1. EVALUATION OF FARMER PREFERRED CASSAVA GENOTYPES FOR DROUGHT TOLERANCE
Prof. DSO Osiru
Mr. Laban Turyagyenda
Dr. Elizabeth Kizito
Dr. Yona Baguma
Dr. Morag Ferguson

2. Introduction Importance of cassava Constraints to cassava
Improved Food security and household income (for over 800 million people in tropics)
Fodder for livestock
Industrial uses (starch powder and alcohol)
Annual world production =241 MT (most from African small-scale farmers and Asia)
Constraints to cassava
Biotic (esp CBSV, CMD etc)
Abiotic (Salinity, soil, temp &water stress)
Post Harvest Physiological Deterioration
Drought/water stress

3. Importance of Drought/water stress
A major limiting factor to crop production worldwide
In Africa cassava growth cycle is interrupted by 3-6 months of drought
Water scarcity increasing/climate change
Pressure on arable land due to Population growth has increased utilization of marginal land.
Unfavourable terrain makes irrigation unaffordable

4. Rationale Considerable progress made on Research on other Traits
e.g yields, Pests and diseases (CGM, CMD,CBB, CBSD)
Much less progress on Research on drought tolerance

5. Genotypes well adapted to drought have been identified elsewhere.
However, response of farmer preferred genotypes to drought in Uganda is not well understood
The aim of the study was to identify the
genetic and physiological traits that
determine drought tolerance in farmer
preferred genotypes

6. KEY OBJECTIVES
Evaluate farmer preferred cassava genotypes for drought tolerance,
Identify morphological/physiological traits associated with drought tolerance mechanism in local germplasm.
Identify drought tolerance genes.
Assess the level of genetic diversity among farmer preferred Ugandan cassava germplasm,
This presentation reports the results of field evaluation of farmer preferred accessions

7. Methodology
53 cassava accessions collected from 15 drought prone Districts
11 improved and 42 landraces
Field screening done in two drought prone Districts of Buliisa and Nakasongola and one normal rainfall site at Kabanyolo in Wakiso District.

8. Parameters measured Number of Roots (NR) Harvest Index (HI)
The field design =RCBD
Two replications of 10 plants of each accession
Spacing 1 m X 1m between plants & 2m between plots
Two cropping seasons (Oct 2008 and Oct, 2009.
Field was rain-fed,
weeding was manually done with hand hoe
neither pesticides nor fertilizers were applied.
Parameters measured
Harvest Index (HI)
Starch content of roots
Leaf Retention (LR)
Dry Matter content (DM),
Number of Roots (NR)
Above Ground Biomass (AGB)
Fresh Root Yield (FRY)
Vigour

9. 1. Vigour
0=dead,
1=drying,
2=wilting,
3=healthy 1 2 3

10. Number of roots: Harvestable roots from four plants of each accession per replication.
Leaf retention: Visually estimated as percentage proportion of leafy stem to the leafless stem for each plant (Fukuda et al., 2010).
Dry matter content (DM) and Starch content (RSC)=(specific gravity method according to Kawano et al., 1987; Chavez et al., 2005)
RSC =

11. Plant height: The plant height was determined using a measuring stick calibrated in centimetres. The measurements were taken on the primary stem , 8 months after planting

12. Genotype x Location (df=104)
Results
Genotype and location effects were highly significant (P<0.01) for all the parameters evaluated
GXL significant except for AGB
Mean squares
SOV
Genotype (df=52)
Location (df=2)
Genotype x Location (df=104)
error
VG a
8.72***
613.04***
4.65***
0.66 HI
0.09***
1.08***
0.02***
0.02 NR
55.328***
214.44***
5.14***
3.534
DMC
246.91***
***
125.12***
89.24
RSC
123.82***
826.07***
62.74***
44.75
FRY
400.38***
***
46.89***
34.74
AGB
240.64***
796.28***
46.99
48.66 LR
551.40***
***
314.60***
162.40 PH
***
***
***
881.30
a Data collected from 2008 planting

13. Percent difference=(R-T)/R*100)
Accession
Kabanyolo (R)
Buliisa (T)
% Difference*
Nakasongola (T)
% Difference
Akena
0.19
0.20
-5.26
0.40
AladoAlado
0.50
0.08
84.00
0.35
30.00
Bao
1.50
94.67
0.23
84.67
Bukalasa
1.25
72.00
0.88
29.60
Dito
2.00
82.50
0.60
70.00
Ecilecili
1.09
0.13
88.07
67.89
Egabu
1.06
0.05
95.28
0.45
57.55
Kabwa
1.66
0.38
77.11
63.86
Kwatamumpare
2.59
91.12
1.63
37.07
Luderudu
0.78
93.59
0.00
Lugbara
1.44
100.00
0.98
31.94
Maburu
1.41
0.03
97.87
0.93
34.04
Magana
0.94
78.72
1.00
-6.38
Mercury
1.13
95.58
0.73
35.40
MH96/0686
1.91
0.10
94.76
1.18
38.22
Mukalasa
1.03
87.38
29.13
Musita
0.75
47.92
Namukoni
1.56
96.79
61.54
Namulalu
0.59
94.92
0.28
52.54
NASE1
0.97
0.48
50.52
0.33
65.98
Safina
0.63
47.62
0.85
-34.92
Sogasoga
86.60
TME14
1.69
80.47
1.53
9.47
TME204
1.16
24.14
Tongolo
2.13
93.90
44.60
Mean
1.34
83.01
0.72
27.56 SE
0.81
Vigour
Percent difference=(R-T)/R*100)

14. Site FRY % Diff AGB LR PH KAB 13.58 14.91 67.64 169.70 BUL 7.90 41.80
Means and percentage difference of cassava genotypes for traits evaluated in filed .
Site VG
% Diff HI NR
DMC
RSC
KAB
1.36
0.46
5.70
40.39
22.76
BUL
0.18
86.56
0.38
18.56
4.07
28.63
32.74
18.94
17.34
23.81
NAK
0.72
47.22
0.35
24.35
5.04
11.53
34.17
15.40
18.36
19.33
LSD(5%)
0.05
0.02
0.29
2.58
1.83
Site
FRY
% Diff
AGB LR PH
KAB
13.58
14.91
67.64
169.70
BUL
7.90
41.80
11.29
24.29
43.31
35.97
142.02
16.44
NAK
8.57
36.86
14.07
5.64
56.72
16.15
141.74
16.60
LSD(5%)
0.96
1.14
1.79
4.17
Percent difference =(R-T)/R*100) where Kabanyolo (KAB) was regarded control (R) and Buliisa (BUL) and Nakasongola(NAK) as treatment (T)

15. Mean performance 53 genotypes under drought (D) and Normal (N) conditionsHI NR
FRY
AGB LR PH D N
Akena
0.43
0.46
6.67
7.83
15.33
16.06
21.08
19.17
60.1
74.73
166.1
188
Bao
0.31
0.47
3.08
6.5
5.09
19.33
11.17
19.67
34.9
70.43
159.5
166.9
Buganda
0.4
2.75
5.5
11.97
17.11
16.88
24.22
40.76
74.4
142.3
161.9
Maburu
0.39
4.64
8.67
9.67
13.61
46.68
60.32
165.1
200.7
Magana
0.52
0.56
7.58
5.67
17.58
19.94
15.28
16.28
58.44
72.68
153.1
184.7
Masindi1
0.32
2.08
1.5
3.2
6.28
8.17
9.33
45.92
67.86
102.8
141.7
Mercury
0.3
0.48
3.92
4.83
3.89
19.44
9.89
21.89
50.35
62.67
164.8
210.6
MH96/0686
9.92 10 21
22.39
22.5
24.94
63.91
72.65
135.3
164.1
Musita
0.53
6.58
7.33
8.33
15.39
11.61
13.06
49.42
66.22
125.5
160.6
Nase1
0.54
5.25
17.79
18.67
15.61
20.78
50.61
72.55
135.6
164.4
Nase11
0.34
5.75
4.67
10.91
22.53
19.91
18.47
50.05
67.89
138.3
153.9
Nase12
0.58
8.75
16.86
23.33
19.19
19.28
53.92
58.11
121.2
121
Nase3
0.49
0.59
14.89
15.89
13.47
51.59
61.04
144.4
174.2
Nase9
0.45
0.38
4.58 6
16.11
13.72
20.83
24.56
53.11
68.45
168.2
184.4
Njule
0.22
0.44
3.67
2.92
11.78
14.31
14.56
52.32
59.69
137.7
132.9
Nyakakwa
0.51
6.33
6.59
13.94
13.22 13
55.93
58.96
152.9
162.1
Nyamutukura
14.79
19.87
18.62
58.51
65.5
177
179
Nyapamitu
0.25 5
2.2
8.41 8
51.63
61.6
146.2
190
TME14
0.5
5.92
15.72 15
16.22 17
57.67
65.02
130.4
TME204
0.55
19.56
15.92
13.44
59.78
66.47
169.1
156.2
Tongolo
0.33
4.17
6.36
8.5
9.86
11.39
39.85
53.84
127.4
144.1
Tongolo2
0.35
4.75
7.17
6.83
12.73
10.44
42.5
54.67
193.1
243.2

16. Examples of tolerant and susceptible genotypes
Putative tolerant
Putative susceptible
Akena
Buganda
MH96/0686
Kwatamumpare
MH97/2961
Namulalu
Nyamutukura
Njule
TME204
Nyapamitu
Magana
Nyaraboke
NASE 12
Rugogoma
Yellow
Bao
MM96/4271
Masindi1

17. Conclusions and recommendations
Water stress affect performance of cassava
There is variability among genotypes for most traits
For successful expansion of cassava growing to marginal areas, efforts should be aimed at breeding for drought tolerant genotypes
Genetic variability observed can be effectively utilised for improvement of cassava
We recommend studies to understand genetic inheritance of drought tolerance

18. Thanks for Listening

19. Acknowledgements
MSI-UNCST and NARO for funding