Plant disease and pest monitoring surveys
in Africa RISING action areas in Tanzania
Africa RISING East and Southern Africa...
Papias Binagwa and Edith Kadege
Selian Agricultural Research Institute (SARI), Tanzania
Jean Claude Rubyogo, Mathew Abang ...
Objective
“To provide current information on the abundance and
distribution of important pests and diseases of common bean...
Geographic coverage
Sub-Humid District
(1610 to 2178 mts)
Njoro
Semi-Arid Districts
(1261- 1527 mts)
Medium elevation
Low ...
 Farmer fields and project sites
were surveyed.
 Structured questionnaires &
field observation methods used
to obtain da...
Variable Groups Frequency (%)
Total land
owned
0-4 acres 90.0
5-10 acres 10.0
11 and above 0.0
Land under
maize
0-4 acres ...
District Altitude
(mts)
Locations Rainfall Total
farms
Babati 1610 –
2178
Long High rain 5 Aphids (4/5)
Stem borer (1/5)
S...
Larvae of Sesamia calamistis Larva of Busseola fusca Stem borer damages
Aphid infestation on maize in Long (Babati district)
Neozygites sp
Neozygites sp
Aphids detected on 3 – 50% of the plants ...
D02_Rm2COI_826285
E02_Rm3COI_826286
G04_RmECR1_826304
G11_RmECR2_826360
H11_RmECR3_826361
A05_RmAIbo_826306
B01_F11TTa_840...
Striga asciatica
Severe incidence of striga in Kongwa (Moleti)
Not observed in any other location
Species Average per 100 g root
sample from 78 samples
(frequency)
Host
Pratylenchus zeae 17804 (N = 51) Maize, groundnut,
...
Species
Total detected
(N=76)
Frequenc
y
Pratylenchus zeae 1964 18
Helicotylenchus multicinctus 620 17
Helicotylenchus dih...
Fungal and viral diseases
District Locations Bipolaris
leaf blight
% inc (sev)
Curvularia leaf
spot
% inc (sev)
Turcicum l...
Babati
Bipolaris leaf blight
Curvularia leaf spot
Turcicum leaf blight
Virus diseases (MLND) in Seleto
Kiteto (Njoro)
Field trial of new maize lines in a
farmer’s field
Brown spot
Termites
Rust ?
Drought and turcicum leaf bli...
Kongwa
Drought
Curvularia leaf spot
Turcicum leaf blight
Turcicum leaf blight Ergot / heat smut Curvularia leaf spot
Maize lethal necrosis in Babati (Seloto)
MCMoV + SCMV
(MLND)
MCMV only
MCMV SCMV SCMV+MCMV MSV SScMV
Maize
stripe
Maize
mosaic
virus
51 (64%) 18 (22.5) 17 (21.5) 1...
S FT W1 W2 W3 W4 E1 E2 E3 E4 E5 E6
MCMV CP1 induced with
1mM IPTG and purified
through Ni-NTA column
S: Soluble fraction; ...
MCMoV Infected sample Healthy MSV Buffer
1:2000 3.00 1.21 1.95 0.16 0.17 0.16
1:10,000 2.62 0.78 1.43 0.16 0.17 0.16
1:20,...
 Production is male
dominated
 63.3% of farmers
intercropped beans and
use of owned saved seed
is most common source
of ...
 54% of farmers consider bean production to be decreasing
 Pests and disease (36%); drought (40%) were cited as the
majo...
 More than 10 pests were observed but aphids and white flies
were the most abundant.
Aphids
White
flies
Foliage
beetles
L...
5e 5f
 Disease varied significantly between sites with Common
bacterial blight(CBB), Angular leaf spot(ALS) & viral
diseases be...
 Pests:
 Beans: Observed in all fields. Aphids and whitefly infestations were the
highest
 Maize: Observed in all field...
 Agricultural extension packages promoting efficient IPDM options
should be a priority in subsequent activities.
 Widen ...
Acknowledgments
Irene Mwasaga
Lazaro Tango
Frank
Africa RISING
Africa Research in Sustainable Intensification for the Next Generation
africa-rising.net
Plant disease and pest monitoring surveys in Africa RISING action areas in Tanzania
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Plant disease and pest monitoring surveys in Africa RISING action areas in Tanzania

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Presented by Lava Kumar (IITA) and Warren Arinaitwe (CIAT) at the Africa RISING East and Southern Africa annual review and planning meeting, Lilongwe, Malawi, 3-5 September 2013

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Plant disease and pest monitoring surveys in Africa RISING action areas in Tanzania

  1. 1. Plant disease and pest monitoring surveys in Africa RISING action areas in Tanzania Africa RISING East and Southern Africa annual review and planning meeting, Lilongwe, Malawi, 3-5 September 2013 Lava Kumar (IITA) and Warren Arinaitwe (CIAT)
  2. 2. Papias Binagwa and Edith Kadege Selian Agricultural Research Institute (SARI), Tanzania Jean Claude Rubyogo, Mathew Abang and Warren Arinaitwe* International Center for Tropical Agriculture (CIAT) Lava Kumar*, C Agboton, M Harun, E Mbiru, F Ngulu, E Swai, D Coyne, F Beed, M Tamo, M Bekunda and I Hoeschle-Zeledon A Owati, O Patricia, I David and M Hema International Institute of Tropical Agriculture (IITA) *Presenting authors Study Team and Authors
  3. 3. Objective “To provide current information on the abundance and distribution of important pests and diseases of common bean, groundnut, maize, pigeonpea and soybean in the Africa RISING project action areas” Approach Interdisciplinary study teams from organizations working in the actions sites participated in the survey Surveys were conducted in April •Filed observation •Sample collection for laboratory analysis •Interview with farmers
  4. 4. Geographic coverage Sub-Humid District (1610 to 2178 mts) Njoro Semi-Arid Districts (1261- 1527 mts) Medium elevation Low rain Moleti Mlali-Lyegu High rain Seleto Long Low rain Sabelo •Maize in all locations •Common bean survey in Babati
  5. 5.  Farmer fields and project sites were surveyed.  Structured questionnaires & field observation methods used to obtain data including, gender, input usage, production trends P&D Management,P&D incidence &severity.  Data was analysed using SPSS Survey Methodology Maize Common bean
  6. 6. Variable Groups Frequency (%) Total land owned 0-4 acres 90.0 5-10 acres 10.0 11 and above 0.0 Land under maize 0-4 acres 100.0 5-10 acres 0.0 Cropping pattern Monocropping 50.0 Intercropping 40.0 Both 10.0 Planting pattern Row planting 100.0 Source of seed Own saved seed 50.0 Seed dealer 40.0 Local market 10.0 Trend of maize production- 5 years: Decreasing: 30% Increasing: 10% Same: 60% Preferred varieties: Local: 40% reason drought tolerant, affordable/ easily accessible Improved: 60% High yield Production constrains Weather: Drought Pests and diseases Access to inputs: usually delayed, improved varieties are expensive Generally farmers perception on IPM option is low. They do not practice crop rotation. Farmer responses (N=10)
  7. 7. District Altitude (mts) Locations Rainfall Total farms Babati 1610 – 2178 Long High rain 5 Aphids (4/5) Stem borer (1/5) Siloto High rain 5 Stem borer (3/5) Sabilo Low-rain 4 Aphids (1/4) Kiteto 1520 – 1527 Njoro Low rain 2 Stem borer (2/2) Kongwa 1132 - 1299 Mlali-Lyegu Low rain 3 Stem borer (2/3) Moleti Low rain 2 Stem borer (2/2) Kwamasingisa Low rain 1 Stem borer • Two maize varieties (Situka and Kilima) were more susceptible to the stem bores attacks (mainly Busseola fusca and Sesamia calamistis). • There may exist other stem borer species to be identified. • Maize aphids also were encountered and its infestation rates were fluctuate • Some aphids parasitoids exist in the fields and other aphids were attacked by Neozygites. Summary of pests
  8. 8. Larvae of Sesamia calamistis Larva of Busseola fusca Stem borer damages
  9. 9. Aphid infestation on maize in Long (Babati district) Neozygites sp Neozygites sp Aphids detected on 3 – 50% of the plants in Long (high altitude ca2100 mts) Biocontrol agents colonized aphid colonies
  10. 10. D02_Rm2COI_826285 E02_Rm3COI_826286 G04_RmECR1_826304 G11_RmECR2_826360 H11_RmECR3_826361 A05_RmAIbo_826306 B01_F11TTa_840483 D03_Rm4COI_826293 C02_Rm1COI_826284 F02_Rm3bCO_826287 HQ112195India-R maidis F01_F12Tan_840487 G01_F9Tanz_840488 A01_F10Tan_840482 C01_F11BTa_840484 TANZF13COF D01_F2Tanz_840485 GU457795RhoKorea-R padi GU140277Canada-P nigronervosa 100 100 99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.02 0.03 0.04 0.01 0.01 Rhopalosiphummaidis Rhopalosiphumpadi Molecular analysis (COI gene-based taxonomy) confirmed aphids found in all locations as R. padi. Only in one location (Long) R. maidis observed on cobs. Aphis craccivora detected on pigonpea in one location (Kiteto) Identification of aphids
  11. 11. Striga asciatica Severe incidence of striga in Kongwa (Moleti) Not observed in any other location
  12. 12. Species Average per 100 g root sample from 78 samples (frequency) Host Pratylenchus zeae 17804 (N = 51) Maize, groundnut, sorghum, sunflower, pigeonpea and common bean Helicotylenchus multicinctus 335 (N=14) Maize, sorghum, bambara Helicotylenchus dihystera 8000 (N=1) Common bean Meladogyne spp. 3297 (N=12) Maize, groundnut, sorghum, sunflower, pigeonpea and common bean Tylenchus semipenetrans 95 (N=6) Maize, common bean Root nematodes
  13. 13. Species Total detected (N=76) Frequenc y Pratylenchus zeae 1964 18 Helicotylenchus multicinctus 620 17 Helicotylenchus dihystera 87 5 Tropholus spp 24 9 Tylenchus semipenetrans 64 14 Aphelenchus spp. 44 6 Aphlencoid 2 1 Hoploliamus spp. 40 8 Paratrichodorus spp. 46 11 Rhadinaphelenchus spp. 7 4 Rhabditis (steinerma and Heterorhabdits spp.) 2542 76 Rotylenchulus reniformis 2 2 Xiphinema spp. 15 6 Trichodorus spp. 0 0 Longidorus spp. 0 0 Meloidogyne spp 2324 23 Nematodes in soil samples Molecular identification of nematode species is in progress
  14. 14. Fungal and viral diseases District Locations Bipolaris leaf blight % inc (sev) Curvularia leaf spot % inc (sev) Turcicum leaf blight % inc (sev) MLND % inc (sev) MSV % inc (sev) Babati Long <10 (3) 10 – 20 (3) 10 - 40 (3) + <2 (3) Siloto 20 (3) 15 (3) 10 – 20 (2) 10 – 50 (4) <2 (3) Sabilo 35 (3) + 10 – 20 (3) - <2 (3) Kiteto Njoro + + <10 (3) + <1 (2) Kongwa Mlali-Lyegu + + <10 (3) + Moleti + + 30 (3) + 10-30 (3) Kwamasingisa + + + - -
  15. 15. Babati Bipolaris leaf blight Curvularia leaf spot Turcicum leaf blight Virus diseases (MLND) in Seleto
  16. 16. Kiteto (Njoro) Field trial of new maize lines in a farmer’s field Brown spot Termites Rust ? Drought and turcicum leaf blight
  17. 17. Kongwa Drought Curvularia leaf spot Turcicum leaf blight Turcicum leaf blight Ergot / heat smut Curvularia leaf spot
  18. 18. Maize lethal necrosis in Babati (Seloto)
  19. 19. MCMoV + SCMV (MLND) MCMV only MCMV SCMV SCMV+MCMV MSV SScMV Maize stripe Maize mosaic virus 51 (64%) 18 (22.5) 17 (21.5) 16 (20) 13 (16.2) 4 (5) 0 Viruses detected (N = 80 samples) SCMV only Incidence of MLN agents differ
  20. 20. S FT W1 W2 W3 W4 E1 E2 E3 E4 E5 E6 MCMV CP1 induced with 1mM IPTG and purified through Ni-NTA column S: Soluble fraction; FT: Flow through W1-W4 : Washings; E1-E6 : Elutions Expressed MCMoV CP Cloning and expression of MCMoV Coat Protein in E. coli
  21. 21. MCMoV Infected sample Healthy MSV Buffer 1:2000 3.00 1.21 1.95 0.16 0.17 0.16 1:10,000 2.62 0.78 1.43 0.16 0.17 0.16 1:20,000 1.92 0.51 0.93 0.16 0.17 0.15 MCMoV antibodies in ELISA 0.00 0.50 1.00 1.50 2.00 2.50 ODat405nm Inf Hel MSV Buf
  22. 22.  Production is male dominated  63.3% of farmers intercropped beans and use of owned saved seed is most common source of planting seed Key findings Variable Group Freq (%) Sex Male 21 70.0 Female 9 30.0 Land under beans 0-4 acres 24 80.0 5-10 acres 20.0 Cropping pattern Monocrop 6 20.0 Intercrop 19 63.3 Both 5 16.7 Seed source Own-saved seed 12 40.0 Agro input deal 3 10.0 Local market 10 33.3 Others 5 16.7
  23. 23.  54% of farmers consider bean production to be decreasing  Pests and disease (36%); drought (40%) were cited as the major to decreasing trends. Production trends in the last 5years Decreasing Same Increasing Don’t know 0 2 4 6 8 10 12 14 16 18 Farmers perception
  24. 24.  More than 10 pests were observed but aphids and white flies were the most abundant. Aphids White flies Foliage beetles Lady birds Large black beetles Village Variety Site In Se In Se In Se In Se In Se Seloto Local NP 70 2 90 3 30 1 70 0 0 0 Kunguru NP 60 2 100 3 60 2 0 0 0 0 Selian 05 BS 50 2 90 3 40 1 0 0 0 0 Selian06 MS 10 0 80 2 80 2 10 0 0 0 Kunguru NP 70 2 90 3 40 1 100 0 0 0 Selian97 NP 50 2 70 2 0 0 30 0 0 0 Mean 51.7 1.7 86.7 2.6 41.7 1.2 35.0 0.0 0.0 0.0 Long Selian06 MS 90 2 40 1 0 0 60 0 50 3 Bwana-shamba NP 100 3 40 1 70 2 80 0 0 0 Selian06 BS 0 0 80 2 0 0 0 0 70 3 Mean 63.3 1.7 53.3 1.3 23.3 0.7 46.7 0.0 40.0 2.0 Sabilo Farm NP 80 3 80 2 0 0 30 0 0 0 Kunguru NP 60 2 70 2 50 1 20 0 0 0 Bwana-shamba NP 40 1 80 2 0 0 30 0 0 0 Lyamungo90 MS 60 3 60 2 40 1 60 0 0 0 Farm NP 60 2 50 2 0 0 0 0 0 0 Mean 60.0 2.2 68.0 2.0 18.0 0.4 28.0 0.0 0.0 0.0
  25. 25. 5e 5f
  26. 26.  Disease varied significantly between sites with Common bacterial blight(CBB), Angular leaf spot(ALS) & viral diseases being the major. CBB* Viral diseases Root rots Rust Ascochyta blight Anthracnose ALS* Village Variety Site In Se In Se In Se In Se In Se In Se In Se Seloto Local NP 45 2 0 0 0 0 0 0 0 0 0 0 40 2 Kunguru NP 0 0 0 0 0 0 10 1 0 0 0 0 70 2 Selian 05 BS 0 0 25 1 0 0 90 3 0 0 0 0 80 3 Selian06 MS 40 2 20 1 0 0 0 0 0 0 0 0 50 2 Kunguru NP 0 0 20 2 0 0 40 2 0 0 0 0 80 3 Selian97 NP 0 0 0 0 10 2 0 0 0 0 0 0 30 2 Mean 14 1 11 1 2 0 23 1 0 0 0 0 58 2 Long Selian06 MS 60 3 0 0 0 0 0 0 80 3 90 3 0 0 Bwana-shamba NP 80 3 0 0 0 0 0 0 0 0 0 0 0 0 Selian06 BS 70 3 0 0 0 0 0 0 80 2 90 3 0 0 Mean 70 3 0 0 0 0 0 0 53 2 60 2 0 0 Sabilo Farm NP 40 2 0 0 70 3 0 0 0 0 35 1 0 0 Kunguru NP 60 2 90 3 0 0 0 0 0 0 0 0 0 0 Bwana-shamba NP 80 2 20 1 0 0 60 3 0 0 0 0 0 0 Lyamungo90 MS 30 1 60 2 40 2 0 0 0 0 0 0 0 0 Farm NP 60 3 0 0 0 0 0 0 0 0 25 1 0 0 Mean 54 2 34 1 22 1 12 1 0 0 12 0 0 0
  27. 27.  Pests:  Beans: Observed in all fields. Aphids and whitefly infestations were the highest  Maize: Observed in all fields. Stem borers and aphids found to cause significant damage, but incidence is less <40%. Work in progress to characterize insect specimens using DNA markers (CO1 gene) Conclusions  Pathogens  Beans: Multiple disease complex wide spread on beans. Individual disease incidence and severity differ among sites. Characterization of pathogens pending.  Maize: Multiple diseases widespread in all locations. Turcicum leaf blight, bipolaris leaf blight, Curviularia leaf spot and MLND are significant.
  28. 28.  Agricultural extension packages promoting efficient IPDM options should be a priority in subsequent activities.  Widen the survey scope by increasing the sample size to at least 60 farmers/village. Also include non-project districts in Tanzania.  Identification of major pests and diseases using more robust methods (in progress)  Utilize the diversity knowledge to establish appropriate diagnostics and isolates for phenotyping germplasm for disease resistance  Promote improved disease resistant varieties to overcome common maize diseases. Future directions
  29. 29. Acknowledgments Irene Mwasaga Lazaro Tango Frank Africa RISING
  30. 30. Africa Research in Sustainable Intensification for the Next Generation africa-rising.net

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