The Interplay Between Smallholder Farmers and Fragile Tropical Agroecosystems in the Kenyan...
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Transcript of The Interplay Between Smallholder Farmers and Fragile Tropical Agroecosystems in the Kenyan...
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