Zijun Zhou

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.

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.

With growing concerns about global warming, it is crucial to adopt agronomic practices that enhance rice yields from paddy fields while reducing greenhouse gas (GHG) emissions for sustainable agriculture. An optimal nitrogen (N) fertilization rate and planting density are vital to ensure high rice yields, minimize GHG emissions, and understand emission behavior for better field management. We hypothesized that optimizing N application rates and planting density to improve nitrogen use efficiency (NUE) in rice cultivation would reduce resource losses and GHG emissions. To test this hypothesis, we implemented five treatments with a rice straw return cultural system: two planting densities (16 hills m⁻² (traditional density, D1) and 20 hills m⁻² (25% higher density, D2)) and three N application rates (no N fertilizer (N0), 180 kg N ha⁻¹ (N1), and 144 kg N ha⁻¹ (N2)). The control treatment (CK) was traditional planting density with no N fertilizer. The four new cropping modes were N1D1, N1D2, N2D1, and N2D2. We investigated the effects of N application rates and planting density on rice grain yield, NUE, and GHG emissions in multiple rice-growing seasons. The N1D2 treatment exhibited the highest grain yield over the three years, with a value of 10,452 kg ha⁻¹, representing an increase of 12.2% compared to CK. Moreover, N uptake in N1D2 was the highest, averaging 39.2% (p < 0.05) higher than CK, and 8.5%, 3.5%, and 2.8% (p < 0.05) higher than N1D1, N2D1 and N2D2, respectively. N2D2 exhibited the highest NUE, with a value of 58.99 kg kg⁻¹, surpassing all other treatments over the three years. GHG emissions, global warming potential (GWP), and greenhouse gas intensity (GHGI) in N2D2 were lower than in N1D1, N1D2, and N2D1. Additionally, reducing N application (comparing N1D1 to N2D1) and increasing plant density (comparing N1D1 to N1D2) improved N agronomic efficiency (NAE) and N partial productivity (PFP_N). The negative correlation between the NAE and PFP_N with GWP and GHG emissions further supports the potential for optimized N management and denser planting density to reduce environmental impact. These findings have important implications for sustainable rice cultivation practices in Southwest China and similar agroecosystems, emphasizing the need for integrated nutrient management strategies to achieve food security and climate change mitigation goals.

Main conclusion: Integrated root phenotypes and transcriptome analysis have revealed key candidate genes responsible for maize root growth and development in potassium deficiency. Potassium (K) is a vital macronutrient for plant growth, but our understanding of its regulatory mechanisms in maize root system architecture (RSA) and K⁺ uptake remains limited. To address this, we conducted hydroponic and field trials at different growth stages. K⁺ deficiency significantly inhibited maize root growth, with metrics like total root length, primary root length, width and maximum root number reduced by 50% to 80% during early seedling stages. In the field, RSA traits exhibited maximum values at the silking stage but continued to decline thereafter. Furthermore, K deprivation had a pronounced negative impact on root morphology and RSA growth and grain yield. RNA-Seq analysis identified 5972 differentially expressed genes (DEGs), including 17 associated with K⁺ signaling, transcription factors, and transporters. Weighted gene co-expression network analysis revealed 23 co-expressed modules, with enrichment of transcription factors at different developmental stages under K deficiency. Several DEGs and transcription factors were predicted as potential candidate genes responsible for maize root growth and development. Interestingly, some of these genes exhibited homology to well-known regulators of root architecture or development in Arabidopsis, such as Zm00001d014467 (AtRCI3), Zm00001d011237 (AtWRKY9), and Zm00001d030862 (AtAP2/ERF). Identifying these key genes helps to provide a deeper understanding of the molecular mechanisms governing maize root growth and development under nutrient deficient conditions offering potential benefits for enhancing maize production and improving stress resistance through targeted manipulation of RSA traits in modern breeding efforts.

Root system architecture (RSA) plays a critical role in the acquisition of water and mineral nutrients. In order to understand the root characteristics that contribute to enhanced crop yield and N accumulation high-yielding and N efficient cultivars under N-stressed conditions. Here, grain yield, N accumulation and RSA traits of six dominant maize cultivars (CD30, ZH311, ZHg505, CD189, QY9 and RY1210) grown in the Southwestern part of China were investigated in field experiment under three different N regimes in 2019–2020; N300 (300 kg N ha⁻¹), N150 (150 kg N ha⁻¹) and N0 (no N supplied). Using Root Estimator for Shovelomics Traits (REST) for the quantitative analysis of maize root image obtained in the field, RSA traits including total root length (RL), root surface area (RA), root angle opening (RO), and root maximal width (RMW) were quantified in this study. The results showed that Yield, N accumulation and RSA were significantly affected by N rates, cultivars and their interactions. Grain yield, N accumulation and root weight showed a similar trend under N300 and N150 conditions compared to N0 conditions. With the input of N fertilizer, the root length, surface area, and angle increase, but root width does not increase. Under the N300 and N150 condition, RL, RA, RO and RMW increased by 17.96%, 17.74%, 18.27%, 9.22%, and 20.39%, 18.58%, 19.92%, 16.79%, respectively, compared to N0 condition. CD30, ZH505 and RY1210 have similar RO and RMW, larger than other cultivars. However, ZH505 and RY1210 have 13.22% and 19.99% longer RL, and 11.41% and 5.17% larger RA than CD30. Additionally, the grain yield of ZH505 and RY1210 is 17.57% and 13.97% higher compared with CD30. The N accumulation of ZH505 and RY1210 also shows 4.55% and 9.60% higher than CD30. Correlation analysis shows that RL, RA, RO and RMW have a significant positive correlation with grain yield while RO and RMW have a significant positive correlation with N accumulation. Linear plus plateau model analysis revealed that when the RO reaches 99.53°, and the RMW reaches 15.18 cm, the N accumulation reaches its maximum value under 0–300 kg N ha⁻¹ conditions. Therefore, selecting maize cultivars with efficient RSA suitable for different soil N inputs can achieve higher grain yield and N use efficiency.

Conservation tillage has attracted increasing attention over recent decades, mainly due to its benefits in improving soil organic matter content and reducing soil erosion. Under intensive conventional tillage systems, some studies have focused on the responses of soil properties in the topsoil to straw retention. However, long-term straw mulching effects on soil physicochemical properties and bacterial communities among different soil depths under a no-till system are still obscure. One twelve-year experiment was conducted that included straw removal (CK) and straw mulching (SM) treatments. Soil samples were collected at 0–5, 5–10, 10–20, and 20–30 cm soil depths. Most soil physicochemical properties and the relative abundances of bacterial phyla were varied with soil depth. Compared with CK, SM increased soil total nitrogen and organic carbon, available phosphorus and potassium, dissolved organic carbon and nitrogen, and water content. SM increased soil bacterial abundance but reduced the Shannon diversity of the bacterial community at 0–5 cm depth. SM increased the relative abundances of Proteobacteria, Bacteroidetes, and Acidobacteria but reduced those of Actinobacteria, Chloroflexi, and Cyanobacteria. SM had different effects on the relative abundances of some C- and N-cycling genera, for instance, increasing Rhodanobacter, Rhizomicrobium, and Terracidiphilus, and reducing Anaeromyxobacter, Mycobacterium, and Syntrophobacter. A principal coordinate analysis indicated that SM largely affected soil bacterial communities at topsoil depth. Soil pH and different nitrogen and organic carbon fractions were the major drivers shaping soil bacterial community. Overall, straw mulch is highly recommended for use under a no-till system because of its benefits to soil fertility and bacterial abundance. However, inorganic nitrogen fertilizer levels may be reduced under straw mulching to maintain or increase soil bacterial Shannon diversity in future studies.

Conservation tillage has attracted increasing attention over recent decades, mainly due to its benefits in improving soil organic matter content and reducing soil erosion. Under intensive conventional tillage systems, some studies have focused on the responses of soil properties in the topsoil to straw retention. However, long-term straw mulching effects on soil physicochemical properties and bacterial communities among different soil depths under a no-till system are still obscure. One twelve-year experiment was conducted that included straw removal (CK) and straw mulching (SM) treatments. Soil samples were collected at 0–5, 5–10, 10–20, and 20–30 cm soil depths. Most soil physicochemical properties and the relative abundances of bacterial phyla were varied with soil depth. Compared with CK, SM increased soil total nitrogen and organic carbon, available phosphorus and potassium, dissolved organic carbon and nitrogen, and water content. SM increased soil bacterial abundance but reduced the Shannon diversity of the bacterial community at 0–5 cm depth. SM increased the relative abundances of Proteobacteria, Bacteroidetes, and Acidobacteria but reduced those of Actinobacteria, Chloroflexi, and Cyanobacteria. SM had different effects on the relative abundances of some C- and N-cycling genera, for instance, increasing Rhodanobacter, Rhizomicrobium, and Terracidiphilus, and reducing Anaeromyxobacter, Mycobacterium, and Syntrophobacter. A principal coordinate analysis indicated that SM largely affected soil bacterial communities at topsoil depth. Soil pH and different nitrogen and organic carbon fractions were the major drivers shaping soil bacterial community. Overall, straw mulch is highly recommended for use under a no-till system because of its benefits to soil fertility and bacterial abundance. However, inorganic nitrogen fertilizer levels may be reduced under straw mulching to maintain or increase soil bacterial Shannon diversity in future studies.