Our findings indicate the importance of paleoclimatic information to improve quantitative predictions of global soil C stocks.
Quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land\uffe2\uff80\uff90cover types.
LocationEurope.
Time period2018.
Major taxa studiedMicrobial community (fungi and bacteria).
MethodsWe collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20\uffc2\uffa0\uffc2\uffbaC and microbial biomass (substrate\uffe2\uff80\uff90induced respiration) using an O2\uffe2\uff80\uff90microcompensation apparatus. Soil and climate data were obtained from the same LUCAS survey and online databases. Structural equation models (SEMs) were used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass.
ResultsSoil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands.
Main conclusionsSoil microbial communities in grasslands and croplands are likely carbon\uffe2\uff80\uff90limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already\uffe2\uff80\uff90degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in future management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change.
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The European Union\u2019s soil strategy underscores the necessity for establishing feasible criteria to assess the soil health condition. In this study, we developed a method to define a critical threshold value for SOC/clay ratio on the basis of crop productivity and resilience. The study integrated data from national soil monitoring (NSM) of Finnish cropland soils (n=505) with satellite-based normalized difference vegetation index (NDVI) obtained from the EcoDataCube (EDC) portal. The study area was confined to the boreal environmental zone to ensure consistent pedo-climatic conditions. The results show that the interannual variation in crop productivity increases rapidly below SOC/clay ratio of 0.09 (95% confidence intervals ranging from 0.07 to 0.16), whereas the corresponding threshold for mean productivity was 0.13 (0.09\u20130.16). The observed threshold values were found applicable for both cereals and temporary ley. The SOC/clay ratio of 1:13 (=0.08), regarded as a criterion for healthy soil in the current Soil Monitoring Law proposal, based on studies by Johannes et al. (2017) and Prout et al. (2021), is lower than the mean thresholds estimated in this study but aligns close to the lower bound of the 95% confidence intervals. In this research, Finnish agricultural land served as the case study area, but the method is easily applicable to various pedo-climatic regions and potentially to different land use types.
Fungi regulate nutrient cycling, decomposition, symbiosis, and pathogenicity in cropland soils. However, the relative importance of generalist and specialist taxa in structuring soil fungal community remains largely unresolved. We hypothesized that generalist fungi, which are adaptable to various environmental conditions, could potentially dominate the community and become the basis for fungal coexisting networks in cropping systems. In this study, we identified the generalist and habitat specialist fungi in cropland soils across a 2,200 kms environmental gradient, including three bioclimatic regions (subtropical, warm temperate, and temperate). A few fungal taxa in our database were classified as generalist taxa (~1%). These generalists accounted for >35% of the relative abundance of all fungal populations, and most of them are Ascomycota and potentially pathotrophic. Compared to the specialist taxa (5\u201317% of all phylotypes in three regions), generalists had a higher degree of connectivity and were often identified as hub within the network. Structural equation modeling provided further evidence that after accounting for spatial and climatic/edaphic factors, generalists had larger contributions to the fungal coexistence pattern than habitat specialists. Taken together, our study provided evidence that generalist taxa are crucial components for fungal community structure. The knowledge of generalists can provide important implication for understanding the ecological preference of fungal groups in cropland systems.
In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km\u00b2), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017\u20132018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD \u2265 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyr\u00e9n\u00e9es.
This study presents a robust framework for spatially explicit monitoring of soil properties and Above Ground Biomass (AGB) estimation in Mediterranean agroforestry and cropland systems by integrating remote sensing (RS) and artificial intelligence (AI). These variables are critical for assimilation into process-based models for Soil Organic Carbon (SOC) dynamics monitoring within a Monitoring, Reporting, and Verification (MRV) system. The framework was developed as part of the MRV4SOC project in Spain, aimed at designing a comprehensive, robust, and cost-effective Tier-3 approach. The primary goal is to produce high-quality geospatial layers of topsoil properties and AGB estima tion, which serve as key inputs for SOC dynamics modeling.The methodology was tested at two long-term demonstration sites in Spain: Quercus ilex Dehesas in Extremadura (SW Spain) and rainfed cereal crops at La Canaleja experimental farm in central Spain. These agroecosystems provide diverse testing grounds for scalable and transferable SOC assessment methodologies within an MRV framework. The approach integrates multi-temporal remote sensing data (2018–2022) from Sentinel-2 and Landsat satellites with machine learning models to predict essential soil properties (SOC, Sand, Silt, Clay, pH, and Total N) and AGB. Ground truth data for AGB estimation were sourced from the Spanish National Forest Inventory (SNFI), while soil property predictions utilized the LUCAS 2018 topsoil libraries due to limited site-specific datasets for model training. A bare soil reflectance composite (2018–2022) derived from Sentinel-2 bands (B02–B12) at 20-meter resolution was employed for geospatial soil property mapping.Given the limited availability of ground truth data, simpler models like Quantile Regression Forests (QRF) and XGBoost were selected. QRF achieved better accuracy for soil texture properties, with R² = 0.62 for clay and outperforming XGBoost for SOC (R² = 0.63) and pH (R² = 0.76) in the agroforestry site. However, XGBoost performed better for SOC (R² = 0.54) and total nitrogen in croplands, as well as for sand, silt, clay, and total nitrogen in the agroforestry site (R² = 0.61 for clay). For AGB estimation in the Dehesas area, a machine learning approach was implemented using SNFI data and remote sensing-derived transformation features. A gradient boosting algorithm (LightGBM) resulted in an R² value of 0.8. In La Canaleja, a bare soil reflectance composite was similarly employed for soil property mapping. Further analysis will be carried out to develop a bottom-up approach for monitoring SOC using these products and process-based modelsUncertainty analysis using Prediction Interval Ratio (PIR) assessment was conducted separately for landscape (L) and sub-landscape (SL) levels. While most properties showed medium to low uncertainty, sand and silt exhibited higher variability in croplands, and SOC displayed the highest uncertainty in the agroforestry site across L and SL levels.This methodology contributes significantly to improving MRV systems by delivering high-quality geospatial layers for SOC dynamics monitoring in complex environments. Increasing ground truth data availability is essential for enhancing model accuracy and minimizing prediction uncertainties further.
Our findings indicate the importance of paleoclimatic information to improve quantitative predictions of global soil C stocks.
Fungi regulate nutrient cycling, decomposition, symbiosis, and pathogenicity in cropland soils. However, the relative importance of generalist and specialist taxa in structuring soil fungal community remains largely unresolved. We hypothesized that generalist fungi, which are adaptable to various environmental conditions, could potentially dominate the community and become the basis for fungal coexisting networks in cropping systems. In this study, we identified the generalist and habitat specialist fungi in cropland soils across a 2,200 kms environmental gradient, including three bioclimatic regions (subtropical, warm temperate, and temperate). A few fungal taxa in our database were classified as generalist taxa (~1%). These generalists accounted for >35% of the relative abundance of all fungal populations, and most of them are Ascomycota and potentially pathotrophic. Compared to the specialist taxa (5\u201317% of all phylotypes in three regions), generalists had a higher degree of connectivity and were often identified as hub within the network. Structural equation modeling provided further evidence that after accounting for spatial and climatic/edaphic factors, generalists had larger contributions to the fungal coexistence pattern than habitat specialists. Taken together, our study provided evidence that generalist taxa are crucial components for fungal community structure. The knowledge of generalists can provide important implication for understanding the ecological preference of fungal groups in cropland systems.