CPWF Volta Science Workshop: Integrated Rainwater Management in Crop-Livestock Systems

1. Landscape level hydrological modeling
&
Farm-scale modeling
Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012

2. Landscape level hydrological modeling
Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012

3. Study objectives
Modeling hydrological dynamics to quantify water
fluxes for achieving optimal crop‐livestock
productivity
o Assess sub‐basin scale water balance thresholds at target
sites
o Develop water allocations framework in target sites
o Recommend best‐fit integrated rainwater management
strategies that maximize productivity
Andes • Ganges • Limpopo • Mekong • Nile • Volta

4. Study sites
Landscape hydrological modeling:
o Conduct sub‐basin water balance thresholds
o Develop a water allocations framework in target sites
o Assess water productivity in specific crop‐livestock systems
Andes • Ganges • Limpopo • Mekong • Nile • Volta

5. Methods
• Baseline characterization has been conducted in
target sites at the household level
• Tools: and
• SWAT hydrological modeling is physically based
– Weather, soil properties, topography, vegetation,
and land management practices data sets
• DEM:
– Used at 90 m resolution
– Watershed delineation; Stream network
Andes • Ganges • Limpopo • Mekong • Nile • Volta

6. Crop water use trends in Golinga
Data Source: Ministry of Food and Agriculture, Ghana
Production estimates and Regional Crop Acreage data for 1992 to 2010
‐ Complemented and verified with V2 Household survey data
Andes • Ganges • Limpopo • Mekong • Nile • Volta
6

7. Water, crops and livestock
distribution for Golinga
Source: Processed from FAO
Source: Ramankutty et al, 2000 Geo‐portal data
Processed from Global Croplands database; ‐Not checked against V2 HH data
Complemented with Ghana MoFA Data
and V2 Household data
Andes • Ganges • Limpopo • Mekong • Nile • Volta
7

8. Water Balance Components for
Golinga
1200
Simulated
Warm‐up Calibration Validation
1000
Rainfall (mm) and Discharge (mm)
800
600
400
200
0
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Rainfall (mm) Surface Water Discharge (mm) Groundwater Discharge (mm)
Percolation (mm) Evapotranspiration (mm)
Andes • Ganges • Limpopo • Mekong • Nile • Volta
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9. Conclusion
Milestones:
• Cropping density and livestock distribution ascertained for all study
sites; Water balance thresholds calculated for all study sites
• Currently developing crop‐livestock water productivity maps for all
target sites
• Landscape outputs from water allocations and water balance will
complement farm‐level flows analysis
Conclusion
• Hydrological analysis indicated that reservoirs play a critical role in
maintaining storage and reducing surface runoff losses at sub‐
basin scale
Andes • Ganges • Limpopo • Mekong • Nile • Volta

10. Farm-scale modeling
Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012

11. Objectives
Identify and evaluate promising interventions for
improved farm productivity
• Extrapolating field results in space and time
• Aggregate field level outputs to farm level
• Scenario analysis: exploring options
• Risk analysis
• Tradeoff analysis (tradeoffs in resource allocation)
• Identifying issues for further (field) research
• Discussion and decision support tool: informing the
innovation platform
Andes • Ganges • Limpopo • Mekong • Nile • Volta

12. NPK
NPK
NPK
Options
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Giller et al. 2010

13. NUANCES-FARMSIM: farm-scale modeling approach
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Tittonell et al. (2007) Fld Crops Res. 100, 348-368; Rufino et al. (2007) Livestock Sci. 112, 273-287; Chikowo et al. (2008) Ag. Syst.
97, 151-166; Tittonell et al. (2009) Ag. Syst. 101, 1-19; van Wijk et al. (2009) Ag. Syst. 102, 89-101; Tittonell et al. (2010) E. J Agron.
32, 10-21.

14. APSIM (Agricultural Production Systems sIMulator)
Andes • Ganges • Limpopo • Mekong • Nile • Volta

15. Constraint analysis
Example of feedbase in villages around Golinga reservoir
In-house
feeding
Grazing
Feed gap
Andes • Ganges • Limpopo • Mekong • Nile • Volta

16. Scenario Analysis
Baseline situation
• 1.5 ha farm
• household of 8 people
• crops: millet, sorghum and cowpea intercropped
• no crop residue stored for cattle
• 3 breeding cows, sells at 4-5 years, herd of 8-10
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Adapted from McDonald (2010)

17. Scenario Analysis
Baseline
Animals sold (10y) 5‐6
Animals on hand 12‐13
Forage deficit 7000
Wet season labour +50
Cattle revenue 34000
Gross Margin* 515000
Cash balance ‐3000
* - including home consumption
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Adapted from McDonald (2010)

18. Scenario Analysis
Baseline Manure
(4 t/ha)
Animals sold (10y) 5‐6 6‐7
Animals on hand 12‐13 13
Forage deficit 7000 6000
Wet season labour +50 +20
Cattle revenue 34000 37000
Gross Margin 515000 637000
Cash balance ‐3000 109000
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Adapted from McDonald (2010)

19. Scenario Analysis
Baseline Manure Crop residue
(4 t/ha) harvesting
Animals sold (10y) 5‐6 6‐7 7‐8
Animals on hand 12‐13 13 13
Forage deficit 7000 6000 3000
Wet season labour +50 +20 +10
Cattle revenue 34000 37000 41000
Gross Margin 515000 637000 671000
Cash balance ‐3000 109000 140000
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Adapted from McDonald (2010)

20. Scenario Analysis
Baseline Manure Crop Sell cow, buy
(4 t/ha) residue 10 sheep &
harvesting fatten
Calves sold (10y) 5‐6 6‐7 7‐8 6‐7
Cattle on hand 12‐13 13 13 9‐10
Forage deficit 7000 6000 3000 4400
Wet season labour +50 +20 +10 +50
Livestock revenue 34000 37000 41000 96000
Gross Margin 515000 637000 671000 739000
Cash balance ‐3000 109000 140000 205000
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Adapted from McDonald (2010)

21. Scenario Analysis
Discussion support tool Learning tool
Andes • Ganges • Limpopo • Mekong • Nile • Volta
Adapted from McDonald (2010)

22. Simulation experiment
Andes • Ganges • Limpopo • Mekong • Nile • Volta

23. Simulation experiment
Lessons:
- Fertilizer increases average yield, but also production risk
- Information on risk is useful for insurance providers (partner in the IPs?)
Andes • Ganges • Limpopo • Mekong • Nile • Volta
- Water and nutrient use efficiency are interlinked

24. Tradeoff analysis
Understanding resource allocation decisions
Resources are finite; directing them to one objective
will penalize other objectives
• Labor: weeding vs. marketing produce
• Cash: fertilizers vs. hiring labor for weeding
• Crop residues: soil organic matter vs. livestock feeding
Andes • Ganges • Limpopo • Mekong • Nile • Volta

25. Tradeoff analysis
concentrates
fertilizer
Andes • Ganges • Limpopo • Mekong • Nile • Volta

26. Tradeoff analysis
concentrates
fertilizer
Andes • Ganges • Limpopo • Mekong • Nile • Volta

27. Tradeoff analysis
concentrates
fertilizer
Andes • Ganges • Limpopo • Mekong • Nile • Volta

28. Tradeoff analysis
concentrates
fertilizer
Andes • Ganges • Limpopo • Mekong • Nile • Volta

29. Tradeoff analysis
concentrates
fertilizer
Andes • Ganges • Limpopo • Mekong • Nile • Volta

30. Tradeoff analysis
concentrates
fertilizer
Lessons:
- Tradeoff analysis helps us in systems understanding
- LinkedAndes • Ganges • Limpopo • Mekong • Nile • Volta
with understanding of socio-institutional settings (e.g. market) and farmers’
objectives, this can be used to design well-adapted interventions

31. Conclusions
Farm systems models are useful tools
for research to
- Understand complex farm dynamics, including farmer
decision making
- Identify topics for further (field) research
for development through
- Assisting in the development of adapted interventions
- Generation of information for discussion support (in IPs)
! Need for high quality input data
Andes • Ganges • Limpopo • Mekong • Nile • Volta

32. Merci pour votre attention!
Thanks for your attention!
Andes • Ganges • Limpopo • Mekong • Nile • Volta