The Interplay Between Smallholder Farmers and Fragile Tropical Agroecosystems in the Kenyan Highland

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The Interplay Between Smallholder Farmers and
Fragile Tropical Agroecosystems in the Kenyan
Highlands

• A.N. Pell 1, 3, D.M. Mbugua 1, 2, 3, L.V.
  Verchot3, C.B. Barrett1, L.E. Blume1, J.G.P.
  Gamarra1, J.M. Kinyangi1, C.J. Lehmann1, A.O.
  Odenyo1, 3, S.O. Ngoze1, B.N. Okumu1, M.J.
  Pfeffer1, P.P. Marenya1, S.J. Riha1, and J.
  Wangila3.
• 1Cornell University, Ithaca, NY, 2Kenya
  Agricultural Research Institute, Nairobi, Kenya,
  3 International Centre for Research in
  Agroforestry, Nairobi, Kenya.

2
National Science Foundation

• Biocomplexity Program
• Coupled Natural and Human Systems
• 2002-2007

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Biocomplexity

• Quantitative, interdisciplinary analysis of
  processes of human and natural systems
• Diverse time and spatial scales
• Emphasis on studies of natural capital, land use
• Model to include uncertainty, resilience and
  vulnerability
• Somewhat different mission than the CLASSES model

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Discussion between Smallholder Farmers in the
Kenyan Highlands and their Agroecosystem
Both people and the environment are at the
margin Small changes in farmers choices
profoundly affect the ecosystem and vice versa.
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Agroecologies

• Among the most tightly coupled of human and
  natural systems
• Conscious decisions made on
• Land use and improvement
• Crop varieties
• Livestock management
• Labor allocation
• Knowledge of how decisions are made is important
  in design of effective interventions

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NSF and BASIS

• NSF project has more emphasis on biophysical
  dynamics (soils, crops and livestock) than BASIS
• Also, the NSF model will have a human
  decision-making module (cognitive maps)
• Both use same socio-economic data

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Soil Depletion and Repletion
How long does it take for soil to become
degraded? What is required for its
replenishment?
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W. Kenya Chronosequence

• Conversions from forest to agriculture 100, 70,
  50, 30, 15, 5, lt 3 and 0 years ago (Nandi and
  Kakamega Forests)
• Identified by discussions with village elders and
  by consulting local records
• 6 Blocks with all 8 time conversions, 4 on heavy
  soils and 2 on sandy soils
• 3 farms per conversion
• Soil chemistry measurements as well as SOM
  fractions (proxy for soil fertility)
• Microbial diversity and soil enzyme activity

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Recent Conversion
15 year Conversion (P first limiting)
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Chronosequence Data Block 1 Heavy textured soil
Soil Carbon Depletion
Soil Enzyme Activities
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Chronosequence Soils
Forest Soil
100 Year Conversion
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Soil Fertility Index Embu and Madzuu
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Soil Repletion

• Solomon Ngoze
• Trials on chronosequence in Madzuu and in
  chronosequence with maize to determine what is
  needed to restore soil fertility in degraded
  soils

What is the shape of this curve with different
soil amendment treatments?
Soil fertility
Time
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Cognitive Maps

• To get information on how farmers make decisions
• Questionnaires in the field now
• Ranking exercises and determining which solutions
  are perceived to be most effective
• Focus groups on risk, crop choice and perceptions
  of soil fertility (qualitative data)

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Livestock

• Two students Florence Nherera and Helen
  Markewich
• FN Evaluating model to predict animal
  performance in Embu
• HM Evaluating model to predict nutrient content
  of manure in Vihiga

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Challenges

• Temporal scales
• Bacterial generation time of 20 min and decades
  to describe intergenerational poverty dynamics
• Spatial heterogeneity
• Difficulty in accounting for spatial
  heterogeneity while capturing human-ecosystem
  dialog
• Finding a common language for the
  interdisciplinary team to speak without losing
  subtleties inherent in disciplinary jargon

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Model Structure
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Model the Measureable
NOT an attempt to model all soil reactions Data
from the chronosequence will be used to
parameterize the soils model Biophysical submodel
will include livestock and crops
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Madzuu
Embu
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Poverty

• 20 of worlds population lives in extreme
  poverty (lt 1 day-1)
• 45-50 of the population in Sub Saharan Africa
  for past 15 years
• Increasing incomes of the extremely poor by 1
  day-1 will require 450 billion year-1
• Need strategic focus on nature and causes of
  extreme poverty

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Sub Saharan Africa

• 70 of population employed in agriculture
• 180 million people food insecure, a number that
  has increased by 100 since 1970
• Maize yields static 1200 kg ha-1
• A 50 kg bag of fertilizer costs a months wages
  for those at the poverty level
• Farm size has decreased from 0.53 to 0.35 ha
  since 1970

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Madzuu
Embu

Source CBS, ILRI, 2003 Poverty Atlas
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Kenyan Highlands (Embu and Madzuu)

• gt 1500 mm rainfall y-1
• 53-55 of populations earn less than .53 d-1
• Fertilizer use 8.8 kg y-1 (Kenyan avg 31.6 kg
  y-1)
• 619 (Embu) and 820 (Madzuu) people km-2
• Farm size 1.0 ha (Embu) and 0.4 ha (Madzuu)
• Tea and dairy more common in Embu

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10 Farms in Madzuu
1/3 of farms in Madzuu are lt 0.2 hectare acre
Annual losses of 112 kg N, 2.5 kg P and 70 kg K
ha-1 ? Serious decline in soil fertility
Smaling et al., 1993
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Welfare Dynamics with Multiple Equilibria
Nonlinear path dynamics with multiple stable
dynamic equilibria and at least one unstable
dynamic equilibrium (threshold)
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Poverty Traps Exist Because of Critical Thresholds
Asset stockt1
Each livelihood strategy has its own accumulation
path. Transitions emerge where switching to
another strategy is optimal. Traps emerge where a
switch is not optimal.
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A4
transitory poor
non-poor
Chronic poor
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A3
2
A2
poverty line
1
A1
T3
T4
T2
Asset stockt
poverty line
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Madzuu Poverty Transitions 1989-2002
Poverty line .53 day-1
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Madzuu Income Distribution - 2002
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Poverty Traps

• Need video, not snap shot of poverty
• Distinguish between chronic and transitory
  poverty
• Chronic poverty implies threshold effects or
  poverty traps