Investigating potential transferability of place-based research in land system science

Much of our knowledge about land use and ecosystem services in interrelated social-ecological systems is derived from place-based research. While local and regional case studies provide valuable insights, it is often unclear how relevant this research is beyond the study areas. Drawing generalized conclusions about practical solutions to land management from local observations and formulating hypotheses applicable to other places in the world requires that we identify patterns of land systems that are similar to those represented by the case study. Here, we utilize the previously developed concept of land system archetypes to investigate potential transferability of research from twelve regional projects implemented in a large joint research framework that focus on issues of sustainable land management across four continents. For each project, we characterize its project archetype, i.e. the unique land system based on a synthesis of more than 30 datasets of land-use intensity, environmental conditions and socioeconomic indicators. We estimate the transferability potential of project research by calculating the statistical similarity of locations across the world to the project archetype, assuming higher transferability potentials in locations with similar land system characteristics. Results show that areas with high transferability potentials are typically clustered around project sites but for some case studies can be found in regions that are geographically distant, especially when values of considered variables are close to the global mean or where the project archetype is driven by large-scale environmental or socioeconomic conditions. Using specific examples from the local case studies, we highlight the merit of our approach and discuss the differences between local realities and information captured in global datasets. The proposed method provides a blueprint for large research programs to assess potential transferability of place-based studies to other geographical areas and to indicate possible gaps in research efforts

Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests

A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (DGVM) and apply it to tropical and sub-tropical South America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America.Fil: Sakschewski, Boris. Potsdam Institute for Climate Impact Research; AlemaniaFil: Von Bloh, Werner. Humboldt-Universität zu Berlin; AlemaniaFil: Drüke, Markus. Humboldt-Universität zu Berlin; AlemaniaFil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Ruscica, Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Langerwisch, Fanny. Universitat Potsdam; AlemaniaFil: Billing, Maik. Universidade Federal de Santa Catarina; BrasilFil: Bereswill, Sarah. Universidade Estadual de Campinas; BrasilFil: Hirota, Marina. Potsdam Institute for Climate Impact Research; AlemaniaFil: Oliveira, Rafael Silva. Potsdam Institute for Climate Impact Research; AlemaniaFil: Heinke, Jens. Potsdam Institute for Climate Impact Research; AlemaniaFil: Thonicke, Kirsten. Potsdam Institute for Climate Impact Research; Alemani

Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests

A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (DGVM) and apply it to tropical and sub-tropical South America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America

A social-ecological approach to identify and quantify biodiversity tipping points in South America’s seasonal dry ecosystems

ropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture.Fil: Thonicke, Kirsten. Institute for Climate Impact Research ; AlemaniaFil: Langerwisch, Fanny. Institute for Climate Impact Research ; Alemania. Czech University of Life Sciences Prague; República ChecaFil: Baumann, Matthias. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Leitão, Pedro J.. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Václavík, Tomáš. Helmholtz Centre for Environmental Research; Alemania. Palacký University Olomouc; República ChecaFil: Alencar, Anne. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Simões, Margareth. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); BrasilFil: Scheiter, Simon. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Langan, Liam. Senckenberg Biodiversity and Climate Research Centre; AlemaniaFil: Bustamante, Mercedes. Universidade do Brasília; BrasilFil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Hirota, Marina. Universidade Federal de Santa Catarina; Brasil. Universidade Estadual de Campinas; BrasilFil: Börner, Jan. Universitat Bonn; AlemaniaFil: Rajao, Raoni. Universidade Federal de Minas Gerais; BrasilFil: Soares Filho, Britaldo. Universidade Federal de Minas Gerais; BrasilFil: Yanosky, Alberto. Consejo Nacional de Ciencia y Tecnología; ParaguayFil: Ochoa Quinteiro, José Manuel. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; ColombiaFil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; ArgentinaFil: Conti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: de la Vega Leiner, Anne Cristina. Universität Greifswald; Alemani

Combined effects of climate and land-use change on the provision of ecosystem services in rice agro-ecosystems

Irrigated rice croplands are among the world's most important agro-ecosystems. They provide food for more than 3.5 billion people and a range of other ecosystem services (ESS). However, the sustainability of rice agro-ecosystems is threatened by continuing climate and land-use changes. To estimate their combined effects on a bundle of ESS, we applied the vegetation and hydrology model LPJmL to seven study areas in the Philippines and Vietnam. We quantified future changes in the provision of four essential ESS (carbon storage, carbon sequestration, provision of irrigation water and rice production) under two climate scenarios (until 2100) and three site-specific land-use scenarios (until 2030), and examined the synergies and trade-offs in ESS responses to these drivers. Our results show that not all services can be provided in the same amounts in the future. In the Philippines and Vietnam the projections estimated a decrease in rice yields (by approximately 30%) and in carbon storage (by 15%) and sequestration (by 12%) towards the end of the century under the current land-use pattern. In contrast, the amount of available irrigation water was projected to increase in all scenarios by 10%–20%. However, the results also indicate that land-use change may partially offset the negative climate impacts in regions where cropland expansion is possible, although only at the expense of natural vegetation. When analysing the interactions between ESS, we found consistent synergies between rice production and carbon storage and trade-offs between carbon storage and provision of irrigation water under most scenarios. Our results show that not only the effects of climate and land-use change alone but also the interaction between ESS have to be considered to allow sustainable management of rice agro-ecosystems under global change

Deforestation in Amazonia impacts riverine carbon dynamics

Fluxes of organic and inorganic carbon within the Amazon basin are considerably controlled by annual flooding, which triggers the export of terrigenous organic material to the river and ultimately to the Atlantic Ocean. The amount of carbon imported to the river and the further conversion, transport and export of it depend on temperature, atmospheric CO2, terrestrial productivity and carbon storage, as well as discharge. Both terrestrial productivity and discharge are influenced by climate and land use change. The coupled LPJmL and RivCM model system (Langerwisch et al., 2016) has been applied to assess the combined impacts of climate and land use change on the Amazon riverine carbon dynamics. Vegetation dynamics (in LPJmL) as well as export and conversion of terrigenous carbon to and within the river (RivCM) are included. The model system has been applied for the years 1901 to 2099 under two deforestation scenarios and with climate forcing of three SRES emission scenarios, each for five climate models. We find that high deforestation (business-as-usual scenario) will strongly decrease (locally by up to 90%) riverine particulate and dissolved organic carbon amount until the end of the current century. At the same time, increase in discharge leaves net carbon transport during the first decades of the century roughly unchanged only if a sufficient area is still forested. After 2050 the amount of transported carbon will decrease drastically. In contrast to that, increased temperature and atmospheric CO2 concentration determine the amount of riverine inorganic carbon stored in the Amazon basin. Higher atmospheric CO2 concentrations increase riverine inorganic carbon amount by up to 20% (SRES A2). The changes in riverine carbon fluxes have direct effects on carbon export, either to the atmosphere via outgassing or to the Atlantic Ocean via discharge. The outgassed carbon will increase slightly in the Amazon basin, but can be regionally reduced by up to 60% due to deforestation. The discharge of organic carbon to the ocean will be reduced by about 40% under the most severe deforestation and climate change scenario. These changes would have local and regional consequences on the carbon balance and habitat characteristics in the Amazon basin itself as well as in the adjacent Atlantic Ocean

Potential effects of climate change on inundation patterns in the Amazon Basin

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Net biome production of the Amazon Basin in the 21st century

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D5.4 Mapping of vegetation indices and metrics, and their utility in FSA mapping at CS scale

This deliverable provides an overview of all work conducted in the context of Activity 5.3.1 (Developing remote sensing indicators) with respect to Farming System Archetype (FSA) Mapping (Task 5.3). This work is based on the FSA definition and mapping in ‘D2.2 - Conceptual Framework’ and ‘D3.5 - Farming System Archetypes for each CS’ and investigates the potential of remote sensing methods to inform different dimensions of FSAs. Findings from this analysis will contribute to the BESTMAP roadmap (Task 5.4). Specifically, methodologies for crop type mapping, crop yield estimation, and field boundary mapping are investigated in different case study regions and their relevance for FSAs are shown

D3.5 Farming System Archetypes for each CS

This deliverable provides an overview of the methods and data used for developing the Farming System Archetypes (FSAs) in the five case studies - Humber, Mulde, SouthMoravia, Bačka and Catalonia. Additionally, it discusses limitations as well as problems and presents solutions. The FSAs are a generalized typology of farming systems that are assumed to have similar response to policy change. FSAs are a major component of the BESTMAP modelling architecture because they provide linkages between many aspects of the project, especially connecting the biophysical and agent-based modelling in the case studies (CS), based on local data (e.g. IACS/LPIS, for explanation see Methodology), with the modelling of policy effects at the EU level, based on FADN micro-data within the FADN regions. The FSA framework defines the main farm characteristics determined by two main dimensions: firstly farm specialization and secondly economic size, both calculated and mapped for each farm in the CSs. ‘Farmer agents’ who belong to the same FSA are then assumed to have similar decision patterns regarding the adoption of agri-environmental schemes, based on the relationships revealed in the CS agent-based models