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Soybeans are globally used as the main source of protein for livestock. However, most soybean production is concentrated in regions in The United States of America, Brazil and Argentina, rendering the supply chain vulnerable to regional disruptions. In 2012, simultaneous soybean losses in these three countries led to shortages in global supplies and to record prices. The losses were linked to anomalous weather conditions in all three countries. In this experiment, we investigate how climate change may affect future events with similar or larger impacts than the one from 2012 for each country individually and simultaneously. For that, we develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or larger impacts than 2012 under different future climatic forcing conditions. We also evaluate the events with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. Results indicate that long-term trends of mean climate increase the occurrence and magnitude of 2012 analogue crop yield losses. Conversely, 2012 analogue crop yield losses that are caused by changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas.

The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 35 years of multifactorial, spatially-explicit simulation data from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM), we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soils across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-determined meta-models are highly accurate (R² = .97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the meta-model’s performance compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-model accurately predicts broad SOC dynamics while it often underestimates the measured SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications.While more accurate input data, calibration, and validation will l be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.

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.

Meteorological conditions can affect crop development and yield in multiple and non-linear ways. Many studies have investigated the influence of climate change on crops by simulating crop responses to the most likely mean climatic projections in the future. However, this approach can potentially overlook changes in extreme-impact events, highly relevant for society, due to their low probability of occurrence and to potential different behaviour with respect to the mean conditions. One way of focusing on extreme-impact events is through the use of physical climate storylines. Storylines enable the construction of self-sustained and physically-plausible chain of events that recreate historical events from source to impact. In addition, storylines allow the exploration of future analogues of the historical events under different circumstances to account for externalities, such as climate change. In this experiment, we use physical climate storylines to reconstruct a historical extreme-impact event and to explore potential analogues of the same event under climate change influence. We develop two types of analogues, event-analogues and impact-analogues, and compare how the future manifestation of the historical event depends on the analogue definition. We use soybean production in the US as an example, with the year of 2012 being the historical extreme event. Based on a random forest model, we link the historical event to meteorological variables to identify the conditions associated with the failure event. To quantify the frequency of occurrence of the different analogues under climate change, we apply the trained random forest model to large ensembles of climate projections from the EC-Earth global climate model. We find that the 2012 failure event is linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. The analogues of the historical event greatly diverge: while event-analogues of the 2012 season are rare and not expected to increase, impact-analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. The results highlight the importance of considering the impact perspective when investigating future extreme crop yields.

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.

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.

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 II. The GGCMI Phase II 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 without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that 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.

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 II 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 II experimental protocol and its simulation data archive. Twelve 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 II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates 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.

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.

In order to achieve climate change mitigation, long-term decisions are required that must be reconciled with other societal goals that draw on the same resources. For example, ensuring food security for a growing population may require an expansion of crop land, thereby reducing natural carbon sinks or the area available for bio-energy production. Here, we show that current impact-model uncertainties pose an important challenge to long-term mitigation planning and propose a new risk-assessment and decision framework that accounts for competing interests. Based on cross-sectorally consistent simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) we discuss potential gains and limitations of additional irrigation and trade-offs of the expansion of agricultural land as two possible response measures to climate change and growing food demand. We describe an illustrative example in which the combination of both measures may close the supply demand gap while leading to a loss of approximately half of all natural carbon sinks. We highlight current limitations of available simulations and additional steps required for a comprehensive risk assessment.