Presented by Kihara J., Kizito F., Lukuyu B., Jumbo B., Sikumba, G., Lyimo S., Marwa L. and Mateete B. at the Africa RISING ESA Review and Planning Meeting, Arusha, Tanzania, 9-11 September 2014

1. Towards increased crop productivity and
sustainability of natural resources in Babati,
Tanzania
Kihara J., Kizito F., Lukuyu B., Jumbo B., Sikumba,
G., Lyimo S., Marwa L., and Mateete B.
Africa RISING ESA Review and Planning Meeting,
Arusha, Tanzania, 9-11 September 2014

2. Project Overview
Piloting scalable farmer technology initiatives
•Increased crop, forages and system productivity;
•Sustainable use of the natural resource base: Soils, water, biomass
Production
constraints
Soil limitations
Climate variability
Low system
productivity
Maize leaf
necrosis
AR Interventions:
System responses
Fertilizer trials
Natural resources
conservation
Forage integration
MLND support
Scenario
assessments
Crop yields
Soil moisture
Organic carbon
Sediment levels
Runoff levels
Farm level
economic gains
Next steps
Scaling of feasible
technologies
Validation of
scenarios
Iterative linkage to
AR4D Platforms
Strategic
partnerships
Targeting policies
for adoption
Tools and
approaches
Farmer
assessments
Participatory field
days
System level
approaches
Soil, Crop &
climate models
Primary data
GIS/Spatial
analysis tools
Surveys

3. Production constraints
CliSmoialt leim vaitraitaiboinlisty
80
70
60
50
40
30
20
10
0
Daily rainfall (mm)
Production
constraints
Soil limitations
Climate variability
Low system
productivity
Variable Low Medium High
Organic carbon <0.5 0.5-0.75 >0.75
Available P <30 30-50 >50
Extractable K <200 200-400 >400
pH <5.5 5.5-7.5 >7.5

4. • Integrated approaches to manage Maize Lethal Necrosis
(MLN) disease in Tanzania
• MLN is caused by Maize chlorotic mottle virus (MCMoV)
in combination with any of the cereal viruses in the
family Potyviridae, such as Sugarcane mosaic virus
(SCMV), & transmitted by insect vectors
Maize leaf necrosis
• Losses due to MLN can reach 100% where the
disease pressure is high
• Plant host resistance combined with good
agronomic and cultural practices are the best &
sustainable approach to manage MLN
Production constraints

5. Response to P sources and different
maize varieties
Trt No. Treatment
T1 Control
T2 Minjingu Mazao
T3 Minjingu granular
T4 DAP
T5 3 tons FYM/ha + Minjingu Mazao
T6 6 tons FYM/ha alone
Control yield (t/ha)
Difference in maize grain yield
from the control (t/ha)
8
6
4
2
0
1 2 3
Village
Hallu
Long
Matufa
Sabilo
Seloto
• Positive but highly
variable responses
in all villages
• Farmers
appreciated
importance of
fertilizer use
• Change in mindset
on fertilizers
• Row planting with
standard spacing

6. Need for site specific
 recommendations
• Some fields are not very responsive
to N and P
• Nitrogen at 45 and 90 Kg N ha-1 is
mainly within “profitable” range.
• Significant N responses in Seloto and
Sabilo, low in other villages
• Need of “simple” tools for site-specific
recommendations

7. Performance of Napier
grass accessions

8. MLN impact on the plant reproductive
system
• Premature drying of ears, no pollen & poor seed
set
Response taken by Africa RISING
• Support to evaluate several new maize varieties
in MLN hotspot areas in Babati, Tanzania.
• Over 1000 new hybrids evaluated in farmers
fields
• Support to conduct trials on application of good
agronomic and cultural practices
• Time of planting
• Timely weeding & fertilizer application
• Pest management
MLND support
MLN Support

9. Table 1. Preliminary results : new maize hybrids evaluated in 33 Babati trials
Name of hybrid GY PH (cm) EH (cm) EP EPP ER GM % NP
CKH124960 6.35 197.35 105.09 0.53 1.56 30.50 18.90 11.02
CKH124718 6.05 198.66 107.47 0.53 1.67 15.76 19.45 10.71
CKH124713 6.04 196.92 100.17 0.52 1.30 13.32 21.35 11.14
CKH124783 6.02 206.37 115.82 0.58 1.31 25.03 17.95 10.03
CKH124727 6.02 195.90 95.90 0.51 1.27 7.27 19.10 13.38
CKH124776 5.75 196.51 120.19 0.57 1.34 19.28 20.70 10.62
CKH124742 5.71 187.09 98.09 0.51 1.41 18.05 21.00 10.09
CKH124712 5.70 190.61 96.35 0.51 1.26 29.78 20.05 11.48
CKH124730 5.68 194.67 96.11 0.52 1.26 22.28 18.35 12.15
CKH124743 5.63 185.44 95.54 0.51 1.23 8.85 20.30 13.46
CKH124775 5.60 213.84 110.66 0.54 1.20 20.54 19.20 10.41
CKH124715 5.50 176.08 97.92 0.56 1.25 -0.27 20.40 8.24
CKH124710 5.46 181.65 109.20 0.60 1.20 39.55 19.20 14.54
CKH124781 5.39 195.97 106.69 0.54 1.40 20.00 19.70 12.57
WH403 3.73 182.20 96.46 0.54 1.16 21.67 24.05 6.48
WH505 3.44 187.86 104.02 0.58 1.19 37.96 19.35 12.91
H520 2.75 223.29 126.32 0.59 1.47 44.05 19.20 11.42
Experiment Mean 4.14 191.50 104.77 0.55 1.33 26.96 20.42 10.26
LSD (0.05) 3.13 23.12 17.59 0.09 0.52 30.44 4.46 5.69
MSe 2.44 133.33 77.19 0.00 0.07 231.03 4.95 8.08
CV 37.8 6.03 8.39 8.24 19.52 56.38 10.90 27.70
p 0.61 0.00 0.00 0.27 0.15 0.23 0.41 0.42
p ns ** ** ns ns ns ns ns
NumSignificantSites 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00
NumReps 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00

10. Farmer technology
assessments
 P-sources and varieties
 Farmers describe characteristics of
varieties/ technologies and did matrix
and pairwise ranking
 DAP and Minjingu Mazao best P sources
 Pioneer 3253 and SC 627 maize varieties
scored good to excellent in most criteria
SC 627
DK 8031
PIONEER
3253
PAN
4M 19
Total
Rank
SC 627
SC 627 Pioneer
3253
SC 627 2 2
DK 8031
Pioneer
3253
DK
8031
1 3
PIONEER
3253
Pioneer
3253
3 1
PAN 4M
19
0 4
 Desmodium was the preferred legume for
intercropping with Napier grass due to high
leafiness, drought, pest and disease resistance
 Three Napier grass accessions were preferred
by farmers: ILRI 16837, KK2 and ILRI 16835
Farmers assessing forages
Farmers assessments of
P sources and varieties

11. Farmer Field days
 2 major field days on fertilizers and varieties and one on
forages:
 215 (58) in Sabilo,
 161 (49) in Hallu
 At least 6 media houses in each
 Matufa, Halu and S/Moyo : 77 (29)

12. Inflows Outflows
Off-farm influxes
Water and nutrients
Off-farm losses
Water and nutrients
OUT 1:Evapotranspiration
Gaseous losses
OUT 2: Runoff
Human excreta
OUT 3: Deep drainage
Leaching
OUT 4: Off farm harvests
Other organic outputs
OUT 5: Vapor wind drifts
Volatilization
OUT 6: Soil evaporation
Erosion
IN 1: Precipitation
Mineral fertilizer
IN 2: Dew fall
Organic inputs
IN 3: Aerial deposition
Atmospheric deposition
IN 4: Irrigation water
Biological N-fixation
IN 5: Upstream run on
Sedimentation
IN 6: Vapor transport
Subsoil exploitation
Farm level-catchment scale
interactions
Internal
Farm-scale
flux flows
Water- Nutrient fluxes
I In-situ field monitoring (2014) Scenario
Tools and
approaches
Assessments
2014-2015
II
Validation, recommendations and scaling
III
2015-2016
Farmer
assessments
Participatory field
days
System level
approaches
Soil, Crop &
climate models
Primary data
GIS/Spatial
analysis tools
Surveys
Data from:
- Field monitoring
- Field surveys
- Historical datasets

13. Scenario assessments
Scenario
assessments
Crop yields
Soil moisture
Organic carbon
Sediment levels
Runoff levels
Farm level
economic gains

14. Next steps
Next steps
Scaling of feasible
technologies
Validation of
scenarios
Iterative linkage to
AR4D Platforms
Strategic
partnerships
Targeting policies
for adoption
AR Interventions:
System responses
Fertilizer trials
Natural Resources
Conservation
Forage-crop
integration
MLND support
Strategic Partnerships:
TUBOCHA
NAFAKA
ILSSI
MISSION INITIATIVES