Soil carbon management in large-scale Earth system modelling: implications for crop yields and nitrogen leaching

Olin, Stefan; Lindeskog, Mats; Pugh, Thomas A. M.; Schurgers, Guy; Wårlind, David; Mishurov, Mikhail; Zaehle, Sönke; Stocker, Benjamin D.; Smith, Benjamin; Arneth, Almut

doi:10.5194/esd-6-745-2015

Cited by 62 publications

(84 citation statements)

References 97 publications

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“…The process-based dynamic global vegetation model (DGVM) LPJ-GUESS simulates vegetation dynamics in response to climate, land-use change (LUC), atmospheric CO 2 , and nitrogen (N) input (Olin et al, 2015a;Smith et al, 2014). The model distinguishes between natural, pasture and cropland land-cover types (Lindeskog et al, 2013), all of which include C-N dynamics (Olin et al, 2015a;Smith et al, 2014).…”

Section: Lpj-guessmentioning

confidence: 99%

“…The model distinguishes between natural, pasture and cropland land-cover types (Lindeskog et al, 2013), all of which include C-N dynamics (Olin et al, 2015a;Smith et al, 2014). Vegetation dynamics in natural land cover are characterized by the establishment, competition, and mortality of 12 plant functional types (PFTs, 10 groups of tree species, C 3 and C 4 grasses) in a number of replicate patches (10 in this study for primary vegetation, 2 for abandoned agricultural areas).…”

Section: Lpj-guessmentioning

confidence: 99%

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Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators

et al. 2017

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Abstract. Land management for carbon storage is discussed as being indispensable for climate change mitigation because of its large potential to remove carbon dioxide from the atmosphere, and to avoid further emissions from deforestation. However, the acceptance and feasibility of land-based mitigation projects depends on potential side effects on other important ecosystem functions and their services. Here, we use projections of future land use and land cover for different land-based mitigation options from two land-use models (IMAGE and MAgPIE) and evaluate their effects with a global dynamic vegetation model (LPJ-GUESS). In the landuse models, carbon removal was achieved either via growth of bioenergy crops combined with carbon capture and storage, via avoided deforestation and afforestation, or via a combination of both. We compare these scenarios to a reference scenario without land-based mitigation and analyse the LPJ-GUESS simulations with the aim of assessing synergies and trade-offs across a range of ecosystem service indicators: carbon storage, surface albedo, evapotranspiration, water runoff, crop production, nitrogen loss, and emissions of biogenic volatile organic compounds.In our mitigation simulations cumulative carbon storage by year 2099 ranged between 55 and 89 GtC. Other ecosystem service indicators were influenced heterogeneously both positively and negatively, with large variability across regions and land-use scenarios. Avoided deforestation and afforestation led to an increase in evapotranspiration and enhanced emissions of biogenic volatile organic compounds, and to a decrease in albedo, runoff, and nitrogen loss. Crop production could also decrease in the afforestation scenarios as a result of reduced crop area, especially for MAgPIE land-use patterns, if assumed increases in crop yields cannot be realized. Bioenergy-based climate change mitigation was projected to affect less area globally than in the forest expansion scenarios, and resulted in less pronounced changes in most ecosystem service indicators than forest-based mitigation, but included a possible decrease in nitrogen loss, crop production, and biogenic volatile organic compounds emissions.

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Section: Lpj-guessmentioning

confidence: 99%

Section: Lpj-guessmentioning

confidence: 99%

Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators

et al. 2017

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“…Agricultural and pastoral systems are 10 represented as a prescribed fractional cover of area under human land use per grid cell. Four crop functional types modelled on winter wheat, spring wheat, rice, and maize were used to simulate croplands , with representation of sowing, harvesting, irrigation, fertilisation, tillage, and the use of cover crops (Lindeskog et al, 2013;Olin et al, 2015;Pugh et al, 2015). Pastures are represented by competing C3 and C4 grass, with 50% of the above-ground biomass removed annually to 15 represent the effects of grazing (Lindeskog et al, 2013).…”

Section: Case Studymentioning

confidence: 99%

Modelling feedbacks between human and natural processes in the land system

Robinson¹,

Vittorio²,

Alexander³

et al. 2017

Preprint

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The unprecedented use of Earth's resources by humans, in combination with the increasing natural variability in natural processes over the past century, is affecting evolution of the Earth system. To better understand natural processes and their potential future trajectories requires improved integration with 5 and quantification of human processes. Similarly, to mitigate risk and facilitate socio-economic development requires a better understanding of how the natural system (e.g., climate variability and change, extreme weather events, and processes affecting soil fertility) affects human processes. To capture and formalize our understanding of the interactions and feedback between human and natural systems a variety of modelling approaches are used. While integrated assessment models are widely 10 recognized as supporting this goal and integrating representations of the human and natural system for global applications, an increasing diversity of models and corresponding research have focused on coupling models specializing in specific human (e.g., decision-making) or natural (e.g., erosion) processes at multiple scales. Domain experts develop these specialized models with a greater degree of detail, accuracy, and transparency, with many adopting open-science norms that use new technology for 15 model sharing, coupling, and high performance computing. We highlight examples of four different approaches used to couple representations of the human and natural system, which vary in the processes represented and in the scale of their application. The examples illustrate how groups of researchers have attempted to overcome the lack of suitable frameworks for coupling human and natural systems to answer questions specific to feedbacks between human and natural systems. We draw from these 20 examples broader lessons about system and model coupling and discuss the challenges associated with maintaining consistency across models and representing feedback between human and natural systems in coupled models.Earth Syst. Dynam. Discuss., https://doi

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“…Olin et al, 2015) and BME performance (magnitudes, trends, and inter-annual variability) by first implementing a global biome-by-biomelevel validation in which the results from the Sahel are highlighted. We then compare BME estimates with LPJ-GUESS NPP simulations (including LPJ-GUESS managed land in order to gauge the effect of agriculture on NPP, keeping in mind that BME is based on a model of potential natural vegetation) that were excluded from BME parameterization.…”

Section: Npp Supplymentioning

confidence: 99%

Future supply and demand of net primary production in the Sahel

et al. 2017

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Abstract. In the 21st century, climate change in combination with increasing demand, mainly from population growth, will exert greater pressure on the ecosystems of the Sahel to supply food and feed resources. The balance between supply and demand, defined as the annual biomass required for human consumption, serves as a key metric for quantifying basic resource shortfalls over broad regions.Here we apply an exploratory modelling framework to analyse the variations in the timing and geography of different NPP (net primary production) supply-demand scenarios, with distinct assumptions determining supply and demand, for the 21st century Sahel. We achieve this by coupling a simple NPP supply model forced with projections from four representative concentration pathways with a global, reduced-complexity demand model driven by socio-economic data and assumptions derived from five shared socio-economic pathways.For the scenario that deviates least from current socio-economic and climate trends, we find that per capita NPP begins to outstrip supply in the 2040s, while by 2050 half the countries in the Sahel experience NPP shortfalls. We also find that despite variations in the timing of the onset of NPP shortfalls, demand cannot consistently be met across the majority of scenarios. Moreover, large between-country variations are shown across the scenarios, in which by the year 2050 some countries consistently experience shortage or surplus, while others shift from surplus to shortage. At the local level (i.e. grid cell), hotspots of total NPP shortfall consistently occur in the same locations across all scenarios but vary in size and magnitude. These hotspots are linked to population density and high demand. For all scenarios, total simulated NPP supply doubles by 2050 but is outpaced by increasing demand due to a combination of population growth and the adoption of diets rich in animal products. Finally, variations in the timing of the onset and end of supply shortfalls stem from the assumptions that underpin the shared socio-economic pathways rather than the representative concentration pathways.Our results suggest that the UN sustainable development goals for eradicating hunger are at high risk for failure. This emphasizes the importance of policy interventions such as the implementation of sustainable and healthy diets, family planning, reducing yield gaps, and encouraging the transfer of resources to impoverished areas via trade relations.

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Soil carbon management in large-scale Earth system modelling: implications for crop yields and nitrogen leaching

Cited by 62 publications

References 97 publications

Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators

Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators

Modelling feedbacks between human and natural processes in the land system

Future supply and demand of net primary production in the Sahel

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