‘Biophysical and economic impact and adaptation options in a changing climate for low rainfall mixed farms, and graziers in the rangelands zone of the Murrumbidgee catchment area.’ Final Report Part 1 Baseline and projected biophysical and economic performance of 2 mixed and 4 grazing only farms in the Riverina Local Lands Services region

Part 2 Evaluation of alternate management strategies on 2 mixed and 4 grazing only farms in the Riverina Local Land Services region

Michael Cashen, Phillip Graham, Kim Broadfoot, Dr John Finlayson & Dr Anthony Clark

April 2015

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Contents Acknowledgements ...... 6 Executive summary ...... 7 Background ...... 9 Scope of this report...... 9 Biophysical models...... 9 Methods ...... 10 Site selection ...... 10 AusFarm setup ...... 11 Soil characterisation ...... 11 Climate (baseline) ...... 11 Farming system verification ...... 11 Climate (projected) ...... 12 CO2 fertilisation ...... 14 Temporal boundaries ...... 14 Assessment indicators ...... 15 Impact assessment ...... 15 Adaptation assessment ...... 17 Results ...... 19 AusFarm Impact assessment - Pleasant Hills ...... 20 Pleasant Hills climate ...... 20 Pleasant Hills economics ...... 21 Pleasant Hills crop production ...... 22 Pleasant Hills pasture production ...... 23 Pleasant Hills livestock production ...... 24 Adaptation assessment – Pleasant Hills ...... 25 Pleasant Hills sowing adaptation ...... 25 Pleasant Hills genetics adaptation ...... 27 Pleasant Hills cover adaptation...... 29 Summary – Pleasant Hills ...... 31 AusFarm Impact assessment - Illabo ...... 32 Illabo climate ...... 32 Illabo economics ...... 33 Illabo crop production...... 34 Illabo pasture production ...... 35 Illabo livestock production ...... 35 Adaptation assessment – Illabo ...... 37 Illabo sowing adaptation ...... 37 Illabo genetics adaptation ...... 39 Illabo cover adaptation ...... 41 Summary – Illabo ...... 43 GrassGro impact assessment – Hay East ...... 44 Hay East climate ...... 44 Hay East production summary ...... 45

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Hay East adaptation summary ...... 46 Summary – Hay East site ...... 46 GrassGro impact assessment – Hay South site ...... 47 Hay South site- climate ...... 47 Hay South site production summary ...... 48 Hay South site adaptation summary ...... 49 Summary – Hay South ...... 49 GrassGro impact assessment – Hay West site ...... 50 Hay West site - climate ...... 50 Hay West site production summary ...... 51 Hay West site adaptation summary ...... 52 Summary –Hay West site ...... 52 GrassGro impact assessment – Narrandera ...... 53 Narrandera climate ...... 53 Narrandera production summary ...... 54 Narrandera adaptation summary ...... 55 Summary – Narrandera ...... 55 Conclusion ...... 56 Future considerations ...... 58 References ...... 59 Appendices ...... 61 Appendix 1. Biophyscial model descriptors ...... 61 Appendix 2. Farming systems literature ...... 62 Appendix 3. Detailed assumptions in AusFarm case study sites ...... 63 Appendix 4. ApSoil soil descriptors for Pleasant Hills and Illabo sites ...... 65 Appendix 5. Baseline and projected biophysical and economic impact ...... 65 Appendix 6. Site statistical climate analysis for the AusFarm sites ...... 70 Appendix 7. Hay and Narrandera GrassGro impact simulation 2030 summary ...... 74 Appendix 8. Summary of site adaptation responses ...... 75

Figure 1. Reference sites across the Southern NSW LLS regions...... 10 Figure 2. RCP and SRES trajectory comparison 1950-2010 (IPCC 2014)...... 14 Figure 3. Boxplot interpretation ...... 19 Figure 4. Long term variability in annual rainfall at Pleasant Hills with the range of climate model projections at 2030...... 20 Figure 5. The maxima and minima monthly temperatures at Pleasant Hills for current climate and the projected period (2030)...... 20 Figure 6. Late winter/spring frost risk at Pleasant Hills, where a frost is a minimum daily temperature below 0 degrees C...... 21 Figure 7. Pleasant Hills gross margin comparison for 2030...... 21 Figure 8. Pleasant Hills crop yield comparison for 2030...... 22 Figure 9. Pleasant Hills harvest day comparison for 2030...... 22

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Figure 10. Pleasant Hills dry matter comparison for 2030...... 23 Figure 11. Pleasant Hills ground cover comparison for 2030 as a proportion of cover (0-1)...... 23 Figure 12. Pleasant Hills supplementary feed intake comparison for 2030...... 24 Figure 13. Pleasant Hills ewe weight comparison for 2030...... 24 Figure 14. Total farm gross margin ($/ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios. ‘BASELINE’ is current management and ‘SOWING’ is adaptation. Dashed blue line is median gross margins under current management. Dashed red line is the breakeven point...... 25 Figure 15. Crop gross margin ($/cropped ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios...... 26 Figure 16. Crop yields (kg/ha) at Pleasant Hills highlighting the effect of implementing the sowing adaptation under different climate scenarios...... 26 Figure 17. Total farm gross margin ($/ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios...... 27 Figure 18. Livestock enterprise gross margin ($/grazed ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios...... 28 Figure 19. Median lamb weight (kg/animal) highlighting the effect of implementing the genetics adaptation under different climate scenarios...... 29 Figure 20. Total farm gross margin ($/ha) highlighting the effect of implementing the cover adaptation under different climate scenarios...... 30 Figure 21. Long term average (%) pasture cover (aggregated to the whole farm as median across all paddocks) highlighting the effect of implementing the cover adaptation under different climate scenarios...... 30 Figure 22. Long term median supplement fed to ewes (kg/head/day) highlighting the effect of implementing the cover adaptation under different climate scenarios...... 31 Figure 23. Long term variability in annual rainfall at Illabo with the range of climate model projections at 2030...... 32 Figure 24. The maxima and minima monthly temperatures at Illabo for current climate and the projected period (2030)...... 32 Figure 25. Late winter/spring frost risk at Illabo, where frost is a minimum daily temperature below 0 degrees C...... 33 Figure 26. Illabo gross margin comparison for 2030...... 33 Figure 27. Illabo crop yield comparison for 2030...... 34 Figure 28. Illabo harvest day comparison for 2030...... 34 Figure 29. Illabo total dry matter comparison for 2030...... 35 Figure 30. Illabo ground cover threshold comparison for 2030...... 35 Figure 31. Illabo supplementary feed intake comparison for 2030...... 36 Figure 32. Illabo ewe weight comparison for 2030 ...... 36 Figure 33. Total farm gross margin ($/ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios...... 37 Figure 34. Crop gross margin ($/cropped ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios...... 37 Figure 35. Crop yields (kg/ha) at Illabo highlighting the effect of implementing the sowing adaptation under different climate scenarios...... 38

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Figure 36. Total farm gross margin ($/ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios...... 39 Figure 37. Livestock enterprise gross margin ($/grazed ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios...... 40 Figure 38. Median lamb weight (kg/animal) highlighting the effect of implementing the genetics adaptation under different climate scenarios...... 41 Figure 39. Total farm gross margin ($/ha) highlighting the effect of implementing the cover adaptation under different climate scenarios...... 42 Figure 40. Long term average (%) pasture cover (aggregated to the whole farm as median across all paddocks) highlighting the effect of implementing the cover adaptation under different climate scenarios...... 42 Figure 41. Long term median supplement fed to ewes (kg/head/day) highlighting the effect of implementing the cover adaptation under different climate scenarios...... 43 Figure 42. Long term variability in annual rainfall at Hay East with the range of climate model projections at 2030 ...... 44 Figure 43. The maxima and minima monthly temperatures at Hay East for current climate and the projected period (2030)...... 44 Figure 44. Late winter/spring frost risk at Hay East, where a frost is a minimum daily temperature below 0 degrees C ...... 45 Figure 45. Long term variability in annual rainfall at Hay South with the range of climate model projections at 2030 ...... 47 Figure 46. The maxima and minima monthly temperatures at Hay South for current climate and the projected period (2030)...... 47 Figure 47. Late winter/spring frost risk at Hay South, where a frost is a minimum daily temperature below 0 degrees C ...... 48 Figure 48. Long term variability in annual rainfall at Hay West with the range of climate model projections at 2030 ...... 50 Figure 49. The maxima and minima monthly temperatures at Hay West for current climate and the projected period (2030)...... 50 Figure 50. Late winter/spring frost risk at Hay West, where a frost is a minimum daily temperature below 0 degrees C ...... 51 Figure 51. Long term variability in annual rainfall at Narrandera with the range of climate model projections at 2030 ...... 53 Figure 52. The maxima and minima monthly temperatures at Narrandera for current climate and the projected period (2030)...... 53 Figure 53. Late winter/spring frost risk at Narrandera, where a frost is a minimum daily temperature below 0 degrees C ...... 54

Table 1. Underpinning soil data for regional case study...... 11 Table 2. Riverina LRZ broad reference farm assumptions ...... 12 Table 3. GCM selection for Riverina low rainfall impact assessment...... 13 Table 4. Key variables used to assess impact scenarios for the AusFarm sites...... 16 Table 5. Key variables used to assess impact scenarios for the GrassGro sites...... 16 Table 6. Key variables used to assess adaptation scenarios for the AusFarm sites...... 17 Table 7. Key variables used to assess adaptation scenarios for the GrassGro sites...... 18

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Table 8. Pleasant Hills lamb performance comparison for 2030...... 25 Table 9. The number of years that the sowing adaptation was enacted...... 27 Table 10. Number of days from lamb weaning to sale date ...... 29 Table 11. Illabo lamb performance comparison for 2030 ...... 36 Table 12. The number of years that the sowing adaptation was enacted...... 38 Table 13. Number of days from lamb weaning to sale date ...... 41 Table 14. 2030 impact assessment % variance of each GCM to the baseline period for the Hay East site...... 45 Table 15. Hay East site adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis...... 46 Table 16. 2030 impact assessment % variance of each GCM to the baseline period for the Hay South site...... 48 Table 17. Hay south site adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis...... 49 Table 18. 2030 impact assessment % variance of each GCM to the baseline period for the site Hay West site...... 51 Table 19. Hay West site adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis...... 52 Table 20. 2030 impact assessment % variance of each GCM to the baseline period for the site at Narrandera...... 54 Table 21. Narrandera adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis...... 55

Acknowledgements The authors would like to acknowledge the significant contributions of staff from NSW Department of Primary Industries and NSW Local Land Services.

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Executive summary In order to assess the potential impact and adaptation options for both mixed and grazing only farms in the low rainfall zone of southern NSW, representative farm simulations have been developed for 6 sites in the Riverina Local Land Services (RLLS) region. Of the 6 sites 2 are mixed farms and 4 are grazing only. Baseline biophysical and economic performance for each site has been established using the either the AusFarm TM or GrassGro TM modelling platform and their sensitivities to future climate scenario’s for the period 2030 assessed using a number of variables. Results suggest that the farming systems selected are indeed sensitive to potential changes in climate and atmospheric

concentrations of C0 2, although the farms were differentially affected likely a function of soil type and climatic pattern.

Across the range of future possible climates assessed, the average impact of the scenarios assessed on the two mixed farms at Pleasant Hills and Illabo were a; • slight decrease of economic returns across the farms, • slight decrease on crop yields at least in part driven by a shortening of the growing season, • positive increases in annual dry matter production but changes in seasonality, with lower pasture volumes available in late autumn and winter, and more in spring, • slight decreases in annual groundcover but with increases in spring, • decreases in the amount of livestock supplement required and increased pasture intake • slight increase in the number of lambs weaned and performance.

An assessment of 3 potential management strategies or adaptation options were made for 2 mixed farm sites. Options assessed included; a. not sowing crops in years where rainfall conditions are not met at the conclusion of multiple sowing windows, b. enhancing terminal lamb growth rates to shorten turn off period, and c. lowering the minimum pasture cover threshold at which stock are moved to reduce livestock supplementation. Results from the biophysical and economic adaptation assessment suggest that both sites had reasonably similar responses to the 3 adaptations assessed i.e. ‘sowing’, ‘genetics’ and ‘cover’. Implementation of the; • ‘genetics’ rule slightly improved gross margins at the farm and livestock level, broadening the probability of return at the Pleasant Hills site and increasing the probability of higher end returns at Illabo. • ‘cover’ rule had no notable impact on farm gross margins, seasonal pasture cover or level of supplementation required. • ‘sowing’ rule only slightly altered both the farm and cropping gross margins at Pleasant Hills, and increased the probability of lower end returns at the farm level. In contrast, Illabo saw a reduction in gross margins at both the farm level and cropping enterprise.

The average impact of the projected change for the GrassGro sites showed; • a varied response in gross margins performance across sites with strong declines in Hay East and Narrandera but increases in Hay South and Hay West sites.

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• a varied response in drainage with on average increases at the Hay South and Narrandera sites but decrease at the other locations. • a slight increase in lambing percentages at all sites considered. • a varied response in minimum ground cover percentages with decreases seen at the 3 Hay sites but slight increases at Narrandera

Across the grazing sites 2 adaptation strategies have been assessed either independently and or in combination. The first being the inclusion of a perennial grasses species ‘pasture’ to the annual mix and the second an improvement in animal genetics ‘genetics’ which increased fleece weight and reduced fibre diameter. At the Narrandera site the adaptation options have been assessed both independently and in combination and at the 3 Hay sites in combination only. Results from the biophysical and economic adaptation assessment suggest that the grazing only sites had reasonably similar responses to the combine adaptations (cover and genetics). Overall there was a positive financial response in gross margin return and either a neutral or negative response on minimum ground cover at all sites relative to the impact assessment.

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Background Extensive dryland agricultural systems are exposed to and dependent on the vagaries of the weather and are inherently sensitive to climate variability and change. The long term maintenance of farm productivity growth is a major priority for the NSW Government. The NSW agriculture sector contributes more than $14.5 billion or 3.4% annually (Fogarty pers comm 2014) to the state’s economy, unfortunately there has been a significant slowdown in agricultural productivity in the broad acre sector since 1990, which can be partly explained by the effects of the Millennium drought. However uncertainties remain about the long term viability of the dryland agriculture sector in a changing but variable climate, particularly in the low rainfall zones (LRZ) of inland NSW.

Scope of this report This project had its genesis at a time of significant change for its two partner organisations; the NSW Department of Primary Industries (NSW DPI) and NSW Local Land Services (NSW LLS), formerly known as the NSW Catchment Management Authority. Under a former government directive LLS’s were to review their strategic plans for their catchment referred to as their Catchment Action Plans and to make those plans 'climate change ready'. The rationale being that some insight into the impact of projected change and viable adaptation options would enable the organisation to more readily facilitate change and increase catchment resilience over the longer term.

The aim of this report is to: • provide an initial assessment of impacts of climate change on representative sites, and • investigate the potential of a number of adaptation options to reduce the biophysical and economic impacts of climate change.

The work does not provide a comprehensive assessment of the adaptive capacity of farms in the region however the information is a valuable start to enable the LLS to engage producers in the region, as a way of developing their own adaptations and increase their resilience to climate stresses.

NSW DPI had demonstrated competence in conducting such investigation through 2 former national impact assessment projects funded by the former Department of Agriculture Fisheries and Forestry (DAFF ) entitled; ‘Developing climate change resilient cropping and mixed cropping/grazing businesses in Australia’ and the ‘Climate change adaptation in the southern livestock industries’ (SLA2030). Results from this work suggested that there was significant opportunity through incremental adaptation of farming systems however little was known about the potential impact of projected changes in climate on mixed farming and grazing only systems in the LRZ receiving less than 350mm growing season rainfall in southern NSW.

Biophysical models Farming systems by their very nature are incredibly complex. Traditional agricultural research in the field and laboratory has over time enabled mathematical relationships to be determined, which through refinement have been built into agricultural computer simulations referred to as biophysical models. Whilst there is a range of sector specific models available, typically they simulate the effects of the environment and management decisions on agricultural production, profits and the environmental variables. In previous projects NSW DPI has favoured the Agricultural Production

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Systems Simulator (APSIM) (Keating B.A et al 2003, McCowan R.L et al 1995) for cropping research and GrassGro TM (Freer M et al 1997, Moore A.D et al 1997) for livestock systems work. In order to assess the impact of changes to climate on mixed farms, components of both the APSIM and GrassGro models were needed, CSIRO’s AusFarm TM model (Moore A.D et al 2004,2007,2014) enables integration of these and additional functionality, specifically the ability to represent the management structure and decisions in complex mixed farms. Biophysical models make no account of plant and animal disease or pest impacts and as such tend to optimise biophysical performance. A full description of each of the models can be found in Appendix 1.

Methods

Site selection To explore the potential impact of projected changes to farming systems in the low rainfall zone of NSW of the Riverina LLS region, 2 mixed farming sites and 4 grazing sites were selected. The mixed farming sites were at Pleasant Hills and Illabo and the grazing sites were at: Narrandera and 3 sites on differing soil types around Hay. The site selection illustrated in Figure 1 has been based on a; 1. spatial analysis of April-October rainfall isohyets within the LLS region, 2. the availability of underpinning soil and historic climate data, and 3. LLS site nomination. This selection of reference sites provides an ideal rainfall analogue from which to make assumptions about impact and adaptation in low rainfall mixed farming and grazing systems across the broader Riverina LLS area. This project is being run concurrently with similar projects in the Western, Murray and Central LLS regions and those sites have also been included in Figure 1.

Figure 1. Reference sites across the Southern NSW LLS regions.

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AusFarm setup

Soil characterisation Simulations in AusFarm are underpinned by appropriate site soil characterisation data including drained upper and lower water capacity limits. An initial spatial review was undertaken of the existing soil data across the LLS region using the Australian Soil Resource Information System (ASRIS) www.asris.csiro.au site . Unfortunately due to limitations in data availability only a single soil type was used on each reference farm. A full descriptor of each site was determined using the ApSoil database http://www.apsim.info/Products/APSoil.aspx. A brief summary can be seen in Table 1 and a more detailed summary of soils at the Pleasant Hills and Illabo sites can be found in Appendix 4.

Table 1. Underpinning soil data for regional case study. Location Latitude Longitude Soil Descriptor Pleasant Hills -35.49 146.87 Henty No701 (Sandy clay loam over light to medium clay) Illabo -34.71 147.54 Junee Reefs No 564 -YP (Red Chromosol) Narrandera -34.5 146.3 Dr 2.33 Northcotte (duplex red horizon hardsetting, B horizon mottled) South Hay -35.6 144.42 Dr 2.33 Northcotte (duplex red horizon hardsetting, B horizon mottled) West Hay -33.57 144.33 Ug 5.2 Northcotte (Clay peds smoothed face) East Hay -34.24 145.30 Dbl 1.33 Northcotte (Duplex brown horizon)

Climate (baseline) Both the AusFarm and GrassGro models utilise daily time stepped climate data. Historic daily climate data in the APSIM format is available through the Queensland Department of Science, Information technology, Innovation and the Arts (QSITIA) via its Longpaddock website, (http://www.longpaddock.qld.gov.au/data). The data available is either actual station data referred to as Patched Point Data (PPD) or as synthetic data which is derived from nearby stations referred to as Data Drill (DD). Given the variation in spatial density and distance of the LLS reference study sites to meteorology stations, DD was determined to be the most representative method. DD accesses grids of data interpolated from point observations by the Bureau of Meteorology. The data in DD are synthetic; there is no original meteorological station data left in the calculated grid fields. Historic climatic data for 2 assessments varied slightly with the period (1957-2012) used for the AusFarm mixed enterprise baseline assessment and the period (1955-2013) used for the GrassGro baseline assessment.

Farming system verification In order to assess the impact of projected changes to the 6 mixed farms along the study transect it was necessary to build farming system simulations which are representative of those at each study site. This was a resource intensive process where regional extension staff from LLS, who have detailed insight into farm systems management and performance, partnered with, departmental agricultural technical specialists and technical model developers to review the relevant literature

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(Appendix 2) and help refine the farming systems. Emerging from this consultation has been a series of underpinning assumptions that have been used for the development of case study sites at: Pleasant Hills, Illabo, Narrandera and the 3 sites around Hay. Table 2 summarises the farming system assumptions and biophysical model used to create the 6 reference farms. A more complete description of the Pleasant Hills and Illabo farms can be found in Appendix 3. The logic and accuracy of model coding for each site was inspected carefully by developers in consultation with regional staff. The output from a broad range of variables was assessed to ensure that management routines were performing satisfactorily, and there were no un-anticipated interactions as the routines became more complex. Model outputs of crop, pasture and animal production were carefully assessed by regional staff to ensure that they were simulated within ranges typically obtained on farms in the region. Often this process involved up to 10 or more model refinements to ensure that the models performed acceptably. This level of acceptability testing, as opposed to formal model validation where independent measures are used, is typical practice in studies where the aim is to assess the sensitivity of a farming system to an external change like a shift in climate variability. The technical reference team engaged in developing the underpinning farming system design have subsequently been involved in the adaptation evaluation for the 3 site simulations in this phase of the project. It should be noted that such simulations are dependent on the availability and quality of plant characterisation modules in the biophysical model with limitations on both species and cultivar data evident during this study for example the 3 grazing site simulations at Hay were based only on local grasses because chenopod shrubs typical of the region aren’t available in the GrassGro package, as such estimates of total ground cover for the landscape will be lower than that normal experienced in the region.

Table 2. Riverina LRZ broad reference farm assumptions

Site Size No Stock Pasture Crop Model Paddocks used Pleasant 4,000ha 10 Merino ewe Lucerne and sub Wheat, canola, wheat, AusFarm Hills *Dorset clover barley, wheat, under sown pasture (5 years) Illabo 4,000ha 10 Merino ewe Lucerne and medic Wheat, canola, wheat, AusFarm *Dorset barley, wheat under sown pasture (5 years) Narrandera Not Not Merino self- Annual grasses Grazing only GrassGro applicable applicable replacing and medics South Hay Not Not Merino self- Annual grasses Grazing only GrassGro applicable applicable replacing and medics West Hay Not Not Merino self- Annual grasses Grazing only GrassGro applicable applicable replacing and medics East Hay Not Not Merino self- Annual grasses Grazing only GrassGro applicable applicable replacing and medics

Climate (projected) Global climate is a system of energy exchange which is subject to different ‘forcing processes’ that influence its state. Global climate has and will continue to change in response to large internal forcing processes like the El Nino Southern Oscillation (ENSO) which influence year to year variability; external forcing events like major volcanic eruptions which influence global climate for 3- 5 years; and much longer term forcing such as changes to the earth’s orbit which have led to the

12 | P a g e glacial and interglacial cycles observed over tens of thousands of years. An important forcing process is the role of greenhouse gases in heat regulation of the atmosphere, and evidence that this has increased considerably over the last century as a by-product of fossil fuel use. The combined effects of these forcing processes are studied intensively, leading to the strong expectation that the global climate will continue the warming trend over the coming century (Stefan et al 2011; AAS 2015; IPCC 2013; Reisinger et al. 2014), as part of this warming global average precipitation is also expected to increase on the whole, but with distinct regional variations. The expectation is major systems like the sub-tropical ridge of pressure which typically dominates the mid latitudes will intensify and move further south leading to reduced winter rainfall, by blocking rain triggering cold fronts from the south west. Inversely this broad scale synoptic change increases the chance of summer storms, by enabling moisture from the summer monsoons to penetrate further (CSIRO 2010). The combined effects of forcing are examined by tracking changes to weather and climate observations as well as Global Climate Models (GCMs). These models are based on the established laws of physics and have improved over time with the latest generation referred to as the CIMP5 models (Coupled Model Intercomparison Project) http://cmip-pcmdi.llnl.gov/cmip5/ . The GCM’s also provide the opportunity to look forward and project what may occur over the coming 30 years into the future. There is moderate to high confidence in their ability to represent changes in global and regional temperature. However for rainfall there is reduced confidence beyond changes to the general global structure of rainfall patterns. For the Murray and Western LLS there is variability between different models in terms of how far south the sub-tropical ridge shifts or how far south the tropics expand over time. It is not uncommon for projections of rainfall from different GCMs to vary markedly when the results are examined at precise locations, such as an individual farm. For this reason this study follows the standard practice of considering multiple climate models as a way of providing a best estimate for anticipating changes which may take place in the future. In order to capture the range of plausible future climates across the LRZ of southern NSW, 3 CIMP5 GCM’s were selected based on the M Skill Score assessment undertaken as part of the 2007 CSIRO and BOM assessment (CSIRO & BoM 2007). Arguably these 3 models quantify a range of possible climate outcomes in the explored scenarios. This selection illustrated in Table 3 enabled a range of likely plausible future climates to be explored and so avoids the issue around ‘picking winners’.

Table 3. GCM selection for Riverina low rainfall impact assessment. Modelling Institution Model abbr eviation used in report Met Office Hadley Centre ‘Hadley’ National Centre for Atmospheric Research ‘CCSM’ Max Planck Institute for Meteorology ‘Mon Plank’

Whilst there is significant uncertainty in predicting the future and indeed human behaviour and technological advances, a number of attempts have been made to standardise assumptions around providing a range of plausible futures. The Representative Concentration Pathways (RCP’s) (IPCC 2014) are the latest attempt in this endeavour and supersede the previous Special Report Emission Scenarios referred to as the SRES (IPCC SRES 2000) which was used in previous NSW DPI impact assessment work. Figure 2 illustrates the relative radiative forcing from the various RCP’s compared to the older SRES. Given the typical planning horizon of the agricultural sector, the period 2030 and RCP 4.5 were regarded as a realistic and useful point at which to examine the biophysical and

13 | P a g e economic impact of changes to climate and potential adaptation options for mixed farmers in the LRZ. As can be seen in Figure 2 there is minimal divergence at 2030 between all the RCP’s and the SRES scenarios in terms of global emissions.

Figure 2. RCP and SRES trajectory comparison 1950-2010 (IPCC 2014). It was initially proposed that CMIP5 projected climate data (Coupled Model Inter-comparison Project; http://cmip-pcmdi.llnl.gov/cmip5/index.html ) would be sourced from the Queensland Department of Science, Information Technology, Innovation and the Arts (DSITIA) via the Consistent Climate Scenarios delivery platform, unfortunately CMIP5 projected data from DSITA was unavailable. A viable alternative was subsequently found, in a weather generator previously developed by CSIRO (Moore et al 2013) as part of the SLA2030 project. The weather generator uses daily historic climate for a site and modifies it based on GCM selection and RCP choice to produce a projected sequential daily weather dataset in a SILO format which is then imported into AusFarm or GrassGro to examine site impacts.

CO2 fertilisation th st Throughout the 20 and 21 century atmospheric CO 2 levels have risen from preindustrial levels of

~280 (ppm) to their current levels of ~395(ppm). Under RCP 4.5, CO 2 levels are expected to rise further to 435(ppm) by 2030. The effect on both temperate (C 3) and tropical (C 4 ) plants of higher

CO 2 concentrations is well documented and often referred to as the CO 2 fertilisation effect. Broadly speaking higher levels of CO 2 improve resource efficiency, productivity and plant production

(Howden et al 2010) although the effect is dependent on photosynthetic pathway. The CO 2 fertilisation has been factored into the biophysical modelling, including feedbacks from the nitrogen and water cycle undertaken as part of this assessment of mixed farm in the LRZ of Riverina LLS region. The assessment has been made on the basis of levels of atmospheric CO 2 levels being at 395 (ppm) for the respective baseline periods and 435ppm for the projected period 2030.

Temporal boundaries The objective of this project has been on the quantitative assessment of biophysical impact and adaptation options of mixed and grazing only farms in the LRZ of NSW using appropriate biophysical

14 | P a g e models ( http://www.grazplan.csiro.au/?q=node/1 , http://www.grazplan.csiro.au/?q=node/3 . This assessment by necessity has required a historic assessment of performance for the periods 1957- 2012 (mixed farms), 1955-2013 (grazing farms) and a futuristic assessment under 3 climate scenarios on the year 2030. Both the Pleasant Hills and Illabo sites are mixed farms, with the pasture phase a key part of the agronomic rotation for the 2 reference farms. To ensure livestock numbers are maintained the program has been set to feed animals at times of low pasture cover in either a feedlot or in the paddock. To enable the simulation to establish pasture in the initial years and the animal system to reach a ‘normal’ state, a run in period of 7 years (period Jan 1950- Dec 1956) was used with biophysical output only reported for the period Jan 1957- Dec 2012 once pasture was established in the system. To enable AusFarm simulations of future climates to stabilise the first 5 years of climate data (period Jan 2015 - Dec 2019) was used to initiate the system with biophysical output only reported for the following 26 years (period Jan 2020 - Dec 2045). The resulting output can be viewed as 26 potential realisations of weather for the year 2030. The GrassGro sites at Narrandera and Hay were a little different in that there was no delay in pasture establishment with pasture established across the site on the first day of the simulation. A period of Jan 1955-Dec 2013 was used as the baseline and projected data was generated for the 30 year period Jan 2015-Dec 2044 with biophysical output presented for the full period.

Assessment indicators Whilst there is a magnitude of output available from each farming simulation, limitations were applied to cap the number of critical indicators of performance for both the impact and adaptation assessments and so enable interpretation, common to both were the economic evaluation using gross margin analysis. The gross margin analysis provides a common metric across all systems and sites and was used as the overarching index to assess impact and adaptation response. The gross margins were derived from system and spatially relevant NSW DPI gross margins 2014 (http://www.dpi.nsw.gov.au/agriculture/farm-business/budgets) with biophysical output for each AusFarm simulation driving the economic analysis which was performed as post process in the statistical package ‘R’ ( http://www.r-project.org/). It should be noted that gross margins are a simple representation of economic performance only taking into account variable income and costs and exclude overheads and any assessment of opportunity costs.

Impact assessment AusFarm mixed farm sites It was determined that 9 indicators of farm performance provide an accurate assessment of baseline biophysical and economic response and the impact of a changing climate. The impact assessment variables used are shown in Table 4. To facilitate communication with stakeholders, the impacts across 3 GCM’s are presented as either an average across the 3 GCM’s or as a boxplot providing some sense of the distribution of outcomes. Details of this analysis can be found in Appendix 5.

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Table 4. Key variables used to assess impact scenarios for the AusFarm sites. Impact Variables analysed Climate Annual rainfall Maximum and minimum temperature Winter/spring frost incidence Economic Gross margin farm Gross margin (crop/livestock) Crop production Crop yield Harvest date Pasture production Dry matter Ground cover Livestock production Supplementary feeding Ewe weight Lamb weight

GrassGro grazing sites It was determined that 5 indicators of farm performance would provide an ideal metric to provide an accurate assessment of baseline biophysical and economic response and the impact of a changing climate. The impact assessment variables used are shown in Table 5. To facilitate communication with stakeholders, the impacts across 3 GCM’s have been summarised as variance of that variable relative to the baseline period 1955-2013. Positive values indicate an increased variance in that variable and negative values show a decrease.

Table 5. Key variables used to assess impact scenarios for the GrassGro sites.

Impact Variables analysed Climate Annual rainfall Average temperature Economic Gross margin (livestock) Landscape Drainage Pasture production Ground cover Livestock production DSE Lambing rate

It should also be noted that only the Narrandera site had overhead costs included in the financial assessment.

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Adaptation assessment AusFarm mixed farm sites 3 agreed potential management strategies or adaptation options have been developed in consultation with advisory staff from the LLS partners including; a. ‘Sowing’: The modelling work conducted for the baseline assessment allowed “dry” sowing to occur at the end of the sowing window if soil moisture triggers had not been reached (insufficient rainfall). The sowing adaptation strategy implemented ensures that crops are not sown in years where rainfall conditions are not met at the conclusion of multiple sowing windows. b. ‘Genetics’: The modelling work conducted for the baseline assessment ran a Merino ewe, terminal sire lamb production enterprise where the terminal sire (Dorset) had a reference weight was set at 80kg. The genetics adaptation strategy implemented an increase in sire reference weight to 90Kg, in order to enhance lamb growth rates and shorten turn off period. This is a moderate change based on 2015 genetics. c. ‘Cover’: The modelling work conducted for the baseline assessment was based on a pasture ground cover threshold of 50%. Once the pasture ground cover threshold was breached (cover fell below 50%), the animals were placed in a feedlot and provided supplementary feed. The cover adaptation strategy decreases this threshold by 5% in order to reduce livestock supplementation and increase grazing time.

The 11 variables used (Table 6) in the adaptation assessment were chosen based on their capacity to summarise the gross impact of the adaptation option selected. As indicated it was determined that only those variables that directly related to each adaptation option would be included in the analysis.

Table 6. Key variables used to assess adaptation scenarios for the AusFarm sites.

Adaptation Variables analysed Sowing Gross margin for farm Gross margin for crop Crop yield Years no crop was sown Genetics Gross margin for farm Gross margin for animal Lamb performance Average days to lamb turn off Cover Gross margin for farm Minimum ground cover farm Supplementation levels to ewes

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GrassGro grazing sites The 2 adaptation strategies that have been examined for the grazing only sites are; a. ‘Pasture’: The baseline modelling work conducted for the baseline assessment ran the livestock enterprise on a pasture mix of annual grasses and medic. The pasture adaptation strategy added a native perennial grass species to the pasture mix. b. ‘Genetics’: The baseline modelling work conducted for the baseline assessment ran a self- replacing merino ewe of 20 um cutting 6 kg of greasy wool at a fleece free and empty body weight of 55kg. The genetics adaptation strategy implemented increased fleece weight of 1 kg and a decreased fibre diameter of 0.5 um. Adaptation response at the 3 Hay case study sites have been examined as a combined response to both the pasture and genetic adaptation, whilst the Narrandera site has examined the effect of the different adaptation separately, and also combined. The 3 variables used (Table 7) to assess each adaptation were chosen based on their capacity to summarise the impact of the adaptation.

Table 7. Key variables used to assess adaptation scenarios for the GrassGro sites.

Adaptation Variables analysed Pasture Gross margin for farm Ground cover

Genetics Gross margin for farm Ground cover

Pasture adaptation In dry environments perennial grasses will in many years act in the same way as an annual species due to the extended period of low soil moisture. Because soil moisture is so limiting the other effect that can occur is that the presence of the perennial species results in a lower germination of annual species which can lead to lower animal production because the perennial species have lower digestibility (energy and protein). This effect can lead to a lower ground cover. This work looks at the pasture between the shrubs as it is the pasture component that drives animal production in the arid zone.

Genetics adaptation The improved wool production reflects where wool cut and fibre diameter could be if producers adopted a structured breeding program from now to 2030. It could be argued that genetic improvement in the flock has historically being used as a strategy to offset the declining terms of trade.

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Results Interpretation of box plots Box and Whisker plots are useful way of examining the distribution of a dataset and have been used extensively in presentation of results in this report. The plot illustrated in Figure 3 is split into quartile groups. The main ‘box’ section of the plot represents the results within the second and third quartile (50% of the data), while the horizontal line splitting the box represents the median value. The diamond represents the average value. The straight lines, ‘whiskers’, extending from the box represent the maximum and minimum values, excluding outliers, which are shown as ‘+’. When interpreting the plot, it is important to look at the distribution of the plot. For example, a shortening of the lower whisker (b) in a gross margin analysis infers that there is a decrease in the probability of receiving lowers end returns. Likewise, an increase in the top whisker (c) suggests an increased chance of hitting higher returns. The symmetry of the ‘box’ is also useful when interpreting the plot. The longer the ‘box’ (c), more variation in the results can be expected. If the box is skewed (a), the smallest quartile reflects more consistent results; whilst the large box shows a greater spread of expected outcomes.

Figure 3. Boxplot interpretation

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AusFarm Impact assessment - Pleasant Hills

Pleasant Hills climate Figure 4, Figure 5 and Figure 6 show the comparison of historical and projected climatic conditions at Pleasant Hills. Figure 4 shows the range of potential rainfall compared to historical measurements. Figure 5 shows that there is an overall expected increase in both average maximum and minimum daily temperatures throughout the year, with increases of up to 2°C not uncommon. The occurrence and projected changes in winter/spring frost (Figure 6) shows a higher probability of later season frosts. This increased incidence of later frosts has the potential of effecting crop performance. A more detailed monthly statistical analysis including an assessment of rainfall and maximum temperatures can be found in Appendix 6.

Figure 4. Long term variability in annual rainfall at Pleasant Hills with the range of climate model projections at 2030.

Figure 5. The maxima and minima monthly temperatures at Pleasant Hills for current climate and the projected period (2030).

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Figure 6. Late winter/spring frost risk at Pleasant Hills, where a frost is a minimum daily temperature below 0 degrees C.

Pleasant Hills economics Figure 7 presents the gross margin calculations for Pleasant Hills for the animal, crop and whole farm component of the farming enterprise, comparing historic conditions to future climate scenarios (CCSM, Hadley, Mon Plank and a combined summary of the three GCMs). The data indicates that across the three GCMs the impact from future climate will have a varying effect on animal, crop and farm gross margins. The Mon Plank climate scenario appears to be the best performing GCM for the cropping and whole farm enterprises. CCSM also shows a positive response for these two enterprises, whilst Hadley show a negative response. The animal component of the farming systems tends to show Mon Plank as the worst performing GCM.

Figure 7. Pleasant Hills gross margin comparison for 2030.

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Pleasant Hills crop production Figure 8 and Figure 9 show the comparison of historical and projected crop performance under climatic conditions at Pleasant Hills. Figure 7 shows the findings of expected crop yields for barley, canola and wheat, comparing the three GCMs with the baseline period. Hadley GCM is consistently the worst performer across all 3 crop types with the lowest median yields of all the GCMs. CCSM and Mon plank both have positive responses for canola and wheat, but like Hadley, both have a negative response when examining barley yields. When comparing harvest date (Figure 9), there are noticeable reductions in the days to harvest across all three crop types, though barley and canola show the most variability between the 3 GCMs.

Figure 8. Pleasant Hills crop yield comparison for 2030.

Figure 9. Pleasant Hills harvest day comparison for 2030.

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Pleasant Hills pasture production Figure 10 and Figure 11 show the comparison of historical and projected pasture performance under climatic conditions at Pleasant Hills. Figure 10 presents the finding of total pasture dry matter at Pleasant Hills, showing Mon Plank and CCMS outperforming the baseline period, and Hadley the worst performer. Relative ground cover as a proportion of total pasture cover at Pleasant Hills is compared in Figure 11. Only Hadley shows noticeable reductions in monthly ground cover. This will have implications on how pastures are managed and the effect this will have on livestock production under future climate scenarios.

Figure 10. Pleasant Hills dry matter comparison for 2030.

Figure 11. Pleasant Hills ground cover comparison for 2030 as a proportion of cover (0-1).

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Pleasant Hills livestock production Figure 12, Figure 13 and Table 8 show the comparison of historical and projected animal performance under climatic conditions at Pleasant Hills. Figure 12 shows the response to the amount of annual ewe supplementation required, where CCSM and Mon Plank have a decrease in the supplementary feed intake and Hadley shows an increase in supplementary fee. This result is supported by Figure 10, relating to the amount of total dry matter available. Figure 13 presents the findings of average ewe weight at Pleasant Hills, showing Mon Plank as the best performing GCM though both CCSM and Mon Plank at or above baseline measurements. Table 8 reports on the average lamb performance at Pleasant Hills, showing only slight variations (from baseline) in the numbers of lambs expected for each of the 3 GCMs. However there was a noticeable increase in lamb weights and animal condition for the Mon Plank GCM.

Figure 12. Pleasant Hills supplementary feed intake comparison for 2030.

Figure 13. Pleasant Hills ewe weight comparison for 2030.

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Table 8. Pleasant Hills lamb performance comparison for 2030.

Ave. Number of Ave. Max Ave. Max Condition Climate Scenario lambs weight (kg) score Baseline (No.) 2930 41.72 3.42 CCSM (% change) 0.0 0.6 2.9 Hadley (% change) 0.7 0.9 -2.0 Mon Plank (% change) -0.2 2.5 6.1 Average 3 GCMs (% change) 0.2 1.3 2.3

Adaptation assessment – Pleasant Hills

Pleasant Hills sowing adaptation Figure 14 and Figure 15 present the economic response to implementation of the ‘sowing’ rule at Pleasant Hills under the 3 future climate scenarios. Figure 14 illustrates the effect on total farm gross margins and Figure 15 shows the effect on crop gross margins. Both figures show the variations of future climate scenarios (CCSM, Hadley and Mon Plank) compared to historic conditions (baseline). Implementing the sowing rule has seen an increase in the median gross margin returns for both the CCSM and Mon Plank GCMs (Figure 14) and a noticeable decrease for Hadley. It is also interesting to note the increase probability of both higher and lower end returns for CCSM when the sowing adaptation is implemented. When examining the effects of the sowing adaptation on crop gross margins (Figure 15), the same trend is observed for each of the 3 GCMs.

Figure 14. Total farm gross margin ($/ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios. ‘BASELINE’ is current management and ‘SOWING’ is adaptation. Dashed blue line is median gross margins under current management. Dashed red line is the breakeven point.

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Figure 15. Crop gross margin ($/cropped ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios. Figure 16 illustrates the effect on crop yield when implementing the sowing adaptation for the 3 crop types present at Pleasant Hills. When examining individual crops, both CCSM and Hadley show improvement in median yields.

Figure 16. Crop yields (kg/ha) at Pleasant Hills highlighting the effect of implementing the sowing adaptation under different climate scenarios.

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Table 9 provides a summary of the number of years when the sowing adaptation was triggered under the future climate scenario, for each of the 3 GCMs. CCSM and Hadley were affected by the sowing adaptation, and Mon Plank showed the least change from baseline. It is also important to consider the number of years that the first crop in the rotation (wheat) was not sown. Not only does this influence yield and gross margins, it also has implications for the pasture phase of the farming system – extending the pasture phase of the rotation for an additional year.

Table 9. The number of years that the sowing adaptation was enacted.

Effect of Sowing Adaptation CCSM Hadley Mon Plank Number of years first crop (wheat) in rotation was not planted @ 3 (12%) 4 (15%) 1 (4%) Number of years canola was not planted 4 (15%) 5 (19%) 0 Number of years no crops were sown 0 0 0 Number of years only one crop was sown 2 (8%) 1 (4%) 0 Number of years only two crops was sown 1 (4%) 1 (4%) 1 (4%) @ therefore extending the pasture phase of the rotation for an additional year

Pleasant Hills genetics adaptation Figure 17 and Figure 18 present the economic response to implementation of the ‘genetics’ adaptation at Pleasant Hills under the 3 future climate scenarios. Figure 17 illustrates the effect of implementing the ‘genetics’ adaptation on total farm gross margins. CCSM and Hadley show slight increases in the median gross margins that could be expected due to the genetics adaptation. When examining the gross margins for the animal component of the farming enterprise, all three GCMs reacted positively to the genetics adaptation. Although the average gross margin did not change for the CCSM climate scenario, there was reduction in the probability of reaching lower end returns and also the probability of reaching higher end return increased. Both Hadley and Mon Plank showed improvement in the median gross margins that could be expected.

Figure 17. Total farm gross margin ($/ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios.

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Figure 18. Livestock enterprise gross margin ($/grazed ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios. Figure 19 and Table 10 illustrate the change in lamb performance from implementation of the ‘genetics’ adaptation at Pleasant Hills. Figure 19 displays the response in lamb weight to implementation of the ‘genetics’ adaptation. All three GCMs showed a noticeable positive response to the genetics adaptation, with Mon Plank even outperforming the current baseline results when the genetics adaptation was implemented. Table 10 displays the comparison in the number of days from lamb weaning to sale from implementation of the ‘genetics’ adaptation. All three GCMs were found to respond positively to the genetics adaptation, with lambs reaching sale criteria quicker than their corresponding GCM baseline. It should be noted that this analysis did not allow for altered selling dates with all animals sold within a static window, potentially higher growth rates could offer opportunities to turn stock off earlier.

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Figure 19. Median lamb weight (kg/animal) highlighting the effect of implementing the genetics adaptation under different climate scenarios.

Table 10. Number of days from lamb weaning to sale date

Climate Adaptation Female Lambs Male Lambs Median Days to sale Median Days to sale Current Baseline 46 28 CCSM Baseline 44 26 CCSM Genetics 33 22 Hadley Baseline 51 31 Hadley Genetics 40 26 Mon Plank Baseline 39 26 Mon Plank Genetics 33 21 * 11 days is the first available sale opportunity, 83 days is the last available sale opportunity

Pleasant Hills cover adaptation Figure 20, Figure 21 and Figure 22 illustrate the response to the implementation of the ‘cover’ adaptation. Figure 20 illustrates the effect of implementing the adaptation on total farm gross margins. There was found to be little effect from applying the cover adaptation for all three GCMs. Figure 21 illustrates the effect of implementing the adaptation on total farm gross margins. There was found to be little effect from applying the cover adaptation for all three GCMs. Figure 22 displays the effect of implementing the ‘cover’ adaptation on ewe supplementation, which shows that there is little to no improvement in the amount of supplement that is fed to the animals. There is a slight decrease in late winter, though this is only a marginal improvement.

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Figure 20. Total farm gross margin ($/ha) highlighting the effect of implementing the cover adaptation under different climate scenarios.

Figure 21. Long term average (%) pasture cover (aggregated to the whole farm as median across all paddocks) highlighting the effect of implementing the cover adaptation under different climate scenarios.

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Figure 22. Long term median supplement fed to ewes (kg/head/day) highlighting the effect of implementing the cover adaptation under different climate scenarios.

Summary – Pleasant Hills

The average impact for the projected change at Pleasant Hills includes; • a slight decrease of economic return across the farm, • a slight decrease in crop yield at least in part driven by a shortening of the growing season, • a positive increase in annual dry matter production but a change in seasonality, with lower pasture volumes available in late autumn and winter and more in spring, • slight decrease in annual ground cover but with increases in spring , • decreases in the amount of livestock supplementation required and increase in pasture intake ,and • slight increases in the number of lambs weaned and performance .

At the Pleasant Hills site implementation of the adaptation option showed; • the ‘sow’ rule only slightly altered gross margins at both the farm and crop level , however increased probabilities of lower end returns at the farm level and broadened probability of returns of crop gross margins. • the ‘genetics’ rule only slightly improved gross margins at both the farm and animal level and broadened probabilities of returns. This result was driven by faster finishing times from increases in lamb weight gain. • The ‘cover’ rule had no notable impact on farm gross margins, seasonal pasture cover or level of supplementation required.

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AusFarm Impact assessment - Illabo

Illabo climate Figure 23, Figure 24 and Figure 25 show the comparison of historical and projected climatic conditions at Illabo. Figure 23 shows the range of potential rainfall compared to historical measurements. Figure 24 shows that there is an overall expected increase in both average maximum and minimum daily temperatures throughout the year, with increase of up to 2°C. The occurrence and projected changes in winter/spring frost (Figure 25) shows the probability of later season frosts. This increased incidence of later frosts (October and November)) has the potential of effecting crop performance. A more detailed monthly statistical analysis including an assessment of rainfall and maximum temperatures can be found in Appendix 6.

Figure 23. Long term variability in annual rainfall at Illabo with the range of climate model projections at 2030.

Figure 24. The maxima and minima monthly temperatures at Illabo for current climate and the projected period (2030).

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Figure 25. Late winter/spring frost risk at Illabo, where frost is a minimum daily temperature below 0 degrees C.

Illabo economics Figure 26 presents the gross margin calculations for Illabo for the animal, crop and whole farm component of the farming enterprise, comparing historic conditions to future climate scenarios (CCSM, Hadley, Mon Plank). When examining the summary boxplot, it suggests that there will be decreases in expected gross margins for each of the farming system components with Hadley as the worst performing GCM.

Figure 26. Illabo gross margin comparison for 2030.

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Illabo crop production Figure 27 and Figure 28 show the comparison of historical and projected crop performance under climatic conditions at Illabo. Figure 27 presents the findings of crop yield for barley, canola and wheat atIllabo, showing reductions in the expected crop yields for both barley and canola. This is most noticeable when comparing the Hadley GCM with baseline conditions. Wheat has performed differently with both CCSM and Mon Plank obtaining slightly higher average yields. Figure 28 shows how the crop harvest date is affected under the climate scenarios. Overall there have been reductions in the days to harvest across all crop types for each of the 3 GCMs.

Figure 27. Illabo crop yield comparison for 2030.

Figure 28. Illabo harvest day comparison for 2030.

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Illabo pasture production Figure 29 and Figure 30 show the comparison of historical and projected pasture performance under climatic conditions at Illabo. Figure 29 shows both CCSM and Mon Plank having similar dry matter available throughout the year and performing better than baseline conditions. Figure 30 presents the findings of relative ground cover as a proportion of total pasture cover at Illabo. Only Hadley shows noticeable reductions in monthly ground cover. This will have implications on how pastures are managed and the effect this will have on livestock production under future climate scenarios.

Figure 29. Illabo total dry matter comparison for 2030.

Figure 30. Illabo ground cover threshold comparison for 2030.

Illabo livestock production Figure 31, Figure 32 and Table 11 show the comparison of historical and projected animal performance under climatic conditions at Temora. Figure 31 shows a varied response to the amount of annual ewe supplement required with both CCSM and Mon Plank showing reductions in the amount of feed required and Hadley showing increases. This supports the finding in Figure 29 relating to the amount of total dry matter available. Figure 32 presents the findings of average ewe weight at Illabo, which again highlights the negative impact of the Hadley CGM on performance.

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Table 11 presents the findings of average lamb performance again showing CCSM and Mon Plank performing better than baseline conditions.

Figure 31. Illabo supplementary feed intake comparison for 2030.

Figure 32. Illabo ewe weight comparison for 2030

Table 11. Illabo lamb performance comparison for 2030

Ave. Number Ave. Max weight Ave. Max Condition Climate Scenario of lambs (kg) score Baseline (No.) 3880 41.27 3.32 CCSM (% change) 0.4 1.5 3.3 Hadley (% change) 0.4 -1.0 -3.9 Mon Plank (% change) 0.1 1.8 3.6 Average 3 GCMs (% change) 0.3 0.8 1.0

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Adaptation assessment – Illabo

Illabo sowing adaptation Figure 33 and Figure 34 presents the economic response to implementation of the ‘sowing’ rule at Illabo under the 3 future climate scenarios. Figure 33 illustrates the effect of implementing the ‘sowing’ rule on total farm gross margins and Figure 34 illustrates the effect of the implementing the ‘sowing’ rule on crop gross margins. Both figures show the variations of future climate scenarios (CCSM, Hadley & Mon Plank) compared to historic conditions (baseline). When examining both the farm (Figure 33) and individual crops (Figure 34), Hadley experiences decreases in expected median gross margins. At the farm scale both Hadley and Mon Plank both show a broadening of return probabilities with an increased chance of receiving lower end returns and all three GCMs can expect an increase chance of reaching high end returns when examining individual crop returns.

Figure 33. Total farm gross margin ($/ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios.

Figure 34. Crop gross margin ($/cropped ha) highlighting the effect of implementing the sowing adaptation under different climate scenarios.

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Figure 35 illustrates the effect on crop yield of implementing the ‘sowing’ rule on each of the 3 crop types represented at the Illabo site. When examining individual crops, there is a noticeable reduction in median yields under the Hadley climate scenario and little changes under CCSM and Mon Plank. Canola responds positively to the CCSM and Hadley scenarios made there is also a positive response to wheat yields for the Hadley climate scenario.

Figure 35. Crop yields (kg/ha) at Illabo highlighting the effect of implementing the sowing adaptation under different climate scenarios. Table 12 provides a summary of the number of years when the ‘sowing’ adaptation was triggered under the future climate scenarios. CCSM and Hadley were the most affected by the sowing adaptation, which would directly relate to the climate conditions experienced under these two climate scenarios. It is interesting to note the number of times that canola was not sown due to the adaptation, with the Hadley scenario 42% of the time. Another consideration is the number of times the first crop in the rotation (wheat) is not sown. Not only does this influence yield and gross margins, it also has implications for the pasture phase of the farming system – extending the pasture phase of the rotation for an additional year.

Table 12. The number of years that the sowing adaptation was enacted.

Effect of Sowing Adaptation CCSM Hadley Mon Plank Number of years the first crop (wheat) in rotation was not sown @ 4 (15%) 5 (19%) 2 (8%) Number of years canola was not sown 4 (15%) 11 (42%) 2 (8%) Number of years no crops were sown 0 0 0 Number of years only one crop was sown 1 (4%) 4 (15%) 0 Number of years only two crops was sown 2 (8%) 1 (4%) 1 (4%) @ therefore extending the pasture phase of the rotation for an additional year

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Illabo genetics adaptation Figure 36 and Figure 37 present the economic response to implementation of the ‘genetics’ adaptation at Illabo under the 3 future climate scenarios. Figure 36 illustrates the effect of implementing the ‘genetics’ adaptation on total farm gross margins. There appears to be a slight change in the median gross margins that would be expected with the implementation of the genetics adaptation (most noticeably for Mon Plank) across all 3 GCMs, however CCSM does show that there is a shift in the distribution of expected gross margins, and an increase in the probability of receiving lower end returns. However when examining he gross margins for the livestock component (Figure 37), CCSM has the most noticeable change in expected gross margins, with a narrowing of the distribution resulting in more consistent return for the livestock component.

Figure 36. Total farm gross margin ($/ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios.

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Figure 37. Livestock enterprise gross margin ($/grazed ha) highlighting the effect of implementing the genetics adaptation under different climate scenarios.

Figure 38 and Table 13 illustrate the change in lamb performance from implementation of the ‘genetics’ adaptation at Illabo. Figure 38 displays the response in lamb weight to implementation of the ‘genetics’ adaptation. All three GCMs showed a noticeable positive response to the genetics adaptation, with all three outperforming the current baseline results when the genetics adaptation was implemented (though this was less noticeable as the lambing season progressed). Table 13 displays the comparison in the number of days from lamb weaning to sale from implementation of the ‘genetics’ adaptation. All three GCMs were found to respond positively to the genetics adaptation, with lambs reaching sale criteria quicker than their corresponding GCM baseline. It should be noted that this analysis did not allow for altered selling dates with all animals sold within a static window, potentially higher growth rates could offer opportunities to turn stock off earlier.

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Figure 38. Median lamb weight (kg/animal) highlighting the effect of implementing the genetics adaptation under different climate scenarios.

Table 13. Number of days from lamb weaning to sale date

Climate Adaptation Female Lambs Male Lambs Median Days to sale Median Days to sale Current Baseline 47 29 CCSM Baseline 43 26 CCSM Genetics 33 22 Hadley Baseline 52 31 Hadley Genetics 41 26 Mon Plank Baseline 43 29 Mon Plank Genetics 32 23 * 11 days is the first available sale opportunity, 83 days is the last available sale opportunity

Illabo cover adaptation Figure 39, Figure 40 and Figure 41 illustrate the response to the implementation of the ‘cover’ adaptation. Figure 39 illustrates the effect of implementing the adaptation on total farm gross margins. There was found to be little effect from applying the cover adaptation for all three GCMs. Figure 40 displays the effect of implementing the ‘cover’ adaptation on long term average pasture cover, again showing little change with implementing the cover adaptation. Figure 41 displays the effect of implementing the ‘cover’ adaptation on ewe supplementation, showing little change in the amount of supplement that is fed to the animals in response to the cover adaptation. However it is interesting to note the difference in the distribution of supplement intake between the three future climate scenarios.

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Figure 39. Total farm gross margin ($/ha) highlighting the effect of implementing the cover adaptation under different climate scenarios.

Figure 40. Long term average (%) pasture cover (aggregated to the whole farm as median across all paddocks) highlighting the effect of implementing the cover adaptation under different climate scenarios.

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Figure 41. Long term median supplement fed to ewes (kg/head/day) highlighting the effect of implementing the cover adaptation under different climate scenarios.

Summary – Illabo The average impact for the projected change for Illabo includes; • a slight decrease of economic return across the farms , • a slight decrease in crop yield at least in part driven by a shortening of the growing season, • a positive increase in annual dry matter production but a change in seasonality, with lower pasture volumes available in late autumn and winter and more in spring, • slight decrease in annual ground cover but with increases in spring , • decreases in the amount of livestock supplementation required and increase in pasture intake ,and • slight increases in the number of lambs weaned and performance . At the Illabo site implementation of the adaptation option showed; • the ‘sow’ rule decreased average gross margins at both the farm and crop level and increased the probability of lower end returns. This was driven by decreases in average yield and broadening yield probabilities. • the ‘genetics’ rule slightly improved gross margins at both the farm and animal level and increased probabilities of higher end returns. This result was driven by faster finishing times from increases in lamb weight gain. • The ‘cover’ rule had no notable impact on farm gross margins, seasonal pasture cover or level of supplementation required.

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GrassGro impact assessment – Hay East

Hay East climate

Figure 42, Figure 43 and Figure 44 shows the comparison of historical and projected climatic conditions at Hay East. Figure 41 shows the range of potential rainfall compared to historical measurements. Figure 43 shows that there is an overall expected increase in both average maximum and minimum daily temperatures throughout the year, with increases of up to 1 0C not uncommon. The occurrence and projected changes in winter/spring frost (Figure 44) shows a higher probability of later season frosts (September and November).

Figure 42. Long term variability in annual rainfall at Hay East with the range of climate model projections at 2030

Figure 43. The maxima and minima monthly temperatures at Hay East for current climate and the projected period (2030).

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Figure 44. Late winter/spring frost risk at Hay East, where a frost is a minimum daily temperature below 0 degrees C

Hay East production summary Table 14 shows the comparison of baseline against projected performance for the 3 GCMs scenarios considered. Results are presented as percentage variance from the baseline period. In terms of gross margins, each of the 3 GCMs recorded a negative impact in farm gross margins, with CCSM being the worst performing future climate scenario (-57% variance from baseline). When examining the impacts on the landscape, drainage was considered to be a useful tool in exploring the impact of future climates. Both Hadley and Mon Plank climate scenarios recorded decreases in drainage whilst CCSM recorded increases (29% variance from baseline). Livestock production was evaluated by examining the impact on lambing rate. All 3 GCMs showed a consistent lambing rate, slightly above baseline expectations. Ground cover has been considered a useful tool when examining the impact of future climate on pasture production. Each of the 3 GCMs responded negatively with decreases in ground cover (between -7 to -14 % variance from baseline).

Table 14. 2030 impact assessment % variance of each GCM to the baseline period for the Hay East site.

Variables Baseline CCSM Hadley 2030 Mon Plank Average (1955-2013) 2030 2030 impact 2030 Annual average rainfall mm 362 2 -13 18 2 Annual average 0 temperature C 17.2 7 8 6 7 DSE/ha 1.3 -54 -8 8 -18 Gross Margin $/ha 23 -57 -4 -9 -23 Drainage mm 38 29 -42 -100 -38 Lambing % 88 3 3 3 3 Ground cover % 59 -14 -7 -10 -10

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Hay East adaptation summary The combined effect of the genetics and pasture adaptation presented in Table 15 illustrates the response to the implementation of the combined adaptation strategies for each GCM scenario assessed. Results are presented as both absolute values and as a percentage variance relative to each GCM’s impact assessment. Care should be taken when looking at percentage changes with very low base numbers. When examining gross margins under future climate scenarios, Mon Plank is the best performing GCM when implementing the adaptation scenario, though all three GCMs show increases in gross margins under the combined adaptation scenario. When comparing ground cover, all three GCMs show a reduction in cover.

Table 15. Hay East site adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis.

Scenario Adaptation Gross Margin Ground cover $/ha Min % Baseline No adaptation 23 59 CCSM Impact 10 51 Adaptation (combined) 12 (20%) 44 (-14%) Hadley Impact 22 55 Adaptation (combined) 31 (41%) 49 (-11%) Mon Plank Impact 21 53 Adaptation (combined) 41 (95%) 49 (-6%)

Summary – Hay East site The average impact for the project change at the Hay East site includes; • 18% lower carrying capacity, • 23% lower gross margin return • 38% decrease in drainage, • 3% increase in lambing rates, and • 10% decrease in ground cover.

At the Hay East site implementation of the adaptation option combination on average showed; • improvements in gross margin return , and • a decrease in minimum % ground cover.

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GrassGro impact assessment – Hay South site

Hay South site- climate Figure 45, Figure 46 and Figure 47 shows the comparison of historical and projected climatic conditions at Hay South. Figure 45 shows the range of potential rainfall compared to historical measurements. Figure 46 shows that there is an overall expected increase in both average maximum and minimum daily temperatures throughout the year, with increases of up to 1 0C not uncommon. The occurrence and projected changes in winter/spring frost (Figure 47) shows a higher probability of later season frosts (September and October).

Figure 45. Long term variability in annual rainfall at Hay South with the range of climate model projections at 2030

Figure 46. The maxima and minima monthly temperatures at Hay South for current climate and the projected period (2030).

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Figure 47. Late winter/spring frost risk at Hay South, where a frost is a minimum daily temperature below 0 degrees C

Hay South site production summary Table 16 shows the comparison of baseline against projected performance of the the 3 GCMs scenarios considered. Results are presented as percentage variance from the baseline period. In terms of gross margins, only CCSM had a negative impact in farm gross margins, with Mon Plank being the best performing future climate scenario (58% variance from baseline). When examining the impacts on the landscape, drainage was considered to be a useful tool in exploring the impact of future climates. Both CCSM and Mon Plank climate scenarios recorded increases in drainage whilst Hadley recorded decreases (-50% variance from baseline). Livestock production was evaluated by examining the impact on lambing rate. All 3 GCMs showed improvements in lambing rate, when compared to baseline expectations. Ground cover has been considered a useful tool when examining the impact of future climate on pasture production. CCSM and Hadley responded negatively with decrease in ground cover between 11 and 21 % (variance from baseline), Mon Plank showed no variation from baseline conditions.

Table 16. 2030 impact assessment % variance of each GCM to the baseline period for the Hay South site.

Variables Baseline CCSM Hadley 2030 Mon Plank Average (1955-2013) 2030 2030 impact 2030 Annual average rainfall mm 376 -5.6 -10.1 14.9 -0.3 Annual average 0 temperature C 16.3 7.4 7.4 6.8 7.2 DSE/ha 1.6 -37.5 12.5 87.5 20.8 Gross Margin $/ha 38 -55 16 58 30 Drainage mm 24 4.2 -50.0 66.7 6.9 Lambing % 96 3.1 1.0 1.0 1.7 Ground cover % 70 -21.4 -11.4 0.0 -11.0

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Hay South site adaptation summary The combined effect of the genetics and pasture adaptation presented in Table 17 illustrates the response to the implementation the 2 adaptation strategies for each GCM scenario assessed. Results are presented as both absolute values and as a percentage variance relative to each GCM impact assessment. Care should be taken when looking at percentage changes with very low base numbers. When examining gross margins under future climate scenarios, only Hadley showed a positive response when implementing the adaptation scenario. Ground cover analysis shows no to little decrease in the expected cover under the adaptation scenarios.

Table 17. Hay south site adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis.

Scenario Adaptation Gross Margin Ground cover $/ha Min % Baseline No adaptation 38 70 CCSM Impact 23 55 Adaptation (combined) 17 (-26%) 55 (0%) Hadley Impact 42 62 Adaptation (combined) 44 (5%) 61 (-2%) Mon Plank Impact 76 70 Adaptation (combined) 60 (-21%) 69 (-1%)

Summary – Hay South The average impact for the project change at the Hay south site includes; • 21% increase in carrying capacity, • 30% increase in gross margin return • 7% increase in drainage, • 2% increase in lambing rates, and • 11% decrease in ground cover.

At the Hay South site implementation of the adaptation option combination on average showed; • declines in gross margins under the CCSM and Mon Plank scenario and only slight increases in the Hadley scenario, and • slight change to minimum % ground cover.

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GrassGro impact assessment – Hay West site

Hay West site - climate Figure 48, Figure 49 and Figure 50 shows the comparison of historical and projected climatic conditions at Hay West. Figure 48 shows the range of potential rainfall compared to historical measurements. Figure 49 shows that there is an overall expected increase in both average maximum and minimum daily temperatures throughout the year, with increases of up to 1 0C not uncommon. The occurrence and projected changes in winter/spring frost (Figure 50) shows a higher probability of frosts in September.

Figure 48. Long term variability in annual rainfall at Hay West with the range of climate model projections at 2030

Figure 49. The maxima and minima monthly temperatures at Hay West for current climate and the projected period (2030).

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Figure 50. Late winter/spring frost risk at Hay West, where a frost is a minimum daily temperature below 0 degrees C

Hay West site production summary In terms of gross margins, only CCSM had a negative impact in farm gross margins (Table 18), with Mon Plank being the best performing future climate scenario (70% variance from baseline). When examining the impacts on the landscape, drainage was considered to be a useful tool in exploring the impact of future climates. No significant changes from baseline were recorded for any of the 3 GCMs. Livestock production was evaluated by examining the impact on lambing rate. Hadley and Mon Plank showed an improvement in lambing when compared to baseline expectations. Ground cover has been considered a useful tool when examining the impact of future climate on pasture production. CCSM and Mon Plank responded negatively (decrease in ground cover between 2 and 13 % variance from baseline) and Hadley showed no variation from baseline conditions.

Table 18. 2030 impact assessment % variance of each GCM to the baseline period for the site Hay West site.

Variables Baseline CCSM Hadley 2030 Mon Plank Average (1955-2013) 2030 2030 impact 2030 Annual average rainfall mm 321 3 -12 30 7 Annual average 0 temperature C 17.9 7 8 6 7 DSE/ha 0.7 -86 29 57 0 Gross Margin $/ha 10 -90 60 70 13 Drainage mm 0 0 0 0 0 Lambing % 88 -1 3 2 2 Ground cover % 48 -13 0 -2 -5

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Hay West site adaptation summary The combined effect of the genetics and pasture adaptation presented in Table 19 illustrates the response to the implementation of the combined adaptation strategies for each GCM. Results are presented as both absolute values and as a percentage variance relative to each GCM impact assessment. Care should be taken when looking at percentage changes with very low base numbers. When examining gross margins under future climate scenarios, all of the GCMs have had a positive response when implementing the adaptation scenario. However the results for the CCSM scenario need to be used with caution due to the very low initial absolute values. Overall there was a decrease in ground cover across all three GCMs with CCSM being the worst performer.

Table 19. Hay West site adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis.

Scenario Adaptation Gross Margin Ground cover $/ha Min % Baseline No adaptation 10 48 CCSM No adaptation 1 42 Adaptation 2 (100%) 38 (-10%) Hadley No adaptation 16 48 Adaptation 21 (31%) 45 (-6%) Mon Plank No adaptation 17 47 Adaptation 24 (41%) 44 (-6%)

Summary –Hay West site The average the impact for the projected change at the Hay West site includes; • No change in carrying capacity, • 13% increase in gross margin return • No change in drainage, • 2% increase in lambing rates, and • 5% decrease in ground cover.

At the Hay West site implementation of the adaptation option combination on average showed; • improvements in gross margin return , and • a decrease in minimum % ground cover.

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GrassGro impact assessment – Narrandera

Narrandera climate Figure 51, Figure 52 and Figure 53 shows the comparison of historical and projected climatic conditions at Narrandera. Figure 51 shows the range of potential rainfall compared to historical measurements. Figure 52 shows that there is an overall expected increase in both average maximum and minimum daily temperatures throughout the year, with increases of up to 1 0C not uncommon. The occurrence and projected changes in winter/spring frost (Figure 53) shows a higher probability of frosts in September.

Figure 51. Long term variability in annual rainfall at Narrandera with the range of climate model projections at 2030

Figure 52. The maxima and minima monthly temperatures at Narrandera for current climate and the projected period (2030).

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Figure 53. Late winter/spring frost risk at Narrandera, where a frost is a minimum daily temperature below 0 degrees C

Narrandera production summary Table 20 shows the comparison of baseline against projected performance for the 3 GCMs scenarios considered. Results are presented as percent variance from the baseline period. In terms of gross margins, each of the 3 GCMs recorded a negative impact in farm gross margins, with Hadley being the worst performing future climate scenario (-108% variance from baseline). When examining the impacts on the landscape, drainage was considered to be a useful tool in exploring the impact of future climates. Both CCSM and Mon Plank climate scenarios recorded increases in drainage whilst Hadley showed decreases (-33% variance from baseline). Ground cover has been considered a useful tool when examining the impact of future climate on pasture production. CCSM and Mon Plank responded positively with increases in ground cover (between 2 and 3 % variance from baseline), Hadley showed a reduction from baseline conditions. Livestock production was evaluated by examining the impact on lambing rate. All 3 GCMs showed an improvement in lambing rate (expressed as a percentage) when compared to baseline expectations.

Table 20. 2030 impact assessment % variance of each GCM to the baseline period for the site at Narrandera.

Variables Baseline CCSM Hadley 2030 Mon Plank Average (1955-2013) 2030 2030 impact 2030 Annual average rainfall mm 469 6.40 -14.29 10.45 0.85 Annual average 0 temperature C 16.6 6.63 9.04 7.23 7.63 DSE/ha 5.5 -49.09 -52.73 -32.73 -44.85 Gross Margin $/ha 49 -102.04 -108.16 -46.94 -85.71 Drainage mm 43 111.63 -32.56 62.79 47.29 Lambing % 90 2.22 3.33 4.44 3.33 Ground cover % 61 3.28 -1.64 1.64 1.09

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Narrandera adaptation summary Table 21 illustrates the response to implementation of the alternate adaptation as individual strategies and then in combination for each GCM scenario. Baseline values have been derived based on an annual pasture system. Results are presented as both absolute values and as a percentage variance relative to each GCM impact assessment. Implementing the ‘pasture’ adaptation significantly improved gross margin performance across all GCM scenarios considered relative to the initial impact assessment with greatest gains under the CCSM scenario, however there was little impact on minimum ground cover. Implementing the ‘genetics’ adaptation improved gross margin performance across both the CCSM and Hadley scenarios but made no difference under the Mon Plank scenario, and had little impact on minimum ground cover. The combined effect of both the ‘pasture’ and ‘genetics’ adaptation strategies significantly improved gross margin return across all scenarios assessed but had little effect on minimum ground cover .

Table 21. Narrandera adaptation assessment comparison for 2030 for each GCM. The variance (percent change) from no adaptation values are shown in parenthesis.

Scenario Adaptation Gross Margin Ground cover $/ha Min % Baseline No adaptation 49 61 CCSM Impact -1 63 Pasture adaptation 16 (1700%) 58 (-8%) Genetics adaptation 5 (600%) 63 (0%) Combined adaptation 27 (2800%) 58 (-0%) Hadley Impact -4 60 Pasture adaptation 18 (550%) 57 (-5%) Genetics adaptation 4 (200%) 63 (5%) Combined adaptation 28 (800%) 58 (-3%) Mon Plank Impact 26 62 Pasture adaptation 31 (19%) 58 (-6%) Genetics adaptation 26 (0%) 63 (2%) Combined adaptation 43 (65% 58 (-6%)

Summary – Narrandera The average impact for the project change at the Narrandera site includes; • 45% decrease in carrying capacity, • 86% decrease in gross margin return, • 47% increase in drainage, • 3% increase in lambing rates, and • 1% increase in ground cover.

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At the Narrandera site implementation of the 3 various adaptation options showed; • Implementing the ‘pasture’ adaptation significantly improved gross margin performance across all GCM scenarios considered relative to the initial impact assessment with greatest gains under the CCSM scenario and had little impact on minimum ground cover. • Implementing the ‘genetics’ adaptation improved gross margin performance across both the CCSM and Hadley scenarios but made no difference under the Mon Plank scenario and had little impact on minimum ground cover. • Implementing both the ‘pasture’ and ‘genetics’ adaptation option significantly improved gross margin return relative to the impact assessment however had little impact on minimum ground cover.

Conclusion Analysis using the three GCM’s provides an indication of the range of change that could occur and its potential biophysical and economic impact on mixed farms in the Riverina LLS region. The Hadley model appeared to be the driest and Mon Plank the wettest models. Whilst there is strong consistency between the models in the direction of temperature change, the CCSM and Hadley models indicate stronger warming than the Mon Plank model. Interestingly across all farms the GCM’s suggest increases in average maximum and minimum temperatures in addition to an increase in the standard deviation, indicating more temperature extremes compared to the baseline period. AusFarm sites Across the 3 GCMs the average impact of the projected change for Pleasant Hills and Illabo include a; • slight decrease of economic returns across the farms, • a slight decrease on crop yields at least in part driven by a shortening of the growing season, • a positive increases in annual dry matter production but changes in seasonality, with lower pasture volumes available in late autumn and winter, and more in spring, • a slight decreases in annual groundcover but with increases in spring, • a decreases in the amount of livestock supplement required and increased pasture intake, and • a slight increases in the number of lambs weaned and performance.

In consultation with LLS staff, 3 adaptation options were assessed for each of the 3 sites. The first was a ‘sowing’ adaptation which enabled a deviation from the baseline system, which sowed crops dry at the end of multiple sowing windows. Applying the ‘sowing’ option tested the impact of not sowing crops in such circumstance. The second was a ‘genetics’ option which tested the impact of increasing lamb weights through genetic selection and the third was a ‘cover’ option, testing the impact of lowering ground cover thresholds to maintain stock in paddocks for longer.

Results from the biophysical and economic adaptation assessment suggest that both sites had reasonably similar responses to the 3 adaptations assessed i.e. ‘sowing’, ‘genetics’ and ‘cover’. Implementation of the; • ‘genetics’ rule slightly improved gross margins at the farm and livestock level, broadening the probability of return at the Pleasant Hills site and increasing the probability of higher end returns at Illabo.

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• ‘cover’ rule had no notable impact on farm gross margins, seasonal pasture cover or level of supplementation required. • ‘sowing’ rule only slightly altered both the farm and cropping gross margins at Pleasant Hills, and increased the probability of lower end returns at the farm level. In contract, Illabo saw a reduction in gross margins at both the farm level and cropping enterprise.

GrassGro sites

The average impact of the projected change for the GrassGro sites showed; • a varied response in gross margins performance across sites with strong declines in Hay East and Narrandera but increases in Hay South and Hay West sites. • a varied response in drainage with increases at the Hay South and Narrandera sites but decrease at the other locations. • a slight increase in lambing percentages at all sites considered. • a varied response in minimum ground cover percentages with decreases seen at the 3 Hay sites but slight increases at Narrandera

Across the grazing sites 2 adaptation strategies have been assessed either independently and or in combination. The first being the inclusion of a perennial grasses species ‘pasture’ to the annual mix and the second an improvement in animal genetics ‘genetics’ which increased fleece weight and reduced fibre diameter. At the Narrandera site the adaptation options have been assessed both independently and in combination and at the 3 Hay sites in combination only. Results from the biophysical and economic adaptation assessment suggest that the grazing only sites had reasonably similar responses to the combine adaptations (cover and genetics). Overall there was a positive financial response in gross margin return and either a neutral or negative response on minimum ground cover at all sites relative to the impact assessment.

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Future considerations This assessment should be viewed as a snapshot in time which was based on the best and most suitable climate data and future projections available, but also limited by the investigators capacity to accurately represent local farming systems using the biophysical and economics models available. However, even given these limitations, a significant legacy now exists in simulation development for mixed farming systems. Options for further work would include a: • rapid reassessment of biophysical and economic impact and adaptation using the Office of Environment and Heritages (OEH’s) NARCLIM dataset, • assessment of applying both the ‘sowing’ and ‘genetic’ adaptations simultaneously, • assessment of increasing sire weights further for the ‘genetic’ adaptation to further test the sensitivity of the farming system, • broader economic assessment including balance sheet assessment, • inclusion of irrigation, and • an assessment of optimal enterprise mix.

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References

Australian Academy of Sciences (2015) The science of climate change: Questions and Answers. Canberra 44 p.

CSIRO (2010) Climate variability and change in south-eastern Australia: A synthesis of findings from Phase 1 of the South Eastern Australian Climate Initiative (SEACI).

Freer M, Moore AD, and Donnelly JR, (1997) GRAZPLAN: Decision Support Systems for Australian grazing enterprises-II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems, 54: 77-126.

IPCC SRES (2000) Nakićenović N, Swart R, ed., Special Report on Emissions Scenarios: A special report of Working Group III of the Intergovernmental Panel on Climate Change (book), Cambridge University Press, ISBN 0-521-80081-1, 978-052180081-5 (pb: 0-521-80493-0, 978-052180493-6).

IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ, (2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267- 288.

McCown RL, Hammer GL, Hargreaves JNG., Holzworth D, Huth NI, (1995) APSIM - An agricultural production system simulation model for operational research. Mathematics and Computers in Simulation 39, 225-231.

Moore A D, Donnelly JR, Freer M (1997) GRAZPLAN: Decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS. Agricultural Systems, 55: 535-582

Moore AD, Holzworth DP, Herrmann NI, Huth NI, Robertson MJ (2007) The Common Modelling Protocol: A hierarchical framework for simulation of agricultural and environmental systems. Agricultural Systems 95(1-3) 37-48.

Moore AD, Holzworth DP, Herrmann NI, Brown HE, de Voil PG, Snow VO, Huth NI (2014) Modelling the manager: representing rule-based management in farming systems simulation models. Environmental Modelling & Software, special issue.

Moore AD, Salmon L,Dove H(2004)The whole-farm impact of including dual-purpose winter wheat and forage brassica crops in a grazing system: a simulation analysis. New directions for a diverse planet: Handbook and Abstracts for the 4th International Crop Science Congress, Brisbane, Australia: 153. www.cropscience.org.au

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Moore AD, Ghahramani A (2013) Climate change and broadacre livestock production across southern Australia. 1. Impacts of climate change on pasture and livestock productivity, and on sustainable levels of profitability. Global Change Biology 19, 1440-1455

Reisinger A, Kitching RL, Chiew F, Hughes L, Newton PCD, Schuster SS, Tait A, Whetton P (2014) Australasia. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1371-1438.

Steffen W (2011) The critical decade: climate science, risks and responses, Australian Climate Commission, Department of Climate Change and Energy Efficiency, Australian Government, Canberra. 72 p.

Stokes C, Howden M (2010) Adapting Agriculture to Climate Change Preparing Australian Agriculture, Forestry and Fisheries for the Future, CSIRO Publishing.

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Appendices

Appendix 1. Biophyscial model descriptors APSIM The Agricultural Production Systems s IM ulator (APSIM) software is a modular modelling framework that has been developed by the APSIM Initiative and its predecessor the Agricultural Production Systems Research Unit (APSRU) in Australia. APSIM was developed to simulate biophysical processes in agricultural systems, particularly as it relates to the economic and ecological outcomes of management practices in the face of climate risk. APSIM is structured around plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, nitrogen and phosphorous transformations, soil pH, erosion and a full range of management controls. APSIM resulted from a need for tools that provided accurate predictions of crop production in relation to climate, genotype, soil and management factor while addressing the long-term resource management issues (Source:http://www.apsim.info/AboutUs/ APSIMModel.aspx).

GrassGro GrassGro is a decision support tool developed by CSIRO Plant Industry to assist decision-making in sheep and beef enterprises. By quantifying the variability in pasture and animal production, farmers and natural resource managers can assess the risks that variable weather imposes on a grazing system. Users can test management options against a wide range of seasons to achieve more profitable and sustainable utilisation of grasslands. GrassGro is a computer program that delivers grazing systems research in a useable form to farmers and their advisers. GrassGro is based on decades of field experimentation from across Australia and lets the user focus on the biophysical and business outcomes of management decisions. Behind GrassGro™s interface, inputs of historical daily weather data drive models of the interacting processes of pasture growth and animal production. Day-to-day changes in water content of soil, pasture growth and decay and responses to grazing are simulated for a chosen enterprise. The user describes their livestock, management, costs and prices. The animal model familiar to users of the decision support tool GrazFeed is built into GrassGro to predict animal intake and production of wool, meat and milk. Seasonal and year-to-year variation in pasture and animal production and gross margins are presented in comprehensive reports for analysis of risk. (Source http://www.grazplan.csiro.au/?q=node/1) AusFarm AusFarm's structure allows models of farm components to be configured and co-ordinated with an infinitely flexible set of management rules. The modular design of the software means it can include models from other scientific groups. This greatly expands the number of crop, livestock and management systems that can be represented and analysed compared to GrassGro for example. AusFarm is primarily intended for research into and analysis of agro-ecosystems. CSIRO Plant Industry has used the Common Modelling Protocol to develop AusFarm, a generic simulation tool for agricultural enterprises that is designed to facilitate the analysis of complex agricultural management questions. The AusFarm interface allows a user to configure simulations for execution within the Common Modelling Protocol. Management activities are conceptualized as a set of "events" that alter the state of a sub-model; the series of events that takes place in a simulation is governed by rules that describe conditions under which management events will take place. (Source http://www.hzn.com.au/ausfarm.php)

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Appendix 2. Farming systems literature

NSW DPI and GRDC (2006) Dryland cropping guidelines for south western New South Wales, Wagga Wagga Agricultural Institute.

Brill R, Speirs S (2007) A review of farming systems of the Western Plains region of NSW. NSW DPI

Edwards J (1999) Sustainable rotations and cropping practices for the marginal cropping areas of NW NSW. NSW DPI and GRDC

Matthews P, McCaffery D(2014) Winter crop variety sowing guide 2014. NSW DPI

Brooke G, McMaster C (2014) ‘Weed control in winter crops 2014. NSW DPI

Fleming J, McNee T, Cook T, Manning B (2012) Weed control in summer crops 2012-13’ NSW DPI

Cameron J, Storrie A (2014) Summer fallow weed management 2014. GRDC

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Appendix 3. Detailed assumptions in AusFarm case study sites

Pleasant Hills Farm area (ha) 4000 No. of paddocks 10 Pasture type Lucerne and sub clover Stocking rate 0.75 No. ewes 3000 Ewes Medium Merino Sire Dorset

Sowing Soil Surface Crop Variety Rainfall Density Depth Spacing Window moisture moisture 23 March – Wheat Wedgetail 25 25 5 150 25 150 6 May 5 May – 8 Wheat Janz 15 0 0 150 25 150 June 19 April – 29 Barley Gairdner 0 25 5 100 25 150 June 31 March – Canola Oscar 25 25 5 50 10 150 14 May 31 March – Canola Oscar 15 0 0 50 10 150 14 May 24 April – 30 Wheat Janz 15 0 0 50 25 150 May

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Illabo Farm area (ha) 4000 No. of paddocks 10 Pasture type Lucerne and medic Stocking rate 1 No. ewes 4000 Ewes Medium Merino Sire Dorset

Sowing Soil Surface Crop Variety Rainfall Density Depth Spacing Window moisture moisture 23 March – Wheat Wedgetail 25 25 5 150 25 150 6 May 5 May – 8 Wheat Janz 15 0 0 150 25 150 June 19 April – Barley Gairdner 0 25 5 100 25 150 29 June 31 March – Canola Oscar 25 25 5 50 10 150 14 May 31 March – Canola Oscar 15 0 0 50 10 150 14 May 24 April – Wheat Janz 15 0 0 50 25 150 30 May

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Appendix 4. ApSoil soil descriptors for Pleasant Hills and Illabo sites

Pleasant Hills The table and accompanying graph summarises site specific soil water content levels and species specific soil water availability.

Illabo The table and accompanying graph summarises site specific soil water content levels and species specific soil water availability.

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Appendix 5. Baseline and projected biophysical and economic impact

Pleasant Hills

Table A5.1. Pleasant Hills site-baseline harvest date (average) for each crop and the variance (percentage change) for the three GCM’s.

Crop Climate Scenario Barley Canola Wheat Baseline 31 Oct 06 Nov 25 Nov CCSM (% change) -5 -4 -3 Hadley (% change) -4 -3 -3 Mon Plank (% change) -5 -4 -3 Average 3 GCMs (% change) -5 -4 -3

Table A5.2. Pleasant Hills site-baseline gross margin (average) for each system and the variance (percent change) for the 3 GCM’s. System Climate Scenario Animal Crop Farm Baseline ($) $153.18 $298.35 $225.77 CCSM (% change) 2 -2 -1 Hadley (% change) 0 -32 -21 Mon Plank (% change) -3 17 10 Average 3 GCMs (% change) 0 -6 -4

Table A5.3. Pleasant Hills site-baseline monthly total dry matter (average) and the variance (percent change) for the 3 GCM’s.

Climate Month Scenario Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Baseline (kg/ha) 1145 1039 976 925 908 830 756 741 865 1163 1364 1246 CCSM (% change) 7 5 8 9 9 14 26 40 45 34 14 12 Hadley (% change) -20 -22 -27 -32 -36 -35 -32 -23 -6 -1 -13 -16 Mon Plank (% change) 19 20 19 19 12 18 29 40 43 35 19 18 Average 3 GCMs (% change) 2 1 0 -1 -5 -1 8 19 27 23 7 5

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Table A5.4. Pleasant Hills site-Baseline monthly and overall groundcover (average) and the variance (percent change) for the 3 GCM’s.

Climate Month Overall Scenario Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (years) Baseline (%) 86 86 88 86 88 89 84 80 79 91 96 95 75 CCSM (% change) 3 3 5 3 -3 -1 5 15 17 1 0 2 3 Hadley (% change) 8 3 -12 -10 -8 -14 -13 -19 13 1 0 -2 -23 Mon Plank (% change) 17 12 5 3 10 12 15 20 27 10 4 6 18 Average 3 GCMs (% change) 9 6 0 -1 0 -1 2 5 19 4 1 2 -1

Table A5.5. Pleasant Hills site-Baseline ewe supplementary intake (average) and the variance (percent change) for the three GCM’s.

Intake (kg/head/year) Climate Scenario Total supp Total Pasture Total Baseline (No.) 36.34 489.1 525.41 CCSM (% change) -19.2 2.7 1.2 Hadley (% change) 20.1 -3.9 -2.3 Mon Plank (% change) -36.4 5.2 2.4 Average 3 GCMs (% change) -11.8 1.3 0.4

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Illabo

Table A5.6 Illabo site-Baseline harvest date (average) for each crop and the variance (percent change) for the three GCMs. Crop Climate Scenario Barley Canola Wheat Baseline 26 Oct 08 Nov 24 Nov CCSM (% change) -4 -3 -2 Hadley (% change) -3 -2 -2 Mon Plank (% change) -3 -2 -2 Average 3 GCMs (% change) -3 -2 -2

Table A5.7. Illabo site-baseline gross margin (average) for each system and the variance (percent change) for the 3 GCM’s. System Climate Scenario Animal Crop Farm Baseline ($) $204.09 $294.63 $249.63 CCSM (% change) 3 -4 1 Hadley (% change) -15 -27 1 Mon Plank (% change) -2 1 0 Average 3 GCMs (% change) -5 -10 1

Table A5.8. Illabo site-baseline monthly total dry matter (average) and the variance (percent change) for the 3 GCM’s. Climate Month Scenario Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Baseline (kg/ha) 1266 1201 1142 1085 1065 960 857 802 867 1107 1303 1286 CCSM (% change) 8 5 7 13 9 9 18 25 28 24 12 11 Hadley (% change) -23 -23 -25 -24 -25 -26 -27 -24 -13 -8 -12 -15 Mon Plank (% change) 10 11 14 14 10 13 21 29 31 22 14 10 Average 3 GCMs (% change) -2 -3 -1 1 -2 -1 4 10 15 13 5 2

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Table A5.9. Illabo site-Baseline monthly and overall groundcover (average) and the variance (percent change) for the 3 GCM’s.

Climate Month Overall Scenario Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (years) Baseline (%) 79 73 71 73 75 79 77 80 88 86 88 88 55 CCSM (% change) 3 -5 -8 -11 -13 -17 5 5 1 -1 -3 1 -10 Hadley (% change) -36 -42 -41 -37 -44 -41 -35 -23 -12 -10 -25 -38 -58 Mon Plank (% change) 13 0 -3 -11 -13 8 15 24 10 17 10 14 -2 Average 3 GCMs (% change) -7 -16 -17 -19 -23 -17 -5 2 0 2 -6 -8 -23

Table A5.10. Illabo site-Baseline ewe supplementary intake (average) and the variance (percent change) for the three GCM’s.

Intake (kg/head/year) Climate Scenario Total supp Total Pasture Total Baseline (No.) 33.72 488.01 521.71 CCSM (% change) -32.9 4.5 2.1 Hadley (% change) 34.2 -6.7 -4.1 Mon Plank (% change) -28.8 4.4 2.3 Average 3 GCMs (% change) -9.2 0.7 0.1

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Appendix 6. Site statistical climate analysis for the AusFarm sites

Pleasant Hills

Table A6.1. Annual climate summary for Pleasant Hills.

Impact % variance on baseline Average Variables Baseline CCSM Hadley Mon Plank GCM's Average Rainfall mm 547 2 -11 10 0 Annual average temperature (max) °C 22.1 6 7 5 6 Annual average temperature (min) °C 9.1 11 14 13 13 Absolute maximum temperature °C 40.8 4 5 6 5 Absolute minimum temperature °C -2.5 -36 -32 -32 -33

Table A6.2. Mean rainfall (mm) summary for Pleasant Hills. Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Current (1957-2012) 39.6 39.6 38.9 39.5 48.6 47.2 56.2 54.4 49.5 50.1 39.8 42.7 CCSM (2030) 30.5 64.8 43.3 46.3 46.3 50.2 42.9 47.0 41.5 39.7 54.4 45.6 change (%) -22.9 63.6 11.2 17.4 -4.6 6.4 -23.8 -13.6 -16.2 -20.8 36.7 6.7 Hadley (2030) 42.2 31.8 29.9 30.3 52.8 47.3 70.5 47.5 48.7 32.8 27.8 29.8 change (%) 6.7 -19.7 -23.2 -23.2 8.8 0.3 25.4 -12.7 -1.6 -34.6 -30.2 -30.3 Mon Plank (2030) 38.3 49.9 44.2 43.4 75.2 39.4 48.6 56.9 56.0 65.0 39.1 48.9 change (%) -3.3 26 13.6 10.1 54.9 -16.4 -13.6 4.5 13.1 29.6 -1.8 14.5

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Table A6.3. Mean temperature (°C) summary for Pleasant Hills.

Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Current (1957-2012) 23.9 23.7 20.4 15.8 11.6 8.9 7.8 9.2 11.5 14.8 18.6 21.5 CCSM (2030) 24.6 25.3 21.1 17.4 12.5 10.1 8.9 10.3 12.6 15.9 19.9 22.6 change (%) 3.1 6.9 3.7 10.4 7.7 14.4 13.5 11.5 9.5 7.3 7.2 5.3 Hadley (2030) 24.8 25.6 22.2 17.7 12.6 9.5 9.0 10.1 12.9 16.6 20.1 23.5 change (%) 3.8 8.3 8.9 11.8 8.4 7.1 14.9 9.4 11.4 12.0 8.0 9.5 Mon Plank (2030) 25.3 24.4 21.4 17.4 12.9 9.8 8.8 10.2 12.1 15.8 20.1 23.3 change (%) 5.9 3.3 4.9 10.1 10.7 10.6 12.8 10.5 5 6.5 8.2 8.2 Table A6.4. Extreme Events - Percent of time exceeding (1957-2012) the 99th percentile of maximum temperatures recorded within the month at Pleasant Hills.

Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Temperature Threshold (99th Percentile) 44.5 45.0 39.9 35.4 27.0 22.5 22.4 25.5 31.0 36.0 41.5 42.0 Current (1957-2012) 0.0 0.0 0.1 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.1 0.1 CCSM (2030) 0.3 0.2 0.2 0.4 1.4 0.8 0.1 0.1 0.3 0.5 0.6 0.3 change (%) 0.3 0.2 0.1 0.4 1.3 0.8 0.1 0.1 0.3 0.5 0.5 0.3 Hadley (2030) 0.7 0.4 1.4 0.0 0.5 0.1 0.0 0.0 0.1 0.3 0.4 1.7 change (%) 0.7 0.4 1.3 -0.1 0.5 0.1 -0.1 -0.1 0.1 0.3 0.4 1.7 Mon Plank (2030) 2.0 0.0 0.5 0.1 1.1 1.2 0.0 0.0 0.1 0.8 0.1 0.8 change (%) 2.0 0.0 0.4 0.1 1.0 1.2 -0.1 -0.1 0.1 0.7 0.1 0.7 Table A6.5. Percent of time in frost (< 0 °C) at Pleasant Hills. Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Current (1957-2012) 0 0 0 0.1 4.9 15.6 21.7 13.0 3.7 0.2 0 0 CCSM (2030) 0 0 0 0.4 5.6 8.1 12.7 7.5 3.8 1.3 0.1 0 change (%) 0 0 0 0.3 0.7 -7.5 -9.0 -5.4 0.1 1.1 0.1 0 Hadley (2030) 0 0 0 0 3.7 6.9 8.2 5.1 2.1 0.4 0.1 0 change (%) 0 0 0 -0.1 -1.2 -8.7 -13.5 -7.9 -1.6 0.3 0.1 0 Mon Plank (2030) 0 0 0 0.4 2.8 10.7 10.9 5.8 3.0 1.0 0.1 0 change (%) 0 0 0 0.3 -2.1 -4.9 -10.8 -7.2 -0.7 0.8 0.1 0

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Illabo

Table A6.6. Annual climate summary for Illabo.

Impact % variance on baseline Average Variables Baseline CCSM Hadley Mon Plank GCM's Average Rainfall mm 516 6 -12 6 0 Annual average temperature (max) °C 22.0 6 7 5 6 Annual average temperature (min) °C 8.7 10 17 14 14 Absolute maximum temperature °C 40.4 7 7 6 7 Absolute minimum temperature °C -3.5 -49 -29 -34 -37

Table A6.7. Mean Rainfall (mm) summary for Illabo. Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Current (1957-2012) 42.8 39.1 37.9 38.2 41.3 39.8 49.9 46.0 45.4 47.8 43.3 42.0 CCSM (2030) 35.1 60.8 42.9 44.7 41.8 41.4 34.5 46.6 40.9 41.3 68.7 45.0 change (%) -18.0 55.6 13.0 17.1 1.0 4.2 -31.0 1.2 -9.9 -13.7 58.6 7.1 Hadley (2030) 47.3 30.2 31.0 25.1 46.6 34.9 65.8 39.3 49.7 36.1 25.6 27.1 change (%) 10.6 -22.6 -18.3 -34.4 12.8 -12.2 31.8 -14.6 9.5 -24.6 -41.0 -35.6 Mon Plank (2030) 41.8 45.4 49.3 39.2 58.3 36.3 39.8 49.0 53.3 54.4 38.1 45.9 change (%) -2.2 16.2 30.0 2.6 41.2 -8.6 -20.3 6.6 17.4 13.8 -12.0 9.2

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Table A6.8. Mean Temperature (°C) summary for Illabo. Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Current (1957-2012) 23.8 23.5 20.3 15.7 11.4 8.5 7.4 8.7 11.2 14.6 18.5 21.4 CCSM (2030) 24.6 25.1 21.1 17.4 12.3 9.8 8.4 9.9 12.2 15.6 19.6 22.4 change (%) 3.4 6.8 3.9 11.0 7.9 15.2 13.2 12.7 9.5 6.5 6.4 4.7 Hadley (2030) 24.9 25.6 22.1 17.6 12.5 9.2 8.7 9.7 12.6 16.6 20.1 23.6 change (%) 4.8 9.0 9.0 12.5 10.0 8.1 16.8 10.4 13.3 13.5 9.1 10.2 Mon Plank (2030) 24.8 24.1 21.2 17.2 12.6 9.5 8.4 9.8 11.7 15.7 20.0 23.2 change (%) 4.2 2.6 4.3 10.1 11.4 12.0 13.2 11.8 5.3 7.3 8.5 8.1 Table A6.9 Extreme Events - Percent of time exceeding (1957-2012) the 99th percentile of maximum temperatures recorded within the month at Illabo.

Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Temperature Threshold (99th Percentile) 44.5 43.5 39.0 34.9 27.0 23.0 23.3 26.4 32.5 35.5 41.5 42.0 Current (1957-2012) 0.1 0.0 0.0 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.1 0.0 CCSM (2030) 0.4 0.9 0.7 0.4 1.5 0.7 0.0 0.0 0.1 0.8 0.2 1.0 change (%) 0.4 0.9 0.7 0.4 1.5 0.7 -0.1 -0.1 0.1 0.7 0.2 1.0 Hadley (2030) 1.1 0.9 1.2 0.4 0.9 0.0 0.0 0.0 0.0 1.0 0.4 1.2 change (%) 1.0 0.9 1.2 0.4 0.8 0.0 -0.1 -0.1 -0.1 0.9 0.4 1.2 Mon Plank (2030) 0.4 0.4 1.1 0.6 1.0 0.7 0.0 0.3 0.0 0.4 0.8 1.4 change (%) 0.4 0.4 1.1 0.5 0.9 0.7 -0.1 0.3 -0.1 0.4 0.7 1.4 Table A6.10. Percent of time in frost (< °0 C) at Illabo. Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Current (1957-2012) 0 0 0 0.8 10.8 22.8 31.9 22.5 10.1 1.4 0.0 0 CCSM (2030) 0 0 0 0.8 8.6 12.8 21.1 13.6 8.2 2.2 0.2 0 change (%) 0 0 0 0.0 -2.2 -10.0 -10.8 -9.0 -1.8 0.7 0.2 0 Hadley (2030) 0 0 0 0.4 5.8 14.4 12.8 10.9 4.3 0.7 0.2 0 change (%) 0 0 0 -0.3 -4.9 -8.4 -19.1 -11.7 -5.7 -0.8 0.2 0 Mon Plank (2030) 0 0 0.1 0.8 5.7 15.6 17.6 12.6 7.4 1.4 0.2 0 change (%) 0 0 0.1 0.0 -5.1 -7.3 -14.3 -9.9 -2.6 0.0 0.2 0

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Appendix 7. Hay and Narrandera GrassGro impact simulation 2030 summary

East Hay GrassGro Variables Baseline (1955-2013) CCSM4 2030 CCSM4 varian ce CCSM4 % change HadGEM2 2030 HasGem2 2030 variance Ha sGem2 % change MPI-ESM 2030 MPI-ESM variance MPI-ESM % change Average 3 GCM’s 2030 Average variance across 3 GCM's Average % change acr oss GCMs Annual average rainfall mm 362 371 9 2.49 314 -48 -13.26 42 7 65 17.96 370.67 8 2.39 Annual average temperature 0C 17.2 18.4 1.2 6.98 18.6 1.4 8.14 18.3 1.1 6.40 18.43 1.2 7.17 DSE/ha 1.3 0.6 -0.7 -53.85 1.2 -0.1 -7.69 1.4 0.1 7.69 1.07 -0.24 -17. 95 Gross Margin $/ha 23 10 -13 -56.52 22 -1 -4.35 21 -2 -8.70 17. 67 -5 -23.19 Drainage mm 38 49 11 28.95 22 -16 -42.11 0 -38 -100.00 23.67 -14 -37.72 Lambing % 88 91 3 3.41 91 3 3.41 91 3 3.41 91.00 3 3.41 Ground cover % 59 51 -8 -13.56 55 -4 -6.78 53 -6 -10.17 53.00 - 6 -10.17

South Hay GrassGro Variables Baseline (1955-2013) CCSM4 2030 CCSM4 varian ce CCSM4 % change HadGEM2 2030 HasGem2 2030 variance Ha sGem2 % change MPI-ESM 2030 MPI-ESM variance MPI-ESM % change Average 3 GCM’s 2030 Average variance across 3 GCM's Average % change acr oss GCMs Annual average rainfall mm 376 355 -21 -5.59 338 -38 -10.1 1 432 56 14.89 375.00 -1 -0.27 Annual average temperature 0C 16.3 17.5 1.2 7.36 17.5 1.2 7.36 17.4 1.1 6.75 17.47 1.2 7.16 DSE/ha 1.6 1 -0.6 -37.50 1.8 0.2 12.50 3 1.4 87.50 1.93 0.3 20.83 Gross Margin $/ha 38 23 -15 -39.47 42 4 10.53 76 38 100.00 47. 00 9 23.68 Drainage mm 24 25 1 4.17 12 -12 -50.00 40 16 66.67 25.67 2 6.94 Lambing % 96 99 3 3.13 97 1 1.04 97 1 1.04 97.67 2 1.74 Ground cover % 70 55 -15 -21.43 62 -8 -11.43 70 0 0.00 62.33 -7 -10.95

West Hay GrassGro Variables Baseline (1955-2013) CCSM4 2030 CCSM4 varian ce CCSM4 % change HadGEM2 2030 HasGem2 2030 variance Ha sGem2 % change MPI-ESM 2030 MPI-ESM variance MPI-ESM % change Average 3 GCM’s 2030 Average variance across 3 GCM's Average % change acr oss GCMs Annual average rainfall mm 321 330 9 2.80 281 -40 -12.46 41 7 96 29.91 342.67 22 6.75 Annual average temperature 0C 17.9 19.2 1.3 7.26 19.3 1.4 7.82 19 1.1 6.15 19.17 1.3 7.08 DSE/ha 0.7 0.1 -0.6 -85.71 0.9 0.2 28.57 1.1 0.4 57.14 0.70 0 0.00 Gross Margin $/ha 10 1 -9 -90.00 16 6 60.00 17 7 70.00 11.33 1 1 3.33 Drainage mm 0 0 0 0.00 0 0 0.00 0 0 0.00 0.00 0 0.00 Lambing % 88 87 -1 -1.14 91 3 3.41 90 2 2.27 89.33 2 1.52 Ground cover % 48 42 -6 -12.50 48 0 0.00 47 -1 -2.08 45.67 -2 -4 .86

Narrandera GrassGro Variables Baseline (1955-2013) CCSM4 2030 CCSM4 varia nce CCSM4 % change HadGEM2 2030 HadGEM2 2030 variance H asGem2 % change MPI-ESM 2030 MPI-ESM variance MPI-ESM % change Average 3 GCM’s 2030 Average variance across 3 GCM's Average % change acr oss GCMs Annual average rainfall mm 469 499 30 6.40 402 -67 -14.29 5 18 49 10.45 473.00 4.00 0.85 Annual average temperature 0C 16.6 17.7 1.1 6.63 18.1 1.5 9.04 17.8 1.2 7.23 17.87 1.27 7.63 DSE/ha 5.5 2.8 -2.7 -49.09 2.6 -2.9 -52.73 3.7 -1.8 -32.73 3.03 -2.47 -44.85 Gross Margin $/ha 49 -1 -50 -102.04 -4 -53 -108.16 26 -23 -46 .94 7.00 -42.00 -85.71 Drainage mm 43 91 48 111.63 29 -14 -32.56 70 27 62.79 63.33 20.33 47.29 Lambing % 90 92 2 2.22 93 3 3.33 94 4 4.44 93.00 3.00 3.33 Ground cover % 61 63 2 3.28 60 -1 -1.64 62 1 1.64 61.67 0.67 1.0 9

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Appendix 8. Summary of site adaptation responses Site Adaptation Variables analysed CCSM Hadley Mon Plank option Pleasant Hills Sowing Gross Margin for No change to average GM + increase No change to average GM + Slight decrease in average and farm in probability of lower returns increase in probability of lower median GM. returns Gross Margin for Small increase in average GM, Small increase in average GM, No change in average GM increase in crop significant broadening in probability and broadening on probability probability of higher returns. returns. of returns. Crop Yield Improvements in average GM across Across species an increase in Slight decrease in average yields. species. average yields. % Years don’t sow Increase in the number of years that Increase in the number of years Slight impact on number of years canola and wheat were not sown. that canola and wheat were not crops were not sown. 4% chance that 15% chance that the first crop sown. 15% chance that the first the first crop rotation was not sown. rotation was not sown. crop rotation was not sown. Genetics Gross Margin farm No increase in average GM and No change in average GM and Increase in average GM and increased probability of high end slight broadening of probability broadening of GM probability returns. of returns. Gross Margin for No change in average GM and No change in average GM and Increase in average GM and animal decrease in probabilities of lower increase in probabilities of low tightening in probabilities of returns returns end return. (more consistent results). Lamb performance Improvements in median lamb Improvements in median lamb Improvements in median lamb weight weight weight Average days to Reduction in number of days to Reduction in number of days to Reduction in number of days to lamb turn off reach sale weight reach sale weight reach sale weight Cover Gross Margin farm No change in average GM, slight No change in average GM slight No change decrease in probability of lower increase in probabilities of return. lower returns Minimum ground No change No change No change cover farm Supplementation Slight decrease in the level of Slight decrease in level of Slight decrease in the level of levels to ewes supplementation required in late supplementation required in supplementation required in late winter late winter winter

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Site Adaptation Variables analysed CCSM Hadley Mon Plank option Illabo Sowing Gross Margin for No change to average GM and an Slight decrease in median GM Slight decrease in median GM farm increase in the probability of return and slight increase in the return and increase in the lower returns probabilities to higher end returns probabilities of low end returns Gross Margin for Slight increase in average GM, No change to average GM return Slight increase in average GM crop significant broadening in and increase in probabilities of return increase in probabilities probability returns. higher end returns. of higher end returns. Crop Yield Decrease in average barley yields, Decrease in average barley and No change in average yield increases in canola and no increase in average wheat and across all species. change in wheat canola yields. % Years don’t sow Increase in the number of years Increase in the number of years Slight increase in the number of that canola and wheat were not that canola and wheat were not years that canola and wheat sown. 15% chance that the first sown. 19% chance that the first were not sown. 8% chance that crop rotation was not sown. crop rotation was not sown. the first crop rotation was not sown. Genetics Gross Margin farm Slight improvement in average No change in average GM return No change in average GM GM return but increase but slight increase in probability of return but slight increase in probabilities of low end returns. high end return. probabilities of low end returns. Gross Margin for No change in average GM return, Slight increase in average GM Slight increase in average GM animal but significant tightening of return and increase in high end return and increase in high end probabilities (more consistent probabilities probabilities result) Lamb performance Improvements in median lamb Improvements in median lamb Improvements in median lamb weight weight weight Average days to lamb Reduction in number of days to Reduction in number of days to Reduction in number of days to turn off reach sale weight reach sale weight reach sale weight Cover Gross Margin farm No change in average GM’s slight No change in average GM return No change in average GM decrease in probabilities of low return end return. Minimum ground No change Minor increase in late No change cover farm autumn/winter Supplementation No significant change No significant change No significant change levels to ewes

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