Rural household income diversification effects on sustainable land

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Rural household income diversification effects on sustainable land

  1. 1. Rural household income diversification effects on sustainable landmanagement in smallholder farming systems: The case of the eastern Africa highlands Joseph Tanui
  2. 2. Overview•This paper forms part ofa study on the “scalingup of sustainable landmanagement in theeastern Africa highlands”•Specifically the studycontributes towardsunderstanding of “theinstitutional economicsof sustainable landmanagement insmallholdercommunities”.
  3. 3. Scale perspectives Systems International treaties, food security and climate change perspectives Vertical and horizontal integration of the biophysical and socialLandscapes economicWatershed 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. 4. A work in progressInstitutional 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 highlands2. Rural household energy poverty and natural resource degradation effects under intense land pressure: the case of smallholder farming systems from Vihiga district of western Kenya3. Social networks and investments in sustainable land management practices by smallholder farmers of the east African highlands: A spatial analytical approach4. Role of poverty in constraining investments in sustainable land management: Modelling an institutional perspective through GAMS
  5. 5. Rural household income diversification effects on sustainable land management in smallholder farming systemsLand degradation is a major threat to food security in theregion
  6. 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. 7. Addressing land degradationDercon 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. 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. 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. 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. 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. 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. 13. Research questions1. What are the impacts of participation in nonfarm and off- farm activities on agricultural production2. How does participation in nonfarm and off-farm sectors condition smallholder activity choices on investments in SLM3. What is the resultant agricultural production efficiency of nonfarm and off-farm incomes
  14. 14. Economic model specificationAn 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 supplyThe labour supply is a summation of farm labour and nonfarm labour
  15. 15. Economic model cont’dThe agricultural household utility function is constrained by an income budget constraint and a land quality constraint represented by an implicit farm production function
  16. 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 labour2) Cash constraints and involvement in nonfarm labour,3) Cash constraint and effects on determinants of agricultural productivity.
  17. 17. Study methodologyA cross-section household survey involving a stratified random sampling procedure is undertaken in Vihiga district.
  18. 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
  19. 19. Empirical analysisThe 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 + +
  20. 20. 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%
  21. 21. Results cont’dNonfarm 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% GovernmentLand Leasing Assistance 4.07% 0.41%
  22. 22. Average incomes from specific Non farm sources 140000 126,419 120000 100000 80000Average 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
  23. 23. 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%
  24. 24. Average incomes from off farm 50000 46,655 40000 33,902 30000Average 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
  25. 25. Results from regression analysis Simple OLS name: <unnamed> log: C:UsersjtanuiDesktopPHDStata filesUntitled22.smcl log type: smclopened 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 = 20257totalOutput Coef. Std. Err. t P>|t| [95% Conf. Interval] farmOutput .9981639 .0042365 235.61 0.000 .9898278 1.0065offfarminc~e .9760731 .0338774 28.81 0.000 .9094135 1.042733nonfarminc~e 1.041591 .0113778 91.55 0.000 1.019203 1.063979slminvestm~t -.0185034 .0850723 -0.22 0.828 -.1858977 .148891 farmsz 1645.16 825.7583 1.99 0.047 20.33928 3269.98fertilizer~t -.0123828 .0368254 -0.34 0.737 -.084843 .0600774hlabour_cost .201218 .0895338 2.25 0.025 .025045 .377391livestock_~s .0338332 .0123501 2.74 0.007 .0095323 .0581342 seedcost -.1619263 .1632162 -0.99 0.322 -.4830821 .1592294households~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
  26. 26. 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 = 26407totalOutput Coef. Std. Err. z P>|z| [95% Conf. Interval]offfarminc~e .4570021 .4965555 0.92 0.357 -.5162287 1.430233nonfarminc~e 1.056538 .0419507 25.19 0.000 .9743156 1.138759 farmOutput .9989499 .0057427 173.95 0.000 .9876944 1.010205slminvestm~t -.0333167 .1117997 -0.30 0.766 -.2524401 .1858066 farmsz 1026.639 1185.784 0.87 0.387 -1297.455 3350.733fertilizer~t -.0228773 .0497169 -0.46 0.645 -.1203205 .074566hlabour_cost .4002554 .2624175 1.53 0.127 -.1140735 .9145843livestock_~s .0388253 .0166502 2.33 0.020 .0061916 .071459households~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 14583.56Instrumented: offfarmincome nonfarmincomeInstruments: farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs householdsize seedcost collegeEdu owntransport distancemainRoad
  27. 27. iv Tobit analysis. ivtobit totalOutput farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs householdsize> seedcost (offfarmincome nonfarmincome = collegeEdu owntransport distancemainRoad), ll twostepTwo-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 1.438546nonfarminc~e 1.057369 .042731 24.74 0.000 .9736181 1.14112 farmOutput .9989967 .0058492 170.79 0.000 .9875325 1.010461slminvestm~t -.0306187 .1138829 -0.27 0.788 -.2538251 .1925878 farmsz 997.0037 1207.884 0.83 0.409 -1370.406 3364.414fertilizer~t -.0221382 .0506403 -0.44 0.662 -.1213914 .077115hlabour_cost .4052859 .2672979 1.52 0.129 -.1186083 .9291802livestock_~s .0388473 .0169588 2.29 0.022 .0056086 .072086households~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 14343.32Instrumented: offfarmincome nonfarmincomeInstruments: farmOutput slminvestment farmsz fertilizercost hlabour_cost livestock_costs householdsize seedcost collegeEdu owntransport distancemainRoadWald test of exogeneity: chi2(2) = 2.87 Prob > chi2 = 0.2387
  28. 28. ReferencesAdato, M., Meinzen-Dick, R., 2002. Assesing the impact of agricultural research on poverty using the sustainable livelihoods framework. EPTD Discussion paper No. 89/FCND Discussion paper No. 128. IFPRI, Washington DC.Ahmed, N., Allison, E.H., Muir, J.F., 2008. Using the Sustainable Livelihoods Framework to Identify Constraints and Opportunities to the Development of Freshwater Prawn Farming in Southwest Bangladesh. Journal of the World Aquaculture Society 39, 598-611.Amsalu, A., de Graaff, J., 2007. Determinants of adoption and continued use of stone terraces for soil and water conservation in an Ethiopian highland watershed. Ecological Economics 61, 294-302.Barret, C.B., Place, F., Aboud, A.A. , 2002. Natural Resource Management in African Agriculture: Understanding and Improving Current Practices. CABI publishing, Oxford.Clay, D., Reardon, T., Kangasniemi, J., 1998. Sustainable Intensification in the Highland Tropics: Rwandan Farmers Investments in Land Conservation and Soil Fertility. Economic Development and Cultural Change 46, 351-377.Davis, B., Winters, P., Reardon, T., Stamoulis, K., 2009. Rural nonfarm employment and farming: household-level linkages. Agricultural Economics 40, 119-123.Davis, L.S., 2006. Growing apart: The division of labor and the breakdown of informal institutions. Journal of Comparative Economics 34, 75.Dercon, S., Christiaensen, L., 2010. Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia. Journal of Development Economics 96, 159-173.Ellis, F., 1998. Household strategies and rural livelihood diversification. Journal of Development Studies 35, 1-38.Ellis, F., 2000. Rural Livelihoods and diversity in developing countries. Oxford university press, Oxford.Feder, G., Just, R.E., Zilberman, D., 1985. Adoption of Agricultural Innovations in Developing Countries: A Survey. Economic Development and Cultural Change 33, 255-298.Gustavo, A., Silvio, D., 2009. Linkages between the farm and nonfarm sectors at the household level in rural Ghana: a consistent stochastic distance function approach. Agricultural Economics 41, 51-66.Haggblade, S., Hazell, P., Brown, J., 1989. Farm-nonfarm linkages in rural sub-Saharan Africa. World Development 17, 1173.

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