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Cultivation of sloping land is a main cause for soil erosion. Conservation practices, such as soil and stone terraces, may reduce the impacts of erosion but their impacts on soil microbial diversity and functioning related to carbon (C) and nutrient metabolisms remain unclear. This study was conducted to evaluate the effects of slope gradients (5°, 8°, 15°, 25°) and conservation practices (cultivated, uncultivated, soil terrace, and stone terrace) on bacterial and fungal diversities, metagenomic and metabolomic functioning associated with basic soil properties. Our results showed that steep slopes at 25° significantly decreased soil pH, silt percentage, and bacterial and fungal abundances, but that soil and stone terraces increased soil organic C (SOC), silt and clay contents, and fungal abundance compared to sloping cultivated lands. In addition, soil and stone terraces increased both bacterial and fungal alpha diversities, and relative abundances of Crenarchaeota, Nitrospirota, and Latescibacterota, but reduced the proportions of Actinobacteriota and Patescibacteria, thus shifting microbial beta diversities, which were significantly associated with increased SOC and silt content. For metagenomics, soil and stone terraces greatly increased the relative abundance of functional genes related to Respiration, Virulence, disease and defense, Stress response, and nitrogen and potassium metabolisms, such as Denitrification and Potassium homeostasis. For soil metabolomics, a total of 22 soil metabolites was enriched by soil and stone terraces, such as Lipids and lipid-like molecules (Arachidonic acid, Gamma-Linolenic acid, and Pentadecanoic acid), and Organoheterocyclic compounds (Adenine, Laudanosine, Methylpyrazine, and Nicotinic acid). To sum up, soil and stone terraces could reduce some of the negative impacts of steep slope cultivation on soil microbial diversity as well as their metagenomic and metabolomic functioning related to C and nutrient metabolism useful for soil health improvement, potentially bolstering the impact of sustainable practices in erosion hotspots around the world.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Inoculation with plant growth-promoting rhizobacteria (PGPR) strains has promoted plant growth and decreased nitrous oxide (N2O) emissions from agricultural soils simultaneously. However, limited PGPR strains can mitigate N2O emissions from agricultural soils, and the microbial ecological mechanisms underlying N2O mitigation after inoculation are poorly understood. In greenhouse pot experiments, the effects of inoculation with Stutzerimonas stutzeri NRCB010 and NRCB025 on tomato growth and N2O emissions were investigated in two vegetable agricultural soils with contrasting textures. Inoculation with NRCB010 and NRCB025 significantly promoted tomato growth in both soils. Moreover, inoculation with NRCB010 decreased the N2O emissions from the fine- and coarse-textured soils by 38.7% and 52.2%, respectively, and inoculation with NRCB025 decreased the N2O emissions from the coarse-textured soil by 76.6%. Inoculation with NRCB010 and NRCB025 decreased N2O emissions mainly by altering soil microbial community composition and the abundance of nitrogen-cycle functional genes. The N2O-mitigating effect might be partially explained by a decrease in the (amoA + amoB)/(nosZI + nosZII) and (nirS + nirK)/(nosZI + nosZII) ratios, respectively. Soil pH and organic matter were key variables that explain the variation in abundance of N-cycle functional genes and subsequent N2O emission. Moreover, the N2O-mitigating effect varied depending on soil textures and individual strain after inoculation. This study provides insights into developing biofertilizers with plant growth-promoting and N2O-mitigating effects. IMPORTANCE: Plant growth-promoting rhizobacteria (PGPR) have been applied to mitigate nitrous oxide (N2O) emissions from agricultural soils, but the microbial ecological mechanisms underlying N2O mitigation are poorly understood. That is why only limited PGPR strains can mitigate N2O emissions from agricultural soils. Therefore, it is of substantial significance to reveal soil ecological mechanisms of PGPR strains to achieve efficient and reliable N2O-mitigating effect after inoculation. Inoculation with Stutzerimonas stutzeri strains decreased N2O emissions from two soils with contrasting textures probably by altering soil microbial community composition and gene abundance involved in nitrification and denitrification. Our findings provide detailed insight into soil ecological mechanisms of PGPR strains to mitigate N2O emissions from vegetable agricultural soils.

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Water management in paddy soils can effectively reduce the soil-to-rice grain transfer of either As or Cd, but not of both elements simultaneously due to the higher mobility of As under reducing and Cd under oxidizing soil conditions. Limestone amendment, the common form of liming, is well known for decreasing Cd accumulation in rice grown on acidic soils. Sulfate amendment was suggested to effectively decrease As accumulation in rice, especially under intermittent soil flooding. To study the unknown effects of combined sulfate and limestone amendment under intermittent flooding for simultaneously decreasing As and Cd in rice, we performed a pot experiment using an acidic sandy loam paddy soil. We also included a clay loam paddy soil to study the role of soil texture in low-As rice production under intermittent flooding. We found that liming not only decreased rice Cd concentrations but also greatly decreased dimethylarsenate (DMA) accumulation in rice. We hypothesize that this is due to suppressed sulfate reduction, As methylation, and As thiolation by liming in the sulfate-amended soil and a higher share of deprotonated DMA at higher pH which is taken up less readily than protonated DMA. Decreased gene abundance of potential soil sulfate-reducers by liming further supported our hypothesis. Combined sulfate and limestone amendment to the acidic sandy loam soil produced rice with 43% lower inorganic As, 72% lower DMA, and 68% lower Cd compared to the control soil without amendment. A tradeoff between soil aeration and water availability was observed for the clay loam soil, suggesting difficulties to decrease As in rice while avoiding plant water stress under intermittent flooding in fine-textured soils. Our results suggest that combining sulfate amendment, liming, and intermittent flooding can help to secure rice safety when the presence of both As and Cd in coarse-textured soils is of concern.

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This study investigates the impact of water levels and soil texture on the migration and transformation of nitrate (NO3--N) and ammonium (NH4+-N) within a soil column. The concentrations of NO3--N gradually decreased from an initial concentration of 34.19 ± 0.86 mg/L to 14.33 ± 0.77 mg/L on day 70, exhibiting fluctuations and migration influenced by water levels and soil texture. Higher water levels were associated with decreased NO3--N concentrations, while lower water levels resulted in increased concentrations. The retention and absorption capacity for NO3--N were highest in fine sand soil, followed by medium sand and coarse sand, highlighting the significance of soil texture in nitrate movement and retention. The analysis of variance (ANOVA) confirmed statistically significant variations in pH, dissolve oxygen and oxidation-reduction potential across the soil columns (p < 0.05). Fluctuating water levels influenced the migration and transformation of NO3--N, with distinct patterns observed in different soil textures. Water level fluctuations also impacted the migration and transformation of NH4+-N, with higher water levels associated with increased concentrations and lower water levels resulting in decreased concentrations. Among the soil types considered, medium sand exhibited the highest absorption capacity for NH4+-N. These findings underscore the significant roles of water levels, soil texture, and soil type in the migration, transformation, and absorption of nitrogen compounds within soil columns. The results contribute to a better understanding of nitrogen dynamics under varying water levels and environmental conditions, providing valuable insights into the patterns of nitrogen migration and transformation in small-scale soil column experiments.

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Soil texture is one of the most important indicators of soil physical properties, which has traditionally been measured through laborious procedures. Approaches utilizing visible near-infrared spectroscopy, with their advantages in efficiency, eco-friendliness and non-destruction, are emerging as potent alternatives. Nevertheless, these approaches often suffer from limitations in classification accuracy, and the substantial impact of spectral preprocessing, model integration, and sample matrix effect is commonly disregarded. Here a novel 11-class soil texture classification strategy that address this challenge by combining Multiplicative Scatter Correction (MSC) with Residual Network (ResNet) models was presented, resulting in exceptional classification accuracy. Utilizing the LUCAS dataset, collected by the Land Use and Cover Area frame Statistical Survey project, we thoroughly evaluated eight spectral preprocessing methods. Our findings underscored the superior performance of MSC in reducing spatial complexity within spectral data, showcasing its crucial role in enhancing model precision. Through comparisons of three 1D CNN models and two ResNet models integrated with MSC, we established the superior performance of the MSC-incorporated ResNet model, achieving an overall accuracy of 98.97 % and five soil textures even reached 100.00 %. The ResNet model demonstrated a marked superiority in classifying datasets with similar features, as observed by the confusion matrix analysis. Moreover, we investigated the potential benefit of pre-categorization based on land cover type of the soil samples in enhancing the accuracy of soil texture classification models, achieving overall classification accuracies exceeding 99.39 % for woodland, grassland, and farmland with the 2-layer ResNet model. The proposed work provides a pioneering and efficient strategy for rapid and precise soil texture identification via visible near-infrared spectroscopy, demonstrating unparalleled accuracy compared to existing methods, thus significantly enhancing the practical application prospects in soil, agricultural and environmental science.

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College and university campuses with a notable arboreal component provide unique opportunities for carrying out ecological research. The University of West Florida Campus Ecosystem Study (UWF CES) was established in 2019 as interconnected research to take advantage of the extensive arborescent nature of the UWF campus, particularly concerning longleaf pine (Pinus palustris). One of these investigations established permanent plots in forested sites of two contrasting types, one dominated by longleaf pine ("pine site") and the other dominated by hardwoods ('hardwood site'). This study used these plots to examine the influence of forest vegetation on light availability and soil processes. Light was measured as photosynthetically active radiation (and expressed as photon flux density-PFD) with a handheld meter in each plot. Soil was sampled to 5 cm in each plot; texture was measured with the hydrometer method. Identical sampling methods were carried out in a persistent canopy opening to assess light and soil conditions under maximum solar radiation. Mean PFD was ~4× higher in pine stands than in hardwood stands; PFD was 12.8 and 3.5% of full light in the pine and hardwood stands, respectively. All soils were dominated by coarse-textured sands, but silt was significantly higher in pine stand soil and higher still in the canopy opening. Among forest stand plots, sand was negatively related to PFD, whereas clay was positively related to PFD. Across the three sites, silt was positively related to PFD. These relationships are consistent with the importance of solar radiation as one of many drivers of soil weathering.

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Growing demands for on-trail activities have aroused challenges of how to balance outdoor recreation and protected area conservation. Unfortunately, most previous studies have focused on only one or two degradation features, so inconsistent views (i.e., no consensus on impacts induced by hiking and mountain biking) were obtained. This study investigated a mountain biking trail, a hiking trail, and an unmanaged trail in a Hong Kong country park for 1.5 years. Five dimensions of trail degradation, namely Trail Morphometry, Soil Texture, Soil Compaction, Erosion Feature, and Recovery Feature, were identified by Explanatory Factor Analysis. Significant correlations were found between hiking and all five dimensions, while mountain biking was significantly associated only with Trail Morphometry, Erosion Feature, and Recovery Feature. Trail grade and landform grade significantly affected Trail Morphometry, Soil Texture, and Erosion Feature, but trail slope alignment was a null predictor. Hiking caused more severe trail degradation than mountain biking, even after controlling for topography. These findings supplement the existing literature by providing a comprehensive understanding of the multitude of adverse impacts caused by hiking and mountain biking. This study provides a valuable reference for sustainable trail management in protected areas that receive both hikers and mountain bikers.

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The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.

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Environmental disturbances such as drought can impact soil health and the resistance (ability to withstand environmental stress) and resilience (ability to recover functional and structural integrity after stress) of soil microbial functional activities. A paucity of information exists on the impact of drought on soil microbiome and how soil biological systems respond to and demonstrate resilience to drought stress. To address this, we conducted a systematic review and meta-analysis (using only laboratory studies) to assess the response of soil microbial biomass and respiration to drought stress across agriculture, forest, and grassland ecosystems. The meta-analysis revealed an overall negative response of microbial biomass in resistance (-31.6 %) and resilience (-0.3 %) to drought, suggesting a decrease in soil microbial biomass content. Soil microbial respiration also showed a negative response in resistance to drought stress indicating a decrease in soil microbial respiration in agriculture (-17.5 %), forest (-64.0 %), and grassland (-65.5 %) ecosystems. However, it showed a positive response in resilience to drought, suggesting an effective recovery in microbial respiration post-drought. Soil organic carbon (SOC), clay content, and pH were the main regulating factors of the responses of soil microbial biomass and respiration to drought. In agriculture ecosystem, soil pH was primarily correlated with soil microbial respiration resistance and resilience to drought, potentially influenced by frequent land preparation and fertilizer applications, while in forest ecosystem SOC, clay content, and pH significantly impacted microbial biomass and respiration resistance and resilience. In grassland ecosystem, SOC was strongly associated with biomass resilience to drought. The impact of drought stress on soil microbiome showed different patterns in natural and agriculture ecosystems, and the magnitude of microbial functional responses regulated by soil intrinsic properties. This study highlighted the importance of understanding the role of soil properties in shaping microbial responses to drought stress for better ecosystem management.

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Deciphering root exudate composition of soil-grown plants is considered a crucial step to better understand plant-soil-microbe interactions affecting plant growth performance. In this study, two genotypes of Zea mays L. (WT, rth3) differing in root hair elongation were grown in the field in two substrates (sand, loam) in custom-made, perforated columns inserted into the field plots. Root exudates were collected at different plant developmental stages (BBCH 14, 19, 59, 83) using a soil-hydroponic-hybrid exudation sampling approach. Exudates were characterized by LC-MS based non-targeted metabolomics, as well as by photometric assays targeting total dissolved organic carbon, soluble carbohydrates, proteins, amino acids, and phenolics. Results showed that plant developmental stage was the main driver shaping both the composition and quantity of exuded compounds. Carbon (C) exudation per plant increased with increasing biomass production over time, while C exudation rate per cm² root surface area h-1 decreased with plant maturity. Furthermore, exudation rates were higher in the substrate with lower nutrient mobility (i.e., loam). Surprisingly, we observed higher exudation rates in the root hairless rth3 mutant compared to the root hair-forming WT sibling, though exudate metabolite composition remained similar. Our results highlight the impact of plant developmental stage on the plant-soil-microbe interplay.

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