Search Results
N2O emissions from the global agricultural nitrogen cycle – current state and future scenarios
2012, Bodirsky, B.L., Popp, A., Weindl, I., Dietrich, J.P., Rolinski, S., Scheiffele, L., Schmitz, C., Lotze-Campen, H.
Reactive nitrogen (Nr) is not only an important nutrient for plant growth, thereby safeguarding human alimentation, but it also heavily disturbs natural systems. To mitigate air, land, aquatic, and atmospheric pollution caused by the excessive availability of Nr, it is crucial to understand the long-term development of the global agricultural Nr cycle. For our analysis, we combine a material flow model with a land-use optimization model. In a first step we estimate the state of the Nr cycle in 1995. In a second step we create four scenarios for the 21st century in line with the SRES storylines. Our results indicate that in 1995 only half of the Nr applied to croplands was incorporated into plant biomass. Moreover, less than 10 per cent of all Nr in cropland plant biomass and grazed pasture was consumed by humans. In our scenarios a strong surge of the Nr cycle occurs in the first half of the 21st century, even in the environmentally oriented scenarios. Nitrous oxide (N2O) emissions rise from 3 Tg N2O-N in 1995 to 7–9 in 2045 and 5–12 Tg in 2095. Reinforced Nr pollution mitigation efforts are therefore required.
To what extent is climate change adaptation a novel challenge for agricultural modellers?
2019, Kipling, R.P., Topp, C.F.E., Bannink, A., Bartley, D.J., Blanco-Penedo, I., Cortignani, R., del Prado, A., Dono, G., Faverdin, P., Graux, A.-I., Hutchings, N.J., Lauwers, L., Özkan Gülzari, Ş., Reidsma, P., Rolinski, S., Ruiz-Ramos, M., Sandars, D.L., Sándor, R., Schönhart, M., Seddaiu, G., van Middelkoop, J., Shrestha, S., Weindl, I., Schönhart, M., Seddaiu, G., van Middelkoop, J., Shrestha, S., Weindl, I., Eory, V.
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. © 2019 The Authors
MAgPIE 4-a modular open-source framework for modeling global land systems
2019, Dietrich, J.P., Bodirsky, B.L., Humpenöder, F., Weindl, I., Stevanović, M., Karstens, K., Kreidenweis, U., Wang, X., Mishra, A., Klein, D., Ambrósio, G., Araujo, E., Yalew, A.W., Baumstark, L., Wirth, S., Giannousakis, A., Beier, F., Meng-Chuen, Chen, D., Lotze-Campen, H., Popp, A.
The open-source modeling framework MAgPIE (Model of Agricultural Production and its Impact on the Environment) combines economic and biophysical approaches to simulate spatially explicit global scenarios of land use within the 21st century and the respective interactions with the environment. Besides various other projects, it was used to simulate marker scenarios of the Shared Socioeconomic Pathways (SSPs) and contributed substantially to multiple IPCC assessments. However, with growing scope and detail, the non-linear model has become increasingly complex, computationally intensive and non-transparent, requiring structured approaches to improve the development and evaluation of the model. Here, we provide an overview on version 4 of MAgPIE and how it addresses these issues of increasing complexity using new technical features: modular structure with exchangeable module implementations, flexible spatial resolution, in-code documentation, automatized code checking, model/output evaluation and open accessibility. Application examples provide insights into model evaluation, modular flexibility and region-specific analysis approaches. While this paper is focused on the general framework as such, the publication is accompanied by a detailed model documentation describing contents and equations, and by model evaluation documents giving insights into model performance for a broad range of variables. With the open-source release of the MAgPIE 4 framework, we hope to contribute to more transparent, reproducible and collaborative research in the field. Due to its modularity and spatial flexibility, it should provide a basis for a broad range of land-related research with economic or biophysical, global or regional focus.
Global food demand scenarios for the 21st century
2015, Bodirsky, B.L., Rolinski, S., Biewald, A., Weindl, I., Popp, A., Lotze-Campen, H.
Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6
2018, Rolinski, S., Müller, C., Heinke, J., Weindl, I., Biewald, A., Leon Bodirsky, B., Bondeau, A., Boons-Prins, E.R., Bouwman, A.F., Leffelaar, P.A., Roller, J.A.T., Schaphoff, S., Thonicke, K.
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-1) but not in temperate regions even at much higher densities (0.4 to 1.2 LSUha-1). Applying LPJmL with the new grassland management options 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.