GENETIC BASIS OF DROUGHT TOLERANCE IN

COTTON (Gossypium hirsutum)

By

Rana Tauqeer Ahmad M.Sc. (Hons) Agri. (Plant Breeding and Genetics)

A thesis submitted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

IN

PLANT BREEDING AND GENETICS

Faculty of Agriculture,

University of Agriculture, Faisalabad 2009

To The Controller of Examinations, University of Agriculture, Faisalabad. We, the supervisory committee certify that the contents and form of

thesis submitted by Mr. Rana Tauqeer Ahmad have been found

satisfactory and recommend that it be processed for evaluation by the

External Examiner(s) for the award of the degree.

SUPERVISORY COMMITTEE Chairman _____________________________ (Dr. Tanwir Ahmad Malik) Member _____________________________ (Dr. Iftikhar Ahmad Khan) Member _____________________________ (Dr. Muhammad Jafar Jaskani)

ACKNOWLEDGEMENTS

Thanks to Almighty “ALLAH” (The Merciful) and to the Holy Prophet

“MUHAMMAD” (Peace be upon him) who is source of guidance for the whole

humanity.

I would express my deep gratitude and appreciation to my supervisor

Dr. Tanwir Ahmad Malik, Associate Professor, Department of Plant Breeding

and Genetics for his keen guidance in these investigations and preparation of this

manuscript. Thanks are also due to Prof. Dr. Iftikhar Ahmad Khan, Dean,

Faculty of Agriculture, Department of Plant Breeding and Genetics and Dr.

Muhmmad Jafar Jaskani, Associate Professor, Institute of Horticultural

Sciences, University of Agriculture, Faisalabad for their valuable suggestions in

research. Special thanks to Mr. Muhammad Fazal, Laboratory Assistant for his

co-operation.

I am also thankful to my wife Ghazala Tauqeer for assistance in

preparation of this manuscript and my daughters Fatima Manyam, Eiza Fatima

and my son Waqir Muhammad for being well-behaved children. Last but not

least, I wish to submit my sincere and earnest thanks to my affectionate brothers,

sister and other family members for their good wishes for my success in studies.

(RANA TAUQEER AHMAD)

DEDICATED

To My Late Parents

and Murshad Sarkar

Haji Munzar Ahmad

Who inspired me to achieve higher goals in life

CONTENTS

Chapter #

Title Page #

I Introduction 1

II Review of Literature 11

2.1 Relative Water Contents 11

2.2 Excised leaf water loss 13

2.3 Net Photosynthesis 14

2.4 Water Use Efficiency 18

2.5 Stomatal size and Frequency 19

2.6 Osmotic Adjustment 20

2.7 Gene action of fibre and yield traits 24

2.8 Correlation Studies 31

III Materials and Methods 42

3.1 Screening of Plant Material 42

3.2 Crossing Work 43

3.3 Field Experiment for Inheritance Studies 43

3.4 Relative Water Contents 45

3.5 Excised Leaf Water Loss 45

3.6 Plant Height 46

3.7 Number of monopodial branches per plant 46

3.8 Number of sympodial Branches per plant 46

3.9 Number of bolls per plant 46

3.10 Boll Weight 46

3.11 Ginning outturn (GOT) 47

3.12 Field trials 47

3.13 Statistical Analysis 47

3.13.1 Generation Means Analysis 47

3.13.2 Analysis of Components of Genetic Variance 48

3.13.3 Heritability Estimates 48

3.13.4 Phenotypic and Genotypic Correlations 50

IV RESULTS AND DISCUSSION 51

4.1.Generation means analysis 51

4.1.1 Plant height 53

4.1.2 Number of monopodial branches 55

4.1.3 Number of sympodial branches 56

4.1.4 Numbers of boll per plant 57

4.1.5 Boll weight 58

4.1.6 Ginning Out-turn (GOT) 59

4.1.7 Fibre Finenes 60

4.1.8 Fibre Strength 61

4.1.9 Staple Length 61

4.1.10 Relative water content (RWC) 62

4.1.11 Excised leaf water loss (ELWL) 63

4.2 Generation Variance analysis 64

4.3 Heritability 67

4.4 Correlation of fibre yield traits 67

4.4.1 Plant height 68

4.4.2 Number of monopodial branches 72

4.4.3 Number of sympodial branches 72

4.5 Correlation of Fibre Quality Traits 73

4.6 Correlation of traits related to drought tolerance 74

4.7 Cotton breeding for drought tolerance 74

V SUMMARY 76

LITERATURE CITED 79

APPENDIX 100

LIST OF TABLES

Table #

Title Page #

3.1 List of selected contrasting parents for crossing. 44 3.2 List of crosses and backcrosses made in the study 44 3.3 Coefficients of the mean (m), additive (d), dominance (h), additive ×

additive (i), additive × dominance (j) and dominance × dominance (l) parameters for the weighted least squares analysis of generation means (Mather and Jinks, 1982).

49

3.4 Coefficients of the D, H, F and E effects for the weighted least squares analysis of generation variances (Mather and Jinks, 1982)

49

4.1 Generation Means for the Plant Height (PH, cm), Monopodia (Mon), Sympodia (Sym), Boll Number (BN), Boll Weight (BW, gm), Ginning Outturn (GOT, %), Fibre Fineness (FF. Mic), Fibre Strength (FS, g/tex), Fibre Length (FL, mm), Relative Water Contents (RWC, %), Excised Leaf Water Loss (ELWL, g/g) in six crosses S-12 x NIAB-78(1), CIM-240 x Karishma(2), Karishma x Acala-15/17(3), CP-1521 x Acala-15/17(4), Acala-15/17 x CIM-240(5), Karishma x CP-1521(6) of cotton

52

4.2 Estimates of the best fit model for generation means parameters (±, standard error) by weighted least squares analysis in respect of Plant Height (PH, cm), Monopodia (Mon), Sympodia (Sym), Boll Number (BN), Boll Weight (BW, gm), Ginning Outturn (GOT, %), Fibre Fineness (FF. Mic), Fibre Strength (FS, g/tex), Fibre Length (FL, mm), Relative Water Contents (RWC, %), Excised Leaf Water Loss (ELWL, g/g) in six crosses S-12 x NIAB-78(1), CIM-240 x Karishma(2), Karishma x Acala-15/17(3), CP-1521 x Acala-15/17(4), Acala-15/17 x CIM-240(5), Karishma x CP-1521(6) of cotton

54

4.3 Variance components D (additive), H (Dominance), F (Additive × Dominance) and E (environmental) following weighted analysis of components of variance, and heritability (ns, narrow sense and F∞ generation) for Plant Height (PH, cm), Monopodia (Mon), Sympodia (Sym), Boll Number (BN), Boll Weight (BW, gm), Ginning Outturn (GOT, %), Fibre Fineness (FF. Mic), Fibre Strength (FS, g/tex), Fibre Length (FL, mm), Relative Water Contents (RWC, %), Excised Leaf Water Loss (ELWL, g/g) in six crosses S-12 x NIAB-78(1), CIM-240 x Karishma(2), Karishma x Acala-15/17(3), CP-1521 x Acala-15/17(4), Acala-15/17 x CIM-240(5), Karishma x CP-1521(6) of cotton

66

4.4.1 Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 1 (S12 x NIAB-78)

69

4.4.2 Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 2 (CIM-240 x Karishma)

69

4.4.3 Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 3 (Karishma x Acala-15/17)

70

4.4.4 Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 4 (CP-1521 x Acala-15/17)

70

4.4.5 Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 5 (Acala-15/17 x CIM-240)

71

4.4.6 Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 6 (Karishma x CP-1521)

71

CHAPTER-1

INTRODUCTION

Cultivation of cotton is very old (Kohel and Lewis, 1984). The time when

cotton fibre was first used by human is not known. However, it is known that

civilizations on both Eastern and Western Hemispheres of the world cultivated

cotton. The first written record of cotton is found in the Hindu Rig Veda, written

during the 15th century B.C. During this period cotton spinning and weaving was

well known. During 800 B.C. Manu ordained that the sacred thread which every

Brahmin had to wear must be made of cotton. The first cotton fabric date back to

approximately as early as 3200 B.C., as revealed by fragments of cloth found at

the Mohenjo-Daro archaeological site on the banks of the River Indus in Pakistan.

Peruvian archaeological excavations found cotton specimens that had been

fabricated into textiles as far back as 2500 B.C.

The latest attempt to trace the history of cotton growing and art of spinning

was made by Silow (1944) and Stephens (1947). There are wild species of cotton

in all the continents except Europe. The old world cotton probably originated

somewhere in the Southern half of Africa and spread Eastwards. The new world

cotton is supposed to have originated in Peru, Ecuador, and Columbia region and

hence its use in this region considered to be very ancient.

Cotton plant belongs to a very large genus, Gossypium, order Malavales

and family Malvaceae. The genus Gossypium comprises 50 species with basic

chromosome number of 13 (Poehlman and Sleper, 1995). Of the known species,

45 are diploid (2n = 2x = 26 chromosomes) grouped in seven genomes A, B, C, D,

E, F and G. Diploid species with the A, B, E and F genomes are African or Asian

and the species with C and G genomes are Australian in origin. Diploid species

containing D genome originated in Western Hemisphere and are referred to as new

world species. There are five allotetraploid species (2n = 4x = 52 chromosomes),

containing AADD genomes, four native to continental America and one to

Hawaii. According to Wendel et al. (1992) the new world tetraploid species arose

some 1-2 million years ago through the hybridization of an old world taxon of the

“A- genome” with a taxon of the “D-genome”.

The origin of allotetraploids is believed to be in the New World, specific

countries being Mexico, Peru, Brazil, Hawaii, and the Galapagos Islands (Smith,

1995). Among the 45 diploid species of cotton, only two (Gossypium arboreum

and Gossypium herbaceum) and of the five allotetraploid species two (Gossypium

hirsutum and Gossypium barbadense) produce spinnable fibres on seed coats and

are cultivated. The species Gossypium barbadense originated in Central and South

America, accounts for about 9% of the world fibre. It produces cotton fibres of the

highest quality. In the USA it was formerly cultivated by the colonists along the

coastal Islands of South Carolina and Georgia, so became known as Sea Island

cotton. It was then introduced to the Nile Valley of Egypt, where it became known

as Egyptian cotton. It is widely grown in irrigated areas of Arizona, New Mexico

Western Texas in the USA and is now generally referred to as American Pima.

Gossypium hirsutum originated in Southern Mexico and Central America. In the

United States it became known as American Upland cotton because it was

cultivated successfully on higher elevations where Sea Island cotton did not

mature. It is the most important agricultural cotton, accounting for more than 90%

of world fibre production.

The cultivated diploid species Gossypium arboreum (originated in Indo-Pak

Sub-continent), and Gossypium herbaceum (originated in Southern Africa) are

called "Old World" or "Asiatic" or “Desi” cottons. These two species have short

staple-length with less commercial value and currently accounts for only 1% of the

world cotton production. The species Gossypium arboreum is currently grown for

local use in India and Pakistan. Whereas, Gossypium herbaceum is grown in the

drier areas of Africa and Asia.

In the Indian Sub-continent the American Upland Cotton (Gossypium

hirsutum) was first introduced into Bombay from America in 1790. Introductions

continued throughout the 19th and early part of the 20th century with varying

degrees of success (Poehlman and Borthakur, 1969).

Cotton’s share of the textile fibre market in the world declined almost

continuously from 68% in 1960 to 39% in 2004, but increased to 40.6% in 2005

(Anonymous, 2006). The dominant position of cotton in the world has been

snatched by the synthetic fabrics, but due to its natural qualities (versatility,

durability and comfort) cotton is still preferred. Many daily used things that we

wear, sleep on, sleep under, walk on, or utilize, contain some percentage of cotton.

Cotton crop mainly grown for fibre purpose. It has many other valuable uses as

well. Cotton seed has 30% starch, 25% oil and 16.20% protein (Cobley and Steele,

1976). It is also used in the manufacture of medicinal supplies, tarpaulin, cordage

and belting. The cotton hulls serve as roughage for livestock and the fuzz (short

seed hair) is used in the manufacture of papers, plastics, carpets, rayon, explosives

and cotton wool (Cobley and Steele, 1976 and Gibben et al., 1985).

Cotton is an important source of vegetable oil. The content of oil varies in

different Gossypium species, e.g., 18.5% in Asiatic cottons, 19.5% in Upland

cottons and 22.4% in Egyptian cottons (Cobley and Steele, 1976). The oil can be

hydrogenated for margarine or used as cooking oil, salad oil and for packing fish

and to cure meats. The lower quality of oil is used in the preparation of vegetable

soap and lubricants. Cotton waste, trash and other residues can be converted into

ethanol with no environmental pollution (Broder et al., 1992). Glucose (9.4%) and

reducing sugars (43%) have been obtained from powdered cotton stalks

(Guhagarkar and Bhatt, 1994). Dried cotton sticks are an important source of fuel

for domestic use. Moreover, burying of cotton sticks at the rate of 5 tones per

hectare increased organic matter from 0.44 to 0.83%, available phosphorus from

8.24 to 9.48 mg kg-1 and exchangeable potassium from 248 to 332 mg kg-1 in the

soil (Malik et al., 1988).

Cotton is grown on an area of about 3.1 million hectares with production of

about 12.4 million bales in Pakistan (Anonymous, 2005-06). Pakistan is the fourth

largest cotton producing country. China is the top producer followed by USA and

India (Anonymous, 2006). Cotton plays a pivotal role in the agriculture based

economy of Pakistan. It generates significant proportion of foreign exchange. The

value of raw cotton and its products exported each year is about Rs. 213 billion

(Anonymous, 2005). It account for 11.5% of the value added in agriculture sector.

Textile, ready-made garments and other cotton based industries are generating

major employment in the country, in addition to the employment involved in its

cultivation and cotton picking/harvesting. Edible oil industry is also heavily

dependent on cotton crop as 35% total edible oil comes from cottonseed. In other

words the national GDP growth is heavily dependent on cotton production in the

country. The statistics indicate that in the years of low cotton production, GDP

growth rate has drastically gone down and vice versa. Cotton crop in Pakistan

feeds 500 textile mills, 1139 ginning factories, 443 spinning mills, 8.45 million

spindles, 2585 oil expelling units and over 5 million labour engaged in cotton and

cotton related business.

The world population is increasing rapidly and needs concurrent increase in

food, fibre and living requirement. It is estimated that Pakistan’s current

population is about 146 million. Each year another 3.2 million people are added to

this number. If this rate continues, Pakistan’s population would reach 217 million

by the year 2020. The present scenario demands to increase yield per unit land

area. It is evident that land, a limiting factor cannot be increased. Therefore,

alternative is to pay more attention to increase cotton yield per unit land area

through new cotton varieties and balanced nutrition to realize high yield potential

of cotton crop. The production of cotton in the country has seen many leaps and

bounds during the last decade. During 1991-92, Pakistan recorded an all time

production level of 12.8 million bales, which declined gradually during later years.

During 1998-99 only 8.79 million bales were produced without any decrease in

the area under cultivation. Cotton failure or poor yields are often outcome of

different stresses.

It is difficult to define “stress” precisely. In engineering and physical

sciences, the term is usually defined as “the force applied per unit area”. However

in biology stress is usually described more loosely as any factor that disturbs the

normal functioning of an organism. The stresses are of two type, biotic stress viz.

insect, pest, disease etc. and abiotic stresses, such as drought, salinity, temperature,

chemical toxicity are serious threats to agriculture and result in the deterioration of

the environment. Abiotic stress is the primary cause of crop loss world wide,

reducing average yields for most major crop plants by more than 50% (Wang,

2003).

In the commonly used terminology, “Drought” is an environmental stress

of sufficient duration to produce a plant water deficit or stress which in turn causes

disturbance of physiological processes. Generally, in arable agriculture, drought

describes a condition in which available soil moisture is reduced to a point when

plant growth is severely affected (Osmanzai et al., 1987). The term “Drought

resistance” has long been used with reference to “the ability of plants to survive

drought or water deficit”. Three primary types of drought resistance mechanism,

drought escape, drought avoidance and drought tolerance have been identified in

plants (Levitt, 1980). Drought escape is the plant’s ability to complete its life cycle

or drought sensitive growth stages before drought begins, while drought avoidance

refers to the plant’s ability to pass water deficit period by not letting the drought

stress develop inside the tissue. So drought tolerance, is the plant’s ability to avoid

drought injury through various physiological and biochemical adaptations at the

cell level.

According to a UNO report (Anonymous, 2002) drought is a great threat all

around the world. Worldwide the area under drought is 36% and the area under

temporary drought is more than 64%. In Pakistan the cultivated area under drought

stress is 75%..

Incident precipitation and river flow are the two major sources of surface

water used to meet the requirements of agriculture and other sectors in Pakistan.

About 60 percent rainfall is received during July to September monsoon. Most

summer rains are not available for crop production because of rapid runoff during

torrential showers.

Higher plants has developed a number of morphological and physiological

adaptations in accordance to their environments. The morphological adaptations to

water stress may be classified as permanent and temporary. Permanent adaptation

include the production of smaller leaves, leaves with changed anatomy, and

senescence or shedding of older leaves. Temporary adaptation involves changes in

leaf angle or orientations such as leaf rolling or folding. Physiological adaptations

to water stress indicate that plants are capable of internal biochemical adaptations

and hormonal control of development under dry condition, which suggests that

plants respond according to a genetically programmed system. Disadvantages of

the morphological adaptations that occur during the growth and development of

the crop is that they are largely irreversible, that have no scope for compensation.

Thus the growth or yield potential that has been lost can not be fully recovered, if

there is a return to more favorable conditions.

High day time temperature (>37°C) delays the formation of buds in cotton

which gives poor yields (Taha et al., 1981; Reddy and Khan, 1999). In Pakistan,

the summer temperature often reaches upto 50°C (Ashraf et al., 1994). Water

requirement varies with the change in evapo-transpiration rate (Gibbon and Pain,

1985) but for obtaining higher yield production, at least 500 mm water either by

rain or irrigation is essential (Waddle, 1984), it should be 1040 mm in hot tropical

areas (Halliday and Trenkel, 1992). According to Grimes and El-Zik (1990),

severe water stress during peak flowering is the most detrimental with the loss of

both squares and young bolls contributing to a severe yield loss. Guinn and

Mauney (1984) reported that yield loss would be expected when leaf water

potential (LWP) is lower than –1.9 MPa.

Drought stress or water deficit is a complex phenomenon affecting the

physiology of cotton plant (Grimes and El-Zik, 1990). The changes in water

content of the soil affects the growth and productivity of plant (Chu et al., 1995).

Water stress applied at different stage of development in cotton (Gossypium

hirsutum) caused abscission (Joseph et al., 1997). Guinn and Mauney (1984)

reported that water stress decreased seed cotton yield, in part because of decreased

flowering and in part because of decreased boll retention. Vigil et al. (1994)

reports that there is a critical window of seed ripening, mainly from approximately

15 to 25 days after anthesis, when cotton seed and fibre are most sensitive to

drought stress. Once seeds pass this 10-day window, they appear not to be

adversely affected by severe drought. Grimes and El-Zik (1990) reported that fibre

length and micronaire are most consistently affected by water stress.

Plants respond to drought stress at molecular, cellular as well as

physiological level (Bray, 1997). The leaves of drought tolerant cotton genotype

maintain higher RWC, photosynthetic activity and proline level (Parida et al.,

2008). In response to water deficit cotton plant show quantitative and qualitative

difference in gene expression. A heat shock protein gene (GHSP-26) has been

isolated and characterized from Gossypium arborum (Maqbool et al., 2007).

Genetic transformations introducing genes for improvement of drought resistance

have been undertaken in crop plants (Junping and John, 2007).

Insufficient soil moisture during the summer months is major cause of yield

losses. Genetic variation for drought tolerance is an essential prerequisite for the

development of stress tolerant varieties. Drought is a major limitation to crop

productivity world wide. For most major crops, improvement in drought tolerance

is an important breeding objective, and significant advance have been made over

the past 10-20 years (Boyer, 1996). Two essential building blocks of any crop

improvement programme are genetic variation and source of germplasm

containing enhanced expression of desired traits. Research is needed to reduce the

chances of crop failure by improving crop and soil management, developing

cultivars to resist drought and achieving a basic understanding of effects of

drought stress on plants. Success in breeding to improve drought resistance of crop

cultivars has been limited in the past by lack of techniques, and a lack of

knowledge of what conditions drought resistance in crop plants (Moss et al.,

1974). The plant breeder attempting to improve drought resistance of his breeding

material has three options, each of which has certain advantages. Selection for

yield stability over dry sites and years (Laing & Fischer, 1977), selecting directly

for performance in controlled drought stress nurseries and selecting for

physiological or biochemical characteristics directly to field drought resistance

(Begg & Turner, 1976). Drought is a multidimensional stress affecting plants at

various level of their organization. The plant response to drought at whole plant

and crop levels is complex because it reflects the integration of stress effects and

responses at all underlying levels of organisation over space and time (Blum,

1996.).

There are number of plant traits like relative water content (RWC), excised leaf water

loss (ELWL), stomatal frequency, stomatal size, osmotic adjustment etc., which have

been reported as related to drought resistance. Present study was initiated to

investigate the inheritance and correlation of RWC, ELWL, plant height, number of

monopodial branches, number of sympodial branches, numbers of boll per plant, boll

weight, staple length, fibre fineness, fibre strength and ginning out turn and their

correlation under drought stress. The information generated by this study would be

helpful for plant breeder to tailor high yielding drought tolerant cotton strains.

CHAPTER-II

REVIEW OF LITERATURE

A number of studies related to drought resistance traits in crop plants have been

reported in the literature and information have successfully been used in breeding

crop plants. There are a number of morphological adaptations like leaf rolling, leaf

angle etc. physiological traits such as net photosynthesis, osmotic adjustment,

water use efficiency, stomatal frequency, relative water content, excised leaf water

loss etc. have been identified which can confer drought resistance in crop plants.

These traits are advocated to be used as screening criteria and have been suggested

to manipulate in crop plants to breed for drought resistance.

2.1 Relative Water Contents

It has been observed that the species which are better adapted to dry environment

have higher relative water content (RWC) at given water potential (Jarvis and

Jarvis, 1963). Dedio (1975) studied five cultivars of wheat to evaluate relative

water content under different levels of soil moisture stress. He found that water

retaining ability of leaf was under the control of dominant genes and concluded

that drought resistant cultivars maintained higher leaf water content under drought.

There is difference of drought tolerance among crop speices. Sorghum suffers a

smaller decrease in relative water content per unit change in leaf water potential

than cotton (Ackerson and Krieg, 1977) or maize (Levitt, 1980). It has also been

observed that un-irrigated plants show a much smaller change in relative water

content per change in water potential (Leviit, 1980). Coyne et al. (1982) studied

correlation of relative water content (RWC) with tissue elasticity and concluded

that low tissue elasticity contribute to drought resistance by maintaining higher

relative water content at zero turgor potential.

Klar (1984) studied RWC in the leaves of wheat under controlled

environment and suggested as drought resistance indicator. It has been reported

that relative water content is an indicator of water status and through its relation to

cell volume; it reflects the balance between water supply to the leaf and

transpiration. So relative water content can be used as the selection criterion for

drought tolerance (Sinclair and Ludlow, 1985, Matin et al. 1989 and Ritchie et al.

1990). Tahara et al. (1990) evaluated two selection groups of plant with high and

low yield potential from F2 population of the cross TAM W-10 x Sturdy. One set

of entries did not receive any water after jointing stage and the other was grown

under well-irrigated conditions. They observed a positive relationship between

grain yield and relative water content as the high yielding selection maintained a

significantly highest relative water content than the low yielding selection.

Khan et al. (1993) evaluated four wheat cultivars for drought resistance and

found that cultivars with smallest decrease in relative water contents produced

higher dry weight and grain yield per plant and were taller compared to other

cultivars. There was also a positive correlation between yield and relative water

contents. Kheiralla et al. (1993) evaluated eight spring wheat cultivars of diverse

geographical origin, 2 (Giza-160 and Sakha-69) from Egypt and 6 from Mexico,

USA, Pakistan and Zimbabwe, for drought resistance. However, high water

retention capacity was not related to high yield under drought stress.

Rajeshwari (1995), evaluated 30 cotton genotypes for drought tolerance

under rainfed conditions. Three genotypes were found to be drought tolerant and

had high yield potential. Earliness, relative water content and chlorophyll stability

index were found to be associated with drought tolerance.

2.2 Excised Leaf Water Loss

It has been observed that the species which are better adapted to dry environment

have low rate of water loss through leaf cuticle, is cited as an important drought

survival mechanism and rate of water loss from excised leaves has been related to

drought resistance in wheat (Salim et al, 1969). It has been suggested that

genotypic differences in rate of water loss, which is presumably an estimate of

cuticular transpiration rate, can be used for screening wheat genotypes for drought

resistance (Clark and McCaig, 1982). Jaradat and Kozak (1983) observed that

excised leaf water retention was positively correlated with yield in wheat. Clarke

and Townley (1986) also concluded that low rate of water loss (high leaf water

retention) was associated with high grain yield potential under drought.

Randhawa et al. (1988) air dried leaves of 10 wheat varieties and one

triticale variety for 48 hours and found varietal differences in leaf water content

and leaf water retention. They concluded that high leaf water retention (low rate of

water loss) may be used as an indicator of drought tolerance. Similarly Winter et

al. (1988) studied several screening techniques to differentiate drought resistant

genotypes in wheat. They used five cultivars (Scout 66, Sturdy, TAM W-101,

TAM-105 and TAMj-108) which differed in drought resistance and yield. Higher

water loss from excised leaves correlated with drought susceptibility. Clark and

Romagosa (1989) reported the association of low rate of excised leaf water loss

with improved yields under very dry environments in wheat. McCraig and

Romagosa (1989) used eight different genotypes of wheat to evaluate excised leaf

water loss as a screening technique for drought resistance in Triticum turgidum

and Triticum aestivum wheat. Among 8 genotypes, those adapted to dry land

exhibited low rate of water loss suggesting that the trait might be used as a

screening criterion for drought resistance.

2.3 Net photosynthesis

It has been found that the effect of drought on net photosynthesis is lower

in drought resistant than in drought susceptible species. Sorghum was able to

photosynthesize at a rate up to 25% of the maximum at a leaf water potential of –

1.2 mpa, a potential at which corn leaves were wilted and had no net

photosynthesis. Lee et al. (1974) studied the effect of drought stress on water

relations and net photosynthesis in pea seedlings and found that drought resistant

genotypes had higher net photosynthesis than drought susceptible ones under

drought stress conditions.

Kaul (1974) measured net photosynthesis in flag leaves of severely drought

stressed wheat cultivars and observed positive correlation between net

photosynthesis and grain yield under drought stress. This suggested that the

relative yield performance of wheat cultivars under drought may be predictable

from the photosynthetic rate of their leaves. Dedio et al. (1976) measured net

photosynthesis of stressed and non-stressed plants. Little difference in net

photosynthesis was observed among non-stressed plants of different cultivars

whereas under drought stress conditions Pitic-62 maintained high net

photosynthesis, while net photosynthesis in the other cultivars declined

dramatically. Their studies suggested that net photosynthesis could be used as a

screening technique for drought resistance. Seropian and Planchon (1984)

concluded that measurement of net photosynthesis may prove very useful for

screening drought resistant plants.

An analysis of the effect of water stress on photosynthesis and water

relations of wheat cultivars C-306 and Kalyansona showed that stress affected

both stomatal and nonstomatal components of photosynthesis (Uprety and Sirohi,

1985). Working on sorghum also concluded that net photosynthesis could be used

as a screening criterion to differentiate drought resistant plants from a population.

Similarly Gupta and Berkowitz (1987) studied the inhibition of photosynthesis due

to water stress in different wheat genotypes. They observed that the genotypes

were different for net photosynthesis under drought conditions. Marco et al.

(1988) studied net photosynthesis in flag leaves of durum wheat cultivars

(Valforte, Produra, Adamello, Karel, Appulo and El-Amel) at different water

potentials in three years. Net photosynthesis was measured under natural

conditions with a LI-CDR portable instrument. It was observed that net

photosynthesis was significantly lower in the un-irrigated leaves.

Ritchie et al (1990) compared gas exchange parameters between drought

resistant winter wheat genotypes and drought susceptible genotype to determine if

these parameters contribute to drought resistance. Photosynthesis was significantly

lower in drought susceptible genotype. Photosynthesis declined by 74% and 84%

in TAM W-101 (drought resistant) and Sturdy (drought susceptible), respectively

at the early vegetative stage. Some studies suggested that increased stomatal

resistance as a result of drought stress was a primary cause of inhibition of

photosynthesis in most crop plants (Chaves, 1991). However, some studies

suggested that in addition to stomatal resistance, mesophyll limitations also

contributed to reduction of photosynthesis in crop plants even within the zone of

cell turgor or flaccidity.

Genotypic differences and reduction in net photosynthesis due to water

stress has also been observed in common bean and tepary bean (Castonguay and

Markhart, 1992). Gent and Kiyomoto (1992) found higher net photosynthesis in

resistant genotypes than susceptible ones in winter wheat. In their experiments,

Xue et al. (1992) subjected wheat cultivar, Shaanhe-6 (drought resistant) and

Zhenyin-1 (drought sensitive) to water stress. Under mild water stress, net

photosynthesis was reduced to about half in Zhenyin-1 but was less affected in

Shaanhe-6. They concluded that the drought resistant cultivar had higher net

photosynthesis, under drought compared to the drought susceptible cultivar.

Hudeckova (1993) used wheat cultivar, viginta to observe the effect of

water stress on net photosynthesis. He observed that net photosynthesis decreased

gradually with increasing drought stress and this effect was reversible by relieving

stress. However, sever desiccation progressively damaged the cell membranes and

reduced net photosynthesis and respiration. Lawlor (1993) reported that in the first

phase of water loss from a leaf under drought the sub-stomatal CO2 decreased

because assimilation was not substantially inhibited. With longer exposure to, and

greater degree of stress, mesophyll cells progressively lost the ability to

photosynthesize. It suggested that factors controlling sub stomatal CO2 did not

significantly affect photosynthesis.

Arnau and Monneveux (1995) studied variation for several morpho-

physiological traits involved in drought stress tolerance, on an F3 segregating

population from a cross between a tall, drought tolerant genotype and a dwarf,

drought susceptible cultivar of barley. There was significant genetic variation in

the population for CO2 assimilation rate, stomatal conductance and CO2

intercellular concentration under drought stress. Rekika (1995) et al. measured

CO2 assimilation rate, stomatal conductance, water use efficiency and

relative water content in 6 barley and 5 durum wheat genotypes subjected

to increasing water stress during seedling stage. Genetic differences were

present even at moderate drought. The results suggested that the gas

exchange parameters could be used as predictive criteria for drought

resistance in durum wheat and barley. However, that variation was not

correlated with yield.

Patel et al. (1996) subjected wheat cultivars, WH-283 and WH-331 to

water stress by withholding water until wilting occurred. Photosynthesis decreased

in both cultivars. However, the proportional reduction in yield of WH-331 was

lower than WH-283 under stress indicating that WH-331 was more tolerant to

water stress. The results showed that the genotype with high net photosynthesis

under stress produced high yield suggesting that net photosynthesis could be used

as selection criterion for drought resistance. However, Lian and Ishii (1996) in pot

experiments, water stressed 16 wheat cultivars from China, Japan and Brazil at

seedling stage and measured net photosynthesis. They observed high

photosynthetic rates compared to drought susceptible ones under water stress. Net

photosynthesis was an important parameter in drought related traits.

Wu et al. (2001) studied weak and strong drought resistant wheat cultivars.

The degree of inhibition of photosynthetic rate and stomatal conductance of strong

drought-resistant cultivars were 17.7-22.5 and 21.06-23.75% less than that of

weak drought-resistant cultivars, respectively.

2.4 Water Use Efficiency (WUE)

It is generally defined as the ratio of dry matter produced per unit loss of

water. Blum et al. (1983) concluded from their studies that the highest yielding

ability under drought was related to higher transpiration efficiency in wheat.

Farquar and Richard (1984) studied variation in water use efficiency in wheat and

the results suggested that selection of water use efficiency to improve wheat

drought resistance might be possible. Similar results were reported by Johnson et

al. (1984).

Rana et al. (1988) studied yield performance and water use efficiency of

two sets of barley varieties (differing in yield performance) under drought. The set

of genotypes with high yield had higher water use efficiency. Singh et al. (1990)

studied 5 wheat varieties for water use efficiency and yield. Variety with highest

water use efficiency produced the highest yield. Nan and Qian (1995) analyzed 10

maize hybrids and observed that water use efficiency of drought resistant group

was 29%-58% higher than of the sensitive group. Similar results were reported by

Rekika et al. (1995). They measured water use efficiency in seedling of 6 barley

and 5 durum wheat genotypes subjected to increasing water stress. Their studies

suggested that water use efficiency can be used as a screening criterion for drought

resistance.

2.5 Stomatal Size and Frequency

Miskin et al. (1972) evaluated inheritance of stomatal frequency and the

effect of Stomatal frequency on photosynthesis, transpiration and stomatal

resistance to diffusion in 5 barley populations. The lines having low stomatal

frequency had higher stomatal resistance and transpired less water than lines with

more stomata. A 25% decrease in frequency of stomata reduced transpiration rate

by about 24%. However, stomatal frequency did not influence the rate of

photosynthesis. Hence the possibility existed for altering transpiration with out

effecting photosynthesis in barley by selecting varieties with fewer stomata.

Kazemi et al. (1978) found genotypic differences in stomatal number in wheat and

suggested that genotypic x environmental interactions would make difficult to

select for stomatal number. Similarly Arshad et al. (1993) reported positive

correlation between number of somata and yield in cotton.

Ramzan (1991) reported that stomatal size, carbohydrate contents, leaf

venation, protein, contents, stomatal frequency and number of fertile tillers per

plant were important selection components that contribute to survival of the plant

under stress conditions. Singh et al. (1992) reported that stomatal behavior

although having less heritability was useful trait for increasing water use

efficiency in cotton. McDaniel et al. (2000) developed different drought and heat

tolerant lines in cotton and reported that drought tolerant lines had lower number

of stomata on leaf.

2.6 Osmotic adjustment

In higher plants this term refers to the lowering of osmotic potential arising

from the net accumulation of solutes in response to water deficit or salinity. The

osmotic adjustment takes place in the leaves, hypocotyls, roots, and reproductive

organs of several plant species. Several factors are now known to influence the

degree of osmotic adjustment, like rate of development of stress, degree of stress,

environmental conditions and differences in cultivars. Osmotic adjustment (OA) is

considered as screening criterion for drought tolerance.

Gao-Ning et al. (1995) reported that in Festuca rubra and Lolium perenne

the relative water content, water potential, osmotic potential and turgor potential

decreased gradually with increase in duration and intensity of water stress. Linear

relationships were observed between water potential and osmotic potential and

turgor potential. Lilley et al. (1996) reported that there was good potential for

increasing dehydration tolerance and osmotic adjustment of rice cultivars. Teulat

et al. (1997) studied 5 barley, 5 durum wheat and one wild emmer wheat

genotypes from different origins under controlled conditions for osmotic

adjustment capacity under water stress conditions. The lower osmotic adjustment

were noted in drought susceptible varieties, while a higher capacity was found in

genotypes exhibiting high yield stability across contrasting environments. Relative

water contents and leaf osmotic potential were highly related to osmotic

adjustment and could be used as criteria for a rapid evaluation of drought tolerant

lines in segregating populations.

Zhang et al. (1998) studied two cultivars to analyze the pressure-volume

curve of sugarcane leaves exposed to water stress. With the elongation of the

drying cycle, the leaf water status substantially deteriorated. In 1998 Sanchez et al.

observed the response of 49 pea cultivars, with different drought tolerance. The

cultivars, which best maintained turgor, were those which were more drought-

tolerant. Turgor maintenance was significantly related to osmotic adjustment

(OA). Sugars and proline played an important role in OA in peas, in response to

water stress.

Rekika et al. (1998) observed that under severe water stress drought

tolerant varieties of wheat maintained higher net photosynthetic (PN) rate. The

decrease in PN was mainly due to stomatal and non-stomatal factors. Blum et al.

(1999) reported that OA of cultivars was positively correlated with biomass and

yield under pre-flowering drought stress in the rain exclusion shelter. It was

concluded that consistent differences in OA existed among wheat cultivars and

that these differences could be associated with plant production under pre-

flowering drought stress. Shangguan et al. (1999) found relationship between

photosynthesis and osmotic adjustment in winter wheat (Triticum aestivum)

studied in a controlled growth chamber under drought conditions. Under gradual

soil drying, there was a progressive osmotic adjustment in the plants, but this did

not occur in response to rapid soil drying. The plants that experienced gradual

drying exhibited higher net photosynthetic gas exchange and photosynthetic

capacity at low water potential than those subjected to rapid drying. It was

suggested that osmotic adjustment allowed maintenance of photosynthesis by

stomatal adjustment and photosynthetic apparatus adjustment at low water

potential.

Gonzalez et al. (1999) grew 3 barley breeding lines and 5 cultivars under a

rain shelter in field trials. Under water stress, there was variation in osmotic

adjustment (OA) and stomatal conductance among the genotypes. Correlations

between yield and OA and stomatal conductance in the water stress treatment were

positive and significant. Under drought yield was negatively correlated with time

to spike emergence and maturity.

Subbarao et al. (2000) studied six pigeon pea genotypes under well-watered

and water deficit conditions to evaluate the contribution of osmotic adjustment

(OA) to growth and productivity of the crop. Genotypic variation in OA (ranging

from 0.1 to 0.5 MPa) was significant. Genotypic variation in leaf relative water

content was correlated with OA. Genotypic variation in leaf relative water content

was correlated with crop growth rate under moisture deficits. The results indicated

that OA could influence crop growth rate by increasing leaf relative water content

during soil moisture deficits.

Khanna (1999) reported about T. aestivum genotypes from India and

Australia grown under field in water variable environments. Osmotic adjustment

was correlated with stability index in yield, biomass and grain number. The

genotypes and species showing high osmotic adjustment maintained higher turgor

and relative water content in stress environments. It was concluded that osmotic

adjustment could be used as a selection criterion for measuring drought resistance

in wheat genotypes and species, but was not necessarily associated with high

yield.

Measurement of net photosynthesis, osmotic potential, stomatal size and

frequency, requires more sophisticated and expensive equipment and time

consuming. Relative water content and excised leaf water loss are preferable

criteria due to their rapid and simplicity in measurement especially for screening

very large segregating population for breeding varieties.

2.7 Gene action of fibre and yield traits

The study of gene action and correlation of the traits to be

manipulated is necessary for breeding varieties. In literature a large number

of genetic studies on cotton are reported. However, most of them are

conducted under well watered conditions. There is interaction of genes and

environment in final development of phenotype. So gene action of the traits

vary in different environments.

Pathak and Singh (1970) investigated the inheritance of yield, number of

bolls, boll weight and ginning outturn in Gossypium hirsutum L. Additive and

epistatic gene effect were important for the characters in all the crosses. El-Adl

and Miller (1971) reported additive genetic effects for yield components like

number of bolls and boll weight. Singh et al. (1971) studied the genetics of yield,

boll number, boll weight, number of monopodial and number of sympodial

branches in 8 cotton varieties. Additive and dominance genetic variances was

significant for the characters along with the genetic interactions.

El-Fawal et al. (1974) studied the gene action in inter-specific crosses of

cotton and observed dominant genetic variance for boll number, boll weight, and

lint length and lint strength. Gad et al. (1974) observed additive gene action for

boll number, boll weight, and lint length. Dominance was also significant for boll

number, boll weight, lint length, strength and fineness. Lefort and Schwendiman

(1974) revealed that boll weight, fibre length, micronaire index and yield had

additive effects. Pathak (1975) used six populations (P1, P2, F1, F2, B1 and B2) of

five upland cotton (Gossypium hirsutum L.) crosses to evaluate genetic effects for

fibre properties in cotton by the analysis of generation means and indicated that

although dominance and dominance × dominance genetic effects were more

important in the inheritance of mean fibre length, additive genetic effects were

also important. Over dominance gene action governed fibre fineness while

additive gene action governed the fibre strength.

Gill and Kalsy (1981) partitioned the components of generation means for

yield of seed cotton, bolls per plant and boll weight for four inter-varietial crosses.

Epistatsis was involved in the inheritance of all characters in all the crosses except

for the boll weight in one cross. Singh and Singh (1981) studied gene action of

yield components and quality traits in F1 and F2 crosses. Additive gene action was

important for yield, boll number, boll weight and ginning outturn. Singh et al.

(1982) studied four yield components in the parents, F1 and BC1 from crosses. The

additive genetic effects were observed for ginning outturn along with the pre

dominated epistasis. Ma et al. (1983) studied 11 characters in P1, P2, F1, F2, BC1

and BC2 of cross between Loumain-I and Acala Sj-3. All the traits except number

of seed per boll showed additive effects. Dominance effects were observed for

fibre fineness. Silva and Alves (1983) studied gene action in cotton (G. hirsutum

L.) and reported that for number of bolls per plant and number of fruiting branches

additive gene action predominated, while dominance affected bolls per plant and

some other character to a minor extent. Raw cotton yield per plant, boll weight,

and fibre percentage were affected significantly by epistasis.

Singh et al. (1983) estimated gene action for six yield related and fibre

quality characters from F1, F2 and backcross generations of a cross in G. hirsutum.

Epistasis was observed for the characters evaluated except halo length. Both

additive and dominance effects were observed for all the characters except the boll

numbers and lint index. Vyahalkar et al. (1984) studied the inheritance of four

fibre characters in the F1 and F2 of a 6 x 6 diallel, indicating that additive

components of genetic variance were important for halo length and non additive

components for fibre strength. It was suggested that fibre length could be

improved by simple selection from segregating material. Desphande et al. (1984)

studied the nature of gene action for yield and fibre traits from a 6 x 6 diallel cross

of G. hirsutum L. and reported dominance gene effects for seed cotton yield. Both

additive and non additive gene actions were important for ginning outturn.

Kaseem et al. (1984) reported additive, dominance and epistatic gene

effects in the inheritance of seed cotton yield, boll weight and bolls per plant.

Randhawa et al. (1986) revealed the presence of epistasis for seed cotton yield,

boll size, number of bolls and plant height. They also found that additive genetic

variance was predominant for seed cotton yield, boll number boll size and plant

height. Kalsy and Garg (1988) performed generation means analysis for yield of

seed cotton per plant, number of bolls per plant, boll weight and plant height. The

results showed that additive and dominant gene action and epistasis were

important for inheritance of seed cotton yield and boll weight. Dominance and

epistasis was also involved. Rehman et al. (1988) reported that number of bolls

was additive and non additive in gene action. The variety B-557 contained the

most dominant genes for bolls per plant, seed cotton yield and boll weight.

Tyagi (1988) investigated genetic architecture of yield and its components

in upland cotton. He reported that genetic control appeared additive for seed

cotton yield, dominant and additive for plant height, number of bolls, and

dominant for boll weight. Lin and Zhao (1988) in a study of three inter varietial

crosses of G. hirsutum L. estimated genetic effects of fibre length, fineness,

strength. The effects of dominance and epistasis varied significantly in different

years and different hybrids. Additive effects were relatively stable for the

characters. Nadarajan and Rangasamy (1990) concluded that staple length and

fibre fineness appeared to be governed by additive gene action and showed high

heritability estimates. Similarly Singh et al. (1990) revealed that lint index was

controlled by additive gene action and ginning out-turns by non-additive genetic

effects.

Wang and Pan (1991) studied 56 F1 populations and 30 F2 populations of

G. hirsutum L. and reported additive genetic effects for boll weight and fibre

length. Rehaman et al. (1993) found that ginning out-turn, staple length and fibre

fineness were governed by over dominance type of gene action. May and Green

(1994) observed significant genetic variation for fibre length, uniformity ratio,

fibre strength, fibre elongation and fibre fineness. Dominance genetic variance

was greater than additive genetic variance for all of the fibre traits.

Tang et al. (1996) studied 64 F2 cotton (Gossypium hirsutum) hybrids

resulting from crosses of four commercial cultivars (DES-119, Deltapine-50,

Stoneville-453 and Coker-315) and 16 pest-resistant germplasm lines for five fibre

and four yield traits in four environments at Mississippi State, USA. Dominance

variance accounted for the major proportion of the phenotypic variances. A low

proportion of additive variance for fibre traits and the significant additive x

environment variance components indicated a lack of substantial useful additive

genetic variability for fibre traits.

Jagtap and Mehetre (1998) performed a 10 X 10 diallel analysis of Upland

cotton (Gossypium hirsutum). Heritability (broad-sense) was moderate to high for

number of bolls/plant, seed cotton yield/plant, boll weight, seed index, plant height

hence there was possibility of improvement of these traits through simple

selection.

Xin and Ming (1998) reported non-additive genetic effects for fibre

fineness whereas, lint percentage and seed index were found to be controlled by

both additive and non-additive genetic effects. Nistor and Nistor (1999) concluded

from their study on 10 cotton genotypes and their F1s that additive and dominance

effects were involved in the genetic control of fibre length. The frequency of

dominant genes was higher in comparison with that of the recessive genes. Punitha

et al. (1999) performed a combining ability analysis of 40 F1 hybrids involving 8

cotton varieties and 5 G. barbadense accessions. Non-additive gene action was

predominant for plant height, sympodia/plant, bolls/plant, boll weight and seed

cotton yield.

Pavasia et al. (1999) concluded from 8×8 diallel that additive type of gene

action was significant for ginning out-turn, seed index, lint index and fibre

fineness. Hendawy et al. (1999) studied the inheritance of fibre length, strength

and fineness in 10 cotton varieties using diallel. Griffing approach revealed that

additive and additive × additive type of gene actions were of greater importance

whereas Hayman approach showed that additive genetic variance was highly

significant for the traits.

Mukhtar et al. (2000) used four cotton genotypes for complete diallel cross

to find gene action of fibre traits in cotton and observed additive type of gene

action with partial dominance for fibre fineness whereas, over dominance type of

gene action was found for fibre strength. Bertini et al. (2001) studied gene action,

heterosis and inbreeding depression in cotton involving 2 parental lines and their

F1, F2, RC1 and RC2 generations. Dominance effects were observed for number of

bolls, boll weight and fibre yield, while in fibre traits additive effects were present.

The presence of a large amount of additive effects for fibre traits suggested that

there was potential for improving fibre quality in cotton.

Khan et al. (2001) revealed from their 5 parent diallel experiment in cotton

that simple additive dominance model was adequate for most of the fibre quality

traits. In another study additive genetic effects were reported to be most important

for fibre traits and weight of 100 seeds (Bertini et al., 2001). Genetic effects for

ginning out turn and staple length were studied by Singh and Yadavendra (2002).

Their studies concluded additive, dominance, additive × additive and additive ×

dominance genetic effects for staple length and additive, dominance and epistatic

effects for the inheritance of ginning out-turn. Mert et al. (2003) used generation

means analysis to find the inheritance of ginning out-turn (GOT) and 100 seed

weight. They reported that additive, dominance and epistatic genetic effects were

responsible for GOT whereas, only dominance effects were involved in the

inheritance of 100 seed weight.

Lint weight and seed yield were characterized by over dominance, whereas

seed index and staple length were governed by partial dominance gene action in a

study conducted by Chandio et al. (2003) on six Gossypium hirsutum genotypes.

Nimbalkar et al. (2004) concluded from their study of 8×8 diallel in desi cotton

(Gossypium arboreum and Gossypium herbaceum) that staple length was only

controlled by additive type of gene action and GOT was controlled by both

additive and non additive parameters. Rahman et al. (2005) studied inheritance of

seed traits in upland cotton using generation means analysis. They observed

different inheritance patterns for seed traits under different temperature regimes.

They found that additive genetic effects controlled seed volume and seed surface

area under heat stress conditions whereas, under non-stress regime, dominant

genetic effects were significant.

2.8 Correlation Studies

Sethi et al. (1960) reported highly significant positive effect of

number of bolls on yield per plant in cotton. Their results revealed that

improvement in respect of one character may be detrimental for another.

They reported that increase in yield or ginning percentage may lead to

reduction of staple length, and vice versa. Kyei (1968) found positive

association between number of bolls and number of fruiting branches. However,

the number of bolls had an inverse association with boll weight, fiber length and

seed weight. Similarly, Singh et al. (1968) reported that the yield was

basically dependent upon number of bolls per plant and number of locules

per boll. The number of sympodial branches per plant had a strong

association with number of bolls per plant.

Schwandiman et al. (1975) reported that yield of seed cotton was

positively correlated with flowering dates, plant height, fiber yield and

number of bolls per plant. Whereas, a negative correlation of seed cotton

yield with seed index was observed. Joshi (1976) studied 14 Gossypium

hirsutum varieties and found that fibre length was negatively correlated

with uniformity ratio and fineness. Lalchand et al. (1978) observed that

number of bolls per plant and seed index had positive correlation with yield

of seed cotton per plant.

Boucherova (1979) reported positive correlation between fibre length and

fibre fineness. There was negative correlation between fibre length and fibre

strength. Tyagi (1988) observed a high positive correlation of yield with

plant height, number of bolls per plant and seed index, while the

correlation was negative with fibre length. Singh et al. (1979) studied 50

genetically diverse Gossypium hirsutum L. varieties. Positive correlations

were found between boll number and number of sympodial branches and

between boll weight and ginning percentage.

Baloch et al. (1979) reported correlation of various characteristics in cotton

cultivars. They found that seed cotton yield positively correlated with plant height

and number of bolls per plant. Waldia et al. (1979) found number of bolls per

plant and boll weight to be positively correlated with seed cotton yield and also

with each other and exhibited positive direct effect on yield in 19 varieties of

Gossypium hirsutum L. Aguilar et al. (1980) reported a positive correlation

between fibre index and seed index as well as between the length and

strength of fibre. They concluded that the association between fibre

percentage and fibre length might be attributed to linkage or pleiotropy.

Bocharova (1980) showed positive correlation between fibre length and

fibre fineness whereas, negative correlation was present between fibre

length, strength and fineness. Number of bolls per plant was highly

significant and negatively correlated with ginning outturn percentage,

staple length and seed index. Boll weight was positively correlated with

staple length. Giri and Updhyay (1980) observed that seed cotton yield per plant

was positively correlated with plant height, internode length, days to maturity and

number of bolls per plant. However, seed cotton yield was negatively correlated

with lint percentage. Nachnani and Abro (1980) found that number of sympodial

branches per plant and number of bolls per plant were positively correlated with

yield.

Sanyasi (1981) described that number of bolls per plant had positive

correlation with seed cotton yield and similarly seed cotton yield had positive

correlation with ginning outturn. Ginning outturn percentage showed positive

correlation with plant height and lint index. Boll weight was negatively correlated

with fibre length, seed index and lint index. Channa and Ahmad (1982) concluded

that number of bolls per plant, number of sympodial branches per plant, and plant

height were positively correlated with seed cotton yield per plant.

Soomro et al. (1982) reported association between seed cotton yield and

other agronomic characters viz., boll weight, ginning outturn, seed index and also

between plant height and number of monopodial branches. Boll weight and seed

index were positively correlated with yield of seed cotton. Negative correlation

was observed between ginning outturn and yield of seed cotton per plant. Dhanda

et al. (1984) studied 120 progenies from Gossypium hirsutum H-777 x H-807. The

studies revealed that seed cotton yield positively correlated with number of bolls

per plant, plant height and seeds per boll. Path coefficient analysis showed that

bolls per plant and seeds per boll contributed the most in yield. Multiple

correlation analysis indicated that bolls per plant and boll weight contributed 60%

towards the yield.

Sokolova and Avtonomov (1985) found that seed oil content positively

correlated with fibre length whereas, fibre yield showed negative correlation with

seed oil content. Al-Rawi et al. (1986) reported that number of bolls per plant,

seed cotton index and plant height were positively correlated with seed cotton

yield. Boll number per plant and seed index had direct effect on yield. Similarly

Zhou (1986) reported that lint yield in Gossypium hirsutum L. positively

associated with bolls per plant. Path coefficient analysis showed that

number of boll per plant contributed most towards yield followed by lint

percentage. Shindo and Thombre (1986) observed positive association of

seed cotton yield with seed index, lint index and fibre length. Seed index

positively correlated with lint index and mean fibre length.

Tyagi (1987) found negative correlation of fibre length with GOT and fibre

fineness. Sangwan and Yadava (1987) reported that seed cotton yield was

positively correlated with number of bolls per plant and boll weight.

Alam and Islam (1991) evaluated genotypic correlation for yield and yield

related components in 20 genetically diverse genotypes. Seed cotton yield per

plant positively correlated with the number of bolls per plant. Chang and Zhao

(1991) found that fibre strength was negative correlated with yield and ginning

outturn was positively correlated with lint yield per plant in 40 cotton cultivars.

Chen and Zhao (1991) studied fibre yield, fibre quality and plant type

characteristics. Lint percentage had significant and positive correlation with fibre

strength.

Khan et al. (1991) found that boll number per plant had negative

association with staple length and positive association with seed index and

seed cotton yield. Lint percentage was negatively correlated with staple

length and seed lint index. Staple length was positively correlated with

seed index and negatively correlated with seed cotton yield. Boll number

and seed index had direct positive effect on yield.

Azhar and Khan (1992) carried out path coefficient analysis of seed

cotton yield and its components and found highly positive correlation of

number of bolls per plant and boll weight with yield of seed cotton per

plant. Boll number and boll weight had positive direct effect on seed cotton

yield. Baloch et al. (1992) found stronger and positive phenotypic correlation

coefficients between number of bolls and seed cotton yield, seed index and boll

weight. Number of bolls had major and direct effect on seed cotton yield. Akbar et

al. (1994) found that the correlation between number of seeds per boll and plant

height was positive. Path coefficient analysis revealed that number of bolls per

plant had maximum positive direct effect on yield of seed cotton. Plant height and

boll weight also contributed to seed cotton yield of a plant via number of bolls per

plant.

Tariq et al. (1992) reported significant genotypic correlation coefficient

between seed index and lint index, followed by number of bolls per plant and yield

of seed cotton per plant. Number of bolls per plant had no correlation with boll

weight. Number of bolls and boll weight had high direct positive effects towards

yield of seed cotton. Wang and Zhao (1992) found a negative correlation between

boll size and number of bolls, bolls size and seed cotton yield in cotton. They

suggested that selection for small bolls and high boll weight may improve seed

yield per plant.

Tian et al. (1993) studied 10 cotton (Gossypium hirsutum L.) lines.

Number of bolls per plant and lint percentage showed positive correlation with

ginned cotton weight per plant. Zhou (1994) reported that most of the yield

components were correlated with lint yield. Whereas the main fibre quality traits,

fibre length, fibre strength and micronaire value, were negatively correlated with

lint yield. Magnitude and type of genetic variation controlling fibre traits in

populations derived from crosses amongst 8 elite F6 cotton parents was estimated

by May and Green (1994). Significant genetic variation was found for fibre length,

uniformity ratio, fibre strength, fibre elongation and fibre fineness. Dominance

genetic variance was greater than additive genetic variance for all of the fibre

traits.

Carvalho et al. (1994) studied six varieties and their 30 hybrids from a

diallel set of crosses for correlation analysis. Seed cotton yield was correlated

positively with number of bolls per plant, boll weight and height while seed cotton

yield had a negative correlation with fibre strength. Fibre length and fibre fineness

showed negative correlation.

Alam (1995) reported that sympodia number was positively correlated with

boll number, boll weight, seed index, lint percentage, lint index and staple length.

Seed cotton yield negatively correlated with sympodia number. Bhatnagar (1995)

found positive correlation of boll weight with yield, seed index, ginning

percentage and lint index. Shah (1995) found that genotypic correlation of

plant height was negative with lint percentage and fibre length. Association

between yield of seed cotton and fibre fineness was negative. Rao and Mary

(1996) reported negative correlation of micronaire with fibre length. Path analysis

revealed that boll number and boll weight were the main contributors towards the

yield.

Amutha et al. (1996) studied 15 parental genotypes and found positive

correlation between boll weight, plant height, and number of bolls per plant. They

also reported that colour of lint had a positive correlation with fibre fineness and

ginning outturn. Bing et al. (1996) studied 64 hybrids resulting from crosses

of four commercial cultivars and 16 pest resistant germplasm lines. They

found that fibre strength had positive genetic correlation with boll weight.

The genotypic correlation of fibre length and strength was negative. Xiang

et al. (1997) reported that in upland cotton fibre strength had negative correlation

with lint yield, which was due to genetic linkage.

Hafeez et al. (1998) analyzed components of variation, heritability,

phenotypic and genotypic correlations for various traits within F3 families of

Gossypium arboreum. The estimates showed moderate to high heritability and

variability for the traits studied. Significant genetic linkage was present between

the seed cotton yield and number of bolls. Murthy (1999) studied ten parents and

45 crosses and found that number of bolls per plant, plant height, number of

monopodial branches, number of sympodia and number of seeds per boll had

positive correlation with seed cotton yield, while negative with ginning %age.

Larik et al. (1999) studied interrelationships of different traits in Gossypium

hirsutum L. in a field trial. Results showed that bolls per plant exhibited strong

positive correlations with sympodia both at phenotypic and genetic levels. Fibre

strength had strong positive association with staple length and ginning outturn

percentage. Fibre fineness revealed strong positive and negative genetic

correlation with fibre strength and staple length respectively. Sultan et al. (1999)

tested 20 genetically diverse genotypes of upland cotton in Bangladesh. They

found that number of sympodia per plant, boll weight and ginning had significant

positive association with fibre yield at both genotypic and phenotypic levels.

Sultan et al. (1999) determined six quantitative characters in 20 genetically

diverse genotypes of upland cotton at Jessore, Bangladesh. Sympodia per plant,

boll number per plant, boll weight and ginning percentage showed highly positive

correlations with fiber yield, at both genotypic and phenotypic level.

Haider and Khan (1998) found that seed index and fibre fineness had

negative correlation with yield of seed cotton. While the staple length and lint

index had positive correlation with seed cotton yield. However, seed index had

negative correlation with staple length, while positive correlation with lint index.

They also observed negative genotypic and phenotypic correlation between lint

index and fibre fineness. Hussain et al. (1998) evaluated 12 upland cotton (G.

hirsutum) genotypes for boll weight, staple length, seed index, lint index and yield

of seed cotton per plant. Correlation studies showed that ginning percentage and

lint index were correlated positively and staple length was correlated with seed

cotton yield.

Hassan et al. (1999) reported that superiority of yield was associated with

number of bolls rather than the boll weight. They also found that number of bolls

per plant, boll weight and 100 seed weight were positively correlated with yield of

seed cotton.

Mukhtar et al. (2000) used four cotton genotypes for complete diallel cross

to find out gene action of fibre traits in cotton and observed additive type of gene

action with partial dominance for fibre fineness whereas, over dominance type of

gene action was found for fibre strength. Satange et al. (2000) evaluated thirty

genotypes of American cotton and noted that number of bolls per plant, sympodia

per plant, seed index and boll weight exhibited positive genotypic and phenotypic

correlation with seed cotton yield per plant. Positive correlation of ginning out-

turn with fibre strength was observed by Badr and Aziz (2000). They also found

that staple length had positive relation with fibre fineness and negative relation

with seed index and ginning out-turn.

Khan and Azhar (2000) conducted correlation studies in cotton. They found

positive correlation of seed cotton yield with number of bolls per plant, boll

weight and staple length. Staple length also had positive correlation with number

of bolls. Lint index and seed index positively correlated. Satange et al. (2000)

studied 30 genotypes of American cotton. They concluded that number of bolls

per plant, sympodial numbers, and seed index and boll weight had positive

correlation with seed cotton yield. Hussian et al. (2000) revealed positive

correlation of seed cotton yield with plant height, monopodial branches and

number of bolls per plant. Plant height also showed strong positive correlation

with number of sympodial branches, number of bolls per plant and GOT. Baloch

et al. (2001) reported that seed cotton yield had positive phenotypic correlation

with number of bolls per plant and lint percentage while seed cotton yield had

negative relationship with boll weight. Multiple correlation coefficients revealed

that about 91.8% of total variation in yield was dependent on variables number of

bolls per plant, boll weight and lint percentage.

Echekwu (2001) evaluated F3 generation for two years. He found that plant

height was positively correlated with seed cotton yield, number of monopodial

branches, number of sympodial braches, seed index, fibre length and micronaire

index. Positive correlation was found between seed cotton yield, lint % age, and

fibre strength. Negative correlations were found between plant height and lint %

age; number of monopodial branches, number of sympodial branches and lint %

age; fibre length, fibre strength and micronaire index. Carvalho (2001) observed

that seed cotton yield was negatively correlated with fibre strength and positively

correlated with micronaire index.

Shahbaz et al. (2004) studied correlation for drought tolerant and agronomic traits

in cotton (G.hirsutum L.). Ginning outturn had positive relationship with boll weight.

Fibre fineness positively correlated with number of monopodial branches, plant height

and stomatal size but was negative with fibre strength, fibre length and relative water

contents.

The traits like relative water content, excised leaf water loss has been

utilized in crops other than cotton for improving drought resistance. Serious

attempt has not been made to study the genetics of these traits and their

incorporation in the genotypes for improving drought resistance in cotton.

Similarly genetic studies for important cotton traits like sympodial branches,

monopodial branches, number of bolls, boll size and fibre traits have been studied

generally under optimum moisture conditions. There are only a few studies in the

literature which report these traits studied under drought conditions.

CHAPTER-III

MATERIALS AND METHODS The research work reported in this dissertation was carried out in the

experimental area at PARS (Postgraduate Agricultural Research Station) of the

Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad.

3.1 Screening of Plant Material

The experimental material was selected from the gemplasm available at

different research stations of Pakistan, Cotton Research Station (CRS) Multan,

Central Cotton Research Institute (CCRI) Multan, Nuclear Institute for Agriculture

and Biology (NIAB) Faisalabad, Cotton Research Institute, AARI, Faisalabad and

the University of Agriculture, Faisalabad. Twenty each of drought resistant and

susceptible genotypes were selected using the data available about the field

response of varieties/genotypes under drought stress in the institutes. Drought

response of selected varieties/genotypes was further assessed by growing them

under drought stress through measurement of relative water content (RWC) and

excised leaf water loss (ELWL). The varieties/genotypes were grown during the

normal crop season of 2004 in a randomized complete block design with three

replications in the area of the Department of Plant Breeding and Genetics to select

contrasting parents for crossing work. To select pair of contrasting parents for a

cross it was tried, that both the parents in the cross had relatively same phenology.

List of varieties/genotypes finally selected is given in Table-3.1.

3.2 Crossing Work Crossing was started in the field during the normal cropping season, 2005.

Sufficient numbers of flowers were selfed and crossed to produce seed. During

December, 2005 the seed of selected varieties/genotypes and their F1s was planted

in pots filled with loamy soil under glasshouse conditions to make further crosses.

Some flower buds of F1 plants were used for back crossing and the others were

selfed to produce seed for F2 population. List of crosses produced is given in

Table-3.2.

3.3 Field Experiment for Inheritance Studies

For inheritance studies parental, F1, F2 and backcross (BC1 and BC2)

generations for each of the crosses were raised in field as separate experiment

during the year 2006. The experiment was sown in a randomized complete block

design having three replications. In each replication there were two rows for each

of the parents and F1, 12 rows for each of the F2, and 10 rows for each backcross

generations. The length of each row was 4 meter. Row to row and plant to plant

distance was 75 and 30 cm, respectively. In Faisalabad rainfall is very low during

the growing season (May to November) is very low so at least 5 supplemental

irrigations are need. Drought stress was imposed by restricting supplemental

irrigation. Supplemental irrigation was applied only two times up to the maturity.

During the year 2006 rain fall the month of July, August and September

was 23.1, 6.6 and 3.7mm respectively. At maturity 30 guarded plants from each of

the parents and F1, 45 plants from each of the backcrosses populations, and 150

plants from each of the F2 populations were selected at random to record the data

on individual plant basis.

Table 3.1: List of selected contrasting parents for crossing.

S .No Genotypes Drought Response 1 S-12 Susceptible (low RWC and high ELWL) 2 NIAB-78 Resistant (high RWC and low ELWL) 3 CIM-240 Susceptible (low RWC and High ELWL) 4 KRISHMA Resistant (high RWC and low ELWL) 5 CP-1521 Resistant (low RWC and high ELWL) 6 ACALA-15/17 Susceptible (high RWC and low ELWL)

Table 3.2: List of crosses and backcrosses made in the study

S. No. Population Parents 1 F1, F2 S-12 x NIAB-78

BC1 (S-12 x NIAB-78) x S-12 BC2 (S-12 x NIAB-78) x NIAB-78

2 F1, F2 CIM-240 x KRISHMA BC1 (CIM-240 x KRISHMA)x CIM-240 BC2 (CIM-240 x KRISHMA) x KRISHMA

3 F1, F2 KRISHMA x ACALA-15/17 BC1 (KRISHMA x ACALA-15/17) x KRISHMA BC2 (KRISHMA x ACALA-15/17) x ACALA-15/17

4 F1, F2 CP-1521 x ACALA-15/17 BC1 (CP-1521 x ACALA-15/17) x CP-1521 BC2 (CP-1521 x ACALA-15/17) x ACALA-15/17

5 F1, F2 ACALA-15/17 x CIM-240 BC1 (ACALA-15/17 x CIM-240) x ACALA-15/17 BC2 (ACALA-15/17 x CIM-240) x CIM-240

6 F1, F2 KRISHMA x CP-1521 BC1 (KRISHMA x CP-1521) x KRISHMA BC2 (KRISHMA x CP-1521) x CP-1521

3.4 Relative Water Content (RWC)

Three fully developed leaf sample were taken from each of the selected

plants during the month of September. When the plants were showing symptoms

of drought stress. The samples were covered with polythene bags soon after

excision and fresh weight was recorded using electronic balance. The leaf samples

were dipped in water overnight for recording the turgid leaf weight. The samples

were oven dried at 70°C for taking dry weight. RWC was calculated using the

following formula.

RWC = [(Fresh weight–Dry weight) / (Turgid weight–Dry weight)] x 100

3.5 Excised leaf water loss (ELWL)

Three fully developed leaf sample were taken from each of the selected

plants, during the month of September. When the plants were showing symptoms

of drought stress. The samples were covered with polythene bags soon after

excision and fresh weight was recorded using electronic balance. The leaf samples

were left on laboratory bench. After six hours the weight of the wilted leaf

samples were taken. The samples were oven dried at 70°C for taking oven dry

weight. Excised leaf water loss was calculated by the following formula.

ELWL = (Fresh weight – wilted weight) / Dry weight

During the end of November, when plants were fully mature, the data about

the following agronomic traits was recorded.

3.6 Plant Height (cm)

The height of each selected plant was measured from ground level to the

top of the main stem.

3.7 Number of Monopodial Branches per Plant

The monpodial branches are the vegetative types of branches in

cotton. At maturity, the monopodial branches per plant were counted for all

the selected plants.

3.8 Number of Sympodial Branches per Plant

The sympodial branches are the fruit branches i.e. bearing the bolls.

At maturity, the sympodial branches on each plant were counted for all

selected plants.

3.9 Number of Bolls per Plant

The number of bolls picked at each picking was recorded from each

individual plant. When final picking was over, picking record was summed

up to calculate the total number of bolls per plant.

3.10 Boll Weight

It is the average weight of seed cotton in a mature boll. Average boll weight

was calculated by dividing the total seed cotton from a plant with the number of

picked bolls.

3.11 Ginning Outturn (GOT)

It is also referred to as lint percentage and is the weight of lint that can be

obtained from a given weight of seed cotton and is expressed as percentage.

Economically, high ginning out-turn is desirable. Dry samples of seed cotton

harvested from individual plants were weighed and ginned separately with a single

roller electrical gin in the laboratory. Electronic balance was used to weigh the

seed cotton and lint. GOT was calculated as %age of lint in seed cotton.

3.12 Fibre Traits

Staple length, fibre strength and fibre fineness was measured by using

Spinlab HVI-900 in the Department of Fibre Technology, University of

Agriculture Faisalabad.

3.13 STATISTICAL ANALYSIS

Standard analysis of variance technique, described by Steel et al. (1997)

was applied to test the significance of differences among the generations used in

the experiment.

3.13.1 Generation Means Analysis

A generation means analysis was performed following the method

described by Mather and Jinks (1982) using a computer programme provided by

Dr. Tanwir Ahmad Malik, Associate professor, Department of Plant Breeding and

Genetics. Means and variances of each population (parents, backcrosses, F1 and

F2) used in the analysis were calculated from individual plants pooled over

replications. The coefficients of the genetic components of generation means are

shown in the Table 3.3. A weighted least square analysis was performed on the

generation means commencing with the simplest model using parameter m only.

Further models of increasing complexity (md, mdh, etc.) were fitted if the chi-

squared value was significant. The best model was chosen as the one which had

significant estimates of all parameters along with non-significant chi-squared

value. For each trait the higher value parent was always taken as P1 in the model

fitting.

3.12.2 Analysis of Components of Genetic Variance

A weighted least squares analysis of variance based on the method as

described by Mather and Jinks (1982) using a computer programme provided by

Dr. Tanwir Ahmad Malik, Associate professor, Department of Plant Breeding and

Genetics was performed on the data of the experiment containing six generations

(Parents, F1, F2, BC1 and BC2). The coefficients of additive (D), dominance (H),

cross product of dominant and additive effects (F) and environmental variation (E)

are shown in Table 3.4. Model fitting was started using the E parameter only, D, H

and F parameters were successively included until a satisfactory fit was obtained.

The best fit model was chosen as the one with all significant parameters and non-

significant chi-squared value.

3.12.3 Heritability Estimates

Heritability in narrow sense (h2ns) was calculated (Mather and Jinks, 1982)

using the components of variance from the best fit model of weighted least squares

analysis using the formula:

Table 3.3: Coefficients of the mean (m), additive (d), dominance (h), additive × additive (i), additive × dominance (j) and dominance × dominance (l) parameters for the weighted least squares analysis of generation means (Mather and Jinks, 1982).

Table 3.4: Coefficients of the D, H, F and E effects for the weighted least squares analysis of generation variances (Mather and Jinks, 1982)

Generation Components of variation

D H F E

P1 0.00 0.00 0.00 1

P2 0.00 0.00 0.00 1

F1 0.00 0.00 0.00 1

F2 0.50 0.25 0.00 1

BC1 0.25 0.25 -0.5 1

BC2 0.25 0.25 0.50 1

Generations Components of genetic effects

m [d] [h] [i] [j] [l]

P1 1 1.0 0.0 1.00 0.00 0.00

P2 1 -1.0 0.0 1.00 0.00 0.00

F1 1 0.0 1.0 0.00 0.00 1.00

F2 1 0.0 0.5 0.00 0.00 0.25

BC1 1 0.5 0.5 0.25 0.25 0.25

BC2 1 -0.5 0.5 0.25 -0.25 0.25

h2ns

(1) = 0.5D/(0.5D + E) (When a simple DE model was adequate without a

significant dominance component)

(2) = 0.5D / (0.5D + 0.25H + E) (When a DHE model had to be fitted)

Heritability in the F∞ generation was also calculated by using the formula:

h2∞ = D / (D + E)

3.12.4 Phenotypic and Genotypic Correlations

Phenotypic and genotypic correlation coefficients between pairs of plant

traits were also determined using the F2 data. Phenotypic correlation coefficients

were calculated by the formula as outlines by Dewey and Lu (1959) using Minitab

computer programme. The genetic correlations (rg) between two characters X and

Y were calculated by the following formula (Ehdaie et al., 1993).

rg = )(.)(

),(YVgXVg

YXCOVg

where,

COVg (X, Y) = COV (X, Y) F2 – COV (X, Y) E

COV (X, Y) E = (1/4) [COV (X, Y) P1 + COV (X, Y) P2 + 2COV (X, Y) F1]

COVg (X, Y), COV (X, Y) E, COV (X, Y) P1, COV (X, Y)P2, COV (X, Y) F1 and COV

(X, Y) F2 are covariances of X and Y associated with genetic effects, non-genetic effects,

P1, P2, F1 and F2 generations respectively and Vg (X) and Vg (Y) are genetic variances of

X and Y respectively.

CHAPTER-IV

RESULTS AND DISCUSSIONS

Analysis of variance revealed significant differences among the

generations for various traits (plant height, number of monopodial

branches, number of sympodial branches, number of bolls per plant, boll

weight, ginning out turn (GOT), fiber length, fiber strength, fiber fineness,

relative water contents and excised leaf water loss) in some crosses while

the traits were not significantly different in the other crosses. The traits

showing significant differences (P≤0.05) among generation means for various

crosses are shown in the Table 4.1. LSD values to compare the generation means

and population effects are also given in the table. The F2 population for the traits

was almost normally distributed showing quantitative inheritance of the traits.

Transgressive segregation was also observed for the traits in some crosses.

4.1 Generation Means Analysis

Results of generation means analysis are given in the Table 4.2. In

quantitatively inherited traits, gene action is described as additive, dominance and

epistatic effects. Additive effect is normally defined as the average effect of genes;

Table 4.1. Generation Means for Plant Height (PH, cm), Monopodia (Mon), Sympodia (Sym), Boll Number (BN), Boll Weight (BW, gm), Ginning Outturn (GOT, %), Fibre Fineness (FF. Mic), Fibre Strength (FS, g/tex), Fibre Length (FL, mm), Relative Water Contents (RWC, %), Excised Leaf Water Loss (ELWL, g/g) in six crosses S-12 x NIAB-78(1), CIM-240 x Karishma(2), Karishma x Acala-15/17(3), CP-1521 x Acala-15/17(4), Acala-15/17 x CIM-240(5), Karishma x CP-1521(6) of cotton

Traits

Cross #

Generations Pop. Effects

LSD (0.05) P1 P2 F1 F2 BC1 BC2

PH 1 91.14 100.36 98.73 97.53 97.68 94.08 ** 2.79 3 97.23 100.46 98.39 97.94 98.96 97.83 * 1.63 4 96.74 101.23 100.35 99.43 98.90 92.95 * 1.83

MONO 1 2.30 3.06 3.12 2.93 3.15 2.97 ** 0.16 5 2.73 2.63 2.72 2.69 2.57 2.43 * 0.13 6 3.12 2.63 2.73 2.54 2.83 2.47 ** 0.14

SYM 1 9.24 7.03 8.26 7.46 8.52 7.91 * 0.64 2 7.32 7.96 7.54 7.84 7.33 7.12 * 0.54 3 8.10 7.17 7.23 7.46 8.13 7.21 * 0.60

NB 1 19.63 18.46 18.36 18.28 17.24 18.56 ** 0.55 4 15.53 15.16 15.78 14.49 15.67 14.87 * 1.28 5 15.74 13.47 14.81 13.53 13.91 12.87 * 0.88

BW 1 4.04 3.54 4.12 3.82 3.90 3.73 * 0.28 4 2.47 3.25 2.93 2.85 2.89 3.21 ** 0.26 6 3.62 2.32 2.97 3.14 2.98 2.71 ** 0.32

GOT 1 39.81 34.87 36.12 35.43 35.23 34.69 ** 0.98 3 45.27 34.35 34.32 33.77 33.96 33.90 * 0.85 5 34.27 35.91 35.03 34.57 34.93 35.76 * 0.92

FF 4 4.87 4.47 4.71 4.67 4.84 4.60 * 0.20 5 4.38 4.63 4.65 4.52 4.58 4.64 * 0.18

6 5.02 4.74 4.95 4.37 4.74 4.83 ** 0.17 FS 1 27.38 26.85 26.72 26.16 26.37 26.23 * 0.60

2 27.23 26.14 26.78 26.35 26.79 26.42 * 0.53 3 26.23 25.21 26.37 25.72 25.85 25.76 * 0.46 4 28.38 25.38 26.36 26.47 26.29 26.14 * 0.41

FL 2 28.07 26.98 28.53 27.39 27.69 26.62 ** 0.62 3 26.82 28.21 28.04 27.62 26.31 28.24 * 0.62 4 27.07 28.16 27.28 27.27 27.24 27.15 * 0.48 5 28.10 28.19 28.25 28.09 27.16 27.81 * 0.60

RWC 1 74.63 77.52 74.43 73.58 75.76 76.64 * 0.81 4 72.28 75.76 72.28 74.69 75.49 74.53 * 0.72

6 74.34 72.15 70.37 70.48 72.64 70.78 ** 0.69 ELWL 1 1.91 1.53 1.83 1.76 1.75 1.69 ** 0.34

3 1.23 1.45 1.89 1.38 1.19 1.31 * 0.57 4 2.11 1.34 1.96 1.61 1.75 1.48 ** 0.45

*, P < (0.05); **, P < (0.01)

dominance as the interaction of allelic genes and epistasis as the interaction of

non-allelic genes that influence a particular trait. Gene action has been estimated

using diallel crosses following the methods described by Hayman (1954) and Jinks

(1954) or by using generation means and variance of different populations

(generally segregating and backcross populations) by the method described by

Mather and Jinks (1982). Generation means and variance analyses have been used

for studying gene action in cotton (Pathak, 1975; Dhillon and Singh, 1980; Singh

and Sandhu, 1985, Yadav and Yadava, 1987, Kalsy and Garg, 1988) and in other

crops (Malik et al., 1999).

4.1.1 Plant height

In the case of plant height the simple model with two parameters (m and [d]

provided a good fit to the data in the two crosses suggesting presence of additive

genetic effects for inheritance of this trait. Singh et al. (1980) tested five

quantitative characters in different cross combinations of G hirsutum L. like plant

height, number of branches etc. and observed that all the characters were affected

by additive type of gene action. Tyagi (1988) investigated genetic architecture of

yield and its components in upland cotton and reported that genetic control

appeared additive for plant height. In the cross CP-1521 x Acala-15/17 the model

with two parameters m and [i] was fit showing the presence of interaction i.e.

additive x additive. Similar results were reported by Kalsey and Vithal (1980).

Their studies revealed additive x additive interactions inheritance of plant height.

Kalsy and Garg (1988) performed generation means analysis for yield components

Table 4.2: Estimates of the best fit model for generation means parameters (±, standard error) by weighted least squares analysis in respect of Plant Height (PH, cm), Monopodia (Mon), Sympodia (Sym), Boll Number (BN), Boll Weight (BW, gm), Ginning Outturn (GOT, %), Fibre Fineness (FF. Mic), Fibre Strength (FS, g/tex), Fibre Length (FL, mm), Relative Water Contents (RWC, %), Excised Leaf Water Loss (ELWL, g/g) in six crosses S-12 x NIAB-78(1), CIM-240 x Karishma(2), Karishma x Acala-15/17(3), CP-1521 x Acala-15/17(4), Acala-15/17 x CIM-240(5), Karishma x CP-1521(6) of cotton

Traits Cross

# Genetic Effects X2

df m [d] [h] [i] [j] [l] PH 1 96.59±0.27 0.91±0.43 - - - 3.11(4)

3 98.47±0.31 1.07±0.15 - - - - 2.23(4) 4 98.37±0.21 - - 1.53±0.73 - - 2.19(4)

MONO 1 2.92±0.07 - 0.28±0.14 - 3.04(4) 5 2.63±0.16 1.01±0.44 - 2.28(4) 6 2.72±0.05 0.16±0.06 - 0.28±0.09 2.95(3)

SYM 1 8.07±0.45 0.39±0.12 1.46±0.63 1.73±0.48 - - 3.60(2) 2 7.52±0.06 0.40±0.09 - 0.44±0.13 - - 3.39(3) 3 7.55±0.07 0.38±0.13 - - - - 2.93(4)

NB 1 18.42±0.26 - - 1.69±0.47 - - 3.54(4) 4 15.25±0.39 - 3.57±0.75 3.20±0.59 - - 3.21(3) 5 14.1±0.18 - - 0.91±0.36 - - 2.51(4)

BW 1 3.86±0.033 0.23±0.04 - - - 0.30±0.08 3.60(3) 4 2.97±0.15 0.29±0.02 - - 0.27±0.05 - 4.84(3) 6 2.96±0.019 0.38±0.03 - - 0.45±0.07 - 5.94(3)

GOT 1 36.02±0.16 - - 1.48±0.35 - - 4.16(4) 3 35.93±0.20 - - 1.02±0.37 - - 2.72(4) 5 36.25±0.19 0.49±0.24 - 0.83±0.35 - - 3.35(3)

FF 4 4.69±0.04 - 0.59±0.20 - - 0.48±0.20 2.34(3) 5 4.57±0.08 - 0.35±0.09 - - - 3.54(4)

6 4.78±0.14 0.12±0.05 1.03±0.21 0.97±0.16 - - 3.99(2) FS 1 26.62±0.13 - - 0.87±0.26 - - 3.22(4)

2 26.62±0.15 - - 0.62±0.29 - - 2.15(4) 3 25.86±0.14 - - 1.08±0.22 - - 4.75(4) 4 26.50±0.09 - - 0.15±0.06 - - 3.49(4)

FL 2 27.55±0.15 - - 0.77±0.28 - - 2.67(4) 3 27.54±0.14 - - 0.63±0.26 - - 3.38(4) 4 27.36±0.08 0.38±0.15 - - - - 2.53(4)

5 27.93±0.14 - - 0.60±0.22 - - 3.64(4) RWC 1 75.43±2.10 0.36±0.09 - 0.37±0.08 4.23(3)

4 74.34±2.06 0.63±0.10 - 0.53±0.11 4.81(3) 6 70.65±2.04 0.54±0.09 0.58±0.10 5.58(3)

ELWL 1 1.75±0.14 - 6.04±0.60 1.05±0.23 - 4.91±0.39 2.44(2) 3 1.41±0.08 - 2.94±0.34 - - 2.77±0.35 7.86(3) 4 1.71±0.06 - - 0.17±0.03 - 5.57(4)

of cotton including plant height. The results showed that additive gene action and

epistasis were important in the inheritance of plant height. Randhawa et al. (1986)

revealed the presence of additive and epistasis for plant height. Singh et al. (1983)

estimated gene action, and observed epistasis for the character.

Plant height is related to yield and drought tolerance in cotton. Taller plant

height will result higher vegetative matter and hence more will be the requirement

for water due to higher water loss. So medium height cotton varieties may perform

better under limited water conditions. The results of present studies showed that

gene action was different in the crosses; hence segregating populations of different

cross combination may be exploited differently depending upon their gene actions.

The variation of gene action in the crosses may be expected under drought stress.

However, well watered studies in the literature also showed this variation (Kalsy

and Garg, 1988; Singh et al., 1983. In the crosses where the additive gene action

was present the selection in early generation may be fruitful however, in the cross

showing epistasis, selection may be postponed to latter segregating generations.

4.1.2 Number of monopodial branches

For number of monopodial branches two parameter model (m, [h] provided

a satisfactory fit to the data in the cross S-12 x NIAB-78 showing the presence of

dominance. Whereas, in the cross Acala-15/17 x CIM-240 two parameter model

(m, [d] was fit showing additive variance and in the cross, Karishma x CP-1521

three parameter model m, [d] and [i] provided a satisfactory fit to the data showing

additive and additive x additive interactions. Similar finding were reported by

Silva and Alves (1983). Singh et al. (1971) found additive and dominance genetic

variances with the genetic interactions in the inheritance of monopodial branches.

Dhillon and Singh (1980) analyzed genetic control of different traits in

cotton using P1, P2, F1, F2, BC1 and BC2 generations. They found that expression

of different components of genetic variation was much influenced by the

environmental effects. Effect of environment on gene action suggests that gene

action for the traits need to be improved should be analyzed under the

environment in which breeding has to be undertaken. A number of studies

regarding gene action of agronomic traits are reported in the literature, however,

only few studies for the traits under drought stress have been conducted. So the

present study was undertaken to investigate the gene action of important traits

under drought. The information would be helpful to breeders involved in breeding

drought resistant cotton. Larger number of monopodial branches may have a

negative effect on plant under drought. Monopodial branches increase vegetative

matter hence water loss is more. The presence of interaction in the crosses in the

present and earlier studies shows that in manipulating monopodial branches

selection of plants in early generations would not be effective. Selection in later

generations would be more effective.

4.1.3 Number of sympodial branches

In the cross, Karishma x Acala-15/17 simple model with two parameter m,

and [d] was found fit with additive type of gene action, whereas in the cross CIM-

240 x Karishma, a model with three components m, [d] and [i] and in the cross S-

12 x NIAB-78 model with four parameters m [d], [h] and [i] which revealed that

additive and dominance as well as additive × additive interaction were involved in

the inheritance. Singh et al. (1971) studied the genetics of the number of

sympodial branches in cotton and reported additive and dominance genetic

variance along with interactions for the character. Silva and Alves (1983)

conducted experiments on the estimation of epistasis, additive and dominance

variation in cotton (G. hirsutum L.) and reported that for number of fruiting

branches (sympodial branches) additive and dominance as well as epistasis was

involved in the inheritance. The presence of interaction in two crosses in the

present study and in the earlier reports suggest that in the improvement of

sympodial branches selection of plants in early generations would not be effective.

Selection in later generations would be more feasible.

4.1.4 Numbers of boll per plant

In crosses, S-12 x NIAB-78 and Acala-15/17 x CIM-240, model with two

parameters m, and [i] provided a good fit to the data suggesting presence of

additive x additive variance. In the cross CP-1521 x Acala-15/17, a model with m,

[h] and [i] provided a good fit to the data suggesting presence of dominance and

additive x additive variance. Pathak and Singh (1970) investigated the inheritance

of number of bolls in Gossypium hirsutum and reported dominance and additive

effects as well as epistasis for number of bolls. Singh et al. (1971) also reported

additive and dominance genetic variances along with the genetic interactions for

the characters. El-Fawal et al. (1974) studied gene action in inter-specific crosses

of cotton and observed dominant genetic variance for boll number. While Gad et

al. (1974) reported additive gene action for boll number.

Similarly Gill and Kalsy (1981) analysed genetic components for bolls per

plant and observed epistatsis in the inheritance of the character. Silva and Alves

(1983) conducted experiments on the estimation of epistasis, additive and

dominance variation in cotton (G. hirsutum L.). They reported additive as well as

dominance affects for bolls per plant. Randhawa et al. (1986) also observed the

presence of epistasis for number of bolls. Kalsy and Garg (1988) performed

generation mean analysis for number of bolls per plant. The results showed that

additive and dominance as well as epistasis were important for inheritance of the

traits.

The presence of interactions in the inheritance of number of bolls per plant

in the present studies and in the earlier studies shows that the trait is not simply

inherited. The plants selected in early segregating generation may not be expected

to breed true. So, selection in later segregating generations may show good results.

4.1.5 Boll weight

All the crosses showed additive as well as interactions for inheritance of

boll weight. Three parameter model m and [d], and [j] with additive type of gene

action with additive x dominance interactions provided fit to the data was

observed in the crosses CP-1521 x Acala-15/17 and Karishma x CP-1521.

Whereas, in the cross, S-12 x NIAB-78 three parameter model m [d], and [l] with

additive and dominance x dominance was fit. Pathak and Singh (1970) observed

additive and epistatic effects for boll weight in cotton. El-Adl and Miller (1971)

reported additive genetic effects for boll weight. Singh et al. (1971) also reported

additive and dominance genetic variance along with the genetic interactions for

boll weight in cotton. Gad et al. (1974) reported additive and dominance genetic

variance for boll weight. Kaseem et al. (1984) reported additive, dominance and

epistatic gene effects for boll weight. Kalsy and Garg (1988) performed generation

means analysis for boll weight and revealed similar results. However, Tyagi

(1988) reported dominance and additive variance for boll weight. The results of

present study and earlier reports the trait is not simply inherited.

4.1.6 Ginning Out-turn (GOT)

All the crosses revealed epistasis for the inheritance of GOT. In the crosses

S-12 x NIAB-78 and Karishma x Acala-15/17 two parameters model m, and [i]

whereas in the cross Acala-15/17 x CIM-240 with three parameters model m, [d]

and [i] provided good fit to the data. Dhillon and Singh (1980) studied genetic

control of ginning out-turn in P1, P2, F1, F2, BC1 and BC2 generations of the cross,

J34 x SS167 grown at two locations. They reported additive dominance and

interactions for the inheritance of GOT. They also observed that expression of

different components of variation was much influenced by the environmental

effects. Singh and Singh (1981) also reported additive genetic effects as well as

interaction for ginning out-turn.

The finding of Pavasia et al. (1999), Singh and Yadavendra (2002) and

Mert et al. (2003) corroborates the results of present study, as they reported

involvement of additive and epistatic genetic effects for the inheritance of GOT.

So breeding for this trait may be relatively difficult.

4.1.7 Fibre Fineness

In the case of fibre fineness the model with four parameters m, d, [h] and

[l.] provided a good fit to the data in the cross Kasihma x CP-1521 suggesting

presence of complex genetic variance for inheritance of this trait. Whereas in the

crosses CP-152 x Acala-15/17 and Acala-15/17 x CIM-240 two parameter m, and

[h] provided a good fit to the data suggesting presence of only dominance variance

for the trait.

Gad et al. (1974) reported additive and dominance gene action for fineness.

Ma et al. (1983) studied inheritance of fibre fineness in P1, P2, F1, F2, BC1 and BC2

of a cross between Loumain I and Acala Sj-3 and found dominance effects for

fibre fineness. Lin and Zhao (1988) in a study of three inter varietial crosses of G.

hirsutum L. estimated genetic effects of fibre fineness. The results showed that

inheritance of the trait fitted a modified additive dominance epistatic model. The

effects of dominance and epistasis varied significantly in different years and

different hybrids. Nadarajan and Rangasamy (1990) reported that fibre fineness

appeared to be governed by additive gene action. Nadarajan and Rangasamy

(1992) studied fibre fineness in six generations derived from crosses of five

genotypes. In general the traits governed by additive, dominance and digenic non

allelic interaction. The presence of epistatic interactions in the present studies and

in the earlier reports show that the inheritance of the traits is complex so, selection

of plants to improve fibre fineness can not be started in early segregating

generations.

4.1.8 Fibre Strength

For fibre strength two parameter model m, and [i] provided a satisfactory fit

to the data in all the crosses. The results showed that all the variance was due to

additive × additive. Lin and Zhao (1988) in a study of three inter varietial crosses

of G. hirsutum L. estimated genetic effects of fibre fineness. Their results showed

that inheritance of fibre traits fitted a modified additive dominance epistatic

model. The effects of epistasis varied significantly in different years and different

hybrids. The presence of interaction in all the crosses in the present study and in

the earlier report shows that the variance observed in the segregating generation

may be due to interactions so selection of plants to improve fibre fineness would

be effective in later generations.

4.1.9 Staple Length

In the cross CP-1521 x Acala-15/17 simple two parameter model m and [d]

provided a good fit to the data suggesting presence of only additive type of gene

action. However, in the crosses CIM-240 x Karishma, Karishma x Acala-15/17

and Acala-15/17 x CIM-240 two parameter model (m and [i] with additive x

additive effects was found to be most appropriate. Lefort and Schwendiman

(1974) reported additive variance for fibre length. Lin and Zhao (1988) in a study

of three inter varietial crosses of G. hirsutum L. estimated genetic effects of fibre

length. The results showed that inheritance of the trait fitted a modified additive

dominance epistatic model. Nadarajan and Rangasamy (1990) concluded that

staple length appeared to be governed by additive gene action. All the traits

showed high heritability estimates. Singh et al. (1983) estimated gene actions for

six yield related and fibre quality characters from F1, F2 and backcross generations

of a cross between LH-95 and 32 OF. Epistasis and both additive and dominance

effects were observed for the character. Singh and Yadavendra (2002), in their

studies observed additive, dominance, additive × additive and additive ×

dominance genetic effects for staple length. Nimbalkar et al. (2004) concluded

from their study of 8×8 diallel in desi cotton (Gossypium arboreum and

Gossypium herbaceum) that staple length was controlled by only additive type of

gene action. Murtaza et al. (2004) reported that epistatic effects controlled staple

length.

Different types of gene actions for the trait in different crosses in the

present study and in the studies reported earlier may be due to difference of

genetic backgrounds of the parents involved. In the present study only one cross

showed additive variance and absence of interactions. Three crosses showed

interactions. The presence of interactions in the inheritance of staple length shows

that the trait may not be simply inherited. So, selection in later segregating

generations may show good results.

4.1.10 Relative water content (RWC) In relative water contents in all the crosses model with three parameters

(m, [h] and [l] was a good fit to data showing dominance along with the

dominance x dominance interaction. Schonfeld et al. (1988) reported additive,

dominance as well as additive x additive genetics effects in wheat. The presence of

interactions in the inheritance of relative water contents shows that the trait is not

simply inherited. The plants selected in early segregating generation may not be

expected to breed true. So, selection in later segregating generations may show

good results.

Trends towards higher RWC at a give water potential has been observed in

species which are better adopted to dry environments (Weatherley and Slatyer,

1975; Jarvis and Jarvis, 1963). Sorghum, for instance, suffers a smaller decrease in

RWC per unit change in leaf water potential than cotton (Ackerson and Kreig,

1977) and maize (Levitt, 1980). Similarly un-irrigated plants showed a much

smaller change in water content per change in water potential, than in the case of

irrigated plant (Levitt, 1980). So simple measurement of relative water content

may not be indicator of drought resistance in plant. However, maintenance of

higher relative water content has been suggested as screening criterion for drought

resistance (Matin et al., 1989; Schonfeld et al., 1988; Ritchie et al., 1990). It has

been observed in the present studies that the genotypes which can maintain

relative water content at the level of 74-77% may perform relatively better under

drought stress conditions. It is important that while selecting plants for drought

resistance, the population should be exposed to uniform level of drought stress.

4.1.11 Excised leaf water loss (ELWL)

In the cross S-12 x NIAB-78, model with four parameter, (m, [h], [i] and

[l]) provided a good fit to the data suggesting presence of dominance mode of

gene action along with the additive x additive and dominance x dominance type of

interactions. Whereas, in the cross Karishma x Acala-1517 three parameters m, [h]

and [l] provided a good fit to the data suggesting presence of dominance gene

action along with the dominance x dominance type of interaction. In the cross, CP-

1521 x Acala-15/17, model with two parameter m and [i] provided a good fit to

the data suggesting presence of additive x additive interaction.

Genetic variation in ELWL has been reported in crop species and the trait

has been suggested as selection criterion for drought resistance (Salim et al. 1669;

Dedio, 1975; Clarke and McCaig, 1982a,b, Clark, 1983; Clarke and Townley-

Smith, 1986, Clarke, 1987; Clarke et al., 1992). Difference in ELWL of the

genotypes may be due to cuticular thickness as stomata close about two minutes

after leaf excision. Most of the water lost from the leaf would be from the

epidermis (except for a small loss from the cut end) so differences in cuticular

thickness would result in difference of ELWL. It has been reported that cuticular

thickness and waxiness of leaf surfaces are genetically controlled and affect

transpiration (Haque et al., 1992).

Different types of gene actions for the trait in different crosses show that

there was wide genetic diversity for the genes for the trait. The presence of

interactions in the inheritance shows that the trait is not simply inherited. The

plants selected in early segregating generation may not be expected to breed true.

Selection in later segregating generations may show good results.

4.2 Generation Variance analysis

Variability of morphological and physiological trait is the result of genetic

differences and of environmental causes. Generation variance analysis has widely

been used by plant breeders for effectively partitioning the total variability into

genetic and environmental components. The partitioning of phenotypic variance

into its genotypic and environmental components is not enough to have deep

insight into the genetic properties of a breeding material. The genotypic variances

needs to be partitioned further into additive (D), dominance (H), environmental

(E) and interaction (F).

It is possible to measure genetic and environmental variance from a

suitably designed experiment which includes some non segregating material (such

as parental inbred lines and F1 etc.) and some segregating populations (such as

backcrosses and F2 etc.). The estimates of variance may also be utilized to

estimate the heritability which reflects the amount of genetic variability relative to

environmental affects. In the present studies a model incorporating additive and

environmental components was sufficient to explain the variation in all the crosses

(Table 4.3). Additive genetic variance of various cotton plant traits has been

reported by Singh and Singh (1981), Singh et al. (1982) and Randhawa et al.

(1986).

Generation means analysis revealed different gene action for various traits

in different crosses, however, in generation variance analysis a model

incorporating D and E was sufficient to explain the variation in all the crosses. The

dominance and interaction components of variance were not detectable in the

generation variance analysis. This discrepancy might arise from differences in the

estimation precision of the two analyses. The generation means analysis is more

robust and reliable compare to generation variance analysis.

Table 4.3: Variance components D (additive), H (Dominance), F (Additive × Dominance) and E (environmental) following weighted analysis of components of variance, and heritability (ns, narrow sense and F∞ generation) for Plant Height (PH, cm), Monopodia (Mon), Sympodia (Sym), Boll Number (BN), Boll Weight (BW, gm), Ginning Outturn (GOT, %), Fibre Fineness (FF. Mic), Fibre Strength (FS, g/tex), Fibre Length (FL, mm), Relative Water Contents (RWC, %), Excised Leaf Water Loss (ELWL, g/g) in six crosses S-12 x NIAB-78(1), CIM-240 x Karishma(2), Karishma x Acala-15/17(3), CP-1521 x Acala-15/17(4), Acala-15/17 x CIM-240(5), Karishma x CP-1521(6) of cotton.

Traits Cross

# Variance Components

(df) Heritability

D H F E Ns F∞ PH 1 82.25±13.12 - - 17.02±2.49 6.81(4) 0.71 0.83

3 57.41±12.28 - - 20.33±2.84 5.32(4) 0.59 0.74 4 57.42±12.15 - - 20.34±2.92 2.35(4) 0.59 0.74

MONO 1 0.67±0.13 - - 0.21±0.03 4.88(4) 0.62 0.76 5 0.38±0.17 - - 0.37±0.05 7.19(4) 0.34 0.51 6 0.52±0.09 - - 0.15±0.02 3.87(4) 0.63 0.78

SYM 1 6.75±1.45 - - 2.41±0.34 7.06(4) 0.58 0.74 2 5.81±1.08 - - 1.61±0.23 4.87(4) 0.64 0.78 3 5.36±0.87 - - 1.15±0.16 2.83(4) 0.70 0.82

NB 1 21.68±3.42 - - 4.39±0.65 1.77(4) 0.71 0.83 4 17.81±3.67 - - 2.37±0.34 2.84(4) 0.79 0.88 5 21.68±3.42 - - 4.39±0.65 2.76(4) 0.72 0.83

BW 1 0.28±0.05 - - 0.21±0.03 4.86(4) 0.17 0.28 4 0.15±0.04 - - 0.14±0.01 2.06(4) 0.65 0.79 6 0.36±0.07 - - 0.13±0.02 4.92(4) 0.58 0.74

GOT 1 14.09±2.34 - - 4.25±0.61 3.96(4) 0.62 0.77 3 10.41±2.25 - - 4.67±0.67 2.76(4) 0.53 0.69 5 13.27±3.14 - - 4.43±0.52 3.74(4) 0.60 0.75

FF 4 1.08±0.25 - - 0.44±0.08 6.95(4) 0.55 0.71 5 1.29±0.26 - - 0.43±0.06 3.92(4) 0.60 0.75

6 1.13±0.21 - - 0.30±0.04 4.16(4) 0.65 0.20 FS 1 13.27±2.54 - - 4.43±0.41 6.76(4) 0.60 0.75

2 14.09±2.74 - - 4.25±0.61 4.98(4) 0.62 0.77 3 10.81±1.77 - - 2.37±0.36 3.84(4) 0.70 0.82 4 17.81±2.67 - - 2.37±0.34 6.84(4) 0.79 0.88

FL 2 10.41±2.74 - - 4.67±0.67 5.94(4) 0.53 0.69 3 10.24±1.69 - - 2.81±0.41 4.65(4) 0.65 0.79 4 9.59±1.81 - - 2.74±0.39 5.52(4) 0.64 0.78 5 9.35±1.43 - - 1.76±0.26 5.82(4) 0.73 0.84

RWC 1 9.59±1.81 - - 2.74±0.39 3.88(4) 0.64 0.79 4 10.81±1.72 - - 2.37±0.34 5.84(4) 0.70 0.82 6 9.35±1.43 - - 1.76±0.26 5.57(4) 0.73 0.84

ELWL 1 0.22±0.08 - 0.16±0.03 7.12(4) 0.41 0.58 3 0.49±0.10 - - 0.18±0.02 6.89(4) 0.61 0.75 4 0.28±0.0.05 - - 0.21±0.04 4.86(4) 0.16 0.28

4.3 Heritability

The success or failure of any crop breeding program depends largely on the

quantum of genetic variability present in the breeding materials in hand.

Heritability is a tool used by plant breeders for effectively isolating the amount of

genetic variation from the total phenotypic variation. The traits studied showed

moderate to high narrow sense heritability estimates. Heritability estimates for the

traits were almost consistent among the crosses. Infinity generation heritability

was consistently higher than the narrow sense heritability for all the traits. High

heritability estimates for the traits show that a large proportion of the genetic

variance was composed of additive genetic component. The proportion of total

phenotypic variability of a character that arises from genetic differences is

attributed as heritability for that character. The information about heritability of

trait helps to predict response to selection in genetically segregating generation.

Ulloa (2006), Singh and Singh (1981) and Gupta (1987) reported high heritability

in cotton whereas, Vyahalkar et al. (1984), found medium to high estimates of

narrow sense heritability.

4.4 Correlation of fibre yield traits

The estimates of correlation among traits are useful for planning a breeding

programme to synthesize a genotype with desirable traits. Correlation was

determined among agronomic and the traits related to drought resistance in cotton.

Six very large F2 segregating populations (150 plants from each population)

involving parents with contrasting traits were used in correlation studies. In F2

population, alleles of parental traits are recombined so, the correlations among the

traits reflect linkage relationships. Generally the correlations for the pair of traits

among the populations were consistent. However, in a few cases correlation was

significant for a trait in one cross but was non-significant in the other. This may be

due to the difference in allele combinations of the parents involved in the

populations.

Correlation matrix among the traits in various crosses is given in

Table 4.4.1-4.4.6. Correlation matrix is shown only for the traits showing

significant differences among the populations in a cross.

4.4.1 Plant height

Plant height was positively correlated with the number of monopodial and

sympodial branches as well as with the number of bolls per plant. Soomro et al.

(1982) reported association between plant height and number of monopodial

branches. Hussian et al. (2000) also reported positive correlation of plant height

and monopodial branches. Similar Amutha et al. (1996) studied 15 cotton

genotypes and found positive correlation of plant height with boll weight and

number of bolls per plant.

Monopodial branches are vegetative branches which do not directly bear

flowers. These branches are also erect in orientation so positive correlation of

plant height and monopodial branches is expected. Taller plant structure may bear

Table 4.4.1. Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 1 (S12 x NIAB-78)

Traits Mono SYM NB BW GOT FS RWC ELWL

PH 0.57

0.47** 0.50 0.44**

0.54 0.47**

-0.13 -0.05

0.14 0.11

0.13 0.05

0.09 0.04

-0.12 -0.09

Mono 0.48 0.38**

0.47 0.28**

-0.07 -0.01

-0.12 -0.07

-0.18 -0.09

0.23 0.18*

-0.13 -0.09

SYM 0.70 0.60**

0.09 0.01

-0.11 -0.02

0.12 0.07

-0.13 -0.05

0.08 0.03

NB -0.12 -0.07

-0.20 -0.11

-0.20 -0.15

0.09 0.04

-0.06 -0.04

BW 0.28 0.13

0.16 0.12

-0.08 -0.01

0.09 0.03

GOT -0.10 -0.04

-0.08 -0.01

-0.09 -0.01

FS 0.15 0.06

-0.09 -0.02

RWC -0.35 -0.26**

Table 4.4.2. Phenotypic (Lower diagonal) and genetic correlation (Upper

diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 2 (CIM-240 x Karishma)

Traits FS FL Sym -0.06

-0.03 -0.09 -0.01

FS 0.25 0.23**

Table 4.4.3. Phenotypic (Lower diagonal) and genetic correlation (Upper

diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 3 (Karishma x Acala-15/17)

Traits SYM GOT FS FL ELWL PH 0.53

0.49** -0.13 -0.09

-0.13 -0.08

0.09 0.02

0.08 0.01

SYM 0.21 0.02

-0.05 -0.01

0.08 0.05

-0.06 -0.04

GOT -0.22 -0.15

0.23 0.24**

-0.09 -0.02

FS 0.32 0.28**

-0.12 -0.07

FL 0.13 0.08

Table 4.4.4. Phenotypic (Lower diagonal) and genetic correlation (Upper

diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 4 (CP-1521 x Acala-15/17)

Traits NB BW FF FS FL RWC ELWL PH 0.64

0.59** -0.16 -0.13

0.07 0.01

-0.07 -0.01

0.16 0.09

0.13 0.06

0.12 0.07

NB 0.09 0.04

0.04 0.03

0.09 0.03

0.19 0.12

-0.01 -0.02

-0.18 -0.12

BW 0.20 0.12

0.13 0.08

0.13 0.08

0.09 0.04

-0.12 -0.07

FF 0.17 0.12

0.25 0.18*

0.12 0.05

-0.09 -0.04

FS 0.21 0.18*

0.15 0.06

0.13 0.08

FL 0.09 0.04

0.08 0.05

RWC -0.25 -0.18*

Table 4.4.5. Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), Relative Water Contents (RWC), Excised Leaf Water Loss in cotton cross – 5 (Acala-15/17 x CIM-240)

Traits NB GOT FF FL Mono 0.56

0.47** 0.18 0.04

0.19 0.04

0.16 0.06

NB -0.23 -0.12

0.08 0.04

0.15 0.08

GOT 0.13 0.11

0.30 0.24**

FF 0.18 0.17*

Table 4.4.6. Phenotypic (Lower diagonal) and genetic correlation (Upper diagonal) matrix for the morphological, and physiological traits Plant Height (PH), Monopodia (Mon), Sympodia (Sym), Boll No (BN), Boll Weight (BW), Ginning Outturn (GOT), Fibre Fineness (FF), Fibre Strength (FS), Fibre Length (FL), s (RWC), Excised Leaf Water Loss in cotton cross – 6 (Karishma x CP-1521)

Traits BW FF RWC MON 0.08

0.01 0.19 0.14

0.31 0.23**

BW 0.12 0.05

0.19 0.10

FF 0.08 0.03

more sympodial branches (reproductive branches) and hence more number of bolls

per plant.

4.4.2 Number of monopodial branches

Number of monopodial branches had positive correlation with number of

sympodial branches and number of bolls per plant. This relationship may be

expected as the monopodial branches bear symopodial branches. Number of

monopodial branches also had positive association with . This suggests that a plant

with higher number of monopodial branches may maintain high . A plant with

high number of monopodial branches may have more cover of the soil in the root

zone allowing less solar radiation to reach ground and hence lower evaporation. A

plant with larger number of monopodial branches might also had deep roots which

would have help plants maintaining higher relative water content which need to be

investigated.

4.4.3 Number of sympodial branches

The number of sympodial branches had positive correlation with number

of bolls per plant. Singh et al. (1968) also reported that the number of

sympodial branches per plant had a strong association with number of bolls

per plant. Similarly Kyei (1968) found positive association between number of

bolls and number of fruiting branches. Singh et al. (1983) studied 50

genetically diverse Gossypium hirsutum L. varieties and observed positive

correlations between boll number and number of sympodial branches. The

sympodial branches are flower bearing branches so higher number of sympodial

branches would result into higher cotton yield (Channa and Ahmad, 1982; Chen

and Zhao, 1991; Hussian et al., 2000).

4.5 Correlation of fibre quality traits

Fibre strength had positive correlation with fibre length. Similarly fibre

fineness had positive correlation with fibre length. The degree of fiber fineness is

recorded as micronaire value. Higher the micronaire value, lesser will be the

fineness of fibre and vice versa. Bocharova (1980) and Lancon et al. (1993)

also reported positive correlation between fibre length and fibre fineness.

Similar findings were reported by Badr and Aziz (2000).

In contrast to these findings, Tyagi (1987) reported negative correlation

between staple length and fibre fineness. However, he mentioned negative

correlation of staple length with micronaire in the table. As the fibre fineness has

inverse relation with micronaire value. So, in the text the result may have been

mistakenly reported as negative correlation between staple length and fibre

fineness, which is actually a positive correlation of staple length and fineness. In

general longer staple cotton has been found as more fine, which is observed within

G. hirsutum as well as between G. hirsutum and G. barbadense cotton genotypes.

The G. barbadense lines are longer in staple length as well as more fine than the

G. hirsutum lines. Staple length positively correlated with fibre strength in the

present studies. Aguilar et al. (1980) and Herring et al. (2004) also reported a

positive correlation between fibre length and strength of fibre.

4.6 Correlation of traits related to drought tolerance

Relative water contents showed positive correlation with only

monopodial branches and negative correlation with excised leaf water loss.

Excised leaf water loss had no correlation with any of the traits studied.

Similar findings were reported by Malik et al. (2006).

The absence of correlation of relative water contents and excised leaf

water loss with agronomic traits suggests that genes controlling the traits

related to drought tolerance are not linked with the genes controlling

agronomic traits. They segregate independently so a breeder can engineer

cotton cultivars having any combinations of agronomic traits with

improved drought tolerance.

Negative correlation of relative water content with excised leaf water

loss shows that the genes which help plant to restrict loss perhaps help

maintaining higher relative water content in leaf.

4.7 Cotton breeding for drought tolerance

The cotton belt of Pakistan stretches between latitude 24°N and 37°N. The

area lies in an arid subtropical continental climate, where cotton cultivation is

possible only with supplemental irrigation. The main features of climate are very

hot, where temperature in summer may shoot up to 50°C (Ashraf et al., 1994).

Climatological data during experimental year depicted in appendix 3.1 also shows

the harsh weather conditions for cotton growth.

Breeding a cotton cultivar, breeder needs to select plants with better combination

of genes for high yield and quality from a very large segregating population. If a

breeder has to select plants with improved drought tolerant as well as with better

yield and quality, he would need a criteria of assessing plants which is easy and

rapid. Relative water content and excised leaf water loss are easy and rapid in

measurements. The genes controlling these traits have no negative correlation with

the genes controlling agronomic traits. So these traits can be manipulated very

easily in breeding for cotton cultivars with improved drought tolerance.

CHAPTER-V

SUMMARY

The objective of the study was to generate genetic information which can help in

breeding cotton cultivars with improved drought tolerance. Plant phenotype is

interaction of genotype and environment. To develop cultivar which may produce

better yield under drought stress environment, breeder needs the information about

the gene action of the traits related to yield and quality under drought stress

environment. The information about linkage relationships of the traits related to

yield and quality as well as the traits which help plant to tolerate drought are also

required.

Gossypium hirsutum genotypes were selected based on the data already

available about the drought response of the genotypes at various cotton research

institutes in Pakistan. The genotypes were further screened by growing under

drought stress in the field to select contrasting parents for drought tolerance. Six

parents were selected to make cross combinations. The parents F1, F2 and

backcross generations of six crosses were studied to find gene action of the traits

related to yield and quality as well as drought tolerance under drought stress. The

individual F2 plants from each of the six crosses (150 plants from each F2

population) were used to find linkage relationship of the traits.

The generation means analysis indicated that all three kinds of gene effects

(additive, dominance and interactions) contributed in the genetic variance of the

traits. However, the generation variances analysis revealed that mainly additive

and environmental variance was involved in the inheritance of traits. Narrow sense

heritability of the traits was almost consistent among the populations and were

moderate to high which suggest that considerable portion of the genetic variance

was additive. Selection in the breeding populations showing only additive variance

may be practiced in early segregating generations while selection of desirable

plants from the populations showing dominance and interactions may be delayed

in later segregating generations.

Generally the correlations for the pair of traits in the populations were

consistent. In F2 population, alleles of parental traits are recombined so,

correlations among traits using large F2 population reflect linkage relationships.

The correlation showed that to engineer plant with higher number of flowering

branches (sympodial branches) and high number of bolls we need to select taller

plants. Fibre length had positive correlation with fibre strength and fibre fineness

(negative correlating of fibre length with micronaire).

Fibre quality traits (fibre length, fibre strength and fibre fineness) did not

correlate with the traits related to yield. This suggests that the genes for fibre

quality traits segregate independent of the traits related to yield. So breeding

cotton cultivars for drought conditions having both the quality and yield traits

would be possible. Similarly the linkage relationship of the fibre traits under

drought stress conditions also suggest that cotton cultivars for drought stress

condition having all the three quality traits can be developed.

Relative water content correlated (positively) with only monopodial

branches. Excised leaf water loss did not correlate with any of the traits related to

yield and quality. It suggests that the genes for relative water content and excised

leaf water loss segregate independent of the yield and quality traits, so breeding

cotton for improved drought tolerance is possible.

Negative correlation of relative water content with excised water loss

shows that the genes which help plant to restrict water loss perhaps help

maintaining higher relative water content in leaf. Relative water content and

excised leaf water loss are easy and rapid in measurements hence may be used in

screening large segregating populations for evolving drought resistant cotton

cultivars. The genes controlling these traits have no negative correlation with the

genes controlling agronomic traits. So these traits can be manipulated very easily

in breeding for cotton cultivars with improved drought tolerance.

Genetic Analysis for some Morpho-physiological Traits Related to Drought

Stress 

RANA TAUQEER AHMAD*, TANWIR AHMAD MALIK*1, IFTIKHAR AHMAD KHAN* AND MUHAMMAD JAFAR JASKANI** *Department of Plant Breeding and Genetics, University of Agriculture Faisalabad **Institute of Horticulture Sciences, University of Agriculture Faisalabad 1Corresponding author’s email: tanwirm@hotmail.com

ABSTRACT 

To develop cultivar, yielding better under drought stress, breeder needs the information about the gene action of the traits related to yield and quality responsible for drought tolerance. The objective of the study was to generate genetic information, which can help in breeding cotton cultivars with improved drought tolerance. Six genotypes of Gossypium hirsutum (S-12, NIAB-78, CIM-240, KRISHMA, CP-1521 and ACALA-15/17) were selected to make cross combinations. The parents F1, F2 and backcross generations of six crosses were studied under drought conditions in the field to find gene action of the traits, plant height, number of monopodial branches per plant, number of sympodial branches per plant, number of bolls per plant, boll weight, ginning outturn (GOT), staple length, fibre strength, fibre fineness, relative water content (RWC) and excised leaf water loss (ELWL). The generation means analysis indicated that all three kinds of gene effects (additive, dominance and interactions) were involved in the inheritance of the studied traits. The plants selected in early segregating generation may not breed true thus selection at later segregating generations may show good results. Key words: Cotton; Gene action; Generation means analysis; Drought stress INTRODUCTION Cotton plays a pivotal role in the agriculture-based economy of Pakistan. Cotton is grown on an area of about 3.1 million hectares with production of about 12.4 million bales in Pakistan (Anonymous, 2006-07). Pakistan is the fourth largest cotton producing country after China, USA and India. It generates significant proportion of foreign exchange. In addition to fibre, cotton is also an important source of vegetable oil in Pakistan. Poor yields of cotton are due to different biotic and abiotic stresses. Precipitation and river flow are the two major sources of surface water used to meet the requirements of agriculture in Pakistan. Most of the rainfall is received during July to September, which is hardly available for crop production because of rapid runoff due torrential showers. There is shortage of water during the growing season of cotton. So we need to breed cotton variety which may produce relatively better yield under drought stress condition.

Drought stress or water deficit is a complex phenomenon affecting the physiology of cotton plant. There are number of plant traits like relative water content (RWC), excised leaf water loss (ELWL), stomatal frequency, stomatal size, osmotic adjustment etc., which are related to drought resistance (Basal et al., 2005; Malik et al., 2006; Parida et al., 2008). Dhillon and Singh (1980) analyzed genetic control of different traits in

cotton using P1, P2, F1, F2, BC1 and BC2 generations. They found that expression of different components of genetic variation was much influenced by the environmental effects. Effect of environment on gene action suggests that gene action for the traits need to be improved should be analyzed under the environment in which breeding has to be undertaken. A number of studies regarding gene action of agronomic traits are reported in the literature (Babar and Khan, 1999; Murtaza, 2005; Rahman and Malik, 2008), however; only a few studies are conducted under drought stress. Present study was initiated to investigate the inheritance of RWC, ELWL, plant height, number of monopodial branches, number of sympodial branches, numbers of boll per plant, boll weight, staple length, fibre fineness, fibre strength and ginning out turn under drought stress. The information generated by this study would be helpful for plant breeder to tailor high yielding drought tolerant cotton strains. MATERIALS AND METHODS The experimental material was selected from the gemplasm available at different research stations of Pakistan, including Cotton Research Station (CRS) Multan, Central Cotton Research Institute (CCRI) Multan, Nuclear Institute for Agriculture and Biology (NIAB) Faisalabad, Cotton Research Institute, AARI, Faisalabad and the University of Agriculture, Faisalabad. Twenty each of drought resistant and susceptible genotypes were selected using the data available on the field response of varieties/genotypes under drought stress in the respective institutes. Drought response of selected varieties/genotypes was further assessed by growing them under drought stress through measurement of relative water content (RWC) and excised leaf water loss (ELWL). The varieties/genotypes were grown during the normal crop season of 2004 in a randomized complete block design with three replications in the area of the Department of Plant Breeding and Genetics to select contrasting parents for making crosses. To select pair of contrasting parents for a cross, it was made sure that both the parents in the cross-had relatively same phenology. Crossing was started in the field during the normal cropping season, 2005. Sufficient numbers of flowers were selfed and crossed to produce seeds. During the December, 2005 the seed of selected varieties/genotypes and their F1s was planted in pots filled with loamy soil under glasshouse conditions to make further crosses. Some flower buds of F1 plants were used for back crossing and the others were selfed to produce seed for F2 population. List of crosses are shown in the Table I. Parental, F1, F2 and backcross (BC1 and BC2) generations for each of the crosses were raised in field as separate experiment during the year 2006. The crop was sown in a randomized complete block design having three replications. In each replication there were two rows for each of the parents and F1, 12 rows for each of the F2, and 10 rows for each backcross generation. The length of row was 4 m. Row to row and plant-toplant distance was 75 and 30 cm, respectively. Control plants were irrigated five times during the experiment. For drought stress the plants were irrigated only twice up to the maturity. At maturity 30 guarded plants from each of the parents and F1, 45 plants from each of the backcrosses populations, and 150 plants from each of the F2 populations were selected at random to record the data on individual plant basis. Relative water content (RWC). Three fully developed leaf sample were taken from each of the selected plants during the month of September when the plants showed the symptoms of drought stress. The samples were covered with polythene bags soon after

excision and fresh weight was recorded. The leaf samples were dipped in water overnight for recording the turgid leaf weight. The samples were oven dried at 70°C for taking dry weight. RWC was calculated using the following formula as by Clarke and Townley-Smith (1986). RWC = [(Fresh weight–Dry weight) / (Turgid weight–Dry weight)] x 100 Excised leaf water loss (ELWL). Three fully developed leaf sample were taken from each of the selected plant, during the month of September when the plants were showing symptoms of drought stress; The samples were covered with polythene bags soon after excision and fresh weight was recorded using electronic balance. The leaf samples were left on laboratory bench. After six hours the weight of the wilted leaf samples were taken. The samples were oven dried at 70°C for taking oven dry weight. Excised leaf water loss was calculated by the following formula by Matin et al. (1889). ELWL = (Fresh weight – wilted weight) / Dry weight During the end of November, when the plants were fully mature, the data about the traits, plant height, number of monopodial branches per plant, number of sympodial branches per plant, number of bolls per plant, boll weight, ginning outturn (GOT), staple length, fibre strength and fibre fineness were recorded. Standard analysis of variance technique, described by Steel et al. (1997) was applied to test the significance of differences among the generations used in the experiment. Generation means analysis was performed following the method described by Mather and Jinks (1982). Means of each population (parents, backcrosses, F1 and F2) used in the analysis were calculated from individual plants pooled over replications. Coefficients of genetic components of generation means used in the analysis are shown in the Table II. A weighted least square analysis was performed on the generation means commencing with the simplest model using parameter m only. Further models of increasing complexity (md, mdh, etc.) were fitted if the chi-squared value was significant. The best model was chosen as the one, which had significant estimates of all parameters along with non-significant chi-squared value. For each trait the higher value parent was always taken as P1 in the model fitting. RESULTS AND DISCUSSION Analysis of variance revealed significant differences among the generations for various traits including plant height, number of monopodial branches, number of sympodial branches, number of bolls per plant, boll weight, ginning out turn (GOT), fibre length, fibre strength, fibre fineness, relative water contents and excised leaf water loss in some crosses, while the traits were not significantly different in the other crosses. Means and LSD values of traits showing significant differences (P≤0.05) among generation means for various crosses were used for genetic analysis and are shown in Table III. The F2 population for the traits was almost normally distributed showing quantitative inheritance of the traits. Transgressive segregation was also observed for the traits in some crosses. Plant height. In the case of plant height the simple model with two parameters (m and [d] provided a good fit to the data in the two crosses suggesting presence of additive genetic effects for inheritance of this trait. Singh et al. (1980) reported that in G hirsutum,

quantitative characters like plant height, number of branches in different cross combinations were affected by additive type of gene action. Tyagi (1988) investigated genetic architecture of yield and its components in upland cotton and reported that genetic control appeared additive for plant height. In the cross CP-1521 x Acala-15/17 the model with two parameters m and [i] was fit showing the presence of interaction i.e. additive x additive. Similar results were reported by Kalsey and Vithal (1980). Their studies revealed additive x additive interactions for inheritance of plant height. Kalsay and Garg (1988) performed generation mean analysis for yield components including plant height. The results showed that additive gene action and epistasis was important in the inheritance of plant height. Randhawa et al. (1986) revealed the presence of additive and epistasis for plant height. Singh et al. (1983) estimated gene action, and observed epistasis for the character. Plant height is related to yield and drought tolerance in cotton. Taller plants result in greater growth and hence require more water due to higher water loss. So, medium height cotton varieties may perform better under limited water conditions. The results of present studies showed that gene action was different in the crosses. Hence segregating populations of different cross combination may be exploited differently depending upon their gene actions. The variation of gene action in the crosses may be expected under drought stress. However, well watered plants also showed such a variation (Kalsy and Garg, 1988; Singh et al., 1983). In the crosses where the additive gene action was present the selection in early generation may be fruitful however, in the cross showing epistasis, selection may be postponed to latter segregating generations. Number of monopodial branches. For number of monopodial branches two parameter model (m, [h] provided a satisfactory fit to the data in the cross S-12 x NIAB-78 showing the presence of dominance. However, in the cross Acala-15/17 x CIM-240 two parameter model (m, [d] was fit showing additive variance and in the cross, Karishma x CP-1521 three parameter model m, [d] and [i] provided a satisfactory fit to the data showing additive and additive x additive interactions, as reported in other studies as well (Silva and Alves, 1983; Singh et al., 1971). Larger number of monopodial branches may negatively affect plant growth under drought. Monopodial branches increase vegetative growth resulting in more water loss. The presence of interaction in the crosses in the present and earlier studies shows that in manipulating monopodial branches selection of plants in early generations would not be effective. Selection in later generations would be more effective. Number of sympodial branches. In the cross, Karishma x Acala-15/17 simple model with two parameter m, and [d] was found fit with additive type of gene action, whereas in the cross CIM-240 x Karishma, a model with three components m, [d] and [i] and in the cross S-12 x NIAB-78 model with four parameters m [d], [h] and [i], which revealed that additive and dominance as well as additive × additive interaction were involved in the inheritance. Singh et al. (1971) reported additive and dominance genetic variance along with interactions for number of sympodial branches in cotton. Silva and Alves (1983) reported the involvement of additive and dominance as well as epistasis for number of fruiting branches (sympodial branches) in cotton. The presence of interaction in two crosses in the present study and in the earlier reports suggested that in the improvement of sympodial branches, selection of plants later rather than earlier generations would be effective.

Numbers of boll per plant. In crosses, S-12 x NIAB-78 and Acala-15/17 x CIM-240, model with two parameters m, and [i] provided a good fit to the data suggesting presence of additive x additive variance. In the cross CP-1521 x Acala-15/17, a model with m, [h] and [i] provided a good fit to the data, suggesting the presence of dominance and additive x additive variance. Pathak and Singh (1970) investigated the inheritance of number of bolls in in cotton and reported dominance and additive effects as well as epistasis for number of bolls. Singh et al. (1971) also reported additive and dominance genetic variances along with the genetic interactions for the characters. El-Fawal et al. (1974) studied gene action in interspecific crosses of cotton and observed dominant genetic variance, while Gad et al. (1974) reported additive gene action for boll number. Randhawa et al. (1986) also observed the presence of epistasis for number of bolls. Kalsy and Garg (1988) showed that additive and dominance as well as epistasis were important for inheritance of boll number in cotton. The presence of interactions in the inheritance of number of bolls per plant in the present andearlier studies indicated that this trait is not simply inherited. The plants selected in early segregating generation may not be expected to breed true. So, selection in later segregating generations may show good results. Boll weight. All the crosses showed additive as well as interactions for inheritance of boll weight. Three parameter model m and [d], and [j] with additive type of gene action with additive x dominance interactions provided fit to the data was observed in the crosses CP- 1521 x Acala-15/17 and Karishma x CP-1521. However, in the cross S-12 x NIAB-78, three parameter model m [d], and [l] with additive and dominance x dominance was fit. Additive, additive and epistatic effects or dominance genetic variance along with the genetic interactions have been reported in cotton for boll weight (Pathak & Singh, 1970; El-Adl & Miller, 1971; Singh et al., 1971; Gad et al., 1974; Kaseem et al., 1984; Kalsy & Garg, 1988). However, Tyagi (1988) reported dominance and additive variance for boll weight. The results of present study and earlier reports the trait is not simply inherited. Ginning outturn (GOT). All the crosses revealed epistasis for the inheritance of GOT. In the crosses S-12 x NIAB-78 and Karishma x Acala-15/17 two parameters model m, and [i] whereas in the cross Acala-15/17 x CIM-240 with three parameters model m, [d] and [i] provided good fit to the data. Dhillon and Singh (1980) studied genetic control of ginning out-turn in P1, P2, F1, F2, BC1 and BC2 generations of the cross, J34 x SS167 grown at two locations with additive dominance and interactions for the inheritance of GOT. They further observed that expression of different components of variation was much influenced by the environmental effects. The finding of Pavasia et al. (1999) and Singh and Yadavendra (2002) corroborates the results of present study. So breeding for this trait may be relatively difficult. Fibre fineness. In the case of fibre fineness the model with four parameters m, d, [h] and [l.] provided a good fit to the data in the cross, Kasihma x CP-1521, suggesting the presence of complex genetic variance for inheritance of this trait. On the contrary, the crosses CP-152 x Acala-15/17 and Acala-15/17 x CIM-240 two parameter m, and [h] provided a good fit to the data, suggesting presence of only dominance variance for the trait. Gad et al. (1974) reported additive and dominance gene action for fineness. Ma et al. (1983) studied inheritance of fibre fineness in P1, P2, F1, F2, BC1 and BC2 of a cross between Loumain I and Acala Sj-3 and found dominance effects for fibre fineness. Lin and Zhao (1988) in a study of three intervarietal crosses of cotton estimated genetic

effects of fibre fineness. The results showed that inheritance of the trait fitted a modified additive dominance epistatic model. The effects of dominance and epistasis varied significantly in different years and different hybrids. Nadarajan and Rangasamy (1990) reported that fibre fineness was governed by additive gene action. Nadarajan and Rangasamy (1992) opined that in general fibre fineness was governed by additive, dominance and digenic non allelic interaction. In concurrence with earlier studies, the presence of epistatic interactions in the present studies showed that the inheritance of the traits is complex. Hence, selection for improved fibre fineness cannot be started in early segregating generations. Fibre strength. For fibre strength two parameter model m, and [i] provided a satisfactory fit to the data in all the crosses. The results showed that all the variance was due to additive × additive. Lin and Zhao (1988) in a study on inter-varietal crosses of cotton showed that inheritance of fibre traits fitted a modified additive dominance epistatic model. The effects of epistasis varied significantly in different years and in different hybrids. The presence of interaction in all the crosses in the present study showed that the variance observed in the segregating generation may be due to interactions. So the selection of plants to improve fibre fineness would be effective in later generations. Staple length. In the cross, CP-1521 x Acala-15/17 simple two parameter model m and [d] gave a good fit to the data suggesting the presence of only additive type of gene action. However, in the crosses CIM-240 x Karishma, Karishma x Acala-15/17 and Acala-15/17 x CIM-240 two parameter model (m and [i] with additive x additive effects was found to be most appropriate. Lin and Zhao (1988) in a study of three inter varietial-crosses of cotton estimated genetic effects of fibre length. The results showed that inheritance of the trait fitted a modified additive dominance epistatic model. Nadarajan and Rangasamy (1990) concluded that staple length was governed by additive gene action. Additive, dominance, additive × additive and additive × dominance genetic effects asnwell as epistatic effects for staple length have also been recorded previously (Singh & Yadavendra, 2002; Nimbalkar et al., 2004; Murtaza et al., 2004). Different types of gene actions for the trait in different crosses in the present study and in the studies reported earlier may be due to difference of genetic backgrounds of the parents involved. In the present study only one cross showed additive variance and absence of interactions, while three crosses showed interactions. The presence of interactions in the inheritance of staple length revealed that the trait may not be simply inherited. So, selection in later segregating generations may be suitable. Relative water content (RWC). In all the crosses, model with three parameters (m, [h] and [l] was a good fit to data showing dominance along with the dominance x dominance interaction of relative water content. Schonfeld et al. (1988) reported additive, dominance as well as additive x additive genetics effects for RWC in wheat. The presence of interactions in the inheritance of relative water contents showed that the trait was not simple in heritance The plants selected in early segregating generation may not be expected to breed true. So, selection in later segregating generations may show good results. Trends towards higher RWC at a give water potential has been observed in species which are better adopted to dry environments (Weatherley and Slatyer, 1957; Jarvis and Jarvis, 1963; Malik et al., 2006; Parida et al., 2008)). Sorghum, for instance, suffered a smaller decrease in RWC per unit change in leaf water potential than cotton (Ackerson and Krieg, 1977) and maize (Levitte, 1980). Similarly, un-irrigated plants

showed a much smaller change in water content per change in water potential, than the irrigated ones (Levitt, 1980). Maintenance of higher relative water content has been suggested as screening criterion for drought resistance (Matin et al., 1989; Schonfeld et al., 1988, Ritchie et al., 1990). Excised leaf water loss (ELWL). In the cross S-12 x NIAB-78, model with four parameter, (m, [h], [i] and [l]) provided a good fit to the data suggesting presence of dominance mode of gene action along with the additive x additive and dominance x dominance type of interactions. In the cross Karishma x Acala-1517 three parameters m, [h] and [l] provided a good fit to the data, suggesting the presence of dominance gene action along with the dominance x dominance type of interaction. In the cross, CP-1521 x Acala-15/17, model with two parameter m and [i] provided a good fit, suggesting the presence of additive x additive interaction. Genetic variation in ELWL has been reported in crop species and FLWL has been suggested as selection criterion for drought resistance (Salim et al., 1669; Dedio, 1975; Clarke and McCaig, 1982a,b, Clark, 1983; Clarke & Townley-Smith, 1986, Clarke, 1987; Clarke et al., 1992). Difference in ELWL of the genotypes may be due to cuticular thickness as stomata close about two minutes after leaf excision. Most of the water lost from the leaf would be from the epidermis (except for a small loss from the cut end) so differences in cuticular thickness would result in difference of ELWL. It has been reported that cuticular thickness and waxiness of leaf surfaces are genetically controlled and affect transpiration (Haque et al., 1992). Different types of gene actions for the trait in different crosses showed a wide genetic diversity for the genes. The presence of interactions in the inheritance shows that the trait is not simply inherited. CONCLUSION Additive dominance as well as interactions was involved for agronomic (plant height, number of monopodial branches per plant, number of sympodial branches per plant, number of bolls per plant, boll weight, ginning outturn), fibre quality (staple length, fibre strength, fibre fineness) and physiological traits (relative water content and excised leaf water loss) under drought stress environments. Hence for breeding drought tolerant cotton selection of plants would be appropriate for late segregating generations. LITERATURE CITED Ackerson, R.C. and D.R. Krieg, 1977. Stomatal and non-stomatal regulation of water use

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Table I.  List of crosses and backcrosses made in the study 

Table II: Coefficients of the mean (m), additive (d), dominance (h), additive × additive (i), additive × dominance (j) and dominance × dominance (l) parameters for the weighted least squares analysis of generation means (Mather and Jinks, 1982).

S. No. Population Parents 1 F1, F2 S-12 x NIAB-78

BC1 (S-12 x NIAB-78) x S-12 BC2 (S-12 x NIAB-78) x NIAB-78

2 F1, F2 CIM-240 x KRISHMA BC1 (CIM-240 x KRISHMA) x CIM-240 BC2 (CIM-240 x KRISHMA) x KRISHMA

3 F1, F2 KRISHMA x ACALA-15/17 BC1 (KRISHMA x ACALA-15/17) x KRISHMA BC2 (KRISHMA x ACALA-15/17) x ACALA-15/17

4 F1, F2 CP-1521 x ACALA-15/17 BC1 (CP-1521 x ACALA-15/17) x CP-1521 BC2 (CP-1521 x ACALA-15/17) x ACALA-15/17

5 F1, F2 ACALA-15/17 x CIM-240 BC1 (ACALA-15/17 x CIM-240) x ACALA-15/17 BC2 (ACALA-15/17 x CIM-240) x CIM-240

6 F1, F2 KRISHMA x CP-1521 BC1 (KRISHMA x CP-1521) x KRISHMA BC2 (KRISHMA x CP-1521) x CP-1521

Generations Components of genetic effects

m [d] [h] [i] [j] [l] P1 1 1.0 0.0 1.00 0.00 0.00 P2 1 -1.0 0.0 1.00 0.00 0.00 F1 1 0.0 1.0 0.00 0.00 1.00 F2 1 0.0 0.5 0.00 0.00 0.25 BC1 1 0.5 0.5 0.25 0.25 0.25 BC2 1 -0.5 0.5 0.25 -0.25 0.25

Table III: Estimates of the best fit model for generation means parameters (±, standard error) by weighted least squares analysis in respect of Plant Height (PH, cm), Monopodia (Mon), Sympodia (Sym), Boll Number (BN), Boll Weight (BW, gm), Ginning Outturn (GOT, %), Fibre Fineness (FF. Mic), Fibre Strength (FS, g/tex), Fibre Length (FL, mm), Relative Water Contents (RWC, %), Excised Leaf Water Loss (ELWL, g/g) in six crosses S-12 x NIAB-78(1), CIM-240 x Karishma(2), Karishma x Acala-15/17(3), CP-1521 x Acala-15/17(4), Acala-15/17 x CIM-240(5), Karishma x CP-1521(6) of cotton. Traits Cross

# Genetic Effects X2

df m [d] [h] [i] [j] [l] PH 1 96.59±0.27 0.91±0.43 - - - 3.11(4)

3 98.47±0.31 1.07±0.15 - - - - 2.23(4) 4 98.37±0.21 - - 1.53±0.73 - - 2.19(4)

MONO 1 2.92±0.07 - 0.28±0.14 - 3.04(4) 5 2.63±0.16 1.01±0.44 - 2.28(4) 6 2.72±0.05 0.16±0.06 - 0.28±0.09 2.95(3)

SYM 1 8.07±0.45 0.39±0.12 1.46±0.63 1.73±0.48 - - 3.60(2) 2 7.52±0.06 0.40±0.09 - 0.44±0.13 - - 3.39(3) 3 7.55±0.07 0.38±0.13 - - - - 2.93(4)

NB 1 18.42±0.26 - - 1.69±0.47 - - 3.54(4) 4 15.25±0.39 - 3.57±0.75 3.20±0.59 - - 3.21(3) 5 14.1±0.18 - - 0.91±0.36 - - 2.51(4)

BW 1 3.86±0.033 0.23±0.04 - - - 0.30±0.08 3.60(3) 4 2.97±0.15 0.29±0.02 - - 0.27±0.05 - 4.84(3) 6 2.96±0.019 0.38±0.03 - - 0.45±0.07 - 5.94(3)

GOT 1 36.02±0.16 - - 1.48±0.35 - - 4.16(4) 3 35.93±0.20 - - 1.02±0.37 - - 2.72(4) 5 36.25±0.19 0.49±0.24 - 0.83±0.35 - - 3.35(3)

FF 4 4.69±0.04 - 0.59±0.20 - - 0.48±0.20 2.34(3) 5 4.57±0.08 - 0.35±0.09 - - - 3.54(4)

6 4.78±0.14 0.12±0.05 1.03±0.21 0.97±0.16 - - 3.99(2) FS 1 26.62±0.13 - - 0.87±0.26 - - 3.22(4)

2 26.62±0.15 - - 0.62±0.29 - - 2.15(4) 3 25.86±0.14 - - 1.08±0.22 - - 4.75(4) 4 26.50±0.09 - - 0.15±0.06 - - 3.49(4)

FL 2 27.55±0.15 - - 0.77±0.28 - - 2.67(4) 3 27.54±0.14 - - 0.63±0.26 - - 3.38(4) 4 27.36±0.08 0.38±0.15 - - - - 2.53(4) 5 27.93±0.14 - - 0.60±0.22 - - 3.64(4)

RWC 1 75.43±2.10 0.36±0.09 - 0.37±0.08 4.23(3) 4 74.34±2.06 0.63±0.10 - 0.53±0.11 4.81(3) 6 70.65±2.04 0.54±0.09 0.58±0.10 5.58(3)

ELWL 1 1.75±0.14 - 6.04±0.60 1.05±0.23 - 4.91±0.39 2.44(2) 3 1.41±0.08 - 2.94±0.34 - - 2.77±0.35 7.86(3) 4 1.71±0.06 - - 0.17±0.03 - 5.57(4)

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Appendix 3.1: Meteorological Data Recorded in Faisalabad During the Crop Year 2006

Parameter Month May June July August September

Mean Max. Temp. (oC) 42.0±0.37 40.9±0.36 37.0±0.34 36.2±0.27 34.7±0.28

Mean Min. Temp. (oC) 29.1±0.49 30.0±0.33 29.0±0.33 28.0±0.24 25.1±0.41

Mean Relative Humidity (%)

8 AM 53.0±1.59 57.4±1.30 71.1±1.50 74.5±0.83 73.8±1.17 5 AM 25.6±1.08 32.1±1.57 52.2±2.05 55.0±0.95 58.6±1.29

Mean Soil Temp. (oC)

8 AM 37.0±0.29 37.5±0.29 35.6±0.56 35.6±0.56 32.0±0.51 5 AM 50.1±0.44 50.3±0.59 44.5±0.81 45.0±0.35 41.0±0.50

Rain fall (mm) 0.00 0.00 23.1 6.6 3.7

Source: Department of Crop Physiology, University of Agriculture Faisalabad, Pakistan.