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Soil fertility management in ethiopia :Decision Support Tools for Soil Fertility Management in Ethiopian Highlands: Dr Tilahun Amede

  1. Decision Support Tools for Soil Fertility Management in Ethiopian Highlands Tilahun Amede, Principal Scientist
  2. Sustainable Intensification Long term Vs Satisfying immediate and growing needs Driven by the need for sustaining higher productivity of land, labour and water; 1. Farm scales : increased productivity per area unit; 2. Landscape: Improved use of resources (land, nutrients, water, labour) at landscape niches; 3. Increased use of capital (e.g. fertilizer, irrigation) 4. Changes in land use: From subsistence to market- oriented, nutrition-rich systems
  3. Community mobilization; Rehabilitated landscapes
  4. Mountain Irrigation from Yewol watersheds Downstream irrigation Increased from 240 ha to 970 ha
  5.  Feed production increased by 35-60% (grazing and crop residue)  Reduction of walking distance to access water: from 9 km to 2 km  Energy for walking is reduced from  1956 MJ ME / TLU to 584 MJ ME / TLU / year  Milk equivalent of1372 ME MJ saved: extra 252 liter of milk per lactation period / TLU  Water: no change in water depleted for feed production Yield gradients, short term..
  6. Integrating insitu water harvesting to maximize productivity Increased water infiltration Concentration of resources (OM, nutrients, water) Year 1 Year 3Year 2
  7. Drivers for Sustainable Watershed Mangt at Scale • Availability of technological options fitting to systems; • Development of convincing approaches; • Financial capacity of the users / risk; • Functional partnership; NGOs • Changing priority of District Administrations • Supportive infrastructure; Roads • Attractive market opportunities
  8. Fertilizer Recommendation: Efficiency and Profitability • Assessing nutrient deficiencies of Cropping systems in Ethiopia through test crops particularly with reference to micro and secondary nutrients • Supporting the National Initiative (ATA) in developing country-wide soil maps and fertilizer recommendations; • Develop a Decision Support Tool targeting systems to achieve increased agricultural production, system resilience and improved livelihoods using fertilizer inputs as entry points.
  9. Confluence points and our sites
  10. Nutrient Zonation within the systems Zone 1 Zone 3 Zone 2
  11. Our treatments (Optimal nutrient applications?) • NP (90/45) • NPK (90/45/61) • NPKS (90/45/61/63) • NPKSZn (90/45/61/63/10 • Minimum application (30% recommended NP) • Farmer’s fields (control)
  12. Our field trials show three types of responses to application of various micro and macronutrients
  13. Enda-Mehoni Zone 1. Good Crop, No effect of blends • Crop is doing well but there is no visible difference among our treatments in terms of growth, height and vigourosity; • Our treatments are not even better than farmers plots • This is where agronomic management played more than nutrient application
  14. Major effect from NP, and in some case K or S Zone 2 farms. Distinct difference among farms
  15. Zone 3. Bad crop, no difference, lost investment (Non-responsive soils) No visible yield margin for the investment
  16. Crop response to fertilizer blends, Enda-Mehoni (Zone 2 and 3) Farmers' Control 33%NP NP NPK NPKS NPKSZn WheatGrainYield(tha-1) 0 1 2 3 4 5 6 Fertile soils Marginal soils
  17. Sinana Zone 1. Good Crop, No effect of blends
  18. Zone 2. Distinct difference among farms
  19. Zonation in DBirhan Zone 1 Zone 2 Zone 3
  20. Farmers' Control 33%NP NP NPK NPKS NPKSZn WheatGrainYield(tha-1) 0 1 2 3 4 5 6 7 Fertile soils Marginal soils Crop response to fertilizer blends, Sinana (Zone 1 and 2)
  21. Wheat response to fertilizer blends, Lemu (Zone 1 and 2) Farmers' Control 33%NP NP NPK NPKS NPKSZn WheatGrainYield(tha-1) 0 1 2 3 4 5 Marginal soils Fertile soils
  22. Farmers' Control 33%NP NP NPK NPKS NPKSZn WheatGrainYield(tha-1) 0 1 2 3 4 5 Fertile soils Marginal soils b ab a ab ab Control / 33%NP/ NP / NPK / NPKS / NPKSZn Crop response to fertilizer blends, Dbirhan (Zone 1 and 2)
  23. Aftereffect of fertilizer blends on yield of Potato, 2015 (Zone 2) 33%NP NP NPK NPKS NPKSZn PotatoYield(tha -1 ) 0 8 12 16 20 24 28 32 36 40 44 48 52 D Birhan Lemu ab ab ab a a b a a a ab
  24. Farmers' Control 33%NP NP NPK NPKS NPKSZn Zinc(mgkg-1) 0 8 12 16 20 Protein(%) 0 8 12 16 20Protein Zinc b b b a a b a a a a 33%NP/ NP / NPK / NPKS / NPKSZn Zinc and Protein as affected by blends, Mehoni
  25. Zinc and Protein as affected by blends, Lemu Zinc(mgkg-1) 0 4 8 12 16 20 24 28 32 36 40 Protein(%) 0 4 8 12 16 20 24 28 32 36 40Protein Zinc a a a a a c b b a a 33%NP/ NP / NPK / NPKS / NPKSZn
  26. Calcium as affected by blends (confounding effect?) 33%NP NP NPK NPKS NPKSZn Ca(gkg-1) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Mehoni Lemu b d d a c b c c b a a
  27. Current Status and Next steps- • National Workshop, Led and Facilitated by EIAR Conducted; Dec 18, 2015 • National Task Force that would revisit the approaches and recommendations created • Next workshop, agree on a similar methodology to test use country wide-across institutions • Expand this work to more regions and Cropping systems • Joint evidence for Policy dialogue? • Functional and easy to use DST
  28. Soil Nutrient DST Soil Nutrient Information Manure/ Compost Information Land parcel location ( Map) and Cropping information “Green Book” of National Crop & Soil Specific Nutrient Advice Advisor – Farmer Consultation Information Farm Tabular Nutrient Recommendation /Report Farm Nutrient Management Land Parcel Specific Fertiliser Plan Decision Support Tool Central Data System Crop Requirement Information Schulte etal, 2015
  29. Thank you
  30. Identifying Nutrient Management Zones  Fields are a mosaic of habitats, each having unique biophysical characteristics that influence soil properties and crop yields.  The effectiveness of matching fertilizer types to soil fertility problems rests on the ability to identify limiting factors, characterize sites, and develop appropriate recommendations.  Approaches for identifying nutrient management zones require collection and interpretation of spatial data (yield, elevation, remote sensed imagery, apparent electrical conductivity, soil nutrient maps, and Farmers’ soil classification criteria).
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