IMAGINE: methodology Pytrik Reidsma Kick-off meeting, March 2015, Wageningen

Research questions  What is a scientifically sound and applicable generic framework linking agronomic, socio-economic, institutional, infrastructural and policy factors, explaining maize yield gaps in SSA?  What are the main biophysical and farm and crop management factors that help to explain yield gaps in the case study countries?  What are the main infrastructural, institutional, socio-economic and policy factors that explain farm and crop management and consequently yield gaps?  Which policies and farm management options are key for increasing yield performance in SSA?

Three stages 1. Benchmark: Calculate potential, highest farmer’s, technical efficient and economic ceiling yields at the national and regional level ● crop growth and economic production models ● combined with actual yield data from surveys to compute the various yield gaps 2. Explain, country-level: Analyse variations in the observed yield gaps in space and relate them to plot-level, farm-level and context determining factors ● econometric techniques ● decisions taken at the farm-household level ● embedded in a wider socio-economic and biophysical context 3. Explain, local: Deepen the analyses of stage 1 and 2 ● local case studies at the village level ● to allow identification of farm and management innovations and policy interventions.

Benchmarks & yield gaps 4 Yield levels Water- limited Gap 1 Highest farmer’s yield Gap 2 Economic ceiling yield Gap 3 Actual farmer yield ModelledFarm and plot level observations Y TEx Y HF Input level (x) Y EE max RUE Production level (y) YWYW

Methodological framework  Frontier analysis ● efficiency gap ● resource gap ● economic efficiency  Crop modelling ● technology gap ● biophysical ranges ● explain (climate, soil, cultivar, timing) 5

Interaction between inputs  Only considering N: efficiency gap  Considering N & P: resource gap of P 6 Input level (N) Production level (y) P resource yield gap Y HF Input level (P) Y HF P resource yield gap Y FARM1

Frontier analysis: resource & efficiency gap  Production function includes inputs & outputs (maize yield)  Inputs: ● Traditional economic: land, labour & capital ● Proposal: growth-defining & -limiting = factors directly required for plant growth 7

Explaining yield gaps: methods  Along with frontier analysis: ● 2 nd stage multiple regression ● if inputs are growth-defining & -limiting ● time, space & from of inputs (~ efficiency) ● farm characteristics, socio-economic & institutional conditions  Crop modelling ● biophysical ranges input-output relationships ● explanation by climate, soil, sowing date, nutrients  Participatory ● interviews, workshops 8 technical inefficiency field management farm, village, region YwYw Technology yield gap Y HF Production level (y) Input level (x)

Explaining yield gaps: data 9 Village Plot Farm Socio-economic conditionsFarm(er) characteristicsBiophysical conditionsField management Soil NPK Soil water Soil type EC pH OM Pest infestation Disease infestation... Farm labour / ha Hired labour / ha Farm area Labour availability Capital availability Age Gender Education HH size Number of plots Off-farm income NKP application Manure application Biocide application Irrigation Sowing density NPK timing Biocide timing Irrigation timing Sowing date Land preparation Weeding Crop residue management Rotations Intercropping Tree cover Erosion control Temperature Radiation Elevation Slope Rainfall + distribution Length growing season Distance to market Input & output prices Market information Extension service – info Subsidy programs Insurance programs Credit programs Age of field Yield

Challenges: methods 1  Frontier analysis ● Method: SFA or DEA ● Functional form: Cobb-Douglas, translog, quadratic,... ● Inputs included: l,l,c / growt-defining & -limiting ● Outputs: only maize (more outputs possible)  2 nd stage multiple regression ● Influences efficiency levels ● Different types  Resource gap ● Not standard output; compare TE with different inputs 10 K-limiting

Challenges: methods 2  Technology gap ● Large difference Y w and Y hf  Explain ● Can be resource or efficiency gap ● Not explained by frontier analysis ● Include high yielding regions & farms! ● Crop modelling: micro-climate, soil, NPK, sowing date ● Experiments: difference with Y hf ? ● Data regions similar AEZ 11 YwYw Technology yield gap Y HF Production level (y) Input level (x)

Challenges: data 1  Country-level ● Ethiopia: LSMS-ISA ● Ghana: ISSER  Additional data sources: ● Relevance of locations, type of data, collaboration?  Local analysis: 12 Region1 Region2 village1village2village3 type2type3type4type1 farm1 farm2 farm3 village1village2village3 type2type3type4type1 farm1 farm2 farm3

Challenges: data 2  Sample ● 2 regions * 3 villages * 4 farm types * 3 farms = 72 farms per country ● size: needed possible  Criteria ● Region/village: AEZ, market access, soil fertility ● Farm types: resource endowment  Timing of surveys: start summer 2015  Set-up of surveys  Responsabilities 13

Time for discussion 14 ?