Gross primary productivity and water use efficiency are increasing in a high rainfall tropical savanna

Despite their size and contribution to the global carbon cycle, we have limited understanding of tropical savannas and their current trajectory with climate change and anthropogenic pressures. Here we examined interannual variability and externally forced long-term changes in carbon and water exchange from a high rainfall savanna site in the seasonal tropics of north Australia. We used an 18-year flux data time series (2001-2019) to detect trends and drivers of fluxes of carbon and water. Significant positive trends in gross primary productivity (GPP, 15.4 g C m²  year^-2 ), ecosystem respiration (R_eco , 8.0 g C m²  year^-2 ), net ecosystem productivity (NEE, 7.4 g C m²  year^-2 ) and ecosystem water use efficiency (WUE, 0.0077 g C kg H₂ O^-1  year^-1 ) were computed. There was a weaker, non-significant trend in latent energy exchange (LE, 0.34 W m^-2  year^-1 ). Rainfall from a nearby site increased statistically over a 45-year period during the observation period. To examine the dominant drivers of changes in GPP and WUE, we used a random forest approach and a terrestrial biosphere model to conduct an attribution experiment. Radiant energy was the dominant driver of wet season fluxes, whereas soil water content dominated dry season fluxes. The model attribution suggested that [CO₂ ], precipitation and T_air accounting for 90% of the modelled trend in GPP and WUE. Positive trends in fluxes were largest in the dry season implying tree components were a larger contributor than the grassy understorey. Fluxes and environmental drivers were not significant during the wet season, the period when grasses are active. The site is potentially still recovering from a cyclone 45 years ago and regrowth from this event may also be contributing to the observed trends in sequestration, highlighting the need to understand fluxes and their drivers from sub-diurnal to decadal scales.

Heavy grazing of buffel grass pasture in the Brigalow Belt bioregion of Queensland, Australia, more than tripled runoff and exports of total suspended solids compared to conservative grazing

Loss of sediment and particulate nutrients in runoff from the extensive grazing lands of the Fitzroy Basin, central Queensland, continue to contribute to the declining health of the Great Barrier Reef. This study measured differences in hydrology and water quality from conservative and heavy grazing pressures on rundown improved grass pastures in the Fitzroy Basin. Conservative grazing pressure was defined as the safe long-term carrying capacity for rundown buffel grass pasture, whereas heavy grazing pressure was defined as the recommended stocking rate for newly established buffel grass pasture. Heavy grazing of rundown pasture resulted in 2.5 times more bare ground and only 8% of the pasture biomass compared to conservative grazing. Heavy grazing also resulted in 3.6 times more total runoff and 3.3 times the peak runoff rate compared to conservative grazing. Loads of total suspended solids, nitrogen and phosphorus in runoff were also greater from heavy than conservative grazing.

Is ground cover a useful indicator of grazing land condition?

Remotely sensed ground cover data play an important role in Australian rangelands research development and extension, reflecting broader global trends in the use of remotely sensed data. We tested the relationship between remotely sensed ground cover data and field-based assessments of grazing land condition in the largest quantitative analysis of its type to date. We collated land condition data from 2282 sites evaluated between 2004 and 2018 in the Burdekin and Fitzroy regions of Queensland. Condition was defined using the Grazing Land Management land condition framework that ranks grazing land condition on a four point ordinal scale based on dimensions of vegetation composition, ground cover level and erosion severity. Nine separate ground cover derived indices were then calculated for each site. We found that all indices significantly correlated with grazing land condition on corresponding sites. Highest correlations occurred with indices that benchmarked ground cover at the site against regional ground cover assessed over several years. These findings provide some validation for the general use of ground cover data as an indicator of rangeland health/productivity. We also constructed univariate land condition models with a subset of these indices. Our models predicted land condition significantly better than random assignment though only moderately well; no model correctly predicted land condition class on >40% of sites. While the best models predicted condition correctly at >60% of A and D condition sites, condition at sites in B and C condition sites was poorly predicted. Several factors limit how well ground cover levels predict land condition. The main challenge is modelling a multidimensional value (grazing land condition) with a unidimensional ground cover measurement. We suggest that better land condition models require a range of predictors to address this multidimensionality but cover indices can make a substantial contribution in this context.

Preventing rangeland degradation: a shared problem for Australia and China

Rangeland degradation continues in Australia, China and elsewhere. The stocking rate/animal production relationship has been a successful concept for pastoralists wanting to avoid degradation and/or raise incomes. However, there are no means available of alerting pastoralists to the approach of critical thresholds that would ‘flip’ rangelands into alternative states when grazing-stressed. Critical threshold forecasting for avoiding degradation (and seizing restoration opportunities) could be made available online. Research has yet to find, assemble and test the set of indicators needed to forecast the approach of critical thresholds envisaged in State-and-Transition thinking. Forecasting at paddock, property and regional scales would have to involve high-performance computing because the thresholds will be space and time dependent. The case for Australia and China to contribute cooperatively to this research effort rests on the large number of contrasting rangeland ecosystems across the two countries that represent rangelands globally. A proven history of past collaboration is extant with existing research programs on plant population dynamics, landscape patchiness/leakiness and soil biota status, and their responses to the separate and combined effects of climate and grazing animals. The road to adoption would involve partnerships with pastoralists throughout the process, remote sensing to identify approaching thresholds in real time, application of high-performance computing and possibly artificial intelligence, and packaging of forecasts for different socio-economic rangeland systems.