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Big data from small farms

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Presented by Mark van Wijk, Romain Frelat, Randall Ritzema and Sabine Douxchamps at the ILRI@40 Livestock and Environment workshop, Addis Ababa, 7 November 2014

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Big data from small farms

  1. 1. Big data from small farms Mark van Wijk, Romain Frelat, Randall Ritzema and Sabine Douxchamps ILRI@40 Livestock and Environment workshop Addis Ababa, 7 November 2014
  2. 2. Farming systems analysis and HH modeling for: - Finding structure in variability in farming systems - Understanding of systems functioning - Targeting of interventions
  3. 3. However… Typically we got stuck in in-depth site specific studies We did not deliver on the targeting promised: What works where for which farmer?
  4. 4. Here… 1. Ongoing work on bringing together HH level characterization data 2. Present a simple analysis of farm household level food security that can be used across many datasets
  5. 5. A key decision: Go simple!!
  6. 6. From food self-sufficiency towards food security
  7. 7. Keep the analysis simple enough to be able to apply it across HH characterization data collected in different surveys!
  8. 8. Food crops produced Cash crops produced Off farm income Livestock products produced
  9. 9. Food crops produced Cash crops produced Off farm income Livestock products produced Food available Consumed Consumed
  10. 10. Food crops produced Cash crops produced Off farm income Livestock products produced Cash available Food available Consumed Consumed Sold Sold
  11. 11. Food crops produced Cash crops produced Off farm income Livestock products produced Cash available Food available Consumed Buy staple crop Expenses Consumed Sold Sold
  12. 12. Food crops produced Cash crops produced Off farm income Livestock products produced Cash available Food available Food need Buy staple crop Expenses Household size and composition Consumed Consumed Sold Sold
  13. 13. Food crops produced Cash crops produced Off farm income Livestock products produced Cash available Food available Food need Buy staple crop Expenses Household size and composition Consumed Consumed Sold Sold Food security ratio
  14. 14. Lushoto, Tanzania (CCAFS)
  15. 15. Rapid intervention analyses
  16. 16. First findings: Agricultural based interventions will not get the poorest 20-60% of the smallholder farmers food secure Alleviation of problems! Goats are an important entry point
  17. 17. First findings: The upper 20 – 50% is intensifying, and linking up to markets In mixed crop-livestock systems this group owns cattle: interventions focusing on cattle productivity address poverty but not so much food security
  18. 18. First findings: 20 – 60% of the farmers: agricultural interventions can make a difference for getting farmers more food secure In high population density areas with small farm sizes: crop interventions can make this difference In medium / low population density areas both livestock and crop intervention can make this difference: production/availability of enough fodder resources
  19. 19. Ongoing activities 1. Expand the database (also CA and SEA) 2. Test results (both the big FS numbers, but also with farmers in field) 3. Adapt and apply mini-survey, on tablet 4. Super cheap survey instrument that is directly linked to an analysis framework: produce rapid results!
  20. 20. Ongoing activities 5. Implement gender component 6. With the mini-survey we fill gaps in the database, but also set up some permanent monitoring sites 7. For more in-depth analyses: use existing tools, but also develop an ‘intermediate complexity HH model’
  21. 21. Targeting Can we identify robust interventions that cut across systems and socio-economic scenarios? (what works where for which group of farmers) Can we upscale the strategies to quantify investment needs in interventions?
  22. 22. Generate bottom up based information to improve large scale impact assessment exercises
  23. 23. Look at changing livelihoods: from that perspective add to the land expansion / intensification – land sharing / sparing debates
  24. 24. Thanks!!

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