Genetic Diversity and Traits Inheritance in Finger Genetic Diversity and Traits Inheritance in Finger millet ( millet ( Eleusine coracana Eleusine coracana ): Implications for ): Implications for Germplasm Conservation and Strategic Breeding for Germplasm Conservation and Strategic Breeding for Multi-stress Tolerant Variety Multi-stress Tolerant Variety D. Lule, K. Tesfaye, M. Fetene, S. de Villiers D. Lule, K. Tesfaye, M. Fetene, S. de Villiers Finger Millet Research Sub-Project Finger Millet Research Sub-Project First Bio-Innovate Regional Scientific Conference United Nations Conference Centre (UNCC-ECA) Addis Ababa, Ethiopia, 25-27 February 2013
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Genetic diversity and traits inheritance in finger millet (Eleusine coracana): Implications for germplasm conservation and strategic breeding for multi-stress tolerant variety
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Genetic Diversity and Traits Inheritance in Finger Genetic Diversity and Traits Inheritance in Finger millet (millet (Eleusine coracanaEleusine coracana): Implications for ): Implications for
Germplasm Conservation and Strategic Breeding for Germplasm Conservation and Strategic Breeding for Multi-stress Tolerant Variety Multi-stress Tolerant Variety
D. Lule, K. Tesfaye, M. Fetene, S. de VilliersD. Lule, K. Tesfaye, M. Fetene, S. de VilliersFinger Millet Research Sub-Project Finger Millet Research Sub-Project
First Bio-Innovate Regional Scientific ConferenceUnited Nations Conference Centre (UNCC-ECA)Addis Ababa, Ethiopia, 25-27 February 2013
I. I. IntroductionIntroduction Cultivated in the tropical & sub-tropical regions of Africa & India;
Widely cultivated in Northern, NW, and Western Ethiopia;
It is the 6th most important cereal crop both in area & production;
It constitutes 10-20% of total cereal production in some regions;
Can produce better yield than other crops under multiple stress & marginal soil;
Has high nutritional value & excellent storage qualities;
Area coverage &production (1999-11)Area coverage in the major regions (2009-11)
Introduction .…Introduction .…
Despite is importance as food security crop, its productivity is suffering from both biotic & abiotic stresses => needs intervention to improve its productivity;
Improvement in any crop usually involves; Exploiting the genetic variability in specific traits; Nature & degree of association between traits; Inheritance & genetic transmissibility;
Limited/insufficient data base for finger millet;
Therefore, the current study was initiated to supplement such pressing needs
II. Objectives Set-I
To assess the extent & pattern of genetic diversity of finger millet germplasms on the basis of phenotypic traits;
To estimate the genetic parameters; heritability
& genetic advance for quantitative traits.
III. III. Materials & Methods Morphological characterization of finger
millet genotypes was conducted at:- Arsi Negele in the central Rift Valley Gute in the western Ethiopia
150 germplasm planted in RCBD with 2 repl.
6 Qualitative Traits
(growth habit, ear shape, ear (glumes) color, grain coverage by glumes; spikelet density and grain color was collected following finger millet descriptors (IBPGR, 1985).
14 Quantitative Morphological (days to 50% to TGW)
No. Country/Region Total1 Amhara 332 Oromia 333 Tigray 274 B/Gumuz 75 SNNP 66 Eritrea 87 Zimbabwe 138 Kenya 79 Zambia 10Sub total 144Released Varieties 6Grand total 150
Qualitative traits ◘ The percentage freq. distribution of each
phenotypic class (using excel computer) program. ◘ Hierarchal clustering of standardized data (using
MINITAB) software◘ The amount of genetic variation was determined
using the Shannon-Weaver diversity index as described by Jain et al. (1975)
Quantitative traits Analysis of variance computed using Agrobase
software; Cluster analysis Using SAS software; Broad sense heritability (H2) & Genetic advance
●Growth habit was observed for Eritrea & Ethiopian (Tigray) materials;● Ear Shape & Grain Color for Kenyan’s;● Grain covering by glumes & spikelet density for Ethiopian (Oromia & SNNP region);
The pooled mean diversity indices for the six traits showed comparatively higher Shannon diversity for Kenyan collection followed Benishangul Gumuz & Oromia region of Ethiopia.
Table _Shannon-Weaver diversity indices (H’) of finger millet accessions collected from 5 regions of Ethiopia and 4 East & South east African countries for 6 qualitative traits
GH= growth habit, ESH= ear shape, EC=Ear/glumes color, GCG=Grain covering by glumes, SPD=Spikelet density, SC=seed color
Based on regional data, 3 clusters groups were formed. ◘ All the five administrative regions of Ethiopia & Eritrea grouped together ◘ Kenya, Zambia and Zimbabwe grouped in the second cluster◘ All released varieties share minimum percentage similarity & with finger millet accessions of all countries & regions.
Adminstrative regions of Ethiopia (Eth), released varieties (V1-V6) and other countries
Sim
ilarity
Bone
ya (V
2)
Wam
a (V4
Gute
(V6)
Tade
sse (V
1)
Pade
t (V3)
Bered
a (V5)
Zambia
Zimba
bwe
Keny
a
SNNP (E
th)
Oromia
(Eth)
Eritri
a
B/Gum
uz (E
th)
Tigray
(Eth)
Amhara
(Eth)
40.91
60.60
80.30
100.00
Fig 2 Similarities for F. millet landraces among regions of Ethiopia, African countries & released varieties evaluated for 6 qualitative traits
Clustering Analysis
Quantitative traitsQuantitative traits
Analysis of variance for quantitative traits Analysis of variance for quantitative traits
The combined analysis of variance across locations showed significant location effects for all quantitative traits.
The genotype mean squares were also significant (P≤0.01) for all quantitative traits except ear weight.
Genotype by environment mean square was highly significant (P≤0.01) for most of the traits considered, indicating that the variation among genotypes for grain yield is more of due to genetic factor than environmental.
Mean squares for 14 quantitative traits of 144 finger millet landraces and 6 released varieties as obtained from combined ANOVA of the two locations (Gute & Arsi Negele)
KEY: TTN=Total tiller number, PTN= productive tiller number, FL= finger length, FN= finger number, EW=ear width, NGPS=number of grain per spikelet, CD=culm diameter, EW= finger weight, GYPLN=grain yield per plant, LOG= lodging index
The genetic relatedness of 144 F. millet landraces for 14 quantitative traits among regions and countries of origin and six released varieties
Regions and count r ies of or igin, and released var iet ies ( v)
Sim
ilari
ty
Wam
a (v)
Tades
se (v
)
Gute (v
)
Pade
t (v)
Bone
ya (v
)
SNNP (
Eth)
Zambia
Zimba
bwe
Keny
a
Bered
a (v)
Oromia
(Eth)
B/Gum
uz (E
th)
Eritre
a
Tigray
(Eth)
Amhara
(Eth)
46.35
64.23
82.12
100.00
Fig. 2 The genetic relatedness of 144 F. millet landraces for 17 quantitative traits among regions & countries of origin & 6 released varieties
The result for cluster analysis indicated that neighboring regions, & countries shared strong similarity
Traits Mean δ2g δ2
p δ2e δ2
gl H2 (%) GA GA (%)
Days to 50% Heading 97.010 66.040 78.850 46.830 2.205 83.754 15.291 15.762
Days to 50% maturity 57.300 11.283 22.315 13.010 15.560 50.560 4.911 3.122
Total tiller number 5.610 0.928 3.005 1.103 3.604 30.865 1.100 19.609
Estimation of the different variances parameters, heritability and genetic advance for 14 major quantitative traits of 144finger millet landraces and 6 released varieties
Pathogen source:- Artificial inoculation by developing the inoculums collected from susceptible genotypes & developed in lab.
Susceptible genotype was planted as spreader row.
No Region/country Sub total1 Oromia 652 Amhara 533 Tigray 464 B/Gumuz 155 SNNP 76 Eritrea 37 Kenya 58 Zambia 99 Zimbabwe 14
Sub total 217
Released Varieties 8
Grand total 225
10 plants were randomly selected/row for data colle;
Blast severity (1-9), Incidence (%), Lesion length (cm), along with other yield parameters were recorded;
Disease assessment was be made every 2 weeks;
Severity score for Leaf, Sheath & Head blast recorded from 10-selected plants were converted to disease index/severity index following standard formula later to calculate the Area Under Disease Progress Curve (AUDPC) of the subsequent recording period.
VIII. Data Collection & Analysis
Analysis of variance Mean squares due to genotypes were highly
significant (P≤0.01) for◊ Leaf blast AUDPC & head blast AUDPC;
◊ Neck blast incidence & lesion length;
◊ Grain yield per plant;
VII. Result and Discussion
Source of variation
dfLeaf blast incidence -days after planting (DAP) Head blast incidence- (DAP)
Result and discussion …….The trends of infection and disease epidemiology Wider ranges of variations were observed among finger millet
accessions for leaf blast, sheath blast, neck blast and head blast infection level.
Maximum range of variation for head and leaf blast incidence were observed among genotypes at 117 &132 days after planting.
The variation among accession gets narrower at later recording period implying that the infection level reaches climax.
Result and discussion ……. As head blast is the major factor in causing yield loss, the accessions
under the study were ranked based on head blast AUDPC value and hence ranges from:- 975%-days for Acc.BKFM0031 collected from western Ethiopia to 4500%-days for 7 finger millet accessions collected from Northern
Ethiopia. Among the top 20 tolerant accessions for leaf & head blast, 16 of
them gave grain yield above average (11.29 g/plant).
Acc. BKFM0031 is the most tolerant landrace with the least head blast AUDPC value (975%-days), but gave lower grain yield per plant (6.78g/plot).
This urges the need to further confirmation for the consistence of its resistance & utilize as a parental line in crossing program.
Table List of the top 20 and last 20 finger millet populations ranked based on head blast resistance (HBAUDP) with their respective mean grain yield, leaf blast, neck blast and sheath blast values.
Fig 1. Patterns of leaf blast severity index of 217 finger millet accessions pooled for regions of origin recorded during the different assessment periods
Leaf blast infection was relatively linear for different countries and regions of origin
Infection pattern with respect to regions/countries of origin
Fig. Patterns of head blast severity index recorded from 217 finger millet accessions pooled for regions of origin recorded during the different assessment periods
• Finger millet accessions from W & SW parts of Ethiopia, and some introduced from Zambia showed relatively better tolerance to leaf blast and head blast during the whole growing periods.
• Infections were high for accessions sampled from Kenya, Eritrea and two Ethiopian regions (Tigray and SNNP).
Higher phenotypic and yield related trait variability observed among finger millet germplasms studied, which worth to apply conventional and modern biotechnological tools to improve the productivity of finger millet;
About 64% of the traits considered in the current study have heritability percentage greater than 50%;
Relatively higher heritability followed by higher genetic advance were recorded for Ear Weight, Lodging Index, Finger Length, Thousand Grain Weight & Grain Yield per Plant.
This in turn offers high chances for improving this traits of finger millet through selection & hybridization.
Finger length (0.33), finger number (0.21), thousand grain weight (0.23) and tiller number (0.28) has positive & significant (P≤ 0.01) correlation with Grain Yield per Plant.
VI. Summary and Future Plan
Clustering goes with geographical proximity indicate the presence of gene flow/seed flow among the local community; Selection by farmers in favor of similar traits across location; Seed from the same sources ; Adaptive role of the traits in similar agro-ecology.
Materials from Western part of Ethiopia should be targeted for in-depth blast screening and conservation.
From Set-I and Set-II experiments:- 30 genotypes advanced to next level yields trials and later some 15
genotypes will be advance to multi-location yield trials.
More than 35 blast tolerant lines advanced to the next level.