Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century

Amyloid-β Triggers the Release of Neuronal Hexokinase 1 from Mitochondria

Brain accumulation of the amyloid-β peptide (Aβ) and oxidative stress underlie neuronal dysfunction and memory loss in Alzheimer's disease (AD). Hexokinase (HK), a key glycolytic enzyme, plays important pro-survival roles, reducing mitochondrial reactive oxygen species (ROS) generation and preventing apoptosis in neurons and other cell types. Brain isozyme HKI is mainly associated with mitochondria and HK release from mitochondria causes a significant decrease in enzyme activity and triggers oxidative damage. We here investigated the relationship between Aβ-induced oxidative stress and HK activity. We found that Aβ triggered HKI detachment from mitochondria decreasing HKI activity in cortical neurons. Aβ oligomers further impair energy metabolism by decreasing neuronal ATP levels. Aβ-induced HKI cellular redistribution was accompanied by excessive ROS generation and neuronal death. 2-deoxyglucose blocked Aβ-induced oxidative stress and neuronal death. Results suggest that Aβ-induced cellular redistribution and inactivation of neuronal HKI play important roles in oxidative stress and neurodegeneration in AD

Soil Organic Matter Temperature Sensitivity Cannot be Directly Inferred From Spatial Gradients

Developing and testing decadal-scale predictions of soil response to climate change is difficult because there are few long-term warming experiments or other direct observations of temperature response. As a result, spatial variation in temperature is often used to characterize the influence of temperature on soil organic carbon (SOC) stocks under current and warmer temperatures. This approach assumes that the decadal-scale response of SOC to warming is similar to the relationship between temperature and SOC stocks across sites that are at quasi steady state; however, this assumption is poorly tested. We developed four variants of a Reaction-network-based model of soil organic matter and microbes using measured SOC stocks from a 4,000-km latitudinal transect. Each variant reflects different assumptions about the temperature sensitivities of microbial activity and mineral sorption. All four model variants predicted the same response of SOC to temperature at steady state, but different projections of transient warming responses. The relative importance of Q max , mean annual temperature, and net primary production, assessed using a machine-learning algorithm, changed depending on warming duration. When mineral sorption was temperature sensitive, the predicted average change in SOC after 100 years of 5 °C warming was −18% if warming decreased sorption or +9% if warming increased sorption. When microbial activity was temperature sensitive but mineral sorption was not, average site-level SOC loss was 5%. We conclude that spatial climate gradients of SOC stocks are insufficient to constrain the transient response; measurements that distinguish process controls and/or observations from long-term warming experiments, especially mineral fractions, are needed

Microbial community-level regulation explains soil carbon responses to long-term litter manipulations

Climatic, atmospheric, and land-use changes all have the potential to alter soil microbial activity, mediated by changes in plant inputs. Many microbial models of soil organic carbon (SOC) decomposition have been proposed recently to advance prediction of climate and carbon (C) feedbacks. Most of these models, however, exhibit unrealistic oscillatory behavior and SOC insensitivity to long-term changes in C inputs. Here we diagnose the source of these problems in four archetypal models and propose a density-dependent formulation of microbial turnover, motivated by community-level interactions, that limits population sizes and reduces oscillations. We compare model predictions to 24 long-term C-input field manipulations and identify key benchmarks. The proposed formulation reproduces soil C responses to long-term C-input changes and implies greater SOC storage associated with CO2-fertilization-driven increases in C inputs over the coming century compared to recent microbial models. This study provides a simple modification to improve microbial models for inclusion in Earth System Models

Publisher Correction: Microbial community-level regulation explains soil carbon responses to long-term litter manipulations.

The original PDF version of this Article contained an error in Table 1. On the right-hand side of the third row, the third equation was missing a β as an exponent on the first CB. This has now been corrected in the PDF version of the Article. The HTML version was correct from the time of publication

Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model

International audienceAdvancements in microbially explicit ecosystem models incorporate increasingly accurate representations of microbial physiology and enzyme-mediated depolymerization of soil organic matter in predicting biogeochemical responses to global change. However, a major challenge with model structural improvements is the requirement for additional parameters, which are often poorly constrained sources of uncertainty. Furthermore, it is often unclear how to best focus data collection efforts toward reducing model uncertainty. Here, we use Dual Arrhenius Michaelis-Menten Microbial Carbon and Nitrogen Physiology, a microbially mediated, coupled soil C and N cycling model, as a tool to explore the influence of microbial physiological and enzyme kinetic parameters on model estimates. We first quantify the potential for constraining model parameters using empirical measurements of soil respiration. We then use simulated data to identify which additional sources of data collection from the field would provide the greatest impact for constraining model estimates. We find that modeled soil C and N pools and fluxes are disproportionately sensitive to only a few parameters (e.g., activation energies and microbial CUE), while others exert less influence (e.g., Michaelis-Menten half-saturation constants). While some parameters can be constrained by the available data on heterotrophic respiration, the collection of additional data on dissolved organic C and N pools in the soil is identified as a high-priority data need. Improving our ability to model the interactions of soil microbial physiology, soil chemistry, enzyme activities, and environmental factors on C and N cycling will require closely considering model uncertainties and prioritizing future data collection opportunities based on their impact on model performance

Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4 parts per thousand target

Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4%(0) at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 +/- 1.2 Mg C ha(-)(1) year(-1) to increase SOC stocks by 4%(0) per year over 30 years, compared to the C input in the control treatments (multi-model median +/- median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition