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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Appendix