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

• 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

Purpose of Farmer-based research is to raise crop yields and water productivity of green water

680 kg/ha

Av. Yield

(shallow sand, 0N, SC401)

0

200

400

600

800

1000

1200

1952 1957 1962 1967 1972 1977 1982 1987 1992 1997

Grai

n yie

ld (k

g/ha

)

(298)

Simulated maize yields, Bulawayo – WP of 1kg grain /mm/ha

Improved germplasmMaize, shallow soil, Bulawayo

0

500

1000

1500

2000

2500

3000

3500

4000

1952 1957 1962 1967 1972 1977 1982 1987 1992 1997

Mai

ze g

rain

(kg

/ha)

Short season cultivar

Long season cultivar

Soil fertility to boosts yieldMaize, shallow soil, Bulawayo

0

500

1000

1500

2000

2500

3000

3500

4000

1952 1957 1962 1967 1972 1977 1982 1987 1992 1997

Ma

ize

gra

in (

kg

/ha

)

Short season cultivar

Long season cultivar

Short season + 1 bag AN

What about under farmer conditions?

Investment Returns - Masvingo

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-10.0 -5.0 0.0 5.0 10.0 15.0

$ return / $ invested

Cu

mu

lati

ve

pro

ba

bili

ty

recommended

1 bag AN/ha

weed competition

1 bag

No weeding

3 bags

Weeded

1 bag

Weeded

So where are the highest payoffs?

Technology option WUE (kg Grain /mm Rain)

Traditional long season cultivar, no N

1.5

short season, no N 1.8

short season, water conservation, no N

2.1

short season, N applied (17kg/ha)

3.2

Short season, N use, water cons. 4.5

CPWF PN17 Activity

• 1 year study (2007-08)

1. To measure crop water use (maize, cowpea, groundnut)

2. Evaluate APSIM performance

3. Use APSIM to extrapolate the field based results of crop water productivity

(APSIM is a point-source model)

• 2 Issues:

1. Establish local credibility of model output (above & below ground)

2. Model outputs as information source for off-site impacts

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

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)

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,

gravimetricallyDates

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

Filling measurement gaps

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

0

100

200

300

400

500

600

700

800

0 0.05 0.1 0.15 0.2 0.25

soil water (mm/mm)

so

il d

ep

th (

mm

)

LL_Oct_1 Sow_Nov_14 Sow_Dec_5 DUL

0

1000

2000

3000

4000

5000

6000

7000

8000

Cowpea Groundnut Maize

Total

Biom

ass (k

g ha

-1)

Obs_TBM

Pred_TBM

0

500

1000

1500

2000

2500

3000

3500

Cowpea Groundnut Maize

Grain

Yield

(kg h

a-1

)

Obs_Grn

Pred_Grn

Total Biomass Grain yield

Obs and Pred Yields

Driver of crop water use Assessment of water productivity

Obs and Pred Soil Water(a) Maize

0

200

400

600

800

0.000 0.050 0.100 0.150 0.200

soil water (mm/mm)

soil

dept

h (m

m)

Pred_D12 Pred_F22 Pred_M29

Obs_Dec12 Obs_Feb22 Obs_Mar29

(b) Groundnut

0

200

400

600

800

0.000 0.050 0.100 0.150 0.200

soil water (mm/mm)

soil

dep

th (

mm

)

Pred_D12 Pred_F22 Pred_M29

Obs_Dec12 Obs_Feb22 Obs_Mar29

(c) Cowpea

0

200

400

600

800

0.000 0.050 0.100 0.150 0.200

soil water (mm/mm)s

oil

de

pth

(m

m)

Pred_D12 Pred_F22 Pred_M29

Obs_Dec12 Obs_Feb22 Obs_Mar29

Water Balance ComponentsCrop In_Crop

rainfall (mm) Ep (mm)

Runoff (mm)

Drain (mm)

Es (mm) Delta_sw (mm)

Maize 485 115 170 78 158 -35 Gnut 485 209 119 65 145 -53 Cowpea 311 101 123 86 112 -86 Season

rainfall

Maize 717 115 201 78 296 +28 Gnut 717 209 150 65 277 +15 Cowpea 717 101 202 95 298 +22

Season rainfall – Oct 1st 2007 to May 28th 2008

Water Productivity (kg/mm/ha)

Crop Yield Price (R/t) R/mm GM/mmMaize 2908 1800 7.59 ??Gnut 2897 3000 12.38 ??Cowpea 1247 4000 7.17 ??

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)

Crop WP1 WP2 WP3Maize 6.0 5.6 4.20Gnut 6.0 5.4 4.10Cowpea 3.8 3.0 1.80

Crops of different value($)

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

Grain yield responseGrain yields_Groblersdal

0

500

1000

1500

2000

2500

3000

3500

Kg g

rain

ha

-1

30N

Gnut

Cowp

Grain yield responseYield Probablity Distribution

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 500 1000 1500 2000 2500 3000 3500

Grain yield (kg/ha)

Pro

bab

ilit

y o

f E

xceed

en

ce

Mz_0N

Mz_30N

Gnut

Cow p

WP response (skip)WP Probability Distribution

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.0 2.0 4.0 6.0 8.0 10.0 12.0

Water productivity (kg grain /mm/ha)

Pro

bab

ility

of

Exc

eed

ence

Mz_0N30NMz_NLNGnutCowp

Rand returnsRand Water Productivity

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.0 5.0 10.0 15.0 20.0

Rand return / mm

Pro

ba

bil

ity

of

Ex

ce

ed

en

ce

0_N

30N

Gnut

Cow p

Deep Drainage (skip)Deep Drainage (below 90cm)

26.2

12.3

7.6

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

Kg g

rain

ha

-1

0_N

30N

NL_N

Deep DrainageDrainage Probability Distribution

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 20 40 60 80 100 120 140

(In_crop) Drainage below 0.9m (mm)

Pro

ba

bil

ity

of

Ex

ce

ed

en

ce

0_N

30N

Gnut

Cowp

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

Thank You

Some issues with Input data

0

10

20

30

40

50

60

70

80

90

Rain

fall

(m

m)

Marble Hall

Tafelkop

Marble Hall – 800masl – Tafelkop > 1200 masl

Used Polokwane Temp data (1230 masl) to adequately simulate crop duration