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Farm level case studies Tanzania

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Presentation by Lieven Claessens at the Africa RISING ESA Project Review and Planning Meeting, Dar es Salaam, Tanzania, 10-11 September 2019.

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Farm level case studies Tanzania

  1. 1. Farm level case studies Tanzania Lieven Claessens International Institute of Tropical Agriculture (IITA) Africa RISING ESA Project review and planning meeting 11 – 12 September 2019, Dar es Salaam, Tanzania
  2. 2. Discussion on application of SIAF in ESA (Accra Nov18 and Malawi Feb19) • The majority of scientists do not have data to meet the needs of the SIAF yet • Generation of SIAF data in subsequent research work plans is the way to go… • Experience with inclusion of domains data in workplan requirements shows gaps - either because of limited knowledge or interest in going beyond comfort zones in data generation (appreciating the needs to generate data in non-familiar domains) • Discipline approaches dominate, reflecting failure to implement together even after planning multiple-interventions guided by influence diagrams
  3. 3. Discussion on application of SIAF in ESA (Accra Nov18 and Malawi Feb19) • How can we synthesize/convert single discipline SIAF data into systems SIAF data? • Most data available are at plot-level. How do we plan for household and community levels, including elevation of available plot data? • Recognise multiple ways of presenting SIAF data (Malawi vs Babati) – for different audiences?
  4. 4. Recommendations • Take stock of all data collected on a given innovation by different scientists in a given location as a team exercise. Draft a site/country manuscript against the data. • Choose system performance indicators that matter to all who have an interest • Measure and make assessments at landscape and community level, providing a baseline • (Jointly) decide on a target and hence the system performance shift that is needed (arrows)
  5. 5. Recommendations • Decide on the multiple interventions needed that are expected (hypothesised) to lead to this shift • Get on with it and see what happens, using an action research approach (try, monitor, adjust) • Trade-offs: Think beyond the results and allow for associations: e.g. What does a productivity outcome mean in the context of farmer decision making for allocation of land and other resources next season?
  6. 6. Pre-planning country meetings July • Data collection tools were developed for 3 farms during country meeting in July (Moshi Maile, Lukumai, Monica Pascale) • Gaps identified • Data were to be delivered by 5th of August…..
  7. 7. Moshi Maile Technology Productivity Economics Environment Human Social Varieties: maize Varieties: pigeonpea Varieties: groundnut Intercropping maize-legume (PP)- gliricidia Poultry (feeding, housing, breeding) SWC (contours) Gliricidia & fodder grass Pigeonpea &Tillage method (tied ridges/flat cultivation)
  8. 8. Lukumai Technology Productivity Economics Environment Human Social Poultry (feeding, housing, breeding) Dairy Improved vegetables & good agronomic practices
  9. 9. Monica Pascale Technology Productivity Economics Environment Human Social Poultry (feeding, housing, breeding) Dairy Improved vegetables & good agronomic practices
  10. 10. General observations • Different seasons for different technologies (not overlapping, contrasting) • Different ‘baselines’, treatments and how to set the maximum for one farm in SIAF diagram? • Data quality issues • As expected, big gaps, especially in human and social domains • No data on linkages/integration of multiple technologies (system diagram) and how to present integrated SIAF diagram….
  11. 11. The Kongwa Kiteto (TZ) example - an attempt at multi-discipline, multi-indicator presentation (farm system performance)
  12. 12. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 prod econ envhuman social SIAF Baseline Technology 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 prod econ envhuman social Baseline Technology Maize variety (QPM), 2015 SWC (Contours re-enforced with Gliricidia & fodder grass) 2018
  13. 13. Farm/HH SYSTEM Productivity Economics Human Social Environment OUTCOME 4 Enabling environment Complementary innovations RESILIENCE Adaptation Mitigation e.g. drought tolerant variety e.g. water harvesting SCALING OUTCOME 5 IMPACT RinD
  14. 14. Conclusions • Big issues with data (availability, quality) • Big gaps for application of SIAF, especially in human and social domains • Team effort needed to consolidate (and clean) existing data (more available? Temporal variability?) • Generating new (systems, SIAF) data should now really be part of the workplans! Let’s engage and collaborate!
  15. 15. Africa Research in Sustainable Intensification for the Next Generation africa-rising.net This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. Thank You

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