Drained agricultural peat soils are hotspots for biogenic CO2 emissions, contributing to elevated atmospheric CO2 levels. Due to microbial mineralization, the organic carbon (OC) content of these soils transitions to that of mineral soils, but it remains unclear how the residual OC content controls the rate of CO2 emission. This hinders the integration of soils with 6-12% OC into national greenhouse gas inventories. Based on a comprehensive laboratory study with organic soils from 103 sites in Denmark, we show that area-scale CO2 emissions from soils with >6% OC are not controlled by OC content and OC density, i.e., that soil OC content (wt/wt) is a poor predictor of area-specific CO2 emissions. The empirical data suggest that CO2 emission factors for 6-12% and >12% OC soils should be considered the same. On the other hand, the data also suggest that disaggregation of emission factors for soils with even higher OC contents is not necessary. We conclude that a global underestimation of CO2 emissions from 6-12% OC soils occurs in countries with large proportions of organic soils in transition from organic to organo-mineral soils due to agricultural management. Refining CO2 emission estimates for 6-12% OC soils is critical for the accuracy of national inventories, but also for recognizing the climate benefits of emerging initiatives to rewet drained organic soils.

Agricultural activity on drained lowlands is a common practice in Denmark and there are suggestions to rewet some of them for climate mitigation purposes. Rewetting those lowlands might result in a change in microbial community composition. This study investigates the current prokaryotic diversity and community composition in soil samples from cultivated lowlands to provide the baseline for monitoring changes after rewetting. Furthermore, variations in soil properties between sites are examined, and the properties driving differences in prokaryotic diversity and community composition are identified. In total, 116 samples were collected from field sites across Denmark that were categorized as one of four different land-use types: Crop, Grass, Fallow, and Other. Soil properties were selected to cover chemical (soil water repellency, pH, electrical conductivity), hydrological (depth to ground-water table, soil water content at field capacity (-100 hPa)), nutrient-related (total nitrogen, organic carbon, carbon-to-nitrogen-ratio, fractions of pyrolizable and residual organic matter), and structural (total porosity, pore size distribution index) functions of the soil. Soil samples exhibited significant variations in their chemical and physical properties, including pH ranging from 2.02 to 7.55, organic carbon ranging from 3 g 100g-1 to 50 g 100g-1, soil water repellency ranging from 71.27 mN m-1 (hydrophilic) to 33.85 mN m-1 (very strongly hydrophobic), and total porosity ranging from 51% to 95%. Soil samples clustered according to soil class (mineral, organo-mineral, organic, highly organic) but not according to land-use type (crop, grass, fallow, other). Prokaryotic alpha diversity, measured as Shannon’s diversity index (H), ranged from 4.16 to 5.89 across samples and could best be predicted by pH, followed by total porosity, fraction of pyrolizable carbon, and pore size distribution index. The pH alone explained 36% of the variation in H between samples. Hierarchical clustering identified three prokaryotic clusters highly correlated with pH. A weak correlation was found between differences in community composition (beta diversity) and geographic distance (r = 0.15, p < 0.001). However, pH was also the main driver of beta diversity, explaining 11% of the variation. At the same time, models including additional variables only had marginally better explanatory power. In conclusion, pH was the predominant driver of prokaryotic alpha and beta diversity across land-use types in lowland soils.

Thinned steep forests are particularly vulnerable to soil physical degradation. Retaining deadwood logs from thinning operations on the forest floor can potentially mitigate soil physical degradation by modifying its physical properties through increased carbon content in steep regions. We aimed to investigate the effect of spruce deadwood logs from thinning operations on the physical properties of a loamy sand Podzol soil in a steep (30(degrees)) temperate spruce forest in Bavaria, Germany. The soil organic carbon (SOC) content was 56% higher under deadwood logs compared to the control areas (p-value = 0.097). Deadwood logs also increased the soil water repellency by 13% (p-value = 0.269), while decreasing the soil shear strength by 35% (p-value = 0.001). Shear strength and water repellency strongly correlated with SOC content, with r = -0.87 and r = 0.86, respectively. Although retaining deadwood logs seems a promising carbon sequestration strategy, it can adversely affect soil shear strength and water repellency and potentially lead to soil degradation. Therefore, the choice to keep deadwood logs on the forest floor may align with specific management goals.

Organic-rich agricultural soils, including drained peatlands, are hotspots for biogenic CO2 emissions. Due to microbial mineralisation, the organic carbon (OC) content of these soils transitions to that of mineral soils, but it remains unclear how the residual OC content controls the rate of CO2 emission. Here we show that area-scaled CO2 emissions from topsoils with >6% OC are not controlled by OC content and OC density in a comprehensive laboratory incubation experiment. National greenhouse gas inventories assign area-scaled CO2 emission factors to soils with >12% OC, while soils with 6-12% OC are mostly disregarded or treated with lower emission factors. In this respect, our results suggest that CO2 emissions from organic soils could be underestimated by up to 40% in the Danish national inventory submission to the United Nations Framework Convention on Climate Change (UNFCCC). We conclude that global underestimation of area-scaled CO2 emissions from 6-12% OC soils occurs in countries with large proportions of organic soils in transition from organic to organo-mineral soils due to agricultural management. Refining CO2 emission estimates for 6-12% OC soils is critical for the accuracy of national inventories, but also for recognising the climate benefits of initiatives to rewet drained organic soils.

FROM IVORY TOWERS TO RURAL POWERS: HIGHER EDUCATION INSTITUTIONS AS KNOWLEDGE PARTNERS IN DIGITAL TRANSFORMATION

Soil specific surface area (SA) reflects the quantity and quality of the soil mineral and organic fractions. For soil samples that have similar clay mineral types, clay content and or organic carbon (OC) contents are good predictors of SA. However, the magnitude of the OC contribution to SA for different soil types is unclear, particularly since SA varies depending on which method or probe molecule is used to measure it. Consequently, we set out to (i) quantify the contribution of soil OC to total SA for soils with varying clay mineralogy, and (ii) assess the effect of probe molecule (EGME or H2O) and water sorption direction on the contribution of OC to the total SA. We utilized 330 soil samples (clay = 1 to 89 %; silt = 2 to 78 %; OC = 0.03 to 34.9 %) that were grouped according to clay mineralogy and OC content. The total SA was measured using EGME adsorption (SAE) and water adsorption and desorption (SAH2O). The contribution of OC to SA was examined using regression and partial correlation analyses that combined clay, silt, and OC as explanatory variables. Results showed that SAE values were strongly correlated to SAH2O, except for OC-rich soils where SAE was significantly lower than SAH2O. Apart from montmorillonite-rich samples, OC contribution to SAE was smaller than for SAH2O. Organic carbon had a positive contribution to desorption SAH2O for all sample groups, except for montmorillonite-rich samples; OC contribution was 7.5, 10.7, and 13.9 m2/g per %C for illite-rich, kaolinite-rich and OC-rich samples, respectively. There was no significant effect of water sorption direction on the OC contribution to SAH2O. Partial correlation analyses that accounted for clay and silt contents confirmed a negative contribution of OC to SAE or SAH2O for soil samples dominated by montmorillonite clay minerals. We can conclude that for the soil types investigated here (except montmorillonitic soils), OC has a positive contribution to total SA, and the magnitude of the contribution depends on the clay type.

The estimation of hydraulic parameters is the basis for establishing soil erosion models. However, the underlying surfaces greatly influence the hydraulic parameters of the overland flow. Since sediment flow and slope surface resistance interact, exploring the mechanism of overland flow movement in relation to different underlying surfaces is essential. A potential relationship between hydrodynamic parameters and slope gradient and flow discharge was investigated by carrying out overland flow experiments, which used four types of non-erosion slope surfaces (stem cover, brown soil, frozen soil, and organic glass), four slope gradients (3 degrees, 6 degrees, 9 degrees, and 12 degrees), and four flow discharges (0.35, 0.45, 0.55, and 0.65 L s-1). The results showed that the hydraulic parameters (i.e., mean flow velocity, flow depth, Froude number, Reynolds number, shear stress, drag coefficient, stream power, and unit stream power) differed with the increased slope gradients and flow discharges. The mean flow velocity of the stem cover slope was lower than that of the frozen soil slope, brown soil slope, and organic glass slope by 33%, 40%, and 50%, respectively. The stem cover slope drag coefficient was higher than the frozen soil slope, brown soil slope, and organic glass slope by 201%, 323%, and 630%, respectively. The critical flow states of the underlying surface were different under different slope gradients and flow discharge conditions. The flow on frozen soil slopes was mainly laminar and transitional, while the other three were transitional. These results help us understand the hydrodynamic mechanisms of the sediment transport process during overland flow. The results of the study provide a scientific basis for understanding the hydrodynamic mechanism of the dynamic changes of the sediment transport on the slope, deepening the understanding of the soil erosion mechanism and improving the prediction accuracy of the soil erosion process model.

Soils host diverse communities of microorganisms essential for ecosystem functions and soil health. Despite their importance, microorganisms are not covered by legislation protecting biodiversity or habitats, such as the Habitats Directive. Advances in molecular methods have caused breakthroughs in microbial community analysis, and recent studies have shown that parts of the communities are habitat-specific. If distinct microbial communities are present in the habitat types defined in the Habitats Directive, the Directive may be improved by including these communities. Thus, monitoring and reporting of biodiversity and conservation status of habitat types could be based not only on plant communities but also on microbial communities. In the present study, bacterial and plant communities were examined in six habitat types defined in the Habitats Directive by conducting botanical surveys and collecting soil samples for amplicon sequencing across 19 sites in Denmark. Furthermore, selected physico-chemical properties expected to differ between habitat types and explain variations in community composition of bacteria and vegetation were analysed (pH, electrical conductivity (EC), soil texture, soil water repellency, soil organic carbon content (OC), inorganic nitrogen, and in-situ water content (SWC)). Despite some variations within the same habitat type and overlaps between habitat types, habitat-specific communities were observed for both bacterial and plant communities, but no correlation was observed between the alpha diversity of vegetation and bacteria. PERMANOVA analysis was used to evaluate the variables best able to explain variation in the community composition of vegetation and bacteria. Habitat type alone could explain 46% and 47% of the variation in bacterial and plant communities, respectively. Excluding habitat type as a variable, the best model (pH, SWC, OC, fine silt, and Shannon's diversity index for vegetation) could explain 37% of the variation for bacteria. For vegetation, the best model (pH, EC, ammonium content and Shannon's diversity index for bacteria) could explain 25% of the variation. Based on these results, bacterial communities could be included in the Habitats Directive to improve the monitoring, as microorganisms are more sensitive to changes in the environment compared to vegetation, which the current monitoring is based on.

Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.

Soil water repellency (WR) is ubiquitous across Greenlandic cultivated fields, which may constrain agricultural production. Fine-grained glacial rock flour (GRF) is available in the surrounding landscape, which could serve as a soil amendment. We tested whether the application of GRF (rates of 0, 50, 100, 300, and 500 ton ha(-1)) reduced the WR across two field trials in South Greenland. The field trials, Upernaviarsuk (UP) and South Igaliku (SI), differed in clay (UP: 0.05-0.11 kg kg(-1); SI: 0.03-0.05 kg kg(-1)) and organic carbon (OC) contents (UP: 0.04-0.13 kg kg(-1); SI: 0.01-0.03 kg kg(-1)). We measured WR across gravimetric water contents (W) from oven-dry to the W where WR ceased (W-NON) to obtain whole WR-W curves. Most soils became hydrophilic around air-dry conditions at application rates of >= 300 ton ha(-1), likely due to increased clay:OC ratios. Application rates of >= 300 ton ha(-1) generally reduced the trapezoidal integrated area of the WR-W curve (WRAREA), W-NON,W- and WR after heat treatments at 105 degrees C (WR105) and 60 degrees C (WR60). The WR105 was significantly reduced in both fields at 500 ton ha(-1), while WR60 was significantly reduced in UP at application rates of >= 300 ton ha(-1). The GRF effects were masked by texture and OC variations. Normalizing WRAREA to the water vapor sorption isotherms (utilizing the Campbell-Shiozawa model) revealed that GRF consistently reduced the normalized WRAREA. The SI field showed the largest reduction in the normalized WRAREA, likely due to its lower OC and clay contents. Thus, GRF could reduce WR across two Greenlandic field trials.

The warming climate is rapidly changing the circumpolar region, presenting new opportunities and challenges for agricultural production in South Greenland. The warming climate is projected to increase the frequency of drought periods, but little is known about the soil-water retention (SWR) and the plant available water (PAW) of the agricultural soils in the region. This study aimed to measure the SWR and PAW of Greenlandic agricultural soils and evaluate the effect of organic carbon (OC) and clay (CL) content using pedotransfer functions based on OC and CL. The study included 464 South Greenlandic agricultural soil samples from 20 fields with a wide distribution in clay (0.016-0.184 kg kg(-1)) and OC contents (0.006-0.254 kg kg(-1)). Pedotransfer functions were successfully developed for estimating the gravimetric water content (w) at five soil-water potentials (-1500, -100, -30, -10, and -5 kPa) and PAW. The OC content was the primary variable governing the gravimetric water content at each soil-water potential, evidenced by R-2 values consistently above 0.80. The effect of OC on the gravimetric water content at -1500 kPa was close to the range reported in the literature, but OC effects were markedly higher between -100 and -5 kPa. Overall, this study highlights a substantial effect of OC on the PAW as a 1% increase in OC increased PAW by more than 4%, which is almost twice the value of a recent meta-study. Our study highlights the potentially dominating effects of organic matter on soil-water balance and availability in high-latitude agriculture.

The soil sorption coefficient (Kd) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of Kd is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis–NIRS) as an alternative method for glyphosate Kd estimation at a country scale and compare its accuracy against pedotransfer function (PTF). A total of 439 soils with a wide range of Kd values (37–2409 L kg−1) were collected from Denmark (DK) and southwest Greenland (GR). Two modeling scenarios were considered to predict Kd: a combined model developed on DK and GR samples and individual models developed on either DK or GR samples. Partial least squares regression (PLSR) and artificial neural network (ANN) techniques were applied to develop vis–NIRS models. Results from the best technique were validated using a prediction set and compared with PTF for each scenario. The PTFs were built with soil texture, OC, pH, Feox, and Pox. The ratio of performance to interquartile distance (RPIQ) was 1.88, 1.70, and 1.50 for the combined (ANN), DK (ANN), and GR (PLSR) validation models, respectively. vis–NIRS obtained higher predictive ability for Kd than PTFs for the combined dataset, whereas PTF resulted in slightly better estimations of Kd on the DK and GR samples. However, the differences in prediction accuracy between vis–NIRS and PTF were statistically insignificant. Considering the multiple advantages of vis–NIRS, e.g., being rapid and non-destructive, it can provide a faster and easier alternative to PTF for estimating glyphosate Kd.

In this chapter, database systems and their applications are introduced. All business applications are based on high-performance database systems, which can process, store, and monitor data and provide error-free access to relevant data. After providing a detailed description of structure and function of database systems, data modeling as the basis for the design of a new database is illustrated with an example from relational databases. Subsequently, the data warehouse, as an information system specially designed for analysis, is described in terms of its conceptualization, its qualities, and its application contexts. The chapter concludes with explanations of the NoSQL and in-memory database systems developed for special application scenarios.

The particle density (rho(s)) is a fundamental physical property needed for calculating the soil porosity and phase distributions. While rho(s) is often estimated using soil organic matter (SOM) content and particle size distribution, the specific densities of each soil component remain unclear in a subarctic agricultural setting. This study aimed to evaluate the rho(s) of soils from Southwest Greenland using a three-compartment model (3CM) based on the mixing ratio of SOM derived from loss-on-ignition, mineral particles <20 mu m (FC), and mineral particles >= 20 mu m (CC). We further evaluated the accuracy of the 3CM against pedotransfer functions (PTFs) and visible near-infrared (vis-NIR) spectroscopic models. A total of 324 soil samples from 16 Greenlandic agricultural fields were investigated, covering a wide range in SOM content (0.021-0.602 kg kg(-1)) and clay content (0.020-0.185 kg kg(-1)). Despite their high SOM content, the Greenlandic soils exhibited relatively high rho(s) (1.936-3.044 Mg m(-3)), which together with a large SOM/organic carbon ratio of 2.16 indicated a high SOM density of 1.493 Mg m(-3). The 3CM fit on all soils indicated FC and CC densities of 3.047 and 2.713 Mg m(-3), respectively, while a subset of soils (n = 203) from the same geological setting resulted in FC and CC densities of 2.738 and 2.731 Mg m(-3). Prediction accuracy of the 3CM (RMSE = 0.067 Mg m(-3)) was similar to PTFs (RMSE = 0.068-0.070 Mg m(-3)) and better than vis-NIR spectroscopic models (RMSE = 0.091 Mg m(-3)).