Search

Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

Carbon sequestration on agricultural land, albeit long-time neglected, offers substantial mitigation potential. Here we project, using an economic land-use model, that these options offer cumulative mitigation potentials comparable to afforestation by 2050 at 160 USD 2022  tCO 2 equivalent (tCO 2 e -1 ), with most of it located in the Global South. Carbon sequestration on agricultural land could provide producers around the world with additional revenues of up to 375 billion USD 2022 at 160 USD 2022  tCO 2 e -1 and allow achievement of net-zero emissions in the agriculture, forestry and other land-use sectors by 2050 already at economic costs of around 80-120 USD 2022  tCO 2 e -1 . This would, in turn, decrease economy-wide mitigation costs and increase gross domestic product (+0.6%) by the mid-century in 1.5 °C no-overshoot climate stabilization scenarios compared with mitigation scenarios that do not consider these options. Unlocking these potentials requires the deployment of highly efficient institutions and monitoring systems over the next 5 years across the whole world, including sub-Saharan Africa, where the largest mitigation potential exists., (© 2024. The Author(s).)

Intensive agriculture with high reliance on pesticides and fertilizers constitutes a major strategy for 'feeding the world'. However, such conventional intensification is linked to diminishing returns and can result in 'intensification traps'-production declines triggered by the negative feedback of biodiversity loss at high input levels. Here we developed a novel framework that accounts for biodiversity feedback on crop yields to evaluate the risk and magnitude of intensification traps. Simulations grounded in systematic literature reviews showed that intensification traps emerge in most landscape types, but to a lesser extent in major cereal production systems. Furthermore, small reductions in maximal production (5-10%) could be frequently transmitted into substantial biodiversity gains, resulting in small-loss large-gain trade-offs prevailing across landscape types. However, sensitivity analyses revealed a strong context dependence of trap emergence, inducing substantial uncertainty in the identification of optimal management at the field scale. Hence, we recommend the development of case-specific safety margins for intensification preventing double losses in biodiversity and food security associated with intensification traps., (© 2024. The Author(s).)

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 5600 unique simulations of crop growth from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM) covering 86,000 simulation units across Europe, we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-explicit meta models are highly accurate (R 2  = 0.97) 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 performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models largely capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of 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 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

This paper studies the joint dynamics of foreign direct investments (FDI) and output growth in European regions by using spatially augmented systems of equations modeling framework that incorporates third-region and spillover effects. The joint framework is used to study the dynamic impacts of regional human capital endowments, which demonstrates the importance of explicitly accounting for an endogenous relationship. The relationship is highlighted in a stylized projection exercise, where the long-run impacts are pronounced in Eastern Europe and capital cities. Overall, ignoring the relationship of regional economic performance and FDI distorts the implied transmission mechanism, which is of utmost importance for policy makers., (© 2023 The Authors. Papers in Regional Science published by John Wiley & Sons Ltd on behalf of Regional Science Association International.)

Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Spillover effects and factors that differ across locations and over time play important roles in these processes. They are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we model connectivity between regions and consider heterogeneous effects to gain more accurate quantitative insights into the inherent complexity of deforestation. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006-2017 considering spatial spillovers and varying impacts over time and space. Spillovers between municipalities that emanate from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in recent years. This suggests a moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that the neglect of the spatial dimension and the assumption of homogeneous impacts lead to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design., (© 2021. The Author(s).)

This paper presents an empirical study of spatial origin and destination effects of European regional FDI dyads. Recent regional studies primarily focus on locational determinants, but ignore bilateral origin- and intervening factors, as well as associated spatial dependence. This paper fills this gap by using observations on interregional FDI flows within a spatially augmented Poisson interaction model. We explicitly distinguish FDI activities between three different stages of the value chain. Our results provide important insights on drivers of regional FDI activities, both from origin and destination perspectives. We moreover show that spatial dependence plays a key role in both dimensions., (© The Author(s) 2021.)

Background: The quantity, quality, and type (e.g., animal and vegetable) of human food have been correlated with human health, although with some contradictory or neutral results. We aimed to shed light on this association by using the integrated data at country level., Methods: We correlated elemental (nitrogen (N) and phosphorus (P)) compositions and stoichiometries (N:P ratios), molecular (proteins) and energetic traits (kilocalories) of food of animal (terrestrial or aquatic) and vegetable origin, and alcoholic beverages with cancer prevalence and mortality and life expectancy (LE) at birth at the country level. We used the official databases of United Nations (UN), Food and Agriculture Organization of the United Nations (FAO), Organization for Economic Co-operation and Development (OECD), World Bank, World Health Organization (WHO), U.S. Department of Agriculture, U.S. Department of Health, and Eurobarometer, while also considering other possibly involved variables such as income, mean age, or human development index of each country., Results: The per capita intakes of N, P, protein, and total intake from terrestrial animals, and especially alcohol were significantly and positively associated with prevalence and mortality from total, colon, lung, breast, and prostate cancers. In contrast, high per capita intakes of vegetable N, P, N:P, protein, and total plant intake exhibited negative relationships with cancer prevalence and mortality. However, a high LE at birth, especially in underdeveloped countries was more strongly correlated with a higher intake of food, independent of its animal or vegetable origin, than with other variables, such as higher income or the human development index., Conclusions: Our analyses, thus, yielded four generally consistent conclusions. First, the excessive intake of terrestrial animal food, especially the levels of protein, N, and P, is associated with higher prevalence of cancer, whereas equivalent intake from vegetables is associated with lower prevalence. Second, no consistent relationship was found for food N:P ratio and cancer prevalence. Third, the consumption of alcoholic beverages correlates with prevalence and mortality by malignant neoplasms. Fourth, in underdeveloped countries, reducing famine has a greater positive impact on health and LE than a healthier diet.

Increased efforts are required to prevent further losses to terrestrial biodiversity and the ecosystem services that it  provides 1,2 . Ambitious targets have been proposed, such as reversing the declining trends in biodiversity 3 ; however, just feeding the growing human population will make this a challenge 4 . Here we use an ensemble of land-use and biodiversity models to assess whether-and how-humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity 5 . We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by the mid-twenty-first century on average across models (confidence interval, 2042-2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34-50%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats-such as climate change-must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy.

International trade enables us to exploit regional differences in climate change impacts and is increasingly regarded as a potential adaptation mechanism. Here, we focus on hunger reduction through international trade under alternative trade scenarios for a wide range of climate futures. Under the current level of trade integration, climate change would lead to up to 55 million people who are undernourished in 2050. Without adaptation through trade, the impacts of global climate change would increase to 73 million people who are undernourished (+33%). Reduction in tariffs as well as institutional and infrastructural barriers would decrease the negative impact to 20 million (-64%) people. We assess the adaptation effect of trade and climate-induced specialization patterns. The adaptation effect is strongest for hunger-affected import-dependent regions. However, in hunger-affected export-oriented regions, partial trade integration can lead to increased exports at the expense of domestic food availability. Although trade integration is a key component of adaptation, it needs sensitive implementation to benefit all regions., Competing Interests: Competing interests The authors declare no competing interests.

Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socio-economic scenarios (SSP1-3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by the model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritize future protection of biodiverse and carbon-rich areas. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

In this paper we use spatial econometric specifications to model daily infection rates of COVID-19 across countries. Using recent advances in Bayesian spatial econometric techniques, we particularly focus on the time-dependent importance of alternative spatial linkage structures such as the number of flight connections, relationships in international trade, and common borders. The flexible model setup allows to study the intensity and type of spatial spillover structures over time. Our results show notable spatial spillover mechanisms in the early stages of the virus with international flight linkages as the main transmission channel. In later stages, our model shows a sharp drop in the intensity spatial spillovers due to national travel bans, indicating that travel restrictions led to a reduction of cross-country spillovers., (© The Author(s) 2020.)

The Indus River Basin faces severe water quality degradation because of nutrient enrichment from human activities. Excessive nutrients in tributaries are transported to the river mouth, causing coastal eutrophication. This situation may worsen in the future because of population growth, economic development, and climate change. This study aims at a better understanding of the magnitude and sources of current (2010) and future (2050) river export of total dissolved nitrogen (TDN) by the Indus River at the sub-basin scale. To do this, we implemented the MARINA 1.0 model (Model to Assess River Inputs of Nutrients to seAs). The model inputs for human activities (e.g., agriculture, land use) were mainly from the GLOBIOM (Global Biosphere Management Model) and EPIC (Environmental Policy Integrated Model) models. Model inputs for hydrology were from the Community WATer Model (CWATM). For 2050, three scenarios combining Shared Socio-economic Pathways (SSPs 1, 2 and 3) and Representative Concentration Pathways (RCPs 2.6 and 6.0) were selected. A novelty of this study is the sub-basin analysis of future N export by the Indus River for SSPs and RCPs. Result shows that river export of TDN by the Indus River will increase by a factor of 1.6-2 between 2010 and 2050 under the three scenarios. >90% of the dissolved N exported by the Indus River is from midstream sub-basins. Human waste is expected to be the major source, and contributes by 66-70% to river export of TDN in 2050 depending on the scenarios. Another important source is agriculture, which contributes by 21-29% to dissolved inorganic N export in 2050. Thus a combined reduction in both diffuse and point sources in the midstream sub-basins can be effective to reduce coastal water pollution by nutrients at the river mouth of Indus., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This article investigates projected changes in temperature and water cycle extremes at 1.5°C of global warming, and highlights the role of land processes and land-use changes (LUCs) for these projections. We provide new comparisons of changes in climate at 1.5°C versus 2°C based on empirical sampling analyses of transient simulations versus simulations from the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) multi-model experiment. The two approaches yield similar overall results regarding changes in climate extremes on land, and reveal a substantial difference in the occurrence of regional extremes at 1.5°C versus 2°C. Land processes mediated through soil moisture feedbacks and land-use forcing play a major role for projected changes in extremes at 1.5°C in most mid-latitude regions, including densely populated areas in North America, Europe and Asia. This has important implications for low-emissions scenarios derived from integrated assessment models (IAMs), which include major LUCs in ambitious mitigation pathways (e.g. associated with increased bioenergy use), but are also shown to differ in the simulated LUC patterns. Biogeophysical effects from LUCs are not considered in the development of IAM scenarios, but play an important role for projected regional changes in climate extremes, and are thus of high relevance for sustainable development pathways.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'., (© 2018 The Authors.)

We analyzed mean height of men born in the 1960s, 1970s and 1980s in 80 countries. Both height and the change in height during the last decades were correlated with N and P intake, as well as the N:P intake ratio. Rich countries had higher per capita N and P intake than poor countries (on average 19.5 ± 0.3 versus 9.66 ± 0.18 kg N y -1 and 2.17 ± 0.04 versus 1.35 ± 0.02 kg P y -1 ), and also larger increases in per capita N intake (12.1 ± 2.0% vs. 7.0 ± 2.1%) and P intake (7.6 ± 1.0% vs 6.01 ± 0.7%), during the period 1961-2009. The increasing gap in height trends between rich and poor countries is associated with an increasing gap in nutrition, so a more varied diet with higher N, P, and N:P intake is a key factor to improve food intake quality in poor countries and thus shorten the gap with rich countries. More N and P are needed with the consequent requirements for a better management of the socioeconomic and environmental associated problems.

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover., (© 2016 John Wiley & Sons Ltd.)

Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity., (© 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)