Soil erodibility (K-factor) is an important parameter in erosion modeling, is one of five factors of the Revised Universal Soil Loss Equation (RUSLE), and generally represents the soil\'s response to rainfall and run-off erosivity. The erodibility could be determined based on direct measurements of soil properties and mathematical calculations. In this study, the K-factor was calculated based on a formula from RUSLE, proposed by Renard et al. (1997). All input parameters: soil organic carbon (SOC), soil structure, and permeability classes were measured by one method, but particle size distribution - in two ways by sedimentation and laser diffraction methods to assess the impact the K-factor variability and the values of soil erosion rates. The 107 soil samples of Chernozems from Kursk Oblast (Russia) were studied. The texture for the most of samples was classified as silty loam in both analyses. However, the laser diffraction underestimates the clay content by an average of 13.2 % compared to the pipette method. The average K-factor estimated based on laser diffraction data was 0.050, and 0.034 t ha h ha-1 MJ-1 mm-1 - sedimentation method. Thus, depending on the method of soil texture analysis, the RUSLE calculated soil loss could underestimated/overstated by 32 % (or 4 t ha-1 yr-1 on average in the study site). Therefore, we propose a regression equation-based conversion method of laser diffraction data to sedimentation method data for Chernozems.•The Laska-TM laser analyzer measured on ∼ 13 % less clay fraction (more on ∼ 8 % silt and ∼ 5 % fine sand) compared with sedimentation method data.•For erosional researchers/modelers it is suggested to state the method of soil texture analysis (based on sedimentation law or laser diffraction) was used for RUSLE K-factor calculations.•To convert K-factor values (for Chernozems) calculated and based on data of the sedimentation method to laser sedimentation - it suggested utilize the coefficient 1.47 (0.68 - vice versa).

Crop yields are affected by hydroclimatic and edaphic conditions, but their interacting roles are often neglected when assessing crop yields at the regional scale. Moreover, often used hydroclimatic conditions such as precipitation and temperature are not as physiologically linked to primary production and yields as actual evapotranspiration. Using statistical models, we quantified the combined effects of edaphic and hydroclimatic conditions on county yields of irrigated rice and rainfed corn, soybean, and spring and winter wheat in the USA (2000-2019). Precipitation and temperature, or actual evapotranspiration, aggregated during the growing season or before and after flowering/silk emergence, in interaction with soil sand content or bulk density, explained up to 87 % of the yield variability. However, actual evapotranspiration explained yields better than precipitation and temperature and their interactions for most combinations of crops and growth periods. At high actual evapotranspiration, yield plateaued or, for spring wheat, decreased. Yields were generally most sensitive to changes in hydroclimatic conditions during part of rather than the entire growing season, and most often after flowering. Soil texture and bulk density modulated the impacts of hydroclimatic conditions: corn and soybean yields were higher in finer soils compared with sandy soils under high evapotranspiration, but lower at low evapotranspiration. Additionally, the yield-maximizing precipitation decreased with sand content and increased with bulk density for most crops. Increasingly available actual evapotranspiration estimates, combined with soil properties, offer an alternative, and more physiologically-based, yield predictor over large climatic gradients to the more widely used precipitation and temperature.

Vineyards hold considerable soil variability between regions and plots, and there is frequently large soil heterogeneity within plots. Clay content in vineyard soils is of interest with respect to soil management, environmental monitoring, and wine quality. However, spatially resolved clay mapping is laborious and expensive. Gamma-ray spectrometry (GS) is a suitable tool for predicting clay content in precision agriculture when locally calibrated, but it has scarcely been tested site-independently and in vineyards. This study evaluated GS to predict clay content with a site-independent calibration and four machine learning algorithms (Support Vector Machines, Random Forest, k-Nearest Neighbors, and Bayesian regulated neuronal networks) in eight vineyards from four German vine-growing regions. Clay content in the studied soils ranged from 62 to 647 g kg-1. The Random Forest calibration was most suitable. Test set evaluation revealed good model performance for the entire dataset with RPIQ = 4.64, RMSEP = 56.7 g kg-1, and R2 = 0.87; however, prediction quality varied between the sites. Overall, GS with the Random Forest model calibration was appropriate to predict the clay content and its spatial distribution, even for heterogeneous geopedological settings and in individual plots. Therefore, GS is considered a valuable tool for soil mapping in vineyards, where clay content and product quality are closely linked.

A 2-month incubation study was carried out using two soil types to determine the nitrogen mineralization of different inorganic-organic amendments. The following seven treatments (Ts) were established: T1 = control (no amendment), T2 = 5 g of dry algae per kg of soil (100%DA), T3 = 136 g of agri-mat per kg of soil (100%GAM), T4 = 61 g of ground grass per kg of soil (100%GG), T5 = 0.6 g of N using lime-ammonium nitrate (LAN) + 2.5 g of dry algae (50%DA50NF), T6 = 50%GAM50NF, and T7 = 50%GG50NF. Three samples per treatment were obtained at 0, 3, 7, 15, 30, 45, and 60 days for N mineral determination. A 2-month glasshouse experiment was established afterward with the following five treatments: T1 = control, T2 = 50%DA, T3 = 50%GAM, T4 = 50%GG, and T5 = 100 NF. The results indicate that nitrogen mineralization was significantly higher in organic-inorganic amendments compared with singular organic amendments. The percentage differences ranged from 157% to 195%. The 50%DA treatment increased the spinach yield by 20.6% in sandy loam and 36.5% in loam soil. It is difficult to fully recommend the 50%DA treatment without field-scale evaluation, but it is a promising option to be considered.

Salinization and sodification are serious and worldwide growing threats to healthy soil functions. Although plants developed a plethora of traits to cope with high salinity, soil bacteria are also essential players of the adaptation process. However, there is still lack of knowledge on how other biotic and abiotic factors, such as land use or different soil properties, affect the bacterial community structure of these soils. Therefore, besides soil chemical and physical investigations, bacterial communities of differently managed salt-affected soils were analysed through 16S rRNA gene Illumina amplicon sequencing and compared. Results have shown that land use and soil texture were the main drivers in shaping the bacterial community structure of the Hungarian salt-affected soils. It was observed that at undisturbed pasture and meadow sites, soil texture and the ratio of vegetation cover were the determinative factors shaping the bacterial community structures, mainly at the level of phylum Acidobacteriota. Sandy soil texture promoted the high abundance of members of the class Blastocatellia, while at the slightly disturbed meadow soil showing high clay content was dominated by members of the class Acidobacteriia. The OTUs belonging to the class Ktedonobacteria, which were reported mostly in geothermal sediments, reached a relatively high abundance in the meadow soil.

Grassland ecology is deteriorating along with a fall in biodiversity and ecosystem services as a result of climate change in the Mediterranean regions. Understanding the mechanism of feedback between soil properties related to available water and increasing aridity is a key component of preserving grassland ecosystems. Structural equation modelling was used to explore a deep understanding of the underlying mechanisms of the feedback between soil properties related to available water and increasing aridity. In most cases, vegetation patches had significantly higher soil properties related to available water than inter-patches. Compared to inter-patches, the fine fractions of silt and clay content, soil organic carbon, saturated hydraulic conductivity, and available water under vegetation patches increased by 3.79%-7.64%, 31.08%-37.64%, 96.65%-141.14%, and 2.63%-9.21%, respectively, under limestone and mica schist lithologies. The fine fractions of silt and clay content, soil organic carbon, and available water were more responsive to the aridity index than the vegetation patch, while saturated hydraulic conductivity was more responsive to the vegetation patch than the aridity index. These complex relationships demonstrated that the available water was significantly positively affected by the vegetation pattern (0.09) and the aridity index (0.21-0.38). Soil texture had a significantly direct effect (0.43-0.53) on available water. Increasing aridity would strengthen the contrast in soil water availability while weakening the contrast in saturated hydraulic conductivity between vegetation patches and inter-patches. Available water was controlled by many aspects except vegetation pattern and aridity index. Understanding these relationships helped in predicting and mitigating the impacts of climate change on soil properties related to available water. The study offered fresh perspectives on the mechanism of vegetation pattern and aridity index on the various soil properties related to available water in arid and semiarid grasslands ecosystems under climate change.

In the quest of achieving sustainable crop productivity, improved soil health, and increased carbon (C) sequestration in the soil, conservation agriculture (CA) is increasingly being promoted and adopted in the Indian subcontinent. However, because some researchers from different regions of the world have reported reduced crop yield under CA relative to agriculture based on conventional tillage (CT), a meta-analysis has been conducted based on published research from India to evaluate the effects of CA on the yield of crops, accumulation of soil organic C as an index of soil health, and C sequestration in the soil in different regions and soil textural groups in the country. The meta-analysis is based on 544 paired observations under CA and CT from 35 publications from India was carried out using Meta Win 2.1 software. The results showed an overall significant (p < 0.05) reduction of 1.15% crop yield under CA compared to CT. Yearwise data showed a reduction of yields under CA from 2009 to 2016, but an increase from 2017 to 2020. Yield reduction was observed in the eastern, north-eastern, and southern regions of India but in western, northern, and north-western regions of the country, an increase was observed under CA rather than CT. Sandy loam and clayey soils exhibited higher crop yield under CA than under CT. Compared to CT, soil organic C content and soil C sequestration under CA increased by 8.9% and 7.3%, respectively. Also, in all the regions and soil textural groups both soil organic C accumulation and soil C sequestration were higher under CA than under CT. Factors such as rainfall, soil depth, available nitrogen (N), and total N significantly influenced the extent of yield increase/decrease and soil organic C accumulation under CA. Overall, results of the meta-analysis suggest that the promotion of CA in India will have to be location-specific taking into consideration the crops, soil attributes, and climatic conditions.

Nontoxic alternatives to chemical soil fumigants for suppressing soilborne pathogens such as Fusarium oxysporum (Fo), one causative agent of strawberry black root rot complex prevalent in the Southeastern United States, are urgently needed. A promising alternative is anaerobic soil disinfestation, in which soil is amended with labile organic materials, irrigated to field capacity, and tarped to induce anaerobic fermentation for a brief period before planting. Pathogen-suppression mechanisms of anaerobic soil disinfestation include anaerobic conditions and generation of reduced metal cations (Fe2+ and Mn2+) and volatile fatty acids (VFAs; e.g., acetic, n-butyric, isovaleric, and others). However, little is known about how the interaction between VFAs, reduced metals, soil texture, and liming influences suppression of Fo. We investigated Fo suppression by VFAs and reduced metal cations in both aqueous and soil-based incubation trials. Inoculum containing Fo chlamydospores was added to aqueous medium containing either 5 or 10 mmol/liter VFAs and either 0.01 or 0.05% (wt/wt) reduced metals. In soil-based incubations, chlamydospore-containing inoculum was applied to sandy, sandy loam, and silty clay soil saturated by solutions containing 10 or 20 mmol/liter VFAs with or without 0.05% (wt/wt) reduced metals. VFAs, particularly in combination with Fe2+ in aqueous solutions and Mn2+ in soils, significantly reduced Fo viability. At the same time, liming and higher soil clay content reduced the effectiveness of VFAs and reduced metals for suppressing Fo, highlighting the influence of soil pH and soil texture on anaerobic soil disinfestation effectiveness.

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil-water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher\'s insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.

The microplastics (MPs) formed by broken plastic film may migrate in the soil under drip irrigation. To investigate the migration distribution of MPs in desert farmland soil under drip irrigation conditions, our study was conducted on farmland in Xinjiang (China). A MP drip irrigation penetration migration testing device was set up in combination with Xinjiang farmland irrigation methods to conduct a migration simulation experiment. The results showed that the migration amount of MPs in soil was significantly positively correlated with the amount of drip irrigation, and significantly negatively correlated with the soil depth; in addition, the relationship between the migration amount of MPs in different types of soil was: clay < sandy loam < sandy soil. Under drip irrigation conditions, the migration rates of MPs were 30.51%, 19.41%, and 10.29% in sandy soil, sandy loam soil, and clay, respectively. The migration ability of these three particle sizes of polyethylene MPs in soil was ranked as follows: 25 to 147 μm > 0 to 25 μm > 147 to 250 μm. When the drip irrigation volume was 2.6 to 3.2 L, horizontal migration distances of MPs exceeded 5 cm, and vertical migration distances reached more than 30 cm. Our findings provide reference data for the study of soil MP migration. Environ Toxicol Chem 2024;43:1250-1259. © 2024 SETAC.