SIMLEZA Site Characterization for Eastern Province of Zambia

Zhe Guo, Carlo Azzarri, Beliyou Haile

SIMLEZA - Africa RISING Meeting 28-29 May 2013, Lilongwe, Malawi

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• Understand the spatial pattern and heterogeneity • Choose appropriate data layers for stratification • Thus help

• Better target interventions • Identify representative or otherwise appropriate action

and control sites• Guide scaling-up/scaling-out efforts within, across, and

beyond

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Objectives

Data Source: Naomi Kamanga and Walter Mupangwa (N=225)

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LundaziChipataKatete

Source: Munyaradzi Mutenje, Menale Kassie, and Kindie Fantaye

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Includes farmers in innovation networks?

Datasets Spatial resolution Year SourcePopulation density 1 sqkm 2009 Land scan

50 sqkm long term (> 50 years) average CRU1 sqkm long term (> 50 years) average WorldClim100 sqkm long term (> 50 years) average NASA POWER50sqkm long term (> 50 years) average GPCC1sqkm long term average interpolated from national weather station

Elevation 1 sqkm USGSSlope 1 sqkm USGSMarket access 1 sqkm 2000 HarvestChoiceLength of growth period ~10sqkm long term (> 50 years) average IIASATemperature 1 sqkm long term (> 50 years) average WorldClim

Precipitation

Review of Spatial Biophysical and Socio-economic Data Layers

; Afripop

• Candidate data layers mapped to visualize their spatial distribution • Final layers aggregated by classes• Results will need refining

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Little within spatial heterogeneity (based on Land scan but result is the same based on Afripop)

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Spatial heterogeneity

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Little within spatial heterogeneity

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Little within spatial heterogeneity

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Little within spatial heterogeneity

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Spatial heterogeneity

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Spatial heterogeneity

• Spatial heterogeneity in rainfall and elevation• Correlation b/n spatial distribution of temperature elevation• Two data layers (9 classes) to stratify SIMLEZA districts

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772 -850mm851-950mm951-1050mm1051-1241mm

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Final Layer 1. Rainfall

369-700 m701-900m901-1100m1101-2237m

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Final Layer 2. Elevation

Excluded area - No farmers in this range based on available farmer location

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Final Classification

Conclusion and Caveats • Candidate layers – population, market access, temperature,

LGP, slope, elevation and rainfall• Maps based on GIS location of 225 farmers from 7 camps in 3

districts • Final layers for stratification - elevation and rainfall; 9 classes• Site/Camp stratification:

Ludanzi (Hoya and Vuu): Low R - High EChipata (Kapara, Mtaya, and Chanje ): Low R and Medium

E; Medium R and Medium E; Medium R and Low E Katete (Kawalala and Kafumbwe): High R and Medium E

• Coarse resolution metadata more suitable to global analysis 17

Next Steps

• Update maps based on • Feedback from Zambia team• Higher-resolution/better data layers from Zambia team• Data for all farmers (e.g., could there more than 1 class

in Ludanzi: low-low), including from Mesekra• Identification of target and control sites • Data collection efforts

• Review of existing data• SIMLEZA baseline survey tool

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Thank you!

Source: Munyaradzi Mutenje, Menale Kassie, and Kindie Fantaye

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