Low-disturbance wild tea plantations provide useful field systems for examining how natural habitat heterogeneity is associated with rhizosphere soil conditions and microbial community organization. In this study, rhizosphere soils were collected from three naturally contrasting habitats in a wild tea plantation in Hainan, China: open sites, slope-shaded sites, and rainforest-shaded sites. Soil physicochemical properties were measured, and shotgun metagenomic sequencing was used to characterize microbial taxonomic composition and KEGG Ortholog-based functional potential. Clear habitat-associated edaphic differentiation was observed. Rainforest-shaded soils had relatively higher pH, organic matter, available phosphorus, and available potassium than the other habitats, whereas nitrogen-related indices remained comparatively stable. Microbial alpha diversity did not differ significantly among habitats, but Bray-Curtis-based beta diversity showed a significant overall habitat effect, with the principal compositional gradient involving RS relative to OP and SS. This pattern indicates that habitat-associated variation was expressed more strongly through community compositional reorganization than through changes in overall richness. Redundancy analysis further indicated that pH, organic matter, and available phosphorus were the main edaphic variables associated with microbial community differentiation. The most abundant KOs showed habitat-associated patterns in relative representation. Open-site soils showed higher representation of metagenome-inferred KOs annotated to substrate transport and environmental sensing, slope-shaded soils showed higher representation of KOs annotated to redox balance and stress-related processes, and rainforest-shaded soils showed higher representation of KOs annotated to genetic information processing and cellular maintenance. These patterns reflect differences in encoded functional potential rather than realized microbial activity. Overall, these results suggest that natural habitat heterogeneity in low-disturbance wild tea systems is associated with coordinated variation in rhizosphere soil fertility and microbial community composition, together with descriptive variation in selected components of metagenome-inferred functional potential. These findings provide field-based evidence for aboveground–belowground linkage in wild tea soils and highlight the importance of edaphic gradients in structuring rhizosphere microbiomes.
The diversity of soil classification systems makes correlation between them challenging. The World Reference Base for Soil Resources (WRB) stands as a reference framework for international correlation of soils. In Portugal, soil mapping remains heterogeneous, as 55.3% of the mainland was mapped at a scale of 1:25,000 using the Classification of the Soils of Portugal (CSP), while the remaining area was mapped at a scale of 1:100,000 using the WRB system. Converting these legacy maps into a unified national 1:100,000 soil map in the WRB system requires the reinterpretation and remapping of the outdated 1:25,000 soil map. The present study discusses the categorical levels of the genetic-based CSP dating from 1974, their correspondence with WRB, and the relevance of updating this classification. The majority of CSP categorical levels show limited correspondence with WRB Reference Soil Groups, since CSP definitions lack the quantitative diagnostic criteria and thresholds required by the WRB. Exceptions include the “Barros” order (83% Vertisols), and the suborders Colluvial soils (100% Regosols) and Humic Litholic soils (78% Umbrisols). Overall correspondence improves when considering soil-forming factors such as lithology, relief, and climate, allowing for a better estimation of dominant and codominant RSGs within mapping units, as well as improved assignment of principal and supplementary WRB qualifiers. The proposed methodology supports the updating and harmonization of soil mapping in Portugal, but, to be successful, it requires the collection of new soil data to refine mapping units. New soil data would also be of great usefulness for revising and modernizing the CSP and improving the use of the WRB system.
Green manuring plays a vital role in enhancing soil fertility and organic matter dynamics in sustainable cropping systems, yet its effectiveness largely depends on the biochemical quality and maturity of the incorporated biomass. To elucidate the influence of crop type and development stage of buried plant biomass on soil traits, a 17-week pot experiment was conducted using safflower (Carthamus tinctorius), radish (Raphanus sativus), and white mustard (Sinapis alba) plants, harvested at two maturity stages: after 11 weeks (biomass A) and 12.5 weeks (biomass B) of cultivation. Biomass maturity exerted a stronger influence than crop identity on plant composition (fat, NDF, cellulose, hemicellulose), amount of residual plant biomass, and on soil properties (β-glucosidase, urease, pH). The more matured biomass (B) contained higher carbohydrates and fat contents, and despite higher recalcitrance indices, it decomposed more completely, resulting in lower residue retention. Crop-specific responses of soil traits were the most distinct at the early harvest stage: radish biomass A enhanced soil enzyme activities and total N, whereas white mustard, particularly biomass B, promoted soil P, Mg, and K availability alongside elevated N-acetyl-β-D-glucosaminidase activity. Safflower decomposed more slowly, indicating potential for longer-term carbon stabilization but limited immediate fertility gains. Overall, harvest timing emerged as a critical determinant in tailoring green manures for both nutrient cycling efficiency and soil carbon stabilization.
While within-plot soil variability strongly influences crop development and agronomic management efficiency, practical and transferable methods for characterizing this variability remain limited. This study proposes a remote sensing framework to assess soil-driven crop heterogeneity using Normalized Difference Vegetation Index (NVDI)-derived parameters and phenological analysis. Crop dynamics were monitored using Sentinel-2 imagery to identify key phenological stages (start of season, maximum development, and senescence), while high-resolution PlanetScope imagery was used to analyze within-plot spatial variability. NDVI dispersion metrics were used as proxies for spatial variability in crop development across soil units and under homogeneous management conditions to isolate soil effects. Results showed that NDVI standard deviation effectively captured spatial heterogeneity in crop development, revealing greater variability in Glacis Slope soils compared with the more homogeneous Platform unit. Crop senescence emerged as the most informative stage for detecting soil-driven variability, while emergence patterns were influenced by sowing dates and early establishment conditions. Analyses under homogeneous management confirmed that soil properties controlling soil moisture dynamics govern spatial variability in crop response. The proposed methodology provides a robust, low-cost, and transferable approach for identifying within-plot variability and supports precision agriculture by enabling site-specific management decisions based on satellite-derived indicators.
