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Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities
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Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities

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This talk presented two sister projects in Ethiopia and India. In both case studies the SWAT model was used to analyze how scenarios of upstream water harvesting and nutrient application interventions …

This talk presented two sister projects in Ethiopia and India. In both case studies the SWAT model was used to analyze how scenarios of upstream water harvesting and nutrient application interventions impact downstream water availability.
The case study in Ethiopia shows that crop yields significantly increase with water harvesting and nutrient applications. By only implementing water harvesting yield scenarios show an increase by 65 % and by adding nutrient applications yields improved by up to 200 %. Water productivity also increases with water harvesting and application of nutrients. However, there is upstream-downstream water availability trade-offs that need to be take into account. More at www.siani.se

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  • 1. “Agricultural Research Towards Sustainable Development Goals ” Agricultural water interventions for sustainable intensification – upstream downstream trade-offs and opportunities Yihun Dile and Louise Karlberg Stockholm Environment Institute Stockholm Resilience Centre
  • 2. Two sister projects on AWI Ethiopia Scattered ponds Subsistence agriculture Implications of potential AWI DSS location + size of dams India Watershed dev. prog. Commercial farming Actual changes Livelihoods SWAT tool used for analysis Implications on downstream water availability
  • 3. Research Area WH suitability study Upper Blue Nile Basin Total area: 10 sq.km  Subbasin size: 1-6ha Hydrological Modelling Lake Tana Basin Understanding implications Meso-scale
  • 4. Water harvesting implementation Suitability class HRUs of slope: <8%; Soil: Luvisols, and vertisols; and agricultural land. Area = 3.79km2 (38% of watershed)  Ponds dimension  size that can store water for ONSEASON and OFFSEASON irrigation  size determined for combination of different climatic years & nutrient application  Crop rotation is applied  ONSEASON (July-Dec) – TEFF  OFFSEASON (Jan-April) – Onion
  • 5. Water Harvesting Implementation Scenarios Nutrient scenarios ONION  Onion – 1st stage: UREA – 85kg/ha, and DAP – 30kg/ha 2nd stage: UREA – 85kg/ha TEFF  Current nutrient application rate (WH + BaselineN)  TEFF – 1st stage: UREA - 15kg/ha and DAP – 30kg/ha 2nd stage: UREA – 15kg/ha  Blanket Nutrient Recommendation (WH + BNR1)  TEFF – 1stage: UREA – 50kg/ha, and DAP – 30kg/ha 2nd stage: UREA – 50kg/ha  Blanket Nutrient Recommendation (WH + BNR2)  TEFF – 1st stage: UREA – 85kg/ha, and DAP – 30kg/ha 2nd stage: UREA – 85kg/ha
  • 6. Crop growth constraints
  • 7. Crop production Scenarios 2.5th percentile WH+Baseline Nutrient 15 WH+BNR1 94.7 WH+BNR2 148.56 Percent change in teff yield Median 97.5th percentile 57.2 667.3 134.3 674.5 217.4 363.6 Onion yield (ton/ha) 2.5th percentile 1.33 median 7.66 97.5th percentile 8.22
  • 8. Change in Teff yield (%)
  • 9. Change in crop yield (%)
  • 10. Onion production (ton/ha)
  • 11. Water Productivity Baseline WH + Baseline N WH + BNR1 WH + BNR2 2.5 0.14 0.17 0.29 0.38 Water productivity Median 0.17 0.27 0.40 0.45 97.5th 0.20 1.12 1.13 0.75 Year 1995 2001 IRR Vol (m3) 532,486 309,326 WYLD (m3) 1,839,334 7,063,383 Percentage 29 3.95 th
  • 12. The Kothapally Case, India
  • 13. Implications on livelihoods 60000 50000 40000 Vegetable crop Main crop ) R N I ( e o c n i m r a F 30000 20000 10000 0 S. No C. No C. Max int. int. int. Dry year S. No C. No C. Max int. int. int. Normal year S. No C. No C. Max int. int. int. Wet year
  • 14. Spatial variability No intervention Dry Wet WDP
  • 15. Water balance Kothapally 100% 80% 60% Outflow 40% GW recharge ET ) m ( i l f o g a t n c r e P 20% 0% No int Dry WDP No int WDP Medium No int WDP Wet
  • 16. Water outflow Kothapally 350 300 250 200 ) m ( w o l f t u O 150 100 50 0 No int WDP Dry No int WDP Medium No int WDP Wet
  • 17. Soil loss analysis 100 70 90 WSD 60 80 50 Soil loss (ton/ha) No int 40 30 ) ( s L o S e v i t a l m u C 20 Scenario-1 70 Scenario-4 60 50 40 30 20 10 10 0 0 0 1 4 7 10 13 16 19 Year 22 25 28 31 50 100 150 Daily rainfall (mm) 200 250 300
  • 18. Downstream consequences 3) 140 120 100 80 Storm flow 60 Base flow m M ( i v s e r S O t a w o l f n I 40 20 0 Current Max int Current Max int Current Max int Dry Medium Wet
  • 19. Conclusions • Total annual runoff reduced by 5 - 30% (Eth) and around 60% (In). At the mesoscale level the total runoff reduction was 30% (In). • Peak flows reduce and low flows increase – flooding problems, bank ersion and channel sedimentation reduce + more water available during dry seasons. • Sediment loss reduction • Crop yield and biomass increase upstream, in particular when combined with nutrient management – food availability and material flow will improve (upstream + downstream) • Drought proofing? Only for some farms during dry seasons, but significantly higher incomes with WDP during normal and wet years • DSS tool for location and size of dams
  • 20. Thanks for the attention
  • 21. Model setup and simulation  Basin Area: 15129 km2  Total No subbasins: 959  Subasin sizes: 500-3000ha  Total No HRUs: 9963  Flow calibrated at 3 gauging stations  Climate data  rainfall, Max & Min - 1990-2011  Global weather data – weather genrator  Evapotranspiration  Hargreaves’ s method  Surface runoff estimation  Curve number method  Stream routing  Variable storage method  Hydrological data  1990-2007
  • 22. Management  Two reserviors Elevation 1784 2135 * Lake Tana Angereb Reservior Principal spillway Area(km2) Volume(Mm3) 2,766 20,300 0.5 3.53 Elevation 1787 2138 Emergency spillway Area(km2) Volume(Mm3) 2983 29,100 0.6 5.16  Fertilizer application  Tillage operations  depth of till of 15cm, and  mixing efficiency of 0.3  tillage frequency of 4  Pescticide application  2.4.D amine weed killer  1 liter/ha ~ 0.379kg/ha 32
  • 23. Model setup and simulations  Subbasins No.: 482  HRUs No.: 786  Total area: 10 sq.km  Subbasin size: 1-6ha Pond  Climate data  rainfall, Max & Min - 1990-2011  Evapotranspiration  Hargreaves’ s method  Global weather data – weather genrator  Surface runoff estimation  Curve number method  Stream routing  Variable storage method
  • 24. Model Calibration and Validation at Megech NSE=0.76 PBIAS=4.0% NSE=0.74 PBIAS=40.2%

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