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- ItemClassifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change(Amsterdam [u.a.] : Elsevier, 2017) Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco; Asseng, Senthold; Baranowski, Piotr; Basso, Bruno; Bodin, Per; Buis, Samuel; Cammarano, Davide; Deligios, Paola; Destain, Marie-France; Dumont, Benjamin; Ewert, Frank; Ferrise, Roberto; François, Louis; Gaiser, Thomas; Hlavinka, Petr; Jacquemin, Ingrid; Kersebaum, Kurt Christian; Kollas, Chris; Krzyszczak, Jaromir; Lorite, Ignacio J.; Minet, Julien; Minguez, M. Ines; Montesino, Manuel; Moriondo, Marco; Müller, Christoph; Nendel, Claas; Öztürk, Isik; Perego, Alessia; Rodríguez, Alfredo; Ruane, Alex C.; Ruget, Françoise; Sanna, Mattia; Semenov, Mikhail A.; Slawinski, Cezary; Stratonovitch, Pierre; Supit, Iwan; Waha, Katharina; Wang, Enli; Wu, Lianhai; Zhao, Zhigan; Rötter, Reimund P.Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
- ItemImproving the use of crop models for risk assessment and climate change adaptation(Amsterdam : Elsevier, 2017) Challinor, Andrew J.; Müller, Christoph; Asseng, Senthold; Deva, Chetan; Nicklin, Kathryn Jane; Wallach, Daniel; Vanuytrecht, Eline; Whitfield, Stephen; Ramirez-Villegas, Julian; Koehler, Ann-KristinCrop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.
- ItemGlobal crop yields can be lifted by timely adaptation of growing periods to climate change([London] : Nature Publishing Group UK, 2022) Minoli, Sara; Jägermeyr, Jonas; Asseng, Senthold; Urfels, Anton; Müller, ChristophAdaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers’ decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes >30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes <30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments.
- ItemAn AgMIP framework for improved agricultural representation in integrated assessment models(Bristol : IOP Publishing, 2017) Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.
- ItemFAIRagro: Ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) für Forschungsdaten in der Agrosystemforschung : Herausforderungen und Lösungsansätze für den Aufbau einer FAIRen Forschungsdateninfrastruktur(Berlin, Heidelber, New York : Springer, 2023) Specka, Xenia; Martini, Daniel; Weiland, Claus; Arend, Daniel; Asseng, Senthold; Boehm, Franziska; Feike, Til; Fluck, Juliane; Gackstetter, David; Gonzales-Mellado, Aida; Hartmann, Thomas; Haunert, Jan-Henrik; Hoedt, Florian; Hoffmann, Carsten; König, Patrick; Lange, Matthias; Lesch, Stephan; Lindstädt, Birte; Lischeid, Gunnar; Möller, Markus; Rascher, Uwe; Reif, Jochen Christoph; Schmalzl, Markus; Senft, Matthias; Stahl, Ulrike; Svoboda, Nikolai; Usadel, Björn; Webber, Heidi; Ewert, FrankFAIRagro ist ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) in Deutschland um Forschungsdaten der Agrosystemforschung FAIR – d. h. auffindbar (F), zugänglich (A), interoperabel (I) und für andere Forschende domänenübergreifend nachnutzbar (R) zu machen. In der deutschen Forschungslandschaft rund um nachhaltige Agrosysteme werden heterogene Forschungsdaten erhoben und nur zum Teil in existierenden Forschungsdatenrepositorien veröffentlicht. Das Spektrum der Datenformate erstreckt sich beispielsweise von Laborergebnissen, über Satellitenbilder bis hin zu qualitativen Interviews mit Landwirt:innen. Um diese Daten zukünftig für Forschende verschiedener Disziplinen besser auffindbar und nachnutzbar zu machen, wird FAIRagro eine Forschungsdateninfrastruktur (FDI) für die Agrosystemforschung einrichten, in der disziplinäre Dateninfrastrukturen miteinander verknüpft werden. Spezifische Herausforderungen im Forschungsdatenmanagement (FDM) fachlicher Disziplinen wie Pflanzenzüchtung, integrierter Pflanzenschutz oder Agrarrobotik werden als Use Cases in FAIRagro adressiert und für diese Lösungen entwickelt. Darüber hinaus wird FAIRagro ein Netzwerk aus direkten Ansprechpersonen für Fragen zum Forschungsdatenmanagement in der Agrosystem-Community bereitstellen. In Übereinstimmung mit den Zielsetzungen der NFDI und der European Open Science Cloud ist FAIRagro aktiv an der konzeptionellen Implementierung eines interoperablen Datenraums beteiligt.
- ItemA regional nuclear conflict would compromise global food security(Washington, DC : National Acad. of Sciences, 2020) Jägermeyr, Jonas; Robock, Alan; Elliott, Joshua; Müller, Christoph; Xia, Lili; Khabarov, Nikolay; Folberth, Christian; Schmid, Erwin; Liu, Wenfeng; Zabel, Florian; Rabin, Sam S.; Puma, Michael J.; Heslin, Alison; Franke, James; Foster, Ian; Asseng, Senthold; Bardeen, Charles G.; Toon, Owen B.; Rosenzweig, CynthiaA limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.