quantifying water productivity in rainfed cropping systems: limpopo province, south africa
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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 - PowerPoint PPT PresentationTRANSCRIPT
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