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“Modeling the impact of natural resource-based poverty traps on food security in Kenya: The Crops, Livestock and Soils in Smallholder Economic Systems (CLASSES) model”

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Abstract

We investigate the interactions between natural resource-based poverty traps and food security for smallholder farms in highland Kenya using a recently developed system dynamics bio-economic model. This approach permits examination of the complex interactions and feedback between farm household economic decision-making and long-term soil fertility dynamics that characterize persistent poverty and food insecurity among smallholders in rural highland Kenya. We examine the effects of changing initial endowments of land and stocks of soil organic matter on smallholders’ well being, as reflected in several different indicators. We show that larger and higher quality land endowments permit accumulation of cash and livestock resources and conservation of soil organic matter relative to smaller or more degraded farms. This suggests the existence of asset thresholds that divide food secure households from food insecure ones.

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Notes

  1. There are several other examples, such as Crissman et al. (1998), Brown (2008). Brown (2000) offers a more complete survey.

  2. System dynamics models are systems of (typically nonlinear) differential equations solved by numerical integration. Additional information and resources on system dynamics can be found on the System Dynamics Society webpage: www.systemdynamics.org.

  3. For simplicity, the perennial cash crop (tea) and the perennial fodder crop (Napier) are omitted from the diagram. However, the stocks, flows and feedbacks are similar to that for the annual food crop (maize).

  4. Note that this process only applies because biomass is accumulated through photosynthesis. For other nutrients (N in most cases and P), external applications are required because there will be nutrient losses with each harvest cycle.

  5. At this stage in model development, we do not model stochastic outcomes from agricultural production. However, the model is structured in such as way as to facilitate the incorporation of risk in later versions

  6. The households also compare on-farm returns to off-farm opportunities.

  7. Although it would be quite feasible to do so, the current version of CLASSES does not include credit access that could facilitate asset acquisition when insufficient cash is available.

  8. The full model and accompanying documentation are available at http://pzacad.pitzer.edu/~estephen/

  9. Summaries of the parameterization of all simulations are included in the appendix.

  10. The dependents add to household consumption levels but do not contribute to the level of available labor on the farm.

  11. The levels of soil nutrients and other farm parameters for each simulation are included in tables in Appendix 2. ‘Median soil quality in our simulations is soil that begins the simulation with half of the initial level of soil nutrient stocks available after the land has been converted from primary forest into agricultural farmland. The stocks, flows and returns related to each plot are modelled individually so as to be able to understand how, for example, soil fertility on each part of the farm evolves over time.

  12. Tropical Livestock Units index livestock quantities across species based on feed intake. The baseline compares all animals to the intake requirements for camels (1 TLU). In our model, cows are also 1 TLU, heifers are 0.7 TLU and calves are 0.3 TLU reflecting differences in intake requirements for male and female cattle as well as adults versus younger animals.

  13. Although the model is deterministic, we have included regular seasonal variation in agricultural yields, with short rains harvests systematically smaller than long rains harvests.

  14. The previous discussion about behavioural modes hypothesized logistic growth or exponential decay based on the simplified feedback structure depicted in Fig. 1. The simulation model results incorporate a number of additional processes (e.g., expenditures for livestock or seasonal crop production) and therefore have additional variation. However, the results are broadly consistent with a bifurcation of behavioural modes—growth for the larger farm and exponential decay and stagnation for the smaller farm.

  15. The 0.5 ha farm is also able to maintain subsistence consumption even though it becomes a net buyer of maize around t = 30 in the simulation (not shown).

  16. Nutrient imports to the farm system resulting from livestock ownership are due primarily to purchased feeds (e.g., maize bran), which are commonly used in the Kenyan highlands, and collection of other feed resources not produced on the farm (such as gathering roadside grasses or weeds). Note that manure per se typically does not represent a source of imported nutrients in this system. Rather, it is one component of nutrient cycling in the farm system.

  17. The baseline transaction cost in the model is 250 KSh/1,000 kg sack of maize, and the baseline price is 1,000 KSh/sack.

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Authors and Affiliations

  1. Pitzer College, 1050 N. Mills Avenue, Claremont, CA, 91711, USA

    Emma C. Stephens

  2. Cal Poly San Luis Obispo, Dept. of Agribusiness, San Luis Obispo, CA, 93407, USA

    Charles F. Nicholson

  3. World Vision International, 1 World Drive, Mississauga, Ontario, L5T 2Y4, Canada

    Douglas R. Brown

  4. University of Tasmania, Agricultural Science, Private Bag 54, Hobart, Tasmania, 7001, Australia

    David Parsons

  5. Cornell University, Applied Economics and Management, 315 Warren Hall, Ithaca, NY, 14853, USA

    Christopher B. Barrett

  6. Cornell University, Crop and Soil Sciences, 909 Bradfield Hall, Ithaca, NY, 14853, USA

    Johannes Lehmann

  7. World Agroforestry Centre (ICRAF), P.O. Box 30677, 00100 GPO, Nairobi, Kenya

    David Mbugua

  8. Cornell University, Earth and Atmospheric Science, Bradfield Hall, Ithaca, NY, 14853, USA

    Solomon Ngoze & Susan J. Riha

  9. Cornell University, Animal Science, 149 Morrison Hall, Ithaca, NY, 14853, USA

    Alice N. Pell

Authors
  1. Emma C. Stephens
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  2. Charles F. Nicholson
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  4. David Parsons
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  5. Christopher B. Barrett
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  6. Johannes Lehmann
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  7. David Mbugua
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  8. Solomon Ngoze
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  10. Susan J. Riha
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Corresponding author

Correspondence to Emma C. Stephens.

Additional information

This work was supported by the Coupled Natural and Human Systems Program of the Biocomplexity Initiative of the National Science Foundation, through grant BCS – 0215890, with additional support from the USAID BASIS CRSP project on Rural Markets, Natural Capital and Dynamic Poverty Traps in East Africa. The Rockefeller Foundation is providing key financial support for many of the Kenyan doctoral students involved in the project.

Appendices

Appendix 1: CLASSES Basic Model Assumptions and Diagrammatic Representation

  1. 1.

    Basic Model Assumptions

    • Initialization: household has 10 patches with either food, cash or Napier grass, has some or no livestock (model will be initialized with different starting conditions to explore poverty traps).

    • On-farm averages are used to calculate expected average value product of labor in: food crops, cash crops (tea), Napier, livestock

    • At time 0: Crop starts to grow, animals are fed, household cash is spent on soil amendments, labor

    • At time = decision point (each quarter): any fully grown food crops are harvested (Napier and tea have continuous harvesting once established), nutrients extracted from soil, farmer sells output.

    • After each decision point: farmer updates the expected average value product of labor in each livelihood activity, reallocates land into highest return activity, invests or disinvests in livestock (based also on expected feed availability, cash constraints and animal health)

    • After each decision point: new soil quality determines crop growth at time 1 and cycle begins again

  2. 2.

    Causal Loop Diagram Representation

    Appendix Fig. 11 indicates in more detail the main feedback relationships in CLASSES. As in the figure in the text, this follows SD diagramming conventions: boxes are stocks, flows are indicated by double arrows and valves, and causal linkages are shown by arrows that also indicate the sign of the relationship. Bold italicized variables indicate initial asset endowments (land, labour and soil nutrients). Italicized variables (not bold) indicate important exogenous values. Red bold variables indicate key outcomes of interest discussed in the text. Pink arrows and variables describe key resource allocation decisions made by the household each planting season (land allocated to three crops: maize, Napier grass, or tea).

    Fig. 11
    figure 11

    Diagrammatic representation of key relationships in CLASSES, emphasizing the linkages among the crops, soils, livestock and economic decision making components. A colour version of this figure may be found in the online version

    Full size image

    This structure is capable of generating the hypothesized fundamental behaviours. However, because the model contains numerous interacting feedback processes, a diagrammatic representation alone is inadequate to determine likely system behaviours (Sterman 2000). Thus, parameterization and simulation are essential to determine likely behaviours for different plausible sets of initial asset endowments. In order to observe a natural resource based poverty trap consistent with food insecurity, there must be a region of increasing economic returns to the farm’s chosen livelihood activities. Households that fall below an identifiable asset threshold, either because of biophysical resource degradation, or economic and/or biophysical shocks, will experience gradually deteriorating welfare outcomes on the farm.

Appendix 2: Selected Model Simulation Parameters

Tables 1 and 2.

Table 1 Simulation #1 on varying farm sizes
Full size table
Table 2 Simulation #2 on varying soil organic matter stocks
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Stephens, E.C., Nicholson, C.F., Brown, D.R. et al. “Modeling the impact of natural resource-based poverty traps on food security in Kenya: The Crops, Livestock and Soils in Smallholder Economic Systems (CLASSES) model”. Food Sec. 4, 423–439 (2012). https://doi.org/10.1007/s12571-012-0176-1

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