Download - Towards increased crop productivity and sustainability of natural resources in Babati, Tanzania
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
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
Validation of scenarios
Iterative linkage to AR4D Platforms
Scaling of feasible technologies
Strategic partnerships
Targeting policies for adoption
Tools and approaches
Participatory field days
Surveys
Soil, Crop & climate models
GIS/Spatial analysis tools
Primary data
System level approaches
Farmer assessments
Production constraints
Soil limitationsClimate variability
12/1/1
990
6/1/1
991
12/1/1
991
6/1/1
992
12/1/1
992
6/1/1
993
12/1/1
993
6/1/1
994
12/1/1
994
6/1/1
995
12/1/1
995
6/1/1
996
12/1/1
996
6/1/1
997
12/1/1
997
6/1/1
998
12/1/1
998
6/1/1
999
12/1/1
999
6/1/2
000
12/1/2
000
6/1/2
001
12/1/2
001
6/1/2
002
12/1/2
002
6/1/2
003
12/1/2
003
6/1/2
004
12/1/2
004
6/1/2
005
12/1/2
005
6/1/2
006
12/1/2
006
6/1/2
007
12/1/2
007
6/1/2
008
12/1/2
008
6/1/2
009
12/1/2
009
6/1/2
010
12/1/2
0100
10
20
30
40
50
60
70
80
Dai
ly ra
infa
ll (m
m)
Production constraints
Soil limitations
Climate variability
Low system productivity
Variable Low Medium HighOrganic carbon <0.5 0.5-0.75 >0.75Available P <30 30-50 >50Extractable K <200 200-400 >400pH <5.5 5.5-7.5 >7.5
• 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
• 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
Maize leaf necrosis
Production constraints
Control yield (t/ha)
Diff
ere
nce
in m
aiz
e g
rain
yie
ld
fro
m th
e c
on
tro
l (t/h
a)
0
2
4
6
8
1 2 3
VillageHalluLongMatufaSabiloSeloto
Response to P sources and different maize varieties
Trt No. TreatmentT1 ControlT2 Minjingu MazaoT3 Minjingu granularT4 DAPT5 3 tons FYM/ha + Minjingu MazaoT6 6 tons FYM/ha alone
• Positive but highly variable responses in all villages
• Farmers appreciated importance of fertilizer use
• Change in mindset on fertilizers
• Row planting with standard spacing
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
Performance of Napier grass accessions
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
Table 1. Preliminary results : new maize hybrids evaluated in 33 Babati trials
Name of hybrid GY PH (cm) EH (cm) EP EPP ER GM % NPCKH124960 6.35 197.35 105.09 0.53 1.56 30.50 18.90 11.02CKH124718 6.05 198.66 107.47 0.53 1.67 15.76 19.45 10.71CKH124713 6.04 196.92 100.17 0.52 1.30 13.32 21.35 11.14CKH124783 6.02 206.37 115.82 0.58 1.31 25.03 17.95 10.03CKH124727 6.02 195.90 95.90 0.51 1.27 7.27 19.10 13.38CKH124776 5.75 196.51 120.19 0.57 1.34 19.28 20.70 10.62CKH124742 5.71 187.09 98.09 0.51 1.41 18.05 21.00 10.09CKH124712 5.70 190.61 96.35 0.51 1.26 29.78 20.05 11.48CKH124730 5.68 194.67 96.11 0.52 1.26 22.28 18.35 12.15CKH124743 5.63 185.44 95.54 0.51 1.23 8.85 20.30 13.46CKH124775 5.60 213.84 110.66 0.54 1.20 20.54 19.20 10.41CKH124715 5.50 176.08 97.92 0.56 1.25 -0.27 20.40 8.24CKH124710 5.46 181.65 109.20 0.60 1.20 39.55 19.20 14.54CKH124781 5.39 195.97 106.69 0.54 1.40 20.00 19.70 12.57WH403 3.73 182.20 96.46 0.54 1.16 21.67 24.05 6.48WH505 3.44 187.86 104.02 0.58 1.19 37.96 19.35 12.91H520 2.75 223.29 126.32 0.59 1.47 44.05 19.20 11.42Experiment Mean 4.14 191.50 104.77 0.55 1.33 26.96 20.42 10.26LSD (0.05) 3.13 23.12 17.59 0.09 0.52 30.44 4.46 5.69MSe 2.44 133.33 77.19 0.00 0.07 231.03 4.95 8.08CV 37.8 6.03 8.39 8.24 19.52 56.38 10.90 27.70p 0.61 0.00 0.00 0.27 0.15 0.23 0.41 0.42p ns ** ** ns ns ns ns nsNumSignificantSites 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00NumReps 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00
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
Farmer technology assessments
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
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)
Farmer Field days
Off-farm influxesWater and nutrients
Off-farm lossesWater and nutrients
OUT 2: RunoffHuman excreta
OUT 3: Deep drainageLeaching
OUT 4: Off farm harvestsOther organic outputs
OUT 6: Soil evaporationErosion
OUT 5: Vapor wind driftsVolatilization
OUT 1:EvapotranspirationGaseous losses
IN 1: PrecipitationMineral fertilizer
IN 2: Dew fallOrganic inputs
IN 3: Aerial depositionAtmospheric deposition
IN 4: Irrigation waterBiological N-fixation
IN 5: Upstream run onSedimentation
IN 6: Vapor transportSubsoil exploitation
Internal Farm-scaleflux flows
Farm level-catchment scaleinteractions
OutflowsInflows
Wat
er-N
utrie
ntflu
xes
In-situ field monitoring (2014)I ScenarioAssessments2014-2015
IIV
alidatio
n, reco
mm
end
ation
s and
scaling
2015-2016III
Data from:- Field monitoring- Field surveys- Historical datasets
Tools and approaches
Participatory field days
Surveys
Soil, Crop & climate models
GIS/Spatial analysis tools
Primary data
System level approaches
Farmer assessments
Scenario assessments
Scenario assessments
Crop yields
Soil moisture
Organic carbon
Sediment levels
Runoff levels
Farm level economic gains
Next steps
Next steps
Validation of scenarios
Iterative linkage to AR4D Platforms
Scaling of feasible technologies
Strategic partnerships
Targeting policies for adoption
AR Interventions: System responses
Fertilizer trials
Natural Resources Conservation
Forage-crop integration
MLND support
Strategic Partnerships:TUBOCHANAFAKAILSSIMISSION INITIATIVES