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- ItemGlobal gridded crop model evaluation: Benchmarking, skills, deficiencies and implications(München : European Geopyhsical Union, 2017) Müller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Glotter, Michael; Hoek, Steven; Iizumi, Toshichika; Izaurralde, Roberto C.; Jones, Curtis; Khabarov, Nikolay; Lawrence, Peter; Liu, Wenfeng; Olin, Stefan; Pugh, Thomas A.M.; Ray, Deepak K.; Reddy, Ashwan; Rosenzweig, Cynthia; Ruane, Alex C.; Sakurai, Gen; Schmid, Erwin; Skalsky, Rastislav; Song, Carol X.; Wang, Xuhui; de Wit, Allard; Yang, HongCrop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.
- ItemParameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble(San Francisco, California, US : PLOS, 2019) Folberth, Christian; Elliott, Joshua; Müller, Christoph; Balkovič, Juraj; Chryssanthacopoulos, James; Izaurralde, Roberto C.; Jones, Curtis D.; Khabarov, Nikolay; Liu, Wenfeng; Reddy, Ashwan; Schmid, Erwin; Skalský, Rastislav; Yang, Hong; Arneth, Almut; Ciais, Philippe; Deryng, Delphine; Lawrence, Peter J.; Olin, Stefan; Pugh, Thomas A.M.; Ruane, Alex C.; Wang, XuhuiGlobal gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
- ItemDiverging importance of drought stress for maize and winter wheat in Europe([London] : Nature Publishing Group UK, 2018) Webber, Heidi; Ewert, Frank; Olesen, Jørgen E.; Müller, Christoph; Fronzek, Stefan; Ruane, Alex C.; Bourgault, Maryse; Martre, Pierre; Ababaei, Behnam; Bindi, Marco; Ferrise, Roberto; Finger, Robert; Fodor, Nándor; Gabaldón-Leal, Clara; Gaiser, Thomas; Jabloun, Mohamed; Kersebaum, Kurt-Christian; Lizaso, Jon I.; Lorite, Ignacio J.; Manceau, Loic; Moriondo, Marco; Nendel, Claas; Rodríguez, Alfredo; Ruiz-Ramos, Margarita; Semenov, Mikhail A.; Siebert, Stefan; Stella, Tommaso; Stratonovitch, Pierre; Trombi, Giacomo; Wallach, DanielUnderstanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
- ItemA meta-analysis of crop response patterns to nitrogen limitation for improved model representation(San Francisco, California, US : PLOS, 2019) Seufert, Verena; Granath, Gustaf; Müller, ChristophThe representation of carbon-nitrogen (N) interactions in global models of the natural or managed land surface remains an important knowledge gap. To improve global process-based models we require a better understanding of how N limitation affects photosynthesis and plant growth. Here we present the findings of a meta-analysis to quantitatively assess the impact of N limitation on source (photosynthate production) versus sink (photosynthate use) activity, based on 77 highly controlled experimental N availability studies on 11 crop species. Using meta-regressions, we find that it can be insufficient to represent N limitation in models merely as inhibiting carbon assimilation, because in crops complete N limitation more strongly influences leaf area expansion (-50%) than photosynthesis (-34%), while leaf starch is accumulating (+83%). Our analysis thus offers support for the hypothesis of sink limitation of photosynthesis and encourages the exploration of more sink-driven crop modelling approaches. We also show that leaf N concentration changes with N availability and that the allocation of N to Rubisco is reduced more strongly compared to other photosynthetic proteins at low N availability. Furthermore, our results suggest that different crop species show generally similar response patterns to N limitation, with the exception of leguminous crops, which respond differently. Our meta-analysis offers lessons for the improved depiction of N limitation in global terrestrial ecosystem models, as well as highlights knowledge gaps that need to be filled by future experimental studies on crop N limitation response.
- ItemThe impact of high-end climate change on agricultural welfare(Washington, DC : American Association for the Advancement of Science, 2016) Stevanović, Miodrag; Popp, Alexander; Lotze-Campen, Hermann; Dietrich, Jan Philipp; Müller, Christoph; Bonsch, Markus; Schmitz, Christoph; Bodirsky, Benjamin Leon; Humpenöder, Florian; Weindl, IsabelleClimate change threatens agricultural productivity worldwide, resulting in higher food prices. Associated economic gains and losses differ not only by region but also between producers and consumers and are affected by market dynamics. On the basis of an impact modeling chain, starting with 19 different climate projections that drive plant biophysical process simulations and ending with agro-economic decisions, this analysis focuses on distributional effects of high-end climate change impacts across geographic regions and across economic agents. By estimating the changes in surpluses of consumers and producers, we find that climate change can have detrimental impacts on global agricultural welfare, especially after 2050, because losses in consumer surplus generally outweigh gains in producer surplus. Damage in agriculture may reach the annual loss of 0.3% of future total gross domestic product at the end of the century globally, assuming further opening of trade in agricultural products, which typically leads to interregional production shifts to higher latitudes. Those estimated global losses could increase substantially if international trade is more restricted. If beneficial effects of atmospheric carbon dioxide fertilization can be realized in agricultural production, much of the damage could be avoided. Although trade policy reforms toward further liberalization help alleviate climate change impacts, additional compensation mechanisms for associated environmental and development concerns have to be considered.
- ItemFossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century(Amsterdam : Elsevier, 2016) Kriegler, Elmar; Bauer, Nico; Popp, Alexander; Humpenöder, Florian; Leimbach, Marian; Strefler, Jessica; Baumstark, Lavinia; Bodirsky, Benjamin Leon; Hilaire, Jérôme; Klein, David; Mouratiadou, Ioanna; Weindl, Isabelle; Bertram, Christoph; Dietrich, Jan-Philipp; Luderer, Gunnar; Pehl, Michaja; Pietzcker, Robert; Piontek, Franziska; Lotze-Campen, Hermann; Biewald, Anne; Bonsch, Markus; Giannousakis, Anastasis; Kreidenweis, Ulrich; Müller, Christoph; Rolinski, Susanne; Schultes, Anselm; Schwanitz, Jana; Stevanovic, Miodrag; Calvin, Katherine; Emmerling, Johannes; Fujimori, Shinichiro; Edenhofer, OttmarThis paper presents a set of energy and resource intensive scenarios based on the concept of Shared Socio-Economic Pathways (SSPs). The scenario family is characterized by rapid and fossil-fueled development with high socio-economic challenges to mitigation and low socio-economic challenges to adaptation (SSP5). A special focus is placed on the SSP5 marker scenario developed by the REMIND-MAgPIE integrated assessment modeling framework. The SSP5 baseline scenarios exhibit very high levels of fossil fuel use, up to a doubling of global food demand, and up to a tripling of energy demand and greenhouse gas emissions over the course of the century, marking the upper end of the scenario literature in several dimensions. These scenarios are currently the only SSP scenarios that result in a radiative forcing pathway as high as the highest Representative Concentration Pathway (RCP8.5). This paper further investigates the direct impact of mitigation policies on the SSP5 energy, land and emissions dynamics confirming high socio-economic challenges to mitigation in SSP5. Nonetheless, mitigation policies reaching climate forcing levels as low as in the lowest Representative Concentration Pathway (RCP2.6) are accessible in SSP5. The SSP5 scenarios presented in this paper aim to provide useful reference points for future climate change, climate impact, adaption and mitigation analysis, and broader questions of sustainable development.
- ItemThe Global Gridded Crop Model Intercomparison phase 1 simulation dataset(London : Nature Publ. Group, 2019) Müller, Christoph; Elliott, Joshua; Kelly, David; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Hoek, Steven; Izaurralde, Roberto C.; Jones, Curtis D.; Khabarov, Nikolay; Lawrence, Peter; Liu, Wenfeng; Olin, Stefan; Pugh, Thomas A. M.; Reddy, Ashwan; Rosenzweig, Cynthia; Ruane, Alex C.; Sakurai, Gen; Schmid, Erwin; Skalsky, Rastislav; Wang, Xuhui; de Wit, Allard; Yang, HongThe Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s).
- ItemSimulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage)(Katlenburg-Lindau : Copernicus, 2019) Lutz, Femke; Herzfeld, Tobias; Heinke, Jens; Rolinski, Susanne; Schaphoff, Sibyll; von Bloh, Werner; Stoorvogel, Jetse J.; Müller, ChristophThe effects of tillage on soil properties, crop productivity, and global greenhouse gas emissions have been discussed in the last decades. Global ecosystem models have limited capacity to simulate the various effects of tillage. With respect to the decomposition of soil organic matter, they either assume a constant increase due to tillage or they ignore the effects of tillage. Hence, they do not allow for analysing the effects of tillage and cannot evaluate, for example, reduced tillage or no tillage (referred to here as “no-till”) practises as mitigation practices for climate change. In this paper, we describe the implementation of tillage-related practices in the global ecosystem model LPJmL. The extended model is evaluated against reported differences between tillage and no-till management on several soil properties. To this end, simulation results are compared with published meta-analyses on tillage effects. In general, the model is able to reproduce observed tillage effects on global, as well as regional, patterns of carbon and water fluxes. However, modelled N fluxes deviate from the literature values and need further study. The addition of the tillage module to LPJmL5 opens up opportunities to assess the impact of agricultural soil management practices under different scenarios with implications for agricultural productivity, carbon sequestration, greenhouse gas emissions, and other environmental indicators.
- ItemEnergy, land-use and greenhouse gas emissions trajectories under a green growth paradigm(Amsterdam : Elsevier, 2016) van Vuuren, Detlef P.; Stehfest, Elke; Gernaat, David E.H.J.; Doelman, Jonathan C.; van den Berg, Maarten; Harmsen, Mathijs; de Boer, Harmen Sytze; Bouwman, Lex F.; Daioglou, Vassilis; Edelenbosch, Oreane Y.; Girod, Bastien; Kram, Tom; Lassaletta, Luis; Lucas, Paul L.; van Meijl, Hans; Müller, Christoph; van Ruijven, Bas J.; van der Sluis, Sietske; Tabeau, AndrzejThis paper describes the possible developments in global energy use and production, land use, emissions and climate changes following the SSP1 storyline, a development consistent with the green growth (or sustainable development) paradigm (a more inclusive development respecting environmental boundaries). The results are based on the implementation using the IMAGE 3.0 integrated assessment model and are compared with a) other IMAGE implementations of the SSPs (SSP2 and SSP3) and b) the SSP1 implementation of other integrated assessment models. The results show that a combination of resource efficiency, preferences for sustainable production methods and investment in human development could lead to a strong transition towards a more renewable energy supply, less land use and lower anthropogenic greenhouse gas emissions in 2100 than in 2010, even in the absence of explicit climate policies. At the same time, climate policy would still be needed to reduce emissions further, in order to reduce the projected increase of global mean temperature from 3 °C (SSP1 reference scenario) to 2 or 1.5 °C (in line with current policy targets). The SSP1 storyline could be a basis for further discussions on how climate policy can be combined with achieving other societal goals.
- ItemImplementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)(Katlenburg-Lindau : Copernicus, 2018) von Bloh, Werner; Schaphoff, Sibyll; Müller, Christoph; Rolinski, Susanne; Waha, Katharina; Zaehle, SönkeThe well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past (1901-2009) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.