Achieving Practical Outcomes through Climate Risk Management in Agriculture Holger Meinke, Roger Stone, Graeme Hammer, Yahya Abawi, Andries Potgieter, Mark Howden, Rohan Nelson, Walter Baethgen and R. Selvaraju
In the beginning Australia, 1791 So little rain has fallen that most of the runs of water in the different parts of the harbour have been dried up for several months and the run which supplies this settlement is greatly reduced. I do not think it is probable that so dry a season often occurs. Captain Arthur Phillip 1791 was an El Niño-related drought.
The early days immediate realisation of the importance of rainfall variability good record keeping, which is now the envy of many countries many decades of trial and error Ad hoc understanding of the importance of climate risk management
the 1970 and 80s Climate scientists re-discovered the the Walker Circulation (Southern Oscillation, ENSO, SOI) Development of the SOI phase system, which is still the most widely used operational forecast system in Australia First production forecasts based on SOI phases and simulation models
the 1970 and 80s In parallel to developments in climate science, the field of crop simulation modelling developed rapidly from 1950 to 1990 (de Wit, Nix, Ritchie, van Keulen, Hammer etc). These models were ideally suited to account for water limitations and given Australia s susceptibility to drought provided a fertile ground for such model applications. This lead to the establishment of APSRU in 1991.
By the end of last century we had come a long way. We 1. developed cropping and grazing systems simulation capability that could address relevant, complex farm management issues 2. realised the importance of participatory research whereby decision makers form part of the research team from the beginning 3. appreciated that climate information needs to be integrated into an overall risk management framework rather than be treated in isolation and 4. developed global partnerships that demonstrated the value of this approach in developing countries
Climate knowledge in the 21 st century part of risk management Effective risk management requires an understanding of the climate system, an understanding of management options in response to climate information, and the ability to change the way agricultural systems are managed. it requires Climate Knowledge
Climate Knowledge is the intelligent use of climate information, including knowledge about climate variability, climate change AND climate forecasting used such that it enhances resilience, increases profits and reduces economic/environmental risks.
Climate - just one of the known risk factors From a risk management perspective we need to understand existing climate variability and know what is predictable and what is not (limits of predictability).
Agricultural systems and climate variability Climate Phenomena Frequency (years) Madden-Julian Oscillation (MJO) SOI phases (ENSO) Decadal variability (IPO, DPO etc) Climate change 0.1 0.2 intraseasonal 0.5 7 seasonal to interannua 10+ decadal to multi-decadal???
Agricultural systems and climate variability Decision Type (eg. only) Logistics (eg. scheduling of planting / harvest operations) Tactical crop management (eg. fertiliser / pesticide use) Crop type (eg. wheat or chickpeas) Crop sequence (eg. long or short fallows) Crop rotations (eg. winter or summer crops) Crop industry (eg. grain or cotton, phase farming) Agricultural industry (eg. crops or pastures) Landuse (eg. agriculture or natural systems) Landuse and adaptation of current systems Frequency (years) Intraseasonal (> 0.2) Intraseasonal (0.2 0.5) Seasonal (0.5 1.0) Interannual (0.5 2.0) Annual / biennial (1 2) Decadal (~ 10) Interdecadal (10 20) Multidecadal (20 +) Climate change
1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 JJA Rainfall (mm). 300 250 200 150 100 50 0 ENSO (El Niño - Southern Oscillation) - - - La Niña El Niño Other Dalby (Qld) JJA rainfall
Probability of exceedance functions 1.0 Probability of exeeding. 0.8 0.6 0.4 0.2 EN LA Other All 0.0 0 50 100 150 200 250 300 JJA rainfall (mm)
Simulation analysis for sorghum using WhopperCropper 10th - 90t h percentile 25th - 75t h percentile Median 6000 Average Yield (kg ha -1 ) 5000 4000 3000 2000 1000 0 15-Sep 15-Oct 15-Nov 15-Dec 15-Jan 15-Feb Sowing date
Emerald Rom a Goondi wi ndi Dalby July 2001 July 2002 NT Legend: 0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% No data NT Legend: 0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% No data # WA WA # SA SA # # NSW NSW (a) VIC (b) VIC TAS TAS Probabilities of exceeding long-term median wheat yields for every wheat producing shire (= district) in Australia issued in July 2001 and July 2002, respectively.
Forecasts issued at the beginning of the season robability of xceeding median hire wheat yield for he 2004 season given he SOI phase consistently negative n June/July WA NT SA QLD Legend: 0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% ± 0 130 260 520 780 1,040 Kilometers NSW VIC
Insight into socio-economic FEASIBILITY Insight into technical POSSIBILITY Insight into climatic PROCESSES Social Science Systems Science Climate Science Policy Economics Dynamic climate modelling FARMER Resource Manager Systems analysis Seasonal climate forecasts
Climate Change
The public image of climate change
Observational evidence indicates that climate changes in the 20th century have already affected a diverse set of physical and biological systems. IPCC (2001)
Model evidence for anthropogenic climate forcing
Australian rainfall trends since the 1950s
Dates of first and last frosts in Australia and Uruguay First and last days of frost at Emerald First and Last days of frost at Estanzuela 0 300 0 0 24 Aug 16 July 29 June Day of Year 250 200 23 Sept 19 Aug 13 Jun 0 3 June 150 5 June 0 1880 1900 1920 1940 1960 1980 2000 2020 100 1900 1920 1940 1960 1980 2000 202 Date of first frost Date of last frost
The benefits of early adoption Increased efficiencies have outweighed all expenditure involved. The costs of tackling climate change are clearly lower than many feared. This is a manageable problem. (Lord Browne, CEO of BP, announcing that BP had reached it s target of reduce carbon emissions to 10% below 1990 levels eight years ahead of schedule) The Economist, Oct 9 th, 2004
Farm and resource management Improved business and better societal outcomes Integrated systems science Well-informed policy development
Quantifying ENSO signal intensity P-values derived from the Log-Rank test applied to compare conditional POEs of 3-monthly JJA rainfall records (1900-2002) from 590 high quality rainfall recording stations across Australia.
Climate risk management in action Adaptation = appropriate responses to the warning Mitigation = development of strategies to reduce or avoid future hazards Climate Warning Adapt Mitigate
Results averaged over 12 climate models indicate Queensland may experience up to 15% less rainfall by 2030 and up to 40% less by 2070 Lower Rainfall Expected in the Future Increasing confidence in more rain Increasing confidence in less rain
General Challenge: Climate Forecasting has no value unless it changes a management decision Management decisions require management tools that open doors of opportunity to turn climate science into commercial climate risk technologies
The business opportunity However, many businesses do not have management structures that incorporate climate variability and climate change in their risk management systems
Defining climate risk technologies Climate risk services: short term consultancy Climate risk products: saleable decision aids Climate risk practices: actionable discoveries developed through co-learning with end users Climate risk training: fee paying short courses Climate risk knowledge: Journal articles, policy informing papers, briefings, postgraduate studies Different technologies require different strategies for delivery/ adoption pathways