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GENETIC VARIABILITY AND INTER-TRAIT ASSOCIATION FOR COOKING AND MICRONUTRIENT (Feand Zn) TRAITS IN ADVANCE LINES OF KALANAMAK AROMATIC RICE.

Byline: S. Kumar, I. D. Pandey, S. A. Rather and H. Rewasia

Keywords: Kalanamak; variability; quality; micronutrient; correlation;

INTRODUCTION

Rice (Oryza sativa L.) is one of the important principal staple food crops for more than half of world population. More than 90 percent of the world rice is grown and consumed in Asia where nearly 60 percent of the world population live and survive. Along with yield, cooking and nutritional quality has also become a primary consideration in rice breeding programs not only in India but also in various rice growing countries across the world. Grain quality of rice plays an important role in consumer acceptability since rice is mainly consumed as whole grains especially in Asia (Seraj et al., 2013). The cooking quality preferences vary within the country, within ethnic groups and from one country to another within different geographical regions (Shobha Rani et al., 2006).

Rice grain quality is primarily assessed based on physical properties such as, milling quality, head rice recovery, chalkiness, grain size and shape, and grain colour, premium quality traits such as aroma, and cooking properties (Dela-Cruz and Khush, 2000). The growing income and food diversification in some Asian and European countries have led consumers to prefer better quality rice, while people in some parts of the world seek improved nutrition. Micronutrient malnutrition, and particularly Fe and Zn deficiency affected over three billion people worldwide, mostly in developing countries (Welch and Graham, 2004). Therefore, in this era, research work should be more focused on enhancement of micronutrient along with cooking quality traits in rice to meeting the demand and combat the malnutrition deficiency. Aromatic rice varieties include long grain basmati, Jasmine rice and small to medium scented indigenous rice of India.

Kalanamak is one of the finest quality aromatic rice of India, famous for taste, palatability, and aroma and it surpasses basmati rice in most of cooking quality traits except grain length. Thus, characterization of Kalanamak aromatic rice for various cooking and micronutrient traits is of prime importance in order to identify genotypes with excellent cooking quality and rich in essential micro-nutrient. In the present investigations, forty eight advance lines of Kalanamak aromatic rice emanated from varietal hybridization of Kalanamak x Kalanamak accessions (3131-SN, 3216-SN and 3327-SN) and Kalanamak x Pusa Basmati-1, along with three checks (Pusa Basmati-1, Pant Sugandh-17 and 3131-SN), were studied and analyzed to assess the magnitude of genetic variability and to estimate inter-trait association amongst thirteen cooking quality and two micronutrient traits (Fe and Zn content).

MATERIALS AND METHODS

The Experimental material for present study compromised of 48 advance lines of Kalanamak aromatic rice genotypes along with three different checks (3131-SN, PB-1 and PSD-17). The experiment was conducted in augmented block design with 6 blocks at organic Farm of Breeder seed production Centre, Pantnagar, (Uttarakhand). Every block had 8 entries and the checks were replicated in each block once. Each plot consisted of 2 rows of 3m length and maintained row to row and plant to plant distance of 20 cm and 15 cm, respectively. Grains were harvested, in 2nd week of December 2014 and sun dried for four days and utilized for cooking and micronutrient quality parameters analysis. Cooking quality and micro-nutrient (Fe and Zn) contents were determined in the rice quality lab of department of Genetics and Plant Breeding and Micro-nutrient lab of department of soil science, college of Agriculture, Pantnagar.

Advance lines have been generated through varietal hybridization of Kalanamak x Kalanamak accessions (3131-SN, 3119, and 3122) and Kalanamak x Pusa Basmati-1. Rough rice samples were dehulled with a Satake laboratory sheller. The sample was poured into the hopper. Entire de-husked kernels were used for milling for a period of 2 minutes by KETT type T-2. Broken and unbroken rice were separated manually. Three fourth to whole kernels were taken as head rice and head rice recovery was calculated by dividing weight of head rice to total weight of brown rice. Various cooking quality traits like alkali digestion value (Little et al., 1958), gel consistency (Cagampang et al., 1973), amylose content (Sowbhagya and Bhattacharya, 1971), kernel length, kernel breadth, cooked kernel length, cooked kernel breadth, kernel elongation ratio (Azees and Shafi, 1966) and aroma were also determined (Juliano and Perez, 1984).

Iron and Zinc contents in brown rice were estimated using atomic absorption spectrophotometer (Varian Model) after wet digestion of the sample (Piper, 1950). The data were analyzed using augmented design to determine statistical significance (Panse and Shukhatme, 1969). The phenotypic and genotypic coefficients of variation (PCV and GCV), Heritability (h2) and expected genetic advance in terms of per cent of mean (GA%) were calculated as suggested by Allard (1960). The estimation of phenotypic and genotypic correlation coefficient was determined in the order to determine the inter-trait association of different cooking and micronutrient traits (Searle, 1961).

Table 1. Analysis of variance (ANOVA) for different quality characters

S.No###Source of###df###Mean sum of squares

###variation

###KL###KB###KL/KB###CKL###CKB###KER###HR (%)###MR###HRR###ASV###GC###A###AC###Fe###Zn

###ratio###(%0###(%)

1###Block

###(eliminating###5###0.012###0.001###0.009###0.113###0.026###0.005###7.899###4.227###3.3979###0.0811###7.6854###0.000###0.5139###2.9292###3.4520

###Check+Var.)

2###Entries###0.883###0.012###0.402###0.090###0.127###17.568###31.758###62.243###0.439###53.955###0.632###3.833###61.057###41.717

###(ignoring###50###**###**###**###2.27 **###*###**###*###**###**###**###**###**###**###**###**

3###Blocks)###4.16###0.036###2.308###12.157###0.722###0.044###37.203###57.055###457.408###1.407###220.191###2.000###15.528###329.420###5.738

###2###2**###**###**###**###**###**###*###**###**###**###**###**###**###**

4###Checks###47###0.221###0.011###0.1015###1.843###0.053###0.065###8.609###25.093###32.658###0.2178###47.500###0.581###3.332###36.525###39.654

###Varieties###**###**###**###**###**###**###*###**###**###**###**

5###Checks vs.###1###25.43###0.041###10.734###2.462###0.544###3.209###399.387###294.414###662.380###8.938###24.885###0.278###3.970 *###677.350###210.642

###Varieties###**###**###**###**###**###**###**###*###**###**###**###**###**

6###ERROR###10###0.019###0.001###0.007###0.071###0.023###0.002###6.362###4.774###3.497###0.038###12.941###0.001###0.780###3.118###3.452

Table 2. General Mean, range, Coefficient of variation and least significant difference for different cooking and micronutrient traits of Kalanamak advanced lines

###Genotype###Checks

S. No.###Traits###Genera###Range###3131-SN###Pusa###Pant Sugandh###CV%###CM###AVSB###AVDB###AVAC

###l mean###Basmati-1###Dhan 17

###1###Kernel length (mm)###4.32###3.66-5.62###4.76###6.12###6.27###13.42###0.18###0.43###0.50###0.38

###2###Kernel breadth (mm)###1.77###1.58-2.19###1.80###1.66###1.67###5.80###0.03###0.08###0.09###0.07

###3###Kernel length/breadth ratio###2.45###2.05-3.51###2.64###3.69###3.74###15.83###0.11###0.27###0.31###0.24

###4###Cooked kernel length (mm)###10.07###8.02-14.26###8.92###11.67###10.94###13.37###0.34###0.84###0.97###0.74

###5###Cooked kernel breadth (mm)###2.76###2.47-3.96###2.96###2.35###2.36###9.35###0.20###0.48###0.55###0.42

###6###Kernel elongation ratio###2.34###1.87-2.99###1.88###1.91###1.74###12.02###0.06###0.15###0.18###0.14

###7###Hulling recovery (%)###79.69###71.56-85.56###76.74###71.77###73.97###4.55###3.24###7.95###9.18###7.01

###8###Milling recovery (%)###68.90###58.81-82.14###67.71###62.56###62.19###7.52###2.81###6.88###7.95###6.07

###9###Head rice recovery (%)###59.60###47.22-71.66###62.42###46.04###48.99###10.36###2.41###5.89###6.80###5.20

###10###Alkali spreading value###5.27###4.30-6.12###5.54###6.32###6.42###10.16###0.25###0.62###0.71###0.54

###11###Gel consistency (mm)###77.42###61.81-93.50###82.92###73.68###71.53###9.07###4.63###11.34###13.09###10.00

###12###Aroma###1.33###1.00-2.00###1.00###2.00###1.00###33.33###0.05###0.11###0.26###0.20

###13###Amylose content (%)###20.87###17.79-24.41###19.57###22.25###22.44###9.16###1.14###2.78###3.21###2.45

###14###Fe(ug/g)###28.37###16.48-41.33###29.34###14.88###19.32###22.43###2.27###5.56###6.43###4.91

###15###Zn(ug/g)###25.59###15.63-46.94###28.93###29.15###30.72###24.38###2.39###5.85###6.76###5.16

Table 3. Mean value of forty eight Kalanamak Advanced lines for different cooking and micronutrient traits.

Geno-###Pedigree###KL###KB###KL###CKL###CKB###KER###HR###MR###HRR###ASV###GC###A###AC###Fe###Zn

types###(mm)###(mm)###/KB###(mm)###(mm)###(mm)###(%)###(%)###(mm)###(%)###(ug/g)###(ug/g)

PMS-29###3131SN x PB-1###3.76###1.79###2.11###8.02###2.57###2.13###85.00###82.14###61.59###5.25###81.80###2.00###21.59###25.064###26.20

PMS-15###3131-SN x 3327-SN###4.26###1.89###2.26###10.62###2.70###2.49###79.36###61.60###57.47###5.59###75.80###1.00###22.87###20.464###21.75

PMS-03###3131-SN x 3327-SN###3.81###1.69###2.26###8.62###2.70###2.26###81.29###74.24###66.47###5.09###73.30###0.00###24.04###32.964###25.40

PMS-81###3131-SN x 3327-SN###3.88###1.71###2.28###8.36###2.55###2.15###84.12###77.29###67.47###5.27###84.58###1.00###21.12###27.914###21.15

PMS-04###3131-SN x 3327-SN###4.16###1.69###2.47###10.12###2.47###2.43###83.14###65.59###59.89###5.75###76.80###1.00###23.44###30.814###33.10

PMS-46###3131-SN x 3327-SN###4.46###2.19###2.05###10.72###2.77###2.40###82.19###68.39###61.42###4.92###70.80###2.00###21.44###36.064###34.40

PMS-45###3131-SN x 3327-SN###4.31###1.74###2.49###10.12###2.72###2.34###80.79###73.39###58.87###4.59###72.80###2.00###17.80###24.664###29.15

PMS-24###3131-SN x PB-1###3.88###1.89###2.06###9.32###2.70###2.40###83.14###65.59###56.87###4.42###82.80###2.00###18.20###28.714###26.50

PMS-18###3216-SN x 3327-SN###3.76###1.69###2.24###11.17###2.72###3.00###82.46###73.76###61.66###5.03###70.50###0.00###19.85###22.681###19.72

PMS-51###3216-SN x 3327-SN###3.81###1.69###2.27###8.95###2.72###2.36###78.51###63.97###51.61###5.36###76.50###1.00###19.32###30.731###25.47

PMS-78###3216-SN x 3327-SN###4.16###1.81###2.31###8.72###2.64###2.10###82.66###75.27###65.43###4.86###81.50###0.00###22.87###16.481###20.72

PMS-55###3216-SN x 3327-SN###4.16###1.69###2.47###8.97###2.64###2.16###78.31###61.77###51.55###4.86###84.50###1.00###19.42###40.531###32.92

PMS-48###3131SN x PB-1###4.36###1.79###2.45###9.57###2.47###2.20###85.39###73.97###66.56###4.53###74.50###2.00###20.27###16.881###25.07

PMS-86###3216-SN x 3327-SN###4.46###1.79###2.50###9.37###2.77###2.10###85.56###77.08###71.66###6.03###79.50###0.00###20.12###20.881###29.15

PMS-26###3131SN x PB-1###4.36###1.69###2.59###10.67###2.57###2.46###75.51###64.02###51.51###4.86###93.50###2.00###17.92###30.881###34.87

PMS-25###3131SN x PB-1###4.56###1.59###2.87###10.27###2.67###2.26###79.56###65.14###55.91###4.36###85.50###2.00###18.17###33.681###46.57

PMS-33###3131SN x PB-1###5.06###1.70###2.97###10.61###2.70###2.10###75.41###65.637###53.22###4.35###88.73###2.00###18.91###29.081###22.15

PMS-07###3216-SN x 3327-SN###4.11###1.90###2.15###8.91###2.60###2.18###77.87###69.637###58.27###5.35###62.73###0.00###24.12###20.631###25.55

PMS-14###3216-SN x 3327-SN###4.16###1.80###2.30###10.36###2.70###2.50###84.72###74.637###69.02###5.52###67.73###0.00###21.07###30.381###24.25

PMS-34###3131SN x PB-1###4.11###1.70###2.41###9.16###2.72###2.24###83.67###64.717###57.71###4.68###71.73###1.00###21.07###37.881###24.75

PMS-47###3131-SN x 3327-SN###4.41###1.96###2.24###12.21###2.50###2.77###79.77###65.837###60.33###4.68###68.73###2.00###22.77###24.681###27.05

PMS-17###3216-SN x 3327-SN###4.07###1.70###2.39###10.11###3.00###2.49###82.17###73.157###64.43###5.35###69.73###1.00###21.73###18.581###16.10

PMS-50###3131SN x PB-1###4.86###1.70###2.85###10.51###2.80###2.16###81.57###71.397###64.01###5.52###72.73###1.00###21.57###29.431###33.80

PMS-72###3216-SN x 3327-SN###3.66###1.60###2.28###10.31###2.70###2.82###82.22###69.187###61.82###5.85###78.73###0.00###22.77###27.931###19.70

PMS-58###3216-SN x 3327-SN###4.12###1.81###2.27###12.31###3.01###2.96###76.05###66.464###56.70###5.80###88.61###1.00###20.83###37.797###29.39

PMS-88###3216-SN x 3327-SN###3.87###1.83###2.10###8.80###2.76###2.25###77.51###69.674###59.46###5.47###74.61###0.00###22.18###33.747###25.14

PMS-21###3131SN x PB-1###4.67###1.71###2.72###11.30###2.81###2.41###78.06###69.904###49.81###5.14###72.61###1.00###21.63###25.997###24.99

PMS-10###3216-SN x 3327-SN###3.87###1.71###2.25###10.18###2.71###2.60###78.36###64.54###55.98###4.97###75.11###0.00###19.31###20.797###23.54

PMS-42###3216-SN x 3327-SN###4.12###1.81###2.27###8.90###2.76###2.14###83.26###73.24###67.11###4.80###81.61###2.00###18.23###29.15###21.84

PMS-35###3216-SN x 3327-SN###3.77###1.81###2.07###10.40###3.10###2.73###80.11###74.52###63.10###5.47###73.61###2.00###19.73###27.40###20.84

PMS-22###3131SN x PB-1###4.37###1.77###2.47###10.50###2.91###2.39###84.36###76.04###69.11###4.30###75.61###1.00###18.88###17.80###22.15

PMS-13###3216-SN x 3327-SN###4.97###1.71###2.90###11.50###2.61###2.31###76.26###64.44###50.76###5.97###79.61###1.00###18.09###37.70###46.94

PMS-49###3131SN x PB-1###4.68###1.81###2.57###11.183###2.50###2.39###81.07###61.95###55.17###6.12###73.81###2.00###22.87###27.20###20.77

PMS-09###3216-SN x 3327-SN###4.20###1.91###2.18###9.58###2.75###2.28###79.92###67.74###61.13###5.79###61.81###2.00###24.42###23.20###25.97

PMS-30###3131SN x PB-1###3.75###1.71###2.18###10.58###2.90###2.81###76.44###62.87###54.18###5.62###79.81###2.00###22.57###26.90###15.63

PMS-27###3131SN x PB-1###5.45###1.81###3.01###13.08###3.098###2.40###79.63###71.49###57.88###5.29###91.81###1.00###19.89###33.05###21.87

PMS-03###3216-SN x 3327-SN###3.80###1.71###2.201###8.68###2.90###2.28###79.17###72.34###64.03###5.29###72.31###1.00###24.07###31.23###24.47

PMS-38###3131SN x PB-1###5.25###1.63###3.211###9.86###2.91###1.88###72.99###61.33###47.23###4.79###82.81###2.00###17.87###29.55###25.07

PMS-32###3131SN x PB-1###4.47###1.81###2.451###9.08###2.60###2.03###77.24###68.55###55.77###4.95###91.81###2.00###18.10###36.14###28.27

PMS-31###3131SN x PB-1###4.65###1.76###2.631###9.18###2.50###1.98###78.47###69.24###67.08###5.94###74.81###2.000###19.47###30.05###19.32

PMS-28###3131SN x PB-1###4.50###1.69###2.657###10.27###2.81###2.30###79.12###71.68###60.17###5.94###77.53###1.000###23.06###33.23###20.37

PMS-23###3131SN x PB-1###5.40###1.79###3.007###14.62###4.10###2.72###72.49###58.81###50.42###5.27###74.53###1.000###20.28###26.88###16.27

PMS-87###3216-SN x 3327-SN###4.30###1.83###2.347###8.78###2.99###2.06###79.41###72.82###67.39###5.70###73.03###1.000###21.38###26.58###26.02

PMS-37###3131SN x PB-1###4.02###1.79###2.247###9.12###2.90###2.29###79.45###68.81###60.42###5.10###76.53###2.000###21.81###24.68###27.87

PMS-01###3131-SN x 3327-SN###4.10###1.79###2.287###9.17###2.70###2.26###76.58###64.15###56.96###5.27###86.53###1.000###19.43###26.33###18.92

PMS-52###3131SN x PB-1###5.62###1.59###3.517###13.02###2.70###2.33###74.91###64.26###58.16###5.94###77.53###2.000###21.28###41.33###28.87

PMS-20###3131SN x PB-1###4.60###1.79###2.567###8.67###2.92###1.89###71.56###63.57###55.58###5.94###71.53###1.000###23.56###28.43###26.12

PMS-83###3216-SN x 3327-SN###4.33###1.77###2.447###9.12###2.70###2.12###78.04###71.24###62.51###5.92###83.10###1.000###20.64###28.79###22.27

3131-SN###C-1###4.76###1.80###2.645###8.92###2.96###1.88###76.74###67.71###62.42###5.54###82.93###1.000###19.57###29.34###28.93

PB-1###C-2###6.12###1.66###3.692###11.67###2.35###1.91###71.78###62.56###46.043 6.32###73.69###2.000###22.26###14.87###29.15

PSD-17###C-3###6.28###1.67###3.745###10.94###2.36###1.75###73.97###62.20###48.99###6.43###71.56###1.000###22.44###19.32###30.73

Mean###4.40###1.76###2.51###10.10###2.75###2.31###79.36###68.62###59.18###5.32###77.34###1.21###20.906###27.95###25.83

Std. Dev.###0.59###0.10###0.40###1.351###0.257###0.277###3.61###5.16###6.129###0.541###7.02###0.749###1.915###6.27###6.30

Std.

Error###0.08###0.01###0.06###0.19###0.036###0.04###0.50###0.72###0.86###0.08###0.98###0.10###0.27###0.88###0.88

C. V. %###13.42###5.80###15.84###13.37###9.35###12.02###4.55###7.52###10.36###10.16###9.07###62.61###9.16###22.44###24.39

Table 4. Phenotypic and genotypic coefficient of variation, heritability and genetic advance (as% of mean) among Kalanamak advanced lines of rice for quality traits

S. No.###Traits###PCV(%)###GCV(%)###Heritability (%)###Genetic advance (as % of mean)

###1###Kernel length ( mm)###9.74###9.21###89.40###17.93

###2###Kernel breadth (mm)###5.22###5.01###92.4###9.94

###3###Kernel length/breadth ratio###11.60###11.05###90.8###21.71

###4###Cooked kernel length (mm)###11.98###11.69###95.1###23.49

###5###Cooked kernel breadth (mm)###7.83###5.59###50.8###8.21

###6###Kernel elongation ratio###9.73###9.50###95.3###19.12

###7###Hulling recovery (%)###3.57###1.67###21.7###1.60

###8###Milling recovery (%)###6.60###5.79###76.9###10.46

###9###Head rice recovery (%)###8.61###8.02###86.70###15.38

###10###Alkali spreading value###8.02###7.12###78.6###13.00

###11###Gel consistency (mm)###8.17###6.72###67.7###11.39

###12###Aroma###29.23###27.60###91.24###56.44

###13###Amylose content (%)###7.98###6.77###71.9###11.83

###14###Fe(ug/g)###19.07###18.03###89.4###35.11

###15###Zn(ug/g)###22.04###20.80###89.1###40.47

Table 5. Estimates of phenotypic and genotypic correlation coefficient for cooking quality and micronutrients traits

Traits###KL###KB###KL/KB###CKL###CKB###KER###HR(%)###MR(%)###HRR(%)###ASV###GC###A###AC###Fe###Zn

KL###P###1.000###-0.073###0.901**###0.615**###0.156###-0.251###-0.292*###-0.248###-0.284*###-0.025###0.199###0.223###-0.268###0.278###0.229

###G###1.000###-0.091###0.908**###0.638**###0.231###-0.228###-0.798**###-0.381**###-0.296*###-0.088###0.364*###0.236###-0.383**###0.319*###0.237

KB###P###1.000###-0.494**###-0.016###0.038###0.015###0.196###0.079###0.183###-0.060###-0.254###0.117###0.165###-0.204###-0.034

###G###1.000###-0.498**###-0.016###0.203###0.021###0.395**###0.081###0.251###-0.003###-0.443**###0.122###0.304*###-0.274###-0.078

KL/KB###P###1.000###0.538**###0.109###-0.228###-0.345*###-0.258###-0.323*###-0.002###0.276###0.167###-0.306*###0.344*###0.233

###G###1.000###0.553**###0.049###-0.212###-0.855**###-0.371*###-0.378**###-0.087###0.509**###0.175###-0.477**###0.421**###0.265

CKL###P###1.000###0.403**###0.601**###-0.273###-0.360*###-0.314*###0.066###0.056###0.015###-0.095###0.165###0.022

###G###1.000###0.581**###0.605**###-0.393**###-0.379**###-0.324*###0.031###0.074###0.015###-0.109###0.155###0.006

CKB###P###1.000###0.301*###-0.196###-0.060###-0.101###-0.122###-0.093###-0.267###-0.028###-0.097###-0.198

###G###1.000###0.442**###0.054###-0.064###-0.206###-0.225###0.265###-0.378*###-0.278###-0.021###-0.298

KER###P###1.000###-0.046###-0.196###-0.123###0.117###-0.132###-0.192###0.135###0.075###-0.207

###G###1.000###0.166###-0.142###-0.118###0.132###-0.222###-0.197###0.196###-0.107###-0.236

HR (%)###P###1.000###0.682**###0.720**###0.035###-0.219###-0.024###0.126###-0.321*###-0.225

###G###1.000###0.767**###0.824**###0.189###-0.686**###-0.051###0.618**###-0.494**###-0.287*

MR(%)###P###1.000###0.777**###0.063###-0.141###-0.135###0.125###-0.349*###-0.254

###G###1.000###0.820**###0.071###-0.202###-0.154###0.267###-0.357*###-0.248

HRR(%)###P###1.000###0.189###-0.267###-0.167###0.243###-0.343*###-0.283*

###G###1.000###0.169###-0.377**###-0.178###0.365*###-0.315*###-0.231

ASV###P###1.000###-0.217###-0.308*###0.479**###0.105###-0.025

###G###1.000###0.183###-0.347*###0.485**###0.166###-0.037

GC###P###1.000###0.219###-0.547**###0.367*###0.102

###G###1.000###0.267###-0.419**###0.383**###0.166

A###P###1.000###-0.344*###0.162###0.202

###G###1.000###-0.405**###0.171###0.214

AC###P###1.000###-0.191###-0.183

###G###1.000###-0.154###-0.284*

Fe###P###1.000###0.450**

###G###1.000###0.482**

RESULTS AND DISCUSSIONS

Genetic variability in any crop is pre-requisite for selection of superior genotypes over the existing cultivars for various agronomic and quality traits. Analysis of variance under augmented design revealed highly significant and exploitable variability among tested genotypes for all the cooking quality and micro-nutrient traits and non-significant for the blocks which indicated the presence of adequate variability in the material under study (Table 1). The general mean (tested genotypes and checks), range and coefficient of variation for the different cooking and micronutrient traits are presented in Table 2. The Kernel length ranged from 3.66 to 5.62 mm with general mean of 4.32 mm and cooked kernel length range was found from 8.02 to 14.26 mm with the general mean of 10.07 mm. Kernel elongation ratio (KER) range was found from 1.87 to 2.99 with the general mean of 2.34. Kernel elongation ratio (KER) is a major quality determinant character for aromatic rice.

The mean values of cooking quality and micronutrient traits of forty eight advance lines along with three checks are presented in Table 3.Based on mean performance, genotypes namely, PMS-33, PMS-27, PMS-52, PMS-38 and PMS-23 were found promising for several desirable cooking quality traits such as kernel length, kernel length-breadth ratio and cooked kernel length with intermediate amylose and gelatinization temperature. These genotypes were at par with basmati rice possessing Kalanamak rice background. These genotypes could be registered as genetic stock or may be utilized for development of fine and super fine Kalanamak aromatic rice varieties. The mean value of intermediate amylose with pleasant strong aroma is of paramount importance in respect of quality rice breeding. Aroma development is also influenced by both genetic factors and environment. The major aromatic compound responsible for aroma is considered is 2-acetyl-1-pyrroline (Buttery et al., 1986).

In the present studies most of the genotypes were analyzed for intermediate amylose content and mild to very strong aroma. Traditional indigenous Kalanmak aromatic rice is reported to be high (35 ug/g in unpolished rice) for iron content (Ravindra Babu et al, 2013). In the present investigation, iron content in brown rice ranged from 16.48 - 41.33 ug/g with the general mean of 28.37ug/g and Zinc content range was found from 15.63 - 46.94 ug/g with the general mean of 25.59 ug/g. Genotypes such as PMS-13 and PMS-46 were observed significantly higher for both Iron and Zinc content than the corresponding checks. Enhancement of micronutrient levels of staple crops through biological processes, such as plant breeding and genetic engineering is referred as biofortification and it could be effective in reducing the problems of malnutrition (Bouis, 2002).

The estimates of genetic parameters of variability viz., phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), heritability in broad sense and genetic advance as percent of mean are presented in Table 4. The phenotypic coefficient of variation (PCV) in general was higher than the genotypic coefficient of variation (GCV) for all traits. The magnitude of difference between GCV and PCV was very narrow indicating less environmental influence and predominant genetic factors involved in expression of cooking and micronutrient traits of different advanced lines of Kalanamak aromatic rice. Vivekanandan and Giridharan (1998) also reported closeness of GCV and PCV for different cooking quality traits indicating higher resistance to major environmental influence. High and moderate estimates of PCV and GCV were observed for Zinc content, Iron content, cooked kernel length and kernel length-breadth ratio.

This indicates the existence of sufficient genetic base among the genotypes taken for study and possibility of genetic improvement through direct selection of these traits. Similar findings were reported by Sala et al. (2012) for Iron and Zinc and Patil et al. (2003) for kernel length after cooking. The estimates of PCV and GCV were low for traits like kernel length, kernel breadth, cooked kernel breadth, kernel elongation ratio, hulling recovery (%), milling recovery (%), head rice recovery (%), alkali spreading value, gel consistency and amylose content. Subbaiah et al. (2011) similarly reported low value of PCV and GCV for hulling recovery (%), milling recovery (%), head rice recovery (%), gel consistency and amylose content in aromatic rice. High heritability coupled with high genetic advance were observed for cooking quality traits viz. cooked kernel length, kernel length breadth ratio and micronutrient traits, viz., Iron and Zinc content.

Similar finding was reported for cooked rice kernel length ratio by Chakraborty et al. (2009) for Iron content by Sala et al. (2012) and Zinc content by Gangashetty et al. (2013). The characters showing high to moderate GCV and PCV, high heritability coupled with high genetic advance indicated that they are governed by additive gene action and breeding method based on progeny testing and mass selection could be useful in improving these traits. High heritability coupled with moderate genetic advance were observed for amylose content, gel consistency, alkali spreading value, head rice recovery, milling recovery and kernel elongation ratio. Similar finding was reported for amylose content by Nayak et al., (2004), for gel consistency by Chakraborty et al. (2009) and for alkali spreading value and kernel elongation ratio by Rathi et al. (2010).

High heritability coupled with low genetic advance was reported for kernel breadth and moderate heritability with low genetic advance was recorded for cooked kernel breadth. Low heritability coupled with low genetic advance was recorded for hulling recovery (%). This indicated that that kernel breadth, cooked kernel breadth and hulling recovery were predominantly governed by non-additive gene action (dominant or epistasis) in Kalanamak advance lines. The characters showing high heritability along with moderate or low genetic advance can be improved by intermating superior genotypes of segregating population developed from combination breeding (Samadia, 2005). The estimates of genotypic correlation coefficient are essential in evaluating the possibility of simultaneous improvement of many characters or improvement of complex traits of agronomical and other quality traits.

In present investigation, phenotypic and genotypic correlation coefficient was studied with cooking and micronutrients traits (Table 5). In general, the magnitude of estimated genotypic correlation coefficient was higher than the corresponding phenotypic correlation coefficients. This showed a strong inherent association among the quality traits under investigation. Kernel length showed significant positive correlation with Kernel length breadth ratio (KL/KB) and cooked kernel length both at genotypic and phenotypic level and it exhibited positive association with Iron content, and gel consistency only at genotypic level. However, on the other hand it showed negative association with hulling recovery (%) and head rice recovery (%). Similar to our finding, Singh et al. (2017) also reported, significant and positive association of kernel length with cooked kernel length and kernel length breadth ratio.

Kernel breadth was found to be negatively associated with kernel length breadth ratio and gel consistency and positively associated with hulling recovery (%) and amylose content at genotypic level only. Kernel length breadth ratio (KL/KB) was significantly and positively associated with cooked kernel length and Iron content (at both phenotypic and genotypic level). KL/KB also had negative association with head rice recovery, head rice recovery and amylose content. Hulling recovery (%) showed highly positive association with milling recovery (%), head rice recovery (%) at both genotypic and phenotypic level. However, it showed negative association with Iron content at the level of both genotypic and phenotypic levels and gel consistency and Zinc content at genotypic level only.

In the present study, the positive significant association of hulling recovery (%) with milling recovery (%) and head rice recovery (%) indicated that the genotypes with higher hulling percent also showed higher estimates for milled rice and head rice recovery. Similar results were reported by several researchers like Chauhan et al. (1995), Nayak et al. (2003) and Nirmala Devi et al. (2015). Hulling recovery (%), milling recovery (%) and Head rice recovery (%) are important quality attributes for rice that enhances commercial success of a variety. Simultaneous improvement of these three quality traits could be achieved through the selection of a single trait is either hulling percent or milling percent or head rice recovery. Head rice recovery showed significant and positive association with amylose content and significant and negative association with Iron and Zinc content. Madhubabu et al. (2017) also reported significant positive correlation between head rice recovery and amylose content.

Alkali spreading value was found to be positively associated with amylose content and negatively associated with aroma. Gel consistency showed significant and positive association with Iron content and negative association with amylose content. The amylose content is a chemical quality trait that determines the texture of cooked rice. Varieties with intermediate amylose content and soft gel consistency are preferred by most rice consumers. In present investigation, the significant negative association between these two chemical quality traits (gel consistency and amylose content) was found. Thus, it indicated that for the selection of quality rice, one must first focus on selecting intermediate amylose content followed by gel consistency test as one trait is not sufficient enough to determine the softness of the rice after cooking. Iron content showed highly significant positive association with Zinc content.

Similarly, positive association of iron and zinc content was also reported by Archna et al. (2018). Graham et al.(1999) reported significant positive correlation between Iron and Zinc in rice, wheat and beans. Furthermore, Stangoulis et al. (2007) too reported significant positive correlation between Zinc and iron content in double haploid rice population indicating co-segregation of concerned factors.

Acknowledgments: The authors are thankful to the Department of Biotechnology Govt. of India (DBT-PMS Phase-II, Project Code: 7003) for financial support to carry out the work.

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