Genetic Variation and Correlation of Spring Rice Genotypes in Mid-Hill's Rainfed Condition of Nepal

Academic Paper 2019 10 Pages

Nature Protection, Landscape Conservation





Experimental details
Statistical analysis
Genotypic and phenotypic coefficient of variation
Broad sense heritability (H2bs)
Genetic advances (GA)


Genotypic and phenotypic variance
Genotypic and phenotypic coefficient of variation
Heritability. 6
Genetic Advance and Genetic Advance as percentage of mean






The present research consists of 12 rice (Oryza sativa L.) genotypes and the experiment was conducted during June- November, 2017 in Randomized Block Design with three replication at NARC, Kumaltar. The data were recorded for ten quantitative characters to study genetic variation , heritability, genetic advance and correlation coefficient analysis .Analysis of variance among 12 genotypes showed significant difference for all characters studied except non- effective tillers. Grain yield had positive significant correlation with SPAD and positive correlation with 1000 grain weight, flag leaf area, days of 50% flowering, plant height, number of effective tiller and panicle length whereas negative significant correlation with number of non-effective tiller and negative correlation with days of 90%maturity. Moderate genotypic coefficient of variation (GCV) and phenotypic coefficient variation (PCV) was observed for flag leaf area and followed by grain yield and number of effective tiller, that these characters could be used as selection for crop improvement. High estimates of heritability were observed for days of 50% flowering and followed by panicle length, flag leaf area, test weight and days of 90% maturity. High genetic advance were found in number of non-effective tiller, grain yield per meter square, no of effective tiller and flag leaf area indicating pre-dominance of additive gene effects and possibilities of effective selection for the improvement of these characters.

Keywords: Correlation, Heritability, Genetic Advance, Genetic Variability and Rice


Rice (Oryza sativaL.) 2n=24 (Hooker,1979), is monocot, annual, semi aquatic cereal crop and a member of family Poaceae. Thiamine, riboflavin, niacin and dietary fibre are good source in rice(FAO 2004). Global production of rice is about 740 million ton with 4,539 kg/ha productivity and habitable in 117 country covering a total area of 163 million hectares (FAOSTAT, 2014) and in Asia for 100 million household it is main source of income and employment (Singh.,et al.,2015). Rice contributes 33.91% to GDP and 48.8% to AGDP in Nepal (MOF, 2016). Rainfed rice, based grown in Asia about 46 million hectares (Haefele and Hijmans,2007) and has alone around 23 million hectares prone to drought Reyes (2009) and affects regularly, out one in five year (Pandey et al.,2005). NARC(2004) indicates still 79% of rice is grown under rainfed condition of which 70% under rainfed lowland and 9% is under upland condition .Pantuwan, et al., (2000) observed that grain yield of rice genotypes under rainfed condition was reduced from 18 % to 54 % as compare to irrigated condition. According to United Nation’s report world population is going to cross 8 billion in 2030 and 9.6 billion by 2050.For rice production, water is critical and important factor. So, to meet future demand resulting from population growth and limited water condition, plant breeders have to identify genotypes with optimal reproductive capacity with high yield potential under stress condition.

Grain yield is polygenically controlled quantitative trait, highly influenced by environment and determined by magnitude and nature of their genetic variability (Singhet al.,2000). Broad sense heritability is influenced by environment so, estimation of heritability along genetic advancement will help know nature of gene action affecting the concerned traits (Sravan, 2012). Higher heritability with higher genetic advance indicates the scope of selection new genotypes with concerned traits (Ajmalet al.,2009). Investigation of the interrelationship between yield and its components will improve the efficiency of breeding program with appropriate selection criteria (Ogunbayoet al.,2014).So, it is important for plant breeders to understand the degree of correlation between yield and its components. Therefore, the objectives of the present study was to find out the nature and magnitude of genetic variations and association between different traits in rice genotypes under rainfed condition.


Experimental details

Coordinate variety trail of advance rice genotypes was conducted during June 2017 – November 2017 at Nepal Agriculture Research Council Kumaltar, Lalitpur. The station is situated at an altitude of 1350 meters above mean sea level at 85010’ E and 27039’N. Twelve rice genotypes NR 11050-B-B-B-B-22, NR 11115-B-B-31-3, NR 11105-B-B-16-2, NR 11105-B-B-49-3, NR 11105-B-B-15-2-1, NR 11216-B-25-1, NR 11139-B-B-B-13-3 , NR 11196-B-25-3,NR 11289-B-16-3, NR 11130-B-B-B-8-3, NR 11130-B-B-B-8 and Khumal-4(standard check) was laid out in Randomized Complete Block Design with three replication. The plants were planted by using crop geometry of 20cm*15cm (RR*PP).Each genotypes received the plots of 8m[2](4m*2m) area with net plot area of 6.4m[2](3.2m*2m).Distance between the replication was 75cm and within plot was 40cm. The nursery was raised on uniform raised beds with no chemical inputs. Forty five days old seedlings were transplanted in main research plot with 4 seedling/hill. Recommended fertilizer of Urea, DAP (P2O5) and MoP (KCL) at the rate of 60:30:30kg ha-[1] NPK. Half dose of urea, full dose of DAP and MoP was applied at planting time whereas half dose of urea was applied at tillering stage. Irrigation wasn’t maintained, single hand weeding at 42 DAT, no weedicide. All the data were obtained from net plot area; five plants were randomly selected for Plant height,Panicle length, Flag leaf area (cm[2]) and Chlorophyll content (SPAD), except for 50% flowering and 90% maturity. Yield and yield related trait like; 1000 grain weight (gm) with adjustment of moisture at 14%. No of effective tiller/m[2], No of non- effective tiller/m[2] were also randomly selected from net plot area.

Statistical analysis

Analysis of variance (ANOVA) was carried out on the data to access the genotypic effects and their interaction using R-Studio version 3.1.1 and the correlation analysis was performed by IBM SPSS Statistics version 20 and MS-Excel 2016 to determine the association of the yield with the agronomic characters.

Genotypic and phenotypic coefficient of variation

The phenotypic and genotypic variance components and coefficients of phenotypic and genotypic of variation to compare the variation among traits were calculated by the methods suggested by Burton and De vane (1953) and Johnson et al., 1955a and 1955b.

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MSg = mean square due to genotypes

MSe = environment variance (error mean square)

r= replication

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X = General mean of the trait

Sivasubramanian and Madhavamenon (1973) categorized the value of GCV and PCV as follows: 0 – 10 %=Low ,10 – 20 %= Moderate and >20 %= High.

Broad sense heritability (H[2]bs)

The ratio of genotypic variance (Vg) to the phenotypic variance (Vp) is called broad sense heritability and expressed in percentage (Hanson et al., 1956). Heritability in broad sense for all characters was computed using the formula given by Falconer (1996) as:

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H = heritability in broad sense

Vp = phenotypic variance

Vg = genotypic variance

The heritability percentage categorized as low, moderate and high as followed by, Robinson, H. F., Cornstock, R. E., & Harvey, P. H. (1949) as follows:0–30% =Low,30–60%= Moderate, > 60%=High.

Genetic advances (GA)

At 5% selection intensity (k) the genetic advance was formulated by the following formula described by (Johnson et al,.1955a).

Genetic Advances (GA) = k.σp.Hb[2]

Where,k = constant (selection differential where k = 2.06 at 5% selection intensity)

σp = phenotypic standard deviation

Hb[2] = broad sense heritability

Genetic advances as percent of mean was calculated for effectiveness of selection in improving the traits, using the formula GAM =GA/X *100 (Falconer, 1996).Where:

GAM=genetic advances as percent of mean

GA=Genetic advances under selection

X = Mean of population in which selection will be employed.

The GA as percent of mean (GAM) was categorized as low, moderate and high as followed by (Johnsonet al.,1955), 0-10%= low, 10-20%=Moderate, 20% and above=High


The analysis of variance ANOVA showed significant difference among all traits except non-effective tiller/m[2] in tested genotypes and indicating the existence of variability among tested genotypes. So, parents used for crossing used for crossing were genetically different.

Table 1: Mean square from analysis of variance of twelve rice genotypes in rainfed condition at NARC,

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Significance probability levels 0.001 *** 0.01** 0.05*

NS= Non Significant

DF=Days of 50% flowering, PH= Plant height ,FA= Flag leaf area, SPAD= Chlorophyll units,

PL=Panicle length, DM= Days of 90% maturity, ET= No of effective tiller per meter square, NET=non effective tiller per meter square, 1000 gw =Thousand grain weight in gram ,GY= Grain yield per meter square.

Genotypic and phenotypic variance

A wide range of variance was observed for all the characters. Phenotypic variance was higher than genotypic variance for the yield and its contributing characters indicating the influence of environmental factors on these traits. Grain yield (1251.33) showed highest genetic variability followed by no of effective tiller per square meter (106.56), flag leaf area (46.93), plant height (28.16), days of 50% flowering(15.55) and SPAD(13.15) as presented in table 2 ,indicating that selection based on these traits would be effective for future crossing. Similar findings were reported earlier by Deviet al.,(2006), Prajapatiet al.,(2011) and Limbaniet al.,(2017) .

Genotypic and phenotypic coefficient of variation

Moderate PCV and GCV recorded for flag leaf area and followed by grain yield and number of effective tiller as presented in Table 2. Akinwaleet al.,(2011) and Sarkaret al.,(2005) also revealed same results suggesting sufficient variability and these character could be used as selection for crop improvement.


Heritability measure the genetic relationship between the parents and their offspring. Heritability indicates as to how much emphasis should be placed in for selection of a particular trait. Heritability was found to be highest in case of- days to 50% flowering (87.45%) followed by panicle length (77.70%), flag leaf area (74.40%), test weight (74.74%) ,days of 90% maturity and plant height (60.95%) whereas moderate heritability was found in trait like grain yield (49.00%), no of effective tiller (45.98%), SPAD (33.24%)and lowest in number of non-effective tiller(15.42%) as presented in Table 2. High heritability values indicate that the traits under study are less influenced by environment in their expression. The plant breeder, therefore, may make his selection safely on the basis of phenotypic expression of these traits in the individual plant by adopting simple selection methods.

Genetic Advance and Genetic Advance as percentage of mean

High heritability estimates and genetic advance were observed higher for grain yield, no of effective tiller and flag leaf area. Based on these results, it is suggested that the high heritability is mostly due to additive gene effects, which indicates that improvement in these character is possible through mass selection and progeny selection. SPAD showed low heritability with low genetic advance indicates that these character are highly influenced by environment factors therefore, selection would be ineffective. In the present research, days of 50% flowering, panicle length, plant height, flag leaf area and 1000 grain weight exhibited high heritability but low genetic advance which indicated that these trait are controlled by non-additive gene action and phenotypic selection may not effective.

High genetic advance of mean were observed for flag leaf area and medium were observed for grain yield,1000 grain weight, panicle length and no of effective tiller/m[2] and low for SPAD, days of 50% flowering and days of 90% maturity. Medium heritability and medium genetic advance of mean was shown by grain yield and effective tiller which suggests that these traits are primarily under genetic control and selection for them can be achieve through their phenotypic performance. Singhet al.,(1980), Patilet al.,(2014) and Fukreiet al.,(2011) revealed same results and similar to our findings in genetic advance and genetic advance of mean.

Table.2. Estimates of Phenotypic (σ 2P) and Genotypic (σ 2g) Variance, Phenotypic coefficient of variability (PCV) and Genotypic coefficient of variability (GCV), Broad sense heritability (H), Expected genetic advances (GA) and Genetic advance as percent of mean (GAM) of twelve rice genotypes in rainfed condition at NARC, 2017.

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PL=panicle length, 1000gw =Thousand grain weight ,FA= Flag leaf area , PH= Plant height, DF= Days of 50% flowering, DM= Days of 90% maturity ET= No of effective tiller per meter square, NET=non effective tiller per meter square SPAD= Chlorophyll units , GY= Grain yield per meter square.


Correlation analysis suggests direction of selection and traits to be improved for grain yield. Phenotypic correlation coefficients among yield and yield attributing traits are presented in Table 3. The results showed that grain yield per meter square had significant positive association with

SPAD (chlorophyll contain r= 0.577), while plant height(r=0.287), no of effective tiller/m[2](r=0.239), 1000 grain weight(r=0.425), panicle length(r=0.156), flag leaf area(r=0.419) and days of 50% flowering(r=0.336) showed non-significant correlation with grain yield .This indicates that all these character were important for yield improvement. Afrinet al.(2017) for SPAD(chlorophyll contain), Patel et al.(2014) and Raoet al.(2014) for 1000 grain weight and number of productive tillers per plant. Grain yield per meter square showed highly negative significance with non-effective tillers/m[2](r=-0.799**). Rahmanet al.(2014) also reported the same result for non-effective tiller/m[2].Grain yield shows non-significant negative correlation with days of 90% maturity(r=-0.291). Seyoumet al.(2012) reported same results for days of 90% maturity with grain yield.

Non effective tiller shows significant negative correlation with 1000 grain weight(r=-0.650**). Rahmanet al.(2014) also found negative correlation with 1000 grain weight..Flag leaf area found to be highly positive correlation(r=0.743**) with chlorophyll contain. Sharifiet al.(2013) found same result for chlorophyll contain. Plant height had significant negative correlation with days of 50% flowering (-0.598**) and similar results was observed by (Meenakshiet al.,1999).


1 Institute of Agriculture and Animal Science, Tribhuvan University, Lamjung, Nepal


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Tribhuvan University
genetic variation correlation spring rice genotypes mid-hill rainfed condition nepal



Title: Genetic Variation and Correlation of Spring Rice Genotypes in Mid-Hill's Rainfed Condition of Nepal