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The stability and resilience of the Earth system and human well-being are inseparably linked 1-3 , yet their interdependencies are generally under-recognized; consequently, they are often treated independently 4,5 . Here, we use modelling and literature assessment to quantify safe and just Earth system boundaries (ESBs) for climate, the biosphere, water and nutrient cycles, and aerosols at global and subglobal scales. We propose ESBs for maintaining the resilience and stability of the Earth system (safe ESBs) and minimizing exposure to significant harm to humans from Earth system change (a necessary but not sufficient condition for justice) 4 . The stricter of the safe or just boundaries sets the integrated safe and just ESB. Our findings show that justice considerations constrain the integrated ESBs more than safety considerations for climate and atmospheric aerosol loading. Seven of eight globally quantified safe and just ESBs and at least two regional safe and just ESBs in over half of global land area are already exceeded. We propose that our assessment provides a quantitative foundation for safeguarding the global commons for all people now and into the future., (© 2023. The Author(s).)

Climate-smart agriculture (CSA) and sustainable intensification (SI) are widely claimed to be high-potential solutions to address the interlinked challenges of food security and climate change. Operationalization of these promising concepts is still lacking and potential trade-offs are often not considered in the current continental- to global-scale assessments. Here we discuss the effect of spatial variability in the context of the implementation of climate-smart practices on two central indicators, namely yield development and carbon sequestration, considering biophysical limitations of suggested benefits, socioeconomic and institutional barriers to adoption, and feedback mechanisms across scales. We substantiate our arguments by an illustrative analysis using the example of a hypothetical large-scale adoption of conservation agriculture (CA) in sub-Saharan Africa. We argue that, up to now, large-scale assessments widely neglect the spatially variable effects of climate-smart practices, leading to inflated statements about co-benefits of agricultural production and climate change mitigation potentials. There is an urgent need to account for spatial variability in assessments of climate-smart practices and target those locations where synergies in land functions can be maximized in order to meet the global targets. Therefore, we call for more attention toward spatial planning and landscape optimization approaches in the operationalization of CSA and SI to navigate potential trade-offs., (© 2020 John Wiley & Sons Ltd.)

Including the parameterization of land management practices into Earth System Models has been shown to influence the simulation of regional climates, particularly for temperature extremes. However, recent model development has focused on implementing irrigation where other land management practices such as conservation agriculture (CA) has been limited due to the lack of global spatially explicit datasets describing where this form of management is practiced. Here, we implement a representation of CA into the Community Earth System Model and show that the quality of simulated surface energy fluxes improves when including more information on how agricultural land is managed. We also compare the climate response at the subgrid scale where CA is applied. We find that CA generally contributes to local cooling (~1°C) of hot temperature extremes in mid-latitude regions where it is practiced, while over tropical locations CA contributes to local warming (~1°C) due to changes in evapotranspiration dominating the effects of enhanced surface albedo. In particular, changes in the partitioning of evapotranspiration between soil evaporation and transpiration are critical for the sign of the temperature change: a cooling occurs only when the soil moisture retention and associated enhanced transpiration is sufficient to offset the warming from reduced soil evaporation. Finally, we examine the climate change mitigation potential of CA by comparing a simulation with present-day CA extent to a simulation where CA is expanded to all suitable crop areas. Here, our results indicate that while the local temperature response to CA is considerable cooling (>2°C), the grid-scale changes in climate are counteractive due to negative atmospheric feedbacks. Overall, our results underline that CA has a nonnegligible impact on the local climate and that it should therefore be considered in future climate projections., (© 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)

Conservation agriculture (CA) is widely promoted as a sustainable agricultural management strategy with the potential to alleviate some of the adverse effects of modern, industrial agriculture such as large-scale soil erosion, nutrient leaching and overexploitation of water resources. Moreover, agricultural land managed under CA is proposed to contribute to climate change mitigation and adaptation through reduced emission of greenhouse gases, increased solar radiation reflection, and the sustainable use of soil and water resources. Due to the lack of official reporting schemes, the amount of agricultural land managed under CA systems is uncertain and spatially explicit information about the distribution of CA required for various modeling studies is missing. Here, we present an approach to downscale present-day national-level estimates of CA to a 5 arcminute regular grid, based on multicriteria analysis. We provide a best estimate of CA distribution and an uncertainty range in the form of a low and high estimate of CA distribution, reflecting the inconsistency in CA definitions. We also design two scenarios of the potential future development of CA combining present-day data and an assessment of the potential for implementation using biophysical and socioeconomic factors. By our estimates, 122-215 Mha or 9%-15% of global arable land is currently managed under CA systems. The lower end of the range represents CA as an integrated system of permanent no-tillage, crop residue management and crop rotations, while the high estimate includes a wider range of areas primarily devoted to temporary no-tillage or reduced tillage operations. Our scenario analysis suggests a future potential of CA in the range of 533-1130 Mha (38%-81% of global arable land). Our estimates can be used in various ecosystem modeling applications and are expected to help identifying more realistic climate mitigation and adaptation potentials of agricultural practices., (© 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)

Understanding the susceptibility of highly mobile taxa such as migratory birds to global change requires information on geographic patterns of occurrence across the annual cycle. Neotropical migrants that breed in North America and winter in Central America occur in high concentrations on their non-breeding grounds where they spend the majority of the year and where habitat loss has been associated with population declines. Here, we use eBird data to model weekly patterns of abundance and occurrence for 21 forest passerine species that winter in Central America. We estimate species' distributional dynamics across the annual cycle, which we use to determine how species are currently associated with public protected areas and projected changes in climate and land-use. The effects of global change on the non-breeding grounds is characterized by decreasing precipitation, especially during the summer, and the conversion of forest to cropland, grassland, or peri-urban. The effects of global change on the breeding grounds are characterized by increasing winter precipitation, higher temperatures, and the conversion of forest to peri-urban. During spring and autumn migration, species are projected to encounter higher temperatures, forests that have been converted to peri-urban, and increased precipitation during spring migration. Based on current distributional dynamics, susceptibility to global change is characterized by the loss of forested habitats on the non-breeding grounds, warming temperatures during migration and on the breeding grounds, and declining summer rainfall on the non-breeding grounds. Public protected areas with low and medium protection status are more prevalent on the non-breeding grounds, suggesting that management opportunities currently exist to mitigate near-term non-breeding habitat losses. These efforts would affect more individuals of more species during a longer period of the annual cycle, which may create additional opportunities for species to respond to changes in habitat or phenology that are likely to develop under climate change., (© 2017 John Wiley & Sons Ltd.)

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.)

Efficient management of biodiversity requires a forward-looking approach based on scenarios that explore biodiversity changes under future environmental conditions. A number of ecological models have been proposed over the last decades to develop these biodiversity scenarios. Novel modelling approaches with strong theoretical foundation now offer the possibility to integrate key ecological and evolutionary processes that shape species distribution and community structure. Although biodiversity is affected by multiple threats, most studies addressing the effects of future environmental changes on biodiversity focus on a single threat only. We examined the studies published during the last 25 years that developed scenarios to predict future biodiversity changes based on climate, land-use and land-cover change projections. We found that biodiversity scenarios mostly focus on the future impacts of climate change and largely neglect changes in land use and land cover. The emphasis on climate change impacts has increased over time and has now reached a maximum. Yet, the direct destruction and degradation of habitats through land-use and land-cover changes are among the most significant and immediate threats to biodiversity. We argue that the current state of integration between ecological and land system sciences is leading to biased estimation of actual risks and therefore constrains the implementation of forward-looking policy responses to biodiversity decline. We suggest research directions at the crossroads between ecological and environmental sciences to face the challenge of developing interoperable and plausible projections of future environmental changes and to anticipate the full range of their potential impacts on biodiversity. An intergovernmental platform is needed to stimulate such collaborative research efforts and to emphasize the societal and political relevance of taking up this challenge., (© 2016 John Wiley & Sons Ltd.)

Historic land-cover/use change is important for studies on climate change, soil carbon, and biodiversity assessments. Available reconstructions focus on the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). This leads to a serious underestimation of land-cover/use dynamics with impacts on the biogeochemical and environmental assessments based on these reconstructions. In this study, we quantified to what extent land-cover/use reconstructions underestimate land-cover/use changes in Europe for the 1900-2010 period by accounting for net changes only. We empirically analyzed available historic land-change data, quantified their uncertainty, corrected for spatial-temporal effects and identified underlying processes causing differences between gross and net changes. Gross changes varied for different land classes (largest for forest and grassland) and led to two to four times the amount of net changes. We applied the empirical results of gross change quantities in a spatially explicit reconstruction of historic land change to reconstruct gross changes for the EU27 plus Switzerland at 1 km spatial resolution between 1950 and 2010. In addition, the reconstruction was extended back to 1900 to explore the effects of accounting for gross changes on longer time scales. We created a land-change reconstruction that only accounted for net changes for comparison. Our two model outputs were compared with five commonly used global reconstructions for the same period and area. In our reconstruction, gross changes led in total to a 56% area change (ca. 0.5% yr(-1)) between 1900 and 2010 and cover twice the area of net changes. All global reconstructions used for comparison estimated fewer changes than our gross change reconstruction. Main land-change processes were cropland/grassland dynamics and afforestation, and also deforestation and urbanization., (© 2014 John Wiley & Sons Ltd.)