Grazing management for soil carbon in Australia: A review.

The livestock industry accounts for a considerable proportion of agricultural greenhouse gas emissions, and in response, the Australian red meat industry has committed to an aspirational target of net-zero emissions by 2030. Increasing soil carbon storage in grazing lands has been identified as one method to help achieve this, while also potentially improving production and provision of other ecosystem services. This review examined the effects of grazing management on soil carbon and factors that drive soil carbon sequestration in Australia. A systematic literature search and meta-analysis was used to compare effects of stocking intensity (stocking rate or utilisation) and stocking method (i.e, continuous, rotational or seasonal grazing systems) on soil organic carbon, pasture herbage mass, plant growth and ground cover. Impacts on below ground biomass, soil nitrogen and soil structure are also discussed. Overall, no significant impact of stocking intensity or method on soil carbon sequestration in Australia was found, although lower stocking intensity and incorporating periods of rest into grazing systems (rotational grazing) had positive effects on herbage mass and ground cover compared with higher stocking intensity or continuous grazing. Minimal impact of grazing management on pasture growth rate and below-ground biomass has been reported in Australia. However, these factors improved with grazing intensity or rotational grazing in some circumstances. While there is a lack of evidence in Australia that grazing management directly increases soil carbon, this meta-analysis indicated that grazing management practices have potential to benefit the drivers of soil carbon sequestration by increasing above and below-ground plant production, maintaining a higher residual biomass, and promoting productive perennial pasture species. Specific recommendations for future research and management are provided in the paper.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

The Potential of Deep Roots to Mitigate Impacts of Heatwaves and Declining Rainfall on Pastures in Southeast Australia.

Declines in growing-season rainfall and increases in the frequency of heatwaves in southern Australia necessitate effective adaptation. The Sustainable Grazing Systems Pasture Model (SGS) was used to model the growth of three pasture species differing in root depth and root distribution under three different climate scenarios at two sites. The modelled metabolisable energy intake (in MJ) was used in a partial discounted net cash flow budget. Both the biophysical and economic modelling suggest that deep roots were advantageous in all climate scenarios at the long growing season site but provided no to little advantage at the short growing season site, likely due to the deep-rooted species drying out the soil profile earlier. In scenarios including climate change, the DM production of the deep-rooted species at the long growing season site averaged 386 kg/ha/year more than the more shallow-rooted species, while at the site with a shorter growing season it averaged 205 kg/ha/year less than the shallower-rooted species. The timing of the extra growth and pasture persistence strongly influenced the extent of the benefit. At the short growing season site other adaptation options such as summer dormancy will likely be necessary.