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: [email protected]
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.