level, 201-217. M. J. Kropff et al. (Eds.), Dordrecht, Kluver Academic Publishers.
Asch, F., Dingkuhn, M., Dörffling, K., 1997. Effects of transpiration on sodium and potassium distribution in salt stressed irrigated rice. Journal of Experimental Botany. 48 supl. : 39.
Bastianns, L., Kropff, M. J., 1993. Effect of leaf blast on photosynthesis of rice canopies. Netherlands Journal of Plant Physiology. 99: 210-215.
Bethke, P. C., Drew, M. C., 1992. Stomatal and non-stomatal components inhibition of photosynthesis in leaves of capsicum annum during progressive exposure to NaCl salinity. Plant Physiology 99: 219-226.
Birch, C. J.,Vos, J., Kiniry, J. R., 1998. Towards a robust method of modelling leaf appearance in plants. Proceedings of the 9th Australian Agronomy Conference, Wagga Wagga.
Bouman, B. A. M., Kropff, M. J., Tuong, T. P., Wopereis, M. C. S., Ten Berge, H. F. M., Van Laar, H. H., 2001. ORYZA2000: Modelling lowland rice. International Rice Research Institute (IRRI), Manila, Philippines, P.O. Box 933.
Cramer, G. R., Nowak, R. S.,1992. Supplemental manganese improves the relative growth, net assimilation and photosynthetic rates of salt-stressed barley. Plant Physiology 84: 600-605.
Dhindsa, R. S., Plumb-Dhindsa, P., Thorpe T. A., 1981. Leaf senescence correlated with increased levels of membrane permeability and lipid
References
60
peroxidation, and decreased levels of superoxide dismutase and catalase. Journal of Experimental Botany 32: 93-101.
Dingkuhn, M., Cruz, R. T., O`Toole, J. C., Dörffling, K., 1989. Net photosynthesis, water use efficiency, leaf water potential and leaf rolling as affected by water deficit in tropical upland rice. Australian Journal of Agricultural Research 40:1171-1181.
Dingkuhn, M., Schnier, H. F., De Datta, S. K., Dörffling, K., Javellana, C., 1991. Relationships between ripening-phase productivity and crop duration, canopy photosynthesis and senescence in transplanted and direct-seeded lowland rice. Field Crops Research 26: 327-345.
Dingkuhn, M., De Datta, S. K., Pamplona, R., Javellana, C., Schnier, H. F., 1992. Effect of late-season N-fertilization on photosynthesis and yield of transplanted and direct-seeded tropical flooded rice. II. A canopy stratification study. Field Crops Research 28: 235-249.
Dahal, K. P.,2004. Salinity effects on tiller and leaf appearance and on development rate of individual leaf positions in irrigated rice. M.Sc thesis, University of Bonn, Germany.
Fageria, N. K., 1985. Salt tolerance of rice cultivars. Plant and Soil 88: 237-243.
Flowers, T. J., Duque, E., Hajibagheri, M. A., McGonigle, T. P., Yeo, A. R., 1985. The effect of salinity on leaf ultra structure and net photosynthesis of two varieties of rice: Further evidence for a cellular component of salt–resistance. New Phytologist 100: 37-43.
Goudriann, J., 1988. The bare bones of leaf angle distribution in radiation models for canopy photosynthesis and energy exchange. Agricultural and Forest Meteorology 43: 155-169.
Gorham, J., Wyn, J., Mcdonell, E., 1985. Some mechanisms of salt tolerance in crop plants. Plant and Soil 89 : 15-40.
Greenway, H., Munns, Rana., 1980. Mechanisms of salt tolerance in non-halophytes. Annual Review of Plant Physiology 31 :149-190.
Hu, Y., Schmidhalter.,1998. Spatial distributions and net deposition rates of mineral elements in the elongating wheat (Triticum aestivum L.) leaf under saline soil conditions. Planta 204: 212-219.
Hashimoto, H., Kura-Hotta., Katoh, M. S., 1989. Changes in protein content and in the structure and number of chloroplasts during leaf senescence in rice seedlings. Plant and Cell Physiology 30: 707-715.
Hodges, T., 1991. Temperature and water stress effects on phenology. In: Hodges (editor). Predicting crop phenology. CRC Press. Boca Raton. FL. 7-13. http://www.sasa.org.za/sasex/about/agronomy/aapdfs/2002/zhou.pdf
Ingram, K.T., Dingkuhn, M., Novero, R.P., Wijangco, E.J., 1991. Growth and CO2 assimilation of low land rice in response to timing and method of N fertilization. Plant and Soil 132: 113-125.
References
61
Kropff, M. J., Van Laar, H. H., Mathews, R. B., Ten Berge, H. F. M., 1993. Oryza1, a basic model for irrigated lowland rice production. International Rice Research Institute (IRRI)- SARP reports, Manila, Philippines, P.O. Box 933.
Kropff, M. J., Van Laar, H. H., Mathews, R. B., 1994. Oryza1: An ecophysiological model for irrigated rice production. SARP research proceedings. International Rice Research Institute (IRRI), Manila, Philippines, P.O. Box 933.
Kura-Hotta, M., Satoh, K., Katoh, S., 1987. Relationship between photosynthesis and chlorophyll content during leaf senescence of rice seedlings. Plant and Cell Physiology 28: 1321-1329.
Lizaso, J. I., Batchelor, W. D., Westgate, M. E., 2003. A leaf area model to simulate cultivar- specific expansion and senescence of maize leaves, Field Crops Research 80: 1-17.
Lösch, R., Jensen, C. R., Andersen, M. N., 1992. Diurnal courses and factorial dependencies of leaf conductance and transpiration of differently potassium fertilized and watered field grown barley plants. Plant and Soil 140: 205-224.
Lutts, S., Kinet, J. M., and Bouharmont, J., 1996. NaCl-induced senescence in leaves of rice (Oryza sativa L.) cultivars differing in salinity resistance. Annals of Botany 78: 389-398.
Major, D. J., Kiniry, J. R., 1991. Predicting day length effects on phonological process. In: T. Hodges (Editor), Predicting crop phenology. CRC Press, Boca Raton, FL. 16-28.
Miglictla, F., 1991b. Simulation of wheat ontogenesis. II Predicting dates of ear emergence and main stem final leaf number. Climatic Research 1: 151-160.
Munns, R., Termaat, A.,1986. Whole-plant responses to salinity. Australian Journal of Plant Physiology 13: 143-160.
Mitsuya, S., Yano, K., Kawasaki, M., Taniguchi, M., Miyake, H., 2002. Relationship between the distribution of sodium and the damages caused by salinity in the leaves of rice seedlings grown under a saline condition. Plant Production Science. 5 (4): 269-274.
Munns, R., Hare, R. A., James, R. A., Rebetzke, G. J., 2000b. Genetic variation for improving the salt tolerance of durum wheat. Australian Journal of Plant Physiology 27: 949-957.
Plaut, Z., Frederick, C. M., Federman, E., 2000. Leaf development, transpiration and ion uptake and distribution in sugarcane cultivars grown under salinity. Plant and Soil 218: 59-69.
Rawson, H. M., Long, M. J., Munns, R., 1988. Growth and development in NaCl treated plants : Leaf Na+ and Cl¯ concentrations do not determine gas exchange of leaf blades in barley. Australian Journal of Plant Physiology. 15 : 519-527.
References
62
Robertson, G.W., 1968. A bio metrological time scale for a cereal crop involving day and night temperature and photo thermal. International Journal. Of Biometeorology 12: 191-22.
Sahu, A. C., Mishra, D., 1987. Changes in some enzyme activities during excised rice leaf senescence under NaCl stress. Biochemie und Physiologie der Pflanzen 182: 501-505.
Schnier, H. F., Dingkuhn, M., De Datta, S. K., Mengel, S. K., Wijangco, E., Javellana, C., 1990a. Nitrogen economy and canopy CO2 assimilation in tropical lowland rice. Agronomy Journal 82: 451-459.
Schachtman, D. P., Munns, P.,1992. Sodium accumulation in leaves of Triticum species that differ in salt tolerance. Australian Journal of Plant Physiology 19: 331-340.
Sie, M., Dingkuhn, M., Wopereis, M. C. S., Miezan, K .M., 1998. Rice crop duration and leaf appearance rate in a variable thermal environment. I. Development of an empirically based model. Field Crops Research 57: 1-13.
Sie, M., Dingkuhn, M., Wopereis, M. C. S., Miezan, K .M., 1998. Rice crop duration and leaf appearance rate in a variable thermal environment. II. Comparison of genotypes. Field Crops Research 58: 129-140.
Spitters, C. J. T., 1986. Seperating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis. Part II: calculation of canopy photosynthesis. Agricultural and Forest Meteorology 38: 231-242.
Sultana, N., Ikeda,T., Itoh, R., 1999. Effects of salinity on photosynthesis and dry matter accumulation in developing rice grains. Environmental and Experimental Botany 42: 211-220.
Tsuchiya, M., Natio, H., Ehera, H., Ogo, T.,1992. Physiological response to salinity in rice plant:I relation between sodium uptake and transpiration under different humidity and salinity conditions. Japanese Journal of Crop Science 61: 16-21.
Tester, M., Davenport, R., 2002. Sodium and potassium transport in higher plants. Annals of Botany 91 (5): 503.
Tivet, M., Silveria, P., B da, Raissac., Dingkuhn, M., 2000. Trophic control of tillering rate of three rice cultivars (Oryza sativa L. and O. glaberrima Steud.) under different drought levels. Poster presentation of Centre de cooperation internationale en recherché agronimique pour le development.
Vries de, P., F. W. T., Jansen, D. M., Ten Berge, H. F. M., Bakema, A., 1989. Simulation of ecophysiological processes of growth in several annual crops. PUDOC, Wageningen, the Netherlands. 271.
White, J. W.,1998. Modelling and Crop Enviornment, In:Tsuji GY, Hoogenboom G and Thornton PK (Eds). Understanding options for agricultural Production, Kluwer Academic Publishers. 179-188.
References
63
Wopereis, M. C. S., Bouman, B. A. M., Tuong, T. P., Ten Berge, H. F.M., Kropff M. J., 1996. ORYZA-W: rice growth model for irrigated and rainfed environments. SARP Research Proceedings, IRRI, P. O. Box 933, Manila, Philippines.
Yeo, A. R., Capron, S. J. M., Flowers, T. J., 1985. The effect of salinity upon photosynthesis in rice (Oryza sativa L): Gas exchange by individual leaves relation to their salt content. Journal of Experimental Botany 36: 1240-1248.
Yeo, A. R., Flowers, T. J., 1983. Varietal differences in the toxicity of sodium ions in rice leaves. Physiologia Plantarum 59: 189-195.
Yeo, A. R., Lee, K. S., Izard, P., Boursier, P. J., Flowers, T. J., 1991. Short and long-term effects of salinity on leaf growth in rice. Journal of Experimental Botany 42: 881-889.
Yeo, A. R., Yeo, M. E., Caporn, S. J. M., Lachno, D. R., Flowers, T.J., 1985. The use of 14C- Ethane diol as a quantitative tracer for the transpirational volume flow of water and an investigation of the effects of salinity upon transpiration, net sodium accumulation and endogenous ABA in individual leaves of Oryza sativa L. Journal of Experimental Botany, Vol. 36, No. 168. 1099-1109.
Yeo, A. R., Yeo, M. E., Flowers, T. J.,1984. Nonosmotic effects of polyethylene glycols upon sodium transport and sodium-potassium selectivity by rice shoots. Plant Physiology. 75: 298-303.
Yeo, A. R., Yeo, M. E., Flowers, T.J., 1987. The contribution of an apoplastic pathway to sodium uptake by rice shoots in saline conditions. Journal of Experimental Botany 38: 1141-1153.
Yin, X., and Kropff, M. J., 1996. The effect of temperature on leaf appearance in rice. Annals of Botany 77: 215-221.
Yin, Xinyou., Kropff, M. J., McLaren, G., Visperas, R. M., 1995. A nonlinear model for crop development as a function of temperature. Agricultural and Forest Meteorology 77:1-16.
Yoshida, S., 1981. Fundamentals of rice crop science. International Rice Research Institute (IRRI), P.O Box 933, Manila, Philippines.
Yoshida, S., Forno, D. A., Cock, J. H., Gomez, K. A., 1976. Laboratory manual for physiological studies of rice. International Rice Research Institute (IRRI), P.O. Box 933, Manila, Philippines.
Zhov, M., Singles, A., Smit, M., 2002. Physiological parameters for modelling varietals differences in sugarcane canopy development in the South Eat East lowveld of Zimbabwe.
Appendix
Appendix I
Preparation of stock solutions
Element Reagent Preparation
(g/10 litres of distilled water)
N
P
K
Ca
Mg
Mn
Mo
B
Zn
Cu
Fe
NH4NO3
NaH2PO4 .2H2O
K2SO4
CaCl2
MgSO4 .7H2O
MnCl2 .4H2O
(NH4 )6 MO7O24 . 4 H2O
H3BO3
ZnSO4 .7H2O
CuSO4 .5H2O
FeCl3 .6H2O
914
403
714
886
3240
15
0.74
9.34
0.35
0.31
77
Preparation for culture solution:
Reagents
1) Sodium hydroxide 1N 40g NaOH were dissolved in 1 liter of demineralized
water.
2) Hydrochloric acid 1N 83 ml of concentrated HCl were given in a 1 liter
volumetric flash and made up to volume with demineralized water.
Appendix
Appendix 2
Abbreviations LLTR = Leaf level transpiration
L1 = Leaf one
L2 = Leaf two
L3 = Leaf three
LDS = Leaf development stage
LDR = Leaf development rate
Upt. = Uptake
Retent. = Retention
Transp. = Transport
Recircul. = Recirculation of senescent leaves
rH = Relative humidity
T = Temperature
WARDA= West Africa Rice Development Association
IRRI = International Rice Research Institute
Appendix
Appendix 3 T1= control, T2= salinity
IR4620 T1 Transpiration rate (L0) y=a+bx+cx^2+dx^3
r^2=0.96984924 DF Adj r^2=0.84924619 FitStdErr=0.63184511 Fstat=21.444439a=-0.040835112 b=0.11082525
c=0.0016881502 d=-3.130962e-05
0 20 40 60 800
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
IR 4630 T1 Transpiration Rate (L1) y=a+bx+cx^2+dx^3
r^2=0.93885885 DF Adj r^2=0.69429427 FitStdErr=0.95316676 Fstat=10.237065a=0.10236361 b=0.079804219
c=0.0023639005 d=-3.3539361e-05
0 20 40 60 800
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Appendix
IR 4630 T1 Transpiration rate (L2)Rank 111 Eqn 2040 y=a+bx+cx^2+dx^3
r^2=0.92525249 DF Adj r^2=0.62626245 FitStdErr=0.85789941 Fstat=8.252248a=-0.032879865 b=0.12211802
c=-0.00019083199 d=-8.4072629e-06
0 20 40 60 80-2
-1
0
1
2
3
4
5
6
7
-2
-1
0
1
2
3
4
5
6
7
IR 4620 T1 Transpiration rate ( L3) y=a+bx+cx^2+dx^3
r^2=0.91339839 DF Adj r^2=0.56699193 FitStdErr=0.83915525 Fstat=7.0314193a=0.075353443 b=0.10822902
c=0.0001849731 d=-1.237179e-05
0 20 40 60 80-2
-1
0
1
2
3
4
5
6
7
-2
-1
0
1
2
3
4
5
6
7
Appendix
IR 4630 T1 Transpiration rate (L4) y=a+bx+cx^2+dx^3
r^2=0.94565712 DF Adj r^2=0.7282856 FitStdErr=0.55183932 Fstat=11.601116a=0.063198933 b=0.13208716
c=-0.0012521403 d=1.5196055e-06
0 20 40 60 80-3
-2
-1
0
1
2
3
4
5
6
7
-3
-2
-1
0
1
2
3
4
5
6
7
IR 4630 T2 Transpiration rate (L0) y=a+bx+cx^2+dx^3
r^2=0.9863379 DF Adj r^2=0.93168951 FitStdErr=0.42065184 Fstat=48.130131a=-0.029077146 b=0.10265308
c=0.0020352448 d=-3.5019891e-05
0 20 40 60 80
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Appendix
IR 4630 T2 Transpiration rate (L1)
y=a+bx+cx^2+dx^3r^2=0.79279401 DF Adj r^2=0 FitStdErr=1.6803107 Fstat=2.5507435
a=-0.094218123 b=0.17074979 c=-0.00038830386 d=-1.5215889e-05
0 20 40 60 80
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
IR 4630 T2 Transpiration rate (L2) y=a+bx+cx^2+dx^3
r^2=0.70256998 DF Adj r^2=0 FitStdErr=2.1204742 Fstat=1.5747569a=-0.14479775 b=0.12924457
c=0.00097618333 d=-2.7217566e-05
0 20 40 60 80
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Appendix
IR 4630 T2 Transpiration rate (L3) y=a+bx+cx^2+dx^3
r^2=0.64469746 DF Adj r^2=0 FitStdErr=2.3510184 Fstat=1.2096686a=-0.13921596 b=0.16041944
c=-1.5541017e-05 d=-2.0344955e-05
0 20 40 60 80
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
IR 4630 T2 Transpiration rate (L4) y=a+bx+cx^2+dx^3
r^2=0.75935845 DF Adj r^2=0 FitStdErr=1.764841 Fstat=2.1037056a=-0.20802097 b=0.18427695
c=-0.00077139475 d=-1.4787375e-05
0 20 40 60 80
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Appendix
IR 31785 T1 Transpiration rate (L0) y=a+bx+cx^2+dx^3
r^2=0.9317524 DF Adj r^2=0.65876201 FitStdErr=1.1060633 Fstat=9.101687a=0.13833804 b=0.09384574
c=0.00018315585 d=-4.4502396e-06
0 20 40 60 800
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
IR 31785 Transpiration rate (L1) y=a+bx+cx^2+dx^3
r^2=0.96852973 DF Adj r^2=0.84264863 FitStdErr=0.70061946 Fstat=20.517345a=0.051232686 b=0.13450815
c=-0.0002918591 d=-5.8487806e-06
0 20 40 60 800
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Appendix
IR 31785 T1 Transpiration rate (L2) y=a+bx+cx^2+dx^3
r^2=0.95373327 DF Adj r^2=0.76866636 FitStdErr=0.81734279 Fstat=13.742536a=0.08828827 b=0.066958488
c=0.00146335 d=-1.8262955e-05
0 20 40 60 800
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
IR 31785 Transpiration rate (L3) y=a+bx+cx^2+dx^3
r^2=0.90469559 DF Adj r^2=0.52347794 FitStdErr=1.0493997 Fstat=6.3284624a=0.10893367 b=0.10212388
c=3.9023629e-05 d=-6.9399371e-06
0 20 40 60 800
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Appendix
IR 31785 T1 Transpiration rate (L4) y=a+bx+cx^2+dx^3
r^2=0.85751567 DF Adj r^2=0.28757835 FitStdErr=0.98443355 Fstat=4.0122104a=0.08771026 b=0.14663869
c=-0.0024657105 d=1.588149e-05
0 20 40 60 80-2
-1
0
1
2
3
4
5
6
7
-2
-1
0
1
2
3
4
5
6
7
IR 31785 T2 Transpiration rate (L0) y=a+bx+cx^2+dx^3
r^2=0.9898217 DF Adj r^2=0.9592868 FitStdErr=0.46397983 Fstat=32.416079a=0.0016078342 b=0.23174668
c=-0.0026893685 d=5.7406323e-06
0 20 40 60 800
1
2
3
4
5
6
0
1
2
3
4
5
6
Appendix
IR 31785 T2 Transpiration rate (L1) y=a+bx+cx^2+dx^3
r^2=0.9879481 DF Adj r^2=0.95179242 FitStdErr=0.50092309 Fstat=27.324834a=-0.001735854 b=0.025668428
c=0.0034626394 d=-3.9499378e-05
0 20 40 60 800
1
2
3
4
5
6
0
1
2
3
4
5
6
IR 31785 T2 Transpiration rate (L2) y=a+bx+cx^2+dx^3
r^2=0.96504473 DF Adj r^2=0.86017891 FitStdErr=0.8498386 Fstat=9.2026625a=0.0029449546 b=-0.11957303
c=0.0061262196 d=-4.7711869e-05
0 20 40 60 800
1
2
3
4
5
6
0
1
2
3
4
5
6
Appendix
IR 31785 T2 Transpiration rate (L3) y=a+bx+cx^2+dx^3
r^2=0.98685521 DF Adj r^2=0.94742084 FitStdErr=0.47941061 Fstat=25.025257a=0.0016613066 b=-0.15842455
c=0.0076653562 d=-6.236566e-05
0 20 40 60 800
1
2
3
4
5
6
0
1
2
3
4
5
6
IR 31785 T2 Transpiration rate (L4) y=a+bx+cx^2+dx^3
r^2=0.99300458 DF Adj r^2=0.97201832 FitStdErr=0.30409394 Fstat=47.3169a=0.0010537799 b=-0.0050641827 c=0.0033566724 d=-3.448835e-05
0 20 40 60 80-2
-1
0
1
2
3
4
5
6
-2
-1
0
1
2
3
4
5
6
Appendix
IR 4630 T1 Leaf area (L0) lny=a+bx+cx^(0.5)
r^2=0.99295786 DF Adj r^2=0.98239465 FitStdErr=6.8774438 Fstat=211.50346a=0.25509684 b=0.01646394
c=0.38645334
0 20 40 60 80-150
-100
-50
0
50
100
150
200
250
300
-150
-100
-50
0
50
100
150
200
250
300
IR 4630 T1 Leaf area (L1)
lny=a+bx+cx^(0.5)r^2=0.96834285 DF Adj r^2=0.92085712 FitStdErr=31.845952 Fstat=45.882656
a=-6.49478 b=-0.11868316 c=2.4335038
0 20 40 60 800
50
100
150
200
250
300
350
0
50
100
150
200
250
300
350
Appendix
IR 4630 T1 Leaf area (L2) lny=a+bx+cx^(0.5)
r^2=0.99086588 DF Adj r^2=0.97716471 FitStdErr=13.298178 Fstat=162.71949a=-16.10058 b=-0.24773782
c=4.6308689
0 20 40 60 80-100
-50
0
50
100
150
200
250
300
350
-100
-50
0
50
100
150
200
250
300
350
IR 4630 T2 Leaf area (L0) lny=a+bx+cx^(0.5)
r^2=0.99734976 DF Adj r^2=0.99337439 FitStdErr=4.5167424 Fstat=564.48548a=-6.2594694 b=-0.057959377
c=1.7851965
0 20 40 60 80-100
-50
0
50
100
150
200
250
300
-100
-50
0
50
100
150
200
250
300
Appendix
IR 4630 T2 leaf area (L1) lny=a+bx+cx^(0.5)
r^2=0.9765241 DF Adj r^2=0.94131025 FitStdErr=30.415551 Fstat=62.395309a=-3.4557951 b=-0.062149506
c=1.6012342
0 20 40 60 800
50
100
150
200
250
300
350
400
0
50
100
150
200
250
300
350
400
IR 4630 T2 Leaf area (L2) lny=a+bx+cx^(0.5)
r^2=0.98535577 DF Adj r^2=0.96338942 FitStdErr=16.157548 Fstat=100.9294a=2.4457692 b=0.040527472
c=-0.020432678
0 20 40 60 80-100
-50
0
50
100
150
200
250
300
350
-100
-50
0
50
100
150
200
250
300
350
Appendix
IR 4630 T2 Leaf area (L3) lny=a+bx+cx^(0.5)
r^2=0.99048506 DF Adj r^2=0.97621264 FitStdErr=6.349366 Fstat=156.14676a=-1.0329038 b=-0.011807638
c=0.75673935
0 20 40 60 80-200
-150
-100
-50
0
50
100
150
200
250
300
-200
-150
-100
-50
0
50
100
150
200
250
300
IR 4630 T2 Leaf area (L4) lny=a+bx+cx^(0.5)
r^2=0.96475413 DF Adj r^2=0.91188532 FitStdErr=6.0826342 Fstat=41.058174a=-1.6941847 b=0.025874002
c=0.41185148
0 20 40 60 80-200
-150
-100
-50
0
50
100
150
200
250
300
-200
-150
-100
-50
0
50
100
150
200
250
300
Appendix
IR 31785 T1 Leaf area (L0) lny=a+bx+cx^(0.5)
r^2=0.9828089 DF Adj r^2=0.95702224 FitStdErr=12.693861 Fstat=85.754436a=2.6185631 b=0.040299835
c=-0.069196706
0 20 40 60 800
50
100
150
200
250
0
50
100
150
200
250
IR 31785 T1 Leaf area (L1) lny=a+bx+cx^(0.5)
r^2=0.99098113 DF Adj r^2=0.97745282 FitStdErr=18.507132 Fstat=164.8179a=-0.30961797 b=-0.032663022
c=0.98311292
0 20 40 60 800
50
100
150
200
250
300
350
400
0
50
100
150
200
250
300
350
400
Appendix
IR 31785 T1 Leaf area (L2) lny=a+bx+cx^(0.5)
r^2=0.99374111 DF Adj r^2=0.98435278 FitStdErr=12.516471 Fstat=238.15924a=-16.217928 b=-0.25072263
c=4.6861618
0 20 40 60 800
50
100
150
200
250
300
0
50
100
150
200
250
300
IR 31785 T1 Leaf area (L3) lny=a+bx+cx^(0.5)
r^2=0.97626561 DF Adj r^2=0.94066402 FitStdErr=21.008082 Fstat=61.699428a=-12.329674 b=-0.15791387
c=3.4069646
0 20 40 60 80-100
-50
0
50
100
150
200
250
300
350
-100
-50
0
50
100
150
200
250
300
350
Appendix
IR 31785 T1 Leaf area (L4) lny=a+bx+cx^(0.5)
r^2=0.9780034 DF Adj r^2=0.9450085 FitStdErr=10.435489 Fstat=66.692358a=1.1084449 b=0.082046059
c=-0.31980393
0 20 40 60 80-150
-100
-50
0
50
100
150
200
250
300
-150
-100
-50
0
50
100
150
200
250
300
IR 31785 T2 Leaf area (L0) lny=a+bx+cx^(0.5)
r^2=0.9828089 DF Adj r^2=0.95702224 FitStdErr=12.693861 Fstat=85.754436a=2.6185631 b=0.040299835
c=-0.069196706
0 20 40 60 80-100
-50
0
50
100
150
200
250
300
-100
-50
0
50
100
150
200
250
300
Appendix
IR 31785 T2 Leaf area (L1) lny=a+bx+cx^(0.5)
r^2=0.9954878 DF Adj r^2=0.98871949 FitStdErr=8.8334827 Fstat=330.9319a=-4.374173 b=-0.078430261
c=1.8037667
0 20 40 60 80-100
-50
0
50
100
150
200
250
300
350
-100
-50
0
50
100
150
200
250
300
350
IR 31785 T2 Leaf area (L2) lny=a+bx+cx^(0.5)
r^2=0.99488405 DF Adj r^2=0.98721011 FitStdErr=6.2723759 Fstat=291.70041a=-10.840833 b=-0.18127507
c=3.3974146
0 20 40 60 80-150
-100
-50
0
50
100
150
200
250
300
-150
-100
-50
0
50
100
150
200
250
300
Appendix
IR 31785 T2 Leaf area (L3) lny=a+bx+cx^(0.5)
r^2=0.95414427 DF Adj r^2=0.88536067 FitStdErr=10.882656 Fstat=31.211288a=-13.635033 b=-0.22668686
c=4.0484374
0 20 40 60 80-200
-150
-100
-50
0
50
100
150
200
250
300
-200
-150
-100
-50
0
50
100
150
200
250
300
IR 31785 T2 Leaf area (L4)
lny=a+bx+cx^(0.5)r^2=0.87517316 DF Adj r^2=0.6879329 FitStdErr=14.102941 Fstat=10.516646
a=-6.2842338 b=-0.093942879 c=2.0157503
0 20 40 60 80-200
-150
-100
-50
0
50
100
150
200
250
300
-200
-150
-100
-50
0
50
100
150
200
250
300
Appendix