ICRAF’S Contribution to the global futures project 16 April 2011, Nanyuki

1. ICRAF’S Contribution to the global futures project16 April 2011, Nanyuki Myriam ADAM

2. Global Futures project • Improve methodologies for ex-ante assessment of promising new agricultural technologies • DSSAT is used to simulate responses of 5 crops to climate, soil, and nutrient availability (SPAM: Spatial Production Allocation Model) • The results are aggregated up to the IMPACT model’s 281 spatial units, called food production units (FPUs)

3. Where do we/AF stand in the VCM context? • Importance of the management aspect G*E*M • Integration of the agronomist perspective • From VCM to virtual systems: • Focus on maize-based system • Better agronomy practices • Micro-dose fertilization

4. Context: low maize production in SSA • Low N and P conditions (low N status of tropical soil) • Low N use efficiency of maize • Limited availability of fertilizer • Low purchasing power of smallholder farmers • Integrated soil fertility management (ISFM) combining mineral fertilizers with organic resources (manure, green manures -legumes, AF-, crop residus) and improved germplasm

5. Promising technologies • Data collection • IMPACT improvement activities

6. ISFM Source: Sanginga and Woomer, TSBF-CIAT, 2009

7. Effects of agroforestry • Effect of agroforestry (Productivity/Income/Environmental services) - nutrient supply through nitrogen fixation and nutrient cycling - more OM - better soil structure and water infiltration -direct production of food, fodder, fuel, and fibre -additional income to farmers from tree products -carbon sequestration -nutrient leaching -biodiversity -resilience  Focus on soil fertility

8. Different systems • Parkland systems • Intercrop systems • Improved fallow/ relay system • Biomass transfer

9. Different systems: focus first on • Parkland systems • Intercrop systems • Improved fallow/ relay system • Biomass transfer MAIZE –BASED SYSTEMS • Monomodal rainfall regime, e.g. Malawi • Maize/gliricidia intercropping • Bimodal rainfall regime, e.g. Kenya • Maize/Sesbania relay

12. What works where and why? • Data analysis Yield gap analysis and derive functional relations: Δy = f(elevation, rainfall, SOC, soil texture)  classification to target the promising technologies in the right locations: mapping + correspondence with food production units (FPU)

14. Identification of the relevant factors : example of Gliricidia

15. Promising technologies • Data collection • IMPACT improvement activities

16. Capitalize on the existing data • Inventory (maize-based systems): • Meta-analysis in Eastern-Southern Africa: improved fallows-intercrops in multiple countries • Multi-locations trials • Biophysical trials in Kenya • Need to get detailed info on : • Weather • Soils • Management practices

18. Soil: AfSIS • 5 countries ( 2-3 sentinel sites) representative of different AEZ: • Kenya, Malawi, Mali, Nigeria and Tanzania • Derive functional relationship for productivity related to soil properties: “ structured database with data …to derive key determinants of crop response and resource use efficiencyand will provide evidence of the efficacy of various soil management practices for improving crop and livestock production, under specific soil and climatic conditions in SSA”

19. Promising technologies Data collection IMPACT improvement/modelling activities

20. IMPACT • Training in May • Adjustment of monocrop yield according to agroforestry systems • Issue of labor • Issue of fertilizer decrease? • Other income

21. Modelling efforts for agroforestry • Existing AF models? FLORES, HiSafe, FALLOW, WaNuLCAS • Use and adaptation of crop models? DSSAT, APSIM, APES, FIELD... • ADAPTATION: use of the modularity of the models • IMPROVEMENT (spatial and temporal effect) Interaction with crop • Resource capture and utilization • Competition: root distribution, water and nutrients competition • Benefit: soil fertility, soil moisture Long term perspective • OM • C sequestration

22. Approach for model adaptation • Directly model the AF system within DSSAT • Inclusion of a tree component + important focus on SOM + intercropping • Correction of monocrop yield from DSSAT • Yield gap analysis  quantification of interaction effect

23. Directly within DSSAT • NO TREE • INTERCROPPING IS NOT (WELL) TACKLED

24. SOM issues • Importance for low input cropping systems • Quality • Litter vs. residues • In DSSAT: external file to provide residue properties • Green vs. senescent organic matter to incorporate • pruning • Trees species

25. Adaptation within IMPACT • Correction of monocrop yield from DSSAT • Yield gap analysis  quantification of interaction effect Yield gap analysis and derive functional relations: Δy = f(elevation, rainfall, SOC, soil texture)

26. Effects of agroforestry I = F – C ± M + P + L Source : Ong and Huxley, 1996

27. Quantification of the interaction Design needed to quantity fertility and competition effect: 4 different systems (usually only 2, maybe 3, are in place in the experimental design)

28. Some modelling activities • Wanulcas: • First runs (no calibration): • Malawi , 1990-1993 • Maize-Gliricidia intercropped

30. From model runs with different scenarios and data analysis, we could quantify the interaction effect Yield gap analysis and derive functional relations: Δy = f(elevation, rainfall, SOC, soil texture)

31. Potential adaptation • Yield response function: Crop yield intercept Product price (fuel, fodder, fruit) Δy = f(elevation, rainfall, SOC, soil texture) Price of input (less fertilizers trees seedlings) Growth rate: Derived from DSSAT, HAS TO BE ADAPTED FOR THE INTERACTION

32. Interaction with the other fellows • IFPRI : FPU, for classification of the agroforestry systems: where, which systems? • CIMMYT: maize-based system • They work on the genetic improvement • We can focus on the management, for better practices • ILRI: fodder issue • Fodder type, productivity • Trade-off for dual purpose • CIAT and ICRISAT: intercropping of leguminous

33. THANK YOU Acknowledgment: Frank PLACE, Edmundo BARRIOS, and Gudeta SILESHI

34. From promising technologies to adoption? • Targeting the technology to the right place, community • Access to inputs • Policy incentives • Scale issues: local vs. national vs. regional vs. global • Uncertainties analysis due to extrapolation/ application of the model in “unvalidated” locations