The Interplay Between The Interplay Between Smallholder Farmers and Smallholder Farmers and

Fragile Tropical Fragile Tropical Agroecosystems in the Agroecosystems in the

Kenyan HighlandsKenyan Highlands

A.N. Pell A.N. Pell 1, 31, 3, D.M. Mbugua , D.M. Mbugua 1, 2, 31, 2, 3, L.V. Verchot, L.V. Verchot33, , C.B. BarrettC.B. Barrett11, L.E. Blume, L.E. Blume11, J.G.P. Gamarra, J.G.P. Gamarra11, J.M. , J.M. KinyangiKinyangi11, C.J. Lehmann, C.J. Lehmann11, A.O. Odenyo, A.O. Odenyo11, 3, S.O. , 3, S.O.

NgozeNgoze11, B.N. Okumu, B.N. Okumu11, M.J. Pfeffer, M.J. Pfeffer11, P.P. , P.P. MarenyaMarenya11, S.J. Riha, S.J. Riha11, and J. Wangila, and J. Wangila33..

11Cornell University, Ithaca, NY, Cornell University, Ithaca, NY, 22Kenya Kenya

Agricultural Research Institute, Nairobi, Agricultural Research Institute, Nairobi, Kenya, Kenya, 33 International Centre for Research in International Centre for Research in

Agroforestry, Nairobi, Kenya.Agroforestry, Nairobi, Kenya.

National Science National Science FoundationFoundationBiocomplexity ProgramBiocomplexity Program

Coupled Natural and Human Coupled Natural and Human SystemsSystems

2002-20072002-2007

BiocomplexityBiocomplexity

Quantitative, interdisciplinary analysis of Quantitative, interdisciplinary analysis of processes of processes of humanhuman and and naturalnatural systems systems

Diverse time and spatial scalesDiverse time and spatial scales Emphasis on studies of natural capital, Emphasis on studies of natural capital,

land useland use Model to include uncertainty, resilience Model to include uncertainty, resilience

and vulnerabilityand vulnerability Somewhat different mission than the Somewhat different mission than the

CLASSES modelCLASSES model

Discussion between Discussion between Smallholder Farmers in the Smallholder Farmers in the Kenyan Highlands and their Kenyan Highlands and their

AgroecosystemAgroecosystem

Both people and the environment are “at the margin” – Small changes in farmers’ choices profoundly affect the ecosystem

and vice versa.

AgroecologiesAgroecologies

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

NSF and BASISNSF and BASISNSF project has more emphasis on NSF project has more emphasis on biophysical dynamics (soils, crops biophysical dynamics (soils, crops

and livestock) than BASISand livestock) than BASIS

Also, the NSF model will have a Also, the NSF model will have a human decision-making module human decision-making module

(cognitive maps)(cognitive maps)

Both use same socio-economic Both use same socio-economic datadata

Soil Depletion and Soil Depletion and RepletionRepletion

How long does it take for soil to become degraded?

What is required for its replenishment?

W. Kenya W. Kenya ChronosequenceChronosequence

Conversions from forest to agriculture 100, 70, 50, 30, 15, 5, < 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

Recent Conversion

15 year Conversion (P first limiting)

Chronosequence Data: Block 1 Heavy textured soil

Soil Carbon Depletion

Soil Enzyme Activities

Chronosequence SoilsChronosequence Soils

Forest Soil 100 Year Conversion

Soil Fertility Index – Embu Soil Fertility Index – Embu and Madzuuand Madzuu

Soil RepletionSoil Repletion

Solomon NgozeSolomon Ngoze Trials on chronosequence in Madzuu Trials on chronosequence in Madzuu

and in chronosequence with maize to and in chronosequence with maize to determine what is needed to restore determine what is needed to restore soil fertility in degraded soilssoil fertility in degraded soils

Soil fertility

Time

What is the shape of this curve with different soil amendment treatments?

Cognitive MapsCognitive Maps

To get information on how farmers To get information on how farmers make decisionsmake decisions Questionnaires in the field nowQuestionnaires in the field now

Ranking exercises and determining which Ranking exercises and determining which solutions are perceived to be most effectivesolutions are perceived to be most effective

Focus groups on risk, crop choice and Focus groups on risk, crop choice and perceptions of soil fertility (qualitative perceptions of soil fertility (qualitative data)data)

LivestockLivestock

Two students: Florence Two students: Florence Nherera and Helen Nherera and Helen MarkewichMarkewich FN: Evaluating model to FN: Evaluating model to

predict animal predict animal performance in Embuperformance in Embu

HM: Evaluating model HM: Evaluating model to predict nutrient to predict nutrient content of manure in content of manure in VihigaVihiga

ChallengesChallenges 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

Model Structure

Model the MeasureableModel 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

Madzuu

Embu

•20% of world’s population lives in extreme poverty (< $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

Poverty

Sub Saharan AfricaSub Saharan Africa

70% of population employed in agriculture70% of population employed in agriculture 180 million people “food insecure”, a 180 million people “food insecure”, a

number that has increased by 100% since number that has increased by 100% since 19701970

Maize yields static 1200 kg haMaize yields static 1200 kg ha-1-1

A 50 kg bag of fertilizer costs a month’s A 50 kg bag of fertilizer costs a month’s wages for those at the poverty levelwages for those at the poverty level

Farm size has decreased from 0.53 to 0.35 Farm size has decreased from 0.53 to 0.35 ha since 1970ha since 1970

Source: CBS, ILRI, 2003 Poverty Atlas

EmbuMadzuu

Kenyan Highlands (Embu Kenyan Highlands (Embu and Madzuu)and Madzuu)

> 1500 mm rainfall y> 1500 mm rainfall y-1-1

53-55% of populations earn less than $.53 53-55% of populations earn less than $.53 dd-1-1

Fertilizer use 8.8 kg yFertilizer use 8.8 kg y-1 -1 (Kenyan avg 31.6 (Kenyan avg 31.6 kg ykg y-1-1))

619 (Embu) and 820 (Madzuu) people km619 (Embu) and 820 (Madzuu) people km-2 -2

Farm size 1.0 ha (Embu) and 0.4 ha Farm size 1.0 ha (Embu) and 0.4 ha (Madzuu)(Madzuu)

Tea and dairy more common in EmbuTea and dairy more common in Embu

10 Farms in Madzuu10 Farms in Madzuu

1/3 of farms in Madzuu are < 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

W*

W* Povertyline

Pov.line

W1 W2 W3

W1

W2

W3

Well-beingt

Well-beingt+1

Welfare Dynamics with Multiple Welfare Dynamics with Multiple EquilibriaEquilibria

Nonlinear path dynamics with multiple stable dynamic equilibria and at least one unstable dynamic equilibrium (threshold)

poverty line

T3 T4

Asset stockt

1

2

3

T2

Asset stockt+1

4

A*2

A*1

A*3

A*4

transitory poor non-poorChronic poor

poverty line

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.

Poverty Traps Exist Because Poverty Traps Exist Because of Critical Thresholdsof Critical Thresholds

Madzuu Poverty Transitions Madzuu Poverty Transitions 1989-20021989-2002

Period 2Period 2

PoorPoorPeriod 2Period 2

Non-PoorNon-Poor

Period Period 11

PoorPoor60.7%60.7% 20.2%20.2%

Period Period 11

Non-Non-poorpoor

10.1%10.1% 9.0%9.0%

Poverty line $.53 day-1

Madzuu Income Madzuu Income Distribution - 2002Distribution - 2002

Poverty TrapsPoverty Traps

Need “video”, not “snap shot” of poverty Distinguish between chronic and

transitory poverty Chronic poverty implies threshold

effects or poverty traps