Grazing is a major land use in Australia’s rangelands. The ‘safe’ livestock carrying capacity (LCC) required to maintain resource condition is strongly dependent on climate. We reviewed: the approaches for quantifying LCC; current trends in climate and their effect on components of the grazing system; implications of the ‘best estimates’ of climate change projections for LCC; the agreement and disagreement between the current trends and projections; and the adequacy of current models of forage production in simulating the impact of climate change. We report the results of a sensitivity study of climate change impacts on forage production across the rangelands, and we discuss the more general issues facing grazing enterprises associated with climate change, such as ‘known uncertainties’ and adaptation responses (e.g. use of climate risk assessment). We found that the method of quantifying LCC from a combination of estimates (simulations) of long-term (>30 years) forage production and successful grazier experience has been well tested across northern Australian rangelands with different climatic regions. This methodology provides a sound base for the assessment of climate change impacts, even though there are many identified gaps in knowledge. The evaluation of current trends indicated substantial differences in the trends of annual rainfall (and simulated forage production) across Australian rangelands with general increases in most of western Australian rangelands (including northern regions of the Northern Territory) and decreases in eastern Australian rangelands and south-western Western Australia. Some of the projected changes in rainfall and temperature appear small compared with year-to-year variability. Nevertheless, the impacts on rangeland production systems are expected to be important in terms of required managerial and enterprise adaptations. Some important aspects of climate systems science remain unresolved, and we suggest that a risk-averse approach to rangeland management, based on the ‘best estimate’ projections, in combination with appropriate responses to short-term (1–5 years) climate variability, would reduce the risk of resource degradation. Climate change projections – including changes in rainfall, temperature, carbon dioxide and other climatic variables – if realised, are likely to affect forage and animal production, and ecosystem functioning. The major known uncertainties in quantifying climate change impacts are: (i) carbon dioxide effects on forage production, quality, nutrient cycling and competition between life forms (e.g. grass, shrubs and trees); and (ii) the future role of woody plants including effects of fire, climatic extremes and management for carbon storage. In a simple example of simulating climate change impacts on forage production, we found that increased temperature (3°C) was likely to result in a decrease in forage production for most rangeland locations (e.g. –21% calculated as an unweighted average across 90 locations). The increase in temperature exacerbated or reduced the effects of a 10% decrease/increase in rainfall respectively (–33% or –9%). Estimates of the beneficial effects of increased CO2 (from 350 to 650 ppm) on forage production and water use efficiency indicated enhanced forage production (+26%). The increase was approximately equivalent to the decline in forage production associated with a 3°C temperature increase. The large magnitude of these opposing effects emphasised the importance of the uncertainties in quantifying the impacts of these components of climate change. We anticipate decreases in LCC given that the ‘best estimate’ of climate change across the rangelands is for a decline (or little change) in rainfall and an increase in temperature. As a consequence, we suggest that public policy have regard for: the implications for livestock enterprises, regional communities, potential resource damage, animal welfare and human distress. However, the capability to quantify these warnings is yet to be developed and this important task remains as a challenge for rangeland and climate systems science.

No abstract

Increased sediment and nutrient losses resulting from unsustainable grazing management in the Burdekin River catchment are major threats to water quality in the Great Barrier Reef Lagoon. To test the effects of grazing management on soil and nutrient loss, five 1 ha mini-catchments were established in 1999 under different grazing strategies on a sedimentary landscape near Charters Towers. Reference samples were also collected from watercourses in the Burdekin catchment during major flow events. Soil and nutrient loss were relatively low across all grazing strategies due to a combination of good cover, low slope and low rainfall intensities. Total soil loss varied from 3 to 20 kg ha(-1) per event while losses of N and P ranged from 10 to 1900 g ha(-1) and from 1 to 71 g ha(-1) per event respectively. Water quality of runoff was considered moderate across all strategies with relatively low levels of total suspended sediment (range: 8-1409 mg l(-1)), total N (range: 101-4000 microg l(-1)) and total P (range: 14-609 microg l(-1)). However, treatment differences are likely to emerge with time as the impacts of the different grazing strategies on land condition become more apparent. Samples collected opportunistically from rivers and creeks during flow events displayed significantly higher levels of total suspended sediment (range: 10-6010 mg l(-1)), total N (range: 650-6350 microg l(-1)) and total P (range: 50-1500 microg l(-1)) than those collected at the grazing trial. These differences can largely be attributed to variation in slope, geology and cover between the grazing trial and different catchments. In particular, watercourses draining hillier, grano-diorite landscapes with low cover had markedly higher sediment and nutrient loads compared to those draining flatter, sedimentary landscapes. These preliminary data suggest that on relatively flat, sedimentary landscapes, extensive cattle grazing is compatible with achieving water quality targets, provided high levels of ground cover are maintained. In contrast, sediment and nutrient loss under grazing on more erodable land types is cause for serious concern. Long-term empirical research and monitoring will be essential to quantify the impacts of changed land management on water quality in the spatially and temporally variable Burdekin River catchment.

This paper (Part 1) describes the development of a new online system that estimates long-term carrying capacity (LTCC) for grazing properties across Queensland, Australia. High year-to-year and multi-year rainfall variability is a dominating feature of the climate of Queensland’s grazing lands, and poses major challenges for extensive livestock production. The use of LTCC is one approach used by graziers to reduce the impact of rainfall variability on land condition and financial performance. Over the past 30 years, scientists, graziers and their advisors have developed a simple approach to calculating LTCC ((average annual pasture growth × safe pasture utilisation) ÷ annual animal intake). This approach has been successful at a property scale (regional south-west Queensland) and in a wider application through Grazing Land Management (GLM) regional workshops. We have built on these experiences to develop an online system (as described in detail in Part 2; Zhang et al. 2021; this issue) that incorporates the simple LTCC approach with advances in technology and grazing science to provide LTCC information for Queensland grazing properties. Features of the LTCC system are: (1) assimilation of spatial datasets (cadastral data, grazing land types, climate data, remotely-sensed woody vegetation cover); (2) a pasture growth simulation model; (3) land type parameter sets of biophysical attributes; and (4) estimates of safe pasture utilisation. The ‘FORAGE LTCC report’ is a major product of the system, describing individual property information that allows detailed analysis and explanation of the components of the LTCC calculation by land type and land condition. The online system rapidly analyses property spatial data and calculates paddock/property LTCC information. For the 10 months between November 2020 and August 2021, over 4000 grazing property reports have been requested in Queensland, and has proven to be a sound basis for ‘discussion support’ with grazier managers and their advisors.

The grazing lands of northern Australia contain a substantial soil organic carbon (SOC) stock due to the large land area. Manipulating SOC stocks through grazing management has been presented as an option to offset national greenhouse gas emissions from agriculture and other industries. However, research into the response of SOC stocks to a range of management activities has variously shown positive, negative or negligible change. This uncertainty in predicting change in SOC stocks represents high project risk for government and industry in relation to SOC sequestration programs. In this paper, we seek to address the uncertainty in SOC stock prediction by assessing relationships between SOC stocks and grazing land condition indicators. We reviewed the literature to identify land condition indicators for analysis and tested relationships between identified land condition indicators and SOC stock using data from a paired-site sampling experiment (10 sites). We subsequently collated SOC stock datasets at two scales (quadrat and paddock) from across northern Australia (329 sites) to compare with the findings of the paired-site sampling experiment with the aim of identifying the land condition indicators that had the strongest relationship with SOC stock. The land condition indicators most closely correlated with SOC stocks across datasets and analysis scales were tree basal area, tree canopy cover, ground cover, pasture biomass and the density of perennial grass tussocks. In combination with soil type, these indicators accounted for up to 42% of the variation in the residuals after climate effects were removed. However, we found that responses often interacted with soil type, adding complexity and increasing the uncertainty associated with predicting SOC stock change at any particular location. We recommend that caution be exercised when considering SOC offset projects in northern Australian grazing lands due to the risk of incorrectly predicting changes in SOC stocks with change in land condition indicators and management activities for a particular paddock or property. Despite the uncertainty for generating SOC sequestration income, undertaking management activities to improve land condition is likely to have desirable complementary benefits such as improving productivity and profitability as well as reducing adverse environmental impact.

Sub-daily rainfall intensity has a significant impact on runoff and erosion rates in northern Australian rangelands. However, it has been difficult to include sub-daily rainfall intensity in rangeland biophysical models using historical climate data due to the limited number of pluviograph stations with long-term records. In this paper a new empirical model (‘Temperature I15’ model) was developed to predict the daily maximum 15-min rainfall intensity (I15) using daily minimum and maximum temperature and daily rainfall totals from 12 selected pluviograph stations across Australia. The ‘Temperature I15’ model accounted for 46% (P < 0.01) of the variation in observed daily I15 for an independent validation dataset derived from 67 Australia-wide pluviograph stations and represented both geographical and seasonal variability in I15. The model also accounted for 70% (P < 0.01) of the variation in the observed historical trend in I15 for the full record period (average record period was 37 years) of 73 Australia-wide pluviograph stations. The ‘Temperature I15’ model was found to be an improvement on a past empirical model of I15 and can be easily implemented in biophysical models by using readily available daily climate data. However, as the ‘Temperature I15’ model only represented 46% of the variation in daily observed I15, the model is best used in simulation studies on ‘timeframes’ in excess of 5 years. The new ‘Temperature I15’ model was implemented in the runoff equation of the Australia-wide spatial pasture growth model AussieGRASS, which predicts daily water balance and pasture growth for 185 different pasture communities. This resulted in an improved simulation of green cover for 71% of pasture communities but was worse for 25% of communities, with no change for 4% of communities.

Surface runoff is an important factor affecting rangeland pasture productivity and off-site sediment transportation. The application of rangeland biophysical models including sub-models of runoff and erosion provides one method to assess how management and climate variability affect the frequency and quantity of surface runoff events. However, there is often limited confidence in extrapolating runoff models developed from site-specific, hillslope field experiments to other locations due to variation in soil types and land condition states. To improve rangeland runoff models, we investigated three potentially important components at 18 paired land condition sites: (1) the importance of a variety of pasture attributes such as biomass and cover on infiltration rates; (2) the impact of surface soil texture on infiltration rates; and (3) whether soil carbon and/or soil bulk density provide valuable indicators of a site’s infiltration rates. The study found that surface soil texture was important when aboveground biomass was low and was found to have a ‘broken-stick’ relationship with infiltration rates (i.e. lowest infiltration occurred at the pivot point of 64% sand). Aboveground biomass, (which included standing grass, grass litter and tree litter) was the best soil or pasture attribute for predicting a plot’s infiltration capacity accounting for 68% of the variability. Plots with surface soil sand content greater than 60% and which had been exclosed for between 4 and 24 years had higher average surface soil carbon mass and concentration (~10%) than adjacent grazed plots. The exclosed plots also had higher surface soil porosity, which was associated with very high infiltration rates.

Dissolved inorganic nitrogen (DIN) movement from Australian sugarcane farms is believed to be a major cause of crown-of-thorns starfish outbreaks which have reduced the Great Barrier Reef coral cover by ~21% (1985–2012). We develop a daily model of DIN concentration in runoff based on >200 field monitored runoff events. Runoff DIN concentrations were related to nitrogen fertiliser application rates and decreased after application with time and cumulative rainfall. Runoff after liquid fertiliser applications had higher initial DIN concentrations, though these concentrations diminished more rapidly in comparison to granular fertiliser applications. The model was validated using an independent field dataset and provided reasonable estimates of runoff DIN concentrations based on a number of modelling efficiency score results. The runoff DIN concentration model was combined with a water balance cropping model to investigate temporal aspects of sugarcane fertiliser management. Nitrogen fertiliser application in December (start of wet season) had the highest risk of DIN movement, and this was further exacerbated in years with a climate forecast for ‘wet’ seasonal conditions. The potential utility of a climate forecasting system to predict forthcoming wet months and hence DIN loss risk is demonstrated. Earlier fertiliser application or reducing fertiliser application rates in seasons with a wet climate forecast may markedly reduce runoff DIN loads; however, it is recommended that these findings be tested at a broader scale.