1. 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. Verchot 3, C.B. Barrett 1, L.E. Blume 1, J.G.P. Gamarra 1, J.M. Kinyangi 1, C.J. Lehmann 1, A.O. Odenyo 1, 3, S.O. Ngoze 1, B.N. Okumu 1, M.J. Pfeffer 1, P.P. Marenya 1, S.J. Riha 1, and J. Wangila 3. 1 Cornell University, Ithaca, NY, 2 Kenya 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

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

4. 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.

5. 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

6. 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

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

8. W. Kenya Chronosequence 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

9. Recent Conversion 15 year Conversion (P first limiting)

11. Chronosequence Data: Block 1 Heavy textured soil Soil Carbon Depletion Soil Enzyme Activities

12. Chronosequence Soils Forest Soil100 Year Conversion

13. Soil Fertility Index – Embu and Madzuu

14. Soil Repletion Solomon Ngoze Solomon Ngoze Trials on chronosequence in Madzuu and in chronosequence with maize to determine what is needed to restore soil fertility in degraded soils Trials on chronosequence in Madzuu and in chronosequence with maize to determine what is needed to restore soil fertility in degraded soils Soil fertility Time What is the shape of this curve with different soil amendment treatments?

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

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

17. 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

18. Model Structure

19. 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

22. Madzuu Embu

23. 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

24. Sub Saharan Africa 70% of population employed in agriculture 70% of population employed in agriculture 180 million people “food insecure”, a number that has increased by 100% since 1970 180 million people “food insecure”, a number that has increased by 100% since 1970 Maize yields static 1200 kg ha -1 Maize yields static 1200 kg ha -1 A 50 kg bag of fertilizer costs a month’s wages for those at the poverty level A 50 kg bag of fertilizer costs a month’s wages for those at the poverty level Farm size has decreased from 0.53 to 0.35 ha since 1970 Farm size has decreased from 0.53 to 0.35 ha since 1970

25. Source: CBS, ILRI, 2003 Poverty Atlas Embu Madzuu

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

27. 10 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

28. W*W* W*W* Poverty line Pov. line W1W1 W2W2 W3W3 W1W1 W2W2 W3W3 Well- being t Well-being t+1 Welfare Dynamics with Multiple Equilibria Nonlinear path dynamics with multiple stable dynamic equilibria and at least one unstable dynamic equilibrium (threshold)

29. poverty line T3T3 T4T4 Asset stock t 1 2 3 T2T2 Asset stock t+1 4 A* 2 A* 1 A* 3 A* 4 transitory poornon-poor Chronic 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 of Critical Thresholds

30. Madzuu Poverty Transitions 1989-2002 Period 2 Poor Non-Poor Period 1 Poor60.7%20.2% Non-poor10.1%9.0% Poverty line $.53 day -1

31. Madzuu Income Distribution - 2002

32. Poverty Traps Need “video”, not “snap shot” of poverty Distinguish between chronic and transitory poverty Chronic poverty implies threshold effects or poverty traps