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Increasing crop productivity while keeping detrimental side-effects on the environment low is a major challenge for global agriculture. Cover crops (CCs), mostly grown during the fallow period and incorporated in soils, are expected to improve soil fertility and crop yields while reducing chemical fertilizer use, with climate change mitigation co-benefits. However, quantifying these ecosystem services across global agricultural lands remains uncertain. In this study we investigate how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects yields and cropland carbon and nitrogen balances using the dynamic global vegetation model LPJ-GUESS. Model performance is evaluated against observations from field trials worldwide as well as other published model-based estimates. LPJ-GUESS generally captures the observed enhanced soil carbon, reduced N leaching, and yield changes caused by CCs. We found that the combination of N-fixing CCs with no-tillage management could potentially increase soil carbon storage by 7% (+0.32 Pg C yr-1 in global croplands) while reducing N leaching by 41% (-7.3 Tg N yr-1) compared with bare fallows after 36 years of simulation. This integrated practice is accompanied by a 2% increase in total crop production (+37 million tonnes yr-1 including wheat, maize, rice, and soybean) in the last decade of the simulation. Legume cover cropping is found to contribute more to increasing the subsequent crop yields than non-legumes. The effects of CCs on crop productivity are highly dependent on the main food crop types, chemical fertilizer use, and management duration, with smallest yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long-term environmental sustainability without compromising crop production in global croplands.

Higher food prices arising from restrictions on exports from Russia or Ukraine have been exacerbated by energy price rises, leading to higher costs for agricultural inputs such as fertiliser. Using a scenario approach with a global land use and food system model (LandSyMM), we quantify the potential outcomes of increasing agricultural input costs and the curtailment of exports from Russia and Ukraine on human health and the environment. We show that, combined, agricultural inputs costs and food export restrictions could increase food costs by 60-100% from 2021 levels, potentially leading to undernourishment of 60-110 million people and annual additional deaths of 400 thousand to 1 million people if the associated dietary patterns are maintained. In additional to lower yields, reduced land use intensification arising from higher input costs would lead to agricultural land expansion of 130-349 Mha by 2030, with associated carbon and biodiversity loss. The impact of agricultural input costs on food prices is larger than that from curtailment of Russian and Ukrainian exports. Restoring food trade from Ukraine and Russia alone is therefore insufficient to avoid food insecurity problem from higher energy and fertiliser prices. While the Black Sea Grain Initiative has been a welcome development and has largely allowed Ukraine food exports to be re-established, the immediacy of these issues appears to have diverted attention away from the impacts of fertiliser prices. While fertiliser prices at the start of 2023 have come down from the peaks of mid-2022, they remain at historically high levels. Our results suggest the costs and lower crop yields achieved through reduced fertiliser use will drive high food price inflation in 2023 and beyond. More needs to be done to break the link between higher food prices and harm to human health and the environment. This study demonstrates how modelling can be used to explore the complexity and interlinked nature of the globalised food system and to quantifying the trade-offs and synergies for health and environmental outcomes of difference scenarios.

Fire-enabled dynamic global vegetation models (DGVMs) can be used to study how fire activity responds to its main drivers, including climate/weather, vegetation and human activities, at coarse spatial scales. Such models can also be used to examine the effects of fire on vegetation, and, when embedded in Earth system models, investigate the feedback of fire on the climate system. Thus they are valuable tools for studying wildfires. Accordingly, the Fire Model Intercomparison Project (FireMIP) was established to evaluate and utilise these models using consistent protocols.Here we present the second round of FireMIP simulations to focus historic wildfire drivers (1901 to present). A six-member ensemble of simulations from fire-enabled DGVMs was compared to remotely-sensed burnt area observations and to the previous round of historical FireMIP simulations. We found that the model skill when simulating spatial patterns of burnt area shows modest improvements compared to the previous FireMIP round, and that the simulations mostly reproduce the decreasing trend in global burnt area found over the last two decades. However, whilst the broad global patterns are reasonable, there are considerable discrepancies with regards to regional agreement and timing of burnt area. Furthermore, the models show diverging trends in the pre-satellite era.To investigate further and inform future model development, we explored the residuals between simulated burnt area from the FireMIP models and remotely-sensed burnt area as a function of climate, vegetation, anthropogenic and topographic variables using generalised additive models (GAMs). We found some common responses across the models, with many over-predicting fire activity in arid/low productivity areas and all models under-predicting at low road density. However, with respect to other variables, such as wind speed and cropland fraction, the models residuals showed divergent responses. It is anticipated that these results should aid further development of global fire models in terms of driving variables, process representations and model structure.

Higher food prices arising from restrictions on exports from Russia or Ukraine have been exacerbated by energy price rises, leading to higher costs for agricultural inputs such as fertilizer. Here, using a scenario modelling approach, we quantify the potential outcomes of increasing agricultural input costs and the curtailment of exports from Russia and Ukraine on human health and the environment. We show that, combined, agricultural inputs costs and food export restrictions could increase food costs by 60–100% in 2023 from 2021 levels, potentially leading to undernourishment of 61–107 million people in 2023 and annual additional deaths of 416,000 to 1.01 million people if the associated dietary patterns are maintained. Furthermore, reduced land use intensification arising from higher input costs would lead to agricultural land expansion and associated carbon and biodiversity loss. The impact of agricultural input costs on food prices is larger than that from curtailment of Russian and Ukrainian exports. Restoring food trade from Ukraine and Russia alone is therefore insufficient to avoid food insecurity problem from higher energy and fertilizer prices. We contend that the immediacy of the food export problems associated with the war diverted attention away from the principal causes of current global food insecurity.

Global food prices are rising rapidly in response to a dramatic increase in global energy prices and the Ukraine-Russia war, causing severe impacts on the world’s poorest people. The FAO Food Price Index increased by 23% from May 2021 to May 2022 and the Cereals Price Index increased by 30%. Sanctions or blockades that restrict exports from Russia or Ukraine – two of the world’s most important food-exporting countries – are exacerbated by energy price rises which further increase food prices through higher costs for agricultural inputs, such as fertiliser. We consider the role of increasing agricultural input costs and the curtailment of exports from Russia and Ukraine to quantify the potential outcomes on human health and the environment. We show that the combination of agricultural inputs costs and food export restrictions could increase food costs by 60-100% in 2023 from 2021 levels, leading to the deaths of 400 thousand to 1 million additional people in 2023 and undernourishment of 60-110 million people. Furthermore, a reduction in land use intensification, arising from higher input costs, leads to agricultural land expansion, especially in the tropics, with potential consequences for deforestation and carbon and biodiversity loss. We find that the impact of agricultural input costs on food prices is larger than the effect of the curtailment of exports from Russia and Ukraine. Restoring food trade from Ukraine and Russia alone would be insufficient to substantially reduce the magnitude of the current food insecurity problem. We contend that the immediacy of the food export problems associated with the Ukraine-Russia war has diverted attention away from the principal causes of current global food insecurity, which may hinder the quest to find solutions.

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon cycles. Yet our understanding of ecosystem fires and their carbon cycle consequences is still limited, largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire, vegetation, climate, and anthropogenic factors. Here, using data from satellite-derived fire observations and ecosystem model simulations, we performed a comprehensive investigation of the spatial and temporal dynamics of China's ecosystem fire disturbances and their carbon emissions over the past two decades (1997–2016). Satellite-derived results showed that on average about 3.47 - 4.53 × 104 km2 of the land was burned annually during the past two decades, among which annual burned forest area was about 0.81 - 1.25 × 104 km2, accounting for 0.33-0.51% of the forest area in China. Biomass burning emitted about 23.02 TgC per year. Compared to satellite products, simulations from the Energy Exascale Earth System Land Model (ELM) strongly overestimated China's burned area and fire-induced carbon emissions. Annual burned area and fire-induced carbon emissions were high for boreal forest in Northeast China's Daxing'anling region and subtropical dry forest in South Yunnan, as revealed by both the satellite product and the model simulations. Our results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China's ecosystem fire disturbances. Our findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon consequences. Further studies are required to improve the methods of observing and modelling China's ecosystem fire disturbances, which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

Climate change, growing populations and economic shocks are adding pressure on the global agricultural system’s ability to feed the world. In addition to curbing the emissions from fossil fuel use, land-based actions are seen as essential in the effort to mitigate climate change, but these tend to reduce areas available for food production, thereby further increasing this pressure. The actors of the food system have the capacity to respond and adapt to changes in climate, and thereby reduce the negative consequences, while potentially creating additional challenges, including further greenhouse gas emissions. The food system actors may respond autonomously based on economic drivers and other factors to adapt to climate change, whereas policy measures are usually needed for mitigation actions to be implemented. Much research and policy focus has been given to land-based climate change mitigation, but far less emphasis has to date been given to the understanding of adaptation, or the interaction between adaptation and mitigation in the land use and food system. Here, we present an approach to better understand and plan these interactions through modelling. Climate change adaptation and mitigation strategies and the impacts on the global food system and socio-economic development can be simulated over long-term predictions, thanks to the new combination of multiple models into the Land System Modular Model (LandSyMM). LandSyMM takes into account the impacts in changes in climate (i.e. temperature, precipitation, atmospheric greenhouse gas concentrations) and land management on crop yields with its implications for land allocation, food security and trade. This new coupled model integrates, over fine spatial scale, the interactions between commodities consumption, land use management, vegetation and climate into a worldwide dynamic economic system. This study offers an outline description of the LandSyMM as well as the perspectives of uses for climate adaptation assessment.

Biological nitrogen fixation (BNF) from grain legumes is significant importance in global agricultural ecosystems. Crops with BNF capability are expected to support the need to increase food production while reducing nitrogen (N) fertilizer input for agriculture sustainability, but quantification of N fixing rates and BNF crop yields remains inadequate. Here we incorporate two legume crops (soybean and faba bean) with BNF into a dynamic vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator). The performance of this new implementation is evaluated against observations from a range of water and N management trials. LPJ-GUESS generally captures the observed response to these management practices on legume biomass production, soil N uptake and N fixation, despite some deviations from observations in some cases. Globally, the simulated BNF is dominated by soil moisture and temperature, as well as amounts of N fertilizer addition. Annual inputs through BNF are modelled to be 11.6±2.2 Tg N for soybean and 5.6±1.0 Tg N for all pulses, with a total fixation of 17.2±2.9 Tg N yr-1 for all grain legumes during the period 1981–2016 on global scale. Our estimates show a good agreement with some previous statistical estimates but are relatively high compared to some estimates for pulses. This study highlights the importance of accounting for legume N fixation process when modelling C-N interactions in agricultural ecosystems, particularly when it comes to account for the combined effects of climate and land-use change on global terrestrial N cycle.

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.

Global biodiversity is rapidly declining and goals to halt biodiversity loss, such as the Aichi Biodiversity Targets, have not been achieved. To avoid further biodiversity loss and aid recovery some have argued for the protection of 50% or 30% of the Earth’s terrestrial land surface. We use a state of the art global land use model, LandSyMM, to assess global and regional human health and food security outcomes when potential area based strategies for conserving biodiversity are modelled. We find diet and weight changes in strictly enforced 30% and 50% land protection scenarios, cause an additional 5.1 million deaths in 2060. At a regional level, South Asia and Sub-Saharan Africa experience high levels of underweight-related mortality, causing an additional 200,000 deaths in these regions alone. Developed regions in contrast are less affected by protection measures. Our results highlight that radical measures to protect areas of biodiversity value may jeopardise food security and human health in the most vulnerable regions of the world.

Forest fires in some regions have intensified over recent decades due to climate change. This trend threatens ecosystems (habitat and biodiversity loss), human health (particulate-matter pollution, smoke), property (burned urban areas, burned forestry yields, monetary loss), and potentially climate mitigation goals (rising carbon dioxide levels, possibly decreased land carbon sink).Here, we investigate whether forest management can reduce future impacts of forest fire and help to control fire regimes in the future. We are using the process-based dynamic global vegetation model LPJ-GUESS with the fire module SIMFIRE-BLAZE to explore this question. The analyzed treatments compare a non-managed stand with stands receiving thinning, prescribed burning, or both. We focus on two regions: The Iberian Peninsula (due to its long history of burning) and Eastern Europe (which may become more fire-prone in the future). Results are compared between CMIP6 scenarios of low-intensity vs. high-intensity climate change (RCPs 2.6 and 8.5, respectively).The results show that prescribed fire raises the amount of burned area but possibly not the property risk because fire line intensities are mitigated; thinning can reduce the amount of prescribed fire required. Thinning reduces fire emissions whereas prescribed burning is the other way around, which could contribute to health and climate risks caused by particulate-matter-pollution. Managements do not seem to have effects on the carbon balance according to end of the century carbon pools, which implies that they do not actively help achieve climate mitigation goals.

Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.

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.

Fires are an important component in Earth system models (ESMs), they impact vegetation carbon storage, vegetation distribution, atmospheric composition and cloud formation. The representation of fires in ESMs contributing to CMIP phase 5 was still very simplified. Several Earth system models updated their representation of fires in the meantime. Using the latest simulations of CMIP6 we investigate how fire regimes change in the future for different scenarios and how land use, climate and atmospheric CO2 concentration contribute to the fire regimes changes. We quantify changes in fire danger, burned area and carbon emissions on an annual and seasonal basis. Factorial model simulations allow to quantify the influence of land use, climate and atmospheric CO2 on fire regimes.We complement the information on fire regime change supplied by ESMs that include a fire module with a statistical modelling approach for burned area. This will use information from simulated changes in climate, vegetation and socioeconomic changes (population density and land use) provided for a set of different future scenarios. This allows the integration of information provided by global satellite products on burned area with the process-based simulations of climate and vegetation changes and information from socioeconomic scenarios.

Significance Impacts of global warming on agricultural productivity have been evaluated extensively. The implications of sudden cooling for global crop growth, however, are as yet little understood. While crop failures after historic volcanic eruptions are documented, a nuclear conflict can cause even more severe and longer-lasting climate anomalies. India and Pakistan are contributing to a de facto Asian arms race and the political instability in South Asia increasingly imposes a global threat. Based on comprehensive climate and crop model ensemble simulations, we provide critical quantitative information on how sudden cooling and perturbations of precipitation and solar radiation could disrupt food production and trade worldwide for about a decade—more than the impact from anthropogenic climate change by late century., A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using

Fire regimes in savannas and forests are changing over much of the world. Anticipating the impact of these changes requires understanding how plants are adapted to fire. In this study, we test whether fire imposes a broad selective force on a key fire-tolerance trait, bark thickness, across 572 tree species distributed worldwide. We show that investment in thick bark is a pervasive adaptation in frequently burned areas across savannas and forests in both temperate and tropical regions where surface fires occur. Geographic variability in bark thickness is largely explained by annual burned area and precipitation seasonality. Combining environmental and species distribution data allowed us to assess vulnerability to future climate and fire conditions: tropical rainforests are especially vulnerable, whereas seasonal forests and savannas are more robust. The strong link between fire and bark thickness provides an avenue for assessing the vulnerability of tree communities to fire and demands inclusion in global models.

The terrestrial biosphere is currently a strong carbon (C) sink but may switch to a source in the 21st century as climate-driven losses exceed CO2-driven C gains, thereby accelerating global warming. Although it has long been recognized that tropical climate plays a critical role in regulating interannual climate variability, the causal link between changes in temperature and precipitation and terrestrial processes remains uncertain. Here, we combine atmospheric mass balance, remote sensing-modeled datasets of vegetation C uptake, and climate datasets to characterize the temporal variability of the terrestrial C sink and determine the dominant climate drivers of this variability. We show that the interannual variability of global land C sink has grown by 50–100% over the past 50 y. We further find that interannual land C sink variability is most strongly linked to tropical nighttime warming, likely through respiration. This apparent sensitivity of respiration to nighttime temperatures, which are projected to increase faster than global average temperatures, suggests that C stored in tropical forests may be vulnerable to future warming.

The global extent of agriculture demands a thorough understanding of the ways it impacts the Earth system through both the modification of the physical and biological characteristics of the landscape as well as through emissions of greenhouse gases and aerosols. People use fire to manage cropland and pasture in many parts of the world, impacting both the timing and amount of fire. So far, much previous research into how these land uses affect fire regimes has either focused on individual small regions or global patterns at annual or decadal scales. Moreover, because pasture is not mapped globally at high resolution, the amount of fire associated with pasture has never been quantified as it has for cropland. The work presented here resolves the effects of agriculture – including pasture – on fire on a monthly basis for regions across the world, using globally gridded data on fire activity and land use at 0.25° resolution. The first global estimate of pasture-associated fire reveals that it accounts for over 40 % of annual burned area. Cropland, generally assumed to reduce fire occurrence, is shown to enhance or suppress fire at different times of year within individual regions. These results bridge important gaps in the understanding of how agriculture and associated management practices influence vegetation fire, enabling the next generation of vegetation and Earth system models more realistically incorporate these anthropogenic effects.

The distributions of tropical ecosystems are rapidly being altered by climate change and anthropogenic activities. One possible trend—the loss of tropical forests and replacement by savannas—could result in significant shifts in ecosystem services and biodiversity loss. However, the influence and the relative importance of environmental factors in regulating the distribution of tropical forest and savanna biomes are still poorly understood, which makes it difficult to predict future tropical forest and savanna distributions in the context of climate change. Here we use boosted regression trees to quantitatively evaluate the importance of environmental predictors—mainly climatic, edaphic, and fire factors—for the tropical forest-savanna distribution at a mesoscale across the tropics (between 15°N and 35°S). Our results demonstrate that climate alone can explain most of the distribution of tropical forest and savanna at the scale considered; dry season average precipitation is the single most important determinant across tropical Asia-Australia, Africa, and South America. Given the strong tendency of increased seasonality and decreased dry season precipitation predicted by global climate models, we estimate that about 28% of what is now tropical forest would likely be lost to savanna by the late 21st century under the future scenario considered. This study highlights the importance of climate seasonality and interannual variability in predicting the distribution of tropical forest and savanna, supporting the climate as the primary driver in the savanna biogeography.

Diets lower in meat could reduce agricultural expansion and intensification thereby reducing biodiversity impacts. However, land use requirements, associated with alternate diets, in biodiverse regions across different taxa are not fully understood. We use a spatially explicit global food and land system model to address this gap. We quantify land-use change in locations important for biodiversity across taxa and find diets low in animal products reduce agricultural expansion and intensity in regions with high biodiversity. Reducing ruminant meat consumption alone however was not sufficient to reduce fertiliser and irrigation application in biodiverse locations. The results differed according to taxa, emphasising that land-use change effects on biodiversity will be taxon specific. The links shown between global meat consumption and agricultural expansion and intensification in the biodiverse regions of the world indicates the potential to help safeguard biodiverse natural ecosystems through dietary change.

The timing and length of burning seasons in different parts of the world depend on climate, land cover characteristics, and human activities. In this study, global fire data from satellite-based instruments are used in conjunction with global gridded distributions of agricultural land cover types (defined as the sum of cropland and pasture area) to separate the seasonality of agricultural burning practices from that of non-agricultural fire. The results presented in this study show that agricultural and non-agricultural land experience broadly different fire seasonality patterns that are not always linked to climate conditions. We highlight these differences on a regional basis, examining variations in both agricultural land cover and associated cultural practices to help explain our results. While we discuss two land cover categories, the methods can be generalized to derive seasonality for any number of land uses or cover types. This will be useful as global fire models evolve to be fully interactive with land use and land cover change in the next generation of Earth system models.