Exploring adaptive responses in dryland cropping systems to increase robustness to climate change.
Samantha Doudle1, Peter Hayman2, Nigel Wilhelm2, Bronya Alexander2, Andy Bates3, Ed Hunt3, Bruce Heddle4, Andrew Polkinghorne3, Brenton Lynch3, Mark Stanley5, Alison Frischke1, Naomi Scholz1, Barry Mudge6 1 SARDI, Minnipa Agricultural Centre, 2SARDI, Waite Agricultural Institute, 3Eyre Peninsula Agricultural Consultant, 4Eyre Peninsula Agricultural Research Foundation, 5 Eyre Peninsula Natural Resources Management Board, 6Rural Solutions SA, Jamestown
Funded by the Department of Climate Change
Project # 0711Doudle Department of Climate Change, Comprehensive Project Report 1. Table of Contents Page No Title Page 1. Table of Contents 2 2. Abstract 3 3. Introduction 5 4. Materials and Methods 7 A. Develop a descriptive climate change adaptation framework for upper EP low rainfall farming systems 7 B. Apply the framework to current farming systems 8 C. Apply the framework to a farming system under climate change 8 D. Use framework for gap analysis 8 5. Results 13 Q1: What are the common characteristics of the robust businesses examined? 13 Q2: What are their important key strengths and associated management strategies? 15 Q3: What are the important vulnerabilities and associated management? 18 SARDI Climate Applications Unit Yield Simulation Analysis of Low Rainfall Areas 18 6. Discussion 21 Q1: What are the common characteristics of robust businesses examined? 21 Q2: What are their important key strengths and associated management strategies? 21 Q3: What are their important vulnerabilities and associated management? 22 Q4: How can their current strengths be maintained or further strengthened and how can the vulnerabilities be minimised in the future? 22 7. Conclusions 24 Feedback from project team 25 8. Communication of key findings 29 9. Acknowledgements 29 10. References 29 11. Appendix 30 1. Climate Analysis for Case Studies 30 2. The challenge of understanding recent rainfall 98 3. Summary of farming systems framework analysis, excel version 100
2 2. Abstract There has long been a debate about the viability of grain farming on the upper Eyre Peninsula (EP), indeed this discussion can be traced back to Goyder who in the late 1860’s delineated a line of reliable cropping that runs through the low rainfall regions in South Australia and traces across the upper EP. Climate change with projections of a warming and drying trend reinvigorates this debate. The Eyre Peninsula Agricultural Research Foundation (EPARF) and SARDI’s Minnipa Agricultural Centre and Climate Applications Unit have collaborated with a small group of farming systems consultants on a project funded by the Department of Climate Change, to assess how the challenging conditions of the past five years compare to the various climate change scenarios for the lower rainfall areas of Eyre Peninsula and the upper North of SA. Case studies were then conducted by the consultants on eleven robust farming businesses who have maintained their strength despite the recent run of poor seasons. The basic premise of this project was that many features of resilience to climate change in coming decades (up to 2030) could be understood from current resilience. However, it is accepted that the projected changes for 2070 include a future that may present challenges not previously met and that there may be more dramatic shifts in climate in the coming decades than what is suggested by the global climate models. Climate change projections from (Suppiah 2006, BoM and CSIRO 2007) indicate high confidence that Eyre Peninsula will be 0.6 to 1.5 degrees warmer by 2030 and that while there is less confidence in rainfall projections, the most likely annual rainfall decline by 2030 is about 5%, with a 1 in 10 chance that it will be 10% drier. For most locations assessed in this project the mean of the last 5 years is about 20% below the mean of the 1980 to 1999 period. Although 5 years is a short period it was considered a guide for evaluating farming systems. The project identified the characteristics of these eleven robust businesses, the strengths and vulnerabilities and the most important requirements for the future to build on the strengths and minimize the vulnerabilities. The range of businesses assessed in this study were diverse in terms of location (from Ceduna in the north- west to Tumby Bay in the south-east of Eyre Peninsula and Pt Germein in the upper North of SA), land zone (calcareous sands and red soils, siliceous sands and deep soils over clay), annual rainfall (300 – 375 mm), agronomic practice (60 -100% cropping), property size (1,500 – 4,570 ha) and many other factors. Not all businesses have come from a strong background, but all have managed to maintain their business strength over the past five very challenging seasons. From this study, there is no one recipe to achieve or maintain strength in terms of agricultural practices across these diverse circumstances; however there were some common business management features and personal characteristics: o They aim to improve their business but in a measured and conservative way. An important business goal is to achieve high equity and to recover that high equity after major expansions or investments. o They are often not the earliest adopters of new technology. When they do adopt they do it well and consolidate before moving on to the next thing. o They are keen to learn (often not formally educated), are organised and allocate time to planning and reviewing. o They recognise they are not experts in every aspect of their business and consult with others for these skills. These characteristics are not rocket science and should be achievable for many businesses. This project team believes that the research, development and extension (RDE) requirements for robust and sustainable businesses in the future under potential climate change impacts should build on what we know is required for low rainfall businesses to better manage short term variability: o An improved ability to identify and analyse potential enterprise costs, benefits and risks. o The flexibility to change the system in response to market and season to develop lower risk, responsive farming systems - including range of crop types, enterprise mixes, input types and levels. o The need to maintain networks and relevant information flow to provide short term support, community confidence and balance to sensational climate change headlines.
3 Given the similarity between short and longer term RDE requirements, increased investment in low rainfall agricultural RDE now is also a solid investment for the future under climate change.
Figure 1: The Eyre Peninsula Agricultural Research Foundation Board, 2008. Left to right, back: Dot Brace (SARDI, EPARF Executive Officer, farmer Poochera), Matt Dunn (farmer Tuckey), Andy Bates (consultant, Streaky Bay), Mike Keller (University of Adelaide), Peter Kuhlmann (EPARF Chairman, SAGIT Chairman, farmer Mudamuckla), Samantha Doudle (SARDI, Leader Minnipa Agricultural Centre), Geoff Thomas (Thomas Project Services), Craig James (farmer Cleve, ABB Ltd), Bruce Heddle (vice Chairman EPARF, farmer Minnipa). Front row: Brent Cronin (farmer Chandada), Dean Willmott (farmer Koongawa), Jim Egan (SARDI Pt Lincoln). Absent: Professor Simon Maddocks (Chief, SARDI Livestock & Farming Systems Division).
4 3. Introduction Project Aim: To explore options that increase the ability of dryland farming systems to respond to climate change through building on key strengths and reducing vulnerabilities, with an initial focus on South Australia’s upper Eyre Peninsula. There has long been a debate about the viability of grain farming on the upper Eyre Peninsula (EP), indeed this discussion can be traced back to Goyder who in the late 1860’s delineated a line of reliable cropping that runs through the low rainfall regions in South Australia (and traces across the upper EP, Figure 2). This map also has grain enterprises overlaid which shows the considerable number of enterprises close to or north of Goyder’s line. Climate change with projections of a warming and drying trend reinvigorates this debate. For example, a key recommendation in the ABARE December 2007 report on the impact of climate change was for policies that encourage adjustment in vulnerable sectors in agriculture, including already marginal farming enterprises.
Figure 2: Goyder's Line from space, 2006 SPOT Vegetation September 21, 2006 image (SPOT Vegetation Programme CNES-VITO, http://free.vgt.vito.be/) Landcover 2003 & Goyder’s Line (SA, Department of Water, Land and Biodiversity Conservation (DWLBC). Information Management Group) 227.2 is the annual rainfall for 2006 at Orroroo Climate change predictions for upper EP have a high confidence in warming and lower confidence but consistent projections on drying. The CSIRO report prepared for SA government (Suppiah et al 2006) and the Climate Change in Australia report (BoM and CSIRO 2007) indicate the following: 1. There is high confidence that Eyre Peninsula will be 0.6 to 1.5 degrees warmer by 2030 and that at 2030 most of the uncertainty is due to different global climate models rather than different emission scenarios. 2. The most likely rainfall decline by 2030 is about 5% with a 1 in 10 chance that it will be 10% drier and a 1 in 10 chance that it will be 2% to 5% wetter. 3. The projections for 2050 and 2070 show a very hot future, especially under high emission scenarios and a wide range of rainfall futures, but the median or best estimate showing an increasing drying.
5 Grain farming on the upper Eyre Peninsula is already considered risky. Future projections of climate change lead to policy makers, Natural Resources Management (NRM) boards, farm advisers and most importantly farmers questioning the viability of the current enterprise mix of the region. While recent years show the vulnerability of many farming enterprises to a run of poor seasons, they also indicate that there are farm enterprises that have remained resilient. This raises the question of what can be learnt from these successful businesses and whether there are common characteristics across these successful businesses which could be adopted by others? Climate change predictions indicate there may be a higher frequency of poor years in the future. Given the recent run of poor years, the other question is how the recent run of seasons compares with projected changes to climate in the coming decades leading up to 2030?
Figure 3: South Australian rainfall isohyet map showing Goyder's Line.
6 3. Materials and Methods To address the project aims the following project methodology was established: A. Develop a descriptive climate change adaptation framework for upper EP low rainfall farming systems B. Apply the framework to current farming systems C. Apply framework to a farming system under climate change D. Use framework for gap analysis E. Communicate key messages
A. Develop a descriptive climate change adaptation framework for upper EP low rainfall farming systems Framework Development In 2007, a team of farmers, researchers and consultants from the Eyre Peninsula Agricultural Research Foundation (EPARF) and Minnipa Agricultural Centre (MAC) constructed a framework to identify and communicate the components and interactions which make up modern farming systems. Initially the framework was constructed as a Powerpoint presentation. The framework was designed as a tool to help in the assessment of farming systems components for this project, by analysing selected farm businesses from low rainfall areas. The categories for this framework were profitability, social, environmental services, soil, climate, external risk, systems choices, land use (cropping, livestock, conservation, other) and land use interactions (cropping and livestock, cropping and environment, livestock and environment). Each category was further divided into a group of factors. This original framework was also used successfully to communicate the type and scope of work conducted from MAC to the EPARF Board, the National Grain & Graze Board and the Low Rainfall Collaboration Group (a collection of five low rainfall farming systems groups across southern Australia). Whilst this framework was a useful communication tool and the process of constructing it documented the structure and components of low rainfall systems, it was not in a format that could be used for comparative and quantitative assessments of those components. Subsequently, the framework was reformatted into a series of spreadsheets, the first columns being the categories and corresponding factors of the farming systems and the top row being the information sought for this project (Appendix 3). What information were we seeking? After finalising the framework format, the information and exact questions to be answered using the framework were determined: 1. What are the common characteristics of the businesses examined? 2. What are the important key strengths and associated management strategies of these businesses? 3. What are the important vulnerabilities and associated management of these businesses? 4. What measures are required to maintain or build on strengths and reduce vulnerabilities to short term climate variability and longer term climate change impacts? In order to generate the information to answer these project questions, we needed to obtain several different types of data for each component of the framework. Therefore the top row of the framework contained the following questions: • Benchmark – the project team of farmers, consultants and researchers agreed on a set of benchmarks or thresholds for each component. The benchmarks chosen were those for which a wide range of data could be easily collected. Benchmarks were included which covered all three aspects of the triple bottom line to ensure that the study went beyond the traditional farming systems focus areas of agronomy and finance. These benchmarks were not a definitive statement to assess ‘good or 7 bad’, but they were a useful tool to stimulate the consultant’s thoughts during the assessment process, and allowed comparative and quantitative assessments to be made. The consultants were asked to provide a rating of how each farming systems component rated against the benchmarks. • Current Management Strategy - how is the business currently managing this farming systems component? • Importance to the Business Now (1 = very important, 5 = not important). • Strength or Vulnerability (1 = very strong, 5 = very vulnerable) – based on these assessments, is this component a strength or vulnerability of the current farming system? • Importance to the Business in the Future (1 = very important, 5 = not important) – based on the interaction with the SARDI Climate Applications Unit, how important do you think this component will be in a future under climate change impacts? • Requirements for the Future – what will be required to better manage this component in the future under climate change impacts? The framework also required a summary of the key points from the analysis and information on the history of the business that contributed to an understanding of how that business had achieved its current robust position. B. Apply the framework to current farming systems, and C. Apply framework to a farming system under climate change Workshop with SARDI Climate Applications Unit An important part of the project was to give the consultants a good understanding of climate change mechanisms and potential impacts, before they conducted the case studies using the farming systems framework. This was achieved through interaction with the SARDI Climate Applications Unit based in Adelaide, Peter Hayman and Bronya Alexander, through a one day workshop held in Port Lincoln. This workshop was also attended by the Department of Climate Change representative, Joanna Pinkas. The project team as a whole was able to come to a common understanding of the basis of climate science, including the different levels of confidence (high for temperature and lower for rainfall). There was room to question climate science, but also for climate applications scientists to question aspects of the farming system and key areas of risk. Preparation with SARDI Climate Applications to provide a recent climate background for the case study locations and to improve understanding of climate change and potential impacts. Recent climate background The recent climatic conditions for the case study businesses chosen by the consultants were analysed by the SARDI Climate Applications Unit. This information confirmed for the consultants that the businesses they had chosen to be successful had endured a very poor run of seasons relative to the historical record. Figure 4 shows the locations for climate analysis. The sites were selected to support the case studies at Ceduna, Darke Peak, Kimba, Lock, Minnipa, Port Kenny, Smoky Bay, Streaky Bay, Tumby Bay, Wharminda and Port Germein. As outlined earlier, the consultants were examining businesses that had maintained business health over the last ten years (1998 to 2007) and had done so despite tough conditions in the past five years. This raises the questions of how, for each location, the 10 year period Figure 4: Climate analysis and case study locations (map from http://maps.google.com.au/). 8 (1998 to 2007) and 5 year period (2003-2007) compared with the long term record and with projections for 2030. Appendix 1 shows the information provided for each location. This was distilled from discussion with the project team on what would be useful. From this discussion we presented the following: • Tables showing monthly rainfalls from start of records to present were listed and the seasonal rankings shown. In climate analysis it is more common to produce percentiles, but the straight ranking is more transparent. We were encouraged to present the raw data (Appendix 1, Table 1). • A presentation of graphs of growing season rainfall (GSR) in cumulative format with the 10th, 90th and median from the long term record and each of the last 10 years shown in separate graphs (Appendix 1, Figure 1). • A time series of growing season rainfall showing individual years and the 5 year running mean (Appendix 1, Figure 2). • The 5 year running mean as a percent departure from the mean and a clear set of dot points that conveyed the exact value. For example, the last 5 years (2003-7) at Minnipa were 26% below the long term average and this 5 year period was the driest 5 year period out of the 89 5 year periods since 1915 (Appendix 1, Figure 3). • A presentation of the same data as shown in the time series of growing season rainfall, but as a bar chart with each bar coloured using cool colours (blues) for decile 8, 9 and 10 and warm colours (orange and reds) for deciles 1 to 3 and intermediate colours (yellow and green) for deciles 4-7 (Appendix 1, Figure 4). • Finally a presentation of the deciles over each 10 year period that shows wetter and drier periods in the past and to see how the current conditions compare (Appendix 1, Figure 5). There are a number of challenges of using recent history as a guide to future resilience to climate change. From a climate science perspective we are moving into a new climate regime and hence the past is an imperfect guide to the future. This problem is not unique to climate science, from a farming perspective, each decade has a unique mix of technologies and economic circumstances. There is also a procedural issue of using the mean of a short period (5 years) to compare with a shift in the long term mean. It is common in climate science to use a 30 year period, the Climate Change in Australia report (BoM and CSIRO 2007) used the 20 year period centered on 1990 (1980 to 1999) as a base to compare the projections of rainfall decline. So a 10% rainfall decline is 10% less than the 1980 to 1990 period (Appendix 2).
GSR
600
500
400
GSR 300 5yr running mean
200 Rainfall (mm) (mm) Rainfall
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 5: Annual growing season rainfall (Apr-Oct) at Minnipa, along with a 5-year running mean (mean of the previous 5-years inclusive).
9 5yr running mean of GSR
40%
30%
20%
10%
0%
-10% Departure from mean mean from Departure -20%
-30% 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Figure 6: Five-year running mean of Apr to Oct rainfall at Minnipa, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
Figures 5 and 6 show an example from Minnipa of how the annual and growing season rainfall over the past 5 year period compares to the historical record: • GSR over the last 5 years (2003-2007) was 26% below the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the driest 5-year period out of 89 5-year periods since 1915. • GSR over the 5 years prior to 2002 (1998-2002) was 3% above the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the 52nd driest 5-year period out of 89 5-year periods since 1915. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 11% below the average of all consecutive 10-year periods from 1915 to 2007. This period was ranked the 12th driest 10-year period out of 84 10-year periods since 1915.
Understanding the run of recent poor seasons as a guide to the future climate The recent run of poor seasons prompt the obvious question, “Is this a drought or climate change?”. This question was raised at the first meeting as it is often posed in the media and is common in farmer discussions, especially with farmers who have long term records of rainfall for the dry periods in the 1930s. Other ways of framing the question are “Is this a cyclical drought or a creeping aridity, is this drought or drying? If we accept the strong evidence for human induced climate change, a more correct question is whether the early stages of climate change are manifest in drying of upper Eyre Peninsula or whether variability will dominate any trend over the next few decades. This is a complex question and the subject of much research at a national and international level. Some general points which have emerged from this research are as follows: • Climate science has much lower confidence in projections of rainfall than temperature. Discussion of what to do with low rainfall farming regions would be different if we had the same confidence in the future of rainfall as temperature. This means that climate science has more to say about the future of say a high chill requirement peach orchard at a site that is currently on the warm margin than dryland farming enterprise at the low rainfall margin. • Although rainfall is the most uncertain projection from the global climate models, there is a worrying consistency amongst the models showing a drying in southern Australia. This is evident in the suite of models used for the Third and Fourth Assessment Rounds of the IPCC and a finding from further research from the South East Australian Climate Initiative (SEACI). Recent research findings seem to lean towards drying. However there is a wide range in the extent of drying. Of all the models used in the BoM CSIRO 2007 report, the middle of the range of models (median) is
10 about 5% decline in rainfall, the driest 1 in 10 model gives a result of 10% drying and the wettest 1 in 10 indicates up to 5% wetting. • A message that growing seasons rainfall for 2006 and 2007 (a decile 1 and decile 2 year) are a guide to the future “2006 and 2007, get used to it…” is overly pessimistic and is a case of confusing extremes and means. • When climate science says that it is unsure about the future of rainfall and how to interpret the recent trends, this is not code for “it is just variability and will return to normal”, it is code for “we don’t know exactly what will happen”. One of the key questions is how much weight to put on recent years. One option is to take the last 10 years as the new regime, but given the degree of variability in rainfall, this seems to be placing undue weight on a very short period. The use of the 100 year record gives no weight to the recent period, a simple compromise is to use the most recent 30 years, but to also include shorter periods in scenario planning. • One approach for 2030 is to factor a 1 to 1.5 degrees rise in temperature and to consider a 5%, 10%, 15%, 20% etc decline in rainfall. This is the approach adopted in this project. A communication challenge is that climate science tends to speak in terms of percent deviations of average (e.g. 5% or 10% decline in average rainfall) while deciles are the language of risk used by agronomists and farmers. When a farmer says that this is a decile 3 year, this is conveying the useful concept that 7 years out of 10 have been wetter and 2 drier. The diagram below is an approach to converting percent decline in rainfall to deciles. The first pie shows equal chance of each decile with the dark brown as decile 1 and dark blue as decile 10. A 5% decline in rainfall means that a farmer should still expect to get poor years and good years, but the ratio of the driest three deciles to the wettest three deciles has changed. A 15% decline means that half the time a farmer should expect to be get the driest three deciles and a 30% decline means that this will occur 75% of the time. The relationship between deciles and climate change projections is unique for each data set. In regions with less variable rainfall where deciles are closer together, a smaller shift in percent rainfall decline will lead to dramatic swings. For example at Maitland on the Yorke Peninsula, a 10% decline in rainfall leads to a 50% chance of being in the bottom three deciles. This approach is holding the coefficient of variation constant with a changing climate, there are alternative methods based on changing the variance, but this approach is the most transparent and is similar to the recent work on drought analysis conducted by the Bureau of Meteorology and CSIRO (Hennessy et al 2008). Rainfall recorded at LOCK POST OFFICE 5% dec li ne 10% decli ne 15% 20% 25% 30%
% reduction
Using APSIM to understand climate change impacts A range of locations across Eyre Peninsula were used for spatial analogues to consider the early stages of climate change. Although using spatial analogues for thinking about climate change has some limitations, they can be a powerful way to consider what a warmer and drier environment might be like. It is also a means of identifying possible adaptation options. Most importantly it provides a basis to consider abstract concepts such as a climate 1 degree warmer and 10% drier. We used the Agricultural Production Simulation model (APSIM) model, which had been verified and parameterised with local data and extensive soil characterisation (Whitbread and Hancock 2008), as a sophisticated climate analysis tool to investigate the sensitivity of simulated wheat yields to rainfall and temperature. By holding soil type constant across all sites (i.e. using the Minnipa soil characteristics at other locations) we can investigate the differences due to climate alone. Following discussion with CSIRO Marine and Atmospheric Research, we decided to undertake a sensitivity analysis using APSIM to investigate the impact on wheat production of changes in temperature of 1, 2, 3 and 4 degrees C and rainfall reductions of 10, 20, 30 and 40%.
11 D. Use framework for gap analysis The basic premise of this project was that resilience to climate change in coming decades (up to 2030) could be understood from current resilience. However, it is accepted that the projected changes for 2070 include a future that may present challenges not previously met and that there may be more dramatic shifts in climate in the coming decades than what is suggested by the global climate models. How to use the framework? Given the modest size of the budget for this project, we carefully considered how the framework could be used to get the best objective information that would be relevant to businesses across the range of rainfall and soil zones. Initially the project team investigated contracting one consultant to conduct a series of one on one interviews using the framework with a number of businesses. The limitations of this approach included the time and expense involved and the potential for business members to not always be objective about how they rated themselves. As a result a method was agreed upon that was effective in meeting the project objectives, affordable and also delivered additional unplanned outcomes to the project. Four independent private consultants were identified on the Eyre Peninsula and one from the upper north of SA who had both a deep farming systems understanding and approach to their business and a very detailed record base for a wide range of clients. The consultants would later be asked to identify case study businesses who had maintained business health over the last ten years, despite the very challenging conditions in the past five years – poor prices in 2005, then two of the worst droughts on record. The logic behind this approach was that if these businesses had managed to remain robust despite these circumstances, there may be identifiable characteristics that contributed to their success. These farm businesses had experienced similar environmental and general financial conditions to others in the same districts but had produced much better business outcomes. Adoption of characteristics which appeared to contribute to this success by other farm businesses in their districts should also improve the viability of these other businesses. The requirements for such businesses to continue to operate well in a future affected by climate change impacts may provide valuable direction for future RDE in low rainfall farming systems. The businesses chosen were not representative of all businesses on the upper EP in terms of scale or type of operation, but were chosen to be in environments which are widespread across the upper EP. This approach not only allowed the project team to get the detailed information they were seeking but it also allowed the five consultants to interact with the SARDI Climate Applications Unit. During this process the consultants learnt more about climate change than they would have through the media and their traditional information networks. How the farm businesses were chosen and process of characterising each business After the initial workshop the consultants chose their case study businesses based entirely on the current robustness of their financial situation and their willingness to participate in the study. They then conducted the analysis using the framework, the background climatic data and their new understanding of potential climate change impacts as developed at the workshop. A framework was filled out by every consultant for each of the farm businesses they had chosen for the study from their own database of client records and financial statements. Gaps in the framework were filled and data verified after personal contact with each farm manager by the consultant. Analysis and summary of frameworks After each framework was completed it was returned to the project team who went through each one in detail to ensure all relevant information was in the appropriate format and location on the spreadsheet. All spreadsheets for each business were then transferred into one continuous spreadsheet to allow the information to be analysed and summarised. Most of the rating type information was summarised using a median value and a range while the comment type information was accumulated and the key common points summarised for all 11 businesses (Appendix 3). Follow up meetings to discuss commonalities A follow up meeting was held with the consultants and the project team to discuss the process and key outcomes.
12 After this meeting a two page summary was produced, based on the most important or highest priorities from the detailed tables for each project question. Representatives from the consultants and the project team then met one final time to ensure this two page summary was an accurate reflection of the most important information generated by the entire process.
13 4. Results
Q 1: What are the common characteristics of the robust businesses examined? Three businesses were chosen by each of the three of the Eyre Peninsula consultants and one by the other Eyre Peninsula and the upper north consultant, giving a total of eleven case studies. The case studies were across a range of land zones in the lower rainfall areas of Eyre Peninsula and the upper north of SA and rainfall ranges from 300 to 375 mm per annum (Table 1). Table 1: Case study businesses, land zone, rainfall and property size summary.
Business Dune Calcareous Calcareous Deep soils Loamy Annual Property Size Number Swale grey Red over clay Sand Rainfall (ha) (mm) % of property in each major land zone Total Leased
1 0 35 40 25 0 350 2463
2 70 0 30 0 0 350 2179
3 0 70 30 0 0 300 3607
4 40 0 60 0 0 375 1868
5 40 20 20 20 0 350 4570
6 60 0 20 20 0 375 2272 7 50 0 0 0 50 300 4100 8 0 60 0 40 0 300 1040 1080
9 0 20 0 0 60 375 920 600
10 30 30 40 0 0 375 1700
11 0 50 50 0 0 300 1600 400
Average 26 26 26 10 10 341 2393
An APSIM analysis was conducted across a subsample of locations (Table 2). These sites cover low to moderate growing season rainfall (GSR) and therefore had correspondingly low to moderate APSIM simulated grain yields. The growing season temperature (GST) ranges by about a degree and the warmer sites have a higher chance of days over 35 degrees. Table 2: Average growing season rainfall (Av GSR), average growing season temperature (Av GST) and average simulated wheat yield (using APSIM) for locations on the Eyre Peninsula from 1900 to 2006. Also shown are the probabilities of getting over 35 degrees C during September to October for at least 1, 2 and 3 days.
Probability of getting over 35oC in Sept/Oct for at least: Location Av GSR Av GST Av simulated 1 day 2 days 3 days (mm) (oC) yield (t/ha) Cowell 187 14.9 1.1 43% 17% 10% Ceduna 216 14.4 1.5 51% 35% 24% Pt Neill 236 14.6 2.1 32% 10% 4% Minnipa 245 14.3 2.2 43% 22% 13% Kimba 251 13.4 2.1 35% 16% 8% Lock 301 13.6 2.8 36% 16% 7% Cummins 349 13.7 3.6 20% 6% 2%
14 To further demonstrate the range of case study businesses, the following are some of the business and production characteristics: • All eleven case studies have maintained business equity at 80% or more over the past five years (Appendix 3, Table 5). • Cropping currently provides the highest percentage of income in all case studies, with a median of 85% and a range of 66 – 100% (Appendix 3, Table 5). • The crop to livestock ratio as a percentage of total arable farm area for five of the case studies was 80:20, four with 60:40, one with 70:30 and one with 100% crop (Appendix 3, Table 11). • In all case studies the dominant grain crop is wheat, with a 5 year median of 52% of the arable area sown to wheat. All case studies also grew barley at a median of 20% of the arable area. Medic dominated pastures formed a median of 29% of the arable area for nine of the case study properties. Two properties sowed a portion of their medic pastures annually, two relied solely on annual regeneration and the rest had a combination of both. Break crops were grown by all but one case study business, with five growing oats (median of 6% of the arable area), three growing peas (1.5% of the arable area) and five growing canola (3% of the arable area) (Appendix 3, Figure 15). • The median wheat yield over the past ten years is 1.49 t/ha, with a range from 1.08- 2.0 t/ha. Over the past two years the median was 0.8 t/ha, with a range from 0.35-1.0 t/ha (Appendix 3, Figure 16). • Nine case studies retain all of their stubbles and six of these are 100% no-till with approximately 30% soil disturbance, with four other case studies sowing their programs using at least 80% no-till. All case studies reported stable or slightly increasing water use efficiency for their cropping programs (Appendix 3, Table 12, Figure 17). • Ten out of eleven case studies identified ryegrass and brome grass as problem weeds. Ten out of eleven case studies identified Rhizoctonia as a problem cropping disease, with Take all and CCN also rating for the majority of case studies. Seven case studies reported snails as a problem pest, with mice and cutworm being common minor pests (Appendix 3, Table 13). • Of the eleven case studies, ten had a livestock enterprise, dominated by self replacing merino flocks. Three case studies had both self replacing merinos and prime lamb enterprises and one ran prime lambs only. Seven out of the ten properties with livestock reported that livestock prevented them returning as much organic matter to the soil as they would like (Appendix 3, Figure 19, Table 14). • All case studies aim towards farming for profit with minimised risk, as opposed to farming for maximum production (Appendix 3, Table 11). • The main compromises between running both a cropping and livestock enterprise included: o Stock knock down stubble, reducing “trash” flow and chemical efficacy. o Weed seeds get buried by livestock over summer and not left on soil surface, therefore germination can be patchy, this can compromise the ability to get an adequate seed kill at spray top time due to uneven head emergence on the grasses. o Lack of dry matter input following a pasture year and subsequent erosion risk. o Paddocks are not grazed uniformly; hills are often grazed out prior to rest of paddock, increasing risk of erosion. Permanent fencing to maximise pasture utilisation compromises cropping machinery flexibility. o Timing of operation – shearing and crop weed control often clash. o Feed utilisation is a balance between the need for maximum nitrogen fixation and animal requirement for food (like to leave medic to bulk up on some lower protein paddocks). o Compromise between good weed control and the need for early, balanced sheep feed. o Withholding period for some pesticides impacts on feed utilisation. o Removal of pasture species during cropping phase necessitates the resowing of pastures on some paddocks annually.
15 Q2: What are important key strengths and associated management strategies of these businesses? Of the 212 farming systems components assessed using the framework, 26 were identified as currently the strongest and most important features of the case study businesses (Table 3). The remainder of the components and their rankings are listed in Appendix 3. Table 3: The most important strengths (listed in order of decreasing average score across all businesses in the study), current management strategies and future requirements from the farming systems framework analysis - refer to Appendix 3 for all farming systems characteristics assessed.
# Category Factor Summary current mgmt Summary requirements for the strategies future
3 Profitability Farm equity - on Strong emphasis on having cash May not be able to rely on capital gain the day as an important component in any to maintain equity as many farmers new major purchase (e.g. land or have in the past 7 years. machinery). Leasing extra land rather than purchasing.
19 Social Experience Willingness to learn. Strong Don't rely on one set of networks, networks for gathering information these guys look further afield than their locals only. Workshops provide opportunities to develop new networks. Critical to maintain the farmer group networks.
27 Social Peer & Industry Major business decisions are made Continue as is. Pressure – e.g. after objective review and new gadgets if consultation with others. you don’t need them!
29 Social External labour Generally, little use of external Short term register for back packer requirement labour. type people, seasonal labour, maybe (external to Eyre Regional Development Board or family) an employment agency.
2 Profitability Debt servicing Maintain low debt exposure Need a data base of actual figures, all (grain) costs/ t through good maintenance measured in a consistent manner, grain - average schedules on machinery, only updated annually, and publically tonnage purchasing on as needs basis (also available. Benchmarks could be see 3, 27). arranged in deciles or risk categories with "triggers".
18 Social Skill & Also see 19. Accessing new Discussion Group Workshops (from Knowledge information and ideas from outside Appendix 2, #1) could include skills Level the farm and the business very audit list (akin to Planning 4 Recovery important (e.g. field days, media, workshops) to encourage people to industry information). Interacting analyse their own skills and external with other farmers very important. requirements. Dry matter vs. grain decisions in responsive farming (people who are confident to crunch their own numbers or have experienced the situation are the ones who can make these decisions easier). Timely access to advice and services (e.g. deep N, soil moisture).
16 # Category Factor Summary current mgmt Summary requirements for the strategies future
21 Social Use experts, See also 18, 19 and 27. External Using external specialists could be mechanic, consultants or advisers used on a included in workshops. Consultants financial adviser, regular basis. have had a role in connecting farmers agronomist to various specialists. When things improve the free market should sort this out. MAC needs to get an extension officer. Funding bodies encourage joint ventures to improve efficiencies.
23 Social Time away from Strong commitment to having Important to flag the point in publicity the farm recreational time away from farm that successful farmers ID this as and business on a regular basis. important. Various ways of getting time out, e.g. church, personal time.
24 Social Physical Health Also see 23. Commitment to Local communities often support short maintaining sound physical health. term health issues; however there are often inadequate facilities to do aerobic exercise in the area. Men's Health nights have been useful.
25 Social Mental Health Also see 23. Strong family support Early warning signs promotion. and/or community involvement. Michael Wallis made a good impact. Shared decision making. Resources in Lincoln have increased.
26 Social Succession Generally, this issue has received A very individual and confidential Planning little attention as most people issue. Often requires a very skilled weren't at that stage of their catalyst/facilitator to get people in business. touch with appropriate support.
31 Social Time to think Careful organisation and good Some people are naturally good at and plan records frees up time for planning. this, others aren't. Access advisor at critical planning times. Make defined times for planning for all members of family.
32 Social Current Strong family support. Positive Enjoyment will return with better satisfaction with outlook. yields. farming
33 Social Attitude to Aware of latest technologies. Continue as is. cropping Commitment to doing things properly.
34 Social Attitude to sheep Robust businesses can be Be flexible and have the self discipline achieved without sheep. If sheep to do everything well, not just the are an enterprise in the business, things you like. then a liking for them is helpful.
35 Social Organisational See also 33. Good organisational Continue as is. ability relative to skills and a strong commitment to peers use them vital.
36 Social Attitude to risk See also 3. Conservative attitudes Continue as is. towards borrowing money very important.
59 Soil Plant Available Maximising access by crops and Need to be able to easily and Characteristics Water pastures to water important e.g. accurately understand plant available summer weed control, early water characteristics in paddock seeding. zones and measure or estimate PAW at critical times. Need to match management and varieties with zone characteristics where practical. Control summer weeds if required.
17 # Category Factor Summary current mgmt Summary requirements for the strategies future
64 Climate Summer rainfall Controlling summer weeds vital. Valuable pastures that are responsive to summer rainfall for use on soils that cannot hold plant available moisture from early summer rains until April.
78 Systems Responsive Generally, maintaining a flexible Reliable market outlook, pricing tools Choices Management outlook and reviewing frequently for and seasonal weather forecasts to both cropping and sheep important. make decisions that lead to profit. Reliable measurement of PAW at the start and throughout the season to aid management decisions on potential pasture and crop growth and likely responses to management treatments. Buffer of seed and fodder on hand. Ability to know when to change plans, i.e. triggers. Need the science behind where and when we can cut inputs.
105 Cropping WUE An objective of improving WUE Ability to use summer/late spring vital. Also see 33. moisture for productive pastures/crops. Weed control techniques to enable early sowing of a greater portion of the program.
113 Cropping Stubble Stubbles retained whenever Snail management without the need Management possible. for stubble bashing or burning to maintain soil cover.
4 Profitability Farm equity - 5 Refer to Appendix 2, #’s 3 &4. Refer to Appendix 2, #’s 3 &4. yrs ago
38 Environment Erosion - do Erosion minimal. Many soil More work to do on the Grain & Graze they experience protection strategies used, e.g. project so that when people increase significant wind conservative stock numbers, no till, stock numbers they have adequate or water stubble retention, feed lotting. feed planning in case of poor erosion? seasons: Understand the place of perennials in the farming system and matching them to soil types (to capitalise on rainfall at any time of the year). Autumn workshops on food storage for various breaks. Publicising lucerne situations, where it does and doesn't grow. Need info to speculate on what potential there is to maximise out of season rain - economics. Maximising feed on offer through electric fence and water points.
167 Livestock Pasture Mix of regenerating pastures and High dry matter pastures or management sown feed. perennials that can be sown or regenerate in summer or early autumn to better address feed gap.
18 Q3: What are the important vulnerabilities and associated management? Of the 212 farming systems components assessed using the framework, 3 were identified as currently the most vulnerable and important features of the case study farming systems (Table 4). The remainder of the components and their rankings are listed in Appendix 3. Table 4: The most important vulnerabilities, current management strategies and future requirements - refer to Appendix 3 for all farming systems characteristics assessed.
# Category Factor Summary current mgmt Summary requirements for the future strategies 70 External Risk Market Volatility Limited use of forward pricing A good personal understanding of & Access schemes. markets, products and what is a good price for my business. Access to good independent, unbiased advice to assist with complex decision making. 65 Climate Heat stress– Early seeding and spreading Improved variety diversity in maturity during flowering maturity times (seeding times and tolerance to drought, heat and frost and/or varieties) common stresses. strategies. 72 External Risk Longer term Reducing up front costs and Require more accuracy in short term climate change increasing flexibility are vital. forecasts and longer term scenarios to scenarios give confidence in planning. Develop information on the potential effects of increased temperatures and potentially more erratic rainfall patterns on crop, pasture, pest, weed and disease dynamics, so that proactive changes can be made. Improve understanding of enterprise risk to develop lower risk, responsive farming systems - including range of crop types, enterprise mixes, input types and levels. Maintain business strength.
SARDI Climate Applications Unit Yield Simulation Analysis of Low Rainfall Areas The Climate Applications Unit analysed the climate at selected upper Eyre Peninsula sites in order to understand current climate risk as a background for climate change scenarios. The analysis showed a tight relationship between average growing season rainfall and average simulated wheat yield (Figure 7), using a constant Minnipa soil type and high nitrogen. This suggests that for the range of study locations, average simulated yields are very closely related to average growing season rainfall (R2 = 0.986).
Simulated yield vs GSR Figure 7: Average April to October rainfall (mm) and 6000 simulated wheat yields using APSIM with non-limiting 5000 Other amounts of soil nitrogen, for case Ceduna 4000 study locations on Eyre Cowell Peninsula. Other locations 3000 Cummins include Clare (GSR 487 mm), 2000 Kimba Spalding (GSR 323 mm), Yield (kg/ha) Lock Jamestown (GSR 342 mm), 1000 Minnipa Peterborough (GSR 219 mm), 0 PtNeill Orroroo (GSR 225 mm), 150 250 350 450 550 Carrieton (GSR 197 mm) and Apr - Oct rainfall Hawker (GSR 202 mm).
19 No doubt there is an impact from other climatic parameters such as growing season temperature, evaporation rates and timing of rainfall, but their impact on average yield seems to be minor compared to the total growing season rainfall. This relationship was also tested with a range of sites across the upper and mid North regions of South Australia and, provided the simulation had high nitrogen and constant soil, there was a similar tight relationship between average growing season rainfall and simulated yield (R2 = 0.9 for all locations in (Figure 7). The slope of this line leads to a water use efficiency (WUE) of 15-20 kg/ha/mm of rainfall. This result would not be surprising if APSIM was programmed with a simple WUE relationship. However, APSIM is a daily time step model and the WUE value is calculated after yield is simulated from a complex interaction of factors. The WUE in any one year is highly variable at a particular location, for example at Minnipa the annual WUE fluctuated between 1-20 kg/ha/mm (standard deviation of 3.5 kg/ha/mm). When local soils were used instead of the constant Minnipa soil, there was very little difference in yield trends (7% or less). When selecting these sites, farmers and agronomists expected that sites on the eastern Eyre Peninsula such as Cowell would have a higher yield for a given growing season rainfall than western sites such as Ceduna. This was thought to be due to harsher spring conditions at Ceduna. Figure 7 suggests there is little difference in simulated WUE. Furthermore, even when local soils are used, this relationship does not change very much.
The sensitivity of temperature, CO2 and rainfall changes on simulated wheat production for Minnipa is shown in Figure 8. As temperature is increased up to 4oC, the lower extent of yield, including the median, stays relatively constant but we see a decrease in the extent of higher yields. The simulated wheat yields are sensitive to CO2 levels. APSIM is also sensitive to rainfall as shown by heavily reduced median and spread in yields for a 40% reduction in rainfall.
APSIM sensitivity for Minnipa
8 7 6 5 4 Me d i a n 3
Yield (t/ha) 2 1 0 No change T1 T2 T3 T4 C570 C760 R10 R20 R30 R40 Climate change scenarios
Figure 8: APSIM simulated wheat yields for Minnipa showing no climate change, an increase in o temperature of 1, 2, 3 and 4 C (T1, T2, T3, T4), an increase in CO2 from 380ppm to 570ppm and 760ppm (C570, C760), and reduction in rainfall by 10%, 20%, 30% and 40% (R10, R20, R30 and R40). Non- limiting amounts of nitrogen were added at sowing. Small squares show the median, large boxes show the range from the 20th to the 80th percentile and minimum and maximum amounts are indicated by the whiskers. When a combination of climate change scenarios are run for Minnipa (Figure 9), with nitrogen supply assumed to be 7 kg/ha at sowing, with a soil reserve of 69 kg/ha NO3 and 1.5 kg/ha NH4, the results show much lower yields compared to Figure 8 in wetter years due to N limitations (note change in scale of axis between Figure 8 and 9). The results also show the high sensitivity to reduction in rainfall.
20 APSIM climate change simulations for Minnipa
2
) 1.5
1 Median
Yield (t/ha Yield 0.5
0 No c hange Temp1 Rain5% Temp1.5 Temp1.5 Temp1.5 Rain10% Rain20% Rain40% Climate change scenarios
Figure 9: APSIM simulations for Minnipa showing no climate change along with combinations of temperature increase (oC) and rainfall decrease (percent reduction in rainfall). Small squares show the median, large boxes show the range from the 20th to the 80th percentile and minimum and maximum amounts are indicated by the whiskers. CO2 concentration under “no change” was taken at 380ppm, compared to 480ppm in all other scenarios. This simulation analysis suggests a strongly precipitation-dependant system. Some of this may be an artefact of APSIM not capturing the full impact of heat stress, however APSIM does capture the effects of higher temperature on phenology and some of the impact on water use. The high sensitivity to precipitation makes it difficult to predict the future as precipitation is the most uncertain aspect of climate change projections. However a highly precipitation-dependant system gives us more confidence in using spatial and temporal analogues to understand scenarios over coming decades and discuss adaptation options. The results of the APSIM analysis was prepared as a refereed poster paper for the Australian Agronomy Conference held in Adelaide in September 2008. Alexander B, Hayman P, Doudle, S and Wilhelm N 2008. Talking about the weather: APSIM, climate change and grain farmers on the Upper Eyre Peninsula, SA. In addition to working with the project team and the consultants, this project provided resources for Peter Hayman to present these findings at various grower forums in South Australia, including GRDC farmer updates at Wudinna and Cummins on the Eyre Peninsula, August 6 and 7, Land and Water Australia and Birchip Cropping Group workshops at Keith and Balaklava, August 21 and 22 and the Minnipa Agricultural Centre Field Day, September 17 2008.
21 5. Discussion This project has been unique in that a group of very experienced researchers, farmers and consultants worked together to develop a process. The outcomes from that process will aid their own professional development and positively influence how they communicate the issue of climate change and manage potential impacts with clients and the agricultural industry into the future. The process used to conduct this project will potentially have just as much impact as the promotion of the project results. From the framework analysis and follow up discussions the most common and important characteristics, strengths and vulnerabilities of the eleven case study businesses were identified. Q1: What are the common and important characteristics of robust businesses examined? • There is no ‘best bet’ farming system for low rainfall farming systems, but rather a range of appropriate farming systems, depending on labour, soil type, rainfall, skills, etc. Within this range it is not specifically what you do, but how well you do it that makes the biggest difference. • Their managers are progressive in their approach to their business and their independent thought processes help them know when to take measured risks and when to be conservative. • They haven’t made major land or machinery purchases until in a strong financial position and then with good planning and communication. They only borrow money for productive assets, not consumer items, e.g. boats, cars, shacks. • They know the limitations of themselves and their business and access outside expertise where necessary. • They are organised and have the capacity to think, plan and communicate clearly under combinations of business, family and community social pressures. These plans are targeted to achieve early seeding of appropriate paddocks. • They may not have always made the best decisions, but they have generally avoided making ‘clanger decisions’ on the big issues such as land and major machinery purchase and succession planning. • Cash surpluses are used wisely and may include off farm investment, superannuation and Farm Management Deposits (FMD’s), which then form an important component of succession planning and risk management for the business. • They have time away from the farm, which may include holidays or pursuing other off farm interests. • They tailor expenditure according to seasonal circumstances. • Strong personal relationships in the business are extremely important and influence the success of processes such as decision making, planning, well being and happiness.
Q2: What are the most common and important key strengths and associated management strategies of these businesses? • Current Farm Equity – Have not always had high equity but have actively pursued strategies to improve equity and business strength before major expansion and technology adoption. Strong emphasis on having cash as an important component in any new major purchase e.g. land or machinery. • Experience - Willingness to learn. Strong formal and informal networks for gathering information. • Attitude - Aware of latest technologies. Commitment to doing things properly before looking for the next step. • Ability to withstand peer and industry pressure - Major business decisions are made after objective review and consultation with others, not to be up with the neighbours or to be fashionable.
22 Q3: What are the most common and important vulnerabilities and associated management strategies of these businesses? • Market vulnerability and access – Constructive marketing strategies based on limited use of forward pricing schemes. • Hostile spring conditions - Early seeding, varietal diversity and limited use of break crops are common strategies. • Longer term climate change scenarios - Managing debt, reducing up front costs and increasing flexibility are seen as vital to managing a warming and drying trend. Flexibility in part referring to managing livestock in the system and being able to scale cropping area up and down depending on prices and start to the season. From the framework analysis the most common and important requirements for the future were discussed, including how to best maintain current strengths and reduce current vulnerabilities in the face of climate change. Q4: How can the current strengths be maintained or further strengthened and how can the vulnerabilities be minimised in the future? Business Management Short to Medium Term • Improved ability to analyse and identify potential enterprise risk. • Flexibility to change system in response to market and season to develop lower risk, responsive farming systems - including range of crop types, enterprise mixes, input types and levels. • A data base of actual, relevant benchmarking figures, all measured in a consistent manner, updated annually, and publicly available. Benchmarks could be arranged in deciles or risk categories with "indicators". Each business will need to have these interpreted individually. • A sound personal understanding of markets, products and what price does their business need to remain profitable, without undue risk. Being able to calculate cost of production for key commodities is a key component of this. Farming Systems Short to Medium Term • Easily and accurately estimate plant available water characteristics in paddock zones at critical times to aid management decisions on potential pasture and crop growth and likely responses to management treatments. • Match management and varieties with zone characteristics where practical. • Accurate, simple, cheap, effective in-paddock tests to determine plant availability of nutrients, prior to cropping and in crop for N. • Need to combine experience with more science re: where and when inputs can be cut. • Improved variety diversity in maturity and tolerance to moisture, heat and frost stresses and lower nutritional situations. • Self regenerating pastures that are poor disease hosts but are responsive to rain at various times of the year, may be diverse pasture with summer and winter activity on poorer paddocks and annually sown pastures on better soils, where cheaply sown pastures can be a crop in a good year. • Ensure decisions to increase stocking capacity include improved background planning to manage livestock in poor seasons to reduce erosion risk, including food storage for various breaks. • Maximise feed on offer and minimise livestock environmental impact through improved grazing management, eg. electric fencing. • Buffer of seed, fodder and cash on hand.
23
Figure 10: " Maximise feed on offer and minimise livestock environmental impact through improved grazing management, e.g. electric fencing”. EP Grain & Graze Research Officers Emma McInerney and Alison Frischke at a strip grazing demonstration of canola and barley on Minnipa Agricultural Centre, 2008.
Longer Term • Need to increase biomass without compromising yield (harvest index). Need higher dry matter pastures and less grazing pressure on stubbles to ensure more dry matter is returned to system each year, to increase soil health, fertility and moisture holding capacity. Investigate the effect of grazing on organic matter levels in low rainfall environments. Investigate soil carbon sequestration and carbon trading potential. • Develop information on the potential effects of increased temperatures and potentially more erratic rainfall patterns on crop, pasture, pest, weed and disease dynamics, so that proactive changes can be made. Confidence in the Future Short to Medium Term • Access to good independent advice to assist with complex decision making. • Increased ability to know when to change plans, i.e. triggers. • Maintaining networks and relevant information flow to provide balance to sensational climate change headlines. Longer Term • Develop farming systems that demonstrate projected viability into the future (the more extreme scenarios).
24 6. Conclusions One of the most important outcomes from this project is the verification of local knowledge that there are businesses in low rainfall areas which have gone through the last five years of very challenging seasons and are still in a strong financial, environmental and social position (Appendix 3). Considering the most likely climate change scenarios are not as bad as the conditions over the last 5 years, this diverse and experienced project team consider there is a future for agriculture on upper Eyre Peninsula, for the mild to moderate climate change scenarios which are most likely over the coming decades. The range of businesses assessed in this study were diverse in terms of location, soil type, rainfall, agronomic practice, property size and many other factors. From this study, there is no one recipe to achieve or maintain strength in terms of agronomy and animal husbandry but there were some common features of business management and personal characteristics for the successful farms: o They aim to improve their business but in a measured and conservative way. An important business goal is to achieve high equity and to recover that high equity after major expansions or investments. o Cash is always a component of every major purchase for the business and the purchase is not made until the business is in a sound financial position. Purchases are made based on objective considerations, not peer group pressure or fashion trends. o Cash surpluses are used to build off farm investment (including farm management deposits, superannuation schemes). o They are often not the earliest adopters of new technology. When they do adopt they do it well and consolidate before moving on to the next thing. o They are keen to learn (often not formally educated), are organised and allocate time to planning and reviewing. o They allocate time away from the business. o They recognise they are not experts in every aspect of their business and consult with others for these skills. These characteristics are not rocket science and should be achievable for many businesses. A study was conducted in 1996, following a series of poor seasons, by Lynch Farm Monitoring to identify successful farmer and farm business characteristics in Eyre Peninsula’s less than 350 mm rainfall zone. The characteristics of successful businesses found in this current study are similar to those identified by the Lynch study in 1996, even though businesses, communities and the economic environment have changed substantially over the past 12 years. Both studies have found that the key ingredients to successful businesses are careful planning, flexibility according to the way the season starts and continues and that timeliness of seeding is vital. This project team believes that the research, development and extension (RDE) requirements for robust and sustainable businesses in the future under potential climate change impacts should build on what we know is required for low rainfall businesses to better manage short term variability: o An improved ability to identify and analyse potential enterprise costs, benefits and risks. o The flexibility to change the system in response to market and season to develop lower risk, responsive farming systems - including range of crop types, enterprise mixes, input types and levels, which in turn requires: The ability to easily and accurately estimate plant available water characteristics and nutrition in paddock zones at critical times to aid management decisions on potential pasture and crop growth and likely responses to management treatments. Improved variety diversity in maturity and tolerance to moisture, heat and frost stresses and lower nutritional situations.
25 Self regenerating pastures that are poor disease hosts but are responsive to rain at various times of the year, may be diverse pasture with summer and winter activity on poorer paddocks and annually sown pastures on better soils, where cheaply sown pastures can be a crop in a good year. o The need to increase biomass without compromising yield (harvest index). o The need to maintain networks and relevant information flow to provide short term support, community confidence and balance to sensational climate change headlines. Given the similarity between short and longer term RDE requirements, increased investment in low rainfall agricultural RDE priorities now is also a solid investment for the future under climate change impacts. The Eyre Peninsula Farming Systems approach of working as teams of researchers, farmers and consultants to address farming systems issues was particularly beneficial for this project. An important outcome from this project is that four of the major private consultants on Eyre Peninsula now have a good understanding of climate change mechanisms and potential impacts in this environment, not only on production and profitability, but also on social and environmental aspects of the farming system. This knowledge will impact positively not only on their clients but also on the community in general and their professional networks. Feedback from project team Project team members were asked to provide feedback on their involvement with the project as part of the evaluation. The following are the outcomes as personally experienced by the team members: Andrew Bates, Consultant, Streaky Bay Interaction with the SARDI Climate Applications Unit has: • Increased my knowledge of climate change science. • Improved my ability to interpret general climate and climate change information. • Provided a basis for me to better Figure 11: "The Eyre Peninsula Farming Systems approach explain the potential impact of of working as teams of researchers, farmers and consultants climate change on farming systems, to address farming systems issues was particularly beneficial and the ability to discuss with clients for this project". Representatives from the EP Farming and ag industry representatives. Systems 3 team, Jason Eglinton (University of Adelaide), Alison Frischke (SARDI), Bruce Heddle (farmer & EPARF), • Shown that meteorological and Ed Hunt (farmer & consultant), Naomi Scholz (SARDI), rainfall data can be represented in a Chris Lymn (farmer & SARDI). manner that assists farmers to understand short term environmental conditions and cycles vs the longer term climate change scenarios – but expert interpretation of the data is required for reliable conclusions to be drawn. • Allowed me to actively participate in discussions regarding the impact of climate change on farming systems – and challenge current thinking on farming systems. Prior to working with the SARDI Climate Applications Unit I was uncertain about interpreting the data represented in various publications, and confused about the value of the data as a predictive tool. The process has been valuable. It is rare to get several consultants in the same room and obtain honest opinion and discussion from most present. Often a consultant will not fully contribute to preserve some of their perceived “IP”.
26 The model provided an excellent way to organise and rank the data collected from several sources, and draw conclusions and identify commonality. I think publicity should consist of a positive article in local papers as a start – despite the challenging years, here are a few things that we identified as common to a number of “healthy businesses”. Good stuff like despite the poor seasons, there are businesses on EP that have maintained strong business health. Independent work has identified that there are some common themes that have assisted the businesses to remain strong and include – using cash as part payment for all land/machinery purchases made during the past 10 years, accessing independent advice on farm and business management and planning, being efficient (doing the job properly), having a period of consolidation after they take a risk, respond quickly to opportunity (after assessment), use cash surplus wisely in the years they are generated, having a spell from farming. Mention in the article that Peter identified the previous 5 seasonal conditions as considerably worse that the worst climate change scenario, but there are businesses are still in good nick. Other farming systems groups should get a summary, and GRDC may like an article in Ground Cover – they thought it was a good survey. Ed Hunt – farmer, consultant, Wharminda Before the project I had strong ideas that the climate change "people" knew a bit about temperature increase and little about rainfall. The interaction with the SARDI Climate Applications Unit confirmed this. What was the greatest benefit was the interaction between all the consultants and the SARDI Climate Applications Unit. What was encouraging was the consistency of farmer type that was chosen by all of us. The process of using the framework was good because it did make you look at all aspects of the farm business. The interaction with the SARDI Climate Applications Unit allowed a gut feel on climate change be confirmed. My original ideas were fairly loose - "none of the bastards have any idea". I now know they have some idea but their knowledge is unlikely to be good enough to make decisions on. It is therefore very important to develop farm businesses and farming systems that can accommodate that unknown. Andrew Polkinghorne – farmer, consultant, Lock I have learnt that climate change is real, especially the effects on temperature. I think the SARDI Climate Applications Unit’s chance wheel demonstrates very well how the odds alter the probability of outcome for a range of years. The information showing that we have weathered rainfalls 20% below normal and the expected drop under climate change is 5% gives reason to expect that we will be able to adapt to climate change readily. The way CO2 levels have risen over the last 20 years demonstrates the impact of the human race on the atmosphere and reinforces the need for emission controls to be implemented - all relatively new information. The survey done by the consultants confirms what I already suspected about resilient farm businesses - that they need to be well capitalised (high equity) and conservative, but progressive when appropriate. Bruce Heddle – farmer, Eyre Peninsula Agricultural Research Foundation representative, Minnipa Things I think about climate change now: • The SARDI Climate Applications Unit message about the sheer severity of the last few years and how none of the models, even at their most extreme end, predict a long term future as severe as the last few seasons gives me more optimism now. • We (probably society in general) are a long way from grasping the difference between climate variability and climate change. There are enormous risks in confusing the two. The politicisation of the whole issue makes the problem even more difficult to address. • The features of farm business that confer the ability to prosper in the face of adversity seem to be more about thought processes and personal behaviours rather than superior circumstances or luck. I now doubt whether the things that favour success for farm businesses are any different to any other business or pursuit, but I do think they can be taught and learned, just as behaviour that weakens or compromises can be taught and learned.
27 • At the most extreme end of the range of outcomes, our problems and challenges would be insignificant compared to the rest of humanity's. • I am optimistic that technological innovation and science can largely mitigate against the problems posed by modest warming and drying for production agriculture. However the upheaval caused to the broader ecosystem and natural systems will be entirely beyond the control or influence of humans, and may well be of far greater significance. • Carbon sequestration and accumulation is critical to the health of agricultural soils but is no magic panacea to negate the downside effects and emissions from agriculture. We still need to find farming systems that reduce the gross emissions output from our activities. • I worry about this challenge because I see ruminants having an important role in production agriculture in environments typified by highly variable climate, but I do now believe the story about the magnitude of their methane output. In a world challenged for food supply and security, I doubt the logic of converting grain to meat, so pastures will be of importance for all sorts of reasons. • The increase in commodity prices which we saw in 2007/08 caused by uncertainty over food supplies may well be just the beginning of much greater upheavals about to hit the world. Much higher prices for food totally change the risk and economics of production even at the margins of our cropping regions, and may mitigate against any decline in production. • The upside of acting to limit the effects of humanity on the world’s climate system and environment massively outweighs the downside of doing nothing. Even if the scientists and climatologists are completely wrong about humanity's contribution to the measured changes we see, reducing our dependence on emissions intensive activities will ultimately be a good thing. I think that the technological and scientific innovation that can be bought about by a disciplined effort to reduce emissions will have economic benefits that will outweigh the costs incurred. • Powerful forces exist to maintain and sustain the status quo - partly the same forces that struggled with the reality that the earth is neither flat nor the centre of the universe, and partly forces driven by greed, self interest or apathy. I am optimistic about the alibility of agriculture to adapt but pessimistic about the capacity of humanity to modify their behaviours quickly enough to avert major detrimental change. Brenton Lynch – consultant, Central Eyre Peninsula Benefits of being involved in the project: • Presentation of data /detail by SARDI Climate Applications Unit to actually quantify the historical climate change picture, past, present, future. Prior to this I found it difficult to source data which gave a factual, reliable presentation of the situation. • Quantifying the changes to average annual rainfall for locations on EP for the past 5 + 10 years and comparing this to projected scenarios of rainfall reduction for the future. This was critical in giving me a reliable /believable projection of what we may have to deal with in farm management in the future, as we could relate it to immediate past experiences. This should be relayed to farmers I feel to give them a perspective (a manageable one) rather than "frightening projections". • "Back testing" management over the past few years to project changes required in future. • Take on board for the future an extra element of production and economic risk due to climate change. Dr Peter Hayman – SARDI Climate Applications, Waite Agricultural Institute, Adelaide If communication is the process of two way flow of information, it was a great experience to be able to present climate science data and have it questioned in three ways: First in terms of the underlying assumptions, second in terms of what it meant for consultants working on farming systems in upper EP and third on how it could be made useful for farmers. It was also really good to consider issues at a farm enterprise level rather than only a paddock agronomy level. The group of consultants were obviously very knowledgeable on both the paddock level and the farm level. In state departments and academia, people tend to specialise in one or the other.
28 Given all the discussion about vulnerability to climate change and poor seasons it was good to hear about successful businesses and to discuss the characteristics of these businesses. The obvious knowledge and thoughtfulness of the project team is in itself a source of confidence in the high level of adaptive capacity in the region. The meetings were well run with an easy environment to challenge ideas and learn from different perspectives. The project provided resources to spend time in preparation and this led to some novel approaches to communicating the risk of climate change which have been useful in a range of other contexts. Bronya Alexander – SARDI Climate Applications, Waite Agricultural Institute, Adelaide Coming from a climate science background, this project has increased my awareness of the importance of climate science in farming systems. Of most value to me was the chance to interact with a motivated group of farmers and consultants from the Eyre Peninsula, and to respond to some of their needs and suggestions about the type of climate information they deemed useful. I have found that the coloured diagrams showing the succession of historical rainfall deciles for each decade that Peter Hayman designed and I helped produce have been really useful in communicating climate risks to other audiences. These include farmers and consultants at a Saddleworth farmer expo and farmers and NRM workers at a climate change forum in Renmark. This project increased my experience in using the crop simulation model APSIM through interacting with Anthony Whitbread from CSIRO, and having the results examined by farmers and consultants. I was also able to present some of the research through a poster at the Australian Society of Agronomy Conference in Adelaide in September. Nigel Wilhelm – Senior Research Officer, SARDI, Waite Agricultural Institute, Adelaide As a career researcher with no first hand experience of managing a commercial farm, involvement in this study gave me very valuable insights into the structures and mechanisms which current farm businesses use to survive the current economic and climatic pressures. This insight will help me focus research priorities into those which have the most impact of business viability and to present research outcomes in more commercially relevant terms. Interaction with the climate change scientists helped clarify the most likely impacts of climate change on our future seasons and, with the commercial consultants present, scope out likely impacts on the viability of low rainfall farming into the future. These discussions substantially improved my confidence for the future of low rainfall agriculture. Samantha Doudle – Leader, Minnipa Agricultural Centre, SARDI As the Manager of a government owned low rainfall research centre, this project has given me an invaluable opportunity to be part of a very diverse, experienced and energetic team of experts who have focused their considerable talent on the issue of climate change in the low rainfall agricultural environment of Eyre Peninsula. The process of coordinating this project has substantially increased my understanding of production, financial, environmental and social aspects of farming systems and how they all interact. The willingness of this project team to openly interact and challenge each other has been a highlight for me and is a strength of the project outcomes. As a professional about to leave this industry to pursue opportunities in the environmental field, I have confidence for the future of low rainfall agriculture and I believe these low rainfall businesses and communities have much to offer in an agricultural landscape that will be influenced by climate change in the future.
29 7. Communication of key findings Summarised outcomes from this project will be publicised through the rural and popular media, such as the Stock Journal, local Eyre Peninsula papers and GRDC’s Ground Cover magazine. A summary article will also feature in the Eyre Peninsula Farming Systems 2008 Summary, which is distributed free to all farmers on Eyre Peninsula. A major vehicle for the communication of the outcomes from this study will be the Eyre Peninsula Farming Systems and Grain & Graze project discussion group forums, conducted in March annually. There are fourteen of these discussion group forums held across the Peninsula and the results from this project will be presented and discussed with representatives from the SARDI Climate Applications Unit and the consultants who conducted the framework analysis. Furthermore, the Low Rainfall Collaboration project will ensure that four other low rainfall farming systems groups in south-eastern Australia will be informed of the outcomes.
8. Acknowledgements Department of Climate Change - Anthony Swirpek, Joanna Pinkas, Tim Thelander Eyre Peninsula Farming Systems Project - GRDC Eyre Peninsula Grain & Graze Project – GRDC, MLA, AWI, L&W Australia Low Rainfall Farming Systems Collaboration Project - GRDC
9. References CSIRO and Bureau of Meteorology (2007 ) Climate Change in Australia www.climatechangeinaustralia Hennessy, K R. Fawcett, D. Kirono, F. Mpelasoka, D. Jones, J. Bathols, P. Whetton, M. Stafford Smith, M. Howden, C. Mitchell and N. Plummer (2007). An assessment of the impact of climate change on the nature and frequesncy of exceptional climateic events. CSIRO and Bureau of Meteorology. Lynch Farm Monitoring (1996) Identifying successful farmer and farm characteristics in Eyre Peninsula’s less than 350 mm annual rainfall zone. Report for the Eyre Peninsula Regional Strategy. Suppiah R, Preston B, Whetton PH, McInnes KL, Jones RN, Macadam I, Bathols J and Kirono D (2006). Climate change under enhanced greenhouse conditions in South Australia. CSIRO Marine and Atmospheric Research, Aspendale, VIC, Australia. Whitbread A, Hancock J (2008) APSIM Modelling for the Eyre Peninsula Farming Systems Project II. CSIRO Sustainable Ecosystems.
30 8. APPENDIX 1 – Climate Analysis for Case Studies Appendix 1a: Climate Analysis – Ceduna
Peter Hayman & Bronya Alexander, SARDI
Ceduna rainfall data has come from the Ceduna AMO composite (met station number 18012).
Table 1a: Monthly rainfall (mm) recorded at Ceduna. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 3rd and 4th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1907 5 0 3 11 32 82 23 50 12 3 36 6 213 53 1908 17 1 11 15 34 53 15 26 32 12 8 2 71 55 187 38 1909 2 8 0 28 40 35 38 91 5 32 38 0 20 6 269 85 1910 0 0 9 0 109 15 51 12 24 38 20 10 47 31 249 76 1911 0 24 3 4 38 47 41 47 24 7 0 18 57 39 208 49 1912 0 34 26 8 6 59 14 19 15 42 26 10 78 60 163 26 1913 1 75 31 4 28 5 12 20 27 37 11 24 143 92 133 12 1914 3 0 25 12 7 17 21 7 12 12 21 8 63 48 88 1 1915 5 24 1 9 35 36 35 45 19 10 0 16 59 43 189 39 1916 2 1 3 12 41 74 75 44 60 22 23 1 22 7 328 98 1917 33 13 29 7 66 46 57 28 43 20 15 9 99 78 267 82 1918 2 0 21 13 24 34 14 33 4 26 6 14 47 31 148 17 1919 15 25 0 16 13 13 24 14 21 15 0 39 60 44 116 8 1920 3 0 5 14 30 42 31 49 38 20 40 12 47 31 224 58 1921 5 58 9 5 80 27 12 25 12 18 27 2 124 88 179 34 1922 14 0 4 40 26 21 46 18 8 7 0 37 47 31 166 28 1923 7 3 0 2 35 54 64 35 23 15 0 23 47 31 228 62 1924 8 32 5 0 20 41 2 34 25 33 21 13 68 52 155 22 1925 1 22 0 34 39 24 42 37 31 9 17 6 57 39 216 54 1926 0 0 12 19 41 62 25 45 11 6 0 8 35 16 209 50 1927 2 10 31 2 18 68 35 20 8 5 13 4 51 33 156 23 1928 8 16 0 1 20 38 43 11 12 26 0 0 41 19 151 18 1929 1 1 8 8 18 29 22 29 20 29 28 1 10 1 155 22 1930 2 1 12 18 37 2 23 43 8 28 18 14 44 24 159 24 1931 3 0 79 34 54 70 47 25 30 12 21 1 114 84 272 86 1932 1 53 2 60 32 53 32 66 44 30 4 16 78 60 317 97 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1933 6 0 13 18 46 15 52 41 26 21 22 2 39 18 219 56 1934 1 10 8 42 3 40 30 71 14 25 47 0 43 22 225 60 1935 6 0 64 25 22 26 21 43 53 55 17 8 117 87 245 74 1936 5 15 0 7 30 34 52 27 4 38 9 23 45 25 192 40 1937 6 9 0 3 35 41 22 46 20 14 67 11 47 31 181 36 1938 3 120 3 33 27 55 66 45 9 11 1 10 204 98 246 75 1939 14 8 43 13 32 63 53 78 8 22 52 3 76 58 269 85 1940 35 15 21 31 35 11 38 6 14 12 10 2 126 89 147 16 1941 18 1 35 33 7 14 50 51 61 24 18 8 66 50 240 67 1942 37 3 25 16 39 94 67 52 34 10 64 45 91 73 312 96 1943 2 46 1 20 14 53 57 38 19 36 7 4 158 94 237 64 1944 0 1 1 32 54 17 53 20 5 6 11 15 13 3 187 38 1945 23 20 0 4 26 34 10 13 20 19 51 91 69 53 126 11 1946 22 51 10 28 32 72 40 19 4 15 19 64 225 100 210 51 1947 9 9 41 21 40 46 34 42 48 50 20 9 142 91 281 90 1948 0 3 0 15 26 22 34 46 8 29 27 26 32 12 180 35 1949 4 14 0 43 33 33 53 20 68 45 45 7 71 55 295 94 1950 8 30 1 5 16 29 32 64 14 81 24 43 91 73 241 69 1951 0 0 13 33 90 65 123 78 11 18 0 22 80 64 418 100 1952 18 3 16 33 88 17 38 35 13 18 42 5 59 43 242 70 1953 12 30 4 7 9 91 35 56 30 46 21 56 93 76 274 87 1954 14 0 1 43 11 51 10 19 13 19 4 26 92 75 166 28 1955 13 38 48 11 55 73 35 38 13 13 22 4 129 90 238 65 1956 0 19 5 54 98 85 71 27 16 97 5 8 50 32 448 101 1957 0 3 2 4 11 54 62 19 16 2 2 39 18 5 168 30 1958 1 0 65 8 68 5 32 61 50 38 9 34 107 81 262 80 1959 1 9 8 6 7 11 40 8 12 9 26 22 61 46 93 2 1960 18 39 3 47 65 21 47 35 64 5 21 9 108 83 284 92 1961 1 11 1 53 9 34 25 27 11 9 32 9 43 22 168 30 1962 1 7 23 2 75 9 22 33 21 67 1 5 72 56 229 63 1963 13 6 2 27 75 62 47 24 7 1 1 3 27 8 243 73 1964 2 5 2 24 48 41 37 20 73 37 39 1 13 3 280 89 1965 1 0 3 5 38 14 28 51 20 13 2 16 44 24 169 31 1966 11 21 42 2 54 40 28 49 56 49 30 125 92 75 278 88 1967 37 27 0 3 18 12 53 26 12 1 3 4 219 99 125 9 1968 41 40 97 24 61 88 41 62 18 18 16 28 185 96 312 96 1969 15 51 89 14 51 42 26 24 39 3 4 3 199 97 199 44 1970 4 0 4 4 26 23 25 38 31 6 18 3 15 4 153 20
32 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1971 3 2 58 84 29 27 39 36 22 4 56 18 84 67 241 69 1972 6 5 0 8 5 24 38 82 9 13 12 1 85 68 179 34 1973 7 11 49 13 21 49 35 87 39 21 21 26 80 64 265 81 1974 4 4 2 21 53 19 50 18 49 58 14 1 57 39 268 83 1975 3 25 18 14 28 13 33 31 44 56 19 11 61 46 219 56 1976 17 56 4 9 19 23 8 10 31 44 24 2 107 81 144 15 1977 11 15 2 8 47 8 14 10 24 5 56 3 54 36 116 8 1978 16 0 5 8 25 46 78 45 74 7 41 4 80 64 283 91 1979 13 2 2 27 77 16 51 47 72 5 43 5 62 47 295 94 1980 6 4 1 44 44 29 23 8 7 40 27 16 59 43 195 41 1981 10 39 15 0 15 73 29 34 1 9 24 1 107 81 161 25 1982 5 7 49 15 21 29 22 7 15 6 0 20 86 69 115 6 1983 5 3 80 99 28 9 60 25 18 21 8 9 108 83 260 79 1984 6 0 6 34 19 13 68 41 49 19 5 15 29 10 243 73 1985 1 0 13 14 14 21 19 74 10 55 7 19 34 14 207 48 1986 0 9 0 13 19 36 85 43 20 38 9 11 35 16 254 78 1987 20 38 2 6 28 35 29 22 10 13 4 22 80 64 143 14 1988 16 11 21 1 32 18 30 7 21 5 30 34 74 57 114 5 1989 7 0 13 17 28 45 108 25 17 3 10 7 84 67 243 73 1990 3 19 3 7 33 48 54 31 28 26 2 82 42 20 227 61 1991 6 0 1 33 17 66 28 33 21 4 27 2 91 73 202 46 1992 1 8 29 29 37 24 20 78 57 107 35 97 67 51 352 99 1993 40 2 3 1 16 13 16 33 30 102 19 30 177 95 211 52 1994 1 16 0 0 17 35 31 14 8 21 10 2 66 50 126 11 1995 88 3 13 22 39 39 51 19 17 12 3 7 116 86 199 44 1996 4 4 11 4 10 40 63 46 49 13 6 4 29 10 225 60 1997 11 4 6 0 51 17 13 29 66 28 35 74 31 11 204 47 1998 6 20 9 41 6 31 85 8 19 8 30 2 144 93 198 42 1999 10 7 9 0 15 14 8 17 40 58 10 5 58 40 152 19 2000 23 27 18 8 16 22 32 68 18 37 8 1 83 65 201 45 2001 13 8 4 11 47 30 21 25 58 31 25 49 34 14 223 57 2002 14 0 1 8 20 44 24 13 22 8 7 11 89 70 139 13 2003 13 18 4 6 10 30 33 55 8 37 10 25 53 35 179 34 2004 4 5 9 22 11 42 42 85 33 4 18 8 53 35 239 66 2005 9 2 0 6 9 67 26 32 87 27 31 16 37 17 254 78 2006 28 7 33 20 8 28 48 2 1 0 17 2 115 85 107 3 2007 2 1 75 38 27 3 15 6 6 16 17 34 97 77 111 4
33 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
300 300
250 250
200 200 1 1 5 5 150 150 9 9 Rainfall (mm) 1998 (mm) Rainfall 1999 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
300 300
250 250
200 200 1 1 5 5 150 150 9 9
Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
300 300
250 250
200 200 1 1 5 5 150 150 9 9
Rainfall(mm) 2002 Rainfall(mm) 2003 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
300 300
250 250
200 200 1 1 5 5 150 150 9 9 Rainfall(mm) 2004 Rainfall(mm) 2005 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
300 300
250 250
200 200 1 1 5 5 150 150 9 9
Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1a: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
34 GSR
500 450 400 350 300 GSR 250 5yr running mean 200
Rainfall (mm) (mm) Rainfall 150 100 50 0 1900 1920 1940 1960 1980 2000 2020
Figure 2a: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
50%
40%
30%
20%
10%
0%
-10% Departure from mean mean from Departure -20%
-30% 1911 1915 1919 1923 1927 1931 1935 1939 1943 1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007
Figure 3a: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 16.5% below the average of all consecutive 5-year periods from 1907 to 2007. This period was ranked the 7th driest 5-year period out of 97 5-year periods since 1907. • GSR over the 5 years prior to 2002 (1998-2002) was 14% below the average of all consecutive 5-year periods from 1907 to 2007. This period was ranked the 12th driest 5-year period out of 97 5-year periods since 1907. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 16% below the average of all consecutive 10-year periods from 1907 to 2007. This period was ranked the 4th driest 10-year period out of 92 10-year periods since 1907.
35 GSR deciles
500 450 400 D1 350 D2 300 D3 250 D4 200 D5
Rainfall (mm) (mm) Rainfall D6 150 D7 100 D8 50 D9 0 D10 1907 1911 1915 1919 1923 1927 1931 1935 1939 1943 1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007
Figure 4a: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 Figure 5a: For each year from 1916 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been two decile 1 years, two decile 2, one decile 4, two decile 5, one decile 6, 7, and one decile 8 year.
36 Appendix 1b: Climate Analysis – Darke Peak
Peter Hayman & Bronya Alexander, SARDI
Darke Peak rainfall data has come from observations at the Darke Peak met station (number 18024).
Table1b: Monthly rainfall (mm) recorded at Darke Peak. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 2nd and 5th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1915 13 0 10 50 40 52 55 133 39 9 4 22 378 81 1916 7 2 11 5 30 119 83 53 71 36 34 3 46 11 397 86 1917 25 9 18 2 64 67 44 89 92 14 38 16 89 47 372 79 1918 2 4 8 20 32 47 19 92 4 45 4 3 68 27 259 38 1919 8 58 0 14 22 14 15 47 38 24 12 14 73 33 174 7 1920 27 0 6 4 50 110 43 65 69 80 53 9 59 21 421 90 1921 43 124 59 38 82 73 25 26 40 27 31 17 288 91 311 63 1922 35 2 1 18 34 34 45 40 19 13 0 53 86 44 203 16 1923 2 0 0 2 76 81 81 44 61 35 1 78 55 14 380 83 1924 18 15 9 8 39 61 10 40 32 79 36 7 121 72 269 43 1925 15 19 0 24 27 23 46 19 70 26 21 1 77 35 235 25 1926 0 7 41 32 59 66 36 55 45 25 1 34 70 29 318 64 1927 2 25 16 8 33 45 41 64 15 4 22 18 78 38 210 20 1928 24 39 2 5 29 52 46 11 19 43 8 8 105 59 205 17 1929 3 7 4 7 21 39 34 48 50 11 36 68 30 3 210 20 1930 1 8 1 13 7 25 78 78 25 80 12 7 114 66 306 61 1931 14 0 25 32 55 75 69 65 37 13 12 5 58 17 346 71 1932 1 66 8 78 51 79 52 88 52 35 5 4 92 49 435 92 1933 13 0 13 11 78 21 52 50 73 12 23 16 35 5 297 57 1934 4 27 18 11 13 38 28 56 72 24 44 0 88 46 242 29 1935 19 0 49 53 43 51 48 48 50 55 30 22 112 64 348 72 1936 51 13 0 9 15 37 52 32 11 33 12 31 116 67 189 12 1937 25 58 16 15 39 51 40 52 60 24 24 44 142 81 281 48 1938 15 75 2 40 13 63 36 53 18 17 3 17 160 87 240 26 1939 33 20 12 9 27 50 49 111 14 20 82 3 85 42 280 47 1940 25 5 13 22 30 11 58 21 22 16 23 4 128 77 180 10 1941 40 2 50 4 8 24 51 44 85 56 24 3 119 71 272 44
37 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1942 18 4 10 38 53 103 59 59 77 25 31 29 59 21 414 89 1943 12 35 1 30 9 28 64 58 27 40 7 2 108 62 256 35 1944 0 18 10 79 44 16 33 13 22 28 27 16 37 6 235 25 1945 27 31 0 1 25 55 20 60 38 43 53 74 101 55 242 29 1946 16 142 17 15 28 47 50 34 12 17 46 60 302 92 203 16 1947 1 18 28 19 14 46 66 45 62 56 22 18 153 86 308 62 1948 1 2 0 28 42 30 31 57 2 53 29 9 43 9 243 30 1949 8 37 0 5 42 24 52 25 47 94 92 8 83 41 289 52 1950 1 27 3 51 54 31 33 65 43 57 17 66 131 78 334 69 1951 0 3 11 29 58 85 92 90 34 36 5 32 97 51 424 91 1952 17 6 6 56 115 33 41 37 52 40 47 14 66 24 374 80 1953 22 11 6 10 17 68 40 41 63 45 27 49 100 53 284 50 1954 19 3 4 47 8 78 39 15 24 42 10 32 102 56 253 34 1955 17 47 7 59 45 115 19 61 45 35 38 9 113 65 379 82 1956 5 68 13 38 102 119 119 50 65 54 19 2 133 80 547 93 1957 0 0 8 1 15 53 40 39 17 14 12 19 29 1 179 8 1958 0 1 36 17 82 5 57 58 95 39 12 55 68 27 353 74 1959 18 29 32 5 8 12 23 17 20 17 25 14 146 83 102 1 1960 19 46 8 58 80 27 51 48 95 4 19 23 112 64 363 76 1961 0 10 7 74 22 19 40 76 28 3 34 3 59 21 262 40 1962 1 19 10 2 76 46 22 29 15 54 7 52 67 25 244 32 1963 21 3 8 92 82 69 72 53 14 10 3 3 91 48 392 85 1964 9 15 0 44 22 44 75 33 71 61 47 5 30 3 350 73 1965 0 0 6 5 48 48 39 106 27 4 24 30 58 17 277 46 1966 18 17 29 6 32 53 50 36 64 32 24 76 118 69 273 45 1967 25 23 1 2 13 11 43 39 35 7 0 4 149 85 150 3 1968 24 59 39 37 62 95 72 86 23 37 16 22 126 76 412 88 1969 13 81 16 29 120 31 54 28 43 0 7 18 148 84 305 60 1970 6 0 3 50 32 53 32 72 48 3 16 23 34 4 290 53 1971 0 3 65 51 54 32 27 69 56 9 64 33 107 61 298 58 1972 45 41 0 12 3 20 74 70 24 12 7 1 183 89 215 21 1973 7 31 35 38 43 60 70 60 56 37 8 18 81 40 364 77 1974 62 12 3 35 66 17 72 42 62 90 8 6 103 58 384 84 1975 5 19 3 4 60 12 42 43 58 73 13 12 41 7 292 54 1976 1 50 4 9 23 35 24 34 32 68 16 7 80 39 225 23 1977 22 18 9 5 27 23 17 17 53 30 72 38 72 31 172 6 1978 4 0 5 24 29 23 85 82 82 8 27 7 119 71 333 68 1979 21 35 11 28 87 15 64 56 125 36 55 17 101 55 411 87
38 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1980 0 0 0 34 34 56 56 12 12 55 18 15 72 31 259 38 1981 16 21 56 3 53 130 50 78 11 18 13 6 126 76 343 70 1982 10 3 45 30 36 64 16 21 13 7 0 13 77 35 187 11 1983 3 22 61 96 26 31 71 35 40 21 14 19 99 52 320 66 1984 5 0 17 15 10 14 74 100 64 43 18 14 55 14 320 66 1985 1 0 14 23 9 47 30 84 20 31 18 32 47 12 244 32 1986 4 5 0 15 28 22 74 73 53 36 13 13 59 21 301 59 1987 38 20 2 8 45 39 51 20 13 24 10 13 86 44 200 13 1988 11 4 4 1 46 42 29 17 38 7 34 26 42 8 180 10 1989 2 0 60 13 77 81 75 56 45 11 25 37 122 74 358 75 1990 18 14 39 9 12 51 69 58 38 26 1 67 133 80 263 41 1991 6 0 4 29 15 85 31 52 53 2 40 4 78 38 267 42 1992 8 7 63 52 42 19 17 71 102 68 51 85 122 74 371 78 1993 91 7 12 1 27 33 44 43 45 53 14 43 246 90 246 33 1994 2 7 0 1 14 63 33 9 14 17 13 11 66 24 151 4 1995 32 8 9 11 58 69 72 16 37 32 32 4 73 33 295 55 1996 3 11 8 4 6 52 64 63 78 18 4 13 58 17 285 51 1997 17 134 0 0 47 21 17 51 65 40 20 43 168 88 241 27 1998 4 22 14 61 6 50 62 37 21 25 18 10 103 58 262 40 1999 13 0 52 5 33 30 31 25 38 48 14 11 93 50 210 20 2000 7 63 23 38 22 48 45 81 38 51 18 5 118 69 323 67 2001 9 7 25 7 48 39 31 58 78 23 28 23 64 22 284 50 2002 17 0 2 10 51 34 53 27 30 15 28 10 70 29 220 22 2003 10 40 0 15 48 55 34 57 14 35 19 9 88 46 258 36 2004 5 3 8 16 12 46 51 52 22 3 18 44 44 10 202 14 2005 6 3 7 7 14 96 28 35 71 45 21 23 78 38 296 56 2006 33 11 19 19 18 28 49 5 1 1 26 49 107 61 121 2 2007 25 0 43 41 44 24 27 18 8 9 26 57 143 82 171 5
39 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9 Rainfall (mm) 1998 (mm) Rainfall 1999 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall(mm) 2002 Rainfall(mm) 2003 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9 Rainfall(mm) 2004 Rainfall(mm) 2005 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1b12: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
40 GSR
600
500
400
GSR 300 5yr running mean
200 Rainfall (mm) (mm) Rainfall
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2b: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
40%
30%
20%
10%
0%
-10% Departure from mean mean from Departure -20%
-30% 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Figure 3b: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 26% below the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the driest 5-year period out of 89 5-year periods since 1915. • GSR over the 5 years prior to 2002 (1998-2002) was 8% below the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the 23rd driest 5-year period out of 89 5-year periods since 1915. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 17.5% below the average of all consecutive 10-year periods from 1915 to 2007. This period was ranked the driest 10-year period out of 84 10-year periods since 1915.
41 GSR deciles
600
500 D1
D2 400 D3
300 D4 D5
Rainfall (mm) (mm) Rainfall 200 D6 D7 100 D8
D9 0 D10 1915 1918 1921 1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 4b13 : The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 5b: For each year from 1924 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been two decile 1 years, two decile 2, one decile 3, 4, 5, two decile 6, and one decile 8 year.
42 Appendix 1c: Climate Analysis – Kimba
Peter Hayman & Bronya Alexander, SARDI
Kimba rainfall data has come from observations at the Kimba Post Office (met station number 18040).
Table 1c: Monthly rainfall (mm) recorded at the Kimba Post Office. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 3rd and 11th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1921 25 102 45 41 80 51 24 18 26 23 46 39 263 51 1922 9 10 0 11 17 23 47 38 13 7 0 35 104 57 156 13 1923 13 0 3 1 59 71 66 40 48 32 0 33 51 17 317 73 1924 21 6 9 6 23 33 10 46 38 64 26 8 69 34 220 32 1925 4 21 0 25 23 16 61 22 42 17 21 0 59 26 206 27 1926 0 8 41 26 41 51 26 59 30 22 0 14 70 35 255 45 1927 4 13 27 4 27 58 37 54 7 4 14 10 58 24 191 24 1928 10 59 0 2 17 25 37 8 20 28 5 0 93 49 137 6 1929 2 6 1 0 23 31 19 40 35 7 29 16 14 1 155 12 1930 0 24 0 23 12 18 55 57 18 76 11 5 69 34 259 48 1931 2 1 17 35 54 64 36 39 27 8 6 3 36 4 263 51 1932 0 53 6 72 32 50 35 67 43 24 1 9 68 32 323 76 1933 9 0 17 14 52 14 31 46 55 10 40 4 36 4 222 33 1934 5 26 18 2 13 19 13 58 33 20 40 1 93 49 158 14 1935 34 0 30 37 20 37 29 37 42 64 15 15 105 60 266 54 1936 19 9 1 6 4 27 48 21 6 41 14 23 59 26 153 10 1937 28 8 18 10 27 28 34 36 39 18 27 69 91 45 192 25 1938 17 69 1 26 5 40 38 42 7 32 3 9 183 81 190 23 1939 22 11 17 9 31 48 49 84 8 11 135 1 62 27 240 41 1940 21 6 13 14 28 9 37 14 13 19 19 7 176 80 134 5 1941 53 2 75 14 10 18 53 42 78 68 14 4 156 77 283 64 1942 22 2 8 57 55 75 52 72 71 20 44 37 50 13 402 85 1943 8 22 1 28 4 27 54 55 36 22 5 1 112 64 226 36 1944 0 44 5 52 48 14 28 13 14 17 30 27 55 20 186 20 1945 38 11 0 2 20 42 20 40 33 22 47 58 106 61 179 17 1946 37 163 21 19 31 30 45 35 15 13 62 56 326 86 188 22
43 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1947 13 25 41 22 12 41 80 43 55 55 15 23 197 83 308 70 1948 0 2 1 21 37 21 23 47 4 51 23 14 41 6 204 26 1949 5 37 0 3 48 18 38 25 33 117 55 12 79 40 282 63 1950 2 25 1 41 68 20 22 46 32 45 21 21 95 50 274 58 1951 1 1 8 19 61 48 79 55 27 30 7 29 52 18 319 74 1952 26 1 8 51 112 44 29 16 71 28 51 8 71 36 351 78 1953 24 14 5 10 14 68 42 50 50 31 28 38 102 55 265 53 1954 32 0 1 54 4 58 23 11 18 89 12 18 99 51 257 47 1955 19 50 18 35 57 81 20 64 21 21 31 8 117 67 299 68 1956 2 59 9 45 88 84 128 39 60 52 14 2 109 63 496 87 1957 0 9 5 3 11 21 34 42 16 12 15 31 30 2 139 7 1958 0 1 53 10 68 1 36 72 99 39 10 36 100 54 325 77 1959 8 20 59 1 3 9 29 24 13 10 25 9 133 74 89 1 1960 9 44 29 45 61 35 51 39 77 3 16 21 116 66 311 71 1961 2 1 5 103 9 9 34 71 18 0 38 2 45 8 244 42 1962 0 27 11 9 63 37 20 38 12 53 2 97 78 38 232 39 1963 11 0 11 73 89 66 80 33 7 6 1 0 121 69 354 80 1964 5 33 0 28 16 39 78 19 66 36 37 6 39 5 282 63 1965 0 0 6 6 43 34 22 77 32 4 13 37 49 11 218 31 1966 29 28 28 3 27 49 54 16 53 15 24 45 135 75 217 30 1967 31 28 0 1 13 11 37 34 30 5 0 5 128 72 131 4 1968 32 39 29 28 60 130 69 71 22 22 30 12 105 60 402 85 1969 28 60 31 20 59 28 71 46 52 1 22 14 161 78 277 59 1970 12 1 2 38 28 43 19 58 46 2 11 18 51 17 234 40 1971 0 0 28 44 32 38 15 48 30 7 47 28 57 22 214 29 1972 91 20 0 9 6 20 65 77 18 12 7 13 186 82 207 28 1973 9 80 24 45 46 62 54 58 62 68 22 19 133 74 395 83 1974 141 13 11 46 113 15 60 30 43 84 10 3 206 84 391 82 1975 1 13 24 13 37 9 42 26 61 92 12 14 51 17 280 61 1976 2 25 5 23 9 19 11 21 28 77 21 4 58 24 188 22 1977 10 18 10 3 27 26 25 15 39 24 39 23 63 28 159 15 1978 20 1 2 17 27 91 63 61 77 16 21 6 85 41 352 79 1979 7 51 15 23 93 8 29 56 138 26 51 15 100 54 373 81 1980 0 2 0 41 37 63 41 8 14 70 12 14 68 32 274 58 1981 37 33 43 3 44 96 46 61 5 14 33 4 139 76 269 56 1982 6 1 56 12 15 42 9 6 19 3 0 5 100 54 106 2 1983 1 9 77 48 32 22 45 29 31 21 6 19 92 47 228 38 1984 8 1 8 15 24 26 37 76 57 33 11 11 42 7 268 55
44 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1985 1 0 28 17 3 20 19 95 21 51 18 25 51 17 226 36 1986 2 9 0 7 32 28 60 54 37 47 23 12 54 19 265 53 1987 63 8 2 7 47 29 58 7 9 8 12 13 108 62 165 16 1988 5 8 11 1 39 31 30 8 31 10 40 12 49 11 150 9 1989 2 0 32 7 57 83 69 30 34 17 28 4 86 42 297 67 1990 6 8 2 10 26 71 50 51 22 23 1 56 48 9 253 44 1991 7 0 51 20 19 110 35 56 49 5 53 2 115 65 294 66 1992 5 8 55 60 51 34 18 66 101 127 53 118 123 71 457 86 1993 128 9 11 0 52 32 44 37 56 72 20 53 319 85 293 65 1994 5 10 0 0 6 62 25 12 16 22 12 3 88 43 143 8 1995 24 3 26 23 44 63 91 22 17 20 27 4 68 32 280 61 1996 9 8 8 3 1 61 72 79 65 25 3 13 56 21 306 69 1997 19 70 0 0 28 12 9 45 96 37 24 41 105 60 227 37 1998 55 48 7 66 9 41 65 41 16 18 35 12 175 79 256 46 1999 26 4 45 1 34 39 20 13 36 82 19 12 122 70 225 34 2000 4 42 15 49 26 39 37 97 31 35 36 10 92 47 314 72 2001 12 12 20 8 54 57 37 44 92 28 41 24 90 44 320 75 2002 12 0 2 6 56 38 41 23 11 8 24 6 79 40 183 19 2003 11 63 0 18 60 33 23 65 14 40 35 19 104 57 253 44 2004 8 2 3 11 11 43 40 47 28 0 15 16 67 29 180 18 2005 10 4 5 8 17 43 35 38 77 43 13 18 50 13 261 49 2006 13 16 14 30 15 25 39 3 2 0 17 36 74 37 114 3 2007 40 0 27 37 27 16 33 18 12 11 36 46 120 68 154 11
45 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150
Rainfall (mm) 150 1998 (mm) Rainfall 1999
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall(mm) 2002 Rainfall(mm) 2003
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 Rainfall(mm) 150 2004 Rainfall(mm) 2005
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1c: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
46 GSR
600
500
400
GSR 300 5yr running mean
200 Rainfall (mm) (mm) Rainfall
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2c: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
40%
30%
20%
10%
0%
-10% Departure from mean mean from Departure -20%
-30% 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Figure 3c: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 23% below the average of all consecutive 5-year periods from 1921 to 2007. This period was ranked the 4th driest 5-year period out of 83 5-year periods since 1921. • GSR over the 5 years prior to 2002 (1998-2002) was 4% above the average of all consecutive 5-year periods from 1921 to 2007. This period was ranked the 52nd driest 5-year period out of 83 5-year periods since 1921. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 10% below the average of all consecutive 10-year periods from 1921 to 2007. This period was ranked the 18th driest 10-year period out of 78 10-year periods since 1921.
47 GSR deciles
600
500 D1
D2 400 D3
300 D4 D5
Rainfall (mm) (mm) Rainfall 200 D6 D7 100 D8
D9 0 D10 1921 1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 4c: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 5c: For each year from 1930 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been one decile 1 year, two decile 2, one decile 3, 4, 5, two decile 6, and two decile 9 years.
48 Appendix 1d: Climate Analysis – Lock
Peter Hayman & Bronya Alexander, SARDI
Lock rainfall data has come from observations at the Lock Post Office (met station number 18046).
Table 1d: Monthly rainfall (mm) recorded at the Lock Post Office. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 1st and 5th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1915 8 1 4 57 40 66 68 120 62 8 3 5 421 87 1916 7 0 10 13 35 151 99 91 47 44 29 8 25 3 480 92 1917 18 58 38 5 73 69 74 92 77 25 83 13 151 86 415 85 1918 3 0 7 11 22 42 31 73 8 33 15 0 106 67 220 17 1919 0 43 0 15 38 18 27 49 32 27 3 15 58 20 206 12 1920 4 0 3 14 35 107 61 79 52 51 42 5 25 3 399 82 1921 51 78 59 17 62 65 43 30 59 24 49 4 235 91 300 56 1922 27 1 3 16 40 44 46 62 20 14 1 62 84 47 242 23 1923 7 3 0 5 66 89 82 62 78 30 2 109 73 37 412 83 1924 11 28 18 13 44 63 20 53 40 88 31 5 168 87 321 61 1925 7 9 0 15 66 33 60 37 51 27 12 0 52 15 289 49 1926 0 6 14 31 69 72 44 59 42 37 3 33 32 7 354 71 1927 1 24 21 8 34 52 52 66 11 2 22 10 82 45 225 18 1928 11 39 1 3 26 52 46 10 21 51 6 0 83 46 209 14 1929 2 13 7 6 26 64 72 81 36 23 41 52 28 4 308 58 1930 1 17 0 19 16 8 65 80 39 61 17 11 111 69 288 47 1931 20 0 13 19 65 108 78 74 56 16 17 7 61 25 416 86 1932 0 63 7 64 47 90 60 71 64 18 36 5 94 55 414 84 1933 22 0 12 15 85 22 57 60 37 14 32 11 75 40 290 51 1934 2 32 19 32 14 31 19 64 41 26 50 0 96 57 227 19 1935 5 0 66 41 42 41 54 54 41 78 24 25 121 76 351 69 1936 39 14 0 8 7 45 70 35 13 33 9 32 102 63 211 15 1937 20 35 8 11 44 47 37 56 46 23 38 37 104 64 264 34 1938 8 128 10 50 12 73 39 55 11 6 0 18 221 90 246 25 1939 23 5 14 13 16 52 77 99 9 20 47 4 60 23 286 45 1940 22 16 13 38 29 14 67 21 21 14 16 2 102 63 204 11 1941 43 2 64 4 8 25 58 43 82 63 26 0 127 77 283 43
49 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1942 9 10 8 26 54 119 83 77 74 18 45 19 53 17 451 91 1943 12 37 0 27 9 27 73 70 29 64 6 5 113 71 299 55 1944 0 16 4 56 56 12 48 10 29 32 22 11 31 6 243 24 1945 33 9 0 4 33 62 16 65 33 52 51 109 75 40 265 36 1946 22 109 15 16 35 62 86 28 12 22 46 104 306 92 261 30 1947 2 18 22 19 19 65 104 42 46 63 21 17 192 88 358 72 1948 1 4 2 27 37 25 43 58 8 34 40 22 45 11 232 21 1949 10 20 0 1 44 25 47 26 51 86 57 4 92 52 280 42 1950 2 13 2 17 53 31 26 59 26 66 19 28 78 43 278 40 1951 2 2 12 38 74 73 107 70 40 32 5 23 63 29 434 88 1952 8 4 7 39 128 38 42 36 65 26 41 21 47 12 374 77 1953 17 25 9 8 16 78 51 44 43 50 24 63 113 71 290 51 1954 14 0 5 69 18 41 45 16 12 29 14 24 106 67 230 20 1955 3 77 12 29 57 112 27 83 22 37 26 8 130 79 367 75 1956 5 11 42 44 93 179 119 48 50 55 22 9 92 52 588 93 1957 2 0 5 4 25 39 54 37 22 20 13 15 38 9 201 10 1958 0 0 34 12 75 6 54 65 79 47 14 72 62 28 338 66 1959 0 15 37 3 20 15 34 16 33 34 42 10 138 82 155 2 1960 33 48 3 91 96 31 56 42 65 5 31 19 136 81 386 80 1961 1 12 7 78 20 23 33 78 20 5 15 4 70 34 257 29 1962 1 16 16 1 86 68 21 44 26 79 6 29 52 15 325 62 1963 17 7 12 56 79 63 109 42 9 2 6 2 71 35 360 73 1964 7 6 2 37 34 49 87 33 51 51 53 5 23 1 342 67 1965 0 0 4 4 79 46 44 88 23 3 15 36 62 28 287 46 1966 10 6 27 7 33 52 51 41 56 25 22 90 94 55 265 36 1967 16 13 1 2 28 10 59 45 34 7 0 2 142 84 185 7 1968 21 12 86 48 62 97 86 71 32 42 25 35 121 76 438 89 1969 6 55 8 34 80 38 52 25 50 0 12 9 129 78 279 41 1970 7 0 3 16 33 48 30 72 69 6 20 5 31 6 274 39 1971 0 1 54 68 83 50 45 76 53 12 48 33 80 44 387 81 1972 40 21 0 18 5 29 76 97 27 12 14 2 142 84 264 34 1973 6 14 40 31 37 76 76 75 56 30 8 16 76 42 381 78 1974 88 1 6 39 85 23 79 50 58 112 14 7 119 74 446 90 1975 6 8 21 5 51 18 44 45 89 64 14 6 56 18 316 60 1976 1 77 4 6 25 36 17 37 42 35 25 5 102 63 198 9 1977 14 14 8 7 36 17 22 21 35 25 65 19 66 31 163 3 1978 8 1 1 11 25 61 90 74 99 8 25 16 94 55 368 76 1979 27 6 1 30 64 30 38 62 124 36 45 13 75 40 384 79
50 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1980 9 0 0 34 65 60 69 11 18 41 27 19 67 32 298 54 1981 21 33 31 2 65 117 52 81 10 25 18 9 131 80 352 70 1982 4 4 35 19 31 54 16 27 20 7 1 15 70 34 174 4 1983 3 21 49 87 38 27 76 49 38 22 11 17 89 50 337 65 1984 4 1 25 20 22 12 65 106 48 25 12 8 58 20 298 54 1985 5 0 37 38 9 39 26 87 24 34 22 10 62 28 257 29 1986 3 5 0 20 21 23 75 63 29 35 11 16 40 10 266 37 1987 20 44 5 17 51 40 77 44 8 26 7 12 96 57 263 31 1988 19 2 11 1 36 47 38 17 33 5 35 27 51 13 177 6 1989 1 0 37 4 66 83 89 49 31 7 18 12 100 60 329 63 1990 11 14 5 10 14 53 68 65 35 24 2 102 60 23 269 38 1991 8 0 6 30 17 73 38 56 45 5 50 0 118 73 264 34 1992 2 1 45 55 44 21 23 75 87 60 52 101 98 59 365 74 1993 39 1 13 2 27 26 28 36 44 45 21 26 206 89 208 13 1994 4 9 0 1 27 76 42 15 14 23 12 12 60 23 198 9 1995 56 1 7 15 36 85 81 12 39 16 16 1 88 48 284 44 1996 4 8 5 9 9 86 69 62 94 18 8 21 34 8 347 68 1997 10 49 1 0 55 23 23 50 99 39 32 45 89 50 289 49 1998 3 13 5 40 7 53 64 60 8 20 13 6 98 59 252 26 1999 16 0 37 7 49 49 26 19 36 70 36 6 72 36 256 27 2000 12 40 16 26 31 49 46 75 36 49 11 4 110 68 312 59 2001 7 11 28 0 47 54 40 60 58 39 31 25 61 25 298 54 2002 17 0 3 9 52 39 63 26 26 27 50 15 76 42 242 23 2003 11 28 1 8 41 66 49 70 30 37 22 7 105 65 301 57 2004 5 1 18 13 16 43 38 71 32 0 27 19 53 17 213 16 2005 7 5 6 6 11 79 38 44 87 65 28 21 64 30 330 64 2006 52 14 32 29 22 23 37 8 4 2 22 37 147 85 125 1 2007 30 0 29 48 40 21 28 15 10 14 15 40 118 73 176 5
51 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9 Rainfall (mm) 1998 (mm) Rainfall 1999 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall(mm) 2002 Rainfall(mm) 2003 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9 Rainfall(mm) 2004 Rainfall(mm) 2005 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1d: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
52 GSR
700
600
500
400 GSR 300 5yr running mean
Rainfall (mm) (mm) Rainfall 200
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2d: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
30%
20%
10%
0%
-10%
Departure from mean mean from Departure -20%
-30% 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Figure 3d: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 23% below the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the driest 5-year period out of 89 5-year periods since 1915. • GSR over the 5 years prior to 2002 (1998-2002) was 9% below the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the 18th driest 5-year period out of 89 5-year periods since 1915. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 16% below the average of all consecutive 10-year periods from 1915 to 2007. This period was ranked the driest 10-year period out of 84 10-year periods since 1915.
53 GSR deciles
700
600 D1 500 D2 D3 400 D4 300 D5
Rainfall (mm) (mm) Rainfall D6 200 D7
100 D8
D9 0 D10 1915 1918 1921 1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 4d: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 5d: For each year from 1924 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been two decile 1 year, one decile 2, three decile 3, one decile 6, and three decile 7 years.
54 Appendix 1e: Climate Analysis – Minnipa
Peter Hayman & Bronya Alexander, SARDI
Minnipa Rainfall data has come from observations at the Minnipa Research Centre (met station number 18052).
Table 1e: Monthly rainfall (mm) recorded at the Minnipa Research Centre. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 3rd and 2nd driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1915 13 0 1 26 42 43 51 83 33 8 0 13 286 71 1916 9 0 11 6 24 97 77 56 53 40 22 4 33 11 353 86 1917 40 7 21 7 67 62 68 64 62 27 34 11 94 63 357 87 1918 4 1 15 19 28 38 26 62 0 37 0 8 65 42 210 33 1919 13 59 0 15 20 19 18 21 34 16 3 38 80 57 143 8 1920 26 0 11 11 54 84 55 72 50 52 36 9 78 53 378 89 1921 68 119 80 14 54 54 22 20 17 27 16 39 312 92 208 32 1922 13 11 1 11 35 28 52 38 13 7 2 43 80 57 184 21 1923 0 0 2 8 31 64 57 46 34 18 0 40 47 20 258 60 1924 12 11 8 6 26 49 5 37 34 65 10 5 71 45 222 41 1925 8 49 1 10 40 22 35 32 33 21 15 0 73 48 193 25 1926 0 4 31 35 42 68 35 56 23 21 2 15 50 25 280 68 1927 1 15 21 11 32 62 62 36 7 5 13 7 54 28 215 36 1928 2 27 2 5 19 57 39 9 17 48 4 0 51 26 194 26 1929 1 0 2 1 26 38 30 36 25 13 26 25 7 1 169 15 1930 0 5 0 21 16 3 55 47 18 45 10 10 56 30 205 30 1931 2 0 20 32 49 72 37 29 24 10 9 0 42 16 253 56 1932 0 56 1 32 27 75 35 68 46 28 3 6 66 43 311 79 1933 12 0 11 15 49 10 54 51 42 11 74 10 32 8 232 48 1934 5 23 13 10 17 16 20 41 34 17 24 0 125 80 155 11 1935 12 0 54 17 22 32 30 33 46 80 15 9 90 59 260 63 1936 0 14 0 6 15 21 84 26 7 26 9 23 38 13 185 23 1937 25 7 4 8 30 36 31 51 34 25 65 18 68 44 215 36 1938 7 176 0 31 14 62 34 49 6 7 0 16 266 91 203 29 1939 31 3 29 6 17 50 57 84 7 11 50 0 79 55 232 48 1940 57 3 23 27 26 7 37 6 12 10 12 1 133 81 125 4
55 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1941 28 0 32 7 4 16 33 16 67 30 17 0 73 48 173 17 1942 21 3 11 13 28 90 63 55 48 9 40 20 52 27 306 78 1943 6 58 1 25 11 29 56 59 25 25 4 3 125 80 230 46 1944 0 10 4 21 54 15 37 13 13 14 15 13 21 4 167 14 1945 8 3 0 0 22 40 7 26 23 30 30 78 39 14 148 10 1946 44 88 10 15 36 54 49 20 10 13 33 81 250 89 197 27 1947 22 18 30 14 6 30 64 41 44 53 12 20 184 88 252 53 1948 0 1 0 16 44 21 26 33 3 38 26 7 33 11 181 20 1949 2 26 0 2 34 17 57 17 43 55 55 14 61 37 225 44 1950 2 25 0 8 30 25 27 57 18 52 9 76 96 65 217 38 1951 3 0 5 18 87 71 107 71 32 35 4 18 93 62 421 92 1952 6 1 2 39 123 21 34 31 39 37 40 12 31 7 324 81 1953 30 29 5 6 12 77 27 52 52 27 21 62 116 73 253 56 1954 28 2 0 42 6 51 23 12 14 37 8 32 113 72 185 23 1955 1 91 30 26 45 101 34 60 22 17 31 10 162 87 305 77 1956 4 23 43 46 74 114 86 39 43 84 9 8 111 71 486 93 1957 0 1 2 2 14 64 44 26 15 10 7 18 20 3 175 18 1958 0 0 51 10 65 3 43 66 70 39 30 34 76 52 296 76 1959 0 9 46 2 17 4 29 2 23 11 18 15 119 76 88 1 1960 16 43 6 56 58 29 57 48 61 3 26 9 98 67 312 80 1961 0 10 1 55 7 18 25 41 14 2 16 1 46 19 162 13 1962 0 16 9 0 68 21 22 35 13 34 5 12 42 16 193 25 1963 10 3 3 37 54 45 77 58 10 6 3 0 33 11 287 72 1964 5 4 1 23 42 50 88 21 65 42 48 8 13 2 331 82 1965 0 0 3 6 43 30 46 61 29 2 11 30 59 33 217 38 1966 20 31 32 4 34 59 41 28 55 25 26 64 124 78 246 52 1967 29 25 0 1 15 9 59 37 36 1 1 2 144 85 158 12 1968 17 52 64 28 65 107 56 88 29 28 19 25 136 83 401 90 1969 9 52 18 19 54 35 42 32 58 1 14 7 123 77 241 50 1970 7 1 0 8 38 35 27 67 50 4 15 5 29 6 229 45 1971 1 2 53 40 61 37 38 49 30 4 64 16 76 52 259 61 1972 27 30 0 20 10 28 56 83 10 18 9 2 137 84 225 44 1973 7 21 36 27 39 70 55 70 41 35 14 28 75 50 337 84 1974 36 6 9 33 90 27 89 23 52 89 12 3 93 62 403 91 1975 5 17 12 9 59 6 32 26 39 69 25 9 49 24 240 49 1976 2 59 8 5 18 41 10 13 32 28 36 4 103 69 147 9 1977 18 13 8 2 53 25 20 16 40 25 32 21 79 55 181 20 1978 11 2 6 19 24 45 67 73 90 16 25 19 72 46 334 83
56 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1979 4 4 3 17 63 11 45 54 137 18 50 8 55 29 345 85 1980 1 1 1 33 47 41 61 15 10 36 42 9 61 37 243 51 1981 36 28 21 0 40 140 38 59 3 14 18 2 136 83 294 75 1982 18 13 56 16 25 44 18 7 25 3 1 17 107 70 138 7 1983 0 6 36 75 35 12 73 28 24 13 8 16 60 35 260 63 1984 3 0 18 17 16 16 66 86 39 23 9 12 45 17 263 64 1985 3 0 5 34 11 22 24 124 17 35 24 24 29 6 267 66 1986 2 7 0 16 39 39 83 41 28 39 9 24 57 31 285 70 1987 35 28 1 5 26 37 49 29 12 14 7 26 97 66 172 16 1988 21 1 9 1 30 34 30 11 23 7 21 22 64 41 136 6 1989 1 0 16 17 50 59 92 32 24 14 25 14 60 35 288 73 1990 6 20 10 15 19 63 56 48 28 25 4 73 75 50 254 58 1991 4 0 5 30 14 98 35 56 58 2 35 0 86 58 293 74 1992 0 19 41 40 39 27 24 73 88 70 48 125 95 64 361 88 1993 77 6 3 0 22 31 28 32 34 75 15 35 259 90 222 41 1994 2 11 0 1 13 37 35 10 15 22 12 7 63 40 133 5 1995 24 3 3 20 55 58 67 10 25 19 18 7 49 24 254 58 1996 1 6 5 10 5 47 63 62 56 22 17 10 37 12 265 65 1997 13 21 1 1 29 18 13 54 64 39 27 61 62 38 218 39 1998 9 10 12 49 4 44 85 43 18 15 14 7 119 76 258 60 1999 7 6 24 1 38 30 21 24 36 58 17 5 58 32 208 32 2000 15 11 15 13 30 44 47 73 26 48 35 18 63 40 281 69 2001 6 18 16 1 42 48 33 54 68 29 10 31 93 62 275 67 2002 6 0 2 7 45 54 56 32 12 17 27 24 49 24 223 42 2003 12 36 0 16 21 32 23 71 13 24 14 9 99 68 200 28 2004 4 5 14 11 14 44 38 71 31 2 25 15 46 19 211 34 2005 7 2 0 6 9 74 32 34 67 31 24 21 49 24 253 56 2006 28 19 54 16 22 19 40 3 1 0 21 9 146 86 101 2 2007 13 0 74 26 37 13 23 14 6 5 19 52 117 74 124 3
57 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150
Rainfall (mm) 150 1998 (mm) Rainfall 1999
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) 2002 Rainfall (mm) 2003
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 Rainfall (mm) 150 2004 Rainfall (mm) 2005
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) 2006 Rainfall (mm) 2007
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1e: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
58 GSR
600
500
400
GSR 300 5yr running mean
200 Rainfall (mm) (mm) Rainfall
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2e: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
40%
30%
20%
10%
0%
-10% Departure from mean mean from Departure -20%
-30% 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Figure 3e: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 26% below the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the driest 5-year period out of 89 5-year periods since 1915. • GSR over the 5 years prior to 2002 (1998-2002) was 3% above the average of all consecutive 5-year periods from 1915 to 2007. This period was ranked the 52nd driest 5-year period out of 89 5-year periods since 1915. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 11% below the average of all consecutive 10-year periods from 1915 to 2007. This period was ranked the 12th driest 10-year period out of 84 10-year periods since 1915.
59 GSR deciles
600
500 D1
D2 400 D3
300 D4 D5
Rainfall (mm) (mm) Rainfall 200 D6 D7 100 D8
D9 0 D10 1915 1918 1921 1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 4e: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 5e: For each year from 1924 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been two decile 1 years, one decile 3, two decile 4, one decile 5, 6, 7, and two decile 8 years.
60 Appendix 1f: Climate Analysis – Port Germein
Peter Hayman & Bronya Alexander, SARDI
Port Germein rainfall data has come from observations at the Port Germein Post Office (met station number 19037).
Table 1f: Monthly rainfall (mm) recorded at the Port Germein Post Office. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 2nd and 5th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1882 0 0 9 49 37 23 14 40 2 46 46 11 211 56 1883 4 9 12 31 84 19 27 30 44 23 22 17 82 58 258 90 1884 46 2 86 38 107 50 5 26 41 12 11 11 173 114 279 99 1885 11 16 27 30 18 24 15 12 46 32 3 28 76 51 177 33 1886 4 2 1 16 5 2 38 80 21 17 34 30 38 10 179 36 1887 4 4 13 94 34 52 25 56 21 53 19 14 85 62 335 116 1888 4 3 17 14 35 28 27 21 21 1 2 6 57 32 147 16 1889 31 7 2 133 80 74 7 37 48 64 17 17 48 17 443 125 1890 57 23 7 24 43 55 82 60 21 22 37 16 121 94 307 107 1891 28 0 10 37 9 34 23 46 16 30 3 6 91 68 195 46 1892 5 6 0 13 13 24 19 48 49 105 32 18 20 2 271 95 1893 0 0 5 56 154 30 12 29 30 10 18 13 55 29 321 111 1894 14 1 6 27 23 30 22 24 22 53 4 47 52 22 201 48 1895 45 0 13 61 1 20 32 15 16 2 5 11 109 82 147 16 1896 55 3 18 11 30 66 30 22 22 23 8 10 92 70 204 50 1897 30 9 4 3 12 25 58 33 31 3 3 1 61 39 165 27 1898 0 21 4 36 12 56 27 36 7 8 7 3 29 4 182 38 1899 4 35 20 21 44 61 4 15 25 8 15 4 69 45 178 34 1900 4 3 10 60 22 51 16 28 25 13 5 52 36 9 215 62 1901 1 4 7 30 10 8 44 42 18 39 3 3 69 45 191 44 1902 15 16 28 10 13 24 14 29 23 30 21 87 65 42 143 14 1903 16 6 50 51 13 36 25 29 51 13 111 15 180 117 218 65 1904 57 20 3 30 23 46 29 20 9 50 5 3 206 121 207 54 1905 29 13 5 65 138 42 40 11 13 33 0 0 55 29 342 118 1906 0 9 44 1 31 77 15 35 52 15 29 29 53 24 226 72 1907 0 0 3 40 33 75 59 28 30 13 73 30 61 39 278 98
61 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1908 34 25 55 9 51 45 9 55 88 67 12 10 217 122 324 114 1909 18 5 6 44 59 68 38 77 18 18 33 3 51 21 322 112 1910 3 5 89 0 98 72 52 46 107 22 21 9 133 104 397 123 1911 8 49 14 0 41 25 31 31 41 15 4 48 101 75 184 39 1912 1 10 12 8 4 49 41 50 27 33 53 21 75 49 212 57 1913 3 11 19 1 19 6 12 36 49 38 17 10 107 79 161 22 1914 6 1 8 19 24 5 9 3 8 17 52 24 42 14 85 1 1915 12 0 1 33 75 17 36 33 44 33 0 16 89 66 271 95 1916 4 1 6 3 41 54 53 48 40 38 62 58 27 3 277 97 1917 47 22 30 2 39 54 37 25 57 42 18 31 219 123 256 87 1918 23 14 58 14 22 26 24 48 8 17 2 10 144 109 159 20 1919 5 37 1 27 48 31 18 37 54 17 17 92 55 29 232 75 1920 0 0 13 18 14 96 23 50 37 52 77 26 122 95 290 100 1921 70 85 96 22 46 36 18 22 32 15 9 9 354 125 191 44 1922 12 3 3 24 87 28 41 19 21 25 2 26 36 9 245 83 1923 2 1 0 2 32 68 70 39 24 56 1 38 31 5 291 102 1924 33 24 11 28 27 29 3 53 47 48 29 31 107 79 235 80 1925 7 4 2 25 31 8 45 12 29 12 4 0 73 48 162 24 1926 0 14 18 16 48 25 27 35 56 6 11 19 36 9 213 59 1927 2 4 25 1 31 23 19 25 28 11 22 6 61 39 138 11 1928 13 36 17 9 40 37 35 10 17 11 0 1 94 71 159 20 1929 2 11 2 4 22 19 10 23 42 6 13 56 16 1 126 8 1930 1 7 0 10 17 19 59 40 21 67 19 34 77 55 233 76 1931 3 4 23 33 38 64 23 40 23 17 19 1 83 60 238 81 1932 0 67 52 111 31 51 41 41 47 21 2 8 139 105 343 119 1933 21 0 24 16 42 16 39 65 35 8 16 13 55 29 221 68 1934 20 29 5 19 5 23 14 47 43 67 33 15 83 60 218 65 1935 36 0 47 35 16 28 11 22 17 56 4 15 131 101 185 41 1936 37 16 0 30 16 26 40 15 9 31 2 59 72 47 167 29 1937 62 16 4 16 43 36 30 65 24 10 42 18 143 108 224 71 1938 10 49 0 25 3 18 42 15 9 53 2 0 119 90 165 27 1939 8 31 18 7 25 87 33 48 12 8 50 8 59 35 220 66 1940 30 20 8 21 25 4 23 16 11 6 11 15 116 87 106 4 1941 111 10 24 4 8 106 39 25 23 62 24 7 171 113 267 93 1942 37 1 3 37 96 40 33 55 43 20 16 36 72 47 324 114 1943 2 113 4 25 9 30 33 31 12 13 13 3 171 113 153 18 1944 5 19 3 45 42 8 10 10 7 20 18 40 43 15 142 12 1945 30 19 3 1 23 43 10 26 27 36 27 47 110 85 166 28
62 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1946 50 171 53 13 34 19 40 14 13 27 35 33 348 124 160 21 1947 7 17 50 31 9 38 36 35 57 70 6 27 142 106 276 96 1948 13 1 0 36 40 21 20 21 12 35 18 19 47 16 185 41 1949 11 62 0 3 61 21 53 12 42 57 39 13 110 85 249 85 1950 2 63 10 5 76 32 27 44 38 35 25 18 127 99 257 89 1951 0 3 14 60 81 91 51 57 30 67 1 24 60 36 437 124 1952 45 0 57 109 79 53 26 20 19 74 66 19 127 99 380 121 1953 13 20 5 3 17 42 19 58 55 53 47 26 123 96 247 84 1954 19 6 0 102 3 38 17 40 12 45 26 61 98 73 257 89 1955 4 49 34 22 56 37 27 35 40 0 48 2 174 115 217 63 1956 27 2 29 14 53 45 53 13 61 52 28 0 108 81 291 102 1957 0 4 22 0 24 19 34 35 13 26 1 37 54 25 151 17 1958 0 0 27 10 23 0 37 56 75 20 23 9 65 42 221 68 1959 7 23 20 0 5 2 49 10 24 25 7 12 82 58 115 6 1960 25 36 26 19 81 18 63 31 87 8 23 2 106 77 307 107 1961 23 20 8 48 6 20 22 38 32 3 56 21 76 51 169 31 1962 9 6 27 7 48 13 17 17 15 47 1 40 119 90 164 25 1963 25 0 22 30 84 68 58 36 17 8 16 13 88 65 301 104 1964 10 18 2 31 30 18 24 35 82 35 18 13 59 35 255 86 1965 0 0 3 2 33 19 36 63 18 5 27 12 34 6 176 32 1966 6 47 27 3 54 64 22 21 19 30 23 75 119 90 213 59 1967 24 41 0 0 25 12 19 28 16 5 0 6 163 111 105 3 1968 55 12 4 42 57 57 31 55 11 39 49 7 77 55 292 103 1969 11 36 17 28 111 25 47 45 49 16 20 4 120 92 321 111 1970 24 0 10 13 20 24 17 41 53 14 52 31 58 33 182 38 1971 0 5 33 69 39 61 61 37 33 3 48 45 121 94 303 105 1972 26 38 0 19 3 3 38 53 10 17 15 17 157 110 143 14 1973 25 130 8 35 48 53 29 67 30 132 30 24 195 119 394 122 1974 86 40 1 88 123 19 79 32 57 88 5 10 181 118 486 126 1975 10 5 19 18 28 1 29 34 40 84 15 14 49 19 234 78 1976 8 10 15 5 17 26 6 14 26 120 32 5 62 40 214 60 1977 12 0 8 7 38 42 9 9 37 27 50 14 57 32 169 31 1978 21 0 1 41 37 60 66 53 58 24 51 6 86 63 339 117 1979 25 10 0 32 69 4 8 45 111 45 49 28 92 70 314 109 1980 53 3 0 50 27 44 38 4 4 68 1 23 133 104 235 80 1981 35 13 12 0 35 54 41 31 17 25 32 11 84 61 203 49 1982 0 6 41 30 14 25 5 4 28 16 3 12 90 67 122 7 1983 0 0 38 31 44 11 46 32 39 28 15 13 53 24 231 73
63 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1984 13 0 1 30 14 17 66 45 66 28 23 3 42 14 266 92 1985 4 0 19 33 22 19 14 84 16 44 13 13 49 19 232 75 1986 0 16 0 11 38 29 90 40 34 66 24 20 42 14 308 108 1987 23 18 13 8 75 23 34 37 25 3 9 22 98 73 205 51 1988 0 29 17 4 48 72 19 4 46 14 37 2 77 55 207 54 1989 1 3 84 14 29 61 72 15 14 2 41 37 127 99 207 54 1990 6 22 4 27 47 58 37 35 20 17 1 19 110 85 241 82 1991 19 0 2 7 2 92 36 20 27 4 67 7 41 11 188 42 1992 2 12 55 44 88 17 9 33 78 83 63 45 143 108 352 120 1993 55 6 8 0 20 22 38 25 28 63 3 64 177 116 196 47 1994 5 28 0 0 5 78 25 7 9 12 9 8 100 74 136 10 1995 52 16 29 29 46 56 46 12 44 29 41 10 114 86 262 91 1996 15 11 0 4 0 27 56 59 68 9 11 5 77 55 223 70 1997 71 42 3 0 22 5 5 35 63 85 15 51 132 102 215 62 1998 9 15 13 53 6 39 34 23 22 31 43 20 103 76 208 55 1999 15 5 25 5 30 42 13 21 24 27 39 35 108 81 162 24 2000 1 30 26 41 38 19 24 58 30 13 29 9 131 101 223 70 2001 1 5 7 25 42 49 31 35 76 67 25 12 51 21 325 115 2002 18 0 2 1 38 32 22 5 25 8 42 10 57 32 131 9 2003 10 55 3 10 85 17 9 40 1 32 24 24 120 92 194 45 2004 12 3 6 6 15 60 35 35 25 3 17 33 69 45 179 36 2005 21 2 7 10 6 31 27 28 63 69 10 18 80 56 234 78 2006 1 25 33 42 7 14 28 1 11 0 17 21 87 64 103 2 2007 109 3 54 21 14 15 20 14 14 12 56 31 204 120 110 5
64 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5
150 9 150 9 Rainfall(mm) 1998 (mm) Rainfall 1999 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5 150 9 150 9
Rainfall (mm) 2000 Rainfall (mm) 2001 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5 150 9 150 9
Rainfall (mm) Rainfall 2002 (mm) Rainfall 2003 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5 9 150 150 9 Rainfall (mm) Rainfall 2004 (mm) Rainfall 2005 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5
150 9 150 9
Rainfall(mm) 2006 Rainfall(mm) 2007 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1f: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
65 GSR
600
500
400
GSR 300 5yr running mean
Rainfall (mm) 200
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2f: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
50%
40%
30%
20%
10%
0%
-10%
Departure from mean -20%
-30%
-40% 1886 1890 1894 1898 1902 1906 1910 1914 1918 1922 1926 1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006
Figure 3f: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 28.5% below the average of all consecutive 5-year periods from 1882 to 2007. This period was ranked the 2nd driest 5-year period out of 122 5-year periods since 1882. • GSR over the 5 years prior to 2002 (1998-2002) was 9% below the average of all consecutive 5-year periods from 1882 to 2007. This period was ranked the 39th driest 5-year period out of 122 5-year periods since 1882. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 19% below the average of all consecutive 10-year periods from 1882 to 2007. This period was ranked the 3rd driest 10-year period out of 117 10-year periods since 1882.
66 GSR deciles
600
500 D1
D2 ) 400 D3
300 D4 D5
Rainfall (mm 200 D6 D7 100 D8
D9 0 D10 1882 1887 1892 1897 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 Figure 4f: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1891 1896 1901 1906 1911 1916 1921 1926 1931 1936 1941 1946 1951 1956 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 Figure 5f: For each year from 1891 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been three decile 1 years, one decile 2, one decile 3, one decile 4, one decile 5, one decile 6, one decile 7, and one decile 10 year.
67 Appendix 1g: Climate Analysis – Port Kenny
Peter Hayman & Bronya Alexander, SARDI
Port Kenny rainfall data has come from observations at Port Kenny/Mount Cooper (met station number 18054).
Table 1g: Monthly rainfall (mm) recorded at Port Kenny. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 2nd and 4th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1939 9 5 15 13 34 104 71 102 10 26 35 13 360 45 1940 25 16 20 60 39 22 78 20 31 23 21 0 109 50 273 21 1941 46 2 52 18 9 22 77 49 98 40 22 0 121 54 313 31 1942 19 4 11 5 39 137 109 78 70 17 79 21 56 14 455 62 1943 4 38 0 30 23 72 69 82 31 30 8 12 142 61 337 39 1944 0 4 4 36 81 20 67 15 17 26 19 17 28 3 262 16 1945 11 14 3 0 43 48 9 50 41 33 52 50 64 24 224 8 1946 41 64 14 21 43 40 73 34 12 21 19 45 221 67 244 11 1947 5 7 49 24 19 50 100 55 54 83 16 16 125 57 385 50 1948 3 6 0 24 48 33 42 50 8 47 38 19 41 8 252 14 1949 3 21 0 8 39 62 69 27 46 75 46 6 81 38 326 35 1950 4 17 0 0 28 38 39 85 28 78 26 36 73 32 296 30 1951 0 0 11 54 111 66 125 98 41 33 0 18 73 32 528 68 1952 5 4 6 57 132 36 61 29 55 37 42 19 33 5 407 55 1953 15 10 10 7 26 101 48 54 50 30 25 55 96 42 316 32 1954 32 0 3 56 17 98 38 20 16 30 18 26 115 51 275 22 1955 5 18 12 31 55 138 46 68 22 29 37 14 79 35 389 52 1956 10 35 22 55 115 184 78 60 58 81 9 6 118 52 631 69 1957 6 0 4 3 22 77 81 35 31 7 9 23 25 2 256 15 1958 2 0 33 12 51 7 54 76 80 46 16 25 67 28 326 35 1959 0 8 16 3 8 14 35 8 32 11 26 16 65 26 111 1 1960 20 58 3 53 100 37 85 48 82 13 25 7 123 56 418 58 1961 0 10 2 71 13 45 46 74 20 14 11 5 44 9 283 25 1962 2 4 41 3 83 44 28 54 32 49 6 28 63 23 293 29 1963 12 11 2 37 85 91 123 50 19 2 4 0 59 19 407 55 1964 2 6 3 27 62 71 102 31 77 45 55 4 15 1 415 57
68 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1965 0 0 2 8 52 37 43 79 25 5 12 11 61 21 249 12 1966 1 16 51 9 44 53 72 52 77 38 28 92 91 41 345 42 1967 27 21 1 1 14 14 111 58 43 3 1 2 169 65 244 11 1968 36 17 77 46 75 88 67 97 56 32 28 42 133 58 461 63 1969 8 41 17 22 67 47 69 18 60 3 9 13 136 59 286 27 1970 10 0 1 15 46 36 39 85 67 5 16 3 33 5 293 29 1971 1 2 101 79 94 47 52 95 52 14 52 31 123 56 433 61 1972 7 19 0 29 11 37 127 87 15 39 6 1 109 50 345 42 1973 12 5 39 27 43 80 62 94 55 26 7 20 63 23 387 51 1974 27 2 2 55 74 31 111 39 64 101 12 8 58 16 475 66 1975 5 9 21 9 50 23 63 60 48 85 29 7 55 13 338 40 1976 1 97 3 9 42 51 14 40 30 32 58 1 137 60 218 6 1977 21 18 5 8 94 30 25 23 46 26 140 11 103 45 252 14 1978 19 0 7 15 44 103 107 103 87 8 25 36 177 66 467 65 1979 2 6 6 23 80 17 78 60 178 28 83 17 75 33 464 64 1980 1 0 0 44 65 45 83 34 10 56 34 24 101 44 337 39 1981 2 60 34 2 43 185 69 96 10 21 20 17 154 62 426 60 1982 10 11 61 20 47 77 32 6 29 7 1 29 119 53 218 6 1983 6 28 43 103 46 29 109 37 35 17 8 43 107 47 376 49 1984 9 0 21 25 21 20 77 103 61 23 13 10 81 38 330 37 1985 4 0 9 37 13 37 29 97 22 50 12 40 36 6 285 26 1986 3 12 0 33 40 41 106 72 54 49 14 24 67 28 395 53 1987 28 39 3 19 57 44 95 32 11 22 2 25 108 48 280 24 1988 34 4 3 1 56 53 47 19 4 6 31 14 68 29 186 3 1989 8 0 6 25 57 83 146 67 31 6 23 25 59 19 415 57 1990 16 23 0 9 21 66 87 67 41 30 0 84 87 40 321 33 1991 7 0 7 27 22 77 54 71 95 3 31 0 98 43 349 44 1992 0 4 42 53 41 43 37 100 92 121 53 129 77 34 487 67 1993 70 4 2 1 14 38 37 50 40 93 24 24 258 68 273 21 1994 0 11 0 0 27 92 38 21 18 35 16 7 59 19 231 9 1995 139 4 3 26 54 104 107 16 31 25 26 4 169 65 363 47 1996 0 5 14 10 7 76 89 83 66 15 32 18 49 10 346 43 1997 25 5 1 0 55 20 22 53 63 50 25 46 81 38 263 18 1998 15 18 2 27 11 59 98 18 36 23 21 14 106 46 272 19 1999 13 5 8 2 24 39 21 41 51 45 18 17 61 21 223 7 2000 16 13 18 39 46 64 94 95 34 47 9 2 82 39 419 59 2001 0 18 23 3 81 67 35 53 87 47 34 20 52 12 373 48 2002 0 0 4 0 54 88 53 24 22 22 20 11 58 16 263 18
69 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 2003 16 16 2 10 34 74 31 125 15 41 26 10 65 26 330 37 2004 6 4 4 28 19 44 42 98 48 0 25 7 50 11 279 23 2005 3 3 0 0 16 115 46 52 81 53 23 20 38 7 363 47 2006 58 18 46 13 29 25 49 3 0 0 20 14 165 63 119 2 2007 10 0 27 58 32 14 40 18 13 33 19 36 71 30 208 4
70 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
500 500 450 450 400 400 350 350 300 1 300 1 5 5 250 250 9 9 200 200 Rainfall (mm) 1998 (mm) Rainfall 1999 150 150 100 100 50 50 0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
500 500 450 450 400 400 350 350 300 1 300 1 5 5 250 250 9 9 200 200
Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001 150 150 100 100 50 50 0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
500 500 450 450 400 400 350 350 300 1 300 1 5 5 250 250 9 9 200 200
Rainfall(mm) 2002 Rainfall(mm) 2003 150 150 100 100 50 50 0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
500 500 450 450 400 400 350 350 300 1 300 1 5 5 250 250 9 9 200 200 Rainfall(mm) 2004 Rainfall(mm) 2005 150 150 100 100 50 50 0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
500 500 450 450 400 400 350 350 300 1 300 1 5 5 250 250 9 9 200 200
Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007 150 150 100 100 50 50 0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1g: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
71 GSR
700
600
500
400 GSR 300 5yr running mean
Rainfall (mm) (mm) Rainfall 200
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2g: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
25% 20% 15% 10% 5% 0% -5% -10%
Departure from mean mean from Departure -15% -20% -25% 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Figure 3g: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 22% below the average of all consecutive 5-year periods from 1939 to 2007. This period was ranked the driest 5-year period out of 65 5-year periods since 1939. • GSR over the 5 years prior to 2002 (1998-2002) was 7% below the average of all consecutive 5-year periods from 1939 to 2007. This period was ranked the 17th driest 5-year period out of 65 5-year periods since 1939. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 15% below the average of all consecutive 10-year periods from 1939 to 2007. This period was ranked the driest 10-year period out of 60 10-year periods since 1939.
72 GSR deciles
700
600 D1 500 D2 D3 400 D4 300 D5
Rainfall (mm) (mm) Rainfall D6 200 D7
100 D8
D9 0 D10 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 4g: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Figure 5g: For each year from 1948 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been three decile 1 years, two decile 3, one decile 5, 6, two decile 7, and one decile 9 year.
73 Appendix 1h: Climate Analysis – Smoky Bay
Peter Hayman & Bronya Alexander, SARDI
Smoky Bay rainfall data has come from observations at the Smoky Bay Post Office (met station number 18077).
Table 1h: Monthly rainfall (mm) recorded at the Smoky Bay Post Office. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 7th and 9th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1913 0 63 14 6 35 6 18 33 46 45 14 17 189 30 1914 1 0 30 26 23 17 13 10 12 10 17 11 62 51 111 4 1915 4 11 0 15 29 26 49 63 14 9 0 8 43 26 205 37 1916 2 2 4 11 37 84 88 40 43 37 16 2 16 6 340 92 1917 29 3 23 4 77 71 47 30 46 22 8 6 73 60 297 80 1918 1 0 11 12 41 43 25 43 3 28 1 21 26 9 195 33 1919 12 23 1 12 21 13 20 21 19 15 0 39 58 46 121 6 1920 8 0 7 13 42 59 60 58 46 29 37 15 54 42 307 82 1921 6 68 5 11 63 36 24 25 18 18 31 1 131 86 195 33 1922 11 0 3 39 34 51 54 24 14 4 0 34 46 28 220 43 1923 11 3 1 7 43 84 63 49 25 15 1 27 49 31 286 77 1924 9 18 9 5 26 48 2 42 24 36 14 9 64 56 183 27 1925 3 12 0 24 49 34 63 26 27 8 16 4 38 20 231 50 1926 0 2 14 39 53 78 30 44 12 3 2 9 36 17 259 67 1927 6 9 24 1 18 65 36 35 8 2 7 4 50 34 165 21 1928 4 15 1 2 16 45 51 10 13 35 0 0 31 14 172 24 1929 1 0 4 11 16 36 32 34 22 19 20 1 5 1 170 22 1930 0 1 0 3 28 2 28 36 9 27 19 3 22 8 133 10 1931 8 0 33 22 41 78 44 21 18 7 21 0 63 53 231 50 1932 0 53 2 29 21 81 36 67 33 20 0 31 76 62 287 78 1933 14 0 11 16 53 18 54 42 25 13 40 4 56 44 221 44 1934 1 10 9 24 4 27 29 47 15 7 40 0 64 56 153 16 1935 3 0 60 23 22 31 21 38 47 37 18 2 103 80 219 42 1936 0 10 0 9 22 41 45 31 2 35 8 18 30 11 185 28 1937 2 28 2 8 43 40 29 50 17 15 49 8 58 46 202 36 1938 13 102 2 35 29 76 58 40 3 4 0 15 174 94 245 63
74 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1939 20 4 47 7 26 60 51 57 1 17 34 0 86 72 219 42 1940 40 7 13 38 14 7 30 1 14 11 4 0 94 77 115 5 1941 13 0 20 21 4 17 70 20 42 20 12 4 37 18 194 31 1942 32 3 15 10 23 103 73 49 30 11 46 34 66 57 299 81 1943 6 29 0 16 25 32 35 34 18 12 3 5 115 84 172 24 1944 0 1 0 32 36 11 41 7 6 9 19 10 9 3 142 13 1945 13 10 0 0 15 23 5 12 23 19 28 61 52 39 97 2 1946 8 40 5 19 23 46 45 16 2 8 18 26 142 90 159 19 1947 5 6 29 23 30 35 44 39 42 37 9 3 84 68 250 65 1948 0 0 0 12 28 20 20 28 6 26 29 16 12 5 140 12 1949 3 20 0 11 29 31 55 18 54 39 45 6 68 58 237 57 1950 3 24 0 0 19 34 46 63 12 69 14 29 78 63 243 61 1951 0 0 9 24 80 48 125 76 18 21 0 16 52 39 392 94 1952 16 3 5 40 128 32 45 44 21 19 53 15 40 22 329 90 1953 10 16 9 6 12 89 48 70 22 34 16 75 103 80 281 75 1954 17 0 1 50 20 79 22 21 34 22 12 22 109 81 248 64 1955 7 43 5 11 68 114 33 46 22 17 30 6 89 75 311 84 1956 2 5 7 64 108 102 61 41 25 79 4 9 50 34 480 95 1957 1 3 2 5 13 70 60 27 20 4 3 41 19 7 199 35 1958 0 0 42 9 86 5 52 56 65 44 10 27 86 72 317 87 1959 0 9 5 2 10 10 48 3 9 5 37 15 51 36 87 1 1960 9 52 1 53 53 23 75 54 66 5 20 12 114 82 329 90 1961 0 17 0 72 5 27 26 39 11 6 22 1 49 31 186 29 1962 0 5 20 4 52 14 25 35 24 45 2 6 48 29 199 35 1963 13 6 0 19 45 85 60 28 7 0 1 0 27 10 244 62 1964 0 7 0 18 61 61 56 30 77 13 34 1 8 2 316 86 1965 0 0 0 7 48 23 22 57 20 6 0 20 35 15 183 27 1966 5 14 47 3 43 57 31 57 40 49 18 63 86 72 280 73 1967 35 17 0 1 8 13 64 24 18 0 1 4 133 88 128 8 1968 50 18 84 24 63 107 51 61 14 10 14 20 157 91 330 91 1969 2 56 67 33 47 39 36 18 39 0 6 2 159 92 212 40 1970 3 0 1 8 35 25 22 41 28 4 9 2 12 5 163 20 1971 1 3 67 53 17 57 33 50 20 3 36 15 82 64 233 51 1972 5 4 0 6 5 41 63 92 4 24 7 0 60 49 235 54 1973 4 9 43 18 26 55 56 72 33 13 9 26 63 53 273 70 1974 3 0 2 55 43 25 85 21 44 46 9 2 40 22 319 88 1975 0 28 11 16 28 13 29 34 57 48 9 2 50 34 225 46 1976 6 66 2 2 23 26 13 12 28 43 18 3 85 69 147 15
75 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1977 15 15 3 0 36 9 7 7 33 7 48 11 54 42 99 3 1978 20 0 4 5 21 46 90 60 58 6 25 4 83 66 286 77 1979 0 0 2 17 89 21 56 45 107 8 46 5 31 14 343 93 1980 2 0 0 52 55 36 45 15 20 36 45 22 53 40 259 67 1981 3 51 16 0 30 130 53 47 2 10 30 4 137 89 272 69 1982 5 12 46 12 25 51 29 4 21 4 0 16 97 78 146 14 1983 3 13 83 127 35 16 57 45 19 11 18 16 115 84 310 83 1984 6 0 12 21 25 16 86 62 42 19 11 14 52 39 271 68 1985 5 0 15 22 15 23 26 101 14 33 5 21 45 27 234 53 1986 5 5 0 11 29 42 90 38 32 39 5 16 36 17 281 75 1987 17 31 0 8 27 39 41 24 13 6 1 26 69 59 158 18 1988 18 8 8 0 33 32 33 13 21 5 24 19 61 50 137 11 1989 3 0 13 17 26 59 74 20 14 1 10 14 59 48 211 39 1990 2 25 8 12 22 48 68 31 36 23 0 87 59 48 240 60 1991 1 0 2 30 16 63 44 56 24 1 29 0 90 76 234 53 1992 2 16 36 33 21 33 25 67 59 74 49 73 83 66 312 85 1993 36 1 1 1 22 20 27 37 27 89 12 21 160 93 223 45 1994 0 10 0 0 25 51 28 12 15 24 11 2 43 26 155 17 1995 105 5 9 32 51 52 72 27 24 20 7 13 132 87 278 71 1996 1 7 10 2 15 53 50 61 42 13 11 8 38 20 236 55 1997 15 15 2 0 48 18 13 37 77 34 26 78 51 36 227 47 1998 5 15 7 46 15 42 90 9 23 6 18 7 131 86 231 50 1999 11 7 12 1 31 24 20 20 61 51 14 3 55 43 208 38 2000 36 20 15 10 29 32 56 90 23 40 4 0 88 74 280 73 2001 17 14 7 0 52 42 28 36 56 25 25 50 42 23 239 59 2002 8 0 1 9 30 54 46 17 10 11 9 27 84 68 177 25 2003 17 17 4 21 17 55 43 100 15 40 11 9 74 61 291 79 2004 4 9 10 25 15 47 38 79 33 0 21 2 43 26 237 57 2005 7 1 0 5 15 63 35 35 65 20 14 11 31 14 238 58 2006 20 11 32 15 37 23 45 5 2 0 10 1 88 74 127 7 2007 2 1 50 49 37 7 14 10 4 9 49 40 64 56 130 9
76 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5
150 9 150 9 Rainfall (mm) 1998 (mm) Rainfall 1999 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5 150 9 150 9
Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5
150 9 150 9
Rainfall(mm) 2002 Rainfall(mm) 2003 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5 9 150 150 9 Rainfall(mm) 2004 Rainfall(mm) 2005 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
350 350
300 300
250 250 1 1 200 200 5 5
150 9 150 9
Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007 100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1h: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
77 GSR
600
500
400
GSR 300 5yr running mean
200 Rainfall (mm) (mm) Rainfall
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2h: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
50%
40%
30%
20%
10%
0%
-10%
-20% Departure from mean mean from Departure -30%
-40% 1917 1920 1923 1926 1929 1932 1935 1938 1941 1944 1947 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007
Figure 3h: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 11% below the average of all consecutive 5-year periods from 1913 to 2007. This period was ranked the 19th driest 5-year period out of 91 5-year periods since 1913. • GSR over the 5 years prior to 2002 (1998-2002) was 1% below the average of all consecutive 5-year periods from 1913 to 2007. This period was ranked the 50th driest 5-year period out of 91 5-year periods since 1913. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 6% below the average of all consecutive 10-year periods from 1913 to 2007. This period was ranked the 24th driest 10-year period out of 86 10-year periods since 1913.
78 GSR deciles
600
500 D1
D2 400 D3
300 D4 D5
Rainfall (mm) (mm) Rainfall 200 D6 D7 100 D8
D9 0 D10 1913 1917 1921 1925 1929 1933 1937 1941 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 Figure 4h: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Figure 5h: For each year from 1922 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been two decile 1 years, one decile 3, 4, 5, 6, two decile 7, one decile 8, and one decile 9 year.
79 Appendix 1i: Climate Analysis – Streaky Bay
Peter Hayman & Bronya Alexander, SARDI
Streaky Bay rainfall data has come from observations at the Streaky Bay Post Office (met station number 18079).
Table 1i: Monthly rainfall (mm) recorded at the Streaky Bay Post Office. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 2nd and 12th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1878 0 4 50 48 24 74 108 41 32 36 10 4 363 108 1879 2 1 12 26 52 23 52 29 59 23 46 9 29 13 264 44 1880 5 0 33 103 33 46 44 52 46 21 8 3 93 94 345 94 1881 14 0 0 8 34 90 15 34 21 13 5 7 25 11 215 17 1882 60 0 5 55 71 38 36 110 10 22 59 14 77 77 342 93 1883 2 4 18 59 120 92 58 62 36 18 24 3 97 99 445 124 1884 8 1 10 61 83 85 14 11 61 13 16 6 46 39 328 83 1885 9 42 33 57 34 93 28 47 32 32 2 1 106 104 323 77 1886 16 0 0 36 22 13 41 84 46 32 3 4 19 7 274 52 1887 4 5 1 30 55 117 49 36 40 50 47 12 17 3 377 110 1888 17 11 2 2 69 95 86 25 22 5 8 12 89 88 304 66 1889 86 1 52 20 72 140 48 69 51 11 11 9 159 125 411 117 1890 1 24 0 28 48 191 153 50 23 60 10 11 45 37 553 129 1891 10 0 0 18 21 76 103 38 33 15 12 7 31 17 304 66 1892 8 0 13 14 59 35 44 52 102 53 13 16 40 31 359 105 1893 6 0 0 23 90 107 54 34 33 16 15 16 35 24 357 102 1894 3 0 10 16 72 57 48 73 22 42 1 1 44 35 330 84 1895 10 4 18 60 3 45 87 38 46 8 5 1 34 23 287 56 1896 74 32 4 15 47 81 87 47 20 0 6 22 116 114 297 62 1897 0 28 23 5 14 75 26 52 16 10 0 5 79 81 198 10 1898 0 9 8 40 32 75 55 33 8 21 5 4 22 9 264 44 1899 8 13 6 10 53 53 33 22 53 10 19 11 36 26 234 25 1900 1 0 62 40 73 92 61 50 33 8 10 2 93 94 357 102 1901 1 119 8 30 38 55 48 56 31 28 7 13 140 124 286 55 1902 12 2 44 1 21 80 50 19 42 44 19 28 78 79 257 37 1903 22 5 25 38 72 54 65 30 44 17 27 9 99 102 320 73
80 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1904 12 20 12 15 25 79 80 22 3 27 6 5 80 83 251 34 1905 11 2 7 37 58 49 65 57 37 35 2 0 31 17 338 88 1906 1 14 7 6 28 42 45 93 61 15 106 1 24 10 290 59 1907 3 0 4 19 34 78 48 53 20 7 29 18 114 111 259 39 1908 15 3 26 14 61 105 30 44 27 12 7 7 91 91 293 60 1909 3 10 4 59 37 73 104 107 13 42 14 0 31 17 435 121 1910 0 1 3 0 122 46 139 21 56 35 19 7 18 5 419 119 1911 0 35 14 1 97 66 46 63 45 24 8 63 75 74 342 93 1912 0 18 26 4 12 90 81 37 48 18 38 1 115 112 290 59 1913 0 59 18 16 33 7 25 39 30 49 7 31 116 114 199 11 1914 0 0 36 31 31 22 35 12 20 16 17 20 74 72 167 3 1915 4 3 4 25 36 43 77 130 15 10 0 4 48 41 336 86 1916 6 0 9 10 31 119 93 45 35 29 25 1 19 7 362 106 1917 32 11 23 15 80 133 86 46 55 21 12 12 92 92 436 122 1918 0 1 15 13 42 44 36 61 10 38 2 12 40 31 244 29 1919 14 32 2 7 23 17 44 27 35 18 1 27 62 63 171 4 1920 3 1 10 20 72 155 92 73 53 25 33 12 42 34 490 128 1921 3 84 7 7 66 55 25 37 20 31 30 7 139 123 241 26 1922 15 0 5 38 37 73 62 48 10 3 1 60 57 56 271 49 1923 13 0 1 6 36 133 67 61 42 26 2 38 75 74 371 109 1924 7 15 19 5 55 47 4 37 43 76 6 6 81 84 267 45 1925 12 11 1 22 96 43 77 45 39 16 14 2 36 26 338 88 1926 0 1 16 36 56 90 24 82 28 24 2 12 33 20 340 90 1927 3 13 24 2 29 90 61 63 13 4 24 3 54 49 262 40 1928 7 12 0 4 38 90 78 30 19 44 1 4 46 39 303 64 1929 1 1 6 8 18 71 64 65 23 21 45 5 13 1 270 48 1930 1 2 1 11 32 4 50 49 10 51 12 2 54 49 207 15 1931 8 2 16 42 62 94 67 38 31 21 29 0 40 31 355 99 1932 1 58 3 41 25 84 55 95 59 24 3 18 91 91 383 112 1933 6 0 11 18 81 15 65 95 30 12 33 7 38 27 316 70 1934 2 5 10 36 3 66 67 39 36 21 44 0 57 56 268 47 1935 9 0 82 34 44 57 62 45 83 38 20 4 135 120 363 108 1936 0 4 0 10 29 38 80 50 6 38 9 22 28 12 251 34 1937 10 9 11 6 46 52 63 81 43 24 64 27 61 62 315 68 1938 7 133 1 28 36 141 43 71 9 3 1 16 232 129 331 85 1939 36 4 42 12 25 71 65 94 9 22 35 9 99 102 298 63 1940 27 7 21 68 44 11 55 15 26 26 11 2 99 102 245 30 1941 22 4 27 18 5 19 66 36 61 58 38 2 66 68 263 41
81 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1942 26 5 7 12 31 148 88 49 45 15 64 27 78 79 388 113 1943 9 36 1 21 18 60 48 61 22 14 5 7 137 121 244 29 1944 0 4 2 36 61 14 63 8 11 11 12 5 18 5 204 13 1945 9 14 0 0 32 54 10 24 43 23 43 56 40 31 186 8 1946 38 42 10 19 34 52 62 28 6 15 44 23 189 127 216 18 1947 10 19 16 30 19 64 84 42 45 56 15 10 112 110 340 90 1948 1 3 1 20 32 35 39 66 9 45 16 7 30 14 246 31 1949 2 25 0 3 43 34 51 20 56 69 44 8 50 46 276 53 1950 2 14 1 6 27 61 44 99 16 73 19 18 69 69 326 82 1951 0 1 12 47 115 71 150 93 23 61 2 15 50 46 560 130 1952 7 4 6 45 129 19 45 53 39 29 43 27 34 23 359 105 1953 9 24 8 6 15 86 42 55 59 31 17 49 111 108 294 61 1954 12 1 1 50 22 81 31 26 17 22 16 24 80 83 249 32 1955 15 28 8 18 42 136 40 48 18 22 27 8 91 91 324 78 1956 6 6 16 55 116 107 66 46 35 55 6 10 63 64 480 127 1957 0 1 4 3 14 50 75 20 17 6 6 33 21 8 185 7 1958 1 1 36 6 48 4 52 67 58 38 13 22 77 77 273 50 1959 0 8 7 18 7 13 26 10 41 9 43 12 50 46 124 1 1960 12 68 3 44 50 30 69 38 52 6 23 8 138 122 289 57 1961 0 14 3 66 10 45 27 41 20 5 27 1 48 41 214 16 1962 0 8 24 4 77 35 22 48 32 46 1 8 60 61 264 44 1963 11 10 2 45 73 70 94 51 17 2 4 1 32 18 352 97 1964 6 3 1 23 65 70 92 37 86 28 48 3 15 2 401 116 1965 1 0 3 7 56 34 57 60 21 7 7 16 55 52 242 27 1966 4 24 43 5 40 60 50 81 42 42 14 44 94 96 320 73 1967 35 13 1 1 9 15 120 49 24 2 1 5 107 107 220 19 1968 26 21 65 59 63 123 50 90 30 29 23 26 118 115 444 123 1969 4 35 33 28 67 49 49 19 41 3 7 9 121 117 256 36 1970 13 0 4 16 47 34 48 69 65 6 16 5 33 20 285 54 1971 6 6 74 76 108 60 43 81 34 10 35 23 107 107 412 118 1972 7 10 1 21 8 47 78 72 18 24 4 1 76 75 268 47 1973 13 7 54 29 46 103 45 93 68 14 11 13 79 81 398 114 1974 11 1 9 27 55 43 114 28 63 69 25 5 45 37 399 115 1975 3 9 22 14 63 27 57 36 51 78 28 13 64 66 326 82 1976 1 113 5 11 34 54 16 33 36 42 40 1 160 126 226 20 1977 21 19 4 2 70 18 21 19 38 23 56 13 85 86 191 9 1978 23 3 5 21 41 88 112 110 87 8 24 14 100 103 467 126 1979 0 1 2 27 76 21 89 60 132 16 45 9 41 32 421 120
82 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1980 2 2 0 51 71 57 61 24 5 56 35 17 58 58 325 79 1981 1 66 15 2 51 149 65 65 5 18 23 8 134 119 355 99 1982 6 19 56 14 41 75 32 6 29 8 0 11 112 110 205 14 1983 1 7 102 118 36 11 89 31 23 8 2 25 121 117 316 70 1984 10 0 14 37 40 20 94 86 50 31 10 16 51 47 358 103 1985 3 0 13 30 17 36 47 118 27 40 8 32 42 34 315 68 1986 5 15 0 21 38 51 120 51 46 52 10 13 60 61 379 111 1987 21 37 1 18 38 59 66 31 10 11 2 18 82 85 233 23 1988 38 3 9 1 37 39 43 20 35 4 22 13 70 70 179 6 1989 5 0 15 14 36 68 128 44 31 5 16 28 55 52 326 82 1990 8 27 7 10 25 63 108 47 41 28 4 87 86 87 322 76 1991 2 0 4 26 35 73 124 48 50 1 15 0 97 99 357 102 1992 2 4 44 38 29 53 39 109 87 91 44 94 65 67 446 125 1993 53 4 2 2 21 23 41 47 28 72 22 17 197 128 234 25 1994 3 15 0 1 26 67 31 15 11 25 6 5 57 56 176 5 1995 76 2 8 26 74 89 90 18 25 26 20 13 97 99 348 95 1996 0 6 10 10 13 73 75 82 61 6 15 11 49 42 320 73 1997 31 5 2 0 50 34 26 51 70 27 22 67 64 66 258 38 1998 8 7 3 43 11 66 88 13 24 10 18 14 107 107 255 35 1999 5 8 10 2 29 43 32 28 51 48 14 6 55 52 233 23 2000 32 13 29 8 42 51 92 92 24 41 10 3 94 96 350 96 2001 10 10 17 1 53 55 36 45 51 33 20 28 50 46 274 52 2002 6 0 4 17 51 55 59 23 11 15 14 13 58 58 231 21 2003 12 31 1 9 28 61 38 128 19 38 23 9 71 71 321 74 2004 4 11 12 31 29 42 63 95 61 1 20 1 59 59 322 76 2005 8 2 3 7 17 99 65 46 77 31 25 13 34 23 342 93 2006 39 20 37 20 39 36 49 5 3 0 16 3 134 119 152 2 2007 5 2 31 42 44 26 34 14 12 30 13 34 57 56 202 12
83 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9 Rainfall (mm) 1998 (mm) Rainfall 1999 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall(mm) 2002 Rainfall(mm) 2003 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9 Rainfall(mm) 2004 Rainfall(mm) 2005 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
450 450
400 400
350 350
300 300 1 1 250 5 250 5 200 9 200 9
Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007 150 150
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1i: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
84 GSR
600
500
400
GSR 300 5yr running mean
200 Rainfall (mm) (mm) Rainfall
100
0 1900 1920 1940 1960 1980 2000 2020
Figure 2i: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
40%
30%
20%
10%
0%
-10%
-20% Departure from mean mean from Departure -30%
-40% 1882 1887 1892 1897 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
Figure 3i: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 13% below the average of all consecutive 5-year periods from 1878 to 2007. This period was ranked the 13th driest 5-year period out of 126 5-year periods since 1878. • GSR over the 5 years prior to 2002 (1998-2002) was 13% below the average of all consecutive 5-year periods from 1878 to 2007. This period was ranked the 14th driest 5-year period out of 126 5-year periods since 1878. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 13% below the average of all consecutive 10-year periods from 1878 to 2007. This period was ranked the 5th driest 10-year period out of 121 10-year periods since 1878.
85 GSR deciles
600
500 D1
D2 400 D3
300 D4 D5
Rainfall (mm) (mm) Rainfall 200 D6 D7 100 D8
D9 0 D10 1878 1883 1888 1893 1898 1903 1908 1913 1918 1923 1928 1933 1938 1943 1948 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 Figure 4i: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1887 1892 1897 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 Figure 5: For each year from 1887 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been two decile 1 years, two decile 2, one decile 3, 4, two decile 6, one decile 7, and one decile 8 year.
86 Appendix 1j: Climate Analysis – Tumby Bay
Peter Hayman & Bronya Alexander, SARDI
Tumby Bay rainfall data has come from observations at the Tumby Bay Post Office (met station number 18086).
Table 1j: Monthly rainfall (mm) recorded at the Tumby Bay Post Office. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 3rd and 11th driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1907 0 0 8 84 28 45 44 35 17 31 13 16 284 64 1908 1 12 7 59 51 72 20 40 66 31 2 11 49 20 339 91 1909 9 10 21 36 28 52 62 96 23 26 30 9 53 24 323 88 1910 4 0 54 4 75 55 130 46 61 42 34 9 97 72 413 99 1911 2 39 6 5 51 50 65 26 55 42 19 53 90 65 294 67 1912 1 11 73 12 14 52 55 24 47 33 30 21 157 93 237 47 1913 1 11 35 6 42 8 29 40 42 50 7 33 98 75 217 32 1914 5 1 53 39 35 5 15 4 14 9 32 35 99 76 121 1 1915 6 1 11 34 29 58 55 62 61 10 4 4 85 61 309 81 1916 6 1 9 23 27 107 95 42 27 42 47 14 24 2 363 95 1917 9 55 28 5 48 49 67 61 91 46 21 19 153 92 367 96 1918 8 3 10 12 41 34 47 68 9 36 2 22 61 35 247 52 1919 18 19 7 7 59 13 24 36 40 19 4 11 68 45 198 22 1920 1 0 4 25 42 89 63 44 57 36 48 33 20 1 356 93 1921 36 13 12 9 46 72 72 35 48 20 55 9 142 90 302 72 1922 16 3 3 21 28 46 53 51 18 15 1 36 86 62 232 42 1923 10 3 1 2 53 75 45 47 63 37 4 56 51 22 322 87 1924 5 9 5 9 48 48 11 39 39 120 47 10 79 56 314 83 1925 19 8 1 38 58 19 44 30 38 8 49 4 85 61 235 46 1926 0 3 18 26 46 44 33 54 47 24 2 52 74 53 274 59 1927 3 33 28 8 41 33 49 49 15 2 39 12 118 86 197 20 1928 7 45 8 7 30 60 38 15 19 43 4 1 111 80 212 28 1929 1 12 8 4 12 38 42 53 59 19 30 36 26 3 227 40 1930 0 50 2 18 9 10 35 67 31 28 16 4 118 86 198 22 1931 12 0 11 17 40 53 78 38 37 5 17 1 43 13 268 55 1932 5 37 10 60 43 76 41 70 76 73 6 2 70 47 439 100
87 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1933 18 0 5 19 64 39 60 51 55 15 14 8 31 5 303 74 1934 2 18 88 86 14 29 18 57 73 26 35 4 130 88 303 74 1935 8 1 33 27 36 50 57 53 21 62 24 19 81 57 306 78 1936 7 12 0 12 26 39 74 33 5 30 16 15 62 36 219 34 1937 17 23 1 5 37 50 26 33 36 18 31 40 72 50 205 26 1938 8 96 2 41 11 55 25 51 10 1 2 17 177 98 194 18 1939 25 15 5 14 17 112 52 99 4 33 62 2 64 38 331 90 1940 19 2 12 24 43 17 110 21 17 39 18 4 97 72 271 56 1941 40 5 23 7 10 16 34 44 63 26 19 20 90 65 200 23 1942 11 39 5 20 72 69 42 40 63 19 37 6 94 69 325 89 1943 21 28 1 16 12 42 64 56 65 27 7 7 93 68 282 62 1944 0 15 3 60 49 9 38 10 21 39 16 12 32 6 226 39 1945 11 15 1 7 30 54 15 42 30 41 45 71 55 26 219 34 1946 31 70 25 13 41 50 85 19 14 8 60 37 242 100 230 41 1947 6 16 10 31 8 34 102 23 51 51 32 13 129 87 300 71 1948 1 8 2 23 17 8 31 38 12 28 26 26 56 28 157 10 1949 8 36 2 6 30 20 41 27 30 60 57 5 98 75 214 29 1950 4 8 2 41 28 36 20 70 20 68 4 13 76 54 283 63 1951 4 20 7 19 46 46 87 64 16 31 4 19 48 19 309 81 1952 28 10 6 28 114 40 28 32 43 22 25 16 67 44 307 79 1953 4 21 4 24 13 68 42 38 47 13 22 56 70 47 245 51 1954 49 2 3 52 23 43 39 17 19 27 23 21 132 89 220 35 1955 4 53 17 41 27 102 19 67 20 29 35 10 118 86 305 77 1956 11 6 25 26 66 117 92 42 41 57 5 15 87 63 441 101 1957 1 1 9 5 15 31 38 28 20 11 6 6 31 5 148 7 1958 6 2 21 11 62 11 87 55 58 37 13 11 41 10 321 86 1959 1 22 24 4 16 13 22 21 38 17 28 10 71 49 131 3 1960 18 46 11 53 61 21 37 42 80 4 26 15 113 81 298 70 1961 0 5 2 62 22 11 45 61 12 12 6 1 48 19 225 38 1962 2 11 13 13 42 27 14 35 19 51 5 26 33 8 201 24 1963 30 31 3 45 60 62 77 36 23 12 2 25 95 70 315 84 1964 6 31 2 56 29 68 109 29 48 20 74 11 66 42 359 94 1965 0 1 6 8 44 42 45 49 22 2 16 16 92 66 212 28 1966 5 3 27 11 30 54 57 41 66 28 18 133 67 44 287 65 1967 7 26 0 7 28 9 38 34 39 14 0 5 184 99 169 15 1968 14 7 47 56 39 70 82 46 28 29 35 22 73 52 350 92 1969 4 70 28 26 33 47 43 33 48 3 12 11 159 94 233 44 1970 14 1 7 27 19 33 21 55 61 6 20 3 45 14 222 36
88 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1971 1 5 44 55 35 37 14 73 60 17 25 37 73 52 291 66 1972 61 27 1 7 6 32 53 57 14 18 2 12 151 91 187 17 1973 3 2 37 16 61 39 48 61 38 42 2 16 56 28 305 77 1974 80 7 4 20 62 7 82 40 16 69 6 1 109 79 296 68 1975 5 7 21 15 47 19 46 56 63 68 17 13 40 9 314 83 1976 12 22 2 9 15 32 19 21 33 37 11 7 66 42 166 14 1977 5 14 6 5 22 32 20 24 35 14 29 5 43 13 152 8 1978 10 1 1 7 27 104 87 78 81 10 15 13 46 16 394 97 1979 1 27 1 23 56 19 40 55 101 25 56 18 57 29 319 85 1980 3 0 0 98 38 47 39 16 16 51 13 14 77 55 305 77 1981 20 25 30 3 30 78 44 65 6 17 18 2 102 78 243 50 1982 12 5 21 14 19 43 9 19 18 10 0 8 58 33 132 4 1983 3 7 80 84 13 35 87 41 21 16 16 32 98 75 297 69 1984 3 1 9 16 15 14 65 58 41 24 20 4 61 35 233 44 1985 10 5 12 36 8 23 14 93 25 24 13 14 51 22 223 37 1986 6 0 0 13 17 17 52 54 30 32 11 12 33 8 215 30 1987 10 23 2 14 41 19 45 22 3 12 3 35 58 33 156 9 1988 17 7 9 2 33 23 47 16 18 2 29 27 71 49 141 5 1989 1 0 8 32 44 50 80 38 24 8 10 13 65 40 276 60 1990 20 11 4 7 12 70 65 60 41 18 0 93 58 33 273 58 1991 4 0 5 20 13 37 26 40 22 4 18 1 102 78 162 12 1992 5 5 53 90 42 29 29 75 82 52 27 99 82 58 399 98 1993 29 3 14 1 16 16 36 49 27 50 20 16 172 97 195 19 1994 3 7 0 2 28 62 15 11 14 15 5 14 46 16 147 6 1995 116 11 18 8 39 60 84 20 26 20 16 9 164 95 257 54 1996 3 12 8 15 12 66 68 63 46 11 8 13 48 19 281 61 1997 10 19 8 0 39 25 11 40 56 45 18 36 58 33 216 31 1998 1 3 5 42 16 58 47 38 16 23 27 14 63 37 240 49 1999 15 2 35 1 34 33 31 18 29 56 19 17 93 68 202 25 2000 8 64 10 33 41 45 14 41 18 43 2 1 118 86 235 46 2001 3 13 23 3 48 36 37 47 48 20 51 28 42 11 239 48 2002 1 0 4 8 24 34 30 22 26 19 40 5 84 59 163 13 2003 21 46 2 8 58 38 28 55 37 48 15 11 114 82 272 57 2004 9 9 9 17 7 35 34 55 20 3 21 11 53 24 171 16 2005 2 4 17 4 5 67 19 35 60 67 19 20 55 26 257 54 2006 30 36 60 13 12 47 44 10 5 0 6 21 165 96 131 3 2007 22 1 15 12 36 38 32 7 12 22 27 39 65 40 159 11
89 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150
Rainfall (mm) 150 1998 (mm) Rainfall 1999
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall(mm) 2002 Rainfall(mm) 2003
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 Rainfall(mm) 150 2004 Rainfall(mm) 2005
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1j: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
90 GSR
500 450 400 350 300 GSR 250 5yr running mean 200
Rainfall (mm) (mm) Rainfall 150 100 50 0 1900 1920 1940 1960 1980 2000 2020
Figure 2j: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
40%
30%
20%
10%
0%
-10% Departure from mean mean from Departure -20%
-30% 1911 1915 1919 1923 1927 1931 1935 1939 1943 1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007
Figure 3j: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 23% below the average of all consecutive 5-year periods from 1907 to 2007. This period was ranked the 2nd driest 5-year period out of 97 5-year periods since 1907. • GSR over the 5 years prior to 2002 (1998-2002) was 16% below the average of all consecutive 5-year periods from 1907 to 2007. This period was ranked the 7th driest 5-year period out of 97 5-year periods since 1907. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 19% below the average of all consecutive 10-year periods from 1907 to 2007. This period was ranked the driest 10-year period out of 92 10-year periods since 1907.
91 GSR deciles
500 450 400 D1 350 D2 300 D3 250 D4 200 D5
Rainfall (mm) (mm) Rainfall D6 150 D7 100 D8 50 D9 0 D10 1907 1911 1915 1919 1923 1927 1931 1935 1939 1943 1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 Figure 4j: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 Figure 5j: For each year from 1916 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been two decile 1 years, two decile 2, one decile 3, three decile 5, and two decile 6 years.
92 Appendix 1k: limate Analysis – Wharminda
Peter Hayman & Bronya Alexander, SARDI
Wharminda rainfall data has come from observations at the Wharminda, Verran site (met station number 18113).
Table 1k: Monthly rainfall (mm) recorded at Wharmindo Verran. The Growing Season Rainfall (GSR) is the cumulative April to October rainfall, and the non-GSR is November and December of the previous year, plus January to March of the given year. Rank is from lowest to highest, so GSR in the last two years were the 3rd and 32nd driest.
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1914 9 2 38 50 34 4 41 6 22 12 11 21 169 15 1915 10 1 56 48 48 75 56 114 51 8 5 6 99 73 400 91 1916 4 1 7 14 29 90 96 43 55 47 7 2 23 5 374 89 1917 13 83 22 12 29 24 57 28 56 10 13 18 127 83 216 40 1918 0 3 4 12 25 42 21 72 7 33 0 7 38 10 212 34 1919 10 57 1 2 37 13 28 32 30 30 0 12 75 47 172 17 1920 14 0 0 15 27 67 56 32 66 47 59 20 26 6 310 80 1921 52 39 31 49 69 67 31 18 50 24 47 0 201 91 308 79 1922 19 0 0 11 26 24 36 40 18 17 0 77 66 36 172 17 1923 3 0 0 7 45 135 81 39 63 44 0 61 80 54 414 94 1924 8 8 7 4 48 47 9 37 32 108 21 4 84 64 285 68 1925 4 10 0 32 46 11 37 54 60 17 45 0 39 11 257 55 1926 0 6 32 41 77 54 59 34 45 37 0 34 83 62 347 84 1927 1 26 17 2 46 38 45 54 8 3 40 4 78 49 196 27 1928 34 31 0 2 12 79 44 21 18 47 3 0 109 77 223 46 1929 2 8 3 1 16 37 29 50 27 0 29 68 16 2 160 9 1930 0 21 0 15 6 0 45 47 23 52 11 33 118 79 188 23 1931 17 0 13 10 47 49 60 41 38 9 10 1 74 45 254 54 1932 2 50 4 57 34 52 60 32 32 29 2 2 67 39 296 71 1933 0 0 13 22 66 42 37 70 50 11 17 7 17 3 298 75 1934 0 12 24 57 1 35 23 25 53 29 37 0 60 33 223 46 1935 11 0 35 39 32 30 45 42 31 67 14 15 83 62 286 69 1936 12 14 0 6 5 26 61 24 9 31 11 23 55 31 162 12 1937 20 27 1 9 26 37 23 36 32 14 27 46 82 60 177 18 1938 4 61 4 32 10 39 27 57 13 2 0 20 142 87 180 21 1939 32 10 10 9 16 57 39 117 9 17 60 1 72 41 264 59
93 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1940 20 2 9 13 27 9 64 16 14 15 15 1 92 68 158 8 1941 42 2 34 3 13 15 35 34 59 40 15 3 94 70 199 29 1942 12 10 4 20 55 71 44 47 66 12 25 16 44 18 315 82 1943 9 23 0 14 6 26 43 48 41 37 1 5 73 42 215 39 1944 0 9 0 58 37 8 31 6 17 21 16 9 15 1 178 19 1945 12 11 0 3 23 46 15 43 40 43 30 86 48 22 213 35 1946 27 92 13 8 32 35 59 16 11 4 31 63 248 93 165 13 1947 2 15 25 19 14 34 69 29 48 47 13 13 136 85 260 57 1948 0 3 0 19 22 13 32 30 9 30 28 32 29 7 155 7 1949 0 22 0 5 41 18 43 19 37 57 67 6 82 60 220 41 1950 0 7 1 40 32 28 19 58 22 55 3 74 81 55 254 54 1951 0 6 7 20 40 59 68 59 27 25 2 20 90 66 298 75 1952 14 5 0 52 87 25 33 28 42 18 23 13 41 14 285 68 1953 0 2 10 13 13 63 43 36 58 27 14 42 48 22 253 51 1954 23 0 1 42 13 53 34 10 20 39 11 23 80 54 211 33 1955 2 32 6 47 34 99 15 65 21 31 23 6 74 45 312 81 1956 5 19 22 26 52 108 92 32 46 46 7 9 75 47 402 92 1957 0 1 5 2 16 23 33 30 18 12 3 23 22 4 134 5 1958 0 0 23 12 55 7 60 59 61 27 6 28 49 23 281 65 1959 0 13 37 2 22 5 16 10 22 15 22 14 84 64 92 1 1960 0 36 6 43 54 17 38 28 77 1 20 12 78 49 258 56 1961 1 10 4 47 14 6 42 60 23 2 19 1 47 19 194 25 1962 0 11 10 4 52 24 14 28 14 63 5 38 41 14 199 29 1963 26 15 8 60 61 51 78 29 14 13 1 2 92 68 306 78 1964 8 22 0 55 30 50 85 34 83 30 64 12 33 8 367 88 1965 0 0 3 5 51 54 28 53 23 8 27 22 79 50 222 44 1966 10 12 32 12 29 55 62 32 76 29 14 142 103 76 295 70 1967 20 24 1 4 17 13 40 39 30 19 1 1 201 91 162 12 1968 32 14 80 67 44 77 73 64 36 43 27 26 128 84 404 93 1969 5 80 12 31 28 47 47 25 40 3 14 7 150 88 221 42 1970 13 1 7 34 16 35 21 72 37 0 21 10 42 15 215 39 1971 0 2 21 45 43 35 43 55 65 12 40 28 54 29 298 75 1972 42 17 0 9 3 26 53 76 24 10 6 7 127 83 201 30 1973 7 2 32 23 31 55 55 88 61 42 12 20 54 29 355 85 1974 72 0 18 27 90 14 83 45 37 70 14 2 122 81 366 86 1975 3 2 17 10 33 12 50 48 71 47 8 3 38 10 271 62 1976 4 38 0 6 28 27 20 31 21 52 7 4 53 27 185 22 1977 14 24 8 5 28 16 22 18 29 26 42 15 57 32 144 6
94 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Non- Rank GSR Rank GSR Non- GSR GSR 1978 10 2 2 7 23 71 81 70 63 11 20 12 71 40 326 83 1979 22 26 2 27 65 15 44 73 114 29 34 16 82 60 367 88 1980 3 0 0 41 63 50 32 7 17 57 10 9 53 27 267 61 1981 27 27 24 1 40 76 61 78 6 15 18 2 97 72 277 64 1982 7 1 27 17 17 37 10 14 17 6 0 12 55 31 118 2 1983 3 10 60 96 19 19 68 47 26 22 18 46 85 65 297 72 1984 6 2 10 7 25 8 62 75 34 26 13 27 82 60 237 47 1985 2 4 21 29 3 28 13 91 27 24 9 24 67 39 215 39 1986 6 5 0 17 18 21 56 59 35 33 6 12 44 18 239 49 1987 16 22 5 18 34 24 50 21 6 16 8 19 61 35 169 15 1988 12 10 2 1 39 41 36 14 27 3 38 32 51 25 161 10 1989 3 0 7 16 94 45 60 41 34 9 26 9 80 54 299 76 1990 16 19 4 8 12 52 58 53 51 20 2 95 74 45 254 54 1991 9 0 31 16 19 67 35 43 38 4 21 2 137 86 222 44 1992 5 5 70 84 38 19 25 60 101 52 55 134 103 76 379 90 1993 38 2 14 2 20 48 30 42 36 37 15 20 243 92 215 39 1994 2 11 0 2 32 54 12 10 6 17 5 11 48 22 133 4 1995 51 4 11 5 44 62 79 18 33 25 19 5 82 60 266 60 1996 1 13 12 9 11 46 63 54 70 23 22 25 50 24 276 63 1997 18 52 5 0 59 18 11 44 57 49 28 48 122 81 238 48 1998 4 8 7 32 11 50 37 42 15 20 26 16 95 71 207 31 1999 9 7 35 8 35 29 16 22 33 52 14 22 93 69 195 26 2000 4 56 14 31 34 27 85 61 31 32 4 6 110 78 301 77 2001 5 15 13 9 41 36 45 36 54 23 53 18 43 16 244 50 2002 7 0 2 8 37 26 45 31 16 17 47 14 80 54 180 21 2003 14 25 0 8 35 72 25 70 26 49 18 8 100 74 285 68 2004 9 11 15 14 15 45 41 46 28 2 18 13 61 35 191 24 2005 0 3 7 5 10 57 33 38 68 51 24 19 41 14 262 58 2006 51 12 48 14 20 25 51 9 1 0 9 15 154 89 120 3 2007 27 0 16 38 35 35 30 12 5 55 23 25 67 39 210 32
95 Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150
Rainfall (mm) 150 1998 (mm) Rainfall 1999
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) Rainfall 2000 (mm) Rainfall 2001
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall(mm) 2002 Rainfall(mm) 2003
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 Rainfall(mm) 150 2004 Rainfall(mm) 2005
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Cumulative Apr-Oct rainfall deciles Cumulative Apr-Oct rainfall deciles
400 400
350 350
300 300
250 1 250 1 5 5 200 200 9 9 150 150 Rainfall (mm) Rainfall 2006 (mm) Rainfall 2007
100 100
50 50
0 0 Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct
Figure 1k: Cumulative Apr-Oct rainfall for the last 10 years (1998-2007), shown against the decile 1, decile 5 and decile 9 amounts calculated using the full historical rainfall record.
96 GSR
450 400 350 300
250 GSR 200 5yr running mean 150 Rainfall (mm) (mm) Rainfall 100 50 0 1900 1920 1940 1960 1980 2000 2020
Figure 2k: Annual growing season rainfall (Apr-Oct), along with a 5-year running mean (mean of the previous 5-years inclusive).
5yr running mean of GSR
30%
20%
10%
0%
-10%
Departure from mean mean from Departure -20%
-30% 1918 1921 1924 1927 1930 1933 1936 1939 1942 1945 1948 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005
Figure 3k: Five-year running mean of Apr to Oct rainfall, in terms of the percent departure from the mean. In red is the 2007 amount (2003-2007) and the 2002 amount (1998-2002).
• GSR over the last 5 years (2003-2007) was 13% below the average of all consecutive 5-year periods from 1914 to 2007. This period was ranked the 15th driest 5-year period out of 90 5-year periods since 1914. • GSR over the 5 years prior to 2002 (1998-2002) was 8% below the average of all consecutive 5-year periods from 1914 to 2007. This period was ranked the 27th driest 5-year period out of 90 5-year periods since 1914. • GSR over the last 10 years from 1998-2007 (not shown in graph) was 10% below the average of all consecutive 10-year periods from 1914 to 2007. This period was ranked the 9th driest 10-year period out of 85 10-year periods since 1914.
97 GSR deciles
450 400 D1 350 D2 300 D3 250 D4
200 D5
Rainfall (mm) (mm) Rainfall 150 D6 100 D7
50 D8 D9 0 D10 1914 1918 1922 1926 1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 Figure 4k: The April-October rainfall each year, coloured according to the rainfall decile. There are equal numbers of each decile throughout the full record.
Deciles in the previous 10 years
100% 90% D10 80% D9 D8 70% D7 60% D6 50% D5 40% D4 30% D3 20% D2 10% D1 0%
1923 1926 1929 1932 1935 1938 1941 1944 1947 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007
Figure 5k: For each year from 1923 to 2007, the ten previous years (inclusive) are shown in terms of their Apr-Oct rainfall deciles, as calculated from the full record. For example, the 2007 column shows that between 1998 and 2007 there has been one decile 1 year, three decile 3, two decile 4, one decile 6, two decile 7, and one decile 9 year.
98 Appendix 3 - The challenge of understanding recent rainfall The long term growing season rainfall at Minnipa is 245mm. Recent years at Minnipa have been dry; the growing season rainfall in 2007 was only 124mm which is 49% below the long term average, and the growing season rainfall for 2006 was 101 mm, which is 58% below the long term average. The average of the two years is 112.5mm which is 54% of the long term average. The average of the last 5 years is 177.8 mm which is 27% of the long term average. The first question is how unusual is it for single year to be 49% below the long term average and for a five year period to be 27% below the long term average? We would expect a single year to vary greatly from the long term average, but the average of 5 or 10 years to vary less. Figure X shows this relationship. There are 11 grey lines which represent, from the bottom, the minimum, 10th, 20th 30th ….90th percentile and maximum. Along the x-axis are the number of years, as you move along the x-axis the grey lines get closer together, this is what we would expect. The dots on figure 1 show the last 1, 2, 3, 4 …20..30 years. Reading off the X axis, the graph shows that 2007 was 49% below the long term average and the grey lines show that it was a decile 1 year. The second dot shows that the 2-year period 2007 and 2006 was 58% below the average and the driest 2-year period compared to all other consecutive 2-year periods in the record. The line of dots along the lowest grey line show that the last 3, 4, 5 and 6-year periods leading up to 2007 were the driest run of seasons on record. It also shows that the last 20 years was not the worst on record. A second question relates to how we should interpret a 5 or 10% reduction in growing season rainfall from a climate change projection. Obviously we can’t say that because we have survived a year that was 49% below the long term average we can handle a future climate that is 10% below the long term average. A 10% decline in rainfall for a single year is expected 4 in 10 years but of all the 20-year consecutive periods since 1915 only 12% have been drier than 10% of long-term average. The last 20-year period has been about 8% below the long-term average, so a projection of 5% would be milder than the last 20 years, but a 10% drying would be worse than the last 20 years, but not the lowest 20-year period on record. a)
How different was GSR in recent years to the long-term (1915-2007) at Minnipa?
120% Max 100% 90% 80% 80% 60% 70% 40% 60% 50% 20% 40% 0% 30% -20% 20% -40% 10% Departure from mean from Departure Min -60% Prior to 2007 -80% 1yr 2yr 3yr 4yr 6yr 7yr 8yr 9yr 5yr 5yr 10yr 11yr 12yr 13yr 14yr 15yr 16yr 17yr 18yr 19yr 20yr 21yr 22yr 23yr 24yr 25yr 26yr 27yr 28yr 29yr 30yr Length of period b)
How different was GSR in recent years to the long-term (1915-2007) at Minnipa?
40% Max 30% 90% 80% 20% 70% 60% 10% 50% 40% 0% 30% -10% 20% 10% Departure from mean from Departure -20% Min Prior to 2007 -30% 6yr 7yr 8yr 9yr 5yr 5yr 10yr 11yr 12yr 13yr 14yr 15yr 16yr 17yr 18yr 19yr 20yr 21yr 22yr 23yr 24yr 25yr 26yr 27yr 28yr 29yr 30yr Length of period
99 c)
How different was GSR in recent years to the average between 1980-1999 at Minnipa?
120% Max 100% 90% 80% 80% 60% 70% 40% 60% 50% 20% 40% 0% 30% -20% 20% -40% 10% Departure from mean from Departure Min -60% Prior to 2007 -80% 1yr 2yr 3yr 4yr 6yr 7yr 8yr 9yr 5yr 5yr 10yr 11yr 12yr 13yr 14yr 15yr 16yr 17yr 18yr 19yr 20yr 21yr 22yr 23yr 24yr 25yr 26yr 27yr 28yr 29yr 30yr Length of period d)
How different was GSR in recent years to the average between 1980-1999 at Minnipa?
40% Max 30% 90% 80% 20% 70% 60% 10% 50% 40% 0% 30% -10% 20% 10% Departure from mean from Departure -20% Min Prior to 2007 -30% 6yr 7yr 8yr 9yr 5yr 5yr 10yr 11yr 12yr 13yr 14yr 15yr 16yr 17yr 18yr 19yr 20yr 21yr 22yr 23yr 24yr 25yr 26yr 27yr 28yr 29yr 30yr Length of period
Figure 14: Growing season rainfall (GSR) anomalies, in terms of departure from the long-term mean from 1915-2007 (a-b) or the mean from 1980-1999 (c-d), for lengths of consecutive years ending 2007 (bold dots) for Minnipa, Eyre Peninsula. A 1-year period refers to 2007, 2-year period refers to 2006-2007, etc up to a 30-year period being 1978-2007. The 11 grey lines show, from bottom up, the minimum, 10th, 20th, .....90th percentile and maximum anomalies from the mean for each length of period. The recent climate change projections, such as those found in the “Climate Change in Australia” report (www.climatechangeinaustralia.com.au) use the base period of 1980-1999. So a projection of, for example, 20% decline in rainfall for 2070 would suggest that the average rainfall in 2070 will be 20% lower than the average during 1980-99. Therefore, to properly compare the recent years with the projections, we repeated the above except, instead of comparing each length of period to the full data record, we compared it to the average between 1980-99. The new figures (Figures 1c-d) are very similar to the initial figures 1a-b. If it’s a 30-year period we’re interested in, people have recorded and lived through significantly drier 30- year periods on average than the last 30 years. But if we’re more interested in a shorter run of years, the recent 2 to 6-year periods have been the worst we’ve ever had on record at Minnipa
100 Appendix 3 - Summary of farming systems framework analysis, excel version NB: The information for each factor (#) is presented either in table or figure format in Appendix 2. Table 5: PROFITABILITY Characteristics # Factors Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to to business strong sit? business 1 = very 5 = Very Median 5 = not 1 = very vulnerable (range) 5 = not
1 % income 3.0 % crop 85 1.0 For those enterprises with 1.0 Key is to stimulate farmers to quantify the role and from livestock (66 -100) sheep, conservative risks of various enterprises to long term viability - to vs. crop stocking rates are used (for do this, they need to use consistent tools. more stability and lower Ed Hunt and Bill Malcolm are working on a project risk). with the Low Rainfall Farming Systems Collaboration to develop a consensus on benchmarks and their measurement using BCG as a focus group. Wait until this is done then use the standard benchmarks to pursue our plans. We can then continue profitability workshops begun under EP Grain & Graze & Farming systems. Get new people interested in the concept to start with, perhaps by using a simple spreadsheet such as Ed Hunt’s. Plan for Profit and the Rural Solutions SA programs are the next step. More thought needs to go into how to address avoiding the ‘big clanger’ decisions, e.g. land and machinery finance, succession planning. All of these activities can be included in the new EPFS 3 and EP Grain & Graze 2 projects, discussion groups. After workshops completed, data then needs to be available to everyone, workshops for the consultants, advisers, accountants, banks, etc. Aim for end of 2009.
2 Debt servicing 2.0 1=< $20 2 1.0 Maintain low debt exposure 1.0 Need a data base of actual figures, all measured in (grain) costs/ t 5 = >$50 (1 -4) through good maintenance a consistent manner, updated annually, and grain - schedules on machinery, publically available. Benchmarks could be arranged average only purchasing on as needs in deciles or risk categories with "triggers". tonnage basis (also see 3, 27).
101 # Factors Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to to business strong sit? business 1 = very 5 = Very Median 5 = not 1 = very vulnerable (range) 5 = not
3 Farm equity - 1.0 1= >90% 1 1.0 Strong emphasis on having 1.0 May not be able to rely on capital gain to maintain on the day 5 = < 60% (1 -2) cash as an important equity as many farmers have in the past 7 years. component in any new major purchase (e.g. land or machinery). Leasing extra land rather than purchasing.
4 Farm equity - 2.0 1= >90% 1 1.5 Maintain. 1.0 5 yrs ago 5 = < 60% (1 -2)
5 Farm equity - 2.0 1= >90% 1.5 2.0 3.0 10 yrs ago 5 = < 60% (1 -3)
6 Market value 3.0 1 = < $150 3 1.5 Careful review of machinery 1.3 Need a data base of actual figures, all measured in machinery / t 5 = > $400 (1 -4) inventory to assure timely a consistent manner, updated annually, and publicly grain average and effective operations but available. Benchmarks could be arranged in deciles - keeping investment in plant or risk categories with "triggers". from being high.
7 Equity in 2.0 1 = >90% 1 2.0 Cash always an important 1.5 Continue as is. Need sufficient machinery capacity machinery - 5 = < 50% (1 -2.5) part of purchase. to be able to rapidly respond to opportunities/ rainfall on the day Second hand machinery events to maximise WUE. often purchased. Hire purchase options kept to a minimum.
8 Drawings / 1.0 comment 0 3.0 Modest and variable, 3.0 Keep off farm income, importance as children get family unit only (0 -0) depending on type of older and education fees. season.
9 % of expected 2.0 1 = >50% 4 3.0 Low expectation to make 2.0 Spend time on record keeping systems to ID poorer annual gross 5 = <10% (1 -5) large income from livestock. gross margin paddocks and do analysis to see if income from more intensive pastures will improve profitability. livestock - average looking forward
102 # Factors Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to to business strong sit? business 1 = very 5 = Very Median 5 = not 1 = very vulnerable (range) 5 = not
10 Off Farm 2.5 1 = > $50K 3 3.5 Variable, some use wife 3.0 May not be possible in the future if communities Income 5 = none (1 -5) income for living expenses, decline and fewer local jobs available - may be some use passive income required to spend all time helping on farm. from shares.
11 Off Farm 3.0 1 = over 1 2.5 Important part of the 1.5 Off farm investment/retirement fund accumulation Investment $250K (1 -5) business. more important in the future as farm saleability is not 5 = nil Important component of guaranteed. succession planning and risk management.
12 Off Farm 2.0 1 = actively 3 2.5 Moderate level of 2.0 Important to maintain and/or grow in the future. Investment managed (1 -5) management of off farm 5 = left investment. alone
13 Use of farm 2.0 1 = build 2 2.0 Very common tool for 1.0 Important to rebuild when good seasons return. management deposits to (1 -5) stabilising incomes. deposits allow expansion 5 = no deposits
14 Record 2.0 1 = good 1.5 2.0 Comprehensive farm and 1.0 Use these records to monitor or change on farm keeping and system, (1 -4) business records. Records decisions. Link with profitability workshops in 1. Tie analysis regularly used in decision making. records to deciles to help interpret the past and plan updated Does not matter if paper or for the future. 5 = none electronic.
103 Table 6: SOCIAL Characteristics
# Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerabilit you importance strategies importance y currently to business to business 1 = Very sit? 1 = very 1 = very strong Median 5 = not 5 = not 5 = Very (range) vulnerable 16 Personal 2.0 1 = optimist 2 2.0 Most managers are 1.5 Maintain or develop good networks, to help Attitude 5 = pessimist (1 -3) optimists and also maintain provide balance to sensational headlines. a healthy balance of Come up with farming systems that show a business and social future during extreme climate change interests. scenarios as a back up. Provide perspective on climate change through our networks. Contribute to EPNRM vulnerability study. 17 Lifestyle 2.0 1 = conservative 2 2.0 Generally conservative. 2.0 Be careful wife's income is not swamped by Choices 5 = flamboyant! (1 -3) the farm so it is available for some lifestyle pursuits. 18 Skill & 2.0 1 = high, read a lot, 2 1.0 Also see 19. Accessing new 1.0 Discussion Group Workshops (from 1) could Knowledge attend (1 -2) information and ideas from include skills audit list (akin to Planning 4 Level workshops/field outside the farm and the Recovery workshops) to encourage people to days, talk to others business very important analyse their own skills and external 5 = low, haven’t (e.g. field days, media, requirements. considered industry information). Dry matter vs. grain decisions in responsive anything new in Interacting with other farming (people who are confident to crunch years farmers very important. their own numbers or have experienced the situation are the ones who can make these decisions easier). Timely access to advice and services (e.g. deep N, soil moisture). 19 Experience 1.0 1 = learnt from 1 1.0 Willingness to learn. Strong 1.5 Don't rely on one set of networks, these guys experience of self (1 -3) networks for gathering look further afield than their locals only. or others information Workshops provide opportunities to develop 5 = don’t learn from new networks. Critical to maintain the farmer experience, think group networks. know it all anyway!
104 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerabilit you importance strategies importance y currently to business to business 1 = Very sit? 1 = very 1 = very strong Median 5 = not 5 = not 5 = Very (range) vulnerable 20 Confidence 2.0 1 = find it easy to 2 2.0 Self-confidence and gaining 1.5 Be careful not to get the message out of make decisions (1 -4) support for decision making perspective from this project. Commodity quickly and from objective assessments prices have alleviated the current situation to accurately important. Sounding out some extent. Workshops with service 5 = always options with others prior to providers will help, if they portray confident indecisive decision making very attitude will have a snow ball effect. Can we valuable. get Pete Hayman to deliver some of the outcomes from this project with the consultants into some small supported discussion groups? 21 Use experts, 2.0 1 = use a range of 1 1.0 See also 18, 19 and 27. 1.0 Using external specialists could be included mechanic, professionals to (1 -3) External consultants or in workshops. Consultants have had a role in financial support the advisers used on a regular connecting farmers to various specialists. adviser, business, e.g. basis. When things improve the free market should agronomist agronomist, sort this out. MAC needs to get an extension financial adviser officer. Funding bodies encourage joint 3 = rely on one ventures to improve efficiencies. external expert 5 = do it within the business 22 Technology 2.0 1 = adopt first and 3 2.0 Most are early adopters 2.0 Independent analysis of cost vs. benefit for adoption adapt myself (1 -3) rather than innovators. new technology adoption, especially if local 3 = adopt after demo's aren't close or become more difficult local adaptation to access. has been done by someone else 5 = Averse to new technology 23 Time away 2.0 1 = spend quality 2 1.0 Strong commitment to 1.0 Important to flag the point in publicity that from the time away every (1 -4) having recreational time successful farmers ID this as important. farm year away from farm and Various ways of getting time out, e.g. church, 5 = never get away business on a regular basis. personal time.
105 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerabilit you importance strategies importance y currently to business to business 1 = Very sit? 1 = very 1 = very strong Median 5 = not 5 = not 5 = Very (range) vulnerable 24 Physical 2.0 1 = good 1 1.0 Also see 23. Commitment to 1.0 Local communities often support short term Health 5 = poor (1 -2) maintaining sound physical health issues; however there are often health. inadequate facilities to do aerobic exercise in the area. Men's Health nights have been useful. 25 Mental 2.0 1 = cope well with 2 1.0 Also see 23. Strong family 1.0 Early warning signs promotion. Michael Wallis Health stress (1 -3) support and/or community made a good impact. Resources in Lincoln 5 = stress affects involvement. Shared have increased. the business decision making. 26 Succession 2.0 1 = well sorted 2 1.0 Generally, this issue has 1.0 A very individual and confidential issue. Often Planning 5 = not considered (1 -4) received little attention as requires a very skilled catalyst/facilitator to most people weren't at that get people in touch with appropriate support. stage of their business. 27 Peer & 1.0 1 = no problem 1 2.0 Major business decisions 1.5 Continue as is. Industry resisting (1 -3) are made after objective Pressure – 5 = always review and consultation with e.g. new succumb others. gadgets if you don’t need them! 28 Community 3.0 1 = plenty & stable 3 2.0 Strong involvement in local 2.0 Mining may provide opportunities for some Structure & 5 = declining & (1 -4) community. inland communities and coastal areas will Services unstable grow anyway. Schooling and shopping is an issue once the wife stops working and has children. 29 External 1.0 1 = casual labour 1 2.0 Generally, little use of 1.5 Short term register for back packer type labour for less than 50 (1 -4) external labour. people, seasonal labour, maybe Eyre requirement days/yr Regional Development Board or an (external to 5 = permanent employment agency. family) employee/s 30 Labour 3.0 1 = good quality 2.5 1.0 Generally, little use of 1.0 Continue as is, review as partners get older Availability &/or quantity (0 -4) external labour. and less able. 5 = poor quality &/or quantity
106 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerabilit you importance strategies importance y currently to business to business 1 = Very sit? 1 = very 1 = very strong Median 5 = not 5 = not 5 = Very (range) vulnerable 31 Time to think 2.0 1 = manage things 2 1.0 Careful organisation and 1.0 Some people are naturally good at this, and plan so have time to (1 -4) good records frees up time others aren't. Access advisor at critical think and plan for planning. planning times. Make defined times for 5 = too busy planning for all members of family. responding to think and plan ahead 32 Current 2.0 1 = very satisfied 2.5 1.0 Strong family support. 1.0 Enjoyment will return with better yields. satisfaction 5 = very unsatisfied (2 -3) Positive outlook. with farming 33 Attitude to 2.0 1 = very interested, 2 1.0 Aware of latest technologies. 1.5 Continue as is. cropping enjoyable (1 -2) Commitment to doing things 5 = not interested, properly. stressful 34 Attitude to 2.0 1 = very interested, 2 1.0 Robust businesses can be 1.5 Be flexible and have the self discipline to do sheep enjoyable (1 -4) achieved without sheep. If everything well, not just the things you like. 5 = not interested, sheep are an enterprise in stressful the business, then a liking for them is helpful. 35 Organisation 2.0 1 = always in top 2 1.0 See also 33. Good 1.0 Continue as is. al ability 10% in district to (1 -4) organisational skills and a relative to finish seeding and strong commitment to use peers harvest them vital. 5 = consistently in bottom 10% 36 Attitude to 2.0 1 = expand 2 1.0 See also 3. Conservative 1.0 Continue as is. risk business using (1.5 -5) attitudes towards borrowing money in hand money very important. 5 = expand business using borrowed money
107 Table 7: ENVIRONMENTAL Characteristics (ecosystems services only, refer to conservation tables for other environmental issues) # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
38 Erosion - do they 2.0 1 = never 2 1.5 Erosion minimal. Many soil 1.0 More work to do on the Grain & Graze project experience 3 = 3 out of (1 -2.5) protection strategies used, so that when people increase stock numbers significant wind 5 yrs e.g. conservative stock they have adequate feed planning in case of or water erosion? 5 = every yr numbers, no till, stubble poor seasons: Understand the place of retention, feed lotting. perennials in the farming system and matching them to soil types (to capitalise on rainfall at any time of the year). Autumn workshops on food storage for various breaks. Publicising lucerne situations, where it does and doesn't grow. Need info to speculate on what potential there is to maximise out of season rain - economics. Maximising feed on offer through electric fence and water points.
39 IPM – do 3.0 1 = every yr 3 3.0 Generally low level of 3.0 Increased knowledge of IPM and risks vs. beneficials assist 3 = 3 out of (2 -5) pesticide usage but mostly short term profit. Monitor for damaging and in pest control? – 5 yrs out of lack of need rather threshold levels before spraying. More use aphids, lady 5 = never than committed IPM information on whether the beneficials are bugs & hoverfly strategies. here and whether they work. e.g..
40 Suppressive Soil 3.0 1 = never 2 3.0 Rhizoctonia not a major 2.0 Important to continue the research program to – how often do 3 = 3 out of (2 -5) problem. investigate if we can create and maintain you get severe 5 yrs Many management options suppressiveness on a range of EP soils. rhizo? 5 = every yr already in place which would discourage Rhizo, e.g. no green bridge, reasonable fertility, deep working points, avoidance of SU herbicides.
108 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
41 Microbial 2.0 1 = label 2 3.0 Generally try to avoid 3.0 Need a pre em herbicide for use with assistance in advice (0 -3) herbicides with residual dry/early sowing with limited plant back herbicide always right activity such as sulphonyl issues and one with excellent spectrum of breakdown - do 5 = label ureas. weed control but with limited carryover for you get herbicide advice never medic and barley on alkaline soils. residue right problems?
42 Nutrient cycling - 3.0 1 = always 2 1.0 Retaining stubbles is almost 1.0 Accurate, simple, cheap, effective tests. N do you think your can rely on (1 -3) universal. Strategic use of mgmt - what would a system look like in the soils can hold slow release fertiliser N. Legume N future that isn't reliant on artificial N? and cycle all the system N important in many systems. nutrients you 5 = never apply in fertiliser? get free system N
43 Recharge - do 2.0 1 = my 2 3.0 Not an issue for most. 3.5 Can we better store out of season rainfall for you believe part system uses (0 -3) Businesses on sandier soils use on crops or pastures? of your farm all of the rain actively using lucerne, trees, contribute that falls saltbush and fencing to recharge to 5 = regularly reduce recharge. catchment lose water to salinity deep problems? drainage
109 Table 8: SOIL Characteristics # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
46 Fertility 2.0 1 = high reserves 3 2.0 Aim to attain high and 1.0 GRDC updates, alternatives and efficiency. of nutrients (1 -4) balanced soil fertility and Fluid fert response curves need calibration to 5 = very infertile then maintain it. other soil types.
47 Transient 2.0 1 = no issue 2 4.5 If present, maintenance of 3.5 Variable rate inputs according to zone to avoid Salinity 5 = magnesia (1 -4) stubble seen as important. wasting money on these smaller unproductive country areas. Some more salt tolerant varieties would be great.
48 Boron 3.0 1 = no issue 3 3.0 Use boron tolerant varieties 3.0 Continued development of boron and salt Toxicity 5 = always (2 -5) where possible. tolerant varieties. symptoms on barley
49 Sodicity 3.0 1 = no issue 3 3.5 Mostly at depth, hard to do 3.5 Monitor current gypsum applications to ID 5 = clay always (1 -4) anything about it. Many benefit vs. cost. loses structure applied gypsum and practice when wet minimum till and stubble retention.
50 Calcium 3.0 1 = non 3 3.0 Generally less intensive 3.0 Efficient, low cost methods to deliver nutrients in Carbonate calcareous (1 -5) cereal, pasture based highly calcareous soils. Productive pastures. 5 = highly rotation with higher P and Zn calcareous input. Sulphonyl ureas (20%+) avoided.
51 Alkalinity 3.0 1 = pH 7 3 3.0 Generally less intensive 3.0 Plant tolerance/ adaptation to this soil type. 5 = pH >9.5 (1.5 -3) cereal, pasture based rotation with higher P and Zn input. Sulphonyl ureas avoided.
52 Acidity 1.0 1 = pH 7 2 4.0 4.0 5 = pH <5 (0 -2)
110 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
53 Non-wetting 2.0 1 = no issue 2 2.0 Most have done some 2.5 Continue delving and clay spreading and sand 5 = severe and (1 -3.5) delving and carefully investigating establishment issues. extensive manage grazing.
54 Compaction 3.0 1 = no issue 3 3.0 Knife points generally used 3.0 Need quantified data on yield loss due to 5 = always (1 -4) for deep working. compaction and cost benefit of treatments, e.g. restricts root deep working, controlled traffic, etc. growth
55 Rock – 2.0 1 = no surface or 2 3.5 Variety of management 3.5 Need records to ID profitability of each surface or subsurface (1 -3) including early maturing or paddock, then reconsider whether to use more shallow 5 = plenty of both tall varieties, shallow seeder, for annual pastures or to rip and roll, go to subsurface hydraulic tynes, stone rolling hydraulic tines. and picking and using these areas for livestock grazing.
56 Texture 2.0 1 = SCL 3 3.0 Wide range of soil types 2.5 Simple methods to understand annual 5 = S or HC (2 -4) managed. production potential of each soils type. Varieties with ability to grow useful in subsoil constraints.
57 Soil finishing 3.0 1 = good 3 2.0 Manage crops and pastures 1.5 Better understanding of bucket size. Varieties ability - 3 = medium (1 -3) for maximum water access that finish well in all seasons with good grain average 5 = poor to prolong finishing ability of size. years soils e.g. early seeding, stubble retention, summer weed control, early varieties.
58 Soil finishing 3.0 1 = good 3 2.0 Many management 2.0 Better understanding of bucket size. Varieties ability - 3 = medium (2 -4.5) strategies employed to that finish well in all seasons with good grain below 5 = poor maximise water access by size. average crops and pastures, e.g. years early seeding, stubble retention, summer weed control.
111 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
59 Plant 2.0 1 = 140 mm (122 2 1.0 Maximising access by crops 1.0 Need to be able to easily and accurately Available to 214), easily (1 -3) and pastures to water understand plant available water characteristics Water extractable important e.g. summer weed in paddock zones and measure or estimate 3 = 100 mm (73 control, early seeding. PAW at critical times. Need to match to 121) management and varieties with zone 5 = 60 mm (26 to characteristics where practical. Control summer 73) or hard to weeds if required. extract
60 Organic 3.0 1 = increasing 3 2.0 Stubble retention, 1.0 Need to increase biomass, however currently matter input 3 = stable (1 -4) conservative stocking rates, lower biomass crops are often best yielders. into system 5 = declining reduced tillage. Need higher DM pastures and less grazing pressure on stubbles to ensure more DM is returned to system each year. Investigate the effect of grazing on organic matter levels. Investigate soil carbon sequestration and carbon trading potential.
112 Table 9: CLIMATE Characteristics # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
62 Annual 3.0 1 = 400 mm+ 2.5 1.0 Early seeding, summer 1.0 Require more accuracy in short term forecasts. rainfall 3 = 300 mm (2 -3) weed control used to Develop responsive, flexible farming systems to 5 = 200 mm- increase use of rainfall maximise moisture use as and when it falls or conserve for winter crops. Require more information on climate change.
63 Early 2.0 1= regularly get 2 2.0 Strong commitment to 1.0 Varieties with early vigour or inoculants to growth good early growing (1 -4) early seeding, conserving control rhizo, pythium and prats for the first 3 conditions conditions summer moisture. weeks after sowing. Provided there is enough compared to district rain to establish the crop, the amount early is 5 = never get good more important for pasture production that crop growing conditions growth.
64 Summer 2.0 1 =can manage 2 1.0 Controlling summer 1.0 Valuable pastures that are responsive to rainfall weeds and store (1 -3) weeds vital. summer rainfall for use on soils that cannot hold moisture for next plant available moisture from early summer crop rains until April. 5 = summer weeds big problem, can't store moisture for later use
65 Heat 4.0 1 = never 3 3.0 Early seeding and 2.0 Improved variety diversity in maturity and stress– 5 = always (3 -4) spreading maturity times tolerance to drought, heat and frost stresses. during (seeding times and/or flowering varieties) common strategies.
66 Wind – cuts 2.0 1 = never 2 2.0 Minimum till, early seeding 2.0 Vigorous pastures and grazing systems that can crops early 5 = always (1 -3) and stubble retention all allow for pasture bulk to be left on the paddock or lodges widely practiced so in autumn. crops at minimised risk for crops harvest
113 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
67 Weather 3.0 1 = never 2 2.0 Strong commitment to 2.0 Varietal tolerance to pre harvest sprouting, damage 5 = always (2 -3) timely harvesting. black point, staining, etc. Use of grain driers, and quality Mix of varieties and crops chaser bins, better headers to improve downgradin to spread out maturity timeliness of harvest. g times.
68 Frost – 2.0 1 = never 2 3.0 Avoid high risk situations 2.0 Genetic tolerance so crops can be sown to during 5 = always (1 -3) e.g. peas. maximise use of available water rather than to flowering Seed a range of varieties minimise risk of frost damage. to reduce risk at any one time.
114 Table 10: EXTERNAL RISK Characteristics # Factor Strength or Benchmark Where do Current Summary current Future Summary Requirements for the Future vulnerability you importance mgmt strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
70 Market 4.0 1 = doesn’t affect 3 1.0 Limited use of 1.0 A good personal understanding of markets, Volatility & my business (1 -5) forward pricing products and what is a good price for my Access 5= viability schemes. business. Access to good independent, unbiased seriously advice to assist with complex decision making. compromised
71 Shorter term 3.0 1 = doesn’t affect 3 3.0 Conservative 1.0 Knowledge of appropriate levels of risk, seasonal my business (2 -4) financing. Early assistance with financial decisions. Reliable variability, 5= viability seeding. Stick to prediction models and tools to indicate risk of poor e.g. drought, seriously reliable crops. production season so decisions on drop and frost compromised pasture management can be made, e.g. graze, frequency hay, keep for seed.
72 Longer term 3.5 1 = not likely to 3.5 2.0 Reducing up front 1.0 Require more accuracy in short term forecasts climate affect my (2 -4) costs and increasing and longer term scenarios to give confidence in change business flexibility are vital. planning. scenarios 5= likely to Develop information on the potential effects of seriously increased temperatures and potentially more compromise erratic rainfall patterns on crop, pasture, pest, viability weed and disease dynamics, so that proactive changes can be made. Improve understanding of enterprise risk to develop lower risk, responsive farming systems - including range of crop types, enterprise mixes, input types and levels. Maintain business strength.
73 Price shocks, 3.0 1 = doesn’t affect 3 2.0 Early purchasing 1.0 Accurate price signals so plans can be modified to e.g. fuel my business (2 -5) options often used for manage input cost increases. Assess alternative 5= viability inputs to avoid energy and fertiliser options. seriously "surprises" and compromised conservative levels of inputs to reduce exposure.
115 # Factor Strength or Benchmark Where do Current Summary current Future Summary Requirements for the Future vulnerability you importance mgmt strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
74 Water 2.0 1 = doesn’t affect 1 4.0 Various, including 2.5 Develop ways to harvest and store more water. Restrictions my business (1 -4) bore, pipeline and 5= viability rainwater for seriously spraying. compromised
75 Input 3.0 1 = doesn’t affect 3 2.0 Strong commitment to 1.0 Good relationships with resellers important. Need shortages / my business (1 -4) early buffer of supplies on farm in order to be delays – e.g. 5= viability purchasing/ordering responsive to rainfall events. labour, seriously of inputs. transport, compromised shearers, seed, fertilizer, chemicals, expert advice
76 Red tape / 3.0 1 = doesn’t affect 3 3.0 Commitment to 2.0 Strong state and national farming organisations restrictive my business (2 -5) comply with all lobbying on issues such as mulesing, single desk, legislation 5= viability regulations. silo charges, chemical use, OHS&W, etc. Efficient seriously Pain eased by good production systems to avoid the need for casual compromised record keeping. labour.
116 Table 11: SYSTEMS CHOICES Characteristics # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
78 Responsive 2.0 1 = always 2 1.0 Generally, maintaining a 1.0 Reliable market outlook, pricing tools and Management plan to play (1 -3) flexible outlook and seasonal weather forecasts to make decisions the season reviewing frequently for both that lead to profit. Reliable measurement of 5 = makes a cropping and sheep PAW at the start and throughout the season to plan and important. aid management decisions on potential pasture sticks to it and crop growth and likely responses to religiously management treatments. Buffer of seed and fodder on hand. Ability to know when to change plans, i.e. triggers. Need the science behind where and when we can cut inputs.
79 Optimising 2.0 1 = farm for 2 2.0 A conservative approach is 1.0 Measure and quantify the risks to businesses Profit, profit with (1 -3) important – supply only with an analysis tools that farmers can use. Minimising minimised sufficient inputs for Access to good advice on profit vs. risk or cost Risk risk conservative productivity, benefit ratios. 5 = farm for maximise opportunities production, through management rather regardless of than inputs. risk
80 Matching 2.0 1 = farm to 2 2.0 All land managed according 1.0 Identification of production zones and land use to land (1 -4) to its capability, even if the prescription farming within zones. Increase natural capability options are fairly limited (e.g. production from shall, heavy soils, e.g. resource 5 = generic different rotations in different boron/salt tolerant grazing varieties with greater base approach paddocks). root depth to extract soil moisture. across all Management within the land types paddock differs by soil type if practical.
81 Diversity 2.0 1 = rely on 2 2.0 Generally a high level of 1.0 Selecting the right balance to retain flexibility in multiple (1 -3) diversity and flexibility. the system to respond to seasonal conditions - enterprises needs good records and analysis tools. for income 5 = rely on one enterprise for income
117 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
82 Ratio – all 2.0 1 = 2 2.0 Majority of farm businesses 1.0 Ability to analyse the profit and risks of each enterprises continuous (1 -3) use crop and livestock mixes enterprise and flexibility to change system in crop to cropper to stabilise incomes and response to market and season. Farming livestock as 3 = 60% reduce risk. systems where livestock don't damage the soils percentage crop, 40% are necessary if there is to be a larger livestock of total arable livestock component, including more productive pasture farm area 5 = livestock species. only
118 Table 12: LAND USE: CROPPING Enterprise Characteristics # Factor Strength or Benchmark Where do Current Summary current Future Summary Requirements for the Future vulnerability you importance mgmt strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
105 WUE 2.0 1 = increasing 2 1.0 An objective of 1.0 Ability to use summer/late spring moisture for 3 = stable (1 -3) improving WUE vital. productive pastures/crops. Weed control 5 = decreasing Also see 33. techniques to enable early sowing of a greater portion of the program.
113 Stubble 2.0 1 = retain all 1 1.0 Stubbles retained 1.0 Snail management without the need for Manageme 5 = burn (1 -3) whenever possible. stubble bashing or burning to maintain soil nt cover.
114 Fertiliser 2.0 1 = N&P adjusted for 2 2.0 Nutrients are applied 1.0 Accurate in paddock tests to determine plant strategy for time of sowing and (1 -4) according to yield availability of nutrients, prior to cropping and cereals stored soil moisture - potential and nutrient in crop for N. Variable rate input delivery yield potential reserves in each systems based on productivity zones. Further 3 = replacement of N paddock. fluid research to identify cost effective & P only N applied strategically fertilisers. 5 = blanket rate with increasing emphasis on less seeding N and in- season N only if deemed appropriate.
115 Use of 2.0 1= strategically used 1 2.0 Analytical tools used 1.0 Accurate, reliable, simple, cheap tests to analytical 5 = never used (1 -3) extensively but mostly indicate plant available nutrients so fertiliser tools, e.g. in a targeted fashion. plans can be confidently constructed. soil tests, tissue tests, disease tests, etc
116 Crop choice soil disease risks 2 Regular monitoring and Regular monitoring, disease resistant – what are (1 -2) use of paddock records. varieties. the main Use CCN resistant drivers that varieties and break dictate your crops. Grass free for rotational takeall. choice?
119 # Factor Strength or Benchmark Where do Current Summary current Future Summary Requirements for the Future vulnerability you importance mgmt strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
117 leaf disease risks 3 Regular monitoring, (2 -5) treat as it develops.
118 weed risks 2 Use regular monitoring, Regular monitoring. (1 -3) grass free and often Clearfield technology.
119 nutrition 3.5 Use regular monitoring Regular monitoring. (1 -5) and apply nutrients as required.
120 early price signals 3 Try to keep balanced Regular monitoring, upgrade marketing skills. (1 -5) program.
121 cash flow 2 Various. Several Regular monitoring, upgrade marketing skills. (1 -5) maintain strong cash flow, others believe it is about marketing not crop choice and that the emphasis should be on profitability instead of cash flow.
122 tradition 5 Not important. (2 -5)
123 Weeds 2.0 1 = occasional 2.5 2.0 A wide variety of control 2.0 Regular monitoring and access to advice. problem (1 -5) methods implemented 5 = expensive (IWM practiced). problem every year
120 # Factor Strength or Benchmark Where do Current Summary current Future Summary Requirements for the Future vulnerability you importance mgmt strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
138 Herbicide 2.5 1 = no resistance, 3 3.0 Herbicide resistance 1.0 Advice on management techniques, resistance manage to prevent (1 -5) acknowledged. measurement of success of management. impacting 5 = resistant weeds A wide range of control Cheap effective seed set controls for on mgmt significantly affect strategies are used on 'persistent' weeds that do not impact on crop and chemical weeds to rotate rotation choice. Cheap, reliable summer weed choice chemicals and slow control options that do not involved onset of resistance, e.g. cultivation. grazing, spray-topping, Clearfield technology strategically.
139 Herbicide & 3.0 1 = decreasing use 3 2.0 A wide variety of control 2.0 Competitive pastures to control some weeds pesticide 5 = increasing use (2.5 -5) methods implemented for free. Low cost IWM techniques that allow use (IWM practiced). for flexible farming systems that make money.
140 Diseases 2.0 1 = occasional 2 2.5 Rotations used to 2.0 Tolerant/resistant varieties. problem (1 -4) manage diseases and 5 = expensive regular monitoring also problem every year common.
150 Pests 2.0 1 = occasional 2 3.0 Monitor and treat when 3.0 Understanding of population dynamics and problem (1 -4) necessary. thresholds and cheap management tools that 5 = expensive are pest specific. problem every year
159 Canopy 3.0 1 = always good grain 3 2.0 Mostly through adjusted 2.0 Accurate soil water and N measurements to Mgmt quality (2 -3) seeding rates and all reduction of up front N. Varieties that don't 5 = always great early delayed N applications. run out of gas at the end of the season. growth, run out of moisture, poor grain quality
121 Cropping program summary - 5 yr av Range of Wheat Yields (t/ha)
100 2.5 80 2
60 1.5 40 1
20 Yield (t/ha) 0.5 % of arable area arable of % 0 0 Wheat Barley Oats Pasture Peas Canola other Av Yield (10 yrs) Av Yield (2 yrs) Type of crop Time Period Av % cropped area Highest Wheat % Lowest Wheat % Highes t Low est Average Figure 15: Cropping enterprise characteristics, factor #’s 80-84, Figure 16: Cropping enterprise characteristics, factor #’s 91- 5 year average of % of arable area sown to various crops and 104, average wheat yields over past 10 and 2 years. pastures over the past five years.
Current tillage type
100% 80% 60%
sown 40% 20% % of program program of % 0% 1234567891011 Business
Zero (<10% disturbance): No Till (<30% disturbance): Direct Drill (sow full disturb): Min till (1 or 2 cults, min disturb): Reduc ed till (1 full cult till, then sow ): Conven (>2 cult prior sow ing): Responsive (do w hat needs to be done) Figure 17: Cropping enterprise characteristics, factor #’s 106- 112, % of tillage types used per case study.
122 Table 13: Cropping enterprise characteristics, weeds, diseases and pests. # & Factor Where do you currently # of businesses sit? highlighting this Median (range) factor as an issue Factor #’s 123- 137 Weeds (1=minor weed, 5=worst weed) Brome Grass 2 (1-4) 10 Ryegrass 2 (1-5) 10 Barley Grass 3 (1-4) 9 Wild Oats 1.5 (1-2) 4 Melons 3 (1-4) 3 Silver Grass 2 (2-2) 2 Skeleton Weed 4 (4-4) 2 Turnip 1.5 (1-2) 2 Medic 3.5 (3-4) 2 Radish 2.5 (2-3) 2 Heliotrope 3 (3-3) 1 Lincoln Weed 3 (3-3) 1 Sheep Weed 4 (4-4) 4 Wards Weed 3 (3-3) 1 Factor #’s 140-148 Cropping Diseases 1=minor disease, 5= major problem disease Rhizoctonia 3 (2-5) 10 Takeall 2 (1-4) 9 CCN 2 (1-3) 8 Cereal Rusts 2 (1-4) 5 Pratylenchus 2.5 (1-4) 4 Crown Rot 2 (2-2) 2 Blackspot 2.5 (1-4) 2 Other 3 (3-3) 1 SFNB 4 (4-4) 1 Factor #’s 150-157 Cropping Pests 1=minor pest, 5= major problem pest Snails 4 (1-5) 7 Mice 1 (1-2) 7 Cutworm 1 (1-3 7 RLEM 3 (1-5) 6 Lucerne Flea 2 (1-5) 4 Other 2.5 (2-3) 2 Aphids (canola, cereal, medic) 2.5 (2-4) 3 Native Budworm 4 (4-4) 1
123 Table 14: LAND USE: LIVESTOCK Enterprise Characteristics # Factor Strength or Benchmark Where do Current Summary current Future Summary Requirements for the vulnerability you importance mgmt strategies importance to Future 1 = Very currently to business business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
167 Pasture management 2.0 1 = sow annually 3 1.5 Mix of regenerating 1.5 High dry matter pastures or perennials 5 = rely on natural (1 -5) pastures and sown that can be sown or regenerate in regeneration feed. summer or early autumn to better address feed gap.
168 Livestock Nutrition 2.0 1 = get all 2 3.0 Most use products to 2.5 Low maintenance stock enterprise. requirements from (1 -3) address specific timing Feed testing and DM measurement to feed or issues, e.g. salt and better match requirements and predict 5 = always calcium for production carrying capacity. deficient, need feeding, B12 for lambs expensive at marking. supplements
169 Use of analytical 3.5 1= strategically 4 4.0 Limited use of analytical 2.8 Skills to test and interpret results as tools, e.g. pasture used (2 -5) tools, some preg there is a lack of independent advice quality & quantity, 5 = never used scanning and feed on EP. Pasture monitoring for carrying blood tests, testing. capacity and production potential scanning, etc based on available moisture.
170 Diseases 2.0 1 = occasional 1 4.0 Vaccinate. 4.0 Continue as is. problem (0 -3) 5 = expensive problem every year
173 Pests 3.0 1 = occasional 1 3.0 Preventative application 3.0 Will need some form of cheap and problem (1 -2) of pesticides used when effective fly control to replace 5 = expensive risks are high, e.g. mulesing. problem every year purchased livestock.
178 Location of water 3.0 1 = maximises 3 3.0 Generally well laid out 2.0 Smaller paddocks on less productive points pasture use (2 -5) and maintained part of farm with additional water 5 = always watering systems. points. compromises pasture use
124 # Factor Strength or Benchmark Where do Current Summary current Future Summary Requirements for the vulnerability you importance mgmt strategies importance to Future 1 = Very currently to business business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
179 Water Supply & 3.0 1 = self sufficient, 3 3.0 Independant water 2.0 Increased water capture and storage. Quality good quality & (1 -5) supplies important and quantity recognition that water 3 = reliant on quality is important pipeline (cropping and 5 = self sufficient, livestock). poor quality & quantity
180 Infrastructure 2.0 1 = regular 2 3.0 Mostly maintained or 3.0 Increased upgrade as improved cash improvement (1 -4) renewed as finances flow allows. 3 = maintenance allow. only 5 = ignoring it!
% of livestock enterprise % of livestock feed
100 100 80 80 60 60 40 40 20 20 % of feed type feed % of % of enterprise % of 0 0 1234567891011 1234567891011 property property
Self replacing merino Prime lambs Pasture Hay Grain Stubble
Figure 19: Livestock enterprise characteristics, factor #’s # 160- Figure 18: Livestock enterprise characteristics, factor #’s # 161, % of livestock enterprise types 163-166, % of livestock feed types.
125 Table 15: Livestock enterprise characteristics, diseases and pests. # & Factor Where do you currently sit? # of businesses highlighting this Median (range) factor as an issue Factor #’s 171-172 Livestock Diseases (1= minor disease, 5 = major problem disease) Toxaemia 1 (1-3) 5 Factor #’s 174-176 Livestock Pests (1 = minor pests, 5 = major problem pest) Flies 2 (1-3) 7 Lice 2 (1-2) 5 Worms 1 (1-2) 6
Table 16: LAND USE: CONSERVATION Characteristics # Factor Strength or Benchmark Where do you Current Summary current mgmt Future Summary Requirements for the Future vulnerability currently sit? importance strategies importance 1 = Very Median to business to business strong (range) 1 = very 1 = very 5 = Very 5 = not 5 = not vulnerable 182 Heritage 0.0 1 = large fenced 5 5.0 Few heritage areas. 4.0 Try to maintain all current scrub and Areas blocks, don’t need (2 -5) manage native animals. mgmt 5 = none 183 Bush 0.0 1 = large blocks, 5 5.0 Few bush management 4.0 Continue to fence, need cheap and quick Management don’t need mgmt (2 -5) agreements. fencing options. Agreements 5 = none 184 Vegetation 3.0 1 = all major blocks 3 4.0 Many blocks of scrub 3.0 Consider when refencing, to allow Corridors linked with corridors (2 -5) linked. regeneration and shelter for stock. 5 = no scrub linked
126 Table 17: LAND USE: OTHER Characteristics # Factor Strength or Benchmark Where do you Current Summary current Future Summary Requirements for vulnerability currently sit? importance mgmt strategies importance to the Future 1 = Very Median to business business strong (range) 1 = very 1 = very 5 = Very 5 = not 5 = not vulnerable
185 Wind 0.0 1 = receive income from wind farms 5 5.0 None. 5.0 Farms 5 = no interest from wind farm (3 -5) companies
186 Water 3.0 1 = good dams and/or groundwater 5 4.0 Various. 2.5 Increased water capture and Capture 5 = no dams or groundwater (2 -5) storage. or Storage
187 Mining 0.0 1 = working mine, I get the 5 5.0 None. 5.0 royalties! (3 -5) 5 = no rumours of exploration
188 Tourism 0.0 1 = receive income from tourism 5 5.0 None. 4.5 asset on property (3 -5) 5 = never likely to have tourism
127 Table 18: LAND USE INTERACTIONS, CROP AND LIVESTOCK Characteristics # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the vulnerability you importance strategies importance Future 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
189 Groundco 3.3 1 = all erosion 3.5 2.0 Many strategies used to protect 2.0 Adoption of more direct drilling on ver – problems attributed to (2 -5) soil surface and is a high priority suitable soils. Use of perennial feed erosion cropping in their management packages. sources, e.g. lucerne. Need vigorous 3 = both enterprises dry matter production and self contribute equally to regeneration from pasture species. erosion Some smaller paddocks and 5 = all erosion containment after opening rain to allow problems attributed to pasture establishment. Increase use livestock of grazing crops. Accumulate more feed on hand and use feedlots more often.
190 Weed 2.5 1 = chemical & 3 2.0 A wide variety of control methods 2.0 Cheap and longer term weed control populatio mechanical weed (2 -4) implemented (IWM practiced). methods that do not reduce soil cover ns & management tools and can perform in adverse conditions, dynamics 3 = both enterprises e.g. dust. contribute to weed management 5 = livestock used as weed management tool
191 Soil 3.0 1 = compaction 3 3.0 Knife points generally used for 2.0 Need quantified data on yield loss due compacti caused by cropping (2 -3) deep working. to compaction and cost benefit of on 3 = compaction treatments, e.g. deep working, caused by both controlled traffic, etc. enterprises 5 = compaction caused by livestock
192 Workload 2.0 1 = can handle both 2.5 2.5 Half rely on family resources, half 2.0 Continued investment in labour enterprises no (1 -5) on casual labour. efficient infrastructure, e.g. stock worries handling facilities, machinery size, 5 = struggle to do grain storage and handling, etc. Need justice to both to be able to quantify the benefit of this enterprises, one is expenditure vs. off farm investment. secondary
128 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the vulnerability you importance strategies importance Future 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
193 Microbial 3.0 1 = cropping reduces 4 3.0 Very conscious of returning as 2.0 Need a vigorous pasture system, biomass the biomass levels I (3 -5) much biomass to soil as perhaps self regenerating annual & activity would like to return to possible. pastures than can respond to summer the soil and autumn rainfall and provide feed 3 = I return good so that other residues can remain levels of biomass to ungrazed. Use feedlots more. A value soil needs to be put on biomass so 5 = livestock reduce economic decisions can be made the biomass levels I about grazing. would like to return to the soil
194 Pasture 2.0 1 = I manage 2 2.0 Pastures managed to maximise 1.0 Self regenerating pastures that are productivi pastures as a break (1 -3) production of both cropping and poor disease hosts but are responsive ty crop livestock. to rain at various times of the year, 1 = I manage may be multi species pasture with pastures to maximise summer and winter activity on poorer production of both paddocks and annually sown pastures cropping and on better soils, where cheaply sown livestock pastures can be a crop in a good year. 5 = I manage pastures for my livestock
195 Feed gap 3.0 1 = cropping doesn't 3 2.0 Using stubbles, hay, grain, sown 2.0 Assess economics of feedlots and cause feed gap (1 -4) feed to reduce and manage feed cheap to establish and vigorous early 5 = cropping causes gap pasture species/system. feed gap
129 # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the vulnerability you importance strategies importance Future 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
196 List top 5 The main compromises between running both a cropping and livestock enterprise included: compromi o Stock knock down stubble reducing "trash' flow and chemical efficacy. ses trying o Weed seeds get buried by livestock over summer and not left on soil surface, therefore germination patchy which can also compromise the ability to get an to have excellent seed kill at spray top time due to uneven head emergence on the grasses. good crop o Lack of dry matter input following a pasture year and subsequent erosion risk. and o Paddocks not grazed uniformly, hills are often grazed out prior to rest of paddock increasing risk of erosion. Permanent fencing to maximise pasture utilisation sheep compromises following cropping machinery flexibility. enterprise o Timing – shearing and crop weed control often clash. s, e.g. o Feed utilisation is a balance between the need for maximum N fixation and animal requirement for food (like to leave medic to bulk up on some lower protein grass free paddocks). o Compromise between good weed control and the need for early, balanced sheep feed. o Withholding period for some pesticides impacts on feed utilisation. o Removal of pasture species during cropping phase necessitates the re sowing of pastures on some paddocks annually.
130 Table 19: LAND USE INTERACTIONS, CROP AND CONSERVATION Characteristics # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the Future vulnerability you importance strategies importance 1 = Very currently to business to business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
202 Herbicide 2.0 1 = never have off 2 4.0 Spray on appropriate 3.0 Record keeping system for compliance. Use target damage (1 -3) conditions with correct Understand chemical drift potential under 5 = always have off spray technology. increased temperatures and ability to apply target damage chemicals at night.
203 Pesticide 2.0 1 = never have off 2 4.0 Spray only when needed, 3.0 Knowledge of population dynamics and Use target damage (1 -5) when conditions are right thresholds of insects and how to favour 5 = always have off with low drift nozzles. build up of beneficial insects for free pest target damage control.
204 Revegetation 3.0 1 = have all potential 4 3.0 Most properties are trying 3.0 Need to manage so can provide some – wind areas planted to (3 -5) to maintain existing shelter for stock and also encourage breaks, perennials vegetation, a few are regeneration. alleys, etc 5 = have no actively revegetating. perennials
205 Fire Risk 3.0 1 = rarely have fires 1 2.0 Conscious of fire risk. 2.0 Whole of district use firebreaks and 5 = often have fires (1 -1.5) Maintain machinery well. observe harvest codes of practice. Firebreaks and fence lines maintained.
206 Roos and 3.0 1 = no problem 3 3.0 Actively manage via 3.0 Continue to monitor and cull as emus 5 = cause significant (1 -5) shooting. appropriate. damage every year
131 Table 20: LAND USE INTERACTIONS, LIVESTOCK AND CONSERVATION Characteristics # Factor Strength or Benchmark Where do Current Summary current mgmt Future Summary Requirements for the vulnerability you importance strategies importance to Future 1 = Very currently to business business strong sit? 1 = very 1 = very 5 = Very Median 5 = not 5 = not vulnerable (range)
208 Preferential 2.0 1 = recruitment, 3 5.0 5.0 grazing, indicator sp healthy (1 -5) indicator 5 =no recruitment, species indicator sp gone
209 Weed 3.0 1 = no invasive weeds 2.5 2.0 Summer weed control 2.0 Low cost long term weed seed bank populations/ 5 = significant weed (2 -4) important. reduction techniques that do not dynamics invasion IWM widely practiced. impact on short term profitability.
210 Revegetation 3.0 1 = planted native 5 5.0 3.0 Identification of the least productive species for grazing, (2 -5) areas and creation of long term, low e.g. saltbush input feed reserves if economics 5 = no planted natives suggest it is more productive than used for grazing annual pastures.
211 Fencing 2.0 1 = significant veg 3 4.0 Some areas fenced. 4.0 Need self regenerating scrub for protected from grazing (2 -4) shelter and wind breaks. Continue to 5 = no veg protected fence when able.
212 Feral pests, 2.5 1 = managed 1 3.0 Most bait foxes. 3.0 Coordinated baiting programs for foxes and 2 = unmanaged (1 -3) whole district. rabbits
132