Translation of the storylines into the household model
The main drivers of the transitions between the farming systems considered in the CLUE modelling
framework were: market access, available technology/extension services, population density, land
fragmentation and off-farm income. For the dynamic variables projections are created for the
coming 20 years under the different scenarios, as described under the section storylines. Error!
Reference source not found. describes the various assumptions made for these projections. For the
in-equitable growth scenario the study area was divided into two zones, based on the current
occurrence of large-scale farming. For the zone with large-scale farming higher growth rate were
assumed for education, extension, and off-farm income than in the zone without the occurrence of
large-scale farming (see figure). The zones were based on the occurrence of large commercials
crops in the un-aggregated original version of the national Africover landcover multipurpose
database (FAO, 2003).
Division of study area in zones with and without large-scale farming
The fourth scenario included climate change. Possible future long-term monthly climate normals
(rainfall, daily temperature, and daily temperature diurnal range) were derived using the WorldClim
v1.3 climate grids at 1-km2 resolution (Hijmans et al., 2005). We used the results of the Hadley
CM3 Global Circulation Model for scenario B2 (Special Report on Emissions Scenarios; IPCC,
2000), to derive climate normals for 2005, 2010, 2015, 2020, 2025 and 2030, using the downscaling methodology described in Jones and Thornton (2003). These normals were then used with
the weather generator MarkSim (Jones and Thornton, 2000) to generate daily weather data
characteristic of the appropriate climate normals. Thirty replicates of weather years for two sites,
Embu and Kiambu, were generated for each of the five-year intervals, and these weather files were
used to run the DSSAT crop models for maize and beans.
Results indicated that there were general tendencies at both sites for some small yield increases and
a general decrease in crop duration, the result of slightly warmer conditions that resulted in faster
accretion of the required heat units for phenological development in both crops.
Predicted changes in growing period in the Kenyan highlands as a result of climate change
Very broadly, model runs indicated something of the order of a 10% crop yield increase to 2030,
coupled with a 10% decrease in crop duration, for Embu and Kiambu. Other results in Thornton et
al. (2005) show that there are other areas, particularly parts of western and coastal Kenya, where
there may be more dramatic decreases in length of growing period to 2030, and in places where
rainfall is more limiting, increased temperatures could cause bigger yield reductions than found
here. Jones and Thornton (2003) indicate a 5% decrease in smallholder maize yields to 2055 for the
country as a whole, although this national figure hides enormous variation, with yields increasing in
some places and dramatic decreases occurring in others.
It should be remembered that the modelling techniques used do not incorporate all the increased
variability that will result because of climate change, particularly the changes in frequency of
extreme events such as droughts and the changes in rainfall variability that may be associated with
the scenarios of climate change. The simulations reported here do not suggest much change in the
down-side risk to householders, but it is highly likely that this risk is underestimated here.
It had been intended to closely integrate the spatial modelling (CLUE) with the household
modelling. However, considerable time was spent adjusting the two models to the specific needs of
the project (i.e. application of CLUE to household data and extension of the household model to
allow cattle keeping as a decision variable). The intention had been that the household model would
be used to refine the parameters of change between one system and the next, based on the
household decision making process, which would then be integrated into the parameters driving the
CLUE model. That was not able to be accomplished, mainly because some of the results of the
household model were not consistent with the observed, statistically based system change. This
appears to have resulted from the extreme heterogeneity in households even within a given system;
the same issue that created difficulty in the household characterisation. Further, in retrospect, more
attention needed to be devoted to understanding how to model shifts between systems at the
household level, based on likelihood of which types of farms can change to others with similar
characteristics.
Nevertheless, the household model was parametrized and run to mirror the changes modelled in the
spatial model. The results were then used to test the spatial systems changes, which are based on
observed system differences, with household level decision predictions based on an optimization
approach. To translate the drivers used in the CLUE modelling framework into the household
structure and information requirements, projections of different variables that changed according to
scenario and period of time were carried out:

Land holding size

Opportunity costs of labour.
These two were selected because they most directly reflect the projected demographic and
economic changes in the alternative scenarios. Real prices cannot be accurately predicted for the
key commodities, and nominal prices will fluctuate depending on inflation, etc. Thus prices were
held fixed, based on the view that assuming that constant real prices was more reasonable that
assuming some real price trend, which would be subject to global commodity trade.
Assumptions, calculations and data sources to create dynamic variables projections for the different scenarios
Drivers
Variable
Level of
analysis for
base data
Source
for base
data
Human
population
density
Total human
population /area
Sub-location
Census
data 1999
Level of
analysis
for
change
District
Baseline scenario
Change and source for change
-
Inequitable scenario
Change and source for change
Two growth rates were used:
a) rural growth rate for sub-locations with population density below 1,000 per km2 and
b) urban growth rate for sub-locations with population density above 1,000 per km2
-
Growth rates varied by district, in proportion to the observed growth rate and based on the national averages. The resulting
average rates for the study areas therefore are calculated from the national averages, although the overall trends are respected.
For ease of presentation, only the national growth rates are presented below.
-
Urban growth rate was the same across the different scenarios and is equal to the observed urban growth rates in the district
between the 1989 and 1999 (Kenya Government, 1989-1999).
-
Rural growth rates differed across scenario with a negative growth rate in the equitable growth scenario in the assumption of
out-migration (from rural to urban areas). Rural growth rate in the baseline scenario is calculated as the observed average rural
growth rates (past 5 years) while for the rural growth rate for the inequitable scenario is the average of the rates in the two other
scenarios.
-
A negative annual trend of 0.01 was applied to take into account the fact that the growth rate increases at a decreasing rate
(results from regression analysis on population panel data (year/ district))
-
The mortality rate due to HIV/AIDS was subtracted from the population (Please see the appendix on how HIV/AIDS data was
incorporated).
Urban growth rate=10.27
Rural growth rate=0.706
Education
Equitable scenario
Change and source for change
Number of
years formal
education of the
household head
Sub-location
Census
data 99
District
Urban growth rate=10.27
Rural growth rate=-0.629
Urban growth rate=10.27
Rural growth rate=0.038
-
The baseline scenario assumed an average national primary education enrolment rate of 1% (Kenya Government, 2002b), 4.5%
for the equitable scenario(Kenya Government (2002b), and 2.25% for the in-equitable growth scenario
-
The district education growth rates in each scenario was assumed to have an inverse relationship to the pupil: teacher ratio in
the district, and it was calculated as:
National pupil : teacher ratio in baseline scenario X education growth rate in each scenario
District pupil : teacher ratio in baseline scenario
-
The education growth rate in the in-equitable scenario assumed three rates:
For the in-equitable growth scenario, the divisions with large scale farms were given a higher growth rate (rate for the equitable
growth scenario), while the ones surrounding the large scale farms as well some divisions with a large proportion of mediumsized farms were the buffer zones. The areas without large scale farms were termed as non-favoured areas, and had a lower
growth rate, which was the average of the baseline and equitable growth scenarios. The growth rates for the buffer zones were
the average of the growth rates between the adjacent favoured and non-favoured areas.
Please see Figure 3 for the map of divisions with large scale farming (favoured divisions), non-favoured areas and the buffer zones
Extension
services
% of farmers
with access to
livestock
extension
services
Sub-location
MOSD
extrapolat
ed data
National
-
The baseline scenario assumed a 0% growth rate. This rate was based on the expert opinion of the November 23rd 2004 panel of
experts meeting.
-
The equitable scenario assumed a 2% growth rate (Madhur, 1999), while the in-equitable base scenario was 1% for areas
without large scale farming and 2% for those with large scale farms.
The buffer zones had an average growth rate of 1.5%, that is the average of the growth rates of the favoured zones (2%) and
non- favoured zones (1%)
Off-farm
employment
Proportion of
household
members with
off farm
employment
Sub-location
Census
data 99
Division
The baseline scenario assumed a 2.25%. growth rate in the proportion of household members with off-farm employment.
(Kenya Government, 2002b), while the equitable growth scenario assumed a 4.5% growth rate (EAMAT. 1999).
-
The off-farm employment growth rate in the in-equitable scenario assumed three rates:
For the in-equitable growth scenario, the divisions with large scale farms were given a higher growth rate (rates for the
equitable growth scenario), while the ones surrounding the large scale farms as well some divisions with a large proportion of
medium sized farms are the buffer zones. The areas without large scale farms were termed as non-favoured areas, and had a
lower growth rate, which was the average of the baseline and equitable growth scenarios. The growth rate for the buffer zones
was the average of the growth rates between the adjacent favoured and non-favoured areas.
Please see Figure 3 for the map of divisions with large scale farming (favoured divisions), non-favoured areas and the buffer zones.
-
The district growth rate was based on the district proportion of off-farm income to the total income (Kenya Government,
2002a), and the national proportion of national off-farm income to the national total income (Kenya Government, 2002b)
The growth rate for each district therefore was calculated as:
Contribution of district non-agricultural income baseline X growth rate for each scenario
Contribution of national non- agricultural income baseline
Market
access
Distance of
tarmac and
murram roads
in kilometers
National
-
For the baseline scenario, the tarmac is 5,033.567km/year between 2004 and 2008, 5,064 km in 2009 to 2024. Length of
murram remains at 6,134.16 km up to year 2024 (Survey of Kenya map revised in 1989 and updated by SDP in 2000. A scale of
1:50,000)
-
For the equitable growth scenario, the length of tarmac increases from 5,843.3945 km in 2004 to 13,657.507 to 2012 and
remains the same up to 2024. The length of murram increases from 7,310.425 km in 2004 to 10,939 (Information from the
Ministry of works on plans for a superhighway from Mombasa to Busia.) (Based on the fact that areas within 20km radius
within airports and airstrips will have their roads improved)
-
For the in-equitable scenario the length of tarmac increases from 5,813.197 km in 2004 to 9,673.145 km to 2013 and remains
the same up to 2024. Length of murram reduces from 9,544.58 km in 2004 to 6,195.719 km in year 2013 and remains the same
up to 2024. In this case murram roads, especially in the favoured areas are being upgraded to tarmac (Based on the Africover
data on cash crop areas for export) (Based on the fact that areas within 20km radius within airports and airstrips will have their
roads improved)
Scenario 4 differs from scenario 2 only by climate change.