Modelling vegetation dynamics in managed grasslands: Responses to drivers depend on species richness

A process-based model reveals the restoration gap of degraded grasslands in Inner Mongolian steppe

Due to the influence of climate change and extensive grazing, a large proportion of steppe grassland has been degraded worldwide. The Chinese government initiated a series of grassland restoration programs to reverse the degradation. However, the limiting factors and the restoration potential remain unknown. Here we present a process-based model to assess the restoration gap (RG) defined as maximum biomass differences between non-degraded and degraded grasslands with different degrees of soil and vegetation degradation. The process-based model Agricultural Production Systems Simulator (APSIM) was evaluated utilizing observation data from both typical and meadow steppes under natural conditions in terms of phenology, dynamics of above-ground biomass and soil water content. Scenario analysis and sensitivity analysis were subsequently performed to address the RG and controlling factors during 1969-2018. The results showed that the calibrated model performed well with r > 0.75 and model efficiency factor EF > 0.5 for all the simulation components. According to our model results, the RG was larger in typical steppe compared to that of meadow steppe and it increased with increasing soil and/or vegetation degradation, to ~60% under extremely degraded scenarios. Both soil and vegetation degradation led to reduced water use efficiency, with an elevated proportion of soil evaporation to evapotranspiration (Es/ET), however, the limiting factor for RG varied. The degradation of soil water holding capacity contributed more to RG regardless of climate conditions for typical steppe in all years and for meadow steppe in dry years. In wet years the importance of vegetation coverage reduction increased for RG in meadow steppe, where the relative importance of vegetation coverage (valued at 62.8%) was 25.6% higher than that of soil degradation. Our results demonstrated the importance of considering climate variations when developing protection and restoration programs for grassland ecosystems.

Confronting an individual-based simulation model with empirical community patterns of grasslands

Grasslands contribute to global biogeochemical cycles and can host a high number of plant species. Both-species dynamics and biogeochemical fluxes-are influenced by abiotic and biotic environmental factors, management and natural disturbances. In order to understand and project grassland dynamics under global change, vegetation models which explicitly capture all relevant processes and drivers are required. However, the parameterization of such models is often challenging. Here, we report on testing an individual- and process-based model for simulating the dynamics and structure of a grassland experiment in temperate Europe. We parameterized the model for three species and confront simulated grassland dynamics with empirical observations of their monocultures and one two-species mixture. The model reproduces general trends of vegetation patterns (vegetation cover and height, aboveground biomass and leaf area index) for the monocultures and two-species community. For example, the model simulates well an average annual grassland cover of 70% in the species mixture (observed cover of 77%), but also shows mismatches with specific observation values (e.g. for aboveground biomass). By a sensitivity analysis of the applied inverse model parameterization method, we demonstrate that multiple vegetation attributes are important for a successful parameterization while leaf area index revealed to be of highest relevance. Results of our study pinpoint to the need of improved grassland measurements (esp. of temporally higher resolution) in close combination with advanced modelling approaches.

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.