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

computed following the standard formula

IV. IV. Data Analysis

Qualitative traits Relatively higher Shannon diversity for:-

●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.

V. V. Result & Discussion

Country/regionQualitative characters

Mean ± SEGH ESH EC GCG SPD SCAmhara/Ethiopia 0.426 0.245 0.212 0.239 0.238 0.312 0.279 ± 0.033B/Gumuz/Ethiopia 0.427 0.253 0.159 0.260 0.338 0.377 0.302 ± 0.040Oromia/Ethiopia 0.329 0.246 0.255 0.335 0.299 0.296 0.294 ± 0.015SNNP/Ethiopia 0.391 0.279 0.194 0.236 0.289 0.326 0.286 ± 0.043Tigray/Ethiopia 0.423 0.277 0.157 0.238 0.288 0.325 0.284 ± 0.033Eritrea 0.458 0.305 0.055 0.111 0.243 0.243 0.236 ± 0.060Kenya 0.317 0.345 0.291 0.234 0.330 0.403 0.320 ± 0.024Zambia 0.325 0.297 0.182 0.284 0.312 0.337 0.289+ 0.240Zimbabwe 0.302 0.293 0.282 0.264 0.283 0.353 0.287 ± 0.012Mean 0.377 0.282 0.199 0.244 0.291 0.330 0.287±0.045

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.

Source of variation df DH DM TTN PTN PLHT FL FN

Location 1 4066.4** 11102.61** 3199.8** 3087.2** 47638.2** 28.12** 36.66**

Genotype 149 315.4** 89.26** 12.02** 11.48** 491.75** 15.1** 4.85**G x E 149 51.24 44.13** 8.31** 8.20** 122.75** 2.45** 1.21**Error 298 46.83 13.01 1.10 1.19 35.58 0.94 0.65

CV (%) 7.05 2.29 18.72 19.55 8.68 12.12 11.09LSD (5%) 7.98 4.21 1.23 1.27 6.95 1.13 0.94Mean 97.01 157.73 5.61 5.55 68.75 7.98 7.23

S source of variation df EW NGPS CD FW TGW GYPLN LOG

Location 1 72.45** 134.6** 2129.8** 13.23** 0.02 28912.1** 228150**

Genotype 149 5.32* 1.07** 0.389** 0.08** 0.754** 182.79** 1546.25**G x E 149 1.09** 0.34 0.32 0.05 0.20 111.90 642.79**Error 298 0.74 0.37 0.27 0.05 0.17 53.61 82.50CV (%) 32.44 12.21 22.01 28.72 18.52 35.85 20.57LSD (5%) 1.00 0.71 0.61 0.26 0.49 8.54 10.59Mean 2.65 4.39 2.37 0.79 2.26 20.42 44.15

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

Productive tiller number 5.550 0.820 2.870 1.187 3.507 28.571 0.995 17.931

Plant height (cm) 68.750 92.250 122.938 35.578 43.586 75.038 17.106 24.881

Finger Length 7.980 3.163 3.775 0.942 0.754 83.775 3.347 41.937

Finger number per ear 7.230 0.910 1.213 0.647 0.282 75.052 1.699 23.501

Ear Weight (g) 2.650 1.058 1.330 0.737 0.177 79.511 1.885 71.143

Number of grain per spike 4.390 0.170 0.268 0.371 0.010 63.551 0.676 15.394

Culm diameter(cm) 2.370 0.018 0.098 0.273 0.024 17.949 0.115 4.862

Finger width (cm) 0.790 0.006 0.020 0.051 0.003 30.000 0.087 11.042

Thousand grain weight (g) 2.260 0.138 0.188 0.176 0.012 73.333 0.653 28.888

Grain yield per plant(g) 20.420 17.673 45.648 53.600 34.150 38.715 5.378 26.336

Lodging percentage 44.150 225.863 386.563 82.500 280.150 58.428 23.619 53.497

Estimation of the different variances parameters, heritability and genetic advance for 14 major quantitative traits of 144finger millet landraces and 6 released varieties

Key: δ2g= genotypic variation, δ2

p=phenotypic variation, δ2e=environmental variance,

δ2gl= genotype by location variance, H2= heritability in broader sense, GA=genetic

advance, GA % =genetic advance as percentage of mean

VI. Screening finger millet genotypes for blast Screening finger millet genotypes for blast diseasedisease

Objective-Objective-Set-II Set-II

To screen blast tolerant genotypes for further utilization in breeding program & yield trials.

VII. Materials and Methods Treatment No. 225 (including 150 from

Set-I experiment) Design: Simple Lattice Experimental Location: Bako ARC Checks: Eight improved 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)

88 102 117 132 147 102 117 132 147

BLOCK 1 4795.5** 23995.** 5760.2** 24053.5** 364.5** 624.22 22022.01** 893.24 338.0**Genotype 224 277.71**

881.32**

637.46* 440.38** 67.36** 2897.1** 1480.49** 1059.1* 58.93

Error 224 159.143 434.52 291.14 174.6 24.1 1009.04 624.66 792.5 48.71CV (%) 29.03 25.59 24.81 16.83 5.15 38.56 31.61 33.11 7.07LSD (0.05) 19.71 33.44 26.01 21.19 8 52.34 38.31 41.16 11.48R-squ(%) 74.97 76.4 78.9 81.34 79.3 78 81 65 62.5Mean 13.22 56.88 63.91 76.86 93.92 45.31 73.33 89.77 98.33RE (%) 111.9 106.1 115.8 106.1 102.8 100.6 116.3 101.6 100.9

Mean squares for blast incidence and severity recorded at different assessment period from different plant parts and grain yield per plant.

Result and discussion …….

S Source of V Variation df SHBDI NBINC LBAUDP HBAUDP LL GYPLN

BLOCK 1 180.3 2572.8** 4653690.3** 4656458.0** 7.45* 73.38*Genotype 224 62.28 800.30** 455942.7** 455947.7** 4.19** 47.26**Error 224 57.79 561.46 119260.4 119246.6 1.54 11.86CV (%) 19.27 25.7 9.39 10.23 21.09 30.12LSD(0.05) 9.46 55.35 509.21 585.8 2.90 5.68R-square 0.79 0.59 0.874 0.799 0.731 0.83Mean 29.54 92.19 3280.1 3506.30 5.89 11.29RE 138.4 100 125.6 102 100 100.5

KEY: SHBDI= sheath blast disease index, NBINC= neck blast incidence, LBAUDP=leaf blast disease progress, HBAUDP= head blast disease progress, LL=Lesion length, GYPLN=grain yield per plant.

Table Cont…….

Result and discussion …….

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.

HBAUDP rank

Acc Country/Region LBAUDPC HB AUDPC SHBDI NBINC GYPPL

1 BKFM 0031 Ethiopia/Oromia 2721.06 975.00 34.72 60.00 6.782 214988 Zambia 2976.79 1425.00 32.63 50.00 11.323 214987 Zambia 2481.48 1597.50 22.22 70.00 13.904 BKFM0010 Ethiopia/B/Gumuz 2322.69 1815.00 23.46 80.00 16.485 203356 Zimbabwe 3067.59 1897.50 23.46 70.00 15.316 BKFM0020 Ethiopia/Oromia 2307.64 1901.25 25.93 70.00 16.117 BKFM0029 Ethiopia/Oromia 3068.98 1912.50 27.78 70.00 9.198 214995 Zambia 2795.37 1912.50 24.07 50.00 14.749 214997 Zambia 3275.23 1935.00 25.31 70.00 15.8510 216035 Ethiopia/Oromia 2404.63 1987.50 22.84 80.00 18.0211 BKFM0024 Ethiopia/Oromia 2656.48 2137.50 24.69 90.00 12.2512 BKFM0018 Ethiopia/Oromia 2368.06 2141.25 24.69 60.00 16.0213 BKFM0063 Ethiopia/Oromia 3025.46 2227.50 28.40 100.00 23.0514 216051 Ethiopia/Oromia 2276.85 2250.00 24.69 70.00 13.3215 BKFM0042 Ethiopia/Oromia 3579.63 2287.50 29.01 100.00 17.0516 BKFM0023 Ethiopia/Oromia 2155.09 2340.00 26.54 60.00 13.7717 BKFM0001 Ethiopia/B/Gumuz 2275.93 2437.50 20.37 20.00 10.2618 216039 Ethiopia/Oromia 2062.04 2445.00 17.90 100.00 11.8319 BKFM0007 Ethiopia/B/Gumuz 3204.17 2475.00 38.27 90.00 11.8220 BKFM0009 Ethiopia/B/Gumuz 3099.54 2475.00 25.31 80.00 18.11

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.

Table . … cont HBAUDP rank

Acc Country/Region LBAUDPC HB AUDPC

SHBDI NBINC GYPPL

206 100002 Ethiopia/Amhara 3305.56 4350.00 29.63 100.00 4.66207 203357 Zimbabwe 3923.61 4350.00 40.74 100.00 15.92208 242114 Ethiopia/Amhara 3224.07 4350.00 27.78 100.00 10.66209 AAUFM-21 Ethiopia/Tigray 3113.89 4387.50 25.93 100.00 11.89210 237475 Ethiopia/Tigray 3697.69 4387.50 33.95 100.00 9.77211 242115 Ethiopia/Amhara 3429.63 4387.50 30.25 100.00 13.20212 238299 Ethiopia/Tigray 4210.65 4425.00 54.94 100.00 9.05213 AAUFM-15 Ethiopia/Tigray 3668.29 4425.00 33.33 100.00 7.93214 AAUFM-2 Ethiopia/Tigray 3692.13 4425.00 27.16 100.00 8.08215 AAUFM-32 Ethiopia/Tigray 3819.91 4425.00 33.95 100.00 12.17216 230105 Eritrea 3501.85 4462.50 27.78 100.00 4.35217 AAUFM-22 Ethiopia/Tigray 4162.50 4462.50 31.48 100.00 6.79218 230104 Eritrea 3844.44 4462.50 26.54 100.00 6.30219 AAUFM-35 Ethiopia/Tigray 3318.52 4500.00 30.25 100.00 16.86220 AAUFM-23 Ethiopia/Tigray 3229.17 4500.00 32.10 100.00 7.73221 AAUFM-44 Ethiopia/Tigray 3906.48 4500.00 31.48 100.00 5.58222 228202 Ethiopia/Amhara 3924.54 4500.00 37.04 100.00 6.46223 238460 Ethiopia/Tigray 3697.22 4500.00 28.40 100.00 4.08224 238308 Ethiopia/Tigray 3748.15 4500.00 35.19 100.00 13.08225 242618 Ethiopia/Tigray 3898.15 4500.00 33.95 100.00 4.12

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.

Summary and Future Plan….

ACKNOWLEDGEMENTS Bio-Innovate Africa

Microbial, Cellular & Molecular Biology-AAU

Bako Agricultural Research Center

Arsi Negele Agricultural Research sub-center

Melkassa Agricultural Research Center

The Institute of Biodiversity Conservation