managing future agricultural production in a variable and changing climate - steven crimp
TRANSCRIPT
Managing Future Agricultural Production in a Variable and Changing Climate
Steven Crimp, Alison Laing, Bronya Alexander, Phil Bowden, Kerry Bridle, Peter Brown,
Howard Cox, Peter deVoil, Jan Edwards, Alex Gartmann, Peter Hayman, Mark Howden,
Philip Kokic, Shaun Lisson, Neil McLeod, Jim Meckif, Barry Mudge, Uday Nidumolu,
David Parsons, Brendan Power, Michael Robertson, Daniel Rodriguez, Janet Walker,
Michael Wurst
17 November 2009
Converting analysis into action
Vulnerability = fn( Impacts , Adaptation)
Exposure & sensitivity
adaptation options; adaptive
capacity & resilience
Effective adaptation will depend on which learning style is appropriate
2000 2010 2020 2030 2040 2050 2060 2070
Low climatic variability
• If there is both increased climate variability as well as underlying trends then broader “social networks” as well as individual re-learning will be required.
2000 2010 2020 2030 2040 2050 2060 2070
High variability
2000 2010 2020 2030 2040 2050 2060 2070
Climate trend and increasing variability
Participatory engagement key part of research
• Expert agricultural knowledge is with farmers.
• Ensuring ‘real’ cropping systems and feasible adaptation options are evaluated.
• Encourages solution-seeking and discussion of many potential options and supports some aspects of individual re-learning.
Project Design
Engagement CMA’s/farmer groups, State Agencies
Project partners and activity locations
• Links with previous research undertaken in NSW (blue dots).
• Provides a national coverage allowing inter-comparison of research between regions (e.g. hotter drier places).
• The project involves nine research partners:
• CSIRO, DEEDI• NSW DPI, BCG• SARDI, TIAR, SFS• DAFWA, Curtin University
• Links with 14 farmer groups across Australia.
• And involves approximately 27 case study sites (red dots).
Project Design
Engagement CMA’s/farmer groups, State Agencies
Group workshops and individual interviews
Farm profit
Economic risk
• Practices• Tillage & ground cover• Moisture seeking
• Tactics• Planting rules• Soil water thresholds• Crop sequences & intensity• Long fallowing• Forage conservation
• Strategies• Crop selection (winter / summer)• Water allocations• Land allocations• Cropping / grazing mix
• Farmers’ preference• Risk preference & trade offs
Alternative farming systems
Less risk
More profit
More of both
Workshops and farmer interviews
Workshops and farmer interviews
Project Design
Engagement CMA’s/farmer groups, State Agencies
Group workshops and individual interviews
Crop modelling, benchmarking and
validation
Exploring adaptation options: modelling
• Crop yields (kg/ha)• Crop N requirements & intake at different crop growth stages• Nutrient leaching & runoff• Soil moisture• Plant growth at each growth stage• Gross margin estimates• Pasture growth and biomass removed in grazing events
Simulations usually run across several years (e.g. 10-50+ years) with output reported annually, usually at harvest
Outputs can be simulated under recent and likely future climates
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1999wheat
2000canola
2001wheat
2002canola
2003wheat
2004lupin
2005wheat
2006triticale
2007lupin
Year and Crop
Yie
ld (
t/h
a)Observed
Simulated
• Crop rotations, sowing rates, fertiliser inputs, and management practices are checked and estimates of yield produced.
• Farmers shown estimated yield information in a range of formats
Modelling – benchmarking and validation
0
10
20
30
40
50
60
70
80
90
100
0 500 1000 1500 2000 2500 3000 3500 4000
Yield (kg/ha)
Cu
mu
lati
ve P
rob
abili
ty
0
500
1000
1500
2000
2500
3000
3500
4000
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
Year
Yie
ld (
kg/h
a)
50% chance of exceeding 2.9 t\ha
Modelling – benchmarking and validation
Time Series Probabilities McClelland (1957-2005)
wheat canola medic (biomass)
Yie
ld (
kg/h
a)
0
1000
2000
3000
4000
5000
6000
Mean/Median Yields as well as distributions and extremes
-40-20
020406080
100
0 1 2 3 4
Gross Margin ($/ha)
Stocking rate (flock ewe hd/farm ha)
Mingenew Previous 10 years
Previous 50 years
Decile 3-4
Modelling – benchmarking and validation
Project Design
Engagement CMA’s/farmer groups, State Agencies
Group workshops and individual interviews
Crop modelling, benchmarking and
validation
Adaption options simulated and evaluated in terms of yield and gross margins
Comparisons across regions
Testing adaptation options – individual crops and single scenarios
Baseline No Change Density Fallow Residue Variety
45%
28%
12%
Baseline No Change Density Fallow Residue Variety
NS
W
Far
m
Vic
toria
n
Far
m
52%
18%
Testing adaptation options – individual crops/multiple scenarios
Testing adaptation options – gross margins
Alternative scenarios for CC scenarios - Roma
Testing adaptation options – whole farm profitability
Project Design
Engagement CMA’s/farmer groups, State Agencies
Group workshops and individual interviews
Crop modelling, benchmarking and
validation
Adaption options simulated and evaluated in terms of yield and gross margins
Comparisons across regions
Group workshops and individual interviews
Project evaluation
Conclusions
• Our research to date has shown that local expert knowledge and modelling can be combined to examine the value of adaptation from local to regional scales.
• The regional variability of adaptation results shows clearly that local knowledge will be required to adapt to projected changes and hence combining local expert knowledge and modelling is a crucial activity.
• The prospect of adapting to significant climate change remains challenging across all of Australia.
Thank you
Steven CrimpPhone: +61 2 6242 1649 or +61 428482940Email: [email protected] Web: www.csiro.au