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C. Folberth
International Institute for Applied Systems Analysis (IIASA), Ecosystem Services and Management Program, Laxenburg, Austria

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Preprint content
Published: 17 June 2021
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Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the Midwest US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present day, pre-industrial +2 °C and 3 °C warming respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure, and construct analogues of these failure conditions in future climate settings. Unlike present-day conditions, future warming may increase the probability of crop failures resulting from univariate meteorological features, reducing the importance of compound failure drivers. Impact-analogues show a significant increase under global warming, with changes in the corresponding drivers. This has implications for risk assessment, as changing drivers of extreme impact events are highly relevant.

ACS Style

Henrique M. D. Goulart; Karin van der Wiel; Christian Folberth; Juraj Balkovic; Bart Van Den Hurk. Weather-induced crop failure events under climate change: a storyline approach. 2021, 2021, 1 -38.

AMA Style

Henrique M. D. Goulart, Karin van der Wiel, Christian Folberth, Juraj Balkovic, Bart Van Den Hurk. Weather-induced crop failure events under climate change: a storyline approach. . 2021; 2021 ():1-38.

Chicago/Turabian Style

Henrique M. D. Goulart; Karin van der Wiel; Christian Folberth; Juraj Balkovic; Bart Van Den Hurk. 2021. "Weather-induced crop failure events under climate change: a storyline approach." 2021, no. : 1-38.

Primary research article
Published: 17 May 2021 in Global Change Biology
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Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.

ACS Style

Florian Zabel; Christoph Müller; Joshua Elliott; Sara Minoli; Jonas Jägermeyr; Julia M. Schneider; James A. Franke; Elisabeth Moyer; Marie Dury; Louis Francois; Christian Folberth; Wenfeng Liu; Thomas A.M. Pugh; Stefan Olin; Sam S. Rabin; Wolfram Mauser; Tobias Hank; Alex C. Ruane; Senthold Asseng. Large potential for crop production adaptation depends on available future varieties. Global Change Biology 2021, 1 .

AMA Style

Florian Zabel, Christoph Müller, Joshua Elliott, Sara Minoli, Jonas Jägermeyr, Julia M. Schneider, James A. Franke, Elisabeth Moyer, Marie Dury, Louis Francois, Christian Folberth, Wenfeng Liu, Thomas A.M. Pugh, Stefan Olin, Sam S. Rabin, Wolfram Mauser, Tobias Hank, Alex C. Ruane, Senthold Asseng. Large potential for crop production adaptation depends on available future varieties. Global Change Biology. 2021; ():1.

Chicago/Turabian Style

Florian Zabel; Christoph Müller; Joshua Elliott; Sara Minoli; Jonas Jägermeyr; Julia M. Schneider; James A. Franke; Elisabeth Moyer; Marie Dury; Louis Francois; Christian Folberth; Wenfeng Liu; Thomas A.M. Pugh; Stefan Olin; Sam S. Rabin; Wolfram Mauser; Tobias Hank; Alex C. Ruane; Senthold Asseng. 2021. "Large potential for crop production adaptation depends on available future varieties." Global Change Biology , no. : 1.

Methods for assessment of models
Published: 23 March 2021 in Geoscientific Model Development
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How global gridded crop models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Intercomparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for simple mechanistic model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer–Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emulator against gridded aboveground maize biomass at the end of the growing season simulated by eight different GGCMs in a given year (2000). Our assumption is that the simple set of equations of SMM, after calibration, could reproduce the response of most GGCMs so that differences between GGCMs can be attributed to the parameters related to processes captured by the emulator. Despite huge differences between GGCMs, we show that if we fit both a parameter describing the thermal requirement for leaf emergence by adjusting its value to each grid-point in space, as done by GGCM modellers following the GGCMI protocol, and a GGCM-dependent globally uniform RUE, then the simple set of equations of the SMM emulator is sufficient to reproduce the spatial distribution of the original aboveground biomass simulated by most GGCMs. The grain filling is simulated in SMM by considering a fixed-in-time fraction of net primary productivity allocated to the grains (frac) once a threshold in leaves number (nthresh) is reached. Once calibrated, these two parameters allow for the capture of the relationship between potential yield and final aboveground biomass of each GGCM. It is particularly important as the divergence among GGCMs is larger for yield than for aboveground biomass. Thus, we showed that the divergence between GGCMs can be summarized by the differences in a few parameters. Our simple but mechanistic model could also be an interesting tool to test new developments in order to improve the simulation of potential yield at the global scale.

ACS Style

Bruno Ringeval; Christoph Müller; Thomas A. M. Pugh; Nathaniel D. Mueller; Philippe Ciais; Christian Folberth; Wenfeng Liu; Philippe Debaeke; Sylvain Pellerin. Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences. Geoscientific Model Development 2021, 14, 1639 -1656.

AMA Style

Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, Sylvain Pellerin. Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences. Geoscientific Model Development. 2021; 14 (3):1639-1656.

Chicago/Turabian Style

Bruno Ringeval; Christoph Müller; Thomas A. M. Pugh; Nathaniel D. Mueller; Philippe Ciais; Christian Folberth; Wenfeng Liu; Philippe Debaeke; Sylvain Pellerin. 2021. "Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences." Geoscientific Model Development 14, no. 3: 1639-1656.

Preprint content
Published: 04 March 2021
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Part of ESA’s Digital Twin Earth Precursor projects, our project focuses on supporting ESA in the definition of the concept of a Digital Twin Earth, and establishing a solid scientific and technical basis to realise this. The project, run by CGI and in close collaboration with Oxford University Innovation, Trillium & IIASA, has a focus on developing a Food Systems Digital Twin, taking on board interdisciplinary systems through the biosphere, atmosphere, and hydrosphere systems. These in turn would allow for new interdisciplinary insights for policies dealing with climate, food production and sustainability. The project is looking at a use case with the prominent use of AI processing, challenges of model integration, ingestion of socio-economic as well as physical measurements, end-to-end chain providing decision support outputs, all with innovation at each stage, and working closely with a series of stakeholders.

The purpose of our use case is to demonstrate the value of the Digital Twin Earth concept to the scientific community, by integrating the outputs of novel algorithms. We will be using selected machine learning extreme precipitation models feeding Global Gridded Crop Models, and after a regional downscaling exercise, the integration into cropland land use and pricing. By taking these steps, the benefits include improvement in routine monitoring with regular seasonal progress, short term policy development including responses to crop shortages due to extremes, and aiding in long term policy development to apply appropriate incentives. The purpose of the architecture and integration within the preparation of the demonstration is to support the use case and draw conclusions for the roadmap. These developments will be based on stakeholder consultations and the drawing together of differing model elements.

This Digital Twin Earth is an exciting project bringing together EO experts, Earth System Scientists, industry, AI experts, modellers, ICT experts and user community. It aims to establish the initial building blocks of an ambitious initiative, and, based on the prototyping activities, to develop a scientific and technology roadmap for the future, addressing current limitations. It ties in closely to both the European Space Agency’s and European Commission’s plan to create a series of interdisciplinary Digital Twin Earths with associated boundary conditions, in order to offer services to public sector users for developing, monitoring and assessing the impact of proposed policy and legislative measures concerning the environment and climate.

ACS Style

Chandra Taposeea-Fisher; Alan Whitelaw; Jon Earl; Christopher Cullingworth; Simon Jackman; Michael Obersteiner; Duncan Watson-Parris; Yarin Gal; Nikolay Khabarov; Christian Folberth; Fernando Orduña-Cabrera; James Parr; Leonard Silverberg. ESA Digital Twin Earth Precursor: Food Systems. 2021, 1 .

AMA Style

Chandra Taposeea-Fisher, Alan Whitelaw, Jon Earl, Christopher Cullingworth, Simon Jackman, Michael Obersteiner, Duncan Watson-Parris, Yarin Gal, Nikolay Khabarov, Christian Folberth, Fernando Orduña-Cabrera, James Parr, Leonard Silverberg. ESA Digital Twin Earth Precursor: Food Systems. . 2021; ():1.

Chicago/Turabian Style

Chandra Taposeea-Fisher; Alan Whitelaw; Jon Earl; Christopher Cullingworth; Simon Jackman; Michael Obersteiner; Duncan Watson-Parris; Yarin Gal; Nikolay Khabarov; Christian Folberth; Fernando Orduña-Cabrera; James Parr; Leonard Silverberg. 2021. "ESA Digital Twin Earth Precursor: Food Systems." , no. : 1.

Preprint content
Published: 03 March 2021
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Climate change poses increasing risks to global food security with more severe heat stress, water scarcity, and flooding. As one of the major adaptation measures, adjusting crop calendars could be a feasible and effective solution to avoid adverse effects on crop yield potentials in a changing climate by allowing crops to grow in more favorable weather conditions. Previous single-crop and single-objective studies on the optimization of crop planting dates lack comprehensive consideration of multi-crop rotation systems, especially rice-based cropping systems with short growing season intervals in Asian tropical monsoon regions. This study seeks to better understand potentials and limitations of adjusting crop calendars for climate change adaptation of double-rice and rice-wheat rotation systems, with a particular focus on the following questions: (1) Is it possible to avoid yield loss of rice and wheat through adjusting crop calendars in the study area? (2) How will fallow period between crop growing seasons change in the future? (3) What are relationships between crop yield improvement, irrigation water requirement, and heat stress mitigation in the study area?

To address these questions, we calibrated a spatial implementation of the Environmental Policy Integrated Climate (EPIC) agronomic model to estimate annual potential yields, irrigation water requirement, and heat stress days of irrigated double-rice and rice-wheat cropping systems in Bangladesh, India, and Myanmar (the BIM countries), and adjusted crop calendars (a) by single-objective optimization with maximum yield and (b) multi-objective optimization with least irrigation water requirement, minimum heat stress days, and highest potential yield under climate change.

Our results indicate that most yield loss in rice and wheat could be avoided through shifting planting dates while considering effects of elevated atmospheric CO2 concentration on biomass assimilation and transpiration. The model indicates that fallow periods between kharif-rice harvest dates and rabi-rice planting dates in double-rice systems are likely to become longer due to shorter growing season duration meanwhile fallow periods between kharif-rice harvest dates and rabi-wheat planting dates in rice-wheat systems are likely to become shorter due to advanced planting dates of rabi wheat, which implies that double-rice systems in the BIM countries will have more flexibility to cope with smaller time windows for crop growth and development in the future. Moreover, nearly half of the study area has the potential to increase yield by more than 10% through changing crop calendars compared to the basic scenario with non-adjusted crop calendars under RCP8.5 in 2080s, but 59% of these areas would face contradictions in obtaining crop yield improvement, saving irrigation water, and mitigating heat stress in the future. We found those areas suitable for adopting shifting planting dates as one of adaptation strategies from the perspective of climate conditions, such as Punjab state in India and Rangpur in Bangladesh, are also the areas with shortened growing season intervals, which requires great efforts to achieve the adaptation objectives under climate change. Thus, the trade-off among climate change adaptation, ecological sustainability, and farmer decision making should be carefully considered for local governments when promoting adjustment of crop calendars in rice-based multiple cropping systems.

ACS Style

Xiaobo Wang; Christian Folberth; Shaoqiang Wang; Rastislav Skalsky; Balkovic Juraj. Potentials and limitations of climate change adaptation with crop calendar optimization in rice-based multiple cropping systems. 2021, 1 .

AMA Style

Xiaobo Wang, Christian Folberth, Shaoqiang Wang, Rastislav Skalsky, Balkovic Juraj. Potentials and limitations of climate change adaptation with crop calendar optimization in rice-based multiple cropping systems. . 2021; ():1.

Chicago/Turabian Style

Xiaobo Wang; Christian Folberth; Shaoqiang Wang; Rastislav Skalsky; Balkovic Juraj. 2021. "Potentials and limitations of climate change adaptation with crop calendar optimization in rice-based multiple cropping systems." , no. : 1.

Journal article
Published: 23 February 2021 in Nature Communications
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Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.

ACS Style

Xuhui Wang; Christoph Müller; Joshua Elliot; Nathaniel D. Mueller; Philippe Ciais; Jonas Jägermeyr; James Gerber; Patrice Dumas; Chenzhi Wang; Hui Yang; Laurent Li; Delphine Deryng; Christian Folberth; Wenfeng Liu; David Makowski; Stefan Olin; Thomas A. M. Pugh; Ashwan Reddy; Erwin Schmid; Sujong Jeong; Feng Zhou; Shilong Piao. Global irrigation contribution to wheat and maize yield. Nature Communications 2021, 12, 1 -8.

AMA Style

Xuhui Wang, Christoph Müller, Joshua Elliot, Nathaniel D. Mueller, Philippe Ciais, Jonas Jägermeyr, James Gerber, Patrice Dumas, Chenzhi Wang, Hui Yang, Laurent Li, Delphine Deryng, Christian Folberth, Wenfeng Liu, David Makowski, Stefan Olin, Thomas A. M. Pugh, Ashwan Reddy, Erwin Schmid, Sujong Jeong, Feng Zhou, Shilong Piao. Global irrigation contribution to wheat and maize yield. Nature Communications. 2021; 12 (1):1-8.

Chicago/Turabian Style

Xuhui Wang; Christoph Müller; Joshua Elliot; Nathaniel D. Mueller; Philippe Ciais; Jonas Jägermeyr; James Gerber; Patrice Dumas; Chenzhi Wang; Hui Yang; Laurent Li; Delphine Deryng; Christian Folberth; Wenfeng Liu; David Makowski; Stefan Olin; Thomas A. M. Pugh; Ashwan Reddy; Erwin Schmid; Sujong Jeong; Feng Zhou; Shilong Piao. 2021. "Global irrigation contribution to wheat and maize yield." Nature Communications 12, no. 1: 1-8.

Accepted manuscript
Published: 06 January 2021 in Environmental Research Letters
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Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of 9 crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1 to -19%) than for CMIP5 (+5 to -13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

ACS Style

Christoph Müller; James Franke; Jonas Jägermeyr; Alex C Ruane; Joshua Elliott; Elisabeth Moyer; Jens Heinke; Pete D Falloon; Christian Folberth; Louis Francois; Tobias Hank; R César Izaurralde; Ingrid Jacquemin; Wenfeng Liu; Stefan Olin; Thomas A M Pugh; Karina E Williams; Florian Zabel. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environmental Research Letters 2021, 16, 034040 .

AMA Style

Christoph Müller, James Franke, Jonas Jägermeyr, Alex C Ruane, Joshua Elliott, Elisabeth Moyer, Jens Heinke, Pete D Falloon, Christian Folberth, Louis Francois, Tobias Hank, R César Izaurralde, Ingrid Jacquemin, Wenfeng Liu, Stefan Olin, Thomas A M Pugh, Karina E Williams, Florian Zabel. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environmental Research Letters. 2021; 16 (3):034040.

Chicago/Turabian Style

Christoph Müller; James Franke; Jonas Jägermeyr; Alex C Ruane; Joshua Elliott; Elisabeth Moyer; Jens Heinke; Pete D Falloon; Christian Folberth; Louis Francois; Tobias Hank; R César Izaurralde; Ingrid Jacquemin; Wenfeng Liu; Stefan Olin; Thomas A M Pugh; Karina E Williams; Florian Zabel. 2021. "Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios." Environmental Research Letters 16, no. 3: 034040.

Journal article
Published: 04 December 2020 in Modelling
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In recent years, the crop growth modeling community invested immense effort into high resolution global simulations estimating inter alia the impacts of projected climate change. The demand for computing resources in this context is high and expressed in processor core-years per one global simulation, implying several crops, management systems, and a several decades time span for a single climatic scenario. The anticipated need to model a richer set of alternative management options and crop varieties would increase the processing capacity requirements even more, raising the looming issue of computational efficiency. While several publications report on the successful application of the original field-scale crop growth model EPIC (Environmental Policy Integrated Climate) for running on modern supercomputers, the related performance improvement issues and, especially, associated trade-offs have only received, so far, limited coverage. This paper provides a comprehensive view on the principles of the EPIC setup for parallel computations and, for the first time, on those specific to heterogeneous compute clusters that are comprised of desktop computers utilizing their idle time to carry out massive computations. The suggested modification of the core EPIC model allows for a dramatic performance increase (order of magnitude) on a compute cluster that is powered by the open-source high-throughput computing software framework HTCondor.

ACS Style

Nikolay Khabarov; Alexey Smirnov; Juraj Balkovič; Rastislav Skalský; Christian Folberth; Marijn Van Der Velde; Michael Obersteiner. Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model. Modelling 2020, 1, 215 -224.

AMA Style

Nikolay Khabarov, Alexey Smirnov, Juraj Balkovič, Rastislav Skalský, Christian Folberth, Marijn Van Der Velde, Michael Obersteiner. Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model. Modelling. 2020; 1 (2):215-224.

Chicago/Turabian Style

Nikolay Khabarov; Alexey Smirnov; Juraj Balkovič; Rastislav Skalský; Christian Folberth; Marijn Van Der Velde; Michael Obersteiner. 2020. "Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model." Modelling 1, no. 2: 215-224.

Journal article
Published: 26 November 2020 in Earth's Future
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The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than five‐fold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia.

ACS Style

Stefan Lange; Jan Volkholz; Tobias Geiger; Fang Zhao; Iliusi Vega; Ted Veldkamp; Christopher P. O. Reyer; Lila Warszawski; Veronika Huber; Jonas Jägermeyr; Jacob Schewe; David N. Bresch; Matthias Büchner; Jinfeng Chang; Philippe Ciais; Marie Dury; Kerry Emanuel; Christian Folberth; Dieter Gerten; Simon N. Gosling; Manolis Grillakis; Naota Hanasaki; Alexandra‐Jane Henrot; Thomas Hickler; Yasushi Honda; Akihiko Ito; Nikolay Khabarov; Aristeidis Koutroulis; Wenfeng Liu; Christoph Müller; Kazuya Nishina; Sebastian Ostberg; Hannes Müller Schmied; Sonia I. Seneviratne; Tobias Stacke; Jörg Steinkamp; Wim Thiery; Yoshihide Wada; Sven Willner; Hong Yang; Minoru Yoshikawa; Chao Yue; Katja Frieler. Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales. Earth's Future 2020, 8, 1 .

AMA Style

Stefan Lange, Jan Volkholz, Tobias Geiger, Fang Zhao, Iliusi Vega, Ted Veldkamp, Christopher P. O. Reyer, Lila Warszawski, Veronika Huber, Jonas Jägermeyr, Jacob Schewe, David N. Bresch, Matthias Büchner, Jinfeng Chang, Philippe Ciais, Marie Dury, Kerry Emanuel, Christian Folberth, Dieter Gerten, Simon N. Gosling, Manolis Grillakis, Naota Hanasaki, Alexandra‐Jane Henrot, Thomas Hickler, Yasushi Honda, Akihiko Ito, Nikolay Khabarov, Aristeidis Koutroulis, Wenfeng Liu, Christoph Müller, Kazuya Nishina, Sebastian Ostberg, Hannes Müller Schmied, Sonia I. Seneviratne, Tobias Stacke, Jörg Steinkamp, Wim Thiery, Yoshihide Wada, Sven Willner, Hong Yang, Minoru Yoshikawa, Chao Yue, Katja Frieler. Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales. Earth's Future. 2020; 8 (12):1.

Chicago/Turabian Style

Stefan Lange; Jan Volkholz; Tobias Geiger; Fang Zhao; Iliusi Vega; Ted Veldkamp; Christopher P. O. Reyer; Lila Warszawski; Veronika Huber; Jonas Jägermeyr; Jacob Schewe; David N. Bresch; Matthias Büchner; Jinfeng Chang; Philippe Ciais; Marie Dury; Kerry Emanuel; Christian Folberth; Dieter Gerten; Simon N. Gosling; Manolis Grillakis; Naota Hanasaki; Alexandra‐Jane Henrot; Thomas Hickler; Yasushi Honda; Akihiko Ito; Nikolay Khabarov; Aristeidis Koutroulis; Wenfeng Liu; Christoph Müller; Kazuya Nishina; Sebastian Ostberg; Hannes Müller Schmied; Sonia I. Seneviratne; Tobias Stacke; Jörg Steinkamp; Wim Thiery; Yoshihide Wada; Sven Willner; Hong Yang; Minoru Yoshikawa; Chao Yue; Katja Frieler. 2020. "Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales." Earth's Future 8, no. 12: 1.

Technical note
Published: 10 November 2020 in Sustainability
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Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s built-in GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the application provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations, and commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the application’s characteristics. More enhancements are planned for inclusion in the future to increase the application’s function as a decision support tool.

ACS Style

Juan Laso Bayas; Andrea Gardeazabal; Mathias Karner; Christian Folberth; Luis Vargas; Rastislav Skalský; Juraj Balkovič; Anto Subash; Moemen Saad; Sylvain Delerce; Jesús Crespo Cuaresma; Jaroslava Hlouskova; Janet Molina-Maturano; Linda See; Steffen Fritz; Michael Obersteiner; Bram Govaerts. AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification. Sustainability 2020, 12, 9309 .

AMA Style

Juan Laso Bayas, Andrea Gardeazabal, Mathias Karner, Christian Folberth, Luis Vargas, Rastislav Skalský, Juraj Balkovič, Anto Subash, Moemen Saad, Sylvain Delerce, Jesús Crespo Cuaresma, Jaroslava Hlouskova, Janet Molina-Maturano, Linda See, Steffen Fritz, Michael Obersteiner, Bram Govaerts. AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification. Sustainability. 2020; 12 (22):9309.

Chicago/Turabian Style

Juan Laso Bayas; Andrea Gardeazabal; Mathias Karner; Christian Folberth; Luis Vargas; Rastislav Skalský; Juraj Balkovič; Anto Subash; Moemen Saad; Sylvain Delerce; Jesús Crespo Cuaresma; Jaroslava Hlouskova; Janet Molina-Maturano; Linda See; Steffen Fritz; Michael Obersteiner; Bram Govaerts. 2020. "AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification." Sustainability 12, no. 22: 9309.

Journal article
Published: 05 November 2020 in Biogeosciences
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Water erosion on arable land can reduce soil fertility and agricultural productivity. Despite the impact of water erosion on crops, it is typically neglected in global crop yield projections. Furthermore, previous efforts to quantify global water erosion have paid little attention to the effects of field management on the magnitude of water erosion. In this study, we analyse the robustness of simulated water erosion estimates in maize and wheat fields between the years 1980 and 2010 based on daily model outputs from a global gridded version of the Environmental Policy Integrated Climate (EPIC) crop model. By using the MUSS water erosion equation and country-specific and environmental indicators determining different intensities in tillage, residue handling and cover crops, we obtained the global median water erosion rates of 7 t ha−1 a−1 in maize fields and 5 t ha−1 a−1 in wheat fields. A comparison of our simulation results with field data demonstrates an overlap of simulated and measured water erosion values for the majority of global cropland. Slope inclination and daily precipitation are key factors in determining the agreement between simulated and measured erosion values and are the most critical input parameters controlling all water erosion equations included in EPIC. The many differences between field management methods worldwide, the varying water erosion estimates from different equations and the complex distribution of cropland in mountainous regions add uncertainty to the simulation results. To reduce the uncertainties in global water erosion estimates, it is necessary to gather more data on global farming techniques to reduce the uncertainty in global land-use maps and to collect more data on soil erosion rates representing the diversity of environmental conditions where crops are grown.

ACS Style

Tony W. Carr; Juraj Balkovič; Paul E. Dodds; Christian Folberth; Emil Fulajtar; Rastislav Skalsky. Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model. Biogeosciences 2020, 17, 5263 -5283.

AMA Style

Tony W. Carr, Juraj Balkovič, Paul E. Dodds, Christian Folberth, Emil Fulajtar, Rastislav Skalsky. Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model. Biogeosciences. 2020; 17 (21):5263-5283.

Chicago/Turabian Style

Tony W. Carr; Juraj Balkovič; Paul E. Dodds; Christian Folberth; Emil Fulajtar; Rastislav Skalsky. 2020. "Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model." Biogeosciences 17, no. 21: 5263-5283.

Model description paper
Published: 03 September 2020 in Geoscientific Model Development
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Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

ACS Style

James A. Franke; Christoph Müller; Joshua Elliott; Alex C. Ruane; Jonas Jägermeyr; Abigail Snyder; Marie Dury; Pete D. Falloon; Christian Folberth; Louis François; Tobias Hank; R. Cesar Izaurralde; Ingrid Jacquemin; Curtis Jones; Michelle Li; Wenfeng Liu; Stefan Olin; Meridel Phillips; Thomas A. M. Pugh; Ashwan Reddy; Karina Williams; Ziwei Wang; Florian Zabel; Elisabeth J. Moyer. The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0). Geoscientific Model Development 2020, 13, 3995 -4018.

AMA Style

James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, Elisabeth J. Moyer. The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0). Geoscientific Model Development. 2020; 13 (9):3995-4018.

Chicago/Turabian Style

James A. Franke; Christoph Müller; Joshua Elliott; Alex C. Ruane; Jonas Jägermeyr; Abigail Snyder; Marie Dury; Pete D. Falloon; Christian Folberth; Louis François; Tobias Hank; R. Cesar Izaurralde; Ingrid Jacquemin; Curtis Jones; Michelle Li; Wenfeng Liu; Stefan Olin; Meridel Phillips; Thomas A. M. Pugh; Ashwan Reddy; Karina Williams; Ziwei Wang; Florian Zabel; Elisabeth J. Moyer. 2020. "The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)." Geoscientific Model Development 13, no. 9: 3995-4018.

Journal article
Published: 17 August 2020 in Journal of Environmental Management
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Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1–0.5 Mg C ha−1 y−1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5–1.5 Mg C ha−1 y−1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.

ACS Style

Juraj Balkovič; Mikuláš Madaras; Rastislav Skalský; Christian Folberth; Michaela Smatanová; Erwin Schmid; Marijn van der Velde; Florian Kraxner; Michael Obersteiner. Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic. Journal of Environmental Management 2020, 274, 111206 .

AMA Style

Juraj Balkovič, Mikuláš Madaras, Rastislav Skalský, Christian Folberth, Michaela Smatanová, Erwin Schmid, Marijn van der Velde, Florian Kraxner, Michael Obersteiner. Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic. Journal of Environmental Management. 2020; 274 ():111206.

Chicago/Turabian Style

Juraj Balkovič; Mikuláš Madaras; Rastislav Skalský; Christian Folberth; Michaela Smatanová; Erwin Schmid; Marijn van der Velde; Florian Kraxner; Michael Obersteiner. 2020. "Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic." Journal of Environmental Management 274, no. : 111206.

Preprint content
Published: 24 June 2020
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How Global Gridded Crop Models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Inter-comparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for Simple Mechanistic Model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer-Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emulator against gridded aboveground maize biomass at the end of the growing season simulated by eight different GGCMs in a given year (2000). Our assumption is that the simple set of equations of SMM, after calibration, could reproduce the response of most GGCMs, so that differences between GGCMs can be attributed to the parameters related to processes captured by the emulator. Despite huge differences between GGCMs, we show that if we fit both a parameter describing the thermal requirement for leaf emergence by adjusting its value to each grid-point in space, as done by GGCM modellers following the GGCMI protocol, and a GGCM-dependent globally uniform RUE, then the simple set of equations of the SMM emulator is sufficient to reproduce the spatial distribution of the original aboveground biomass simulated by most GGCMs. The grain filling is simulated in SMM by considering a fixed in time fraction of net primary productivity allocated to the grain (frac) once a threshold in leaves number (nthresh) is reached. Once calibrated, these two parameters allow to capture the relationship between potential yield and final aboveground biomass of each GGCM. It is particularly important as the divergence among GGCMs is larger for yield than for aboveground biomass. Thus, we showed that the divergence between GGCMs can be summarized by the differences in few parameters. Our simple but mechanistic model could also be an interesting tool to test new developments in order to improve the simulation of potential yield at the global scale.

ACS Style

Bruno Ringeval; Christoph Müller; Thomas A.M. Pugh; Nathaniel D. Mueller; Philippe Ciais; Christian Folberth; Wenfeng Liu; Philippe Debaeke; Sylvain Pellerin. Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences. 2020, 2020, 1 -39.

AMA Style

Bruno Ringeval, Christoph Müller, Thomas A.M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, Sylvain Pellerin. Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences. . 2020; 2020 ():1-39.

Chicago/Turabian Style

Bruno Ringeval; Christoph Müller; Thomas A.M. Pugh; Nathaniel D. Mueller; Philippe Ciais; Christian Folberth; Wenfeng Liu; Philippe Debaeke; Sylvain Pellerin. 2020. "Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences." 2020, no. : 1-39.

Preprint content
Published: 24 June 2020
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Bruno Ringeval; Christoph Müller; Thomas A.M. Pugh; Nathaniel D. Mueller; Philippe Ciais; Christian Folberth; Wenfeng Liu; Philippe Debaeke; Sylvain Pellerin. Supplementary material to "Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences". 2020, 1 .

AMA Style

Bruno Ringeval, Christoph Müller, Thomas A.M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, Sylvain Pellerin. Supplementary material to "Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences". . 2020; ():1.

Chicago/Turabian Style

Bruno Ringeval; Christoph Müller; Thomas A.M. Pugh; Nathaniel D. Mueller; Philippe Ciais; Christian Folberth; Wenfeng Liu; Philippe Debaeke; Sylvain Pellerin. 2020. "Supplementary material to "Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences"." , no. : 1.

Journal article
Published: 15 June 2020 in Environmental Research Communications
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Rafaela Flach; Marianela Fader; Christian Folberth; Rastislav Skalský; Kerstin Jantke. The effects of cropping intensity and cropland expansion of Brazilian soybean production on green water flows. Environmental Research Communications 2020, 2, 071001 .

AMA Style

Rafaela Flach, Marianela Fader, Christian Folberth, Rastislav Skalský, Kerstin Jantke. The effects of cropping intensity and cropland expansion of Brazilian soybean production on green water flows. Environmental Research Communications. 2020; 2 (7):071001.

Chicago/Turabian Style

Rafaela Flach; Marianela Fader; Christian Folberth; Rastislav Skalský; Kerstin Jantke. 2020. "The effects of cropping intensity and cropland expansion of Brazilian soybean production on green water flows." Environmental Research Communications 2, no. 7: 071001.

Perspective
Published: 19 May 2020 in Nature Food
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Future technologies and systemic innovation are critical for the profound transformation the food system needs. These innovations range from food production, land use and emissions, all the way to improved diets and waste management. Here, we identify these technologies, assess their readiness and propose eight action points that could accelerate the transition towards a more sustainable food system. We argue that the speed of innovation could be significantly increased with the appropriate incentives, regulations and social licence. These, in turn, require constructive stakeholder dialogue and clear transition pathways.

ACS Style

Mario Herrero; Philip K. Thornton; Daniel Mason-D’Croz; Jeda Palmer; Tim G. Benton; Benjamin L. Bodirsky; Jessica R. Bogard; Andrew Hall; Bernice Lee; Karine Nyborg; Prajal Pradhan; Graham Bonnett; Brett A. Bryan; Bruce M. Campbell; Svend Christensen; Michael Clark; Mathew T. Cook; Imke J. M. De Boer; Chris Downs; Kanar Dizyee; Christian Folberth; Cecile M. Godde; James S. Gerber; Michael Grundy; Petr Havlik; Andrew Jarvis; Richard King; Ana Maria Loboguerrero; Mauricio A. Lopes; Cathrine McIntyre; Rosamond Naylor; Javier Navarro; Michael Obersteiner; Alejandro Parodi; Mark B. Peoples; Ilje Pikaar; Alexander Popp; Johan Rockström; Michael Robertson; Pete Smith; Elke Stehfest; Steve M. Swain; Hugo Valin; Mark Van Wijk; Hannah H. E. Van Zanten; Sonja Vermeulen; Joost Vervoort; Paul C. West. Innovation can accelerate the transition towards a sustainable food system. Nature Food 2020, 1, 266 -272.

AMA Style

Mario Herrero, Philip K. Thornton, Daniel Mason-D’Croz, Jeda Palmer, Tim G. Benton, Benjamin L. Bodirsky, Jessica R. Bogard, Andrew Hall, Bernice Lee, Karine Nyborg, Prajal Pradhan, Graham Bonnett, Brett A. Bryan, Bruce M. Campbell, Svend Christensen, Michael Clark, Mathew T. Cook, Imke J. M. De Boer, Chris Downs, Kanar Dizyee, Christian Folberth, Cecile M. Godde, James S. Gerber, Michael Grundy, Petr Havlik, Andrew Jarvis, Richard King, Ana Maria Loboguerrero, Mauricio A. Lopes, Cathrine McIntyre, Rosamond Naylor, Javier Navarro, Michael Obersteiner, Alejandro Parodi, Mark B. Peoples, Ilje Pikaar, Alexander Popp, Johan Rockström, Michael Robertson, Pete Smith, Elke Stehfest, Steve M. Swain, Hugo Valin, Mark Van Wijk, Hannah H. E. Van Zanten, Sonja Vermeulen, Joost Vervoort, Paul C. West. Innovation can accelerate the transition towards a sustainable food system. Nature Food. 2020; 1 (5):266-272.

Chicago/Turabian Style

Mario Herrero; Philip K. Thornton; Daniel Mason-D’Croz; Jeda Palmer; Tim G. Benton; Benjamin L. Bodirsky; Jessica R. Bogard; Andrew Hall; Bernice Lee; Karine Nyborg; Prajal Pradhan; Graham Bonnett; Brett A. Bryan; Bruce M. Campbell; Svend Christensen; Michael Clark; Mathew T. Cook; Imke J. M. De Boer; Chris Downs; Kanar Dizyee; Christian Folberth; Cecile M. Godde; James S. Gerber; Michael Grundy; Petr Havlik; Andrew Jarvis; Richard King; Ana Maria Loboguerrero; Mauricio A. Lopes; Cathrine McIntyre; Rosamond Naylor; Javier Navarro; Michael Obersteiner; Alejandro Parodi; Mark B. Peoples; Ilje Pikaar; Alexander Popp; Johan Rockström; Michael Robertson; Pete Smith; Elke Stehfest; Steve M. Swain; Hugo Valin; Mark Van Wijk; Hannah H. E. Van Zanten; Sonja Vermeulen; Joost Vervoort; Paul C. West. 2020. "Innovation can accelerate the transition towards a sustainable food system." Nature Food 1, no. 5: 266-272.

Model experiment description paper
Published: 18 May 2020 in Geoscientific Model Development
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Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

ACS Style

James A. Franke; Christoph Müller; Joshua Elliott; Alex C. Ruane; Jonas Jägermeyr; Juraj Balkovic; Philippe Ciais; Marie Dury; Pete D. Falloon; Christian Folberth; Louis François; Tobias Hank; Munir Hoffmann; R. Cesar Izaurralde; Ingrid Jacquemin; Curtis Jones; Nikolay Khabarov; Marian Koch; Michelle Li; Wenfeng Liu; Stefan Olin; Meridel Phillips; Thomas A. M. Pugh; Ashwan Reddy; Xuhui Wang; Karina Williams; Florian Zabel; Elisabeth J. Moyer. The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0). Geoscientific Model Development 2020, 13, 2315 -2336.

AMA Style

James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Juraj Balkovic, Philippe Ciais, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, Munir Hoffmann, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Nikolay Khabarov, Marian Koch, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Xuhui Wang, Karina Williams, Florian Zabel, Elisabeth J. Moyer. The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0). Geoscientific Model Development. 2020; 13 (5):2315-2336.

Chicago/Turabian Style

James A. Franke; Christoph Müller; Joshua Elliott; Alex C. Ruane; Jonas Jägermeyr; Juraj Balkovic; Philippe Ciais; Marie Dury; Pete D. Falloon; Christian Folberth; Louis François; Tobias Hank; Munir Hoffmann; R. Cesar Izaurralde; Ingrid Jacquemin; Curtis Jones; Nikolay Khabarov; Marian Koch; Michelle Li; Wenfeng Liu; Stefan Olin; Meridel Phillips; Thomas A. M. Pugh; Ashwan Reddy; Xuhui Wang; Karina Williams; Florian Zabel; Elisabeth J. Moyer. 2020. "The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)." Geoscientific Model Development 13, no. 5: 2315-2336.

Preprint content
Published: 21 April 2020
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Tony W. Carr; Juraj Balkovič; Paul E. Dodds; Christian Folberth; Emil Fulajtar; Rastislav Skalsky. Supplementary material to "Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global-gridded crop model". 2020, 1 .

AMA Style

Tony W. Carr, Juraj Balkovič, Paul E. Dodds, Christian Folberth, Emil Fulajtar, Rastislav Skalsky. Supplementary material to "Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global-gridded crop model". . 2020; ():1.

Chicago/Turabian Style

Tony W. Carr; Juraj Balkovič; Paul E. Dodds; Christian Folberth; Emil Fulajtar; Rastislav Skalsky. 2020. "Supplementary material to "Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global-gridded crop model"." , no. : 1.

Preprint content
Published: 21 April 2020
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Water erosion in agricultural fields can reduce soil fertility and agricultural productivity. Despite the impact of water erosion on crops, it is typically neglected in global crop yield projections. Furthermore, previous efforts to quantify global water erosion have paid little attention to the effects of field management on the magnitude of water erosion. In this study, we analyse the robustness of simulated water erosion estimates in wheat and maize fields between the years 1980 to 2010 based on daily model outputs from a global gridded version of the Environmental Policy Integrated Climate (EPIC) crop model. Using the MUSS water erosion equation and country-specific and environmental indicators determining different intensities in tillage, residue handling and cover crops, we simulate global annual median and average water erosion rates of 6 t ha−1 and 19 t ha−1 and an annual soil removal of 7 Gt in global wheat and maize fields. A comparison of our simulation results with field data demonstrates an overlap of simulated and measured water erosion values for the majority of global cropland. Slope inclination and daily precipitation are key factors in determining the agreement between simulated and measured erosion values and are the most critical input parameters controlling all water erosion equations included in EPIC. The many differences between field management methods worldwide and the varying water erosion estimates from different equations add uncertainty to the simulation results. To reduce the uncertainties addressed here and to improve global water erosion estimates generally, more data on global field management and more field data from study sites representing the diversity of environmental conditions where crops are grown are necessary.

ACS Style

Tony W. Carr; Juraj Balkovič; Paul E. Dodds; Christian Folberth; Emil Fulajtar; Rastislav Skalsky. Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global-gridded crop model. 2020, 2020, 1 -24.

AMA Style

Tony W. Carr, Juraj Balkovič, Paul E. Dodds, Christian Folberth, Emil Fulajtar, Rastislav Skalsky. Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global-gridded crop model. . 2020; 2020 ():1-24.

Chicago/Turabian Style

Tony W. Carr; Juraj Balkovič; Paul E. Dodds; Christian Folberth; Emil Fulajtar; Rastislav Skalsky. 2020. "Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global-gridded crop model." 2020, no. : 1-24.