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While soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. 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 higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find long-term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.

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.

Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analysed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models’ sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

In this presentation we discuss methodologies, modeling tools and case studies on linking distributed disciplinary food, water, energy, environmental (FWEE) systems’ models into multi-systems multi-disciplinary integrated models for truly integrated analysis and managing of FWEE security NEXUS. Models’ linkage approaches enable to operationalize the concept of modeling and data platforms for distributed independent models’ “integration” and integrated FWEE security NEXUS management.Local, national and global FWEE security in the presence of climate change and risks of various kinds depend on the consistent coordination between and within the interdependent FWEE systems regarding sustainable resource supply and utilization. Detailed independent sectoral and regional systems’ models are often used to address these challenges. However, the independent approaches overlook the close linkages and feedbacks between and within the systems and, therefore, possible cross-sectoral implications. Critical cross-sectoral FWEE systemic supply-demand imbalances can trigger a disruption in a FWEE systems network. Disruptions and failures can be induced by human decisions in combination with natural shocks. For example, overuse of water in one system, e.g., agricultural, can lead to drying up of wells, decrease of reservoir water level, shortage of water in other systems, e.g., for colling power plants or hydropower production; an extra load in a power grid triggered by a power plant or a transmission line failure can cause cascading failures with catastrophic systemic outages; a hurricane in combination with inappropriate land use management can result in a catastrophic flood and human and economic losses, similar to the induced by Hurricane Katrina. These are examples of systemic risks motivating the development of proper models’ linkage approaches and integrated systems analysis.The linkage algorithms are becoming widely demanded in connection with the need for decentralized planning of distributed systems and technologies emerging in agriculture, water, energy, environmental systems, e.g., distributed precision agriculture technologies; hydro-economic models’ linkages; bio-physical crop modeling; distributed energy production.In this presentation we define and illustrate the two main linkage methodologies:linkage of distributed FWEE optimization models (land use, water, energy systems models); linkage of simulation and optimization models (crop-yield meta-model from EPIC and a land-use GLOBIOM model). Both methodologies are based on iterative sequential stochastic quasigradient (SQG) procedures of, in general, non-smooth nondifferentiable stochastic optimization, which converge to socially optimal solution maximizing an implicit nested nondifferentiable social welfare function. The linkage problem can be viewed as a general endogenous reinforced learning problem. The models act as “agents” that communicate with a “central hub” (a regulator) and take decisions in order to maximize the “cumulative reward". The procedure does not require models to exchange full information about their specifications. The distributed models can operate on distant computers of individual agents and “negotiate” with a central computer of a regional planner through the linkage procedure.

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated. Climate change affects agricultural productivity. New systematic global agricultural yield projections of the major crops were conducted using ensembles of the latest generation of crop and climate models. Substantial shifts in global crop productivity due to climate change will occur within the next 20 years—several decades sooner than previous projections—highlighting the need for targeted food system adaptation and risk management in the coming decades.

This document describes the methodology used to simulate spatiotemporal water regime data (i.e., water table depth and soil moisture – see dataset at https://doi.org/10.5281/zenodo.7355372) for a range of rewetting scenarios and land use classes in Indonesian peatlands. The large-scale modelling methodology is based on the Environmental Policy Integrated Climate (EPIC) model and gridded soil and meteorological information available from Indonesia. This document also describes findings from a regional assessment addressing rice productivity in potentially restored Indonesian peatlands carried out as a part of the RESTORE+ project. The impact assessment explores rice yields following assumptions on (a) nutrient management, namely the good agricultural practice and management that was back-calibrated against statistically reported regional yields, (b) separate rice growing seasons, and (c) different rewetting intensity scenarios. The analysis was carried out by the EPIC-IIASA modelling framework, addressing all peatland resources suitable for rice production, including mineral soils and shallow peat soils with a depth of less than 1 m., Funding acknowledgment: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a decision adopted by the German Bundestag.

Food stability – or the lack thereof has caused several issues on global food security in the past. Prime examples are the COVID-19 pandemic, the Ukraine-Russian war, and the 2008 African food crisis. Meanwhile, the occurrence of extreme meteorological events has been increasing rapidly (droughts, floods, …), resulting in substantial harvest losses putting food security under pressure with strong indications that these will become even more prevalent under climate change. Yet, the amount of studies assessing food stability or potential climate change effects is scarce – hindering purposeful policymaking. This makes a methodological framework to assess food stability urgent.

The document describes version 0.1 of the Spatial simulation infrastructure for the bio-physical modelling in Indonesia (https://doi.org/10.5281/zenodo.6997967) and the procedures of its creation. Primary purpose of the dataset was to provide a consistent interface for effective integrating of the data coming from variety of resources, as well as to provide the RESTORE+ project bio-physical and economic optimization modelling suite (EPIC-IIASA, G4M, WaNuLCAS, and GLOBIOM) with necessary inputs on terrain, soil, and sub-national administrative units., Funding acknowledgement: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) based on a decision adopted by the German Bundestag.

The document describes version 0.1 of the Harmonized peatland dataset for bio-physical modelling in Indonesia (https://doi.org/10.5281/zenodo.6998051) and the procedures of its creation. Primary purpose of the dataset was to provide bio-physical models (e.g. EPIC-IIASA and WaNuLCAS) with consistent input data on peat and mineral soil distribution in Indonesia and with the peat parameters necessary for proper representation of peat hydrology in simulations., Funding acknowledgement: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) based on a decision adopted by the German Bundestag.

A high-resolution nutrient emission inventory can provide reliable and accurate identification of priority control areas, which is crucial for efficient decisions on water quality restoration. However, the inventories widely used in large-scale modeling are usually based on provincial inputs, which induce the challenges of lacking localized parameters and missing localized characteristic when provincial scale inputs are converted to finer scales with the down-scale methods. Based on elaborate investigations and statistical data at the county scale with multi-scale data conversion, the China Emission Inventory of Nutrients (CEIN) was developed with a spatial resolution of a 0.1° grid and sub-basin scales. The Yangtze River Basin was used as a case study to illustrate the potential applications of CEIN. The emissions of total nitrogen (TN) and total phosphorus (TP) of Yangtze River Basin is 0.43 Mt and 0.04 Mt for point sources, 11.09 Mt and 4.64 Mt for diffuse sources in 2017. The hotspot analysis for 2606 sub-basins indicated that cropland is the key source of nutrient emissions, accounting for 58.88% and 79.15% of TN and TP, respectively. Industrial sewage and freshwater aquaculture accounted for 27.39% (TN) and 21.98% (TP) of the point sources, which is substantial due to their direct discharge into surface waters. The current results also reveal that, in contrast to CEIN, the previously used common emission factors based on GDP per capita produced considerable overestimations of 2.37 and 2.65 times the actual TN and TP emissions, respectively. Additional advantages of the CEIN have been demonstrated in identifying priority control areas more accurately with reduced bias and quantifying the effects of policies at much smaller scales. For example, the CEIN helps to distinguish hotspots, which was neglected when identifying sources at the level-III sub-basin scale, and indicates that the management of fractional areas (TN: 16.97%; TP: 13.44%) provides the highest nutrient emissions control (TN: 44.34%; TP: 48.65%) for the entire basin. The evaluation of China's toilet revolution policy demonstrates that achieving equitable access to safe sanitation has resulted in a reduction of 7240 t of TN and 833 t of TP, which is extremely critical for rural water quality and health.

Agricultural ecosystems provide essential services mainly through food, feed, fiber and consequently income but they also contribute cultural, supporting and regulating services. In turn, farming can adversely affect ecosystem services, especially those from natural ecosystems, if farming practices are unsustainable.Recently, a Sustainable Agriculture Matrix (SAM; https://doi.org/10.1016/j.oneear.2021.08.015) of indicators across environmental, economic, and social dimensions has been developed by an international research team to coherently quantify the sustainability of countries’ farming systems globally. The focus was on indicators that can be tracked over time and relate to performance to facilitate analyzes of synergies and trade-offs. At present, this indicator system is being co-evaluated with stakeholders in ten countries within an international consortium including Austria, to elicit stakeholders’ appraisal of the framework’s applicability in their specific geographical and socioeconomic context and eventually co-design a revised matrix based on stakeholders’ requirements.A first workshop has shown that most indicators from the environmental dimension are useful for stakeholders in the Austrian context, but some need further refinements. Biodiversity, for example, is only considered via land cover change whereas threats to (agro-)biodiversity in Austria and the EU foremost occur in-situ. The economic dimension is ranking second in its usefulness for Austrian stakeholders with few indicators such as food loss being of little relevance. The indicators presently included in the social dimension are least relevant as they cover aspects such as land rights, undernourishment, and rural poverty, which do not pose major issues in Austria and more broadly the EU.General concerns of stakeholders are the directionality of indicator ratings and their scope which is in part considered too narrow. E.g., high government expenditure for agriculture is considered positive in the matrix regardless of its purpose and may cause dependencies. Human nutrition is only included via undernourishment and soil nutrient status solely as surplus, whereas in both cases also the other extreme may be adverse. Accordingly, a bell-shaped indicator and rating would be favored in such cases. A general requirement was expressed for an additional context dimension. Governance arrangements and the overall socioeconomic situation are so far deliberately not included due to the focus on performance in the existing SAM. Yet, indicators describing such framework conditions can be essential to interpret synergies and trade-offs and the effectiveness of policy measures aiming at achieving SDGs. Beyond the evaluation of existing indicators, the stakeholder process yielded comprehensive suggestions for additional indicators, covering biodiversity, research and education, self-sufficiency, as well as various aspects of resilience and stability. Overall, the co-evaluation with stakeholders highlights that only few globally defined indicators are readily applicable in a regional context where consideration of local conditions and specifics is vital.The proposed revisions are now being matched with available data across geographic scales to revise the matrix and perform further analyses on trade-offs and synergies. This will also include further context information to facilitate the evaluation of policies, ultimately allowing for improved policy-making to attain agricultural sustainability. Results will be further co-evaluated iteratively with stakeholders to eventually produce a globally applicable indicator system.

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.

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., There are big uncertainties in the contribution of irrigation to crop yields. Here, the authors use Bayesian model averaging to combine statistical and process-based models and quantify the contribution of irrigation for wheat and maize yields, finding that irrigation alone cannot close yield gaps for a large fraction of global rainfed agriculture.

Water erosion removes soil nutrients, soil carbon, and in extreme cases can remove topsoil altogether. Previous studies have quantified crop yield losses from water erosion using a range of methods, applied mostly to single plots or fields, and cannot be systematically compared. This study assesses the worldwide impact of water erosion on maize and wheat production using a global gridded modeling approach for the first time. The EPIC crop model is used to simulate the global impact of water erosion on maize and wheat yields, from 1980 to 2010, for a range of field management strategies. Maize and wheat yields were reduced by a median of 3% annually in grid cells affected by water erosion, which represent approximately half of global maize and wheat cultivation areas. Water erosion reduces the annual global production of maize and wheat by 8.9 million tonnes and 5.6 million tonnes, with a value of $3.3bn globally. Nitrogen fertilizer necessary to reduce losses is valued at $0.9bn. As cropland most affected by water erosion is outside major maize and wheat production regions, the production losses account for less than 1% of the annual global production by volume. Countries with heavy rainfall, hilly agricultural regions and low fertilizer use are most vulnerable to water erosion. These characteristics are most common in South and Southeast Asia, sub-Saharan Africa and South and Central America. Notable uncertainties remain around large-scale water erosion estimates that will need to be addressed by better integration of models and observations. Yet, an integrated bio-physical modeling framework – considering plant growth, soil processes and input requirements – as presented herein can provide a link between robust water erosion estimates, economics and policy-making so far lacking in global agricultural assessments.

Potential climate-related impacts on future crop yield are a major societal concern first surveyed in a harmonized multi-model effort in 2014. We report here on new 21st-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean, and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5 to -6% (SSP126) and +1 to -24% (SSP585) — explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9 shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts — when the change signal emerges from the noise — consistently occurs earlier in the new projections for several main producing regions before 2040. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

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.

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

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.

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.

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations., Global gridded crop models are increasingly used to assess climate change impacts on food production. Here, the authors assess crop yield uncertainty associated with soil data input, reporting that soil type strongly influences yield estimates, and may either buffer or amplify climate-related impacts.

Agriculture is fundamental to all three pillars of sustainability, environment, society, and economy. However, the definition of sustainable agriculture and the capacities to measure it remain elusive. Independent and transparent measurements of national sustainability are needed to gauge progress, encourage accountability, and inform policy. Here, we developed a Sustainable Agriculture Matrix (SAM) to quantify national performance indicators in agriculture and to investigate the trade-offs and synergies based on historical data for most countries of the world. The results reveal priority areas for improvement by each country and show that the trade-offs and synergies among indicators often differ. Exceptions to common economic-versus-environmental trade-offs, for example, offer opportunities to learn from countries with synergistic pathways for multiple sustainability indicators. These SAM indicators will improve as data become more available, but this version offers a useful starting point for evaluating progress, identifying priorities for improvement, and informing national policies and actions toward sustainable agriculture., One Earth, 4 (9), ISSN:2590-3322

Agriculture has a massive and growing footprint. This study finds that optimizing fertilizer and major crops globally could reduce by 50% needed global cropland, allowing restored vegetation on spared land to sequester carbon. The global expansion of cropland exerts substantial pressure on natural ecosystems and is expected to continue with population growth and affluent demand. Yet earlier studies indicated that crop production could be more than doubled if attainable crop yields were achieved on present cropland. Here we show on the basis of crop modelling that closing current yield gaps by spatially optimizing fertilizer inputs and allocating 16 major crops across global cropland would allow reduction of the cropland area required to maintain present production volumes by nearly 50% of its current extent. Enforcing a scenario abandoning cropland in biodiversity hotspots and uniformly releasing 20% of cropland area for other landscape elements would still enable reducing the cropland requirement by almost 40%. As a co-benefit, greenhouse gas emissions from fertilizer and paddy rice, as well as irrigation water requirements, are likely to decrease with a reduced area of cultivated land, while global fertilizer input requirements remain unchanged. Spared cropland would provide space for substantial carbon sequestration in restored natural vegetation. Only targeted sparing of biodiversity hotspots supports species with small-range habitats, while biodiversity would hardly profit from a maximum land-sparing approach.