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Forage offtake, leaf biomass and soil organic carbon storage are important ecosystem services of permanent grasslands, which are determined by climatic conditions, management and functional diversity. However, functional diversity is not independent of climate and management, and it is important to understand the role of functional diversity and these dependencies for ecosystem services of permanent grasslands, since functional diversity may play a key role in mediating impacts of changing conditions. Large-scale ecosystem models are used to assess ecosystem functions within a consistent framework for multiple climate and management scenarios. However, large-scale models of permanent grasslands rarely consider functional diversity. We implemented a representation of functional diversity based on the competitor, stress-tolerator and ruderal (CSR) theory and the global spectrum of plant form and function into the Lund Potsdam Jena managed Land (LPJmL) dynamic global vegetation model (DGVM) forming LPJmL-CSR. Using a Bayesian calibration method, we parameterised new plant functional types (PFTs) and used these to assess forage offtake, leaf biomass, soil organic carbon storage and community composition of three permanent grassland sites. These are a temperate grassland and a hot and a cold steppe for which we simulated several management scenarios with different defoliation intensities and resource limitations. LPJmL-CSR captured the grassland dynamics well under observed conditions and showed improved results for forage offtake, leaf biomass and/or soil organic carbon (SOC) compared to the original LPJmL 5 version at the three grassland sites. Furthermore, LPJmL-CSR was able to reproduce the trade-offs associated with the global spectrum of plant form and function, and similar strategies emerged independent of the site-specific conditions (e.g. the C and R PFTs were more resource exploitative than the S PFT). Under different resource limitations, we observed a shift in the community composition. At the hot steppe, for example, irrigation led to a more balanced community composition with similar C, S and R PFT shares of aboveground biomass. Our results show that LPJmL-CSR allows for explicit analysis of the adaptation of grassland vegetation to changing conditions while explicitly considering functional diversity. The implemented mechanisms and trade-offs are universally applicable, paving the way for large-scale application. Applying LPJmL-CSR for different climate change and functional diversity scenarios may generate a range of future grassland productivities. [ABSTRACT FROM AUTHOR]

To represent the impact of grazing livestock on carbon (C) and nitrogen (N) dynamics in grasslands, we implement a livestock module into LPJmL5.0-tillage, a global vegetation and crop model with explicit representation of managed grasslands and pastures, forming LPJmL5.0-grazing. The livestock module uses lactating dairy cows as a generic representation of grazing livestock. The new module explicitly accounts for forage quality in terms of dry-matter intake and digestibility using relationships derived from compositional analyses for different forages. Partitioning of N into milk, feces, and urine as simulated by the new livestock module shows very good agreement with observation-based relationships reported in the literature. Modelled C and N dynamics depend on forage quality (C:N ratios in grazed biomass), forage quantity, livestock densities, manure or fertilizer inputs, soil, atmospheric CO 2 concentrations, and climate conditions. Due to the many interacting relationships, C sequestration, GHG emissions, N losses, and livestock productivity show substantial variation in space and across livestock densities. The improved LPJmL5.0-grazing model can now assess the effects of livestock grazing on C and N stocks and fluxes in grasslands. It can also provide insights about the spatio-temporal variability of grassland productivity and about the trade-offs between livestock production and environmental impacts. [ABSTRACT FROM AUTHOR]

Forage supply and soil organic carbon storage are two important ecosystem functions of permanent grasslands, which are determined by climatic conditions, management and functional diversity. However, functional diversity is not independent of climate and management, and it is important to understand the role of functional diversity and these dependencies for ecosystem functions of permanent grasslands. Especially since functional diversity may play a key role in mediating impacts of changing conditions. Large-scale ecosystem models are used to assess ecosystem functions within a consistent framework for multiple climate and management scenarios. However, large-scale models of permanent grasslands rarely consider functional diversity. We implemented a representation of functional diversity based on the CSR theory and the global spectrum of plant form and function into the LPJmL dynamic global vegetation model forming LPJmL-CSR. Using a Bayesian calibration method, we parameterised new plant functional types and used these to assess forage supply, soil organic carbon storage and community composition of three permanent grassland sites. These are a temperate grassland, a hot and a cold steppe for which we simulated several management scenarios with different defoliation intensities and resource limitations. LPJmL-CSR captured the grassland dynamics well under observed conditions and showed improved results for forage supply and/or SOC compared to LPJmL 5.3 at three grassland sites. Furthermore, LPJmL-CSR was able to reproduce the trade-offs associated with the global spectrum of plant form and function and similar strategies emerged independent of the site specific conditions (e.g. the C- and R-PFTs were more resource exploitative than S-PFTs). Under different resource limitations, we observed a shift of the community composition. At the hot steppe for example, irrigation led to a more balanced community composition with similar C-, S- and R-PFT shares of above-ground biomass. Our results show, that LPJmL-CSR allows for explicit analysis of the adaptation of grassland vegetation to changing conditions while explicitly considering functional diversity. The implemented mechanisms and trade-offs are universally applicable paving the way for large-scale application. Applying LPJmL-CSR for different climate change and functional diversity scenarios may generate a range of future grassland productivity. [ABSTRACT FROM AUTHOR]

Grazing livestock alter the fluxes of carbon (C) and nitrogen (N) in grasslands. We implement a livestock module representing grazing dairy cows into LPJmL5.0-tillage, a global vegetation and crop model with explicit representation of managed grasslands and pastures, forming LPJmL5.0-grazing. The new module explicitly accounts for feed quality on dry matter intake and feed digestibility using relationships derived from compositional analyses for different forages. Partitioning of N into milk, feces, and urine are simulated by the new livestock module shows very good agreement with observation-based relationships reported in the literature. Modeled C and N dynamics depend on feed quality (C:N ratios in grazed biomass), feed quantity, livestock densities, manure or fertilizer inputs, soil, atmospheric CO2 concentrations, and climate conditions. Due to the many interacting relationships, C sequestration or emission rates as well as N losses and N use efficiency (NUE) show substantial variation in space and across livestock densities. The improved LPJmL5.0-grazing model can now assess the effects of management and climate change on grazing dairy productivity, C dynamics and N losses. Optimal stocking densities can be derived per location for different optimization targets, such as maximization of milk production, lowest emission intensity, best NUE, or highest C sequestration. [ABSTRACT FROM AUTHOR]

The article focuses on a study related to the multimodel evaluation of nitrous oxide (N2O) emissions from an intensively managed grassland. It mentions that process‐based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands; and N2O concentrations in atmosphere impacts Earth's system in ways including global warming effect as the third most important greenhouse gas.

Grassland management affects the carbon fluxes of one-third of the global land area and is thus an important factor for the global carbon budget. Nonetheless, this aspect has been largely neglected or underrepresented in global carbon cycle models. We investigate four harvesting schemes for the managed grassland implementation of the dynamic global vegetation model (DGVM) Lund--Potsdam--Jena managed Land (LPJmL) that facilitate a better representation of actual management systems globally. We describe the model implementation and analyze simulation results with respect to harvest, net primary productivity and soil carbon content and by evaluating them against reported grass yields in Europe.We demonstrate the importance of accounting for differences in grassland management by assessing potential livestock grazing densities as well as the impacts of grazing, grazing intensities and mowing systems on soil carbon stocks. Grazing leads to soil carbon losses in polar or arid regions even at moderate livestock densities ( <0.4 livestock units per hectare -- LSUha enables assessments of the global grassland production and its impact on the terrestrial biogeochemical cycles but requires a global data set on current grassland management. [ABSTRACT FROM AUTHOR]

Multi-species grasslands are reservoirs of biodiversity and provide multiple ecosystem services, including fodder production and carbon sequestration. The provision of these services depends on the control exerted on the biogeochemistry and plant diversity of the system by the interplay of biotic and abiotic factors, e.g., grazing or mowing intensity. Biogeochemical models incorporate a mechanistic view of the functioning of grasslands and provide a sound basis for studying the underlying processes. However, in these models, the simulation of biogeochemical cycles is generally not coupled to simulation of plant species dynamics, which leads to considerable uncertainty about the quality of predictions. Ecological models, on the other hand, do account for biodiversity with approaches adopted from plant demography, but without linking the dynamics of plant species to the biogeochemical processes occurring at the community level, and this hampers the models' capacity to assess resilience against abiotic stresses such as drought and nutrient limitation. While setting out the state-of-the-art developments of biogeochemical and ecological modelling, we explore and highlight the role of plant diversity in the regulation of the ecosystem processes underlying the ecosystems services provided by multi-species grasslands. An extensive literature and model survey was carried out with an emphasis on technically advanced models reconciling biogeochemistry and biodiversity, which are readily applicable to managed grasslands in temperate latitudes. We propose a roadmap of promising developments in modelling. [ABSTRACT FROM AUTHOR]

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: −64 ± 74 g C m −2  yr −1 (animal density reduction) and −81 ± 74 g C m −2  yr −1 (N and animal density reduction), against the baseline of −30.5 ± 69.5 g C m −2  yr −1 (LSU [livestock units] ≥ 0.76 ha −1  yr −1 ). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N 2 O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m −2  yr −1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU −1  yr −1 across sites). The highest N 2 O-N intensities (N 2 O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs. [ABSTRACT FROM AUTHOR]

Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change. [ABSTRACT FROM AUTHOR]

Biogeochemical models of vegetation dynamics could potentially be used to complement empirical studies on the effect of plant species richness. A key precondition is the simulation of species coexistence. While community scale models regularly incorporate respective processes, models at the field or landscape scale used for larger scale assessments, require additional model development. However, it is unclear how the particular process description within these models affects simulations of species performance and resulting ecosystem functions. We compare simulations of two grassland models of different complexity for monocultures and two-species mixtures in a grassland experiment in Jena, Germany. By providing an in-depth analysis of the models' process descriptions, we evaluate their ability to simulate the response of different species, their interactions and their joint performance to drought and mowing. Both models simulated similar average above-ground biomass (AGB) but showed different intra-annual variability. Generally, the models had difficulties representing a balanced species composition in multiple species mixtures and competition for space was the main driver of community composition in both models. The resulting communities were dominated by the more competitive species, while the weak competitor was only marginally present in most mixtures independent of drought and mowing. The competitive strength which we derived from the calibrated parameter sets of the species differed between the models and the agreement on which species dominate specific mixtures was mixed. While both models simulated reduced soil water content and above-ground biomass in response to drought, the strength and duration of these responses differed. Despite these differences, simulated species interactions were barely affected, and strong competitors remained dominant. Mowing had opposing effects on the competition for space in the models, which could be attributed to the different representations of plants in the two models. The models selected for the comparison are two representatives for local- and large-scale applications and use widely applied approaches for which our comparison highlighted strengths and weaknesses. To enable the investigated models (and those with similar complexity) to simulate coexistence of multiple species, niche differentiation needs to be improved. This requires a stricter separation of access to different resources and improved representation of different ecological strategies for which community scale models that are able to simulate coexistence may be an inspiration. Our approach may serve as an example for other modellers looking for ways to identify important model processes for further model development in the context of species interaction. • The model comparison showed similar biomass (AGB) with different seasonal patterns. • Competition for space was the main driver of species interaction. • Assessed grassland models have deficiencies in simulating stable coexistence. • To improve niche differentiation, incorporation of below-ground traits is needed. • Approaches from community scale models could inspire models at larger scales. [ABSTRACT FROM AUTHOR]

\begin{document}\title{Fire practice on Brazilian managed grasslands and its implementation in LPJmL 4.0}\author{Marie Brunel$^{1,2}$, Henrique Barbosa$^{3}$, Markus Dr\"{u}ke$^{1}$,\\Christoph M\"{u}ller$^{1}$, Anja Rammig$^{4}$, Kirsten Thonicke$^{1}$,\\Stephen Wirth$^{1}$, Susanne Rolinski$^{1}$}\date{} Brazilian farmers follow varying management fire practices during the year, depending on climatic conditions and status of the vegetation.Although since the 1990s, the agricultural use of fire is prohibited in entire Brazil and fires are still used on a regular base to manage pastures, remove winter dead biomass or prevent wildfire events.With increasing agricultural areas, fire practices on agricultural land might play a significant role in fire seasonality, burnt area and GHG emissions.However, studies on fire practices are still insufficient to appropriately described fire purposes and spread.Such investigations provide the basis for political interventions as formulating strategies for reducing GHG emissions and enable future fire impact assessments.\smallbreakIn order to develop a tool for assessing future fire impacts on grasslands in a process-based way, we implement the SPITFIRE module on managed grasslands depending on seasonality, climate and vegetation conditions in Brazil.SPITFIRE is a process-based fire module implemented in the dynamic global vegetation model Lund-Potsdam-Jena managed land model (LPJmL 4.0) that simulates fire spread and intensity.Currently SPITFIRE computes natural and human-caused fires only in natural vegetation.We develop and test fire prescription scenarios depending on the ratio of burnt area or the date of setting fire.As validation data set, we use a combination of the Mapbiomas land use data set and the MODIS burnt area estimates.We analyse and compare the outputs provided by the two different LPJmL SPITFIRE versions and burnt area validation data like carbon emissions, vegetation cover and soil carbon content in Amazon, Cerrado and Pampas regions.\smallbreakThe comparison of the results from the SPITFIRE versions and the validation data set enables to distinguish the actual amount of each type of fire event.Moreover, in the context of main global warming, the scientific community favours strongly environmental protection and the reduction of GHG emissions from grassland fires.On the other hand, farmers prefer these practices because of economical concerns.The results of this study give a first overview of the actual human-caused fire impacts on managed grasslands including CO$_{2}$ emissions.This is an important first step for fire impact assessments, future projections and environmental impact studies on the global carbon budget in times when deforestation in combination with fire is increasing.\end{document} [ABSTRACT FROM AUTHOR]

Grassland ecosystems cover about one-quarter of the earth's land area, exist in various environments and with different functional diversity (FD). Climate, soil, disturbances and management practices influence sequestration and storage of carbon. Functionally diverse grassland ecosystems may have increased productivity and carbon storage. State of the art dynamic global vegetation models (DGVMs) rarely consider flexible plant traits underpinning FD.We extend the Lund-Potsdam-Jena managed land version 4 (LPJmL4) DGVM implementing additional C3 and C4 grass plant functional types (PFTs). Trait values are selected randomly from prescribed ranges in a manner that added grass PFTs follow either an exploitative or conservative strategy for resource acquisition. Results are compared to observations and analyzed to evaluate the effect of different trait values. Further a fixed PFT combination assembled from the mean trait values of the prescribed ranges is used for global simulations. For these we analyze environmental gradients of net primary productivity (NPP) and discuss expected and simulated niche differentiation. All simulations are conducted under three precipitation regimes (potential irrigation, actual and reduced precipitation).Our results reveal the potentials and limitations of the model's current implementation of competition. We derive essential changes necessary to improve the representation of niche differentiation and FD in grasslands in LPJmL4. The model is able to reproduce existing findings from empirical studies showing that leaf traits (specific leaf area and leaf turnover) and leaf to root ratio strongly influence productivity. Modeled productivity depends on light and water availability. We identified an overlap of factors determining the allocation of light and water between the different PFTs. Therefore, the model is currently unable to simulate niche differentiation and trade-offs between strategies.We propose to adjust the model to independently determine resource allocation to include these trade-offs and assemble PFTs following the leaf economic spectrum. Achieving this our aim is to contribute to the understanding of facilitation and competition in differently managed grassland ecosystems with variable FD. [ABSTRACT FROM AUTHOR]