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

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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