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Background and Aims Crop residues are important for the redistribution of alkalinity within soils. A net increase in pH following residue addition to soil is typically reported. However, effects are inconsistent in the field due to confounding soil processes and agronomic practises. Methods A column experiment investigated the effects of canola, chickpea and wheat residues, differing in alkalinity content and C:N ratio, on soil pH changes in a Podosol (Podzol; initial pH 4.5) and Tenosol (Cambisol; initial pH 6.2) under field conditions. Results Residues (10 g dry matter kg⁻¹ soil; 0-10 cm) increased soil pH, and temporal changes in alkalinity depended on the residue and soil type. Alkalinity was generated via abiotic association reactions between H⁺ and added organic matter and via ammonification and decarboxylation processes during decomposition. Alkalinity from canola and chickpea residues moved down the soil profile (10-30 cm) and was attributed to nitrate immobilisation and organic anion decomposition by soil microbes. Conclusions The application of residues to acid and moderately acid soils increased the pH of both topsoil and subsoils, which persisted over 26 months. Maximal increase of pH observed at 3 months was correlated with the concentration of excess cations in the residues.

The sixth edition of the Polish Soil Classification (SGP6) aims to maintain soil classification in Poland as a modern scientific system that reflects current scientific knowledge, understanding of soil functions and the practical requirements of society. SGP6 continues the tradition of previous editions elaborated upon by the Soil Science Society of Poland in consistent application of quantitatively characterized diagnostic horizons, properties and materials; however, clearly referring to soil genesis. The present need to involve and name the soils created or naturally developed under increasing human impact has led to modernization of the soil definition. Thus, in SGP6, soil is defined as the surface part of the lithosphere or the accumulation of mineral and organic materials permanently connected to the lithosphere (through buildings or permanent constructions), coming from weathering or accumulation processes, originated naturally or anthropogenically, subject to transformation under the influence of soil-forming factors, and able to supply living organisms with water and nutrients. SGP6 distinguishes three hierarchical categories: soil order (nine in total), soil type (basic classification unit; 30 in total) and soil subtype (183 units derived from 62 unique definitions; listed hierarchically, separately in each soil type), supplemented by three non-hierarchical categories: soil variety (additional pedogenic or lithogenic features), soil genus (lithology/parent material) and soil species (soil texture). Non-hierarchical units have universal definitions that allow their application in various orders/types, if all defined requirements are met. The paper explains the principles, classification scheme and rules of SGP6, including the key to soil orders and types, explaining the relationships between diagnostic horizons, materials and properties distinguished in SGP6 and in the recent edition of WRB system as well as discussing the correlation of classification units between SGP6, WRB and Soil Taxonomy.

Background Agricultural food production is at the base of food and fodder, with fertilization having fundamentally and continuously increased crop yield over the last decades. The performance of crops is intimately tied to their microbiome as they together form holobionts. The importance of the microbiome for plant performance is, however, notoriously ignored in agricultural systems as fertilization disconnects the dependency of plants for often plant-beneficial microbial processes. Moreover, we lack a holistic understanding of how fertilization regimes affect the soil microbiome. Here, we examined the effect of a 2-year fertilization regime (no nitrogen fertilization control, nitrogen fertilization, and nitrogen fertilization plus straw amendment) on entire soil microbiomes (bacteria, fungi, and protist) in three common agricultural soil types cropped with maize in two seasons. Results We found that the application of nitrogen fertilizers more strongly affected protist than bacterial and fungal communities. Nitrogen fertilization indirectly reduced protist diversity through changing abiotic properties and bacterial and fungal communities which differed between soil types and sampling seasons. Nitrogen fertilizer plus straw amendment had greater effects on soil physicochemical properties and microbiome diversity than nitrogen addition alone. Moreover, nitrogen fertilization, even more together with straw, increased soil microbiome network complexity, suggesting that the application of nitrogen fertilizers tightened soil microbiomes interactions. Conclusions Together, our results suggest that protists are the most susceptible microbiome component to the application of nitrogen fertilizers. As protist communities also exhibit the strongest seasonal dynamics, they serve as the most sensitive bioindicators of soil changes. Changes in protist communities might have long-term effects if some of the key protist hubs that govern microbiome complexities as top microbiome predators are altered. This study serves as the stepping stone to promote protists as promising agents in targeted microbiome engineering to help in reducing the dependency on exogenous unsustainably high fertilization and pesticide applications.

Soil types mapping and the spatial variation of soil classes are essential concerns in both geotechnical and geoenvironmental engineering. Because conventional soil mapping systems are time-consuming and costly, alternative quick and cheap but accurate methods need to be developed. In this paper, a new optimized multi-output generalized feed forward neural network (GFNN) structure using 58 piezocone penetration test points (CPTu) for producing a digital soil types map in the southwest of Sweden is developed. The introduced GFNN architecture is supported by a generalized shunting neuron (GSN) model computing unit to increase the capability of nonlinear boundaries of classified patterns. The comparison conducted between known soil type classification charts, CPTu interpreting procedures, and the outcomes of the GFNN model indicates acceptable accuracy in estimating complex soil types. The results show that the predictability of the GFNN system offers a valuable tool for the purpose of soil type pattern classifications and providing soil profiles.

Biochar has been widely used for soil improvement, but uncertain results persist due to diverse biochar characteristics, soil properties, and crop responses. Therefore, the effects of biochar on crop yields and soil quality were evaluated using effect size method from 1011 paired data points from field trials, based on a global meta-analysis method. The results indicated that biochar with a higher total phosphorus concentration (≥1.0%), total carbon concentration (≥70%), and specific surface area (≥50 m 2 g ‒1) were optimal for improving crop yields. For improving crop yields, biochar made from manure (effect size, 42%) outperformed that made from ligneous (22%) or cereal (12%) material. Porous, acidic, or young soil types were optimal for biochar application, while sandy and clay soils were preferred over loam soil. Soils with lower available nitrogen (<80 mg kg ‒1), phosphorus (<10 mg kg ‒1), potassium (<120 mg kg ‒1), pH (<4.5), and cation exchange capacity (<10 cmol kg ‒1) were more effective on crop yield increases. The effect of biochar on yield was higher for cash crops (oil plants: 37%, vegetables: 28%) compared to food crops (legumes: 26%, maize: 20%, wheat: 12%, rice: 6%), but with no significant effect observed on rice ( P=0.788). Finally, biochar increases crop yields by improving soil quality through enhanced levels of soil organic carbon, total nitrogen, ammonium-nitrogen, nitrate-nitrogen, and soil pH while reducing soil bulk density. Our research enhances understanding of the relationships between biochar, soil, and crops, aiding researchers, manufacturers, and farmers in making informed decisions regarding biochar selection, planting locations, and crop choices.

Aims All components of the soil-plant-atmosphere (s-p-a) continuum are known to control berry quality in grapevine (Vitis vinifera L.) via ecophysiological interactions between water uptake by roots and water loss by leaves. The scope of the present work was to explore how the main hydraulic components of grapevine influence fruit quality through changes in liquid- and gas-phase hydraulic conductance. Methods To reach our objectives, determinations of shoot growth, berry size and sugar content, leaf gas exchange, predawn leaf water potential (as a proxy of soil water potential), midday stem water potential and leaf water potential were performed in conjunction with anatomical measurements of shoot xylem. All measurements were conducted in two different cultivars (Cabernet franc and Merlot) and on three different soil types (clayey, gravelly, and sandy). Results Shoot xylem morphometric characteristics and whole-plant hydraulic conductance were influenced by cultivar and soil type. Differences in leaf gas exchange parameters and water potentials were determined by soil type significantly more than by cultivar. Between the two extremes (gravelly soil imposing drought conditions and sandy soil with easily accessible water) the clayey soil expressed an intermediate plant water consumption and highest sugar accumulation in berry. Conclusions Hydraulic and non hydraulic limitations to vine/berry interactions supported the conclusion that water availability in the soil overrides differences due to cultivar in determining the productive potential of the vineyard. Non hydraulic stomatal control was expected to be an important component on plants grown on the clayey soil, which experienced a moderate water stress. Possible links between hydraulic traits and berry development and quality are discussed.

This study investigated the factors affecting bacterial diversity in purplish and calcareous soils under the same vegetation type. The results showed that obvious differences in pH, water content, total phosphorus (TP), free iron oxide (Fed) and amorphous iron oxide (Feox) existed between the two soils. The bacterial Shannon indexes of the two soils varied significantly, and Shannon index of purplish soil were higher than that of calcareous soils. The co-occurring network structures in two soil bacterial communities showed obvious variations, with higher edge counts, average degrees and clustering coefficients in calcareous soils than that in purple soils. The key species group identified in the two soils was Acidobacteria; belonging to the family Thermoanaerobaculaceae in purplish soil, and to the family Vicinamibacterales in calcareous soil. Also, the results of multivariate stepwise linear regression showed that Feox was a significant explanatory factor for changes in bacterial diversity (68.8%, P < 0.01) compared to other physical and chemical factors. Overall, our study indicated that soil parent material may be an important factor influencing soil bacterial communities.

Growing concerns about the global antimicrobial resistance crisis require a better understanding of how antibiotic resistance persists in soil and how antibiotic exposure impacts soil microbial communities. In agroecosystems, these responses are complex because environmental factors may influence how soil microbial communities respond to manure and antibiotic exposure. The study aimed to determine how soil type and moisture alter responses of microbial communities to additions of manure from cattle treated with antibiotics. Soil microcosms were constructed using two soil types at 15, 30, or 45% moisture. Microcosms received biweekly additions of manure from cattle given cephapirin or pirlimycin, antibiotic-free manure, or no manure. While soil type and moisture had the largest effects on microbiome structure, impacts of manure treatments on community structure and individual ARG abundances were observed across varying soil conditions. Activity was also affected, as respiration increased in the cephapirin treatment but decreased with pirlimycin. Manure from cattle antibiotics also increased NH 4 + and decreased NO 3 − availability in some scenarios, but the effects were heavily influenced by soil type and moisture. Overall, this work demonstrates that environmental conditions can alter how manure from cattle administered antibiotics impact the soil microbiome. A nuanced approach that considers environmental variability may benefit the long-term management of antibiotic resistance in soil systems.

This study aims to create soil zonation maps (SZM) using a spatial interpolation approach relying on vast geotechnical subsoil data gathered through field and laboratory analysis. Islamabad, a rapidly growing city and capital of Pakistan, is used as a case study. The sub-soil data were evaluated from 210 geotechnical investigation reports in terms of soil type, standard penetration (SPT-N value), undrained shear strength, and consolidation parameters. The data were digitally analyzed in ArcGIS using the ordinary kriging interpolation technique, and SZM were developed based on SPT-N and soil type. For the developed SZM, settlement and allowable bearing capacity (ABC) are evaluated for shallow foundations. The results showed that the study area was divided into three main zones based on SPT-N [i.e., zone-1(4–15), zone-2(16–30), and zone-3(> 30)], and six sub-zones based on lithology. The lean/silty clay is predominant up to 15 m, underlain by gravel and shale/sandstone up to 50 m. Correlations were presented based on linear regression analysis with R2 = 0.98 to predict the SPT-N with depth for expeditious appraisal of stiffness and strength of sub-soils throughout preliminary planning and feasibility studies of several construction projects. The ABC for the shallow foundation in Islamabad found to be above 100 kPa, indicating an excellent safe ABC to support foundations of lightly loaded structures. Moreover, the correlation coefficient to predict SPT-N values is around 85%, while about 94% for soil type. Furthermore, reliable information on geotechnical properties of the subsoil’s layers will work as a complement for the site characterization and identification of hazard for upcoming projects.

Machine learning has shown great potential in predicting soil properties, but individual models are often prone to overfitting, limiting their generalization. Ensemble models address this challenge by combining the strengths of multiple algorithms. This study applies a voting-based ensemble model (VEM), integrating Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), to gain a deeper understanding of soil type evolution, which is crucial for land management and soil conservation. The research, conducted in the Tongzhou District of Beijing, uses 5,000 sampling points selected via genetic algorithms for model training, 237 surface samples for consistency testing, and 97 profiles for field validation. The VEM demonstrates high accuracy and robustness, producing a detailed soil type map and identifying key trends in soil type evolution. This study highlights the effectiveness of ensemble models in understanding soil evolution and offers valuable insights into soil system dynamics.