Enhancing Water Productivity in Crop-Livestock Systems of SSA:  Minimizing trade-offs and maximizing benefits Tilahun Amed...
Livestock a livelihood strategy; increasing demand USA 140
Fetching water for household use  hugely  competes for labour and  limited resources Water scarcity is a real threat  Phot...
Negative impact of livestock on  water and land resources   <ul><li>Physical destruction, soil structure : </li></ul><ul><...
Nile basin
Principles of water productivity <ul><li>Water productivity  refers to the amount or value of product over volume or value...
Case studies from Eth and Zim Lenche Dima,  Dry, crop-livestock 650mm Nkayi Dry, agropastoral 550 mm Kuhar wet, crop-lives...
Land use evolution
System changes and its drivers in Kuhar
Drivers of Land use changes   <ul><li>Fogera: Introduction of Rice and better access to markets </li></ul>Implication : Re...
open grazing exclosure Drivers of Land use changes  @:  Lenche Dima : NGO led exclosures  Implications:  More access to bi...
Contribution of crop residue for livestock feed and their water budget Kuhar Michael 60% 2% 28% 6% 2% 2% crop residues gre...
Land and water productivity of major feed classes
Feed imbalance Source. Endale Bekele, 2007 <ul><li>Crop land grazing: 10% </li></ul><ul><li>Improved forage  : 5% </li></u...
Lenche Dima Daily Energy Requirement (ME/TLU) = 40.61 Current Energy supply (ME/TLU)   = 21.70 Deficit = 46.6% Additional ...
Livestock feed and energy Energy spent for walking  ± 2-3 times energy for milk production and growth
Water productivity variables for Lenche Dima Watershed (1546 ha) with water flows
Quantifying the LWP variables Average contributions of the different livestock outputs to overall LWP at household level.
Water budgets of the farming systems in two contrasting sites  water budget analysis High unproductive water losses = indi...
Interventions for Improving LWP and managing trade-offs <ul><li>Fertilizer application </li></ul><ul><li>Reducing livestoc...
1. Fertilizer effects on Maize in Zimbabwe   FP = Farmer practice, MD = Micro-dose, RC = recommended
Simulated maize stover over a period of 30 years under 3 SFM   FP = farmer practice, MD = micro-dose, RC = recommended rat...
Effects of soil fertility gradients on Enset biomass 355.1 267.9 146.0 Transpiration (Ta) 203.5 285.4 446.1 Evaporation (E...
2. Minimizing mortality rates <ul><li>DynMod model was employed to simulate the different scenarios (current, acceptable) ...
3. Multipurpose legumes:  applied as whole plant,  shoot or roots, on  wheat grain yield. <ul><li>Below ground effect was ...
4. Intensifying cropping systems:   Contribution of maize stover and mucuna to dry season feed at 20, 40 and 60% of daily ...
5. Rehabilitating degraded lands
6. Watering points  <ul><li>Reduction of walking distance to access water: from 9 km to 2 km </li></ul><ul><li>Energy for ...
7. If we changing breeds
Estimating water requirements for  various breeds  4063 lt /lt milk No No Water requirements 70% of total milk No need to ...
Markets as incentives for improving water productivity  <ul><li>Assist local actors to identify NRM-oriented  marketable e...
Facilitating ‘change’ for increased uptake <ul><li>Intervention options:  to address differing demands </li></ul><ul><li>S...
Political  change  Women’s   empowerment Good  Leadership Institutional changes Feed  management Water  management Animal ...
http://www.publish.csiro.au/nid/202/issue/5185.htm
Acknowledgements  <ul><li>Bundesministerium für wirtschaftliche Zusammenarbeit und Entwicklung (BMZ) </li></ul><ul><li>ICR...
Thank you !
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Enhancing Water Productivity in Crop-Livestock Systems of SSA: Minimizing trade-offs and maximizing benefits

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Presentation by Tilahun Amede, Katrein Descheemaeker, E. Mapedza et al (IWMI) to the CGIAR Systemwide Livestock Programme Livestock Policy Group Meeting, 1 December 2009

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Enhancing Water Productivity in Crop-Livestock Systems of SSA: Minimizing trade-offs and maximizing benefits

  1. 1. Enhancing Water Productivity in Crop-Livestock Systems of SSA: Minimizing trade-offs and maximizing benefits Tilahun Amede, Katrein Descheemaeker, E. Mapedza et al. Presentation: CGIAR Systemwide Livestock Programme Livestock Policy Group, 1 December 2009
  2. 2. Livestock a livelihood strategy; increasing demand USA 140
  3. 3. Fetching water for household use hugely competes for labour and limited resources Water scarcity is a real threat Photo: Getachew Bayafers Traveling long distance to access drinking water
  4. 4. Negative impact of livestock on water and land resources <ul><li>Physical destruction, soil structure : </li></ul><ul><li>Wind and water erosion </li></ul><ul><li>Biological and chemical degradation : decreasing water quality </li></ul><ul><li>Removal of biomass from the system: </li></ul><ul><li>reduced soil organic matter, </li></ul><ul><li>nutrient mining </li></ul><ul><li>Decline in water holding capacity: </li></ul><ul><li>changing hydrology </li></ul>
  5. 5. Nile basin
  6. 6. Principles of water productivity <ul><li>Water productivity refers to the amount or value of product over volume or value of water depleted/diverted </li></ul><ul><li>Livestock water productivity (LWP) is often neglected in water productivity studies </li></ul><ul><li>Livestock products & services are very important in mixed systems </li></ul>
  7. 7. Case studies from Eth and Zim Lenche Dima, Dry, crop-livestock 650mm Nkayi Dry, agropastoral 550 mm Kuhar wet, crop-livestock 1300 mm
  8. 8. Land use evolution
  9. 9. System changes and its drivers in Kuhar
  10. 10. Drivers of Land use changes <ul><li>Fogera: Introduction of Rice and better access to markets </li></ul>Implication : Reduced grazing area by almost 65%, wet and dry season shortage of feed
  11. 11. open grazing exclosure Drivers of Land use changes @: Lenche Dima : NGO led exclosures Implications: More access to biomass, less competition, more productivity
  12. 12. Contribution of crop residue for livestock feed and their water budget Kuhar Michael 60% 2% 28% 6% 2% 2% crop residues green forage grazing hay weeds tree fodder Lenche Dima 67% 1% 17% 7% 3% 5% crop residues green forage grazing hay weeds tree fodder
  13. 13. Land and water productivity of major feed classes
  14. 14. Feed imbalance Source. Endale Bekele, 2007 <ul><li>Crop land grazing: 10% </li></ul><ul><li>Improved forage : 5% </li></ul><ul><li>Feed Deficit: 25-77% </li></ul>
  15. 15. Lenche Dima Daily Energy Requirement (ME/TLU) = 40.61 Current Energy supply (ME/TLU) = 21.70 Deficit = 46.6% Additional land required 250 ha System = Semi-arid, one cropping seasons, sorghum-livestock
  16. 16. Livestock feed and energy Energy spent for walking ± 2-3 times energy for milk production and growth
  17. 17. Water productivity variables for Lenche Dima Watershed (1546 ha) with water flows
  18. 18. Quantifying the LWP variables Average contributions of the different livestock outputs to overall LWP at household level.
  19. 19. Water budgets of the farming systems in two contrasting sites water budget analysis High unproductive water losses = indicator of productivity gap
  20. 20. Interventions for Improving LWP and managing trade-offs <ul><li>Fertilizer application </li></ul><ul><li>Reducing livestock mortality </li></ul><ul><li>Multipurpose legumes </li></ul><ul><li>Intensifying cropping systems </li></ul><ul><li>Rehabilitating degraded lands through exclosures </li></ul><ul><li>Watering points for livestock drinking </li></ul><ul><li>Changing breeds </li></ul>
  21. 21. 1. Fertilizer effects on Maize in Zimbabwe FP = Farmer practice, MD = Micro-dose, RC = recommended
  22. 22. Simulated maize stover over a period of 30 years under 3 SFM FP = farmer practice, MD = micro-dose, RC = recommended rates
  23. 23. Effects of soil fertility gradients on Enset biomass 355.1 267.9 146.0 Transpiration (Ta) 203.5 285.4 446.1 Evaporation (Ea) Non limiting Near optimal Poor Soil fertility conditions Water balance components
  24. 24. 2. Minimizing mortality rates <ul><li>DynMod model was employed to simulate the different scenarios (current, acceptable) of mortality </li></ul><ul><li>Feed shortages account for about 8% mortality </li></ul><ul><li>Farmers do not grow forages for dry season feeding </li></ul><ul><li>Diseases account for about 80% of mortality </li></ul><ul><li>Low investment in disease prevention/ control (vaccination, dipping, dosing), </li></ul>
  25. 25. 3. Multipurpose legumes: applied as whole plant, shoot or roots, on wheat grain yield. <ul><li>Below ground effect was more </li></ul><ul><li>substantial on crop yield </li></ul><ul><li>Yield loss is minimal if the biomass </li></ul><ul><li>is fed to the animals </li></ul>6.51 (1.45) TSP (50 kg /ha) and Urea (100 kg/ha) 6.38 (1.69) Control 6.50 (0.76) 6.41 (0.85) 7.03 (1.23) Vetch 5.14 (0.29) 2.67 (0.76) 7.38 (0.54) Stylosanthus 8.86 (0.81) 8.29 (0.54) 9.75 (1.18) Mucuna 6.38 (0.38) 4.31 (0.93) 7.85 (0.96) Lablab Wheat yield after only roots (t/ha) Wheat yield after biomass transfer (t/ha) Wheat yield after total biomass (qt/ha) (SE) Type of legume
  26. 26. 4. Intensifying cropping systems: Contribution of maize stover and mucuna to dry season feed at 20, 40 and 60% of daily CP requirements. (CP=0.228 kg/day) for 300 kg live weight.
  27. 27. 5. Rehabilitating degraded lands
  28. 28. 6. Watering points <ul><li>Reduction of walking distance to access water: from 9 km to 2 km </li></ul><ul><li>Energy for walking is reduced from 1956 to 584 MJ ME / TLU per year </li></ul><ul><li>Milk equivalent of the 1372 ME MJ saved: extra 252 liter of milk/lactation / TLU: in reality from 343 to 463 liter/ lactation per cow </li></ul><ul><li>No change in water depleted for feed production </li></ul><ul><li>Milk WP improves by 35% (survey) to 75% (theoretical) </li></ul>
  29. 29. 7. If we changing breeds
  30. 30. Estimating water requirements for various breeds 4063 lt /lt milk No No Water requirements 70% of total milk No need to keep No need to keep Scenario I: If feed availability was reduced by 50% 4101 4232 No Water demand (lt/lt) Increase stock by 60% Reduce stock by 10% No need to keep If feed availability is reduced by 10%, no reduction in milk Scenario I: 3175 4663 12174 Water requirement (per lt of milk) 38964 27095 17194 ME demand MJ year 365 270 210 Lactation period 12.50 8.0 2.5 Milk yield (lt day -1) 450 400 250 Live weight (kg) Cross breed 50% Cross breed 25% Local Zebu Breed
  31. 31. Markets as incentives for improving water productivity <ul><li>Assist local actors to identify NRM-oriented marketable enterprises (market information, facilitators, processors) </li></ul><ul><li>Facilitate integration of market options with win-win effects (food, feed, cash, conservation) </li></ul><ul><li>Facilitate communities and district officers in identifying niches, what fits best where (guides, tools, methods) </li></ul><ul><li>Develop policies to combine short term marketable enterprises with long term benefits </li></ul>
  32. 32. Facilitating ‘change’ for increased uptake <ul><li>Intervention options: to address differing demands </li></ul><ul><li>Scaling out; using adaptive systems </li></ul><ul><li>Key drivers: emerging from bright spots (case studies) could lead to a jump start </li></ul><ul><li>Power relations : shared beliefs and interests, but also diverse and often conflicting values and resource priorities (struggled and ‘bargained); </li></ul><ul><li>LWP gender perspective ; focus on women will yield disproportionately greater system-wide benefits; </li></ul><ul><li>Leadership ; willingness to shoulder political and social risk, inspires trust, externally-sourced innovations </li></ul>
  33. 33. Political change Women’s empowerment Good Leadership Institutional changes Feed management Water management Animal productivity 1.Technologies 2. Institutions More grain and livestock product per unit of investment of labour, water and land Community Innovation & empowerment <ul><li>Impact </li></ul><ul><li>Poverty </li></ul><ul><li>Environment </li></ul><ul><li>Resilience </li></ul>Targeting and dissemination L W P 3. Supportive policy Amede et al., 2009 ‘ Enabling integration and innovations in LWP’
  34. 34. http://www.publish.csiro.au/nid/202/issue/5185.htm
  35. 35. Acknowledgements <ul><li>Bundesministerium für wirtschaftliche Zusammenarbeit und Entwicklung (BMZ) </li></ul><ul><li>ICRISAT </li></ul><ul><li>Department of Agricultural Research and Extension (AREX), Zimbabwe </li></ul><ul><li>Amhara Regional Agricultural Research Institute (ARARI) </li></ul><ul><li>ZEF-University of Bonn, Germany </li></ul>
  36. 36. Thank you !

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