Use of models – can they help
● Benchmarking
F&S vs computer simulation
Financial vs physiological
● Yield-gap analysis/diagnosis
The French & Schultz water-use efficiency concept
5
4
Grain yield (t/ha)
Grain
Yield
(t/ha)
20 kg/ha/mm
3
0m
2
1
110 mm
1m
0
0
100
200
400
300
1 m 500
Water use (mm)
April to October
rainfall (mm)
French and Schultz (1984)
WUE data – southern NSW 1990-2005
Grain yield (t/ha)
10
Evaporation?
Bethungra
Gundibindjal
Harden
Condobolin
8
6
20 kg/ha.mm
Rainfall
Distribution
4
2
Stored water?
0
0
100
200
300
400
500
April - October rainfall (mm)
600
Accurate benchmarking
Fine-tuning French and Schultz to account for:
- stored moisture at sowing [Fallow rainfall - 80 x 0.5]
- un-used water at harvest [Post-flower rain - 50 x 0.5]
- impact of late sowing
[-10% WUE per month delay]
(Robertson and Kirkegaard AJAR, 2005)
500
500
y = 0.9034x
- 24.435
2
450
RR =0.54
= 0.5472
2
400
350
300
250
200
150
100
300
250
200
150
100
50
0
0
100
200
2
350
50
0
0.9106x - 79.766
Ry2==0.78
R = 0.7822
400
g/m2
Yield
Simulated
yield (g/m2)
Simulated
yield (g/m2)
g/m2
Yield
450
300
400
April-Oct rain (mm)
April – Oct rainfall
500
600
0
100
200
300
400
500
in-crop rain + start SW -end SW (mm)
Seasonal water supply
600
Accurate benchmarking
Simulation models (APSIM) – Yield-ProphetTM:
Advantages:
Can account for specific season, soil and management factors
Can investigate “what-ifs” e.g. response to inputs
but…
Requires accurate and detailed soil information, validation
Diseases and other undiagnosed constraints
Are they tuned to the present, or the possibilities?
Measured yield (t/ha)
Benchmarking yields
Predicted yield (t/ha)
Consultants: Chris Duff, Geoff Pitson, John Sykes, Greg Condon
Financial vs physiological benchmarks
System innovation which increase $GM without excessive risk
Water-limited potential
20 kg/ha.mm
Leading
RETURN
Ave $GM/ha
Current status
Average
INVESTMENT RISK
12 kg/ha.mm
Investment framework
- map current grower performance
Efficiency Frontier
Production ($)
H
G
A
C
H = Environmental
potential
G = Maximum
marginal return
A = region’s best
growers
B
B = underperforming
growers
Expenditure on inputs ($)
Current fro ntier
A
C = risk-adverse
growers
Yield gap analysis and diagnosis
GENETICS
MANAGEMENT
INFLUENCE
Previous history
3+ yrs
Soil structure
Soil fertility
Weed seedbank
MANAGEMENT
Previous crop
Pre-crop fallow
1 yr
0.5 yr
Diseases
Nitrogen
Water
Weeds
Weed control
Stubble
Grazing
In-crop
Sowing date
Variety, N
Tillage/residue
Fertiliser
Plant protection
Increase soil water capture and storage
Crop vigour/reduce evaporative loss
Canopy management/harvest Index
Kirkegaard and Hunt (2010) Journal Experimental Botany
The gap is significant but closing slowly
Western Australia (Fischer 2010)
3.0
WL Potential yield
Grain yield (t/ha)
2.5
y = 0.0141x - 25.7
R² = 0.489
slope= 0.5%
2.0
y = 0.0248x - 47.9
2
R = 0.355
slope= 1.4%
1.5
Farm yield
1.0
0.5
0.0
1975
1980
1985
1990
Year
1995
2000
2005
2010