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Quantifying water productivity in rainfed cropping systems: Limpopo Province, South Africa

Quantifying water productivity in rainfed cropping systems: Limpopo Province, South Africa. John Dimes CPWF PN17 Final Project Workshop 15-18 June 2009, Univ of Witwatersrand, Johannesburg, South Africa. Impact target. Smallholder farming systems in Limpopo Basin

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Quantifying water productivity in rainfed cropping systems: Limpopo Province, South Africa

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  1. Quantifying water productivity in rainfed cropping systems:Limpopo Province, South Africa John Dimes CPWF PN17 Final Project Workshop 15-18 June 2009, Univ of Witwatersrand, Johannesburg, South Africa

  2. Impact target Smallholder farming systems in Limpopo Basin • Largely Rainfed systems (highly variable) • Perennial low productivity (poor fertility) • Resource-poor farmers • Highly risk-averse • Poor market access • Largely an issue of ‘Green Water’ productivity – near term and longer term

  3. Purpose of Farmer-based research is to raise crop yields and water productivity of green water Av. Yield 680 kg/ha (298) Simulated maize yields, Bulawayo – WP of 1kg grain /mm/ha

  4. Improved germplasm

  5. Soil fertility to boosts yield

  6. 1 bag Weeded 3 bags Weeded 1 bag No weeding What about under farmer conditions?

  7. So where are the highest payoffs? Short season, N use, water cons. 4.5

  8. CPWF PN17 Activity • 1 year study (2007-08) • To measure crop water use (maize, cowpea, groundnut) • Evaluate APSIM performance • Use APSIMto extrapolatethe field based results of crop water productivity (APSIM is a point-source model)

  9. 2 Issues: • Establish local credibility of model output (above & below ground) • Model outputs as information source for off-site impacts

  10. Approach • Did not initiate new experimentation • Added value to existing field activities by monitoring soil water. • Partnerships • Sasol Nitro/Univ Limpopo – NxP in Maize • ARC-GCI:- Gnut and Bambara variety trials • Venda Univ/ACIAR Project – P trial in Gnut

  11. This Presentation • Experimental data and simulation results from 1 site – Tafelkop, ARC-GCI • Higher potential ( > 1200masl, >500mm, Sekhukune District, 2007/08 = 717mm) • Sandy Loam • Gnut and Bambara variety trial, on-farm • Improved varieties of Maize and Cowpea Demonstration plots (30m x30m)

  12. Exptn. Details • Different Planting Dates: • Nov 14th, 2007, Maize (29kgN ha-1) and Gnut • Dec 5th, 2007, Cowpea • Soil water measurements • 0-10, 10-30, 30-60, 60-90cm, gravimetrically • Dates • Dec 12th 2007, Gnut and Bambara • > 300mm, DUL for soil layers • Feb 22nd , 2008, All crops • almost 1 month without rain – Crop LL of soil layers • Mar 29th, 2008, Mz, Cwp, Gnut • Physiological Maturity – Mar rains 70mm, 30mm on 27th – refilling of soil profile

  13. Filling measurement gaps SOC 0-10cm = 0.51%, PAWC 0-90cm = 90mm : Oct-Nov14= 180mm, to Dec 12th = 134mm

  14. Obs and Pred Yields Total Biomass Grain yield Driver of crop water use Assessment of water productivity

  15. Obs and Pred Soil Water

  16. Water Balance Components Season rainfall – Oct 1st 2007 to May 28th 2008

  17. Water Productivity (kg/mm/ha) WP1 = grain/ m3 in_crop rainfall WP2 = kg grain/ (m3 of rainfall +delta SW storage sowing to harvest – using model outputs) WP3 = kg grain/ m3 of seasonal water balance (Oct 1st 2007 to May 28th 2008) Crops of different value($)

  18. Simulation Analysis • Tafelkop soil • Groblersdal climate (1974-2004) • In Addis, Nov 2008 • Maize response to N (0N, 30N, Non-limiting N) • maize is the dominant crop grown by SHF’s • Today • Include legume options • Bag of LAN increased from R200 to > R500

  19. Grain yield response

  20. Grain yield response

  21. WP response (skip)

  22. Rand returns

  23. Deep Drainage (skip)

  24. Deep Drainage

  25. Conclusions • Crop modelling (hydrological modelling AND Livestock modelling) are essential tools for systems analysis and WP assessment: • Caution: need to establish local credibility for these tools. • Crop/soil simulation output can provide important data (drainage/runoff) to inform catchment level analysis for different crop management interventions (the green-blue interaction) • Crop modelling adds value to field experimentation • Helps fill measurement gaps • APSIM performed well in simulation of crop yields and soil water use in Limpopo Basin

  26. Thank You

  27. Some issues with Input data Marble Hall – 800masl – Tafelkop > 1200 masl Used Polokwane Temp data (1230 masl) to adequately simulate crop duration

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