1. Introduction

AJPS

American Journal of Plant Sciences

2158-2742

Scientific Research Publishing

10.4236/ajps.2016.710142

AJPS-69323

Articles

Biomedical&Life Sciences

Genetic Diversity of Quantitative Traits of Sugarcane Genotypes in Ethiopia

Esayas

Tena

¹Firew

Mekbib

²Amsalu

Ayana

Integrated Seed Sector Development Ethiopia Program, Addis Ababa, Ethiopia

Sugar Corporation of Ethiopia, Research and Training, Wonji, Ethiopia

School of Plant Sciences, Haramaya University, Haramaya, Ethiopia

22072016

0710149815206 March 2016accepted 26 July 29 July 2016

2014

This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

Information about the amount and distribution of genetic variation in germplasm collections is important for their efficient management and effective utilization in plant breeding. Therefore this study was conducted to assess genetic diversity of sugarcane germplasm in Ethiopia. An experiment comprising of 400 sugarcane genotypes (174 local and 226 introduced) was conducted between March 2012 and October 2013 at Wonji and Metehara Sugar Estates using partial balanced lattice design with two replications . Data was recorded on 21 quantitative characters which included cane yield and its components, sugar yield and sugar quality traits. ANOVA portrayed highly significant differences (P < 0.01) among the genotypes for 21 quantitative traits. Cluster analysis revealed intra cluster D²values ranging from 2.16 - 10.60 and inter cluster from 7.24 - 5864. There were six principal components accounting for 79.26% of the total variation in the tested materials. Millable stalk count, single cane weight, stalk diameter, cane yield, sugar yield and sugar quality traits showed high positive loading on the first two PCs and accounted for most of the variation observed among the genotypes. Therefore, this study suggested that the important characters responsible for diversity in the sugarcane genotypes could be grouped in two principal components namely “Yield” and “Quality” with “Yield” traits being comparatively more important than “Quality”. Genotypes clustered for high mean values of various traits could be exploited for further improvement of the crop either through selection or through hybridization. The clusters having high mean value for yield could be selected for yield per se as well.

ANOVA Cluster Analysis Local and Introduced Sugarcane Genotypes PCA

1. Introduction

Saccharum is a complex genus characterized by high ploidy levels and composed of at least six distinct species―S. officinarum, S. barberi, S. sinensi, S. spontaneum, S. robustum and S. edule. Accurate assessment of genetic diversity is very important in crop breeding as it helps in the selection of desirable genotypes, identifying diverse parental combination for further improvement through selection in the segregating populations, and introgressing desirable genes from diverse germplasm into the available genetic base. Therefore, genetically diverse germplasm is needed in breeding programs to enhance the productivity and diversity of cultivars. Utilization of introduced germplasm and the knowledge of genetic remoteness among them are vital for their manipulation in crop improvement program [1] . In any breeding program collection of germplasm is always the first step as it provides plant breeders with sources of useful traits. Especially collecting local germplasm would be crucial as they provide locally adapted genes for better crop improvement. Towards this effort, an exploration and collection of local sugarcane germplasm in different geographic regions of Ethiopia has been conducted and more than 300 materials were collected [2] . Documented in a history of the monastery in Northern Ethiopia, it was learnt during this survey that sugarcane had been growing in the country since around 16^th century [2] . It is presumed that sugarcane was introduced into Ethiopia in the 16^th century by the Portuguese with other food crops like rice, banana, lime, mandarin and ginger [3] .

Sugarcane has commercially been grown in Ethiopia for the manufacture of white sugar in the Upper Awash River Basin at Wonji on 5000 ha since 1951 which was started by a Dutch Handles Vereening Ammsterdam (HVA) company [4] . The second sugar estate at Metahara started production in 1969/70 and the third at Fincha in 1998. At present sugarcane is cultivated on 37,000 ha and the four sugar mills in different parts of the country produce about 300,000 ton sugar per annum. Data from the last 10 years (2004-2013) indicated that the average cane yield at Wonj and Metahara ranged from 1300 - 1500 qt/ha and 1700 - 1800 qt/ha, respectively. Similarly, the average sugar percent obtained from the sugar mills indicated 11.5% - 12.5% at Wonji and 10% - 11% at Metahara. Accordingly the sugar yield ranged from 162.5 - 187.5 qt/ha and 187 - 198 qt/ha at Wonji and Metahara respectively.

As it has never had its own breeding program, the sugar industry of Ethiopia has been relying on imported varieties to satisfy the varietal requirements of the sugar cane plantations. So far more than 300 varieties were imported. Currently only 6 to 7 varieties are grown widely and commercially across Ethiopian Sugar Estates. This is because most of the varieties were not adaptable to the local agro ecological conditions of the country. Even the varieties under cultivation now are of old generations and are contracted with many problems and consequently of low yielders. In light of these, the Sugar Corporation of Ethiopia is currently on its way of establishing sugarcane breeding program. Therefore, establishment of good sources of sugarcane germplasm, of both exotic and local origin, and its characterization are of great importance to provide a diverse genetic base and efficient management of the germplasm source for sugarcane improvement program of Ethioipa.

Information about the amount and distribution of genetic variation in the germplasm collections is important for their efficient management and effective utilization in the breeding program. Multivariate statistical analysis techniques like Principal Component Analysis (PCA) and Cluster Analysis techniques could be used for evaluating genetic diversity among sugarcane genotypes. In studies on genetic divergence using cluster analysis, Mahalanobis’ generalized distance (D²) is commonly used as a measurement of proximity [5] due to the fact that characteristics with different measurement units and normally correlated are being considered, the optimization method of Tocher is also frequently used as a clustering algorithm, as described by [6] .

These analyses have been used successfully to study genetic diversity. Reference [7] studied 30 hybrid clones involving Saccharum barberi, S. officinarum, and co-hybrid to evaluate their seven parents to find out the nature and pattern of genetic divergence. The clones were grouped in 15 clusters and grouping of progeny clones was independent of parent cross combination. They concluded that hybridization among clones from diverse clusters may help in isolating progenies with higher sugar yield and its traits. Reference [8] evaluated sugarcane germplasm from field plots of four Saccharum species and four commercial cultivars by means of analysis of sugar composition. Cluster analysis indicated heterogeneity within and among these species. They concluded that information on sugar composition should assist breeders in selecting superior clones for the relevant breeding programs. Ninety-four genotypes of S. spontaneum were studied by [9] for principal component and cluster analysis based on seven quantitative traits of S. spontaneum. The three principal components obtained provided 82.47% cumulative variance. Based on these seven traits, the 94 S. spontaneum genotypes were grouped into 4 clusters.

The present study was conducted to quantify the genetic diversity of quantitative traits using multivariate methods for locally collected and introduced germplasm in Ethiopia.

2. Materials and Methods2.1. Description of the Study Sites and Plant Material

The experiment was conducted at Wonji and Metehara sugar estates during 2012/2013.

2.1.1. Wonji

Wonji Sugar Factory is located in Oromia Regional Government State, Eastern Shewa Zone, Adama Woreda, About 110 km from Addis Ababa and about 10 km south of Adama Town with latitude 8˚31'N and longitude 39˚12'E with elevation of 1550 masl. The average annual rainfall is 800 mm with maximum and minimum temperatures 26.9˚C and 15.3˚C respectively [10] .

2.1.2. Metehara

Metehara sugar factory is located in Oromia Regional Government State, Eastern Shewa Zone about 200 Km from Addis Ababa and about 8 km south of Metehara Town with latitude and longitude 8˚51'N and 39˚52'E respectively and with elevation of 950 masl. Annual rainfall is 554 mm with temperature maximum and minimum of 32.6˚C and 17.5˚C respectively [10] .

2.2. Plant Materials

The plant materials for this study consisted of a total of 400 accessions of which 174 were local sugarcane germplasm collected from different regional states of Ethiopia and 226 were introduced sugarcane germplasm collections maintained at conservation garden of Research and Training, Sugar Corporation, found at Wonji (see Appendix in Supplementary Material available online at http://dx.doi.org/10.4236/ajps.2016.710139). Selection among the local genotypes was made based on geographical regions where the materials were collected and the morphological variations noted during the collection work and when the varieties were quarantined in their collection areas for one year. In exotic/introduced genotypes selection was made taking into consideration the variation in place of origin i.e. source countries and different periods of introductions to the country.

2.3. Experimental Design and Field Layout

The experiment was laid out in 20 × 20 partial balanced lattice design with two replications. Canes were cut into three budded sets and planted in single row plots of 5 m × 1.45 m and 20 cm between plants within a row. Uniform crop management practices like irrigation, cultivation and fertilization were applied to all entries in the trial as recommended for the areas. Urea was applied 2.5 months after planting at a rate of 200 kg∙ha⁻¹ at Wonji and 400 kg∙ha⁻¹ at Metehara. The crop was harvested 20 months after planting as plant cane takes 18 - 20 months to mature at the two sugar estates.

2.4. Data Collected

Data on quantitative stalk characters (Table 1) was recorded viz sprout count 1 and 2 months after planting (SPC1MAP and SPC2MAP), tiller counts 4 and 5 month after planting (TC4MAP and TC5MAP), stalk count 10 months after planting (STC10MAP), hand refractometer brix reading 10 months after planting (HRBrix 10MAP), Millable stalk count per hectare (MSCHA), single cane weight (SCW), number of internode (NOI), internode length (IL), stalk height (SH), stalk diameter (SD), leaf length (LL), leaf width (LW), leaf area (LA), Cane yield per hectare (CYHA), Sugar yield quintal per hectare (SY). Data on juice quality parameters i.e. brix percent (brix%), pol percent (pol%), purity percent (purity%) and sugar percent (SR%) was also recorded. For every accession, ten plants were used for recording data for quantitative characters, which were recorded on plot basis. Count data and cane yield was recorded considering all cane stalks from the whole plot. For quantitative leaf characteristics measurement, a procedure developed by [12] was used.

Table 1 List of quantitative characters recorded in the study

Quantitative Traits	Code	Description
Sprout count	SPC	Number of primary/mother shoot emerged from planted bud
Tiller count	TC	Number of secondary, tertiary, etc shoots emerged from primary shoots
Leaf length (cm)	LL	Length of the third leaf from the flag leaf
Leaf width (cm)	LW	Width of the third leaf from the flag leaf
Leaf area (cm²)	LA	Area of the third leaf from the flag leaf, computed as (leaf length × leaf width × 0.747) suggested by Stickler et al. (1961)
Stalk count 10 month after planting	STC10MAP	The number of millable stalks 10 month after planting
HRBrix percent 10 month after planting	HRBrix10MAP	Hand rifractometer brix reading 10 month after planting
Number of millable canes (count)/plot	MSCPL	stalks with four or more visible internodes at 10 months or after
Number of millable canes (count)/hectare	MSCHA	Number of millable cane produced per hectare calculated from millable stalk count per plot
Stalk thickness/diameter (cm)	SD	Width of stalk at mid internode
Stalk height/Cane length/ (cm)	SH	Height of a sugarcane plant measured from ground level to the top visible dewelap.
Number of internodes (count)	NI	Count of total internodes per plant
Internode length (cm)	IL	Length of the third internode counted from the ground surface
Cane yield per plot (Kg)	CYPL	Weight of cane harvested from an experiment plot
Cane yield per hectare (qt/ha)	CYHA	The weight of millable sugarcane produced per hectare of land or calculated from cane yield per plot
Single cane weight (kg)	SCW	Weight of cane harvested from an experiment plot divided by the number of millable cane per plot
Brix percent	Brix%	Juice Brix refers to the total solids content present in the juice expressed in percentage. Brix includes sugars as well as non-sugars as indicated by a brix hydrometer.
Pol percent/ Juice Sucrose percent	Pol%	The juice sucrose per cent is the actual cane sugar present in the juice determined by reading on the scale of polarimeter.
Purity percent	Purity%	The ratio of pol to brix. Pty = Pol/ Bx × 100
Sugar percent	SR%	Amount of sugar recovered from the cane. Obtained by the formula: = ((pol% − (brix% − pol%) * 0.7)) * 0.75 as described in Winter Carp indirect method of cane juice analysis [11] .
Sugar yield per hectare (qt/ha)	SY	Amount of crystal sugar produced per hectare of land. Obtained by multiplying cane yield per hectare with sugar percent

2.5. Statistical Analysis2.5.1. ANOVA

All the quantitative agro-morphological characters and sugar juice quality parameters considered (Table 1) in the study were statistically analyzed as simple partial balanced lattice design using the statistical procedures described by [13] . Characters with count data were log transformed before analysis [13] . ANOVA was done first separately for the two locations. Combined ANOVA was done over locations after the homogeneity of error variance was tested using the F-max method of [14] , which is based on the ratio of the larger mean square of error (MSE) from the separate analysis of variance to the smaller mean square of error as:

If the larger error mean square is not three-fold larger than the smaller error mean square, the error variance was considered homogeneous [13] .

For characters having significant mean differences, the difference between treatment means was compared using Tukey’s Studentized Range (HSD) Test at 5% of probability. All statistical analyses and data processing was performed using SAS software V9.

2.5.2. Cluster Analysis

Cluster analysis was employed by average linkage method using the appropriate procedure of SAS software V9. Means of each quantitative character were standardised prior to clustering as suggested by [15] to avoid the effect due to difference in scale. The genotypes were grouped into different clusters using Tocher’s method as described by [16] . The resulting cluster was subjected to Mahalanobis’ D² statistics to assess inter and intra divergence among clusters.

2.5.3. Principal Component Analysis

Principal component analysis (PCA) was used as a data reduction tool to summarise the information from phenotypic data so that the influence of noise and outliers on the clustering results is reduced. Principal component analysis was performed on the traits using SAS software V9 in order to study the relationship among the genotypes and to complement and confirm the grouping obtained through cluster analysis [17] [18] .

3. Results and Discussion3.1. Analysis of Variance

Analysis of variance results for 400 genotypes indicated significant differences for all the characters under study (Table 2). All phenotypic traits including sugar quality traits showed highly significant variation revealing a high level of genetic diversity among them. Therefore, the existence of the genetic variability among the studied clones demonstrated a favorable situation to practice the breeding program. This result indicates that there was significant amount of phenotypic variability and all the genotypes differed with each other with regard to the characters that opened a way to proceed for further improvement through simple selection. Genetic variability in germplasm resources is a prerequisite to practice selection [19] [20] . The relatively large genotypic mean squares indicated that clones differed in their potential for the traits. Significant genotype × location interactions for most of the traits revealed that mean performances of the genotypes were influenced by the locations. This interaction was largely due to changes in the relative ranking of the genotypes across the locations which suggest that at this stage evaluating sugarcane genotypes in more locations rather than one may be satisfactory.

Comparative advantages of means of characters of the 5% best selected accessions (Appendix 1) for most of the agronomic traits showed that local varieties collected from different geographic regions of the country had superiority over the standard varieties B52298 and NCO334 and mean of commercial cane cultivars (MCV) (Table 3) and those of the introduced varieties amongst the 5% best selected. Though the sucrose recovery percent was relatively higher for the introduced varieties amongst the best 5% selected, the higher cane yield per plot recorded for the local varieties compensated for their superior sugar yield over the standard varieties and mean of MCV. The local variety Nech Ageda collected from Amhara Region, Debub Welo Zone, Borena Wereda showed the highest sugar yield and 60.66%, 38.13% and 127.85% comparative sugar yield advantage over B52298, NCO334 and MCV respectively.

This variety had the highest stalk count per plot recorded 10 months after planting during which time that is 9 - 10 months after planting when the stalk population stabilizes and the potential number of millable stalk would be known. The highest cane yield was also recorded for this variety.

Relatively higher tiller counts per plot four and five months after planting was recorded for the local varieties Ye Beskula Shenkora, Nech Kechacha Shenkora/Getr, Moris and Engda and among introduced varieties like CO810, CO991, CP72/2083, DB386/60 showed higher tiller counts (Appendix 1). The highest millable stalk count at harvest was recorded for B4425, B45154, CO842, B4906, CO957, Ye Beskula Shenkora, Nech Ageda, Aladi, and Moris. With regard to cane yield among the 5% best selected (20 clones) 18 were local varieties and only two introduced varieties namely B4425 and N55/805. This was also true in measure of single cane weight where 17 of the 20 selected were local varieties. Relatively higher inter node counts were recorded for the local clones whereas higher inter node length was observed in the introduced varieties. Among the 20 best selected

Table 2 Analysis of variance for morphological and juice quality traits of sugarcane tested over two locations (Wonji and Metehara 2012/2013)

Characters^†	Location	Replication	block(Replication)	Accession	Location*Accession	Error	CV (%)
	(1)	(1)	(19)	(380)	(399)	(780)
SPC1MAP	10.675**	6.887*	1.102^ns	1.978**	1.938**	1.055	49.24
SPC2MAP	386.921**	20.108**	1.240^ns	1.498**	1.237*	1.044	35.89
TC4MAP	567.169**	23.802**	0.983^ns	1.875**	1.237**	0.957	25.27
TC5MAP	44.092**	0.082^ns	0.626^ns	0.953**	0.919**	0.559	18.63
STC10MAP	46.119**	17.248**	0.372^ns	1.066**	0.616**	0.289	13.88
HRBrix10MAP	591.961**	50.116**	3.531^ns	3.138^ns	3.430^ns	3.424	12.10
MSCHA	40.712**	3.074**	0.409*	1.196**	0.590**	0.252	4.49
SCW	1.180**	3.156**	0.145^ns	0.517**	0.166**	0.123	23.12
NOI	779.806**	250.431**	18.531^ns	52.037**	26.659**	14.623	13.57
IL	20.473^ns	1.962^ns	7.351^ns	15.618**	10.229^ns	9.235	34.35
SH	88352.063**	9279.902**	640.917^ns	3337.181**	1243.085**	872.007	12.29
SD	5.050**	0.001^ns	0.042^ns	0.337**	0.089**	0.060	9.18
LL	8953.181**	5978.962**	212.970^ns	526.532**	359.608**	189.594	10.76
LW	0.483^ns	27.152**	0.483^ns	1.844**	0.792**	0.622	18.39
LA	132290.602**	545322.802**	6137.660^ns	25433.571**	12832.068**	9734.180	23.89
CYHA	5857126.000**	7375569.600**	284712.100^ns	1667648.400**	619916.600**	279325.000	39.12
Brix	4.364^ns	24.310**	1.271^ns	5.225**	2.164*	1.791	6.90
Pol	34.281**	32.627**	1.499^ns	6.202**	2.410**	1.861	7.52
Purity	354.399**	19.678^ns	7.269^ns	13.471**	8.545**	6.697	2.77
SR	61.297**	20.338**	1.036^ns	4.052**	1.601**	1.199	8.44
SY	38976.630**	79538.100**	5466.260^ns	30595.530**	11103.880**	5298.920	41.00

^†SPC1MAP and SPC2MAP = Sprout count 1 and 2 months after planting; TC4MAP and TC5MAP = Tiller counts 4 and 5 month after planting; STC10MAP = Stalk count 10 months after planting; HRBrix10MAP = Hand rifractometer brix reading 10 months after planting; MSCHA = Millable stalk count per hectare; SCW = Single cane weight (Kg); NOI = Number of internode; IL = Internode length (cm); SH = Stalk height (cm); SD = Stalk diameter (cm); LL = Leaf length (cm); LW = Leaf width (cm) LA = Leaf area (cm²; CYHA = Cane yield (qt/ha); Brix = Brix percent; Pol = Pol percent; Purity = Purity percent; SR = Sugar percent; SY = Sugar yield (qt/ha); *P = 0.05, **P < 0.01, ns = non significant, numbers in parenthesis are degrees of freedom.

Table 3 Mean of 21 quantitative characters* for 10 commercial varieties in Ethiopian sugar estates

Accessions	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O	P	Q	R	S	T	U
B 41227	30	126	118	51	33	16.00	119310	1.42	28	28.00	246.70	2.30	119.00	3.30	392.26	1962.10	18.59	17.48	93.85	12.53	269.02
B 52298	18	33	70	67	75	15.45	122414	1.77	26	7.68	197.15	2.76	147.63	4.28	470.29	2169.25	20.23	18.80	92.92	13.40	289.75
CO 449	9	22	33	55	45	15.05	105172	0.98	23	10.84	244.73	2.34	135.80	4.49	456.73	995.00	19.66	18.45	93.79	13.24	135.00
CO 678	2	11	49	44	35	16.37	41379	1.01	19	9.89	192.33	2.48	135.30	4.13	413.25	475.75	16.64	14.77	88.65	10.16	51.25
CO 680	10	22	46	51	46	14.76	56207	2.04	31	7.91	243.35	2.65	136.55	4.72	482.91	1160.50	20.40	19.15	93.81	13.75	162.75
CO 740	3	23	42	95	41	15.12	74828	1.57	29	8.42	235.38	2.77	110.20	4.66	385.05	1257.75	19.73	18.17	92.12	12.86	161.25
DB 377/60	13	32	63	58	59	16.50	79483	1.41	30	7.82	231.89	3.04	136.38	4.36	450.66	1106.50	19.76	18.30	92.47	13.00	144.50
Mex 54/245	8	5	40	49	42	14.76	95517	1.78	27	10.12	256.68	2.81	123.75	5.56	518.19	1710.50	19.96	18.59	93.05	13.24	230.00
N 14	18	25	85	90	76	17.26	113104	1.61	29	8.69	250.40	2.53	137.10	3.76	383.62	1819.25	20.82	19.91	95.61	14.48	262.50
NCO 334	6	37	134	62	109	13.51	166207	1.46	27	9.06	239.75	2.83	133.75	4.98	665.12	2424.50	19.96	19.15	95.98	13.94	337.00
Mean	12	33	68	62	56	15.48	97362	1.50	27	10.84	233.83	2.65	131.55	4.42	461.81	1508.11	19.57	18.27	93.23	13.06	204.30

*A = Sprout count 1 month after planting; B = Sprout count 2 month after planting; C = Tiller count 4 month after planting; D = Tiller count 5 month after planting; E = Millable stalk count 10 month after planting; F = Hand refractometer brix reading 10 month after planting; G = Millable stalk count per hectare at harvest; H = Single cane weight (kg); I = Number of internodes; J = Internode length (cm); K = Stalk height (cm); L = Stalk dimeter (cm); M = Leaf length (cm); N = Leaf width (cm); O = Leaf area (cm²); P = Cane yield (qt/ha); Q = Labratory brix%; R = Pol%; S = Purity%; T = Sugar%; U = Sugar yield (qt/ha).

(5%) for stalk diameter 16 were local varieties where medium thick stalk diameters ranging from 3 - 3.5 [21] was recorded. The highest and lowest stalk diameter was recorded for the local varieties Kay Sidancho and Nech Ye Abesha Shenkora respectively. In terms of leaf area the standard variety NCO334 scored the highest value followed by the local varieties Ye Kenya Ageda and Nech Shenkora (code 35 as in Appendix 1). Among the best 5% selected, higher values of brix%, pol%, purity% and sugar% were recorded mostly for the introduced varieties.

This information helps to determine the genetic variability and contribution of some morphological traits in cane yield and sucrose recovery and can largely facilitate the formulation of appropriate selection strategies to develop the clones of best commercial merits, which are suitable for the cultivation in different climate zones.

3.2. Cluster Analysis

Cluster (segmentation) analysis for phenotypic traits showed a clear demarcation between sugarcane accessions (Table 4). Furthermore, Table 5 showed differences among clusters by summarizing cluster means for the 21 quantitative traits. Based on these traits, the accessions were grouped into different clusters. The dendrogram divided the accessions into nineteen main clusters and a singleton. The first cluster included 136 genotypes out of which 62 were introduced while the rest 74 were local clones. This indicated that these local genotypes have close similarity with the group of exotic sugarcane accessions belonging to this group. This cluster is characterized by accessions having HRBrix10MAP, number of internodes, leaf length values close to the grand mean. Furthermore, it has brix, pol, purity and sugar percent greater than the grand mean averaged over all clusters. Cluster two consisted of 120 accessions where 67 were introduced and 53 local accessions. The genotypes in this cluster demonstrated values greater than the grand mean for most of the traits which included millable stalk number, cane yield, single cane weight, stalk height, stalk diameter, leaf area, brix, pol, purity and sugar present and sugar yield. Genotypes in this cluster could contribute in the future breeding program with regard to these traits. Cluster three had only six local accessions out of the total 80, which were collected from different geographic regions of the country. This indicated that these accessions had genetic similarity with the rest of exotic accessions within the cluster. TC5MAP, STC10MAP, HRBrix10MAP, MSCHA, SH, brix%, pol%, purity% and SR% had values greater than the grand mean in this cluster.

Cluster four comprised seventeen accessions all of which were local accessions. This accessions, though collected from different geographic regions of the country, they tend to cluster together indicating source of origin is not the criteria for clustering. Amazingly these genotypes had 18 of the 23 quantitative traits with means greater than the grand mean averaged over all the 20 clusters. Out of these traits TC5MAP, SCW, SH, SD, LL, CYHA and SY had the second largest means from all the clusters. These genotypes reliably would be major contributors to improve these traits in the crossing programs. Cluster five consisted of seven accessions four of which were foreign varieties namely CP 1/441, M112/34, M377/5, Mex53/142 introduced from three source countries i.e. Canal Point, Mauritius and Mexico, respectively. The other three accessions America, Nech Shenkora /Shenkora Adi and Nech Shenkora were local collections from three different regions in the country SNNP, Oromia and Amhara. This cluster had accessions with stalk height, stalk diameter, leaf width, brix, pol, purity, and sugar percent which had values greater than the grand mean and a mean leaf area comparable to the grand mean. Cluster six had eight genotypes all locals except one exotic accession CO945 form Coimbatore, India. This variety should have close similarity with the local accessions with which it cluster together. Accessions in this cluster had mean values greater than the grand mean for number of internode, stalk diameter, leaf length and width and leaf area. Other traits had means lower than the grand mean. Cluster seven contained four exotic accessions B45154 and B58230 from Barbados and CO842 and CO957 from Coimbatore, India. These varieties might share same parents in their genealogical history; this could be the reason for their clustering together. The genotypes in this cluster showed mean performance greater than the grand mean for tiller counts 4 and 5 months after planting, stalk count 10 month after planting, millable stalk number, cane yield, internode length, stalk height and leaf length. However, they had low single cane weight. Furthermore, they had lower means than the grand mean for all sugar quality parameters.

Cluster eight consisted of five local accessions collected from different parts of the country. No exotic variety has clustered with these clones. These accessions demonstrated the shortest internode length, shortest stalk height, the narrowest stalk diameter, lowest single cane weight, narrowest leaf width and the lowest leaf area of all the clusters. They have also showed lower mean than the grand mean for all the traits including sugar quality

Table 4 Clustering of 400 sugarcane genotypes into twenty clusters using mean of 21 quantitative characters (numbers refer to code of genotypes (see Appendix in Supplementary Material available online at http://dx.doi.org/10.4236/ajps.2016.710139)

Cluster	No. of genotypes	Genotypes
C₁	136	32, 49, 76, 93, 15, 113, 69, 74, 36, 33, 53, 134, 371, 84, 101, 26, 118, 198, 48, 99, 23, 41, 14, 265, 97, 429, 21, 180, 379, 47, 183, 54, 232, 152 165, 266, 360, 71, 78, 110, 115, 37, 166, 215, 227, 131, 98, 392, 45, 162, 400, 412, 182, 223, 295, 174, 197, 172, 6, 333, 417, 199, 246, 280, 239, 393, 127, 394, 238, 437, 34, 171, 230, 240, 116, 359, 44, 175, 196, 288, 144, 25, 121, 409, 123, 217, 252, 374, 119, 132, 107, 126, 105, 269, 299, 178, 184, 179, 388, 362, 408, 231, 440, 35, 5, 173, 11, 31, 287, 315, 302, 439, 237, 250, 257, 435, 436, 390, 347, 310, 19, 111, 117, 208, 133, 2, 10, 59, 378, 283, 150, 330, 404, 234, 209, 421
C₂	120	9, 170, 72, 80, 247, 383, 91, 106, 70, 68, 289, 281, 423, 219, 340, 274, 85, 30, 206, 366, 143, 334, 94, 216, 42, 271, 142, 161, 399, 441, 403, 406, 79, 83, 39, 81, 195, 292, 158, 351, 62, 186, 233, 160, 418, 270, 415, 309, 373, 248, 370, 332, 380, 29, 188, 89, 327, 12, 290, 372, 255, 314, 308, 303, 114, 224, 176, 51, 103, 122, 192, 324, 343, 229, 300, 348, 73, 92, 331, 46, 3, 254, 363, 304, 82, 241, 401, 141, 136, 228, 187, 364, 63, 163, 24, 275, 356, 124, 226, 235, 50, 307, 75, 87, 311, 318, 395, 276, 317, 305, 422, 432, 253, 177, 27, 156, 191, 56, 427, 20
C₃	80	398, 402, 419, 428, 272, 312, 244, 286, 405, 410, 326, 349, 282, 396, 251, 365, 213, 339, 204, 298, 245, 341, 263, 431, 212, 220, 211, 301, 325, 367, 90, 243, 221, 420, 354, 323, 338, 222, 345, 335, 214, 64, 278, 218, 337, 321, 320, 202, 382, 264, 328, 242, 355, 357, 361, 268, 350, 194, 260, 306, 344, 154, 96, 397, 8, 13, 313, 384, 259, 353, 207, 426, 381, 377, 193, 249, 368, 201, 433, 225
C₄	17	40, 159, 60, 4, 7, 140, 189, 164, 190, 149, 139, 151, 100, 67, 77, 38, 1
C₅	7	129, 389, 346, 22, 58, 386, 391
C₆	8	145, 167, 55, 65, 57, 185, 61, 294
C₇	4	205, 296, 291, 236
C₈	5	146, 155, 18, 128, 16
C₉	3	293, 430, 28
C₁₀	3	138, 157, 43
C₁₁	2	297, 385
C₁₂	3	169, 407, 66
C₁₃	2	267, 434
C₁₄	3	153, 203, 210
C₁₅	2	279, 425
C₁₆	1	273
C₁₇	1	424
C₁₈	1	181
C₁₉	1	104
C₂₀	1	200

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