Rural household income diversification effects on sustainable land management in smallholder farming systems: The case of the eastern

Africa highlands

Joseph Tanui

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”.

Scale perspectives

Landscapes

Farm

Plot

Tree Tree tenure, Niche compatibility and multipurpose use

Crop productivity, nutrient cycling, soil (fertility, depth, slope)

Watershed

Systems

Agricultural productivity, land tenure , income and expenditure flows

Local governance, biodiversity , common property regimes

Vertical and horizontal integration of the biophysical and social economic

International treaties, food security and climate change perspectives

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

Land degradation is a major threat to food security in the region

Rural household income diversification effects on sustainable land management in smallholder farming systems

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.

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

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

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)

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.

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.

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

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

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

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

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 labour2) Cash constraints and involvement in nonfarm labour,3) Cash constraint and effects on determinants of agricultural productivity.

Study methodology

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

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

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

+ +

+ +

+ +

Emerging results thus far..

17.22%

8.71%

0.21%2.07%2.28%

48.96%

20.54%

Specific Non farm income streams

Bussiness Employment Government Assistance Land Leasing

Landlord Remittance Wage Labour

Results cont’d

Bussiness33.74%

Employment17.07%

Government Assistance0.41%

Land Leasing4.07%

Landlord4.47%

Wage Labour40.24%

Nonfarm income streams without remittances

Bussiness

Employment

Government Assistance

Land Leasing

Landlord

Wage Labour

Bussiness Employment Government Assistance

Land Leasing Landlord Remittances Wage Labour0

20000

40000

60000

80000

100000

120000

140000

48,131

126,419

6,00010,225

41,423

15,886 18,866

Average incomes from specific Non farm sources

OFF Farm Income sources

Average incomes KSH

Charcoal

5.92%

Firewood59.87%

Fishing, 0.66%Fodder7.24%

Forest honey1.32%

Quarrying3.95%Sand Harvesting

1.32%

Timber15.79%

Tree Nurseries3.95%

Percentage of farmers that practise specific off farm income source

CharcoalFirewoodFishingFodderForest honeyQuarryingSand HarvestingTimberTree Nurseries

Charcoal Firewood Fishing Fodder Forest honey Quarrying Sand Harvesting Timber Tree Nurseries0

10000

20000

30000

40000

50000

13,222

16,458

6,250

33,902

1,000

23,267

3,200

46,655

3,183

Average incomes from off farm

Non farm income sources

Average Incomes in KSH

Results from regression analysis Simple OLS

_cons 5258.352 3477.969 1.51 0.132 -1585.147 12101.85households~e 129.4905 413.9276 0.31 0.755 -684.9828 943.9637 seedcost -.1619263 .1632162 -0.99 0.322 -.4830821 .1592294livestock_~s .0338332 .0123501 2.74 0.007 .0095323 .0581342hlabour_cost .201218 .0895338 2.25 0.025 .025045 .377391fertilizer~t -.0123828 .0368254 -0.34 0.737 -.084843 .0600774 farmsz 1645.16 825.7583 1.99 0.047 20.33928 3269.98slminvestm~t -.0185034 .0850723 -0.22 0.828 -.1858977 .148891nonfarminc~e 1.041591 .0113778 91.55 0.000 1.019203 1.063979offfarminc~e .9760731 .0338774 28.81 0.000 .9094135 1.042733 farmOutput .9981639 .0042365 235.61 0.000 .9898278 1.0065 totalOutput Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 3.1217e+13 319 9.7859e+10 Root MSE = 20257 Adj R-squared = 0.9958 Residual 1.2680e+11 309 410361531 R-squared = 0.9959 Model 3.1090e+13 10 3.1090e+12 Prob > F = 0.0000 F( 10, 309) = 7576.33 Source SS df MS Number of obs = 320

> vestock_costs seedcost householdsize. regress totalOutput farmOutput offfarmincome nonfarmincome slminvestment farmsz fertilizercost hlabour_cost li

opened on: 17 Oct 2011, 17:11:58 log type: smcl log: C:\Users\jtanui\Desktop\PHD\Stata files\Untitled22.smcl name: <unnamed>

2SLS

householdsize seedcost collegeEdu owntransport distancemainRoadInstruments: farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costsInstrumented: offfarmincome nonfarmincome _cons 5348.641 4711.778 1.14 0.256 -3886.273 14583.56 seedcost -.2425868 .2345734 -1.03 0.301 -.7023422 .2171685households~e 673.8686 747.4567 0.90 0.367 -791.1197 2138.857livestock_~s .0388253 .0166502 2.33 0.020 .0061916 .071459hlabour_cost .4002554 .2624175 1.53 0.127 -.1140735 .9145843fertilizer~t -.0228773 .0497169 -0.46 0.645 -.1203205 .074566 farmsz 1026.639 1185.784 0.87 0.387 -1297.455 3350.733slminvestm~t -.0333167 .1117997 -0.30 0.766 -.2524401 .1858066 farmOutput .9989499 .0057427 173.95 0.000 .9876944 1.010205nonfarminc~e 1.056538 .0419507 25.19 0.000 .9743156 1.138759offfarminc~e .4570021 .4965555 0.92 0.357 -.5162287 1.430233 totalOutput Coef. Std. Err. z P>|z| [95% Conf. Interval]

Root MSE = 26407 R-squared = 0.9929 Prob > chi2 = 0.0000 Wald chi2(10) =39471.92Instrumental variables (2SLS) regression Number of obs = 320

> ldsize seedcost (offfarmincome nonfarmincome = collegeEdu owntransport distancemainRoad). ivregress 2sls totalOutput farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs househo

iv Tobit analysis

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

Prob > chi2 = 0.0000 Wald chi2(10) = 38050.46Two-step tobit with endogenous regressors Number of obs = 320

> seedcost (offfarmincome nonfarmincome = collegeEdu owntransport distancemainRoad), ll twostep. ivtobit totalOutput farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs householdsize

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