2. Introduction
• Vulnerable populations are often food insecure
• Food security:
– Sufficient calories
– Diverse nutrients
• Sources of food
– farm production
– market
– Landscape
• Nutrition-sensitive landscapes
– optimise production, NRM and nutrition
3. Objectives
1. Characterise the current landscape and
determine landscape performance in terms of
production, environmental outcomes and
nutrition.
2. Explore trade-offs and synergies of proposed
interventions at landscape level.
3. Identify and test entry points for improvements
in farming, diets and ecosystem services
provided by the landscape
5. The case studies
• Western Kenya
– Densely populated (1 044 persons km-2)
– Food crops (maize, beans)
– Cash crops (tea, vegetables)
– Food insecurity, land degradation, poverty
• Northwest Vietnam
– Low smallholder agricultural productivity
– Degradation of natural resources
– Low income and access to markets
– Malnutrition
6. Site selection
1. Review of secondary sources & experts
2. Field visits to 10 sub-locations
- Mambai: tea-based & Masana: maize-based
3. Participatory mapping 4. Transect Walk
7. Landscapes in Vihiga County
• Mambai landscape
- river + ‘forest’
- Impactlite survey to
10 households
• Masana sub-location
- circumcision forest
-10 households
surveyed
8. Preliminary results
• Soil fertility Masana > Mambai
• Majority of land holdings < 0.5 ha
- Some Hhs had fields away from landscape
Mambai (50%) & Masana (30%)
• No major forests, nearby lakes or dams
• Hh size: Masana 5(±2.5) and Mambai 4.5 (±1.4)
10. Food – produced and purchased
• All farms produced food crops
– Beans purchase Masana > Mambai
– Banana purchase low
– Low consumption of traditional vegetables?
11. Way forward
• Building of farms into FarmDesign on-going
• Farms to be aggregated in LandscapeIMAGES
• Field visits for additional data collection
• Modeling and community feed backs
Carl Timler 2/27/2015
perhaps also include other comparisons such as agro ecologies, population densities, topography, access to main roads/markets
In mambai – 4/5 away fields under maize-bean, 2/5 fields under tea including farmer 7, Masana all3 hh with away fields under maize, 1 sweetpotato, woodlot
Mambai – without tea farmer 1: employed at school farmer 7: small area within landscape, away field under tea
Masana: maize 55%) > banana (18%) > napier (12%)
Mambai: maize (37%) > tea (28%) > eucalyptus (22%)
- Area under banana in Mambai quite low cfd to Masana
- Napier (12%) in both highlighting the importance of livestocks in both sub-locations!