Landscape level hydrological modeling

&
Farm-scale modeling
Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps

3 / 7 ...
Landscape level hydrological modeling

Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps

3 / 7 / 2012
Study objectives
Modeling hydrological dynamics to quantify water
fluxes for achieving optimal crop-livestock
productivit...
Study sites
 Landscape hydrological modeling:
o Conduct sub-basin water balance thresholds
o Develop a water allocations ...
Methods
• Baseline characterization has been conducted in
target sites at the household level
• Tools:

and

• SWAT hydrol...
Crop water use trends in Golinga

Data Source: Ministry of Food and Agriculture, Ghana
Production estimates and Regional C...
Water, crops and livestock
distribution for Golinga

Source: Ramankutty et al, 2000
Processed from Global Croplands databa...
Water Balance Components for
Golinga
1200

Calibration

Validation

800

600

400

200

0
1980
1981
1982
1983
1984
1985
19...
Conclusion
Milestones:
• Cropping density and livestock distribution ascertained for all study
sites; Water balance thresh...
Farm-scale modeling

Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps

3 / 7 / 2012
Objectives
Identify and evaluate promising interventions for
improved farm productivity
•
•
•
•
•
•
•

Extrapolating field...
NPK
NPK
NPK

Giller et al. 2010

Andes • Ganges • Limpopo • Mekong • Nile • Volta

Options
NUANCES-FARMSIM: farm-scale modeling approach

Andes • Ganges • Limpopo • Mekong • Nile • Volta
Tittonell et al. (2007) Fl...
APSIM (Agricultural Production Systems sIMulator)

Andes • Ganges • Limpopo • Mekong • Nile • Volta
Constraint analysis
Example of feedbase in villages around Golinga reservoir

In-house
feeding

Grazing

Feed gap
Andes • ...
Scenario Analysis
Baseline situation
• 1.5 ha farm
• household of 8 people
• crops: millet, sorghum and cowpea intercroppe...
Scenario Analysis
Baseline
Animals sold (10y)

5-6

Animals on hand

12-13

Forage deficit

7000

Wet season labour

+50

...
Scenario Analysis
Baseline
Animals sold (10y)

Manure
(4 t/ha)

5-6

6-7

Animals on hand

12-13

13

Forage deficit

7000...
Scenario Analysis
Baseline
Animals sold (10y)

Manure
(4 t/ha)

Crop residue
harvesting

5-6

6-7

7-8

Animals on hand

1...
Scenario Analysis
Baseline

Calves sold (10y)

Manure
(4 t/ha)

Crop
residue
harvesting

Sell cow, buy
10 sheep &
fatten

...
Scenario Analysis
Discussion support tool  Learning tool

Andes • Ganges • Limpopo • Mekong • Nile • Volta

Adapted from ...
Simulation experiment

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

Lessons:
- Fertilizer increases average yield, but also production risk
- Information on risk is us...
Tradeoff analysis
Understanding resource allocation decisions
Resources are finite; directing them to one objective
will p...
concentrates

Andes • Ganges • Limpopo • Mekong • Nile • Volta

fertilizer

Tradeoff analysis
concentrates

Andes • Ganges • Limpopo • Mekong • Nile • Volta

fertilizer

Tradeoff analysis
concentrates

Andes • Ganges • Limpopo • Mekong • Nile • Volta

fertilizer

Tradeoff analysis
concentrates

Andes • Ganges • Limpopo • Mekong • Nile • Volta

fertilizer

Tradeoff analysis
concentrates

Andes • Ganges • Limpopo • Mekong • Nile • Volta

fertilizer

Tradeoff analysis
concentrates

fertilizer

Tradeoff analysis

Lessons:

- Tradeoff analysis helps us in systems understanding
- LinkedAndes...
Conclusions
Farm systems models are useful tools

for research to
- Understand complex farm dynamics, including farmer
dec...
Merci pour votre attention!
Thanks for your attention!

Andes • Ganges • Limpopo • Mekong • Nile • Volta
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Landscape Level Hydrological Modeling and Farm Scale Modeling in the Volta River Basin

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Study Objectives:
Modeling hydrological dynamics to quantify water fluxes for achieving optimal crop-livestock productivity

- Assess sub-basin scale water balance thresholds at target sites
- Develop water allocations framework in target sites
- Recommend best-fit integrated rainwater management strategies that maximize productivity

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Landscape Level Hydrological Modeling and Farm Scale Modeling in the Volta River Basin

  1. 1. Landscape level hydrological modeling & Farm-scale modeling Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012
  2. 2. Landscape level hydrological modeling Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012
  3. 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. 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. 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. 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. 7. Water, crops and livestock distribution for Golinga Source: Ramankutty et al, 2000 Processed from Global Croplands database; Complemented with Ghana MoFA Data and V2 Household data Source: Processed from FAO Geo-portal data -Not checked against V2 HH data Andes • Ganges • Limpopo • Mekong • Nile • Volta 7
  8. 8. Water Balance Components for Golinga 1200 Calibration Validation 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) and Discharge (mm) 1000 Simulated Warm-up Rainfall (mm) Surface Water Discharge (mm) Percolation (mm) Groundwater Discharge (mm) Evapotranspiration (mm) Andes • Ganges • Limpopo • Mekong • Nile • Volta 8
  9. 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 subbasin scale Andes • Ganges • Limpopo • Mekong • Nile • Volta
  10. 10. Farm-scale modeling Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012
  11. 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. 12. NPK NPK NPK Giller et al. 2010 Andes • Ganges • Limpopo • Mekong • Nile • Volta Options
  13. 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. 14. APSIM (Agricultural Production Systems sIMulator) Andes • Ganges • Limpopo • Mekong • Nile • Volta
  15. 15. Constraint analysis Example of feedbase in villages around Golinga reservoir In-house feeding Grazing Feed gap Andes • Ganges • Limpopo • Mekong • Nile • Volta
  16. 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. 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. 18. Scenario Analysis Baseline Animals sold (10y) Manure (4 t/ha) 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. 19. Scenario Analysis Baseline Animals sold (10y) Manure (4 t/ha) Crop residue harvesting 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. 20. Scenario Analysis Baseline Calves sold (10y) Manure (4 t/ha) Crop residue harvesting Sell cow, buy 10 sheep & fatten 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. 21. Scenario Analysis Discussion support tool  Learning tool Andes • Ganges • Limpopo • Mekong • Nile • Volta Adapted from McDonald (2010)
  22. 22. Simulation experiment Andes • Ganges • Limpopo • Mekong • Nile • Volta
  23. 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 • - Water and Ganges • Limpopo • Mekong • Nile • are interlinked nutrient use efficiency Volta
  24. 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. 25. concentrates Andes • Ganges • Limpopo • Mekong • Nile • Volta fertilizer Tradeoff analysis
  26. 26. concentrates Andes • Ganges • Limpopo • Mekong • Nile • Volta fertilizer Tradeoff analysis
  27. 27. concentrates Andes • Ganges • Limpopo • Mekong • Nile • Volta fertilizer Tradeoff analysis
  28. 28. concentrates Andes • Ganges • Limpopo • Mekong • Nile • Volta fertilizer Tradeoff analysis
  29. 29. concentrates Andes • Ganges • Limpopo • Mekong • Nile • Volta fertilizer Tradeoff analysis
  30. 30. concentrates fertilizer Tradeoff analysis Lessons: - Tradeoff analysis helps us in systems understanding - LinkedAndes • Ganges • Limpopo • Mekongsocio-institutional settings (e.g. market) and farmers’ with understanding of • Nile • Volta objectives, this can be used to design well-adapted interventions
  31. 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. 32. Merci pour votre attention! Thanks for your attention! Andes • Ganges • Limpopo • Mekong • Nile • Volta

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