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

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. 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. 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. 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. 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. 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. 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. Lukumai
Technology Productivity Economics Environment Human Social
Poultry (feeding, housing,
breeding)
Dairy
Improved vegetables & good
agronomic practices

9. Monica Pascale
Technology Productivity Economics Environment Human Social
Poultry (feeding, housing,
breeding)
Dairy
Improved vegetables & good
agronomic practices

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….

12. The Kongwa Kiteto (TZ) example - an attempt at
multi-discipline, multi-indicator presentation
(farm system performance)

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SIAF
Baseline Technology
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Baseline Technology
Maize variety (QPM), 2015 SWC (Contours re-enforced
with Gliricidia & fodder grass)
2018

14. 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

15. 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!

16. 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