The co-application of biochar, plant growth-promoting rhizobacteria (PGPR), and phosphorus- and potassium-bearing minerals has emerged as a promising strategy for improving soil nutrient availability. However, the synergistic effects and impact factors that facilitate this optimization are yet to be fully elucidated. To address this knowledge gap, we conducted a pot experiment to evaluate the effects of these amendments on tea yield and phosphorus (P)/potassium (K) availability, while employing Random Forest (RF) and Partial Least Squares Structural Equation Modeling (PLS-SEM) to reveal the underlying mechanisms driving these improvements. The results demonstrated that the tripartite combination significantly enhanced tea yield, leaf P/K concentrations, and soil available P (AP)/available K (AK) levels compared to individual applications or pairwise combinations. Analytical modeling identified Chloroflexi bacteria containing pqqc functional genes as key drivers of AP enhancement. The AP was further modulated by β-glucosidase activity, NaHCO₃-P, and AK levels. Critical determinants of AK dynamics included phosphorus-solubilizing bacterial populations, catalase activity, and fundamental soil chemical properties. In summary, our research conclusively shows that the co-application of phosphorus- and potassium-bearing minerals, PGPR, and biochar represents an effective approach to enhancing P and K accessibility in soil, thereby offering a viable alternative to conventional P and K fertilizers in tea cultivation.

Remote sensing change captioning aims to describe disaster-related changes from bi-temporal imagery, yet existing methods typically produce image-level captions without explicit regional evidence, limiting interpretability and weakening the link between generated language and actual changed areas. We present ChangePixel, a single-backbone framework that upgrades remote sensing change captioning into pixel-level, evidence-grounded change narration without introducing new manual grounding labels. ChangePixel incorporates three lightweight modules: a Bi-Temporal Change-Aware Transfer Adapter (BCTA) that converts shared pre- and post-event visual features into change-aware grounding representations, a Change Region Grounding Planner (CRGP) that localizes a compact set of informative changed regions before narration begins, and a Weak Evidence Alignment Bridge (WAB) that converts released change captions into phrase-to-region weak supervision. Through this design, the model jointly produces a global change caption and region-level evidence in the form of pixel masks paired with corresponding local change phrases. Experiments on the Remote Sensing Change Caption (RSCC) dataset and LEVIR-CC demonstrate that ChangePixel provides caption quality (ROUGE 19.52/ST5-SCS 76.91 on RSCC; CIDEr-D 56.82 on LEVIR-CC under zero-shot transfer) that is competitive with general-purpose vision–language models (VLMs) while adding pixel-level spatial evidence to change narration; additionally, evidence localization is quantified on LEVIR-MCI through semantic change-mask metrics, reaching Change mIoU 33.8 (15.3 points higher than a non-learned pixel-difference floor of 18.5), whereas phrase-to-region alignment is assessed qualitatively pending a dedicated grounding benchmark. The proposed framework offers a practical path from coarse image-level captioning to evidence-grounded disaster understanding.

In this study, bacterial α-diversity, represented by Chao1 richness and Shannon diversity, was quantified and mapped across the acidic red and yellow soils of southern China by integrating a comprehensive meta-database of 920 georeferenced observations with multi-source environmental data and machine-learning models. Chao1 and Shannon were modeled using Random Forest, eXtreme Gradient Boosting, and Support Vector Machine algorithms. Among these, Random Forest consistently achieved the highest predictive accuracy and robustness (R² > 0.83). SHapley Additive exPlanations (SHAP) analysis revealed that soil pH acted as the fundamental filter for both diversity dimensions, while they were further shaped by distinct environmental controls. The Normalized Difference Vegetation Index (NDVI) negatively constrained species richness, reflecting the prevalence of intensively managed monoculture systems in this region, whereas temperature exerted a strong negative control on community evenness. Spatial predictions showed pronounced regional contrasts among the Southwest, Middle–Lower Yangtze, and Southern China agricultural zones, with uncertainty patterns closely linked to terrain complexity and data density. The results demonstrate that bacterial richness and evenness respond to distinct yet interacting environmental controls, highlighting the necessity of multidimensional diversity assessment. This study provides a scalable and interpretable framework for digital soil mapping of microbial diversity, offering spatially explicit insights for managing acid-sensitive agroecosystems under environmental change.

1. As a globally significant economic crop, tea cultivation directly influences regional agricultural economies (Y. Zhao et al. 2022). Precise, dynamic monitoring of tea plantation distribution is ...