The 160 million ha of grazing land in Queensland support approximately 10 million beef equivalents (9.8 million cattle and 10.7 million sheep) with native pastures as the major forage source. The complexity of these biophysical systems and their interaction with pasture and stock management, economic and social forces limits our ability to easily calculate the impact of climate change scenarios. Models of åsafeÇ carrying capacity developed from property data and expert opinion were used to assess the impacts of plausible climate change scenarios on Queensland regional åsafeÇ carrying capacity. Climate change impacts varied considerably across the State depending on whether moisture, temperature or nutrients were the limiting factors. A major finding of the sensitivity study was the potential importance of doubling CO2 in mitigating or amplifying the effects of warmer temperatures and changes in rainfall.
Introduction
In Crimp et al. (this conference) the importance of climate change as an issue for rangeland users was described and the temporal trends in climate variables averaged across QueenslandÇs grazing lands were documented. It has been suggested that the low priority of climate change issues, as perceived by the users of rangelands, is in part due to a lack of information on global climate change and current climatic trends, and a lack of knowledge of the potential impact of climate change on grazing industries.
Climate change scenario and representation
As examples of climate change scenarios we chose a doubling of CO2 accompanied by a 3oC increase in temperature and rainfall changes of ± 10%. The impact of these changes on QueenslandÇs native pastures was simulated using the pasture growth model GRASP (Littleboy and McKeon 1997) which has been extensively calibrated and validated in Queensland. Modifications to GRASP were made to represent the general effects of doubling atmospheric CO2 levels on the following characteristics of C4 pasture growth (Howden et al. 1998): 1) potential regrowth rate (kg DM/ha/day, +10%); 2) transpiration efficiency (kg DM/ha/mm @ 20hPa, +40%); 3) green yield at which potential transpiration is 50% of potential evapotranspiration (kg DM/ha, +40%); 4) rate of nitrogen uptake (kg N/ha per 100 mm transpiration, +20%); and 5) radiation use efficiency (kg/ha per MJ/m2, +5%).
The change in temperature was represented by simply adding 3oC to daily minimum and maximum temperatures and dew point in the control daily climate file for 12 locations throughout Queensland. Vapour pressure deficit and Class A pan evaporation were then recalculated. Rainfall decrease and increase were represented by multiplying the rainfall for each day in the control file by 0.9 and 1.1 respectively. No adjustments were made to daily solar radiation. For the climate change studies, the climate for the period 1961 to 1990 was used as the control and the control CO2 level was taken as 350 ppm. Thus the experimental design for the sensitivity tests was a 2 * 2 * 3 factorial design:
CO2
Temperature
Rainfall
Control
Control
90% of Control
2 x Control level
Control temperatures + 3oC
Control
110% of Control
Simulation methodology
For three regions of Queensland (south-west, south-east, north-east), relationships have been found to exist between åsafeÇ carrying capacity and average annual pasture as estimated by graziers for the different land systems within their properties (Scanlan et al. 1994; Johnston, McKeon and Day 1996; Day, Scattini and Osbourne 1997). Analysis of the pooled data from these regions showed that pasture growth accounted for 77% of the variation in carrying capacity across a wide range of soil types, land systems, pasture communities, grazing enterprises and climatic zones. When estimates of åsafeÇ carrying capacity were expressed as the ratio of animal intake per ha to average annual pasture growth (i.e. pasture utilisation), 47% of estimated åsafeÇ carrying capacities fell between 15 and 25% pasture utilisation. Hall et al. (1998) combined these analyses with expert opinion at other locations and found that åsafeÇ carrying capacity (%SU) was correlated with the percentage of days per year when the simulated pasture growth index (Littleboy and McKeon 1997) exceeded 0.05 (% gidays):
åSafeÇ carrying capacity for each of 12 regions in Queensland was then calculated using the following equation:
Safe CC = % SU* pasture growth
100feed intake
where Safe CC was the åsafeÇ carrying capacity of each region expressed in 400 kg beast equivalents (BE) / ha; pasture growth was average annual pasture growth (kg DM/ha); and feed intake was the annual feed intake for a 400 kg BE and estimated to be 2700 kg DM. (A BE consumes ª 10 kg DM/day for six months of the growing season and ª 5 kg DM/day for six months of the dormant period).
Simulation results
There was considerable interaction between location and the impact of changes in rainfall, CO2 and temperature on the calculated Safe CC. Locations in south-east Queensland had the lowest sensitivity to changes in rainfall and CO2 because of nitrogen limitation on pasture growth but warmer temperatures increased % gidays and hence increased åsafeÇ utilisation rate and Safe CC. The combined changes resulted in increases of 7 to 27% in Safe CC across rainfall scenarios (± 10%) and south-east locations. At central Queensland locations, the percentage changes in Safe CC were equivalent to the imposed changes in rainfall (± 10%). Doubling CO2 alone resulted in a 7 to 14% increase and compensated for the drier rainfall scenario when the two changes were imposed together. Warmer temperatures alone increased Safe CC (6 to 30%) with considerable increases for the warmer, wetter scenario depending on location (16 to 44%). The combined changes resulted in increases of 8 to 61% in Safe CC across rainfall scenarios (± 10%) and locations in central Queensland. For north-east Queensland locations, changes in Safe CC were directly related to changes in rainfall. Doubling CO2 alone increased Safe CC by 9 to 20% whilst increased temperature resulted in reduced Safe CC (-2 to à10%) especially under the warmer drier scenario depending on location (-14 to à29%). The combined scenarios resulted in changes of -12 to +18% in Safe CC across rainfall scenarios and locations. In central-west Queensland there was considerable amplification of the ± 10% rainfall change (à25 to +23%) and strong positive effects of doubling CO2 (30%). Increased temperature had a negative effect depending on location (à12 to à19%) and the combined change scenario effects ranged from à9 to +49% across rainfall scenarios and locations. For the site in the south-west, Safe CC had the highest sensitivity to the ± 10% changes in rainfall (-30 to +35%). Doubling CO2 and warmer temperatures resulted in strongly positive effects (30 and 25% respectively) and hence there was a very wide range of possible effects across the combined scenarios (+15 to +115%). These effects reflect the major limitations of moisture and temperature on C4 grass growth in the south-west region.
Discussion
A major finding in this study was the mitigating effect of CO2 on the combined negative effects of lower rainfall and warmer temperatures on Safe CC. This study also showed strong interactions between location and climate change scenarios. To some extent this is to be expected given that there is considerable variation in where and when pasture growth is limited by nitrogen, moisture or temperature. The application of the same climate change scenario across the 12 regions showed that there are both åwinnersÇ and ålosersÇ in terms of åsafeÇ carrying capacity and the potential for amplification of small climatic changes.
