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)

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. 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. 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. 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. 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. Ethiopia

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. Kenya

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. Simile demo

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
Business as usual scenario

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
Fuelwood self-sufficiency scenario

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
Meeting household fuel demand with on-farm trees could provide indirect
benefits in terms of food security, especially for the HRE household …

18. 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…

19. 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…

20. 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 ...

21. 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 …

22. 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)

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
Farmer practice scenario

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
Planting basins scenario

25. 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 …

26. 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.

27. 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…

28. 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 …

29. 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?

30. 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

31. 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

32. 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