Interrelationship and Path analysis of Tuber yield and related traits in yam (Dioscorea spp.) from Ethiopia

 

M. Tewodros1*, M. Firew2, H. Shimelis3, G. Endale4

1Jimma Agricultural Research Center, P.O. Box 192, Jimma, Ethiopia,

2Haramaya University, School of Plant Sciences, P.O. Box 138, Dire Dawa, Ethiopia

3African Centre for Crop Improvement, School of Agriculture, Earth and Environmental Sciences,

University of Kwa Zulu-Natal, Private Bag X01, Scottsville, 3209, Pietermaritzburg, South Africa.

4Ethiopian Institute of Agricultural Research, P.O. Box 2003, Addis Ababa, Ethiopia

*Corresponding Author E-mail: tewodrosmulualem@gmail.com

 

ABSTRACT:

Understanding the nature of associations among economically important traits is essential to improve selection efficiency in plant breeding programs. This study aimed to determine the magnitude of association between tuber yield and related traits and to identify the most influential character(s) involving 36 landrace collections of yams for effective selection and conservation. Field evaluations were conducted at Jimma Agricultural Research Center in Ethiopia using a 6x6 lattice design with two replications during 2015. Data on 12 qualitative and 19 quantitative traits were collected and subjected to analysis of variance, correlation and path analyses. Highly significant differences (p<0.01) were detected among collections for the studied traits. Significant and positive correlations were detected between tuber fresh weight (TFW) with vine length (VL), days to maturity (DM) and tuber diameter (TDi). Tuber length (TL) positively and significantly correlated with leaf length (LL), vine length (VL) and internodes length (IL). Tuber diameter (TDi) positively correlated with LL, TuL, IL, DM and leaf width (LW). Leaf color (LC), leaf size (LSi), petiole color (PC), vine color (VC), tuber skin color (TSC) and tuber flesh color (TFC) had significantly negative correlations with TFW and TuL. Path analysis revealed high direct path coefficient value (1.112) between LL and TuL. Also, positive direct path coefficient value (1.018) was exhibited between DM and TFW. Relatively high direct path coefficient value (0.356) was exhibited between leaf shape (LS) and TuL. This study revealed that selection for increased above ground biomass and days to maturity may improve genetic gain in storage tuber yield and length of tuber in yam breeding. Using the overall analyses, the following collections such as: 27/02, 56/76, 08/02, 10/002,39/87, 45/03,6/02,116, and 7/83 were selected for breeding and conservation.

 

KEYWORDS: Correlation analysis, path coefficients, yam, tuber yield.

 

 

1.    INTRODUCTION:

Yam (Dioscorea species) belonging to the family Dioscoreaceae is cultivated in Africa, Asia, South America, Caribbean and the South Pacific islands for its storage tubers (Asieduand Alieu, 2010). The genus comprises over 600 species (Sesay et al., 2013), Only 10 are cultivated for human food and medicinal values such as Dioscore aalata, D. esculenta, D. batatas or D. opposite, D. bulbifera, D. cayenensis, D. rotundata complex, D. dumetorum, D. trifida, D. nummularia and D. pentaphylla (Girma et al. 2012; Dansi et al., 2013). Only few species like D. alata, D. bulbifera, D. cayenensis and, D. rotundata complex are the most widely cultivated species in Africa with greater economic significance (Lebot, 2009; Asiedu and Alieu, 2010; Norman et al., 2012). Ethiopia is considered to be the center of origin and diversity of most yam species (Coursey, 1967; Zeven and De Wet, 1982; Tamiru et al., 2011). In the country a large number of yam landraces are cultivated but no systematic genetic conservation has yet been done (Hildebrand et al., 2003). In south west Ethiopia, yams are predominantly grown by smallholder farmers for food, traditional medicine, and to earn cash incomes. Women farmers are the main producers and traders of yams in Ethiopia. Yam is increasingly showing high market value owing to its consumer demand for food and medicine purposes making it an ideal candidate for market-driven production (Mulualem and Weldemichel, 2013).

 

Yams have low tuber yield levels in Ethiopia estimated at 4 t/ha (Tamiru, 2011) when compared to West African countries where yields of 25 t/ha are reported (Asiedu and Alieu, 2010). Therefore, systematic genetic improvement of the crop is required in Ethiopia to boot productivity. A well-characterized genetic pool is required to develop high yielding yam varieties incorporating farmers’ and market demand (Dansi et al., 2013). Therefore, assessment of genetic variability and association between agro-morphological traits in the existing genetic resources remain a key component of yam breeding and conservation programs (Norman et al., 2011).

 

Tuber yield and yield components are important selection criteria of promising yam genetic resources. Tuber yield is directly or indirectly affected by yield components requiring selection of relevant traits positively correlated with it (Mulualem et al., 2013). Therefore, knowledge on the interrelationship and degree of association of yield and yield components is useful to improve selection efficiency in yam breeding and conservation efforts. Simple correlation analysis describes the mutual associations of variables without regard to cause and effect inter-relationship.

 

Component characters during selection are often inter-dependent showing direct or indirect interrelationships and influencing selection efficiency for yield and yield components. Simple correlation coefficients can be misleading if a high correlation between two traits is a consequence of the indirect effect of other traits (Dewey and Lu, 1959). Path coefficient analysis is a standardized partial regression which was developed by Wright (1921) and later described by several authors (Wright, 1934; Li, 1956; Bhatt, 1973; Mohammadi et al., 2003). Unlike simple correlation analysis, path coefficient analysis separates the total correlation coefficients into the direct and indirect effects in order to make selection more effective (Dewey and Lu, 1959; Falconer and Mackey, 1996). Path coefficients give the relative contribution of various yield determining traits, enabling breeders to decide between direct and indirect selection procedures (Paul et al., 2013). The direct and indirect effects of traits on economic yield can be determined through path analysis which has been used in a number of crops to study the relationships between yield and yield components (Christopher, 2000). There is no information regarding the application of path analysis in the selection and conservation of yam genotypes. This information could provide valuable insight when evaluating diverse genotypes of yam for targeted breeding. Further, isolating the most influential yam traits would enhance the selection responses in yam improvement programs. Therefore, the objectives of this study were to determine the magnitude of association between tuber yield and related traits and to identify the most influential character (s) involving 36 landrace collections of yams for effective selection and conservation.

 

2.    MATERIALS AND METHODS:

2.1. Study area:

The experiment was conducted at Jimma Agricultural Research Center (JARC). The center is located at latitude 7o 40.00' N and longitude 36o 47’.00’ E with an altitude of 1753 meters above sea level (m.a.s.l.). The area receives mean annual rainfall of 1432mm with mean maximum and minimum temperatures of 29.20C and 8.900C, respectively. The soil at the study site is Eutric Nitosol (reddish brown) with pH of 5.3. These environmental conditions are conducive for yam cultivation.

 

2.2. Plant materials, experimental design and field management:

A total of 36 yam landraces was collected from Jimma, Sheka and Bench-maji zones of southwest Ethiopia (Table 1). The experiment was laid out in a 6 x 6 simple lattice design with two replications. Plants were field established using a 7 m long rows using inter-row spacing of 1.5 m and intra-rows spacing of 1 m. Tubers of the same size which started sprouting were used as planting material. One month after planting, seedlings were earthed up followed by frequent weeding. All other agronomical practices were followed according to the recommendations and farmers practices of the areas. Each yam plant was tended using dried coffee sticks of 3.5-4.5 m long to provide support and induce good canopy and vine development. Five middle plants within a row were sampled and tagged for data collection and final harvest.

2.3. Data collection:

Both qualitative and quantitative data were collected according to the descriptors of yam (Dioscorea spp.) developed by Bioversity International (IPGRI, 1997). The qualitative data collected with their descriptors are summarized in Table 2. These traits are the most important farmers preferred attributes of yam landraces for various purposes. For example, leaf color, leaf shape, leaf size, leaf density, petiole color, twinge direction, vine color, tuber shape, tuber skin color, tuber branching, tuber surface texture and tuber flesh color. Data on 19 quantitative traits were collected as follows.

 

2.3.1. Data collected on five selected plants:

·       Leaf length (cm): the mean length of fully expanded young leaves was measured on the main vine from collar to the tip of the leaves at maturity.

·       Leaf width (cm): the mean width of fully expanded young leaves was measured on the main vine from widest part at maturity.

·       Length of leaf lobe (cm): the mean length of five fully expanded leaf lobs from the main vine of leaves was measured from the middle at maturity.

·       Petiole length (cm): the mean length of petioles on the main vine was measured from the base to the point of insertion of the leaf lob at maturity.

·       Distance between lobs (cm): the mean distance of fully expanded leaf lobs from the main vine of leaves was measured from the base at maturity.

·       Vine length (cm): the length of the longest vine was measured with a tape from the ground level to the tip at the time of maturity.

·       Number of tubers plant-1: counted from five plants and the mean calculated

·       Tuber length (cm): measured from the lower to the upper tip using venire caliper at harvest.

·       Tuber diameter (cm): measured at the middle using venire caliper at harvest.

·       Internodes length (cm): mean length was measured from the middle of vine at maturity.

·       Number of internodes vine-1: counted from five plants and the mean calculated

·       Number of vines hill-1: counted at maturity.

·       Tip length (cm): the mean length of tip was measured from the apex at maturity.

 

2.3.2. Data collected on plot basis:

·       Vine fresh weight: the vine of all the plants from each plot were bulked and weighed at harvest and the vine fresh weight/plot was then converted to tonnes per hectare (t/ha).

·       Vine dry weight: the total vine dry weight was estimated by drying 100gm of fresh vine in a forced air circulation oven at 700C for about 72 hours and expressed in tonnes per hectare (t/ha) of the vine fresh weight.

·       Tuber fresh weight: the tuber of all the plants of each plot were bulked and weighed at harvest and the tuber fresh weight/plot was then converted to tonnes per hectare (t/ha).

·       Tuber dry weight: the total vine dry weight was estimated by drying 100gram of fresh vine in a forced air circulation oven at 700C for about 72 hours and expressed in tonnes per hectare (t/ha) of the tuber fresh weight.

·       Days to maturity: was recorded from date of emergence to the date when the crop was ready for harvesting, i.e. tubers had become mature and the plant had started drying.

·       Harvest Index (%): was calculated as a ratio of total marketable tuber yield to the sum of biomass and total marketable tuber yield and expressed in percentage.

 

2.4. Data analysis:

Qualitative data were analyzed using Statistical Package for Social Sciences 16.0 (SPSS, 1996). Quantitative data were subjected to analysis of variance (ANOVA) using the lattice procedure as suggested by Gomez and Gomez (1984) using SAS version 9.0 (SAS, 2000) and Genres (2008) statistical software packages. Means were separated using the Least Significant Difference (LSD) procedure at the 5% and 1% level of significance. The Spearman’s rank non-parametric correlation coefficients were calculated to describe the degree and pattern of associations of observed qualitative and quantitative traits of yam landraces.

 

Path coefficient analysis was conducted using a genotypic correlation matrix set up as G= K×L both for tuber fresh weight and tuber length. In this matrix vector ‘G’ represents the genotypic correlation coefficients of storage tuber fresh weight (a) or tuber length vs. qualitative and quantitative traits. In the same vector ‘K’ is the value of genotypic correlation for all possible combinations among the traits and vector ‘L’, the path coefficients. The path coefficients were calculated as the product of vector G and each row of K-1using the Genres (2008) statistical software. Direct and indirect path coefficients were calculated for both qualitative and quantitative traits as proposed by Wright (1934) and later described by Williams et al. (1990) using genotypic correlation coefficients. For path analysis of quantitative traits, tuber fresh weight (TFW) and tuber length (TuL) were considered as response variables whereas, leaf length, leaf width, petiole length, vine length, internodes length, number of internodes per vine, days to maturity, number of tubers per hill, TuL, tuber diameter, TFW, tuber dry weight and harvest index were considered as causal variables. For path analysis of qualitative traits, TFW and TuL were considered as response variables. Furthermore, leaf color, leaf shape, leaf size, leaf density, petiole color, twinge direction, vine color, tuber shape, tuber skin color, tuber branching, tuber surface texture and tuber flesh color were regarded as casual variables.


 

Table 1: Descriptions the 36 yam accessions used for the study.

S. No.

Name of landraces

Zone

District

Latitude

Longitude

Altitude

1

59/02

Jimma

Mana

07040’37N

036049’10E

1718

2

68/01

Jimma

Dedo

07030’63N

036053’45E

1784

3

6/02

Bench maji

Sheko

06059’66N

035034’11E

1728

4

75/02

Jimma

Kersa

07040’43N

036048’76E

1734

5

3/87

Jimma

Manna

07040’58N

036048’75E

1731

6

56/76

Jimma

Manna

07041’89N

036048’06E

1837

7

54/02

Bench maji

Sheko

07002’03N

035032’77E

1892

8

46/83

Jimma

Dedo

07031’28N

036053’59E

1771

9

08/02

Jimma

Kersa

07040’46N

036048’79E

1740

10

116

Jimma

Dedo

07031’28N

036053’63E

1683

11

01/75

Sheka

Yeki

07011’30N

035026’22E

1171

12

06/83

Jimma

Dedo

07031’32N

036053’64E

1692

13

17/02

Sheka

Yeki

07011’27N

035026’26E

1176

14

07/03

Jimma

Dedo

07031’50N

036053’60E

1733

15

45/03

Jimma

Mana

07041’86N

036048’08E

1810

16

27/02

Jimma

Sekachekorsa

07035’06N

036041’91E

1877

17

37/87

Jimma

Mana

07041’87N

036048’13E

1940

18

10/002

Bench maji

Sheko

07002’91N

035029’76E

1668

19

76/02

Jimma

Kersa

07040’64N

036048’84E

1728

20

06/2000

Jimma

Sekachekorsa

07035’43N

036041’86E

1850

21

7/83

Jimma

Sekachekorsa

07035’06N

036041’91E

1898

22

58/02

Sheka

Yeki

07011’22N

035026’25E

1192

23

39/87

Jimma

Sekachekorsa

07035’42N

036042’94E

1885

24

32/83

Jimma

Shebesombo

07026’74N

036024’01E

1372

25

24/02

Jimma

Shebesombo

07026’75N

036024’07E

1379

26

2/87

Jimma

Shebesombo

07026’76N

036024’12E

1365

27

60/87

Sheka

Yeki

07011’72N

035026’48E

1199

28

15/2000

Bench maji

Sheko

07004’13N

035037’74E

1320

29

34/87

Jimma

Dedo

07031’37N

036053’44E

1911

30

21/02

Jimma

Sekachekorsa

07036’48N

036045’09E

1785

31

57/76

Bench maji

Sheko

07002’88N

035029’74E

1654

32

0001/07

Jimma

Shebesombo

07026’74N

036024’12E

1367

33

0004/07

Jimma

Kersa

07040’55N

036048’75E

1741

34

7/84

Bench maji

Sheko

07002’88N

035029’74E

1661

35

7/85

Sheka

Yeki

07014’30N

035026’17E

1173

36

06/2001

Bench maji

Sheko

06059’69N

035034’09E

1387

 

Table 2: List of qualitative traits with detailed descriptions used in the study.

Qualitative traits

Description

Acronym

Leaf color

1= yellow green, 2= pale green, 3=dark green, 4= purplish green, 5=purple, 99=other

LC

Leaf shape

1= ovate, 2= chordate 3= chordate long, 4=chordate broad, 5= sagittate

LS

Leaf size

1= small, 2= medium, 3= large

LSi

Leaf density

1= low, 2= medium, 3= high

LD

Petiole color

1= all green with purple base, 2= all green with purple leaf junction, 3= all green with purple at both ends, 4= all purplish green with purple base, 5= all purplish green with purple leaf junction, 6= all purplish green with purple at both ends, 7= green, 8= purple, 9= brownish green, 10= brown, 11= dark brown, 99=other.

PC

Twinge direction

1= clockwise, 2= anticlockwise

TD

Vine color

1= yellowish, 2= green, 3= light green, 4=purple, 99=other

VC

Tuber shape

1= round, 2= oval, 3= oval-oblong, 4= cylindrical, 5= flattened, 6= irregular,

TS

Tuber skin color

1= grayish, 2= light brown, 3=dark brown, 4= 0ther

TSC

Tuber branching

0= none, 1= slightly branched, 2= branched, 3= highly branched

TBr

Tuber surface texture

1= smooth, 2= rough

TSte

Tuber flesh color

1= white, 2= yellowish white, 3= yellow, 4= orange, 5= light purple, 6= purple 7= purple with white, 8= white with purple, 9= outer purple/ inner white, 99= other

TFC

 


3.    RESULTS:

3.1. Characterization of yam landraces using qualitative trait:

The common qualitative traits observed among the tested yam collections are presented in Table 3. The most farmers-preferred qualitative traits of yam include: TS, irregular, cylindrical and oval tuber shape. The following landraces show these traits: 59/02, 6/02, 08/02, 01/75, 45/03, 37/87, 76/02, 7/83, 32/83, 2/87, 57/76, 0004/07 and 7/84 which are valued for food and medicine. The yam landraces with cylindrical and oval tuber shape that fetch high market values included: 68/01, 75/02, 56/76, 3/87, 07/03, 17/02, 10/002,06/2000, 24/02,15/2000, 21/02 and 7/85.


 

Table 3: Mean response of 36 yam landraces evaluated for qualitative traits.

No

 

Qualitative parameters

Name of landraces

LC

LS

Lsi

LD

PC

TDI

VC

TS

TSC

TBr

TSt

TFC

1

59/02

1.0

1.0

3.0

2.0

1.0

1.0

3.0

6.0

2.0

1.0

2.0

7.0

2

68/01

3.0

5.0

1.0

3.0

5.0

1.0

2.0

4.0

6.0

2.0

1.0

8.0

3

6/02

3.0

2.0

1.0

3.0

4.0

1.0

2.0

6.0

2.0

3.0

2.0

1.0

4

75/02

3.0

2.0

1.0

3.0

2.0

1.0

2.0

4.0

2.0

3.0

2.0

1.0

5

3/87

3.0

2.0

2.0

3.0

7.0

1.0

2.0

4.0

2.0

3.0

2.0

6.0

6

56/76

2.0

5.0

1.0

3.0

1.0

2.0

3.0

2.0

3.0

3.0

2.0

1.0

7

54/02

3.0

2.0

1.0

3.0

5.0

1.0

3.0

3.0

2.0

2.0

1.0

6.0

8

46/83

3.0

2.0

3.0

2.0

4.0

1.0

2.0

3.0

2.0

0.0

2.0

9.0

9

08/02

1.0

2.0

1.0

3.0

4.0

1.0

2.0

6.0

3.0

2.0

2.0

1.0

10

116

3.0

2.0

1.0

3.0

7.0

1.0

2.0

5.0

3.0

2.0

2.0

9.0

11

01/75

1.0

2.0

1.0

3.0

2.0

1.0

2.0

6.0

3.0

2.0

1.0

6.0

12

06/83

3.0

2.0

3.0

2.0

4.0

1.0

2.0

3.0

2.0

3.0

2.0

1.0

13

17/02

1.0

2.0

2.0

3.0

2.0

1.0

2.0

4.0

2.0

0.0

2.0

7.0

14

07/03

3.0

2.0

2.0

3.0

4.0

1.0

2.0

4.0

2.0

3.0

2.0

8.0

15

45/03

2.0

5.0

2.0

3.0

1.0

1.0

3.0

6.0

2.0

2.0

2.0

8.0

16

27/02

3.0

2.0

2.0

3.0

4.0

1.0

3.0

4.0

3.0

2.0

2.0

1.0

17

37/87

3.0

5.0

2.0

2.0

4.0

1.0

3.0

6.0

3.0

3.0

2.0

9.0

18

10/002

3.0

1.0

2.0

3.0

7.0

1.0

3.0

4.0

2.0

2.0

2.0

7.0

19

76/02

3.0

2.0

2.0

3.0

6.0

1.0

3.0

6.0

2.0

2.0

2.0

1.0

20

06/2000

1.0

2.0

2.0

3.0

6.0

1.0

3.0

4.0

3.0

2.0

1.0

8.0

21

7/83

3.0

5.0

2.0

3.0

6.0

1.0

2.0

6.0

2.0

0.0

2.0

1.0

22

58/02

3.0

2.0

2.0

2.0

6.0

1.0

3.0

4.0

3.0

2.0

2.0

8.0

23

39/87

2.0

2.0

3.0

3.0

4.0

1.0

3.0

3.0

3.0

2.0

2.0

8.0

24

32/83

2.0

1.0

2.0

3.0

4.0

1.0

3.0

6.0

3.0

2.0

1.0

6.0

25

24/02

2.0

2.0

3.0

2.0

6.0

1.0

3.0

4.0

3.0

3.0

2.0

6.0

26

2/87

3.0

2.0

2.0

3.0

6.0

1.0

3.0

6.0

2.0

2.0

2.0

1.0

27

60/87

2.0

1.0

3.0

3.0

4.0

1.0

3.0

5.0

3.0

3.0

2.0

6.0

28

15/2000

3.0

2.0

2.0

3.0

5.0

1.0

2.0

2.0

2.0

2.0

1.0

8.0

29

34/87

3.0

2.0

2.0

3.0

6.0

1.0

3.0

4.0

2.0

2.0

2.0

1.0

30

21/02

3.0

2.0

1.0

3.0

4.0

1.0

3.0

4.0

3.0

0.0

2.0

7.0

31

57/76

3.0

2.0

2.0

3.0

5.0

1.0

2.0

6.0

2.0

2.0

1.0

6.0

32

0001/07

2.0

5.0

3.0

2.0

1.0

2.0

3.0

2.0

2.0

3.0

2.0

9.0

33

0004/07

2.0

5.0

2.0

3.0

1.0

1.0

3.0

6.0

3.0

3.0

2.0

8.0

34

7/84

3.0

2.0

2.0

3.0

4.0

1.0

2.0

6.0

3.0

1.0

2.0

6.0

35

7/85

3.0

1.0

2.0

2.0

6.0

1.0

3.0

4.0

3.0

3.0

1.0

8.0

36

06/2001

2.0

2.0

2.0

3.0

6.0

1.0

3.0

5.0

3.0

0.0

2.0

7.0

LC= leaf color, LS= leaf shape, LSi= leaf size, LD=leaf density, PC=petiole color, TDI=twinge direction, VC=vine color, TS=tuber shape), TSC=tuber skin color, TBr=tuber branching, TSt=tuber surface texture and TFC=tuber flesh color.

 


3.2. Characterization of yam landraces using quantitative traits:

The analysis of variance of quantitative characters revealed highly significant difference (p<0.01) among the landraces for 13 of the 19 characters (Table 4). Significant differences among accessions in most traits indicated the existence of inherent genetic variability among accessions. The observed variability among accessions was due to leaf width, leaf, petiole, vine and internodes length, number of internodes vine-1, number of vine hill-1, days to maturity, tuber length and diameter, tuber fresh weight and dry weight and harvest index.


 

Table 4: Partial analysis of variance showing mean squares and significant tests and summary statistics for 19 quantitative characters of 36 yam collections evaluated at Jimma, Ethiopia during 2015

No.

Quantitative character

Mean square

Accession (DF=35)

Error

R2

CV (%)

P-value

1

Leaf length (cm)

2.92**

0.38

0.93

5.89

<0.0001

2

Leaf width (cm)

4.92**

0.08

0.89

6.99

<0.0001

3

Petiole length (cm)

8.52**

3.05

0.82

17.04

0.0046

4

Length of leaf lobe (cm)

0.46

0.59

0.68

38.70

0.7478

5

Distance between lobs (cm)

0.37

0.56

0.55

22.04

0.8781

6

Vine length (cm)

188.1**

34.28

0.95

2.29

<0.0001

7

Internodes length (cm)

0.30**

0.11

0.88

3.54

0.0080

8

Tip length (cm)

0.23

0.45

0.48

26.64

0.9701

9

Number of internodes vine-1

10.96**

2.56

0.88

6.17

0.0002

10

Number of vines hill-1

0.59**

0.26

0.78

11.99

0.018

11

Days to maturity

187.32**

70.78

0.83

6.07

0.0067

12

Number of tubers hill-1

0.10

0.12

0.63

8.16

0.7411

13

Tuber length (cm)

4.88**

1.89

0.88

3.54

0.0079

14

Tuber diameter (cm)

5.61**

1.94

0.84

9.34

0.0036

15

Vine fresh weight (t/ha)

12.31

20.45

0.52

36.58

0.9182

16

Vine dry weight (t/ha)

0.85

1.13

0.61

34.22

0.7871

17

Tuber fresh weight (t/ha)

256.07**

10.80

0.97

10.99

<0.0001

18

Tuber dry weight (t/ha)

4.58**

0.88

0.90

4.53

<0.0001

19

Harvest Index (%)

187.32**

70.78

0.83

12.29

0.0067

*and **Significant difference at the 0.05and 0.01 probability level, DF: Degree of freedom.

 


Mean performance amongst the yam landraces are summarized in Table 5. Promising yam landraces were identified considering three important yield determining traits namely: TFW, TuL and number of tubers per hill. The top ten performing landraces with respect to high TFW were: 10/002, 56/76, 17/02, 27/02, 7/83, 08/02, 59/02, 45/03 and 6/02. The TFW ranged from 35.1to 63.0 t/ha. Landraces which produced the highest TuL included: 27/02, 68/01, 08/02, /6/02, 75/02, 56/76, 13/87, 116, 39/87 and 07/03. The top ten landraces with respect to number of tubers per hill were: 34/87, 0001/07, 15/2000, 27/02, 45/03, 60/87, 39/87, 10/002, 56/76 and 24/02. Number of tubers per hill among these landraces ranged from 3.92 to 4.83.

3.3. Correlation analysis:

Highly significant (p <0.001) and positive correlations were observed between leaf length with leaf width (r = 0.77), internodes length (r = 0.45) and TuL (r =0.45) (Table 7). Significant negative correlation was observed between leaf length with number of internodes per vine (r = – 0.33). Significant and positive correlations were observed between leaf width with petiole length (P <0.001; r = 0.45), internodes length (P <0.005; r = 0.35), TuL (P <0.005; r = 0.35) and tuber diameter (P <0.001; r = 0.44).

 


Table 5: Mean response of 36 yam collections evaluated for 13 quantitative traits at Jimma in 2015.

Acc

LL

LW

PL

VL

IL

NIPV

DM

NT

PH

TuL

TuDi

TFW

TDM

HI%

1.0

12.68

5.13

7.13

262.33

9.81

26.83

147.75

4.08

39.22

15.84

40.40

23.36

77.75

2.0

11.91

4.67

14.00

265.42

10.46

24.55

142.29

4.33

41.83

15.76

30.00

21.48

72.29

3.0

14.16

5.00

12.88

267.00

10.34

26.30

140.04

4.08

41.35

16.74

35.10

21.62

70.04

4.0

11.07

4.54

8.77

280.85

10.29

29.00

145.53

4.17

41.17

14.88

33.80

21.36

75.53

5.0

10.07

4.16

7.98

251.85

9.51

27.90

125.88

4.33

38.03

13.12

16.80

23.57

55.88

6.0

11.82

4.44

11.53

267.25

10.26

25.40

157.57

4.50

41.04

16.40

53.80

20.96

87.57

7.0

8.65

3.72

9.58

267.00

9.44

30.00

143.44

4.50

37.77

12.40

30.40

22.45

73.44

8.0

9.83

3.84

13.28

257.25

9.34

26.80

142.68

4.17

37.37

13.92

28.55

20.81

72.68

9.00

11.81

4.66

10.17

256.50

10.45

23.10

144.84

4.33

41.81

18.00

41.40

20.74

74.84

10.0

9.05

4.66

11.15

250.10

10.08

21.40

139.72

4.33

40.33

16.08

29.60

19.81

69.72

11.0

10.09

3.50

10.50

253.75

9.66

22.70

146.11

4.33

38.66

13.38

33.80

21.97

76.11

12.0

10.20

4.08

9.40

252.25

9.66

25.60

145.25

4.50

38.66

13.76

44.00

21.80

75.25

13.0

10.16

4.31

9.77

240.50

9.12

27.10

149.39

4.17

36.49

17.20

49.20

18.14

79.39

14.0

9.29

3.00

9.80

263.50

9.95

28.60

136.59

4.08

39.79

15.04

24.00

22.51

66.59

15.0

11.28

4.86

11.73

260.75

10.03

26.80

142.79

4.67

40.13

17.76

37.40

18.47

72.79

16.0

10.93

4.49

12.38

271.50

10.96

25.10

142.31

4.75

43.83

15.92

47.40

18.86

72.31

17.0

8.75

3.58

5.82

296.75

10.10

33.00

141.29

3.92

40.42

12.48

30.60

22.50

71.29

18.0

10.28

4.43

9.60

263.85

9.68

28.30

152.13

4.58

38.72

15.60

63.00

17.83

82.13

19.0

10.17

4.06

8.23

247.00

9.28

26.03

144.25

3.92

37.13

12.16

33.40

23.33

74.25

20.0

9.67

4.08

11.83

240.38

9.52

23.78

142.93

3.92

38.08

13.92

26.80

19.70

72.93

21.0

10.78

4.72

11.57

254.25

9.53

26.93

151.14

4.17

38.13

16.48

45.60

19.10

81.14

22.0

9.71

4.05

10.23

241.75

8.89

24.35

143.71

4.42

35.56

19.52

29.20

16.93

73.71

23.0

12.34

4.73

13.50

258.50

9.97

23.33

113.78

4.58

39.89

16.08

11.40

20.76

43.78

24.0

9.62

4.05

8.10

242.25

9.02

27.00

125.77

4.42

36.08

13.68

16.20

23.17

55.77

25.0

8.28

3.48

7.48

259.25

9.40

31.80

130.80

4.50

37.61

12.72

19.40

21.22

60.80

26.0

11.20

4.42

12.92

239.00

9.25

22.10

126.75

4.33

37.01

14.48

14.80

20.53

56.75

27.0

10.65

4.58

11.12

254.25

9.93

25.43

145.06

4.58

39.71

14.64

27.80

21.30

75.06

28.0

11.86

4.22

8.17

243.25

9.51

24.50

114.93

4.75

38.06

12.10

11.80

20.86

44.93

29.0

10.72

4.16

8.17

235.00

9.01

22.50

98.98

4.83

36.06

12.64

6.80

20.55

28.98

30.0

9.76

4.30

9.92

237.50

8.92

26.40

139.13

4.50

35.69

16.72

28.40

20.61

69.13

31.0

9.88

4.42

10.45

246.35

9.22

26.33

141.72

4.25

36.90

12.72

32.00

19.17

71.72

32.0

10.20

4.23

11.12

239.75

9.17

25.20

142.30

4.75

36.69

9.11

23.40

22.06

72.30

33.0

10.04

4.33

10.78

243.05

9.20

25.76

142.01

4.50

36.79

10.92

27.70

20.62

72.01

34.0

8.77

3.61

8.92

243.75

9.30

26.38

133.89

4.42

37.19

13.78

23.67

21.71

63.89

35.0

12.39

4.63

12.60

252.25

9.75

26.00

115.42

4.17

39.02

12.39

9.60

19.91

45.42

36.0

8.53

3.50

6.15

241.25

9.47

24.90

130.39

4.17

37.90

10.61

14.40

19.84

60.39

Mean

10.46

4.24

10.19

254.09

9.65

26.03

138.02

4.36

38.61

14.88

29.77

20.82

68.02

St.div

1.31

0.47

2.07

13.33

0.49

2.50

12.22

0.24

1.98

2.27

12.93

1.61

12.33

Min

8.28

3.00

5.82

235.00

8.89

21.40

98.98

3.92

35.56

11.47

6.80

16.93

28.98

Max

14.16

5.13

14.00

296.75

10.96

33.00

157.57

4.83

43.83

19.52

63.00

23.57

87.57

LL=Leaf length (cm); LW= Leaf width (cm); PL= Petiole length (cm); VL= Vine length (cm); NTPP= Number of tubers hill-1;

TL=Tuber length (cm); TuDi= Tuber diameter (cm); IL= Internodes length (cm); NIPV= Number of internodes vine-1; DM= Days to maturity; TFW=Tuber fresh weight (t/ha); TDW=Tuber dry weight (t/ha); and HI= Harvest Index (%).

St.div-=Standard deviation; Min=Minimum and Max= Maximum

 

Table 6: Spearman’s rank correlation coefficients showing pair-wise association of qualitative traits assessed among 36 yams landraces

Traits

LC

LS

Lsi

LD

PC

TDI

VC

TS

TSC

TBr

TSt

TFC

LC

1.00

0.013

-0.118

-0.020

0.485**

-0.157

-0.148

-0.146

-0.096

0.118

0.071

-0.139

LS

 

1.00

-0.132

-0.022

-0.389**

0.476**

0.028

-0.023

0.243

0.109

0..131

0.062

Lsi

 

 

1.00

-0.547**

-0.032

0.020

0.268

-0.158

-0.266

0.001

0.257

0.240

LD

 

 

 

1.00

0.080

-0.162**

-0.180

0.216

0.058

-0.137

-0.125

-0.276

PC

 

 

 

 

1.00

-0.433

-0.025

0.008

0.063

-0.077

-0.137

-0.034

TDI

 

 

 

 

 

1.00

0.204

-0.471**

-0.026

0.249

0.129

-0047

VC

 

 

 

 

 

 

1.00

-0.046

0.055

0.115

0.090

0.108

TS

 

 

 

 

 

 

 

1.00

0.025

-0.131

0.062

-0.157

TSC

 

 

 

 

 

 

 

 

1.00

0.037

-0.293

0.242

TBr

 

 

 

 

 

 

 

 

 

1.00

-0.068

-0.105

TSt

 

 

 

 

 

 

 

 

 

 

1.00

-0.253

TFC

 

 

 

 

 

 

 

 

 

 

 

1.00

**Significant difference at 0.01 probability level.

LC= leaf color, LS= leaf shape, LSi= leaf size, LD=leaf density, PC=petiole color, TDI=twinge direction, VC=vine color, TS=tuber shape), TSC=tuber skin color, TBr=tuber branching, TSt=tuber surface texture and TFC=tuber flesh color.

 


Vine length had significant and positive correlation with internodes length (P < 0.001; r = 0.73), number of internodes per vine (P < 0.001; r = 0.97), TuL (P < 0.001; r = 0.73), storage TFW (P < 0.001; r = 0.39), days to maturity (P < 0.005; r = 0.34) and harvest index (P < 0.005; r = 0.34). Further, leaf length, leaf width and petiole length were significantly and negatively correlated with number of internodes per vine (r = - 0.33), (r = - 0.33) and (r = - 0.45), respectively.

 

The correlation coefficients between qualitative traits with the two important quantitative traits (TFW and TuL) are presented in Table 8. Significant positive correlations were recorded between TFW and TuL with leaf shape with r=0.32. Likewise, non-significant positive correlations were recorded between TFW and TuL with leaf size, leaf density, tuber shape and tuber branching. Twinge direction and tuber surface texture showed negative non-significant correlation with TFW and TuL.


 

Table 7: Spearman’s rank correlation coefficients showing pair-wise association of quantitative traits measured among yam landraces.

Traits

LL

LW

PL

VL

IL

NIPV

DM

NTPH

TuL

TUDi

TFW

TDW

HI

LL

1.00

0.77**

0.47**

0.15

0.45**

-0.33*

-0.08

0.05

0.45**

0.36*

0.11

-0.09

-0.08

LW

 

1.00

0.45**

0.05

0.35*

-0.33*

0.10

0.12

0.35*

0.44**

0.27

-0.25

0.10

PL

 

 

1.00

-0.02

0.29*

-0.45**

0.11

0.11

0.29

0.35*

0.11

-0.33*

0.11

VL

 

 

 

1.00

0.73**

0.97**

0.34*

-0.22

0.73**

0.18

0.39**

0.20

0.34*

IL

 

 

 

 

1.00

0.06

0.25

-0.05

0.99**

0.34*

0.30*

0.04

0.25

NIPV

 

 

 

 

 

1.00

0.22

-0.24

0.02

-0.16

0.17

0.25

0.22

DM

 

 

 

 

 

 

1.00

-0.29

0.25

0.33*

0.85**

-0.13

1.00**

NTPH

 

 

 

 

 

 

 

1.00

-0.05

-0.03

-0.08

-0.21

-0.29

TuL

 

 

 

 

 

 

 

 

1.00

0.33*

0.36*

0.04

0.25

TuDi

 

 

 

 

 

 

 

 

 

-1.00

0.48**

-0.44**

0.33*

TFW

 

 

 

 

 

 

 

 

 

 

1.00

-0.29

0.85**

TDW

 

 

 

 

 

 

 

 

 

 

 

1.00

-0.13

HI

 

 

 

 

 

 

 

 

 

 

 

 

1.00

*, **Significant difference at the 0 .05and 0.01 levels of probability.

LL=Leaf length (cm); LW= Leaf width (cm); PL= Petiole length (cm); VL= Vine length (cm); IL= Internodes length (cm); NIPV= Number of internodes vine-1, DM= Days to maturity, NTPH= Number of tuber per hill; TL=Tuber length (cm); TDi= Tuber diameter (cm); TFW=Tuber fresh weight (t/ha); TDW=Tuber dry weight (t/ha) and HI= Harvest index (%).

 

Table 8: Spearman’s rank correlation coefficients showing pair-wise association between qualitative traits with TFW and TuL in yam landraces.

Traits

LC

LS

Lsi

LD

PC

TDI

VC

TS

TSC

TBr

TSt

TFC

TFW

0.03

0.32*

0.06

0.26

-0.28

-0.04

-0.05

0.11

-0.16

0.24

-0.11

-0.26

TuL

-0.24

0.32*

0.06

0.24

-0.28

-0.04

0.37*

0.11

-0.16

0.24

-0.11

-0.25

*Significant difference at the 0.05 probability level.

LC= leaf color, LS= leaf shape, LSi= leaf size, LD=leaf density, PC=petiole color, TDI=twinge direction, VC=vine color, TS=tuber shape), TSC=tuber skin color, TBr=tuber branching, TSt=tuber surface texture and TFC=tuber flesh color.

 


3.4. Path coefficient analysis of qualitative traits in yam:

Path coefficient values of qualitative traits with TFW and TuL as the response variate are presented in Table 9. High direct path coefficients were estimated between leaf shape (0.349), leaf density (0.238), tuber branching (0.188), leaf color (0.177) and leaf size (0.116) with tuber fresh weight, respectively. Further, positive direct path coefficient values and non-significant positive genotypic correlation were exhibited between leaf density (0.257) followed by tuber branching (0.118) and leaf color (0.177) with TFW. However, leaf shape showed significant and positive correlation with TFW (r=0.32) and TuL (r=0.32).


 

Table 9: Estimates of direct (boldfaced main diagonals) and alternate/indirect path coefficient values of qualitative traits with TFW (top) and TuL (bottom) in yam landraces.

Traits

LC

LS

Lsi

LD

PC

TDI

VC

TS

TSC

TBr

TSt

TFC

TFW

LC

0.177

-0.074

0.022

-0.049

-0.017

0.065

-0.027

0.007

-0.004

-0.022

0.020

-0.068

0.03

LS

0.037

0.349

-0.003

-0.014

-0.01

-0.058

-0.003

-0.016

0.016

0.015

0.001

0.009

0.32*

LsI

-0.033

-0.001

0.116

-0.031

0.005

0.090

-0.077

-0.002

0.002

0.046

-0.019

-0.033

0.06

LD

0.036

-0.021

-0.015

0.238

0.117

0.007

-0.003

-0.001

0.012

-0.050

0.001

-0.064

0.26

PC

-0.014

0.017

-0.002

-0.130

-0.213

-0.018

0.026

0.010

-0.016

0.011

0.023

0.031

-0.29

TDI

0.049

0.087

-0.045

-0.008

-0.017

-0.233

0.0704

0.001

-0.006

0.011

0.013

0.034

-0.04

VC

-0.029

0.006

0.055

0.005

0.034

0.100

-0.162

-0.012

0.036

-0.005

-0.043

-0.032

-0.05

TS

0.023

0.094

0.003

0.064

0.038

0.006

-0.033

-0.059

0.003

0.010

-0.020

-0.022

0.11

TSC

-0.010

-0.073

-0.002

-0.038

-0.046

-0.002

0.076

0.003

-0.077

0.004

0.022

-0.015

-0.16

TBr

0.021

0.028

0.028

-0.063

-0.012

-0.014

0.004

-0.003

-0.002

0.188

-0.006

0.074

0.24

TSt

0.021

0.000

0.012

0.000

0.029

0.018

-0.040

-0.006

0.010

0.007

-0.173

0.017

-0.11

TFC

-0.047

-0.013

0.015

0.061

0.026

0.032

-0.022

-0.005

-0.004

-0.055

0.011-

-0.252

-0.26

Traits

LC

LS

Lsi

LD

PC

TDI

VC

TS

TSC

TBr

TSt

TFC

TuL

LC

-0.186

-0.055

0.016

-0.074

-0.028

0.041

-0.006

0.016

-0.001

0.082

-0.026

-0.019

-0.24

LS

0.029

0.356

-0.003

-0.015

-0.009

-0.060

-0.003

-0.022

0.020

0.021

0.001

0.009

0.32*

LsI

-0.029

-0.001

0.102

-0.034

0.004

0.093

-0.099

-0.002

0.002

0.066

-0.008

-0.032

0.06

LD

0.054

-0.021

-0.014

0.257

0.103

0.008

-0.004

-0.022

0.015

-0.072

0.001

-0.063

0.24

PC

-0.028

0.017

-0.002

-0.141

-0.188

-0.019

0.033

0.014

-0.021

0.016

0.010

0.031

-0.28

TDI

0.032

0.088

-0.039

-0.008

-0.015

-0.241

0.090

0.002

-0.001

0.017

0.006

0.0340

-0.04

VC

-0.006

0.006

0.048

0.005

0.030

0.104

-0.207

-0.016

0.466

-0.007

-0.019

-0.031

0.37*

TS

0.036

0.096

0.003

0.069

0.033

0.006

-0.042

-0.082

0.004

0.015

-0.009

-0.022

0.11

TSC

-0.002

-0.074

-0.002

-0.041

-0.040

-0.002

0.098

0.003

-0.099

0.007

0.010

-0.015

-0.16

TBr

-0.056

0.028

0.025

-0.068

-0.011

-0.015

0.005

-0.004

-0.002

0.272

-0.002

0.072

0.24

TSt

-0.062

0.001

0.011

0.001

0.025

0.018

-0.052

-0.009

0.013

0.010

-0.077

0.016

-0.11

TFC

-0.014

-0.013

0.013

0.066

0.023

0.033

-0.027

-0.007

-0.006

-0.079

0.005

-0.246

-0.25

LC= leaf color, LS= leaf shape, LSi= leaf size, LD=leaf density, PC=petiole color, TDI=twinge direction, VC=vine color, TS=tuber shape), TSC=tuber skin color, TBr=tuber branching, TSt=tuber surface texture and TFC=tuber flesh color.

 


3.5. Path coefficient analysis of quantitative traits in yam:

Results on the path coefficients with TFW as the response variable are summarized in Table 10. High direct path coefficient value (1.018) and highly significant genotypic correlation (r= 0.85, P <0.001) were exhibited between the days to maturity and TFW. Selection on the bases of early days to maturity and increased tuber length may maximize storage tuber yield in yam. Results on the path coefficients with TuL as the response variable and leaf length, leaf width, petiole length, vine length, days to maturity, number of internodes per vine, internodes length, TFW, tuber dry weight and harvest index as independent variables are summarized in Table 11. Values of direct effects were <1, indicating that inflation due to multicollinearity was low. Relatively high direct path coefficient value (0.112) and highly significant genotypic correlation (r=0.45, P < .001) were exhibited between leaf length and TuL. Path analysis indicated that selection for increased LL would bring about simultaneous increase in vine and internodes length (Table 11).


 

Table 10: Estimates of direct (bold and underlined) and indirect path coefficient values of TFW with quantitative traits amongst yam landraces.

Traits

LL

LW

VL

PL

DM

NIPV

NVPH

IL

TuL

TDW

HI

TFW

LL

-0.016

0. 056

0.001

0.042

0.210

-0.013

0.031

0.165

-0.399

0.080

0.004

0.11

LW

-0.022

0. 114

-0.015

0.024

1.389

-0.065

0.004

-0.838

0.039

0.062

-0.319

0.27

VL

0.008

0.053

-0.023

-0.083

0.780

0.113

0.005

-0.199

0.039

0.054

-0.350

0.39**

PL

-0.126

0.284

0.067

-0.034

0.181

-0.028

0.005

-0.474

0.454

-0.229

0.013

0.11

DM

0.054

-0.202

0.120

-0.066

1.018

-0.114

-0.001

-1.068

1.020

0.127

-0.029

0.85**

NOI

0.040

-0.010

-0.001

-0.032

0.169

0.022

-0.039

-0.049

0.164

0.003

-0.095

0.17

NVPH

-0.260

0.064

-0.187

0.027

0.458

-0.219

-0.065

0.556

-0.509

0.002

0.054

-0.08

IL

-0.195

0.305

0.081

-0.172

0.369

-0.154

0.014

-0.238

0.250

0.117

-0.064

0.30*

TuL

-0.023

0.141

0.108

-0.070

0.216

-0.046

0.016

-0.239

0.213

0.116

-0.071

0.36*

TDW

0.146

-0.129

0.002

-0.093

0.224

-0.143

-0.077

-0.484

0.242

0.053

-0.037

-0.29

HI

-0.179

0.086

0.042

0.082

1.234

-0.208

0.004

-0.254

0.060

0.060

-0.080

0.85**

LL=Leaf length(cm); LW= Leaf width(cm); VL= Vine length(cm); PL= Petiole length (cm); DM= Days to maturity,NIPV=Number of internodes/vine;NVPH= Number of vine per hill;IL=Internodes length (cm);TuL=Tuber length(cm); TDW=Tuber dry weight (t/ha) and HI= Harvest Index (%).

 

Table 11: Estimates of direct (bold and underlined) and indirect path coefficient values of TuL with quantitative traits amongst yam landraces

Traits

LL

LW

VL

PL

DM

NIPV

NVPH

IL

TFW

TDW

HI

TuL

LL

0.112

0.009

-0.001

-0.006

-0.001

0.299

0.007

0.009

0.007

-0.002

0.008

0.45**

LW

0.551

-0.31

0.001

-0.067

0.001

0.186

0.021

0.096

0.007

-0.094

-0.029

0.35*

VL

1.409

-0.009

0.001

-0.012

0.008

-0.89

0.016

0.078

0.067

0.063

-0.001

0.73**

PL

0.228

0.01

-0.021

-0.007

0.001

0.119

0.009

0.011

0.002

-0.072

0.001

0.29

DM

0.336

-0.006

0.001

0.001

0.001

-0.125

0.011

-0.002

0.031

0.017

-0.001

0.25

NOI

0.005

0.002

0.001

-0.007

0.001

0.012

0.001

0.002

-0.003

0.001

-0.001

0.02

NVPH

0.001

0.005

-0.031

-0.027

-0.001

0.050

0.009

0.001

-0.02

-0.02

-0.011

-0.05

IL

0.225

-0.001

0.001

0.002

-0.001

0.458

0.094

0.003

-0.005

0.211

0.001

0.99**

TFW

0.699

0.019

-0.001

-0.002

-0.001

-0.395

0.024

0.002

0.008

0.001

0.001

0.36*

TDW

0.505

0.001

0.001

-0.007

0.001

-0.473

0.007

0.002

-0.004

0.002

-0.001

0.04

HI

-1.254

-0.001

0.001

0.006

-0.001

1.503

0.004

0.006

-0.016

-0.013

0.001

0.25

LL=Leaf length(cm); LW= Leaf width(cm); VL= Vine length(cm); PL= Petiole length (cm); DM= Days to maturity, NIPV=Number of internodes/vine;NVPH= Number of vine per hill; IL=Internodes length (cm);TFW=Tuber fresh weight (t/ha); TDW=Tuber dry weight (t/ha) and HI= Harvest Index (%).

 


4.    DISCUSSION:

Knowledge on the nature of associations among economically important traits is essential for direct or indirect selection and could consequently improve the efficiency of selection gains in plant breeding programs. In this study, correlation and path coefficient analyses were used to determine associations between qualitative and quantitative traits and also to determine the best selection criteria. Simple correlations among qualitative traits showed positive associations between leaf color and petiole color (Table 6). Positive correlations were also noted between leaf shape and twinge direction. Further, highly significant and negative correlations were observed between leaf size and density, leaf density and twinge direction as well as twinge direction and tuber shape. Further, petiole color, twinge direction, tuber surface texture and tuber flesh color were negatively correlated with tuber fresh weight and length (Table 8) signifying that these traits may negatively limit yield gains.

 

Correlations between qualitative traits have not been reported in yam. Therefore, this study could be very useful in commercial breeding of yam. Further, the lack of correlation between leaf density and tuber shape with other qualitative traits may be of interest for further investigation. Simple correlation analysis among quantitative traits revealed highly significant (p< 0.001) and positive correlations between leaf length with leaf width (r=0.77), internodes length (r=0.45) and TuL (r =0.45) (Table 7). This suggests that targeted selection to improve these traits would increase TuL (Mulualem and Dagne, 2013). Results in this study are in agreement with the report of Pandey et al. (1996), Alam et al. (2014) and Mashilo et al. (2016) who independently worked on taro, greater yam and bottle gourd landrace collections. The authors indicated that several quantitative traits including plant height, number of female flowers and number of branches per plant were important yield components influencing storage TFW in yam. Tuber length significantly and positively correlated with leaf length (r=0.45) and leaf width (r=0.35), vine color (r=0.73) and internodes length (r=0.99), respectively (Table 7). So far, there are no studies that reported correlation of storage TFW with related traits in yam.

 

Associations among traits are often expressed using simple correlation coefficients. This may limit prediction on the success of selection. Therefore, correlation coefficients between various characters were partitioned into direct and indirect effects using path coefficient analysis. Path coefficient analysis is increasingly being used in plant breeding to improve selection efficiency through pinpointing traits with significant effects on yield or yield components (Dominic et al., 2014). In this study, positive direct effect was exhibited between TFW and TuL. High direct effects were recorded between leaf shape followed by leaf density, tuber branching, leaf color and leaf size with TFW. Furthermore, these traits were well-correlated with TuL (Table 9). The poor correlation of leaf size and tuber shape with tuber yield suggests that direct selection for these traits may not provide storage tuber yield improvement. High positive direct effect was exhibited between days to maturity and storage TFW (Table 10) suggesting simultaneous selection of the two traits may improve genetic gain of tuber yield in yam breeding. This finding is in agreement with Mulualem and Weldemichael (2013) who reported that TuL is an important character in making selection in aerial yam. Further, Tsegaye et al. (2006) reported that storage TuL and dry matter contents are the best character to select Ethiopian sweet potato accessions. Hence, selection on the bases of early days to maturity and increased TuL may maximize storage tuber yield in yam. The negative direct effect of internodes length on TFW may be explained by the fact that selection based on internodes length might reduce TFW. Similar finding was reported by Monkola (2013) who indicated that the direct effect of internodes length on TFW of greater yam was small and negative. Whereas these are in contradiction to the result of Kifle (2006) and Dominic et al. (2014) who reported that number of verticals contributed more for tuber yield on taro and cassava, in that order. However, TuL, leaf width, number of internodes per vine and tuber dry weight had positive direct effects (Table 10). Vine length also had positive indirect effect on TFW through most of the traits except leaf width and number of internodes per vine where the direct effect of vine length was negative (Table 10). Vine length components seem to have less competitive effect with TFW at path coefficient analysis level and have not been selected against. LW had negative indirect effect through days to maturity, number of internodes per vine and tuber dry weight. It is interesting to note that days to maturity itself had positive direct effect on TFW and positive indirect effect through all characters except number of vines per hill. Mulualem and Dagne (2013) described the similar result. These authors reported that days to maturity being the novel character and had higher direct effect on fresh root yield on cassava.

 

The low negative association of tuber dry weight with TFW, which is not as such important on the basis of correlation estimates, revealed positive direct and indirect supplier to TFW via path analysis. Thus, selecting accessions based on this character would contribute for rapid yam tuber yield enhancement programmes. Leaf width had comparatively high positive direct effect (0.114) on TFW. Besides, it had positive and non-significant association with TFW. This is in agreement with Norman et al. (2011) who reported that taro accessions with wider leaves had high TFW. It had high negative indirect effects via days to maturity and tuber dry weight and negligible indirect negative effect through number of internodes per vine. Therefore, it is important to consider accessions with wider leaves in improving tuber yield in yam, as it was positively associated with TFW and its direct and indirect positive effect through yield contributing traits to TFW. The direct effect of TuL on TFW was positive and high (0.213). Positive direct effect of TuL on TFW was also reported by Mulualem and Weldemichael (2013) on aerial yam. Besides, the indirect effect of TuL on TFW through petiole length, days to maturity, tuber dry weight, number of vines per hill, internodes length and number of internodes per vine was high and positive. In contrast, the indirect influence through leaf length was higher and negative. Therefore, selection based on TuL is important to maximize TFW. Internodes length had significant and positive association with TFW. This positive association did not contribute to fresh tuber yield directly but indirectly through petiole length, days to maturity, vine length, tuber dry weight and TuL. However, harvest index and vine length had contributed indirectly to TFW. The value of harvest index had small and negative direct (-0.080) effect on TFW. Therefore, efforts required in breeding cultivars with a higher length of storage tuber can be achieved through selection of landraces. Landraces which produced the highest TFW were 10/002, 56/76, 17/02, 27/02, 7/83, 08/02, 59/02, 45/03 and 6/02. A high path coefficient value indicates that the change will result in a proportional (or inversely proportional) change in another correlated trait, whereas a strong correlation coefficient indicates that the change will have high effect on the second trait (Cramer and Wehner, 2000). Landraces which produced the highest TuL included: 27/02, 68/01, 08/02, 6/02, 75/02, 56/76, 13/87, 116, 39/87 and 07/03. These are useful genetic resources for yam breeding emphasizing storage tuber yield.

 

Knowledge of the associations among qualitative traits is important for simultaneous selections especially for yam which shows considerable variability in tuber qualitative parameters. The present study further showed that selection for qualitative traits such as LS may influence TuL whereas selection for leaf size and leaf density may not influence TFW and TuL in yam.

 

5.    CONCLUSION:

Overall, the correlation and path analyses allowed selection of the yam landraces such as: 27/02, 56/76, 08/02, 10/002, 39/87, 45/03, 6/02, 116 and 7/83 which can be very useful landraces in breeding and conservation of this valuable horticulture crop in Ethiopia.

 

6. ACKNOWLEDGEMENTS:

Ethiopian Institute of Agricultural Research (EIAR) and Jimma Agricultural Research Center (JARC) are acknowledged for financial support of this study.

 

7. REFERENCES:

1.      Alam, S, Euphemia, S, Bora, P., Saud, BK., 2014. Genetic variation in different cultivars of greater yam (Dioscorea alata). Journal of root crops, 40(1): 1-5.

2.      Asiedu, R, Alieu, S., 2010. Crops that feed the world 1. Yams, Journal of food Science, 2:305-315.

3.      Bhatt, GM., 1973. Significance of path coefficient analysis in determining the nature of character association. Euphytica, 22:338–343.

4.      Christopher, SC., 2000. Path Analysis of the correlation between fruit number and plant traits of cucumber populations. HortScience, (4):708–711.

5.      Coursey, DG., 1967. Yams: an account of the nature, origins, cultivation and utilization of the Useful members of the Dioscoreaceae.Longmans. Green and Co Ltd (London), p 230.

6.      Cramer, CS., Wehnerm TC., 2000. Path analysis of the correlation between fruit number and plant traits of cucumber populations. HortScience. 35:708–711.

7.      Dansi, A, Dantseyand, BH., Vodouhè, R., 2013. Production constraints and farmers’ cultivar preference criteria of cultivated yams (DioscoreacayenensisDioscorearotundata complex) in Togo.International J. Biol. Chem. 9(1): 388-408.

8.      Dewey, DR, Lu, KH., 1959. A correlation and path coefficients analysis of components of crested wheat grass seed production. Agronomy Journal, 51:515-518.Dominic, AO., Ukaobasi, E., Ogon T., 2014. Association and Path Coefficients Analysis of Fresh Root Yield of High and Low Cyanide Cassava (Manihot esculenta Crantz) Genotypes in the Humid Tropics. Journal of Crop Science and Biotechnology,17(2): 103-109

9.      Dominic AO., Ukaobasi E, Ogon T. 2014. Association and path coefficients analysis of fresh root yield of high and low cyanide cassava (Manihot esculenta Crantz) genotypes in the humid tropics. Journal of Crop Science and Biotechnology,17(2): 103-109

10.   Falconer, DS., Mackey, FC., 1996. Introduction to quantitative genetics. 4th ed. London: Longman Genres, 2008. Genres statistical software User’s Guide Version 16, Genres Inc. USA.

11.   Girma, G., Korie, S., Dumet, D., Franco, J., 2012. Improvement of accession distinctiveness as an added value to the global worth of the yam (Dioscoreaspp) genebank. International Journal of Conservation Sciences, 3(3):199-206.

12.   Gomez, KA., Gomez, AA., 1984. Statistical Procedures for Agricultural Research. 2nd ed. John Wiley and Sons, inc., New York.

13.   Hildebrand, EA., 2003. Motives and opportunities for domestication: an ethno- archaeological study in southwest Ethiopia. Journal of Anthropological Archeology, 22:358-375.

14.   IPGRI/IITA., 1997. Descriptors for yam (Dioscorea spp.). International Institute for Tropical Agriculture, Ibadan, Nigeria/International Plant Genetic Resources Institute, Rome, Italy, p.66.

15.   Kifle, A., 2006. Characterization and divergence analysis of some Ethiopian taro [Colocasiaesculenta (L.)] accessions M.Sc. Thesis, Haramaya University, Haramaya, Ethiopia, p.84.

16.   Lebot, V., 2009. Tropical tuber and tuber crops: cassava, sweet potato, yams, aroids. CABI: Wallingford, Oxfordshire.

17.   Li, CC., 1956. The concept of path coefficient and its impact on population genetics. Biometrics, 12:190–210.

18.   Mashilo, J., Hussein, S., Alfred, O., 2016. Correlation and path coefficient analyses of qualitative and quantitative traits in selected bottle gourd landraces. Acta Agriculturae Scandinavica, Section B. Soil and Plant Science, 1-13.

19.   Mohammadi, SA., Prasanna, BM., Singh, NN., 2003. Sequential path model for determining interrelationships among grain yield and related characters in maize. Crop Sci., 43:1690–1697.

20.   Monkola, M., 2013. Genetic variability and association among yield and yield related traits in cassava (Manihotesculenta Crantz) in southwest Ethiopia. MSc. Thesis presented at School of Graduate studies, Jimma University, Jimma, Ethiopia. p.85.

21.   Mulualem, T., Dagne, Y., 2013.Studies on correlation and path analysis for root yield and related traits of Cassava (ManihotesculentaCrantz) in South Ethiopia.Journal of Plant Sciences,1(3):33-38.

22.   Mulualem, T., Weldemichel, G., 2013. Agronomical evaluation of aerial yam (Dioscorea bulbifera) accessions collected from South and Southwest Ethiopia. Greener Journal of Agricultural Sciences, 3(9): 693-704.

23.   Mulualem, T., Weldemichael, G., Benti, T., Walle, T., 2013.Genetic Diversity of Cassava (Manihot esculenta Crantz) Genotypes in Ethiopia. Greener Journal of Agricultural Sciences, 3(9): 636-642

24.   Norman, PE., Tongoona, P., Shanahan, P.E., 2011. Determination of interrelationships among agro morphological traits of yams (Dioscorea spp.) usingcorrelation and factor analyses. Journal of Applied Biosciences, 45: 3059– 3070.

25.   Norman, P. E., Tongoona P., Danson J., Shanahan, PE., 2012. Molecular characterization of some cultivated yam (Dioscorea spp.) genotypes in Sierra Leone using simple sequence repeats. International Journal of Agronomy Plant Production, 3(8): 265-273.

26.   Pandey, G., Dhobal, V, Sapra, RL., 1996. Genetic variability, correlation and path analysis in Taro [(Colocasia esculenta (L.)]. Journal ofhill. Res.,9: 299-302.

27.   Paul, KK., Bari, MA., Debnath, SC., 2013. Correlation and path coefficient studies of yield and yield attributing characters in taro [(Colocasia esculenta (L.)]. Journal of Bangladesh Academy of Sciences, 37(2): 131-137

28.   SAS, Institute. 2000. Statistical Analytical Systems SAS/STAT user’s guide version, 8(2) cary NC: SAS institute inc.

29.   Sesay, L, Norman, P.E., Massaquo, i A., Gboku, M.L., Fomba, SN., 2013. Assessment of farmers’ indigenous knowledge and selection criteria of yam in Sierra Leone. Sky Journal of Agricultural Research, 2(1):1–6.

30.   Statistical Package for Social Sciences., 1996. SPSS for windows. User’s guide: Statistics version 16. Inc. Cary, NC.

31.   Tamiru, M., Heiko, C.B, Brigitte, LM., 2011. Comparative analysis of Morphological and farmers cognitive diversity in yam landraces (Dioscorea spp.) from Southern Ethiopia. Tropical Agriculture development, 55(1): 28-43.

32.   Tsegaye, E., Devakara, FV., Nigussie, D., 2006. Correlation and path analysis in sweet potato and their implication for clonal selection. Journal of Agronomy, 5(3):391-395.

33.   Williams, WA., Jones, MB., Demment, MW., 1990. A concise table for path analysis statistics. Agronomy Journal, 82:1022–1024.

34.   Wright, S. ,1921. Theory of path coefficients. Genetics. 8:239–255

35.   Wright, S., 1934. The method of path coefficients. Annal Mat Stat. 5:161–215.

36.   Zeven, A.C., De Wet, J.M., 1982. Dictionary of cultivated plants and their region of diversity: Center for Agricultural Publication and Documentation, (Wageningen), p.259.

 

 

 

Received on 05.08.2020         Modified on 25.08.2020

Accepted on 07.09.2020  ©AandV Publications All right reserved

Res. J. Pharmacognosy and Phytochem. 2020; 12(4):207-218.

DOI: 10.5958/0975-4385.2020.00035.7