1. Rural household income diversification effects on sustainable land
management in smallholder farming systems: The case of the eastern
Africa highlands
Joseph Tanui

2. Overview
•This paper forms part of
a study on the “scaling
up of sustainable land
management in the
eastern Africa highlands”
•Specifically the study
contributes towards
understanding of “the
institutional economics
of sustainable land
management in
smallholder
communities”.

3. Scale perspectives
Systems International treaties, food security and climate change
perspectives
Vertical and horizontal integration of the biophysical and social
Landscapes
economic
Watershed Local governance, biodiversity , common property regimes
Farm Agricultural productivity, land tenure , income and expenditure
flows
Plot Crop productivity, nutrient cycling, soil (fertility, depth, slope)
Tree Tree tenure, Niche compatibility and multipurpose use

4. A work in progress
Institutional economics of sustainable land management research
has produced the following outputs (papers):
1. Rural household income diversification effects on sustainable land
management in smallholder farming systems: The case of the eastern
Africa highlands
2. Rural household energy poverty and natural resource degradation effects
under intense land pressure: the case of smallholder farming systems from
Vihiga district of western Kenya
3. Social networks and investments in sustainable land management
practices by smallholder farmers of the east African highlands: A spatial
analytical approach
4. Role of poverty in constraining investments in sustainable land
management: Modelling an institutional perspective through GAMS

5. Rural household income diversification effects on sustainable land
management in smallholder farming systems
Land degradation is a major threat to food security in the
region

6. Land degradation manifestation
• In smallholder farming landscapes, land degradation is
complex and associated with changes in socio-ecological
conditions:
– Increased vulnerability of agro-ecosystems to shocks and uncertainties
– Diminishing soil/ land productivity
– Poor market access limiting productive investments
– High and increasing population
• Waithaka et, al., (2007) asserts that among the difficulties
smallholder farmers face is that of optimization in an
environment of competing needs.

7. Addressing land degradation
Dercon and
Christiaensen, (2010)
assert that addressing
land degradation in the
region require two
fundamental steps:
– An examination of
smallholder farming
systems to better
understand factors
that explain low
technology adoption
– Size opportunities for
facilitating wide scale
investments in
sustainable land
management

8. Farmer decision
Making
• Need to gain a
wider
understanding of
farm level
resource
allocation, a basis
for relating
agricultural
productivity to
investments in
land quality

9. What do we know so far…
• Determinants of agricultural technology adoption and on what guides
natural resource management (NRM) practices in Sub-Saharan Africa (SSA)
(Lee, 2000; Barret, 2002; Feder et al., 1985) )
• Studies that have applied the livelihoods approach to better understand
smallholder farming systems (Ellis, 1998; Ellis, 2000; Adato, 2002; Ahmed et
al., 2008)
• Understanding social economic and institutional factors that influence the
adoption of specific SLM technologies and practices (Sheikh et al., 2003;
Amsalu and de Graaff, 2007; Pender and Gebremedhin, 2008; Tiwari et
al., 2008; Shiferaw et al., 2009)
• The increasing role of rural non-farm and off-farm income generating
activities (Haggblade et al., 1989; Reardon et al., 1994; Davis, 2006; Davis et
al., 2009; Gustavo and Silvio, 2009)

10. So what do we know in rural nonfarm and off-farm…
• Savadogo et al., (1998) analyzed the farm productivity raising
investments effects of animal traction resulting from non-farm
income.
• Kilic et al.,(2009) investigated the impact of non-farm income on
agricultural spending on crop inputs and other crop expenses
• Maertens (2009)addressed the impact of non-farm and farm wage–
labour derived from horticultural based agro-industry.
• Takahashi (2009) identifies the effect of non-farm income on the
use of tractors and threshers and on the employment of hired labor.
• Lien et, al.,(2010) found out that among Norwegian farmers, in
addition to demographic, time trend, and some regional
effects, nonfarm income has significant negative effect on farm
output.

11. Identifying gaps in literature
• (Clay et al., 1998; Mazvimavi and Twomlow, 2009): Difficult
data requirement reason for the dearth of empirical research
on determinants of land improvement investments by African
rural households
• Davis et al (Davis et al. 2009): Little information on how
participation in the nonfarm and off farm sector affects the
choice of farming technologies and the mix of farming activity.

12. Definition of the study
• Study contributes to information gap by examining how
participation in the nonfarm and off-farm sectors affects
investments in SLM
• In the context of reduced farm sizes and agricultural
productivity, the study tests the following hypothesis:
– That nonfarm and off-farm income streams will affect investments in
SLM

13. Research questions
1. What are the impacts of participation in nonfarm and off-
farm activities on agricultural production
2. How does participation in nonfarm and off-farm sectors
condition smallholder activity choices on investments in SLM
3. What is the resultant agricultural production efficiency of
nonfarm and off-farm incomes

14. Economic model specification
An agricultural household maximizes a quasi-concave utility
function:
The utility is dependent on a vector of consumption C and Leisure time, which
is expressed as total available time T minus labour supply
The labour supply is a summation of farm labour and nonfarm labour

15. Economic model cont’d
The agricultural household utility function is constrained by an income budget
constraint and a land quality constraint represented by an implicit farm
production function

16. First order conditions ..
The first-order conditions provide insight into a number of relationships
necessary for empirical estimation including the following:
1) Farm labour and nonfarm labour
2) Cash constraints and involvement in nonfarm labour,
3) Cash constraint and effects on determinants of agricultural productivity.

17. Study methodology
A cross-section household survey involving a stratified random sampling
procedure is undertaken in Vihiga district.

18. Sampling framework
• Village lists of households were made up based on the 2009
national census lists
• From the list every 9th household member was interviewed
• Total number of households interviewed were 320
• Plot level soil sampling and analysis were undertaken in 490
farm plots
• A structured survey questionnaire was used to collect
biophysical and social economic data
• Community level and district level information was collected
through focus group meetings
• Desk top research was also undertaken

21. Empirical analysis
The empirical analysis strategy provides a basis for determining the
following:
• Impacts of participation in nonfarm and off-farm activities on
agricultural production
+ +
• Effect of nonfarm and off-farm earnings on investment in
sustainable land management
+ +
• Estimating the agricultural productivity and efficiency effects of
nonfarm and off-farm
+ +

22. Emerging results thus far..
Specific Non farm income streams
Bussiness Employment
Government Assistance Land Leasing
Landlord Remittance
Wage Labour
20.54%
48.96%
17.22%
8.71%
2.28%
2.07% 0.21%

23. Results cont’d
Nonfarm income streams without remittances
Bussiness
33.74%
Wage Labour Bussiness
40.24%
Employment
Government Assistance
Land Leasing
Employment
17.07%
Landlord
Landlord Wage Labour
4.47%
Government
Land Leasing Assistance
4.07% 0.41%

24. Average incomes from specific Non farm sources
140000
126,419
120000
100000
80000
Average incomes KSH
60000
48,131
41,423
40000
18,866
20000 15,886
10,225
6,000
0
Bussiness Employment Government Land Leasing Landlord Remittances Wage Labour
Assistance
OFF Farm Income sources

25. Percentage of farmers that practise specific off farm income source
Firewood
59.87%
Charcoal
Firewood
Fishing
Fodder
Charcoal Forest honey
Fodder
5.92% 7.24% Quarrying
Fishing, 0.66%
Sand Harvesting
Timber Timber
15.79%
Tree Nurseries
Tree Nurseries
Forest honey
3.95% 1.32%
Quarrying
Sand Harvesting
3.95%
1.32%

26. Average incomes from off farm
50000
46,655
40000
33,902
30000
Average Incomes in KSH 23,267
20000
16,458
13,222
10000
6,250
3,200 3,183
1,000
0
Charcoal Firewood Fishing Fodder Forest honey Quarrying Sand Timber Tree
Harvesting Nurseries
Non farm income sources

27. Results from regression analysis
Simple OLS
name: <unnamed>
log: C:UsersjtanuiDesktopPHDStata filesUntitled22.smcl
log type: smcl
opened on: 17 Oct 2011, 17:11:58
. regress totalOutput farmOutput offfarmincome nonfarmincome slminvestment farmsz fertilizercost hlabour_cost li
> vestock_costs seedcost householdsize
Source SS df MS Number of obs = 320
F( 10, 309) = 7576.33
Model 3.1090e+13 10 3.1090e+12 Prob > F = 0.0000
Residual 1.2680e+11 309 410361531 R-squared = 0.9959
Adj R-squared = 0.9958
Total 3.1217e+13 319 9.7859e+10 Root MSE = 20257
totalOutput Coef. Std. Err. t P>|t| [95% Conf. Interval]
farmOutput .9981639 .0042365 235.61 0.000 .9898278 1.0065
offfarminc~e .9760731 .0338774 28.81 0.000 .9094135 1.042733
nonfarminc~e 1.041591 .0113778 91.55 0.000 1.019203 1.063979
slminvestm~t -.0185034 .0850723 -0.22 0.828 -.1858977 .148891
farmsz 1645.16 825.7583 1.99 0.047 20.33928 3269.98
fertilizer~t -.0123828 .0368254 -0.34 0.737 -.084843 .0600774
hlabour_cost .201218 .0895338 2.25 0.025 .025045 .377391
livestock_~s .0338332 .0123501 2.74 0.007 .0095323 .0581342
seedcost -.1619263 .1632162 -0.99 0.322 -.4830821 .1592294
households~e 129.4905 413.9276 0.31 0.755 -684.9828 943.9637
_cons 5258.352 3477.969 1.51 0.132 -1585.147 12101.85

28. 2SLS
. ivregress 2sls totalOutput farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs househo
> ldsize seedcost (offfarmincome nonfarmincome = collegeEdu owntransport distancemainRoad)
Instrumental variables (2SLS) regression Number of obs = 320
Wald chi2(10) =39471.92
Prob > chi2 = 0.0000
R-squared = 0.9929
Root MSE = 26407
totalOutput Coef. Std. Err. z P>|z| [95% Conf. Interval]
offfarminc~e .4570021 .4965555 0.92 0.357 -.5162287 1.430233
nonfarminc~e 1.056538 .0419507 25.19 0.000 .9743156 1.138759
farmOutput .9989499 .0057427 173.95 0.000 .9876944 1.010205
slminvestm~t -.0333167 .1117997 -0.30 0.766 -.2524401 .1858066
farmsz 1026.639 1185.784 0.87 0.387 -1297.455 3350.733
fertilizer~t -.0228773 .0497169 -0.46 0.645 -.1203205 .074566
hlabour_cost .4002554 .2624175 1.53 0.127 -.1140735 .9145843
livestock_~s .0388253 .0166502 2.33 0.020 .0061916 .071459
households~e 673.8686 747.4567 0.90 0.367 -791.1197 2138.857
seedcost -.2425868 .2345734 -1.03 0.301 -.7023422 .2171685
_cons 5348.641 4711.778 1.14 0.256 -3886.273 14583.56
Instrumented: offfarmincome nonfarmincome
Instruments: farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs
householdsize seedcost collegeEdu owntransport distancemainRoad

29. iv Tobit analysis
. ivtobit totalOutput farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs householdsize
> seedcost (offfarmincome nonfarmincome = collegeEdu owntransport distancemainRoad), ll twostep
Two-step tobit with endogenous regressors Number of obs = 320
Wald chi2(10) = 38050.46
Prob > chi2 = 0.0000
Coef. Std. Err. z P>|z| [95% Conf. Interval]
offfarminc~e .4472133 .5057914 0.88 0.377 -.5441196 1.438546
nonfarminc~e 1.057369 .042731 24.74 0.000 .9736181 1.14112
farmOutput .9989967 .0058492 170.79 0.000 .9875325 1.010461
slminvestm~t -.0306187 .1138829 -0.27 0.788 -.2538251 .1925878
farmsz 997.0037 1207.884 0.83 0.409 -1370.406 3364.414
fertilizer~t -.0221382 .0506403 -0.44 0.662 -.1213914 .077115
hlabour_cost .4052859 .2672979 1.52 0.129 -.1186083 .9291802
livestock_~s .0388473 .0169588 2.29 0.022 .0056086 .072086
households~e 723.4686 761.9132 0.95 0.342 -769.8538 2216.791
seedcost -.2433766 .2389228 -1.02 0.308 -.7116566 .2249034
_cons 4923.527 4806.105 1.02 0.306 -4496.266 14343.32
Instrumented: offfarmincome nonfarmincome
Instruments: farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs
householdsize seedcost collegeEdu owntransport distancemainRoad
Wald test of exogeneity: chi2(2) = 2.87 Prob > chi2 = 0.2387

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