Karl-Heinz Erb

Improved rangeland grazing could mitigate climate change through carbon dioxide (CO ₂ ) sequestration in soils and vegetation. However, altering grazing practices to increase ecosystem carbon storage may also decrease livestock production and/or increase greenhouse gas emissions through the supply chain, such that the net emissions impacts remain unclear. Here, we assess the global net mitigation potential of improving grazing intensity by quantifying potential CO ₂ sequestration alongside systems-level impacts of plant productivity changes, livestock emissions, feed requirements, and production constraints. Improving grazing intensity in global rangelands could sequester 2.2 ± 0.43 gigatons of carbon dioxide equivalent (Gt/CO ₂ eq) per year in the near term, but maintaining livestock production through supplemental feeding would reduce net mitigation by 2 to 31% (to 1.8 ± 0.45 GT/CO ₂ eq per year). Our results suggest that neglecting systems-level emissions impacts may substantially overestimate the global climate benefits of improved grazing.

The impact of land use on ecosystems has reached critical levels, jeopardizing biosphere integrity. A key indicator that quantifies, monitors, and analyses such impacts is the Human Appropriation of Net Primary Production (HANPP). Assessing HANPP consists of a comparison of actual net primary production and potential vegetation properties—i.e., vegetation properties that would prevail in the absence of land use (i.e., the NPP of the potential vegetation; NPPpot). While technical advancements in remote sensing have led to global land cover data at high spatial resolution up to 30 arcsec (i.e., 1 km at the equator) and higher, spatial explicit global NPPpot, mainly provided by Dynamic Global Vegetation Models (DGVMs), are usually generated at 30 arcmin (i.e., 55 km at the equator). This spatial mismatch causes uncertainties in HANPP assessments. We here present a workable solution for downscaling NPPpot until global, high‐resolution results, for example, from DGVMs, become feasible. This method uses a simple NPPpot model with readily available auxiliary information at higher spatial resolution to generate fine‐scale patterns to downscale high‐quality coarse scale NPPpot. It includes a smoothing procedure to reduce downscaling artifacts. We demonstrate this Smooth Auxiliary Data (SAD) downscaling by applying it to NPPpot results from the LPJ‐GUESS DGVM model for the year 2010 to downscale from 30 arcmin to 30 arcsec resolution. This approach generates fine‐scaled NPPpot patterns and aligns with alternative models for smaller geographic units while computational cost is acceptable.

The impact of land use on ecosystems has reached critical levels, jeopardizing biosphere integrity. A key indicator that quantifies, monitors, and analyses such impacts is the Human Appropriation of Net Primary Production (HANPP). Assessing HANPP consists of a comparison of actual net primary production and potential vegetation properties - that is vegetation properties that would prevail in the absence of land use (i.e., the NPP of the potential vegetation; NPPpot). While technical advancements in remote sensing have led to global land cover data at high spatial resolution up to 30arcsec (i.e. 1km at the equator) and higher, spatial explicit global NPPpot, mainly provided by Dynamic Global Vegetation Models (DGVMs), are usually generated at 30arcmin (i.e. 55km at the equator). This spatial mismatch causes uncertainties in HANPP assessments. We here present a workable solution for downscaling NPPpot until global, high-resolution results e.g. from DGVMs become feasible. This method uses a simple NPPpot model with readily available auxiliary information at higher spatial resolution to generate fine-scale patterns to downscale high-quality coarse scale NPPpot. It includes a smoothing procedure to reduce downscaling artifacts. We demonstrate this Smooth Auxiliary Data (SAD) downscaling by applying it to NPPpot results from the LPJ-GUESS DGVM model for the year 2010 to downscale from 30arcmin to 30arcsec resolution. This approach generates fine-scaled NPPpot patterns and aligns with alternative models for smaller geographic units, while computational cost is acceptable.

Land use is intimately linked to key components of the Earth system, including the climate system, biodiversity and biogeochemical cycles. Advanced understanding of patterns and dynamics of land use is vital for assessing impacts on these system components and for developing strategies to ensure sustainability. However, thematically detailed data that enable the analyses of spatiotemporal dynamics of land use, including land-use intensity, are currently lacking. This study presents a comprehensive land-use data cube (LUIcube) that traces global land-use area and intensity developments between 1992 and 2020 annually at 30 arcsecond spatial resolution. It discerns 32 land-use classes that can be aggregated to cropland, grazing land, forestry, built-up land and wilderness. Land-use intensity is represented through the framework of Human Appropriation of Net Primary Production, which allows to quantify changes in NPP, respectively biomass flows, induced by land conversion and land-management. The LUIcube provides the necessary database for analyzing the role of natural and socioeconomic drivers of land-use change and its ecological impacts to inform strategies for sustainable land management.

Emissions from agricultural activities constitute 11% of global greenhouse gas emissions and are hard to abate. Here, we present and analyze a consistent empirical assessment of global emissions from agricultural activities from 1910–2015. Agricultural emissions increased 3.5-fold from 1910–2015, from 1.9 to 6.7 GtCO₂eq yr⁻¹. CH₄ emissions, emissions from enteric fermentation and from livestock products contributed the highest fractions of emissions by gases, processes, and products, respectively. A decomposition analysis quantifies the contribution of major drivers of agricultural emissions dynamics. It reveals that globally and across the entire period, changes in population, agricultural production per capita (‘output’), regional distribution of production (‘regional mix’), and composition of final products (‘product mix’, i.e. a shift towards livestock production) all contributed to increasing agricultural emissions. Conversely, declining emissions per unit of production (‘emissions intensity’), particularly for livestock, partly counterbalanced the emissions increase. Significant variations prevail across regions and time periods. Most notably, the composition of final products counteracted agricultural emissions increase from 1910–1950, but growing livestock production has become an increasingly important driver of emissions growth in more recent periods. This finding unravels that increases in livestock production offset the improvements in emissions intensity of industrial agricultural intensification. Our findings underscore the large potential of reducing livestock production and consumption for mitigating the climate impacts of agriculture.

Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets describing these drivers were combined with a process model for soil P dynamics to reconstruct the past and current distribution of P in cropland and grassland soils. The objective of the present update, called GPASOIL-v1, is to incorporate recent advances in process understanding about soil inorganic P dynamics, in datasets to describe the different drivers, and in regional soil P measurements for benchmarking. We trace the impact of the update on the reconstructed soil P. After the update we estimate a global averaged inorganic labile P of 187 kgP ha⁻¹ for cropland and 91 kgP ha⁻¹ for grassland in 2018 for the top 0–0.3 m soil layer, but these values are sensitive to the mineralization rates chosen for the organic P pools. Uncertainty in the driver estimates lead to coefficients of variation of 0.22 and 0.54 for cropland and grassland, respectively. This work makes the methods for simulating the agricultural soil P maps more transparent and reproducible than previous estimates, and increases the confidence in the new estimates, while the evaluation against regional dataset still suggests rooms for further improvement.