Progress in forecasting carbon, water and energy fluxes in improved and degraded pastures: data-model comparison near Sydney Australia

Livestock grazing contributes to greenhouse gas (GHG) emissions, soil degradation and erosion, and loss of biodiversity. Regenerative pasture management includes improvements such as sowing high-diversity seed mixtures with legumes and other deep-rooted forbs in addition to C3 and C4 grasses, alternating intensive grazing with rest periods, bio-based fertilizers, etc. These improvements may alleviate degradation and restore multiple ecosystem services, including soil carbon sequestration and heat wave mitigation. Predictive understanding of management impacts requires process-based models that accurately simulate herbaceous growth and allocation in response to grazing and irrigation events. Moreover, accurate and timely model forecasts depend on well-validated data collected at appropriate temporal and spatial scales, delivered with low latency.

We used four years of eddy covariance data in combination with vegetation indices and a process-based model to improve estimates of Net Ecosystem Production (NEP) and energy balance in response to livestock and wildlife grazing in an area with fluctuating soil moisture availability. The enhanced vegetation index (EVI) for the degraded pasture, grazed mainly by native wildlife (kangaroos), demonstrated wide seasonal variations of 0.2 to 0.6, whereas EVI was maintained more consistently close to 0.5 for an improved pasture, grazed intermittently by cattle or sheep. Across three wet years, NEP for the improved pasture averaged 12% higher compared to the degraded one (153 vs. 137 g C m-2 y-1), associated with average 20% greater gross primary production (GPP; 1822 vs. 1521 g C m-2 y-1). However, NEP on the improved pasture was lower than on the degraded pasture in two of those three years, possibly due to grazing-related differences in biomass removal. Sensible heat fluxes were higher from the degraded pasture, especially during hot/dry periods. Ongoing analyses are evaluating soil C storage for benchmarking flux data. Model predictions are also being improved by validating representation of productivity by C3 and C4 species and carbon allocation to roots and crowns. This work contributes to enhancing environmental sustainability in managed grasslands with near-real-time forecasting ability for grazing and irrigation management.