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Mary Crossland
PhD student at Bangor University
afp43d@bangor.ac.uk
Livelihood trajectory models reveal the importance of
...
Why model livelihoods?
Benefits of new technologies are often
measured in terms of yield per hectare or
income per hectare...
Land restoration
in Kenya and Ethiopia
IFAD-EC funded project:
“Restoration of degraded land for food security
and poverty...
Important questions
How many planting basins can a household dig and maintain?
How many planting basins would a household ...
Photos: Leigh Winowiecki
How many trees would a specific household need to plant to be self-sufficient in fuelwood?
How mu...
Ethiopia
Ethiopian scenarios
High resource
endowed
Medium resource
endowed
Low resource
endowed
Household size 7 6 5
Farm size (acr...
Kenya
Kenyan scenarios
Farmer practice scenario:
No basins, total area of cultivated land under
farmers normal practice
Planting...
Simile demo
Ethiopia results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of trees 419 2622 419 2247 419 ...
Ethiopia results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of trees 419 2622 419 2247 419 ...
Ethiopia results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of trees 419 2622 419 2247 419 ...
Ethiopia results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of trees 419 2622 419 2247 419 ...
Ethiopia results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of trees 419 2622 419 2247 419 ...
Ethiopia results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of trees 419 2622 419 2247 419 ...
Ethiopia results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of trees 419 2622 419 2247 419 ...
Kenya results
Basins performed best under low rainfall conditions and were only
marginally outperformed by farmer practice...
Kenya results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of planting basins 0 261 0 174 0 1...
Kenya results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of planting basins 0 261 0 174 0 1...
Kenya results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of planting basins 0 261 0 174 0 1...
Kenya results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of planting basins 0 261 0 174 0 1...
Kenya results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of planting basins 0 261 0 174 0 1...
Kenya results
High resource
endowed
Medium resource
endowed
Low resource
endowed
Number of planting basins 0 261 0 174 0 1...
The uptake of planting basins:
1. Unlikely to increase household incomes but could provide a critical safety
net in terms ...
What about the share
of benefits within the
household?
In Ethiopia, on-farm trees for
fuelwood could free-up
substantial t...
In Kenya, has the use of basins shifted labour between men and women?
Who is involved in land preparation
using your farme...
1. While the innovations explored here are unlikely to lift the majority
of farmers out of poverty on their own, they may ...
Livelihood trajectory models reveal the importance of interactive effects on increasing rural households’ capacity for tra...
Livelihood trajectory models reveal the importance of interactive effects on increasing rural households’ capacity for tra...
Livelihood trajectory models reveal the importance of interactive effects on increasing rural households’ capacity for tra...
Livelihood trajectory models reveal the importance of interactive effects on increasing rural households’ capacity for tra...
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Livelihood trajectory models reveal the importance of interactive effects on increasing rural households’ capacity for transformational change

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CGIAR Research Program on Policies, Institutions, and Markets Workshop on Rural Transformation in the 21st Century (Vancouver, BC – 28 July 2018, 30th International Conference of Agricultural Economists). Presentation by Mary Crossland (World Agroforestry Centre, Kenya / Bangor University, Wales, UK), Fergus Sinclair (World Agroforestry Centre, Kenya / Bangor University, Wales, UK), Tim Pagella (World Agroforestry Centre, Kenya / Bangor University, Wales, UK), Jasper Taylor (Simulistics Ltd., Edinburgh, UK), Lalisa Duguma (World Agroforestry Centre, Kenya), Leigh Winowiecki (World Agroforestry Centre, Kenya)

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Livelihood trajectory models reveal the importance of interactive effects on increasing rural households’ capacity for transformational change

  1. 1. Mary Crossland PhD student at Bangor University afp43d@bangor.ac.uk Livelihood trajectory models reveal the importance of interactive effects on increasing rural households’ capacity for transformational change Mary Crossland1, 2, Fergus Sinclair1, 2, Tim Pagella1, 2, Jasper Taylor3, Lalisa Duguma 1, Leigh Winowieck1 1World Agroforestry Centre (ICRAF), Nairobi, Kenya; 2Bangor University, Wales, UK; 3Simulistics Ltd., Edinburgh, UK
  2. 2. Why model livelihoods? Benefits of new technologies are often measured in terms of yield per hectare or income per hectare However, farmers make decisions on the basis of their whole livelihood system What are the specific benefits a household and its individuals can expect to receive? Do narrow evaluations underplay the less direct benefits of innovations and the extent to which they can contribute to transformational change?
  3. 3. Land restoration in Kenya and Ethiopia IFAD-EC funded project: “Restoration of degraded land for food security and poverty reduction in East Africa and the Sahel: taking successes in land restoration to scale” Research ‘in’ development approach through systematically testing promising options across a range of contexts (Coe at al., 2014) Over 500 farmers in Kenya and 200 farmers in Ethiopia conducting on-farm trials of planting basins and tree planting practices
  4. 4. Important questions How many planting basins can a household dig and maintain? How many planting basins would a household need to be self-sufficient in maize? What other benefits could planting basins provide? Kenya
  5. 5. Photos: Leigh Winowiecki How many trees would a specific household need to plant to be self-sufficient in fuelwood? How much dung could then be applied to cropland instead of burnt as fuel? What other benefits could on-farm trees for fuelwood provide? Important questions Ethiopia
  6. 6. Ethiopia
  7. 7. Ethiopian scenarios High resource endowed Medium resource endowed Low resource endowed Household size 7 6 5 Farm size (acre) 9.3 5.6 3.5 Livestock (TLU) 6.90 3.04 1.75 Business as usual scenario: Current number of on-farm trees found in woodlots (419 trees per farm based on Duguma and Hager, 2010) Fuelwood self-sufficiency scenario: Number of on-farm trees needed to meet household fuel demand Duguma and Hager (2010)
  8. 8. Kenya
  9. 9. Kenyan scenarios Farmer practice scenario: No basins, total area of cultivated land under farmers normal practice Planting basins scenario: Where labour is allocated to digging and maintaining basins each season High rainfall: 2000mm/year Medium rainfall: 1500mm/yr Low rainfall: 700mm/yr High resource endowed Medium resource endowed Low resource endowed Household size 6 5 2 Cultivated area (acre) 7 4 1 Hired labourers 1 0 0 Off-farm earners 2 1 0 Livestock (TLU) 2.2 1.1 0
  10. 10. Simile demo
  11. 11. Ethiopia results High resource endowed Medium resource endowed Low resource endowed Number of trees 419 2622 419 2247 419 1873 Dung applied to cropland (kg/farm/year) 255.3 2487.7 169.7 1310.2 103.3 966.0 % of annual cereal demand met by additional wheat yield 2.8 20.6 2.1 10.6 1.5 7.3 Time spent collecting wood (hours/year) 1151.1 -- 1043.9 -- 849.5 -- Days with fodder from trees (days/year) 57 213 129 213 213 213 Additional fodder days based on surplus (days/year) -- 144 -- 481 12 793 Income per capita per day from fodder sales (USD/person/day) -- 0.24 -- 0.41 0.01 0.70 Percentage of farm under trees 4.5 27.9 7.4 39.6 11.8 52.9 Business as usual scenario
  12. 12. Ethiopia results High resource endowed Medium resource endowed Low resource endowed Number of trees 419 2622 419 2247 419 1873 Dung applied to cropland (kg/farm/year) 255.3 2487.7 169.7 1310.2 103.3 966.0 % of annual cereal demand met by additional wheat yield 2.8 20.6 2.1 10.6 1.5 7.3 Time spent collecting wood (hours/year) 1151.1 -- 1043.9 -- 849.5 -- Days with fodder from trees (days/year) 57 213 129 213 213 213 Additional fodder days based on surplus (days/year) -- 144 -- 481 12 793 Income per capita per day from fodder sales (USD/person/day) -- 0.24 -- 0.41 0.01 0.70 Percentage of farm under trees 4.5 27.9 7.4 39.6 11.8 52.9 Fuelwood self-sufficiency scenario
  13. 13. Ethiopia results High resource endowed Medium resource endowed Low resource endowed Number of trees 419 2622 419 2247 419 1873 Dung applied to cropland (kg/farm/year) 255.3 2487.7 169.7 1310.2 103.3 966.0 % of annual cereal demand met by additional wheat yield 2.8 20.6 2.1 10.6 1.5 7.3 Time spent collecting wood (hours/year) 1151.1 -- 1043.9 -- 849.5 -- Days with fodder from trees (days/year) 57 213 129 213 213 213 Additional fodder days based on surplus (days/year) -- 144 -- 481 12 793 Income per capita per day from fodder sales (USD/person/day) -- 0.24 -- 0.41 0.01 0.70 Percentage of farm under trees 4.5 27.9 7.4 39.6 11.8 52.9 Meeting household fuel demand with on-farm trees could provide indirect benefits in terms of food security, especially for the HRE household …
  14. 14. Ethiopia results High resource endowed Medium resource endowed Low resource endowed Number of trees 419 2622 419 2247 419 1873 Dung applied to cropland (kg/farm/year) 255.3 2487.7 169.7 1310.2 103.3 966.0 % of annual cereal demand met by additional wheat yield 2.8 20.6 2.1 10.6 1.5 7.3 Time spent collecting wood (hours/year) 1151.1 -- 1043.9 -- 849.5 -- Days with fodder from trees (days/year) 57 213 129 213 213 213 Additional fodder days based on surplus (days/year) -- 144 -- 481 12 793 Income per capita per day from fodder sales (USD/person/day) -- 0.24 -- 0.41 0.01 0.70 Percentage of farm under trees 4.5 27.9 7.4 39.6 11.8 52.9 … free up time that would otherwise be spent collecting fuelwood and reduce degradation pressures on forest resources…
  15. 15. Ethiopia results High resource endowed Medium resource endowed Low resource endowed Number of trees 419 2622 419 2247 419 1873 Dung applied to cropland (kg/farm/year) 255.3 2487.7 169.7 1310.2 103.3 966.0 % of annual cereal demand met by additional wheat yield 2.8 20.6 2.1 10.6 1.5 7.3 Time spent collecting wood (hours/year) 1151.1 -- 1043.9 -- 849.5 -- Days with fodder from trees (days/year) 57 213 129 213 213 213 Additional fodder days based on surplus (days/year) -- 144 -- 481 12 793 Income per capita per day from fodder sales (USD/person/day) -- 0.24 -- 0.41 0.01 0.70 Percentage of farm under trees 4.5 27.9 7.4 39.6 11.8 52.9 …meet dry season fodder demand and provide surplus fodder, meaning livestock can be stall-fed for more days of the year…
  16. 16. Ethiopia results High resource endowed Medium resource endowed Low resource endowed Number of trees 419 2622 419 2247 419 1873 Dung applied to cropland (kg/farm/year) 255.3 2487.7 169.7 1310.2 103.3 966.0 % of annual cereal demand met by additional wheat yield 2.8 20.6 2.1 10.6 1.5 7.3 Time spent collecting wood (hours/year) 1151.1 -- 1043.9 -- 849.5 -- Days with fodder from trees (days/year) 57 213 129 213 213 213 Additional fodder days based on surplus (days/year) -- 144 -- 481 12 793 Income per capita per day from fodder sales (USD/person/day) -- 0.24 -- 0.41 0.01 0.70 Percentage of farm under trees 4.5 27.9 7.4 39.6 11.8 52.9 … or if surplus fodder is sold, could increase per capita income, especially for the LRE household ...
  17. 17. Ethiopia results High resource endowed Medium resource endowed Low resource endowed Number of trees 419 2622 419 2247 419 1873 Dung applied to cropland (kg/farm/year) 255.3 2487.7 169.7 1310.2 103.3 966.0 % of annual cereal demand met by additional wheat yield 2.8 20.6 2.1 10.6 1.5 7.3 Time spent collecting wood (hours/year) 1151.1 -- 1043.9 -- 849.5 -- Days with fodder from trees (days/year) 57 213 129 213 213 213 Additional fodder days based on surplus (days/year) -- 144 -- 481 12 793 Income per capita per day from fodder sales (USD/person/day) -- 0.24 -- 0.41 0.01 0.70 Percentage of farm under trees 4.5 27.9 7.4 39.6 11.8 52.9 However. Meeting demand requires a lot more trees than currently found on farms and a large change in land use for the smallest farm …
  18. 18. Kenya results Basins performed best under low rainfall conditions and were only marginally outperformed by farmer practice scenarios under medium and high rainfall conditions. Example output for the LRE household’s maize store under high rainfall conditions Very small difference in total yield between farmer practice and basins scenarios (a mean difference of 13 kg per acre across households)
  19. 19. Kenya results High resource endowed Medium resource endowed Low resource endowed Number of planting basins 0 261 0 174 0 174 Maize yield (kg/farm/year) 592.7 787.9 317.2 447.0 110.9 231.1 Days with no maize (days/year) 0 0 47 0 239 101 Days with no maize from farm (days/year) 50 0 153 68 239 101 Surplus maize (kg/year) -- 120.8 -- -- -- -- Income per capita per day (USD/person/day) 0.70 0.72 0.84 0.84 -- -- Days with stover for livestock (days/year) 20 27 23 32 NA NA Surplus fodder (kg/year) -- -- -- -- 174.1 358.5 Low rainfall conditions Farmer practice scenario
  20. 20. Kenya results High resource endowed Medium resource endowed Low resource endowed Number of planting basins 0 261 0 174 0 174 Maize yield (kg/farm/year) 592.7 787.9 317.2 447.0 110.9 231.1 Days with no maize (days/year) 0 0 47 0 239 101 Days with no maize from farm (days/year) 50 0 153 68 239 101 Surplus maize (kg/year) -- 120.8 -- -- -- -- Income per capita per day (USD/person/day) 0.70 0.72 0.84 0.84 -- -- Days with stover for livestock (days/year) 20 27 23 32 NA NA Surplus fodder (kg/year) -- -- -- -- 174.1 358.5 Low rainfall conditions Planting basins scenario
  21. 21. Kenya results High resource endowed Medium resource endowed Low resource endowed Number of planting basins 0 261 0 174 0 174 Maize yield (kg/farm/year) 592.7 787.9 317.2 447.0 110.9 231.1 Days with no maize (days/year) 0 0 47 0 239 101 Days with no maize from farm (days/year) 50 0 153 68 239 101 Surplus maize (kg/year) -- 120.8 -- -- -- -- Income per capita per day (USD/person/day) 0.70 0.72 0.84 0.84 -- -- Days with stover for livestock (days/year) 20 27 23 32 NA NA Surplus fodder (kg/year) -- -- -- -- 174.1 358.5 Low rainfall conditions Basins increased the number of days households were able to meet their maize demand, especially for the LRE household, but only the HRE household reached maize self-sufficiency …
  22. 22. Kenya results High resource endowed Medium resource endowed Low resource endowed Number of planting basins 0 261 0 174 0 174 Maize yield (kg/farm/year) 592.7 787.9 317.2 447.0 110.9 231.1 Days with no maize (days/year) 0 0 47 0 239 101 Days with no maize from farm (days/year) 50 0 153 68 239 101 Surplus maize (kg/year) -- 120.8 -- -- -- -- Income per capita per day (USD/person/day) 0.70 0.72 0.84 0.84 -- -- Days with stover for livestock (days/year) 20 27 23 32 NA NA Surplus fodder (kg/year) -- -- -- -- 174.1 358.5 Low rainfall conditions In order to reach maize self-sufficiency, MRE and LRE households would need an extra 206 and 160 basins, respectively.
  23. 23. Kenya results High resource endowed Medium resource endowed Low resource endowed Number of planting basins 0 261 0 174 0 174 Maize yield (kg/farm/year) 592.7 787.9 317.2 447.0 110.9 231.1 Days with no maize (days/year) 0 0 47 0 239 101 Days with no maize from farm (days/year) 50 0 153 68 239 101 Surplus maize (kg/year) -- 120.8 -- -- -- -- Income per capita per day (USD/person/day) 0.70 0.72 0.84 0.84 -- -- Days with stover for livestock (days/year) 20 27 23 32 NA NA Surplus fodder (kg/year) -- -- -- -- 174.1 358.5 Low rainfall conditions Due to lack of surplus grain, basins were unable to increase per capita income…
  24. 24. Kenya results High resource endowed Medium resource endowed Low resource endowed Number of planting basins 0 261 0 174 0 174 Maize yield (kg/farm/year) 592.7 787.9 317.2 447.0 110.9 231.1 Days with no maize (days/year) 0 0 47 0 239 101 Days with no maize from farm (days/year) 50 0 153 68 239 101 Surplus maize (kg/year) -- 120.8 -- -- -- -- Income per capita per day (USD/person/day) 0.70 0.72 0.84 0.84 -- -- Days with stover for livestock (days/year) 20 27 23 32 NA NA Surplus fodder (kg/year) -- -- -- -- 174.1 358.5 Low rainfall conditions …and had limited benefits in terms of increased stover production for livestock fodder …
  25. 25. The uptake of planting basins: 1. Unlikely to increase household incomes but could provide a critical safety net in terms of food security during low rainfall years, especially for lower resource endowed households 2. However, in areas with high rainfall and where the risk of drought is low, planting basins may not be such a worthwhile investment. Meeting fuel demand with on farm trees: 1. Unlikely to raise per capita income substantially without big changes in land use, especially for smaller farms 1. Larger farms present a greater opportunity for on-farm tree planting 1. Presents indirect benefits such as increased food security, surplus fodder and time saved – greater options and capacity for change?
  26. 26. What about the share of benefits within the household? In Ethiopia, on-farm trees for fuelwood could free-up substantial time - but who’s time? Primarily women who collect firewood How do on-farm trees for fuelwood impact the livelihoods of individuals within the household? Importance of intra-household dynamics
  27. 27. In Kenya, has the use of basins shifted labour between men and women? Who is involved in land preparation using your farmer practice? Who is involved in land preparation under basins? Data from monitoring of planned comparisons in Kenya 2017/2018
  28. 28. 1. While the innovations explored here are unlikely to lift the majority of farmers out of poverty on their own, they may have indirect and implications on the wider livelihood system, and offer households greater options (i.e., surplus fodder for livestock or for sale, extra time for other activities). 1. The extent to which innovations could increase a household capacity for change, however, depended heavily on household characteristics (i.e., farm size, household size, labour availability, livestock). 2. Modelling livelihood systems could therefore provide a useful tool for exploring the impact of interventions on individual households in a much broader sense than currently done within agricultural research and development. Key messages

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