Experience of using sewage sludge in soil for green building

Wastewater sludge from municipal wastewater treatment plants in Nizhny Novgorod and soils based on them, prepared with different ratios of sludge, sawdust and peat, have been investigated. Sewage sludge-based soils contain plant nutrients and are suitable for use as fertilizing materials in green building. Under the conditions of a three-year lysimetric experiment on cereal lawn grasses, a significant efficiency of organic soils was noted in comparison with traditional soils. The lawn maintenance regime revealed differences in the mixtures of fertilizing materials in terms of the effect on the productivity of the phytocenosis. Use of sewage sludge containing large amounts of heavy metals as one of the soil components undoubtedly leads to increase in the amount of these toxic elements in the soil as a whole.

Estimating cation exchange capacity and clay content from agricultural soil testing data

Clay content and the ability to reversibly retain cations affect many essential chemical and physical properties of soil, such as pH buffering and carbon sequestration. Cation exchange capacity (CEC) and base saturation are also commonly used as criteria in soil classification. However, determination of CEC and particle-size distribution is laborious and not included in routine soil testing. In this study, pedotransfer functions including soil test cations (STCat; Ca2+ + Mg2+ + K+), pH and soil organic carbon (SOC, %) as explanatory variables were developed for estimating CEC, titratable acidity (TA; H+ + Al3+) and clay content (clay, %). In addition, reference values for potential CEC and its components were determined for Finnish mineral and organic soils. The mean of potential CEC extracted by 1 M ammonium acetate at pH 7.0 ranged from 14 (range 6.4−25) in coarse soils to 33 (21−45) cmol(+) kg-1 in heavy clay soils, and from 42 (24−82) in mull soils to 77 (25−138) cmol(+) kg-1 in peat soils. The average CEC of clay and SOC were 27 and 160 cmol(+) kg-1, respectively. Titratable acidity occupied 53% and around 40% of the CEC sites in organic and mineral soils, respectively, evidencing that it is a prominent component of the potential CEC in these predominantly acidic soils. STCat, pH and SOC explained 96% of the variation in potential CEC. STCat and pH can be used in estimating the clay content especially for soils containing over 30% clay. In coarse textured soils, in contrast, SOC hampers the STCat based estimation of clay content.

Quantification and comparison of ecosystem services of grasslands versus another fodder crop (maize) based on mineral nitrogen content in the 60–90 cm soil layer

Grasslands provide many ecosystem services. Apart from being a source of fodder for animals, grasslands regulate water and soil quality by reducing nitrogen emissions to the environment. The aim of the study was to determine the biophysical and monetary value of ecosystem services of grassland based on the “mineral nitrogen content in the soil layer 60–90 cm” indicator depending on the method of use and the type of soil, against the cultivation of maize for green fodder. The study area encompassed three provinces, different in terms of soil use, livestock population and intensity of grassland use. The investigation indicated that the value of ecosystem services provided by grasslands varied spatially and depended on the type of use and type of soil. In mineral soils, the lowest levels of this index were recorded from sites used for pasturing, while the highest levels were found under maize crops. In organic soils (without maize crops), the smallest losses of Nmin were observed in meadows while the highest losses were in pastures. Nmin losses in organic soils were higher than in mineral soils. The losses observed were highest in Opolskie Province, followed by Podlaskie Province, with the lowest losses in Lubelskie Province.

Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints

Cosmic-ray neutron sensing (CRNS) allows for non-invasive soil moisture estimations at the field scale. The derivation of soil moisture generally relies on secondary cosmic-ray neutrons in the epithermal to fast energy ranges. Most approaches and processing techniques for observed neutron intensities are based on the assumption of homogeneous site conditions or of soil moisture patterns with correlation lengths shorter than the measurement footprint of the neutron detector. However, in view of the non-linear relationship between neutron intensities and soil moisture, it is questionable whether these assumptions are applicable. In this study, we investigated how a non-uniform soil moisture distribution within the footprint impacts the CRNS soil moisture estimation and how the combined use of epithermal and thermal neutrons can be advantageous in this case. Thermal neutrons have lower energies and a substantially smaller measurement footprint around the sensor than epithermal neutrons. Analyses using the URANOS (Ultra RApid Neutron-Only Simulation) Monte Carlo simulations to investigate the measurement footprint dynamics at a study site in northeastern Germany revealed that the thermal footprint mainly covers mineral soils in the near-field to the sensor while the epithermal footprint also covers large areas with organic soils. We found that either combining the observed thermal and epithermal neutron intensities by a rescaling method developed in this study or adjusting all parameters of the transfer function leads to an improved calibration against the reference soil moisture measurements in the near-field compared to the standard approach and using epithermal neutrons alone. We also found that the relationship between thermal and epithermal neutrons provided an indicator for footprint heterogeneity. We, therefore, suggest that the combined use of thermal and epithermal neutrons offers the potential of a spatial disaggregation of the measurement footprint in terms of near- and far-field soil moisture dynamics.

Estimation of litter input in hemi-boreal forests with drained organic soils for improvement of GHG inventories

Assessments of net greenhouse gas (GHG) emissions in forest land with drained organic soils conducted within the scope of National GHG inventories requires reliable data on litter production and carbon (C) input to soil information. To estimate C input through tree above-ground litter, sampling of above-ground litter was done in 36 research sites in Latvia representing typical forests with drained organic soils in hemiboreal region. To estimate C input through tree below-ground litter and litter from ground vegetation, modelling approach based on literature review and data on characteristics of forest stands with drained organic soils in Latvia provided by National Forest Inventory (NFI) was used. The study highlighted dependence of C input to soil through litter production from the stand characteristics and thus significant differences in the C input with litter between young and middle age stands. The study also proves that drained organic soils in middle age forests dominated by Silver birch, Scots pine and Norway spruce may not be the source of net GHG emissions due to offset by C input through litter production. However, there is still high uncertainty of C input with tree below-ground litter and ground vegetation, particularly, mosses, herbs and grasses which may have crucial role in C balance in forests with drained organic soils. Key words: forests, drained organic soils, litter production, carbon input, National GHG inventory

Performance of three machine learning algorithms for predicting soil organic carbon in German agricultural soil

Soil organic carbon (SOC), as the largest terrestrial carbon pool, has the potential to influence climate change and mitigation, and consequently SOC monitoring is important in the frameworks of different international treaties. There is therefore a need for high resolution SOC maps. Machine learning (ML) offers new opportunities to do this due to its capability for data mining of large datasets. The aim of this study, therefore, was to test three commonly used algorithms in digital soil mapping – random forest (RF), boosted regression trees (BRT) and support vector machine for regression (SVR) – on the first German Agricultural Soil Inventory to model agricultural topsoil SOC content. Nested cross-validation was implemented for model evaluation and parameter tuning. Moreover, grid search and differential evolution algorithm were applied to ensure that each algorithm was tuned and optimised suitably. The SOC content of the German Agricultural Soil Inventory was highly variable, ranging from 4 g kg−1 to 480 g kg−1. However, only 4 % of all soils contained more than 87 g kg−1 SOC and were considered organic or degraded organic soils. The results show that SVR provided the best performance with RMSE of 32 g kg−1 when the algorithms were trained on the full dataset. However, the average RMSE of all algorithms decreased by 34 % when mineral and organic soils were modeled separately, with the best result from SVR with RMSE of 21 g kg−1. Model performance is often limited by the size and quality of the available soil dataset for calibration and validation. Therefore, the impact of enlarging the training data was tested by including 1223 data points from the European Land Use/Land Cover Area Frame Survey for agricultural sites in Germany. The model performance was enhanced for maximum 1 % for mineral soils and 2 % for organic soils. Despite the capability of machine learning algorithms in general, and particularly SVR, in modelling SOC on a national scale, the study showed that the most important to improve the model performance was separate modelling of mineral and organic soils.

Addressing Peatland Rewetting in Russian Federation Climate Reporting

Rewetting is the most effective way to reduce greenhouse gas (GHG) emissions from drained peatlands and must significantly contribute to the implementation of the Paris Agreement on Climate within the land sector. In 2010–2013, more than 73 thousand hectares of fire-prone peatlands were rewetted in the Moscow Region (the hitherto largest rewetting program in the Northern Hemisphere). As the Russian Federation has no national accounting of rewetted areas yet, this paper presents an approach to detect them based on multispectral satellite data verified by ground truthing. We propose that effectively rewetted areas should minimally include areas with wet grasslands and those covered with water (cf. the IPCC categories “rewetted organic soils” and “flooded lands”). In 2020, these lands amounted in Moscow Region to more than 5.3 and 3.6 thousand hectares, respectively. Assuming that most rewetted areas were former peat extraction sites and using IPCC default GHG emission factors, an overall GHG emission reduction of over 36,000 tCO2-eq year−1 was calculated. We furthermore considered the uncertainty of calculations. With the example of a 1535 ha large rewetted peatland, we illustrate the estimation of GHG emission reductions for the period up to 2050. The approach presented can be used to estimate GHG emission reductions by peatland rewetting on the national, regional, and object level.