• Energy Research
• 2016-2025close

AbstractRestoring ecosystems is an imperative for addressing biodiversity loss and climate change, and achieving the targets of the Kunming–Montreal Global Biodiversity Framework. One form of restoration, rewilding, may have particular promise but may also be precluded by requirements for other forms of land use now or in the future. This opportunity space is critical but challenging to assess. We explored the potential area available for rewilding in Great Britain until the year 2080 with a multisectoral land‐use model with several distinct climatic and socioeconomic scenarios. By 2080, areas from 5000 to 7000 km2 were either unmanaged or managed in ways that could be consistent with rewilding across scenarios without conflicting with the provision of ecosystem services. Beyond these areas, another 24,000–42,000 km2 of extensive upland management could provide additional areas for rewilding if current patterns of implementation hold in the future. None of these areas, however, coincided reliably with ecosystems of priority for conservation: peatlands, ancient woodlands, or wetlands. Repeatedly, these ecosystems were found to be vulnerable to conversion. Our results are not based on an assumption of support for or benefits from rewilding and do not account for disadvantages, such as potential losses of cultural landscapes or traditional forms of management, that were beyond the modeled ecosystem services. Nevertheless, potential areas for rewilding emerge in a variety of ways, from intensification elsewhere having a substantial but inadvertent land‐sparing effect, popular demand for environmental restoration, or a desire for exclusive recreation among the wealthy elite. Our findings therefore imply substantial opportunities for rewilding in the United Kingdom but also a need for interventions to shape the nature and extent of that rewilding to maintain priority conservation areas and societal objectives.

AbstractClimate‐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.

AbstractConservation 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.

AbstractIncluding 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.

AbstractUnderstanding 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.

AbstractBumblebees (Bombus ssp.) are among the most important wild pollinators, but many species have suffered from range declines. Land‐use change, agricultural intensification, and the associated loss of habitat have been identified as drivers of the observed dynamics, amplifying pressures from a changing climate. However, these drivers are still underrepresented in continental‐scale species distribution modeling. Here, we project the potential distribution of 47 European bumblebee species in 2050 and 2080 from existing European‐scale distribution maps, based on a set of climate and land‐use futures simulated through a regional integrated assessment model and consistent with the RCP–SSP scenario framework. We compare projections including (1) dynamic climate and constant land use (CLIM); (2) constant climate and dynamic land use (LU); and (3) dynamic climate and dynamic land use (COMB) to disentangle the effects of land use and climate change on future habitat suitability, providing the first rigorous continental‐scale assessment of linked climate–land‐use futures for bumblebees. We find that direct climate impacts, although variable across species, dominate responses for most species, especially under high‐end climate change scenarios (up to 99% range loss). Land‐use impacts are highly variable across species and scenarios, ranging from severe losses (up to 75% loss) to considerable gains (up to 68% gain) of suitable habitat extent. Rare species thereby tend to be disproportionally affected by both climate and land‐use change. COMB projections reveal that land use may amplify, attenuate, or offset changes to suitable habitat extent expected from climate impact depending on species and scenario. Especially in low‐end climate change scenarios, land use has the potential to become a game changer in determining the direction and magnitude of range changes, indicating substantial potential for targeted conservation management.