European Journal of Soil Science最新文献

The conversion of natural ecosystem to pasture or agricultural fields is the main factor of soil fertility and aboveground biomass decline in the Amazon basin. Our aim here was to present the impacts on soil chemical properties and aboveground biomass associated with four land covers (Coffea canephora, natural ecosystem, pasture and Theobroma cacao) from the Amazon basin, Amazonas, Brazil. The soil pH, soil organic carbon (SOC), SOC stock, phosphorus (P), Ca²⁺, Al³⁺, H⁺+Al³⁺, sum of bases, cation exchange capacity (CEC), base saturation and aluminium saturation varied among the studied land covers. We observed a decrease in SOC stock by 82.9%, 33.1% and 79.5% when comparing the results of C. canephora, pasture and T. cacao with the natural ecosystem.Principal component analysis (PCA) showed that bulk density, soil pH, base saturation, Ca²⁺, K⁺, aboveground biomass increment (ABI), SOC stock, Al³⁺ and aluminium saturation were the main factors contributing to 86.45% of the data variance. Additionally, the proposed predictive models indicated that: (i) ABI was strongly influenced by soil pH, SOC_stock, K⁺, Ca²⁺, base saturation and bulk density and (ii) SOC_stock was strongly influenced by K⁺, Ca²⁺ and Al³⁺. The results of our study highlighted the importance of considering the conversion of natural ecosystems, aiming to find more suitable systems (e.g., agroforestry systems) in Brazil's Legal Amazon, based on the sustainable ways that simulate similar conditions to that occuring in the soil of natural ecosystems, since even the studied monocropping systems (T. cacao and C. canephora) that aimed to recover land degradation showed lower ABI and SOC stocks. Thus, future studies must consider the use of agroforestry systems with these crops to exploit positive results among soil chemical properties and aboveground biomass increment.

Extraction of plastic particles from soil is challenging and, thus, exceptionally little spatial information on plastic distribution at the field scale has been gathered. However, for environmental risk assessment, adequate sampling should complement coherent plastic profiling. In this study, we investigated the spatial distribution of mesoplastics (MePs; from >5 mm up to 130 mm) in arable soil (Haplic Cambisol) managed intensively by 12 years of compost application. Geo-referenced samples (n = 128) and five different sampling designs (n = 45) of variable sampling volume (from 2 to 300 L) were collected at a three hectare study site in Northern Germany (0–30 cm soil depth). Soil properties such as pH and soil organic carbon (SOC) were measured to evaluate dispersion measures of these data. In total, we found 259 MePs with a predominance of transparent packaging foils made of polyethylene and coloured fibres of polypropylene. Average particle metrics were a projection area of 47 (3–400) mm², a Feret diameter of 18.5 (5.4–130) mm and a mass of 1.89 (0.11–221) mg. Caution is advised when measuring the particle mass due to still strongly adhering soil material, especially for fibre bundles with 0.544 mg soil mg⁻¹ particle. We recommend using a 0.1 mol L⁻¹ tetrasodium pyrophosphate solution to purify MePs by removing attached soil before weighing for further environmental risk assessment. The MePs count with a median value of 0.50 (0–3.2) particles kg⁻¹ and median mass of 2.26 (0–221) mg kg⁻¹ featured the highest coefficient of variation (CV) with 103% and 187%, respectively. This is 10–20 times larger in comparison to the CV of SOC (9.2%) and even 50–93 times larger than CV of soil pH (2.2%). This leads to the need of larger sample numbers to delineate plastic metrics in comparison with soil properties to identify a reliable mean value of the field within a predefined allowable error. Mesoplastics in the soil were characterized by a pure nugget effect variogram (no spatial correlation), revealed no intrafield variability and the sample volume yielded inconclusive results. Sampling for plastics in soil should either (i) drastically increase the sample number for a single field or (ii) communicate transparently that the allowable error is by far enhanced in comparison with classical soil properties like pH and SOC. More systematic studies featuring geo-spatial analysis of MePs and smaller-sized plastics in soils are required to propose adequate sampling designs across multiple land uses and plastics fingerprints. A larger database would, thereupon, pave the way for best-practice guides on how to treat ‘outliers’ and search for robust estimators for spatial mapping of plastics in soils.

The increasing global demand for sustainable agriculture requires accurate and efficient soil analysis methods. Conventional laboratory techniques are often time-consuming, costly and environmentally damaging. To address this challenge, we developed and validated locally calibrated mid-infrared (MIR) spectroscopy models for predicting key soil properties pH, phosphorus (P) and exchangeable cations in soil samples from South Africa's Western Highveld region, using a dataset of 979 soil samples and machine learning algorithms Cubist, partial least squares regression (PLSR) and random forest (RF). A subset of spectra was also submitted to the newly developed Open Soil Spectral Library's (OSSL) prediction models to determine whether global prediction models could be used for local soil property prediction. Accurate predictions for pH, calcium (Ca) and magnesium (Mg), with coefficient of determination (R²) values exceeding 0.76 were obtained with the local calibration algorithms. The predictions for P, potassium (K) and sodium (Na) did not meet the requirements for reliability. Soil spectroscopic prediction models calibrated with local soils outperformed the corresponding global prediction models considered. The OSSL prediction results were inaccurate, with a RPIQ <1, and consistently underpredicted all soil properties. Furthermore, the OSSL collection of prediction models does not include a pH (KCl) model, the routinely used pH measurement method in South Africa. These findings highlight the importance of local calibration for accurate soil property prediction and underscore the need for regional representation in global spectral libraries. This research serves as the first local calibration of MIR spectroscopy models for the Western Highveld region of South Africa and provides a foundation for future local soil property inference model development. It also serves as a potential starting point for a comprehensive South African soil spectral library that can be contributed to global spectral libraries.

Understanding the nuances of soil health is more important than ever to improve the quality and sustainability of agroecosystems. However, it is poorly understood how the variety of metrics currently in use to evaluate soil health relate to each other, and in what situations their use is not sensitive enough to indicate environmental changes. The use of faunal co-occurrence networks is a novel, potentially valuable tool that has hitherto received little attention in the context of soil health. Here, we used a meadow land-use intensity gradient to compare the response of a number of soil community metrics, including chemical and ecological indicators as well as faunal co-occurrence network parameters. Our findings indicate that the examined metrics displayed distinct, often contrasting patterns to one another, and that network analysis detected patterns that strongly aligned with the land-use effects. This pattern was qualitatively different from patterns arising from traditionally used metrics. The soils with conventional farming, that is, the least regenerative land-use, generally scored well in traditionally used metrics, including C:N ratio, faunal abundance and the ratio of Acari to Collembola. Regenerative farming was comparable with conventional farming in all conventional metrics—however, network analysis revealed that the soil faunal communities under regenerative farming had the highest species connectivity out of all research areas potentially due to grazing increasing the connectivity of faunal networks. Overall, these results suggest that network analyses are best suited to capture subtle land-use intensity differences while traditional metrics performed well in big changes. While more research is needed to better interpret soil faunal co-occurrence networks, our findings imply that it could be a useful method to provide further insight in aspects of soil health.

Isotopic dilution has been widely used to measure isotopically exchangeable phosphorus (P) in soil (E value), as a measure of potentially plant-available P. However, in soils with low E values and/or strong P sorption, measurement of E values can be challenging due to very low solution concentrations and the interference of colloidal non-exchangeable species, thus confounding the measurements in the soils of most interest. The addition of a complexing compound could increase solution concentrations and reduce these analytical issues, as has been found in the case of metals. Therefore, we investigated the addition of Fe-humic acid (Fe-HA) as a P-complexing compound to the soil suspension prior to isotopic exchange. This results in the formation of P-Fe-HA complexes, thus increasing P solution concentrations by solubilizing P from the labile pool and reducing errors caused by suspended colloids. We used this method to measure E values in five soils with low P status, without or with the addition of carrier P. The addition of Fe-HA (at 50 or 200 mg Fe-HA/L to the equilibration solution) substantially decreased the measured E value without carrier P addition in four of the five soils, while there was no or little effect when carrier P was added. The higher Fe-HA rate increased solution concentrations of stable and radioactive P more than the lower rate, but there was no significant difference in measured E values between the two Fe-HA rates. The method was also applied to 15 subsoils with low P status. Overall, our results indicated that the addition of Fe-HA provides an easy and robust way to avoid analytical issues in the determination of E values in soils with low concentrations of P in solution.

Acid sulfate soils are found globally and have significant environmental impact as a source for metals and acidity to surrounding streams that can cause, for example, large-scale fish kills. In the face of changing climate and its effect on groundwater fluctuations, the environmental risk associated with these soils needs to be thoroughly investigated. This study examined the water-soluble concentrations of multiple elements from the oxidized, transition and reduced zones of acid sulfate soil profiles situated on the Swedish coastal plains. By comparing untreated (naturally oxidized in field) and incubated samples from these zones, we gain insight into the current and near-future mobilization and leaching of acidity and metals that occur in these soils. The results showed that concentrations of Al, Cd, Co, Mn, Ni, S and Zn mobilized from incubated samples were about an order of magnitude higher than from the untreated samples. Notably, the concentrations of mobilized Co, Mn and Ni were higher than released by 1 M HCl at the same sites, highlighting the particularly high mobility of these metals from in situ oxidation of acid sulfate soils. Conversely, Fe and Cu showed lower than expected water-soluble concentrations and were also low compared to the 1 M HCl-extractable element concentrations, likely due to rapid re-mobilization of secondary Fe minerals. Arsenic, Cr and Pb showed overall low water-soluble concentrations in both the incubated and untreated samples, consistent with these elements not being abundantly leached from acid sulfate soils. This observation was further supported by the retention of these metals in secondary Fe-mineral phases such as jarosite and schwertmannite as reported in previous studies. A strong correlation between acidity and near-total S indicated that S can serve as an indicator for the acidification risks associated with acid sulfate soil oxidation. Overall, the findings demonstrated that even a small lowering of the groundwater table can lead to significant mobilization of metals and acidity. This highlights the increased risks of environmental degradation in the face of climate change and intensified drainage operations and, thus, the need for proper management to reduce the risks.

Over the last decade, the fact that novel perspectives on various aspects of soils have remained strongly controversial long after they emerged, without any kind of consensus being reached about them, raises question about the underlying reasons for this phenomenon. The on-going debate on the usefulness of aggregates to describe the functions of soils illustrates some of the key aspects of that question. Similar debates on other soil-related issues also appear stalled, or have been for a long time and are only now moving forward. This might suggest a fundamental aversion to change, which when it gets overcome, only does so slowly. However, at the same time, somewhat surprisingly, researchers appear willing to quickly seize opportunities provided by new idea or novel perspectives on other topics. In that context, the objective of the present article is to analyse in detail what may cause such contrasting reactions to novelty. We consider, then ultimately dismiss, explanations based on how strongly or not novel perspectives have been actively promoted, on how access to suitable technology may impede or only slow down perspective shifts and on whether a recent theory of the ‘slowed canonical progress in large fields of science’ applies to the relatively small soil science community. Then, taking soil aggregates as a case in point, we come to realize that it is the extent to which a novel perspective mandates an interdisciplinary approach that determines whether or not it is adopted quickly. From that standpoint, we envisage a number of practical actions that could be taken to facilitate in the future the emergence in soil science of interdisciplinary research efforts, which we argue are absolutely essential to successfully tackle the enormous complexity of soils and to come up with satisfactory answers to the daunting environmental and food security problems we currently face in their management.

Cover crops are grown between two main crops to reduce periods of bare fallow. In highly diverse crop rotations, the lengths of break periods between two main crops vary highly over time and consequently the cover cropping management differs from year to year. Long-term field trials are thus of limited use because the same cover cropping approach only appears once in several years. This increases the need to better determine the immediate effects of different cover cropping strategies on soil properties. This study evaluated two cover cropping strategies and monitored the temporal development of several soil properties on six fields in Eastern Switzerland in the 9 months period between harvest of winter wheat and sowing of spring crops. The two tested strategies were (a) double cover cropping (DCC) where two cover crops mixtures were grown subsequently and shallowly (3 cm) incorporated into the topsoil and (b) permanent soil cover (PSC) with one grass-clover mixture, which was harvested and thus not incorporated into the soil. Soil samples at three different soil depths (0–5, 5–10 and 10–20 cm) were sampled four times in high spatial resolution and analysed using a combined approach of visible near infrared spectroscopy and conventional lab methods. Differences between the sampling times and field sites were stronger than effects of different treatments. For soil organic carbon (SOC), no significant difference was measured between treatments in 0–20 cm soil depth. Only when analysed per depth segment, the PSC treatment showed significantly higher SOC increase in 5–10 cm soil depth than the DCC treatment. This could be due to the longer soil cover and thereby associated longer root growth period in the PSC treatment, leading to higher below ground C inputs than in the DCC treatment. On the other hand, the DCC treatment showed generally higher increases in permanganate oxidizable carbon stocks (0–5 cm), microbial C (0–10 cm), microbial N (0–10 cm) and mineral N (0–10 cm) than the PSC treatment. We conclude that maximizing cover crop above ground biomass input by planting two cover crops (DCC) benefitted soil microorganisms on most fields but was less beneficial on SOC than permanent soil cover (PSC) in 5–10 cm soil depth.

Simple humus balance calculators were developed for farmers and consultants to determine the best crop rotation and amount of organic fertilizer required to improve soil quality and prevent nutrient leaching in croplands. Although the potential of these tools to infer the impact of different agricultural practices on soil organic carbon (SOC) dynamics in croplands is not well studied, they have been integrated in several farm-level climate or environmental impact assessment calculators. Here we examine the correlation between humus balance values estimated with two different tools developed in Germany/Central Europe and observed changes in SOC content at 14 long-term sites in Switzerland. The first tool was developed by the Association of German Agricultural Investigation and Research Institutes and is referred to as the VDLUFA. The humus balance calculator STAND is a descendent of the VDLUFA that accounts for pedoclimatic factors in Central Europe. Crop rotations were distinguished based on cultivation practice, whereby those with mixed fertilization were supplied with mineral fertilizer alone and in combination with organic materials, while those with organic fertilization include unfertilized and organic fertilizer treatments. An analysis of 133 short-term observations (i.e. individual crop rotations of five and 6-year duration) and 26 long-term observations (i.e. several crop rotations with a total duration of ≥10 years) showed that humus balance values (kg C ha⁻¹ year⁻¹) of short-term crop rotations were not or only poorly correlated with the observed change in SOC content (%) (R² = 0.06 in STAND and R² = 0.05 in VDLUFA for crop rotations with organic fertilization, and R² < 0.01 for crop rotations with mixed fertilization). The correlation did not improve when the humus balance values of long-term observations with mixed fertilization were compared with decadal SOC development (R² = 0.04 for STAND and R² = 0.06 for the VDLUFA). Stronger correlations were found only for long-term observations with organic fertilization (R² = 0.68 for STAND and R² = 0.64 for the VDLUFA). These findings underline that while the studied humus balance calculators are able to distinguish the effect of different fertilizers (organic vs. mineral) on a farm's humus supply on the longer term, neither are suited for predicting SOC trends over single crop rotations. Although this study was carried out in Switzerland, the results should apply to any region with temperate climate and similar soil properties.

The quantification of soil strength parameters is a crucial prerequisite for constructing physical models related to hydro-geophysical processes. However, due to ignoring soil spatial variability at different scales, traditional parameter assignment strategies, such as assigning values depending on land use classification or other classification systems, as well as those extrapolation and interpolation methods, are insufficient for physical process modelling. This work addressed this deficiency by proposing a method to derive stochastic soil strength parameters under hybrid machine learning (ML) models, taking into account the grain-size distribution (GSD) of soil with scaling invariance. The nonlinear connection between GSD parameters (derived from GSD curves, such as μ and D_c), moisture content, and soil shear strength parameters was fitted by the suggested hybrid ML model. An analysis of a case study revealed that: (i) the Multi-layer Perceptron optimized by the African Vulture Optimization Algorithm (AVOA) algorithm performs the best and can estimate the shear strength parameters of soil mass on vegetated slopes; (ii) all the selected ML models showed significant improvements in predictive performance after optimization with the AVOA, with R² scores increasing by 24.72% for Support Vector Regressor, 34.04% for eXtreme Gradient Boosting, and 35.53% for Multi-layer Perceptron; and (iii) soil cohesion has an increasing relationship with the GSD parameter μ, while soil internal friction angle has a negative correlation with the grain-size parameter D_c. The proposed methodology can give predictions of soil shear strength distribution parameters, providing parameter support for the physical modelling of surface process dynamics.