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Biometrical character interrelationship and morphological variations in some upland rice (Oryza Sativa L.) varieties.

INTRODUCTION

In Northern Nigeria, upland rice (Oryza Sativa L.) is an annual crop that is typically cultivated on dry land. Rice is the sixth major crop cultivated in Nigeria after sorghum, millet, cowpea, cassava and yam [1]. It is grown in four major rice growing environments: upland, rain-fed lowland, irrigated lowland and deep water. Rainfed upland is the major rice growing ecology in West Africa, accounting for nearly 60% of the total regional rice area [2]. It is not only consumed by human and fed to livestock, but is also a major raw material to agro-allied industries as it can be processed into acetic acid, glucose and starch while its husks can serve as a fuel and ash use as fertilizers [3].

The morphological method is the oldest and is considered as the first step in the description and classification of germplasm [4]. It had been reported that although agro-morphological characters are often influenced by environmental conditions, the method is still useful and easy to apply for classification, estimating diversity and registration of cultivars. Also, it was reported that morphological data showing continuous distributions or that are polygenically controlled, are particularly useful in inter-group classification below species level [5, 6, 7].

Many researchers had successfully used agro-morphological characters to classify and estimate diversity in a variety of crop species [8- 15]. Likewise, 67 hot pepper accessions had been classified into six clusters using 35 morphological and physiological characters [16]. These results revealed the importance of identification of important agro-morphological traits for diversity analysis in different crop species. The objective of this study was to characterize and classify 25 upland rice varieties. It also intended to identify morphological traits responsible for selection of important characters and study the interrelationship among the traits.

MATERIALS AND METHODS

Experimental materials and data collection

This study utilized twenty five upland rice cultivars (Table 1) extracted from 100 ergo-listed in National Cereal Research Institute (NCRI), Badeggi, Niger State, Nigeria. The trial experiment was conducted at Edozhigi, situated on Latitude 9[degrees]05'N and Longitude 5[degrees]50'E in 2004/2005 cropping season. The experiment was laid out in a randomized complete block design with three replicates. Seeds were planted in 2 rows of 5m in each replication. Nitrogen Phosphorus Potassium ([N.sub.2][P.sub.2][O.sub.2][K.sub.2]) fertilizers were applied at the rate of 80: 40: 40kg/ha. Hoe and hand pulling of weeds were done three times during the crop growth cycle. Sixteen agro- morphological characters were taken in each experimental plot (Table 2). The morphological traits considered in this study are plant height, leaf width, leaf length, leaf area, first and second iron toxicity score, panicle per meter square, African rice gall midge (AFRGM) score, number of tillers/stand, panicle length, panicle weight, number of spikelet, number of panicle branches, 100 grain weight, grain length and total grain weight. Data was subjected to statistical analysis of variance and multivariate statistical models (factor and cluster analysis), which are useful tools for dimensional reduction of variables and ease of interpretation [17].

Statistical Methods

Descriptive statistics and analysis of variance (ANOVA) for all the traits were conducted to estimate diversity of the traits. Simple Pearson moment correlation coefficients were computed between pairs of morphological traits. Factor analysis was also performed on mean of traits according to varimax rotation method [18] using the correlation matrix to reduce the dimensionality of the data on the 16 agromorphological traits. Cluster analysis was also performed and its dendrogram constructed to study the genetic relationship of the varieties using their agromorphological traits. For identifying superior traits in each class, the mean and standard error for class were computed based on group membership. All data was analyzed using Commercial packages Minitab Inc. [19] version 14 and SAS Inc. [20] version 8.1.

RESULTS

The Morphological traits

Descriptive statistical estimates and ANOVA test for the morphological traits are presented in Table 3. The ANOVA results indicated that the cultivars were significantly different in some traits like plant height (F=2.085, P<0.05), leaf length (F=2.085, P<0.05), first and second iron toxicity scores(F=3.011, P<0.01) and (F=2.774, P<0.01) respectively, number of panicle /[m.sup.2] (F=2.362, P<0.01), phenotype (F=3.051, P<0.01), panicle length (F=7.364, P<0.01), number of spikelets (F=8.692, P<0.01), panicle branches (F=8.443, P<0.01), panicle weight (F=5.562, P<0.01), 100 seed weight (F = 5.586, P<0.01), and total grain weight (F = 5.157, P<0.01). ANOVA results did not show significant different for leaf width (F = 1.185, P>0.05), leaf area (F = 0.838, P>0.05), AFRGM scores (F=0.738, P>0.05), number of tillers per stand (F=1.032, P>0.05) and grain length (F = 0.916, P>0.05). The minimum and maximum values are also presented in the Table 3, which indicated the two extreme values of all possible values for each trait. Standard deviation measured the variability within each variable and mean value is the average over all varieties.

Relative diversity of the traits: factor analysis

To determine the relative diversity of the traits among the tested ecotypes, factor analysis based on varimax rotation method was carried out on morphological traits and resulted into 6 common factors, which accounted for about 68% of the total variation (Table 4). Prior to the factor analysis, the variables were first standardized to the remove the effect of different measuring scales. Factor 1(yield contribution factors) accounted for 23.8% of the total variation. This included panicle length, number of spikelets, and number of panicle branches, total grain weight, panicle weight, second iron toxicity score and panicle per meter square. Factor 2(vegetative factor) accounted for 11.8% of the total variation, which included components such as leaf width, leaf length and plant height. Factor 3(seed quality) included 100-seed grain weight accounted for 8.8 % of the proportion variance. Factor 4 included number of tillers per stand and first iron toxicity score and accounted for 8.5% of the total variation. Factor 5 (genetic property) included AFRGM score and accounted for 7.7% of the total variation. Factor 6 included leaf area which accounted for 6.9% of the total variation.

Correlation between traits

In determining the interrelationship between pairs of traits, Pearson product moment correlation was performed and the results showed significant correlations between some paired traits (Table 5). Plant height was positively and significantly correlated with leaf width (r = 0.23, P<0.05), leaf length(r = 0.31, P<0.05), panicle length (r = 0.27, P<0.05) and total grain weight(r = 0.28, P<0.05) and number of spikelets (r = 0.33, p<0.01). Leaf width was only positively and significantly correlated with leaf length (r = 0.46, p<0.01) and leaf area (r = 0.36,p<0.01) while leaf length was positively and significantly correlated with leaf area(r = 0.25, P<0.05), number of spikelets(r = 0.29, P < 0.05), panicle weight(r = 0.30, p< 0.01), number of panicle branches (r = 0.33, p<0.01) and total grain weight (r = 0.29, p < 0.05).

It is worth mentioning that African Rice gall midge (AFRGM) score and first iron toxicity score (scored at 40 days after transplanting) were not significantly correlated with any other parameter, but, second iron toxicity score (scored at 60 days after transplanting) was negatively and significantly correlated with yield components such as panicle length(r = -0.48, p<0.01), number of spikelets (r = -0.38, p<0.01), panicle weight(r = -0.45, p<0.01), number of panicle branches(r = -0.35, p<0.01) and total grain yield weight (r = -0.33, p<0.01). Furthermore, number of tillers/stands was significantly and positively correlated with panicle length(r = 0.27, p<0.05) and total grain yield weight (r = 0.29, p<0.05). Panicle length was positively and significantly correlated with number of spikelet(r = 0.67, p<0.01), panicle weight (r = 0.54, p<0.01), number of panicle branches (r = 0.58, p<0.01) and total grain weight (r = 0.56, p<0.01), while number of spikelets was positively and significantly correlated with panicle weight (r = 0.45, p<0.01), number of panicle branches (r = 0.61, p<0.01) and total grain weight(r = 0.55, p<0.01). Panicle weight was positively and significantly correlated with number of panicle branches (r= 0.34, p<0.01) and total grain yield weight (r = 0.50, p<0.01). Number of panicle branches was significantly correlated with total grain weight (r = 0.48, p<0.01) and 100-seed weight was not significantly correlated with any morphological trait.

Cultivars classification

Cluster analysis was performed on the rice varieties to evaluate the genetic distance between the 25 different ecotypes based on the 16 agro-morphological traits. All the 25 rice cultivars were classified into four clusters and the corresponding dendrogram was presented (Figure 1). Referring to Table 1, Cluster I consisted of 15 cultivars: 1 2 3 6 7 9 13 14 16 18 20 21 22 23 and 25. Cluster II comprises of 6 cultivars: 4, 10, 15, 17, 19 and 24. Cluster III consisted of 2 cultivars: 5 and 8 and Cluster IV consisted of ecotypes 11 and 12. The mean and standard error of the each cluster were computed from their group membership (cultivars made up of each cluster in Table 6). The findings indicated that the cultivars in each group possess more similar genetic relationship than cultivars in different groups. Therefore, cultivars in each group could commonly be used for hybridization programs with regard to the mean value of their desirable characters.

The average values of cultivars made up of the first cluster (based 15 cultivars) had their mean values greater than overall cultivars include such traits as leaf area, second iron toxicity score, AFRGM score and 100-seed weight. In this class, the mean for 100- seed grain weight was much more than the overall mean. In the second class (consisted of 6 cultivars), their average value greater than overall cultivars for such traits like plant height, leaf length, first iron toxicity score, panicle per square meter, number of tillers/stand, panicle length, number of spikelets, panicle weight, number of panicle branches, 100- seed weight and total grain weight. The characteristics of this class could be suitable for improving the yield components. In the third class, there were only 2 cultivars (5 and 8, which are Philippines and International Institute of Tropical Agriculture (IITA-Nigeria) developed respectively). Important characters of this class were based on those traits whose mean values are found to be greater than overall cultivars, and they included traits in cluster II and leaf width and leaf length.

[FIGURE 1 OMITTED]

DISCUSSIONS

In the present study, 25 upland rice cultivars were analyzed and classified using 16 agro-morphological characters; it was found that these cultivars can be characterized into four groups, which was in line with previous works [10-15]. Cluster analysis indicated that the cultivars in each group possessed similar genetic relationship as opposed to cultivars in different groups. Therefore, cultivars in each group could commonly be used for hybridization programs with regards to the mean value of the desirable characters. A similar study conducted by Assefa et al. [8] using 320 teff lines classified these lines into 14 groups using 20 morphological, agronomical and phenological characters. Upadhyaya et al. [21] reported days to 50% flowering, plant width, apical secondary branches, tertiary branches, dots on seed testa, 100 seed weight, flowering duration, basal secondary branches, seed colour and seed testa texture as important traits in explaining multivariate polymorphism in a chickpea core collection. These results revealed the importance of identification of important agro-morphological traits for diversity analysis in different crop species.

The findings from correlation analysis indicated that the total yield weight was positively and significantly correlated with panicle length, number of panicles branches, and number of spikelets, panicle weight, and panicle/[m.sup.2]. These traits are a good indicator for yield improvement selection. However, second iron toxicity score was negatively and significantly correlated with total yield weight; this could be an indication of negative effect of soil stress on the yield. There are similar works by other researchers [21, 22] that traits, which had high positive correlations on yield should be emphasized for selecting yield.

Factor analysis characterized the 16 morphological characters into six common factors. The factors observed are yield components, vegetable factors, seed characteristics and forage factors. The most important factor was yield contribution components, which accounted for 23.8 % of the total variation. The versatility of the methodology employed in this study had been used on various crop varieties by previous researchers [14-16]. Zewdie and Zeven [16] reported that fruit weight, 1000 seed weight and fruit number per plant were the most important characters among the 35 morphological and physiological characters in clustering 67 hot pepper accessions. It was also reported that days to 50% flowering, plant width, apical secondary branches, tertiary branches, dots on seed testa, 100 seed weight, flowering duration, basal secondary branches, seed colour and seed testa texture as important traits in explaining multivariate polymorphism in a chickpea core collection [20]. Similarly, the diversity of 50 grass pea genotypes were also evaluated using morphological traits [13]. These results revealed the importance of identification of important agro-morphological traits for diversity analysis in different crop species.

CONCLUSION

This study revealed systematic diversity within this population of upland rice varieties. It is hoped that the information generated will provide a basis for selecting cultivars to be used for hybridization programs. Varieties in similar genetic groups did not generate much variability. Distant cultivars in cluster analysis could better provide a means for broad genetic base for yield improvement and other attributes of rice. Grain yield components could also be used for selecting those cultivars for any meaningful cultivar improvement as they contributed more to the total variability. The relationship in the correlation studies will allow breeders to select characters that have direct association with one another and therefore improve breeding activities in this crop.

ACKNOWLEDGEMENT

We greatly acknowledge the cooperation of Editor--in--Chief and comments of the reviewers in improving the quality of this article. We are also highly indebted to the references cited below.

REFERENCES

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[3.] Schalbroceck JJ Rice (Oryza sativa L.) In: Raemackers R.H. (Ed.). Crop Protection in Tropical Africa, Directorate General for International Cooperation (DGIC); pp.59-78, 2001.

[4.] Smith JSC and OS Smith The Description and Assessment of Distances between Inbred lines of Maize: The utility of Morphological, Biochemical and Genetic descriptors and a Scheme for the Testing of Distinctiveness between Inbred lines. Maydica. 1989; 34: 151-161.

[5.] Camussi A, Ottaviano E, Calinski T and Z Kaczmarek Genetic distances based on quantitative traits. Genetics, 1985; 111: 945-962.

[6.] Hardon JJ, Vosman B and TL Hintum Identification of genetic resources and their origin: The capabilities and limitations of modern biochemical and legal systems commission on plant genetic resources. First extraordinary session Rome, 1994, 7-11 November 1994.

[7.] Newbury HJ and BV Ford-Lloyd The use of RAPD for assessing variation in plants. Plant Growth Reg. 1993; 12: 43-51.

[8.] Assefa K, Ketema S, Tefera H, Nguyen HT, Blum A, Ayele M, Bai G, Simane B and T Kefyalew Diversity among germplasm lines of the Ethiopian cereal tef (Eragrostis tef (Zucc.) Trotter). Euphytica. 1999; 106: 87-97.

[9.] Elhoumaizi MA, Saaidi M, Oihabi A and C Cilas Phenotypic diversity of date-palm cultivars (Phoenix dactylifera L.) from Morocco. Gen. Res and Crop Evol. 2002; 49: 483-490.

[10.] Gomez OJ, Blair MW, Frankow-Lindberg BE and U Gullberg Molecular and phenotypic diversity of common bean landraces from Nicaragua. Crop Sci. 2004; 44: 1412-1418.

[11.] Lucchin M, Barcaccia G and P Parrini Characterization of a flint maize (Zeamays L. convar. mays) Italian landrace: I. Morpho-phenological and agronomic traits. Gen. Res. and Crop Evol. 2003; 50: 315-327.

[12.] Mars M and M Marrakchi Diversity of pomegranate (Punica granatum L.) Germplasm in Tunisia. Gen. Res. and Crop Evol. 1999; 46: 461-467.

[13.] Tadesse W and E Bekele Phenotypic diversity of grass pea (Lathyrus sativus L.) in relation to geographical regions and altitudinal range. Gen. Res. and Crop Evol. 2003; 50: 497-505.

[14.] Upadhyaya HD Phenotypic diversity in groundnut (Arachis hypogaea L.) core collection assessed by morphological and agronomic evaluations. Gen. Res and Crop Evol. 2003;50: 539-550.

[15.] Van de Wouw M, Maxted N and BV Ford-Lioyd Agro-Morphological characterization of common vetch and its close relatives. Euphytica. 2003; 130: 281-292.

[16.] Zewdie Y and AC Zeven Variation in Yugoslavian hot pepper (Capsicum annuum L.) accessions. Euphytica. 1997; 97: 81-89.

[17.] Richard AJ and DW Wichern Applied Multivariate Analysis. Third Edition Published by Prentice- Hall of India, New Delhi, India, 2001.

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[19.] SAS Inc. Statistical Analysis System for window, Version 8.1,Copyrighted by SAS,2000.

[20.] MINITAB Inc. MINITAB software for window Version 14, copyrighted Minitab,2005.

[21.] Upadhyaya HD, Ortiz R, Bramel PJ and S Singh Phenotypic diversity for morphological and agronomic characteristics in chickpea core colloction. Euphytica. 2002; 123: 333-342.

[22.] Vange T Biometrical Studies on Genetics Diversity of Some Upland Rice (Oryza Satiba L.) Accessions; Inter. J. of Food and Agric. Res. 2006: 3 (2).

Adeyemi RA * (1), Gana AS (1) and ST Yusuf (1)

* Corresponding author email: adeyemira@yahoo.ca

(1) Department of Crop Production, School of Agricultural and Agricultural Technology, Federal University of Technology, P.M.B. 65 Minna--Nigeria.
Table 1: Names and sampling locations
of 25 upland rice ecotypes

Code Ecotypes Name Origin

1 WAB 35 - 2 - FX WARDA
2 IDSA 10 Cote d'ivoire
3 IDSA 85 Cote d'ivoire
4 FARO 46 Nigeria/IITA
5 IR47701 - 6- 3-1 Philippines
6 WAB 96 - 1 - 1 WARDA
7 TOX1010-21-5 -12 - 4 -7 Nigeria/IITA
8 IRAT 226 Nigeria/IITA
9 WAB 450 - 224 - 1 WARDA
10 FARO 43 Nigeria/NCRI
11 LAC 23 N/I
12 WAB 450 - 1 - 13-P-163-41 WARDA
13 WAB 450 - 24 - 2 -3 - P-33 WARDA
14 WAB 450 - 1 - 3-P-33-HB WARDA
15 ITA 321 Nigeria/IITA
16 FARO 49 Nigeria/NCRI
17 ITA 323 Nigeria/IITA
18 WAB 99 - 100 WARDA
19 WABC165 WARDA
20 WAB 99 - H- 14 -HB WARDA
21 IRAT 216 Nigeria/IITA
22 WAB 56 - 39 WARDA
23 CAN - 6650 N/I
24 BROWN GORA Nigeria/NCRI
25 WAB32- 133 WARDA

N/I means No information

Table 2: Sixteen agro-morphological traits and abbreviations

Variable s Abbreviation Descriptions

Plant Height PHT Measured from
 the base to the
 tip of the
 tallest plant

Leaf width LWT Measured from
 the middle of
 the leaf using
 five sample per
 plot for test
 entry

Leaf length LLT Measured with a
 meter rule from
 the leaf base
 the tip of the
 leaf

Leaf Area LAR Length of the
 leaf multiply
 by its width
 multiply by
 0.75

First Iron ITSI Score base on
toxicity the standard
score evaluation
 system for rice
 developed by
 IRR (1996) at
 40 days
 transplanting

Second Iron ITS2 Score base on
toxicity score the standard
 evaluation
 system for rice
 developed by
 IRR (1996) at
 60 days after
 transplanting

Panicle per PAM Panicles
meter square counted on five
 hills/plot

AFRGM Score AGRS Galls were
 counted on ten
 hills on each
 plot. The
 percentage was
 determined

No. of tillers/ NTS Numbers of
stand tillers on a
 hill was
 counted using 5
 per test entry
 per replication

Panicle length PAT Measured from
 the base to the
 tip of the last
 grain

No. of spikelet NSP Taken by
 counting the
 number of
 spikelets

Panicle weight PAW Five panicles
 were weighed
 per each
 treatment per
 replicate

No. of panicle NPB Braches were
branches counted on the
 average of five
 selected
 panicle per
 replicates

100 grain 100W Taken weight of
weight 100 grains per
 replication for
 each test entry

Grain length GLT Measured from
 tip to tip of 5
 randomly
 selected grain

Total grain TOT Total grain of
weight all the
 grains/plot
 after threshing

Table 3: Descriptive statistics and analysis of variance
of agro-morphological traits 25 upland rice ecotypes

Variables Min. Max. Mean

Plant Height 4.8 110.4 74.64
Leaf width 0.34 1.32 0.79
Leaf length 11.2 29.6 21.66
Leaf Area 2.86 106 14.49
First Iron Toxicity score 1 5 3.07
Second Iron toxicity score 1 7 5.83
Panicle/[m.sup.2] 40 325 107.20
AFRGM Score 0 110 28.39
Number of tillers/stand 3 23 8.84
Panicle length 10 23 14.88
Number of spikelet 21 96 43.50
Panicle weight 0.304 16.72 6.45
Panicle branch 2.4 8 5.09
100 grain weight 1.57 4.49 2.58
Grain length 4.4 11.4 8.22
Total grain weight 5 155 46.66

 ANOVA
Variables Std.dev (F-value)

Plant Height 17.17 2.085 *
Leaf width 26.58 1.186ns
Leaf length 19.02 1.77 *
Leaf Area 81.56 0.838ns
First Iron Toxicity score 48.64 3.636 **
Second Iron toxicity score 27.70 3.012 **
Panicle/[m.sup.2] 45.38 2.362 **
AFRGM Score 68.03 0.74ns
Number of tillers/stand 39.93 1.032ns
Panicle length 17.91 7.34 **
Number of spikelet 38.06 8.692 **
Panicle weight 46.96 5.561 **
Panicle branch 22.92 8.443 **
100 grain weight 17.95 5.586 **
Grain length 12.96 0.916ns
Total grain weight 73.43 5.157 **

ns--not significant ; * indicates significant at 5% level;
** indicates significant at 1% level across all
cultivars

Table 4: Results of factor analysis (Varimax rotation)

Variances Yield Vegetative Seed
 Contribution factor quality
property

Eigen value 3.8119 1.8801 1.4016
1.1043
Prop. Variance 0.238 0.118 0.088
0.069
Cumm. Prop var. 0.238 0.346 0.434
0.675
Affected traits PAL LFW 100w
 NSP LLT
 PAB PHT
 TOT
 PAW
 ITS2
 PAM

Variances factor Genetic forage
 IV response
property
Eigen value 1.3597 1.2362
1.1043
Prop. Variance 0.085 0.077
0.069
Cumm. Prop var. 0.519 0.596
0.675
Affected traits ITS1 ARGM LAR
 NTS

Table 5: Correlation coefficients of pairs of morphological
traits of 25 upland rice ecotypes

 PHT LWT LLT LAR ITS1

PHT 1.00
LWT 0.23 * 1.00
LLT 0.31 ** 0.46 ** 1.00
LAR 0.08 0.36 ** 0.25 * 1.00
ITS1 0.02 0.01 0.05 0.12 1.00
ITS2 -0.12 0.07 -0.10 0.03 0.12
PAM 0.20 0.06 0.16 0.06 0.09
ARGM 0.03 -0.10 -0.12 0.08 0.20
NST 0.15 0.01 -0.01 0.03 0.13
PLT 0.27 * 0.03 0.23 0.01 0.07
NSP 0.33 ** 0.13 0.29 * 0.13 0.06
PAW 0.16 0.03 0.30 ** 0.03 0.11
PAB 0.15 0.20 0.33 ** 0.01 0.04
100w 0.18 0.08 0.03 0.01 0.07
GLT 0.00 -0.19 -0.16 0.03 0.21
TOT 0.28 * 0.15 0.29 * 0.05 0.14

 ITS2 PAM ARGM NST PLT

PHT
LWT
LLT
LAR
ITS1
ITS2 1.00
PAM -0.21 1.00
ARGM 0.02 -0.13 1.00
NST -0.10 0.20 -0.13 1.00
PLT -0.48 ** 0.30 ** 0.07 0.27 * 1.00
NSP -0.38 ** 0.30 ** 0.10 0.20 0.67 **
PAW -0.45 ** 0.26 * 0.10 0.16 0.54 **
PAB -0.35 ** 0.24 * 0.09 0.20 0.58 **
100w -0.20 -0.21 0.16 -0.09 -0.05
GLT -0.10 ** -0.07 -0.06 0.05 -0.09
TOT -0.33 ** 0.60 ** -0.11 0.29 * 0.56 **

 NSP PAW PAB 100w GLT TOT

PHT
LWT
LLT
LAR
ITS1
ITS2
PAM
ARGM
NST
PLT
NSP 1.00
PAW 0.45 ** 1.00
PAB 0.61 ** 0.34 ** 1.00
100w -0.12 0.08 -0.17 1.00
GLT -0.02 -0.05 -0.13 0.16 1.00
TOT 0.55 ** 0.50 ** 0.48 ** 0.02 0.01 1.0

* Significant at 5% level, ** Significant at 1%level, PHT= plant
height, LWT= leaf width, LLT= leaf length, LAR= leaf area,
ITS1= First Iron toxicity score, ITS2= Second Iron toxicity
Score, PAM= Panicle/[m.sup.2], ARGM=argm score, NTS= Number of
Tillers, PAL=Panicle length, NSP=Number of spikelet, PAW=
panicle weight, PAB= Number of panicle branches, 100w= 100
seed weight, GLT= grain length, TOT= total yield weight

Table 6: Mean and Standard Error of agro-morphological
traits of ecotypes based on class membership formation

Class PHT LWT LLT LAR ITS1 ITS2 PAM ARGM

Cluster 1 72.8 (a) 0.79 21.2 15.0 2.84 6.2 105.0 29.1
1 2 3 6 7 9 1.37 (b) 0.03 0.57 2.2 0.23 0.2 4.68 3.3
 13 14 16 18
 20 21 22 23
 25

Cluster 2 81.8 0.81 22.1 13.7 3.33 5.9 108.9 28.2
 4 10 15 17 2.15 0.05 1.21 1.29 0.33 0.33 14.8 3.44
 19 24

Cluster 3 66.9 0.80 24.5 14.9 3.0 3.0 163.3 29.3
5 8 13.3 0.062 1.21 1.67 0.52 0.73 31.8 6.54

Cluster 4 74.87 0.693 21.02 12.54 4.00 5.67 62.50 23.0
 11 12 3.09 0.058 1.45 1.94 0.447 0.67 9.90 5.44

Class NTS PAL NSP PAW PAB 100W GLT TOT

Cluster 1 8.04 13.9 38.9 5.51 4.86 2.62 8.28 37.0
 1 2 3 6 7 9 0.40 0.26 1.84 0.32 0.17 0.06 0.18 3.36
 13 14 16
 18 20 21
 22 23 25

Cluster 2 10.0 16.4 50.9 6.72 5.27 2.65 8.1 65.2
 4 10 15 17 0.71 0.68 4.42 0.79 0.23 0.15 0.18 8.2
 19 24

Cluster 3 11.3 18.5 59.5 11.9 6.35 2.27 7.77 90.8
 5 8 2.54 1.35 11.4 1.34 o.61 0.05 0.30 23.4

Cluster 4 8.83 14.05 39.80 7.28 5.05 2.42 8.70 19.17
 11 12 2.34 0.82 1.95 0.67 0.34 0.24 0.19 5.33

(a) Mean, (b) Standard Error of mean, PHT = plant height, LWT =
leaf width, LLT = leaf length, LAR = leaf area, ITS1 = Iron
toxicity score Primary, ITS2 = Iron toxicity Score Second, PAM =
Panicle/m2, ARGM = argm score, NTS = No. of Tillers , PAL =
Panicle length, NSP = No. of spikelets, PAW = panicle weight, PAB
= number of panicle branches, 100w = 100 seed weight, GLT = grain
length, TOT = total yield weight
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Article Details
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Author:Adeyemi, Rasheed A.; Gana, A.S.; Yusuf, S.T.
Publication:African Journal of Food, Agriculture, Nutrition and Development
Article Type:Report
Geographic Code:6NIGR
Date:Mar 1, 2011
Words:4635
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