Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.

Life cycle impact assessment (LCIA) is a lively field of research, and data and models are continuously improved in terms of impact pathways covered, reliability, and spatial detail. However, many of these advancements are scattered throughout the scientific literature, making it difficult for practitioners to apply the new models. Here, we present the LC‐IMPACT method that provides characterization factors at the damage level for 11 impact categories related to three areas of protection (human health, ecosystem quality, natural resources). Human health damage is quantified as disability adjusted life years, damage to ecosystem quality as global species extinction equivalents (based on potentially disappeared fraction of species), and damage to mineral resources as kilogram of extra ore extracted. Seven of the impact categories include spatial differentiation at various levels of spatial scale. The influence of value choices related to the time horizon and the level of scientific evidence of the impacts considered is quantified with four distinct sets of characterization factors. We demonstrate the applicability of the proposed method with an illustrative life cycle assessment example of different fuel options in Europe (petrol or biofuel). Differences between generic and regionalized impacts vary up to two orders of magnitude for some of the selected impact categories, highlighting the importance of spatial detail in LCIA. This article met the requirements for a gold – gold JIE data openness badge described at http://jie.click/badges.

This study estimates the potential losses of vascular plant species richness due to terrestrial acidification for different world's biomes. We used empirical occurrence data of 2409 species from 140 studies and estimated the relative species richness - pH response curves using logistic regressions. The regressions were then used to quantify the fraction of species that are potentially lost due to soil pH changes. Although we found considerable variability within biomes, out results show that the pH at which species richness was maximized was found to be the lowest in (sub)tropical forests (pH = 4.1) and the highest in deserts (pH = 7.4). We also found that (sub)tropical moist forests are highly sensitive to decreases of in soil pH below 4.1. This study can be coupled with existing atmospheric deposition models to quantify the risk of species richness loss following soil acidification.

No abstract