Modelling carbon and water exchange of a grazed pasture in New Zealand constrained by eddy covariance measurements

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

Could patterns of animal behaviour cause the observed differences in soil carbon between adjacent irrigated and unirrigated pastures?

Previous soil sampling from grazed pastures in New Zealand compared the changes of soil organic carbon (SOC) in adjacent irrigated and unirrigated portions of the same paddocks. It showed that irrigated portions had lower SOC stocks than unirrigated portions, with an average difference of 7.0 tC ha^-1 or 0.6 tC ha^-1 yr^-1. These findings have formed the basis of an assessment for the net effect of conversion of New Zealand's grazed pastures to irrigation. However, since cattle could move freely between irrigated and unirrigated portions of the studied paddocks, there could have been different grazing intensities and/or excreta transfer between the irrigated and unirrigated portions of the same paddocks. Both these factors could have affected SOC stocks. In this study, we used the process-based model, CenW, to simulate the consequences of this possible carbon transfer via animal excreta and different grazing intensities. We found that the observed increase of 0.6 tC ha^-1 yr^-1 in SOC stock in the unirrigated portions could result from a transfer of 20% excreta from the irrigated to unirrigated portions (with an area ratio of 6:1) of a paddock and with the unirrigated portions being grazed only lightly with 2.0 tDM ha^-1 in foliage biomass residuals remaining after grazing. That means that the observed higher SOC stocks in the unirrigated portions could potentially be attributable to the behaviour of grazing animals. We suggest that a realistic extent of carbon transfer and/or differences in grazing intensities could be sufficient to account for the observed differences in SOC stocks even if irrigation per se caused no differences in carbon stocks. It is therefore inappropriate to ascribe the change of SOC to irrigation effects based on experimental findings where SOC changes can be affected by the behaviour of grazing animals.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Combining eddy covariance measurements with process-based modelling to enhance understanding of carbon exchange rates of dairy pastures

Many temperate grasslands are used for dairying, and ongoing research aims to better understand these systems in order to increase animal production and soil organic carbon (SOC) stocks. However, it is difficult to fully understand management effects on SOC because most changes are slow and difficult to distinguish from natural variability, even if changes are important over years to decades. Eddy covariance (EC) measurements can overcome this problem by continuously measuring net carbon exchange from pastures, but net balances are very sensitive to even small systematic measurement errors. Combining EC measurements with detailed process-based modelling can reduce the risks inherent in total reliance on EC measurements. Modelling can also reveal information about the underlying processes that drive observed fluxes. Here, we describe carbon exchange patterns of five paddocks situated at four different locations in New Zealand and France where EC data and detailed physiological modelling were available. The work showed that respiration by grazing animals was often only incompletely captured in EC measurements. This was most problematic when fluxes were based on gap-filling, which could have estimated incorrect fluxes during grazing periods based on observations from periods without grazing. We then aimed to extract plant physiological insights from these studies. We found appreciable carbon uptake rates even at temperatures below 0 °C. After grazing, carbon uptake was reduced for up to 2 weeks. This reduction was larger than expected from reduced leaf area after grazing, but the factors contributing to that difference have not yet been identified. Detailed physiological models can also extrapolate findings to new management regimes, environmental conditions or plant attributes. This overcomes the limitation of experimental studies, which are necessarily restricted to actual site and weather conditions allowing models to make further progress on predicting management effects on SOC.

Modelling the effects of pasture renewal on the carbon balance of grazed pastures

In New Zealand, pasture renewal is a routine management method for maintaining pasture productivity. However, knowledge of the renewal effects on soil organic carbon (SOC) stocks is still limited. Here we use a process-based model, CenW, to comprehensively assess the effects of pasture renewal on the carbon balance of a temperate pasture in the Waikato region of New Zealand. We investigated the effects of renewal frequency, length of fallow period, renewal timing, and the importance and quantification of age-related reductions in productivity. Our results suggest that SOC change depends on the combined effects of renewal on gross primary productivity (GPP), autotrophic and heterotrophic respiration, carbon removal by grazing and carbon allocation to roots. Pasture renewal reduces grazing removal proportionately more than GPP because newly established plants need to allocate more carbon to re-build their root system following renewal which limits foliage production. That lengthens the time before above-ground biomass has grown sufficiently to be grazed again. New plants have a lower ratio of autotrophic respiration to GPP, however, which partly compensates for the GPP loss during renewal. Our simulations suggested an average SOC loss of 0.16 tC ha^-1 yr^-1 if pastures were renewed every 25 years, but could gain an average of 0.3 tC ha^-1 yr^-1 if pastures were renewed every year. For maximizing pasture production, the optimal renewal frequency depends on the rate of pasture deterioration with more rapid deterioration rates favouring more frequent renewal. Additionally, the length of the fallow period, renewal timing, and associated environmental conditions are important factors that can affect SOC temporally, but the importance of those effects diminishes at the annual or longer time scales. A major uncertainty for a full understanding of the renewal effect on SOC lies in the rate of pasture deterioration with time since previous renewal.

Modeling Carbon and Water Fluxes of Managed Grasslands: Comparing Flux Variability and Net Carbon Budgets between Grazed and Mowed Systems

The CenW ecosystem model simulates carbon, water, and nitrogen cycles following ecophysiological processes and management practices on a daily basis. We tested and evaluated the model using five years eddy covariance measurements from two adjacent but differently managed grasslands in France. The data were used to independently parameterize CenW for the two grassland sites. Very good agreements, i.e., high model efficiencies and correlations, between observed and modeled fluxes were achieved. We showed that the CenW model captured day-to-day, seasonal, and interannual variability observed in measured CO2 and water fluxes. We also showed that following typical management practices (i.e., mowing and grazing), carbon gain was severely curtailed through a sharp and severe reduction in photosynthesizing biomass. We also identified large model/data discrepancies for carbon fluxes during grazing events caused by the noncapture by the eddy covariance system of large respiratory losses of C from dairy cows when they were present in the paddocks. The missing component of grazing animal respiration in the net carbon budget of the grazed grassland can be quantitatively important and can turn sites from being C sinks to being neutral or C sources. It means that extra care is needed in the processing of eddy covariance data from grazed pastures to correctly calculate their annual CO2 balances and carbon budgets.

Implications of accounting for management intensity on carbon and nitrogen balances of European grasslands

European managed grasslands are amongst the most productive in the world. Besides temperature and the amount and timing of precipitation, grass production is also highly controlled by applications of nitrogen fertilizers and land management to sustain a high productivity. Since management characteristics of pastures vary greatly across Europe, land-use intensity and their projections are critical input variables in earth system modeling when examining and predicting the effects of increasingly intensified agricultural and livestock systems on the environment. In this study, we aim to improve the representation of pastures in the dynamic global vegetation model LPJ-GUESS. This is done by incorporating daily carbon allocation for grasses as a foundation to further implement daily land management routines and land-use intensity data into the model to discriminate between intensively and extensively used regions. We further compare our new simulations with leaf area index observations, reported regional grassland productivity, and simulations conducted with the vegetation model ORCHIDEE-GM. Additionally, we analyze the implications of including pasture fertilization and daily management compared to the standard version of LPJ-GUESS. Our results demonstrate that grassland productivity cannot be adequately captured without including land-use intensity data in form of nitrogen applications. Using this type of information improved spatial patterns of grassland productivity significantly compared to standard LPJ-GUESS. In general, simulations for net primary productivity, net ecosystem carbon balance and nitrogen leaching were considerably increased in the extended version. Finally, the adapted version of LPJ-GUESS, driven with projections of climate and land-use intensity, simulated an increase in potential grassland productivity until 2050 for several agro-climatic regions, most notably for the Mediterranean North, the Mediterranean South, the Atlantic Central and the Atlantic South.

The trade-offs between milk production and soil organic carbon storage in dairy systems under different management and environmental factors

A possible agricultural climate change mitigation option is to increase the amount of soil organic carbon (SOC). Conversely, some factors might lead to inadvertent losses of SOC. Here, we explore the effect of various management options and environmental changes on SOC storage and milk production of dairy pastures in New Zealand. We used CenW 4.1, a process-based ecophysiological model, to run a range of scenarios to assess the effects of changes in management options, plant properties and environmental factors on SOC and milk production. We tested the model by using 2years of observations of the exchanges of water and CO₂ measured with an eddy covariance system on a dairy farm in New Zealand's Waikato region. We obtained excellent agreement between the model and observations, especially for evapotranspiration and net photosynthesis. For the scenario analysis, we found that SOC could be increased through supplying supplemental feed, increasing fertiliser application, or increasing water availability through irrigation on very dry sites, but SOC decreased again for larger increases in water availability. Soil warming strongly reduced SOC. For other changes in key properties, such as changes in soil water-holding capacity and plant root:shoot ratios, SOC changes were often negatively correlated with changes in milk production. The work showed that changes in SOC were determined by the complex interplay between (1) changes in net primary production; (2) the carbon fraction taken off-site through grazing; (3) carbon allocation within the system between labile and stabilised SOC; and (4) changes in SOC decomposition rates. There is a particularly important trade-off between carbon either being removed by grazing or remaining on site and available for SOC formation. Changes in SOC cannot be fully understood unless all four factors are considered together in an overall assessment.