Genetic analysis of drought tolerance in cowpea (Vigna unguiculata L.)

Genetic analysis of drought tolerance in

cowpea (Vigna unguiculata L.)

Eugene M. Agbicodo Feb. 8th 2010

Outline

Introduction

Phenotyping

Genotyping and genetic map construction

QTL mapping

Implications for breeding

Cowpea

Vigna unguiculata L. Walp

Family: Fabaceae

Diploid: 2n = 22

Genome size: ~620 Mbp

Self pollinated crop

www.iita.org

Cowpea Production

FAO:http://www.fao.org/inpho/content/compend/text/ch32/ch32.htm

Major Constraints of Cowpea Production

Drought stress and cowpea production

Drought stress occurred throughout the cropping season (early- mid- and terminal)

Mid- and late-season drought stress have received considerable attention given their negative effects on yield (Hall et al. 2004)

However, drought tolerance at seedling is receiving attention Singh et al. (1999 a,b) Muchero et al. (2008; 2009a)

Plant MaterialsTVu7778Danila

Recombinant inbred lines (RILs)

Susceptible parent

DanilaX

Danila

Tolerant parent

XDanila

XDanila TVu7778

XDanila

Susceptible parent

TVu7778

XDanila

Phenotyping

RILs derived from Danila (tolerant) and TVu7778

(susceptible) were evaluated for:

1. Drought tolerance at seedling

2. Terminal drought tolerance

130 cm length, 65 cm width and 15 cm depth

1:1 mixture of top soil and sand

Drought phenotyping at seedling stage

1 2 3 4 5 61 2 3 4 5 61 2 3 4 5 6

Parameters measured:

• Stem greenness (Stg)

• Drought-induced trifo-

liate senescence (DTS)

• Survival after rewatering

Phenotyping for terminal drought

RCBD with 2 treatments, 3 rep

Drought imposed at flowering

Locations: Kano and Ibadan

Terminal drought field trials

•Ibadan

Phenotyping for terminal drought tolerance

Parameters measured:

stomatal conductance (Gs), RWC

days to flowering

delay leaf senescence (DLS): visual scoring 1-5 classes

number and weight of pods, total seed weight,

100 seed-weight, fresh and dry fodder weight

Genotyping

RILs/parents genotyped Illumina Goldengate SNP array, 1,536 SNPs mined from EST sequences from several sources (UCR, IITA)

1,248 SNP were selected from ESTs derived from 11 cowpea genotypes

After data processing, only 322 SNPs with minor allele frequency > 0.30 were used to construct genetic map using JoinMap 4

QTL mapping

Entry mean of terminal (field) and seedling

(greenhouse) drought tolerance traits

SNP genetic linkage map of 282 loci with 11 LGs

QTL analysis was performed using MapQTL 5,

(Kruskal-Wallis test, Interval Mapping, Multiple-

QTL Model mapping )

Susceptible

Parent

RiL-117 Tolerant

Parent

RiL-106 Susceptible

Parent

RiL-87Susceptible

Parent

RiL-117 Tolerant

Parent

RiL-106 Susceptible

Parent

RiL-87

Results

Stg, DTS and Sur varied

significantly between RILs

and parents

Seedling phenotyping

Correlation of traits at seedling

Seedling survival mostly depend on stem greenness

(0.911)

Stem greenness is negatively correlated with

drought-induced trifoliate senescence (-0.714)

Results

Variation of RILs for delayed leaf senescence (DLS)

(a) (b) (c) (d)

5 10 15 20 25 30 35

Stomatal conductance

(mmol m-2s-1)

Delayed leaf senescence (DLS) Days to flowering

Number of Pods/plant

Number of seeds/pod

Grain yield (g/plant)

Dry Dry

Fodder yield (g/plant)

Seed weight (g)

50 100 150 200 250 300 36 39 42 45 48 511 2 3 4 5

1 2 3 4 5 50 100 150 200 250 300

3 6 9 12 15 18 0,03 0,06 0,09 0,12 0,15 0,18 0,21

0,03 0,06 0,09 0,12 0,15 0,18 0,21

3 6 9 12 15 18 21

5 10 15 20 25 30 35

3 6 9 12 15 18 2136 39 42 45 48 51

3 6 9 12 15 18

3 6 9 12 15 18 21

(a) (b) (c) (d)

5 10 15 20 25 30 35

(mmol m-2s-1)

Number of seeds/pod

Dry Dry

Seed weight (g)

50 100 150 200 250 300 36 39 42 45 48 511 2 3 4 5

1 2 3 4 5 50 100 150 200 250 300

3 6 9 12 15 18 0,03 0,06 0,09 0,12 0,15 0,18 0,21

0,03 0,06 0,09 0,12 0,15 0,18 0,21

3 6 9 12 15 18 21

5 10 15 20 25 30 35

3 6 9 12 15 18 2136 39 42 45 48 51

3 6 9 12 15 18

3 6 9 12 15 18 21

5 10 15 20 25 30 35

(mmol m-2s-1)

Number of seeds/pod

Dry Dry

Seed weight (g)

50 100 150 200 250 300 36 39 42 45 48 511 2 3 4 5

1 2 3 4 5 50 100 150 200 250 300

3 6 9 12 15 18 0,03 0,06 0,09 0,12 0,15 0,18 0,21

0,03 0,06 0,09 0,12 0,15 0,18 0,21

3 6 9 12 15 18 21

5 10 15 20 25 30 35

3 6 9 12 15 18 2136 39 42 45 48 51

3 6 9 12 15 18

3 6 9 12 15 18 21

(mmol m-2s-1)

Number of seeds/pod

Dry Dry

Seed weight (g)

50 100 150 200 250 300 36 39 42 45 48 511 2 3 4 5

1 2 3 4 5 50 100 150 200 250 300

3 6 9 12 15 18 0,03 0,06 0,09 0,12 0,15 0,18 0,21

0,03 0,06 0,09 0,12 0,15 0,18 0,21

3 6 9 12 15 18 21

5 10 15 20 25 30 35

3 6 9 12 15 18 2136 39 42 45 48 51

3 6 9 12 15 18

3 6 9 12 15 18 21

Frequency distribution of traits in Kano

(i) (j) (k) (l)

(mmol m-2s-1)

Number of seeds/pod

Dry Dry

Seed weight (g)

5 10 15 20 25 30 35

50 100 150 200 250 300 43 46 49 52 55 58

43 46 49 52 55 58

1 2 3 4 5

1 2 3 4 5 50 100 150 200 250 300

2 4 6 8 10 12

0,03 0,06 0,09 0,12 0,15 0,18 0,21

4 8 12 16 20 24 28 32

5 10 15 20 25 30 35

4 8 12 16 20 24 28 32

5 10 15 20 25 30 35

(i) (j) (k) (l)

(mmol m-2s-1)

Number of seeds/pod

Dry Dry

Seed weight (g)

5 10 15 20 25 30 35

50 100 150 200 250 300 43 46 49 52 55 58

43 46 49 52 55 58

1 2 3 4 5

1 2 3 4 5 50 100 150 200 250 300

2 4 6 8 10 12

0,03 0,06 0,09 0,12 0,15 0,18 0,21

4 8 12 16 20 24 28 32

5 10 15 20 25 30 35

4 8 12 16 20 24 28 32

5 10 15 20 25 30 35

(mmol m-2s-1)

Number of seeds/pod

Dry Dry

Seed weight (g)

5 10 15 20 25 30 35

50 100 150 200 250 300 43 46 49 52 55 58

43 46 49 52 55 58

1 2 3 4 5

1 2 3 4 5 50 100 150 200 250 300

2 4 6 8 10 12

0,03 0,06 0,09 0,12 0,15 0,18 0,21

4 8 12 16 20 24 28 32

5 10 15 20 25 30 35

4 8 12 16 20 24 28 32

5 10 15 20 25 30 35

Frequency distribution of traits in Ibadan

Traits Irrigation Drought RR% Irrigation Drought RR%

Days to flowering

No of seed/pod

Total yield (g/plant)

Gs (mmol m-2s-1)

No of pod/plant

100 Seed sweight (g)

19.3 (7-65) 51.0

49.5 (38-62) 48.3 (33-60) 2.4 43.4 (36-76) 40.6 (34-57) 6.4

35.5 (19-130) 18.4 (6-70) 48.2 39.3 (20-86)

6.5 (0-21) 46.7

20.8 (11-61) 10.3 (2-41) 50.5 27.2 (11-54) 12.8 (3-45) 52.9

14.7 (4-74) 8.1 (0-30) 44.7 12.1 (4-45)

10.0 (0-17) 16.6

6.9 (3-19) 6.4 (0-15) 6.3 7.6 (3-14) 7.4 (0-14) 1.4

12.1 (8-20 ) 10.0 (0-16) 16.6 12.0 (8-18)

86.0 (62-99) 0.4

17.1 (4-40) 10.9 (0-26) 36.4 13.7 (8-26) 8.1 (0-19) 40.6

89.5 (52-98) 89.4 (74-99) 0.1 86.3 (61-100)

Kano Ibadan

175.4 (51-411) 95.2 (18-228) 45.7 246 (136-404) 121.1 (11-298) 50.7

Effect of water stress on plant performances

Results

Correlation and path analyses

Direct and indirect effects of variables on GYD [rij] x [Pi-Y] [ri-Y]

Indirect effect via seed weight 0.431 x 0.086 0.037

Indirect effect via number of seed/pod 0.501 x 0.200 0.100

Indirect effect via stomatal conductance 0.232 x 0.055 0.013

Indirect effect via flowering time 0.001 x 0.034 0.000

Indirect effect via delayed leaf senescence 0.045 x 0.023 0.001

Indirect effect via fodder yield 0.438 x 0.246 0.107

Direct effect of number of pods/plant with GYD 0.587

Total (correlation between number of pod/plant and GYD) 0.845

Correlation between No of pod/plant and GYD is 0.845

Drought Irrigation

GYDP=0.055

(X)Residualfactors

P=0.064

(X)Residualfactors

Correlations and

path coefficient for

7 traits with direct

and indirect effect

on grain production

under drought in Kano

Results

LG No Markers Length cM Distance between Markers

1 58 111.6 2

2 21 31.6 1.5

3 26 66.3 2.6

4 27 59.6 2.2

5 28 52.8 2

6 27 78.6 3

7 26 60.4 2.3

8 21 44.1 2

9 19 33.7 1.7

10 17 40 2.3

11 12 54.3 4.5

Total 282 633 2.2

SNP genetic linkage map

This genetic linkage map is integrated in consensus map of 7 RILs

populations with 928 SNPs (Muchero et al. 2009b)

Genetic linkage map and QTL identified at seedling

Kruskal-Wallis Range % Variation

QTL LG Position (cM) Flanking Markers Significant level Dry Wet RR Dry Wet RR Dry Wet RR Explained

Gs-1 2 40.03 - 50.98 1_0595 - 1_1158 0.01 -0.001 na na na 2.91 1.34 0.28 1.12 1.03 0.62 2.1 - 10.7

Gs-2 7 2.06 - 10.95 1_1249 - 1_0559 0.01 -0.001 na na na 1.97 1.05 0.45 2.29 0.94 0.24 3.4 - 9.7

Gs-3 7 2.06 - 20.68 1_1249 - 1_1414 0.01 -0.001 na na na 2.63 1.14 1.32 2.15 0.66 0.87 3.8 - 12.8

Gs-4 8 23.13 - 31.32 1_1168 - 1_0530 0.01 -0.001 na na na 3.12 2.35 0.92 1.82 2.05 1.05 5.5 - 18.5

DLS-1 3 7.73 -32.66 1_0853 - 1_1349 0.01 - 0.001 na na na 4.20 4.31 2.34 3.78 4.09 2.08 9.3 - 17.8

DLS-2 3 19.33 - 28.96 1_1195 - 1_0104 0.01 - 0.001 na na na 3.97 2.87 1.98 4.33 3.11 2.19 3.9 - 16.2

DLS-3 3 54.25 - 70.41 1_1027 - 1_0594 0.05 - 0.001 na na na 3.91 2.71 2.03 3.75 2.06 2.02 6.1 -19.4

DLS-4 5 5.81 - 38.03 1_0309 - 1_0037 0.05 - 0.001 na na na 4.87 3.67 1.98 2.76 2.32 1.82 7 - 15.2

DLS-5 7 17.68 - 37.68 1_1414 - 1_0056 0.001 - 0.0001 na na na 10.75 4.89 3.10 5.21 2.59 2.26 10.3 - 46.3

DLS-6 7 20.68 - 41.89 1_1414 - 1_1249 0.001 - 0.0001 na na na 3.91 2.82 2.81 7.86 3.95 3.00 8.7 - 32.7

Flow-1 8 0 - 15.19 1_0298 - 1_0141 0.01 - 0.001 4.07 2.90 1.77 7.19 5.42 2.32 3.39 2.01 3.26 3.8 - 27.8

Flow-2 8 0 - 9.19 1_0298 - 1_1370 0.01 - 0.0001 3.89 3.10 2.46 5.29 3.42 3.64 4.61 3.43 3.29 5.6 - 19.6

Flow-3 5 16.34 - 31.70 1_0924 - 1_0800 0.001 - 0.0001 1.78 1.07 0.37 3.62 3.10 2.09 1.97 0.59 0.21 7.9 - 16.2

Flow-4 9 23.18 - 39.99 1_1467 - 1_1408 0.01 - 0.001 2.65 2.03 1.02 2.05 3.54 0.93 1.04 0.22 0.09 3.9 - 8.6

Flow-5 7 7.37 - 13.21 1_0056 - 1_0270 0.01 - 0.0001 3.36 1.39 0.96 3.43 0.98 1.01 0.97 0.23 0.09 2.2 - 10.2

Pod-1 3 73.83 - 78.30 1_0299 - 1_1349 0.01 - 0.0001 2.17 0.97 0.76 3.06 2.32 0.47 2.33 1.98 0.83 5.3 - 10.8

Pod-2 4 11.46 - 15.842 1_0275 - 1_0856 0.01 - 0.001 1.88 0.43 1.02 2.36 3.24 1.04 1.99 2.88 1.02 0.6 - 12.6

Pod-3 4 9.88 - 18.22 1_0304 - 1_1013 0.05 - 0.001 2.10 0.89 0.32 3.24 3.23 1.21 2.88 2.00 1.22 5.2 -12.7

Pod-4 5 25.58 - 32.70 1_0346 - 1_0800 0.05 - 0.001 1.22 0.88 0.46 0.27 0.43 0.01 2.21 0.37 0.08 2.5 - 8.5

Pod-5 8 18.68- 22.12 1_1370 - 1_0530 0.01 - 0.001 0.25 0.76 0.03 2.27 0.91 0.23 0.27 0.08 0.02 8.5 - 10.1

Pod-6 9 57.72 - 69.64 1_0221 - 1_1236 0.05 - 0.001 0.09 0.06 0.37 2.00 3.50 1.98 0.87 0.24 0.05 9.1 - 13.9

Pod-7 10 19.28 - 31.81 1_0416 - 1_0598 0.05 - 0.001 3.20 0.97 0.54 1.20 0.49 0.19 0.76 0.06 0.01 12.1 - 13.7

Days to flowering; permutation threshold 3.20

Stomatal Conductance Gs; permutation threshold (GW)* 2.10

Dealyed leaf senescence (DLS); permutation threshold 3.60

Number of pod/plant; permutation threshold 2.10

Year 1 (Kano) Year 2 (Kano) Year 2 (Ibadan)

QTL mapping for terminal drought tolerance traits

Kruskal-Wallis Range % Variation

QTL LG Position (cM) Flanking Markers Significant level Dry Wet RR Dry Wet RR Dry Wet RR Explained

Seed/P-1 2 23.05 - 29.95 1_1352 - 1_1230 0.05 - 0.001 2.29 1.98 0.69 1.64 1.97 0.41 0.90 0.76 0.25 5.8 - 9.2

Seed/P-2 3 13.328 - 18.42 1_1073 - 1_0373 0.05 - 0.001 0.01 0.02 0.01 2.26 2.20 0.43 1.03 2.40 0.02 4.2 - 10

Seed/P-3 4 11.46 - 17.22 1_0275 - 1_0398 0.05 - 0.001 0.81 0.82 0.01 1.56 2.25 0.87 1.22 2.73 0.51 5.3 - 9.4

Seed/P-4 4 25.03 - 33.41 1_0106 - 1_0774 0.05 - 0.001 0.02 0.01 0.01 0.87 2.30 0.20 2.10 4.66 1.92 4.7 - 17.3

Seedwt-1 4 7.88 - 21.27 1_0304 - 1_0106 0.001 - 0.001 2.59 2.30 0.90 3.62 2.49 0.65 2.10 1.04 0.72 5.7 - 13.8

Seedwt-2 6 2.93 - 8.32 1_1381 - 1_0943 0.01 - 0.001 5.97 2.58 0.45 1.93 1.22 0.32 2.06 2.01 0.67 8.8 - 10.9

Seedwt-3 6 0 - 12.32 1_0911 - 1_0943 0.001 - 0.001 5.97 2.58 1.93 2.46 0.99 0.39 2.29 2.21 1.09 6 - 22.8

Seedwt-4 10 47.37 - 55.99 1_0840 - 1_0007 0.001 - 0.001 2.41 0.90 0.08 2.19 4.29 1.93 2.03 2.89 1.03 12.1 - 19.3

Seedwt-5 10 6.77 - 19.28 1_1189 - 1_1049 0.01 - 0.001 3.41 0.53 0.12 2.93 0.90 1.32 1.22 0.87 0.09 7.4 -15.1

GY-1 8 17.19 - 27.13 1_1370 - 1_0530 0.01 - 0.001 0.21 0.09 0.00 2.71 0.91 0.04 1.24 1.01 0.38 8.5 - 13.8

GY-2 5 32.72 - 43.25 1_0419 - 1_0819 0.01 - 0.001 0.91 2.01 0.82 1.24 2.77 0.03 0.78 0.54 0.19 4.6 - 10.1

GY-3 9 10.5 - 30.49 1_0703 - 1_0137 0.01 - 0.001 0.02 0.98 0.32 2.36 2.92 1.03 1.87 2.44 0.21 8.5 - 12.2

GY-4 7 2.06 - 13.52 1_0248 - 1_0270 0.01 - 0.001 2.63 1.25 0.52 2.76 1.98 1.00 1.98 1.32 0.08 4.2 - 8.3

GY-5 3 0 - 14.09 1_0105 - 1_1065 0.001 - 0.001 1.98 0.91 0.09 2.10 3.11 1.08 2.13 1.92 0.05 6.7 - 12.9

GY-6 6 16.95 - 23.59 1_0706 - 1_0123 0.001 - 0.001 1.34 1.21 0.03 0.91 0.42 0.02 2.67 0.76 0.01 4.7 - 9.8

FY-1 5 23.54 - 29.67 1_0081 - 1_0800 0.05 - 0.001 1.23 1.98 0.91 3.10 2.77 1.22 2.12 0.03 0.01 6.7 - 9.1

FY-2 6 19.61 - 32.06 1_1381 - 1_0943 0.01 - 0.001 0.65 0.90 0.01 2.03 2.97 0.90 0.01 0.01 0.07 6.3 - 9.6

FY-3 10 31.42 - 53.05 1_0865 - 1_0354 0.05 - 0.001 0.03 0.01 0.01 1.82 1.43 0.76 4.53 2.41 0.09 6.7 -12.50

FY-4 6 8 - 31.06 1_0323 - 1_0943 0.05 - 0.001 2.01 0.04 0.06 0.98 0.62 0.03 2.07 3.96 0.21 7.4 - 13

FY-5 4 35.75 - 52.68 1_1221 - 1_1147 0.01 - 0.001 2.95 2.15 1.02 1.06 0.92 0.01 0.18 1.03 0.10 7.2 - 17.2

Fodder yield; permutation threshold 2.00

Number of seed/pod; permutation threshold 2.00

Seed weight; permutation threshold 2.40

Grain yield; permutation threshold 2.00

Year 1 (Kano) Year 2 (Kano) Year 2 (Ibadan)

QTL mapping for terminal drought tolerance triats

QTL-environment interactions

Grain and fodder yield and grain yield

components were most affected by environment

with QTL specific to water regime, location and

QTL-water treatment and location effects were

also observed for stomatal conductance, but to

the lesser extend for DLS and flowering time

QTL-water treatment, location and year effects

might be due to:

1. Experimental errors during measurements

2. Unstable weather conditions in Ibadan

3. Soil in Ibadan is richer (N, Zn, Fe, Mn)

4. Differences in soil water retention capacity with

more clay in Ibadan.

The QTL-environment interactions suggest that

some specific sets of genes account for traits

under water stress and well-watered conditions

Similar results were reported by (Sangara et al.

2001; 2004; Levi et al. 2009)

Co-localization of physiological

and productivity QTLs

Common genomic regions

These 4 common regions on LG3, LG5, LG7, LG8 where QTLs for Gs, DLS, flowering time, yields and yield components co-localized represent hot spot for drought tolerance traits.

These results demonstrate that some genes loci that regulate plant ability to DLS, maintain higher stomatal conductance, promote flower and pod formations.

Conclusion and implications for breeding

Despite the higher number of 42 QTLs, four

chromosomal regions contain several QTLs for

physiological and productivity traits

Breeding for such QTLs in the common regions

may help to improve several aspects of the

drought response in cowpea

Muchero et al. 2009b

Comparative genomics

Implications for breeding

Marker loci defining the QTLs in the common

chromosomal regions represent candidates for

further molecular studies for MAS

The fact that our genetic map is integrated in a

consensus map of 7 RILs population will help for

comparative genomic studies.

Acknowledgements

WUR Plant Breeding

Richard Visser

Gerard van der Linden

Abiotic research group

Christian Fatokun

Satoru Muranaka

R. Bandyopadhyay

All Technicians

Financial Support

IITA-Lukas Brader Fund, all members of IITA board,

Leventis Foundation and IFAR, Wageningen UR Plant

Breeding

Jeff Ehlers

Tim Close

P. Roberts

M. Muchero

NN. Diop

Dr Lukas Brader

Thanks for your attention

Genetic analysis of drought tolerance in cowpea (Vigna unguiculata L.)

Technology

Transcript of Genetic analysis of drought tolerance in cowpea (Vigna unguiculata L.)

INCLUSIÓN DE Vigna unguiculata Y Musa spp. PARA ...

Nutritional Value of Cowpea (Vigna Unguiculata L ... - bsss.bg · Issue 2. 112. Introduction . Cowpea (Vigna unguiculata L. Walp) is a widely grown legume food crop of the tropics

Development of new cowpea ( Vigna unguiculata) mutant ... · 22/05/2020 · 81 tolerance to Maruca vitrata pod borer which were observed among cowpea mutants [9,10,11] 82 [12,13,14].

VARIASI CAMPURAN KACANG TUNGGAK (Vigna unguiculata) …eprints.poltekkesjogja.ac.id/543/1/1. Awal.pdfvi ABSTRAK VARIASI CAMPURAN KACANG TUNGGAK (Vigna unguiculata) PADA PEMBUATAN TAHU

1a snake bean(vigna unguiculata )

Volume 12 No. 7 December 2012 THE EFFECT OF COWPEA … · Volume 12 No. 7 December 2012 THE EFFECT OF COWPEA (Vigna unguiculata) FLOUR AS AN EXTENDER ON THE PHYSICO-CHEMICAL PROPERTIES

Transformation of cowpea ( Vigna unguiculata L. Walp.) by … · 2010-08-30 · Adesoye et al. .… J. Appl. Biosci. 2010. Transformation of cowpea by Agrobacterium infiltration 1846

Assessment of Cowpea (Vigna Unguiculata(L.) Walp ...

EVALUATION OF PERFORMANCE OF COWPEA (Vigna unguiculata L) VARIETIES IN AGRONOMY FARM OF LAMJUNG CAMPUS

Long bean ( Vigna unguiculata )

Vigna Unguiculata as Source of Angiotensin-I Converting Enzyme ...cdn.intechopen.com/pdfs/42423/InTech-Vigna_unguiculata_as_source... · Vigna Unguiculata as Source of Angiotensin-I

Effect of Cu toxicity on growth of Cowpea (Vigna unguiculata)

“RESPUESTA FISIOLÓGICA DEL FRIJOL CAUPÍ (Vigna unguiculata ...

(Vigna unguiculata (L.) Walp.)

in Cowpea (Vigna Unguiculata L. Walp) from Hainan Province ...

Phenotypic variability studies in selected accessions of ... · Vigna unguiculata 1. Introduction Cowpea (Vigna unguiculata L. Walp) is a legume belonging to the family Fabaceae.

Evaluation and Utilization of Cowpea (Vigna unguiculata (L ...semenarska.rs/UNS-PSU/radovi/2/02 Evaluation 06-18.pdf · Production area of cowpea about 14 million hectares worldwide

Cowpea (Vigna unguiculata) Seed Productionafricasoilhealth.cabi.org/wpcms/wp-content/uploads/2017/03/Cowpea-Seed-Trainers-Notes.pdfCowpea (Vigna unguiculata) Seed Production Module

The genome of cowpea (Vigna unguiculata [L.] Walp.) · 2 ABSTRACT . Cowpea (Vigna unguiculata [L.] Walp.) is a major crop for worldwide food and nutritional security, especially in

REMEDIATIVE ABILITIES OF COWPEA (Vigna unguiculata ...