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

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

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

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

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

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

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.

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

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

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 =

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

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*** 1647.27*** 125.12*** 89.24 RSC 123.82*** 826.07*** 62.74*** 44.75 FRY 400.38*** 2762.04*** 46.89*** 34.74 AGB 240.64*** 796.28*** 46.99 48.66 LR 551.40*** 54881.50*** 314.60*** 162.40 PH 8647.80*** 95423.60*** 2710.40*** 881.30 a Data collected from 2008 planting

Percent difference=(R-T)/R*100) Accession Kabanyolo (R) Buliisa (T) % Difference* Nakasongola (T) % Difference Akena 0.19 0.20 -5.26 0.40 -110.53 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)

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)

Mean performance 53 genotypes under drought (D) and Normal (N) conditions   HI 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

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

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

Thanks for Listening

Acknowledgements MSI-UNCST and NARO for funding