What if the secret to higher sugarcane yields isn't just more fertilizer, but smarter timing? This study reveals how controlled-release fertilizers rewire soil microbes to boost sugar production. Root-soil interactions mediated by microorganisms are crucial for fertilizer efficiency and crop growth regulation.

This study combined soil microbial profiling with non-targeted metabolomics to investigate these mechanisms under varying fertilizer rates. Sugarcane is the world's most important sugar crop, but its long growing season requires a large continuous supply of fertilizer.

This high demand leads to increased cultivation costs and poor economic benefits for farmers. Controlled release fertilizer releases nutrients slowly and continuously to support plant growth over time. Compared to traditional fertilizers, this method reduces nutrient wastage and environmental pollution while improving crop yield.

Functional soil microorganisms promote plant growth by producing hormones like glutathione and gibberellin. These microbes also help plants cope with environmental stresses like drought and salinity by producing antioxidants. The experiment tested five field application rates of controlled-release fertilizer alongside a traditional fertilizer control.

Table 1 presents the effects of different controlled-release fertilizer application rates on key sugarcane agronomic traits, including plant height, stem diameter, and sucrose content. The authors report these indicators alongside population metrics like effective stems and final yields to illustrate how varying fertilizer levels influence crop performance.

By comparing treatments such as D25 against the control group, the table highlights specific shifts in biomass accumulation and sugar production that define the optimal fertilization strategy for this study. While the highest fertilizer rate produced the maximum cane stem yield, the sugar yield peaked at the D25 condition.

For superior sugar yield and economic returns, the effective threshold for fertilizer application was found under D25 conditions. Table 2 presents the soil nutrient profiles across different controlled-release fertilizer application rates, including a control group.

The authors report measurements for pH, soil organic carbon, and available nitrogen, phosphorus, and potassium, with statistical significance indicated by letters. For instance, the D15 treatment shows a soil organic carbon value of 56.06 plus or minus 9.64 grams per kilogram, while the D3 treatment records an available potassium level of 302.43 plus or minus 86.76 milligrams per kilogram.

These data illustrate how varying fertilizer levels alter the chemical composition of the sugarcane rhizosphere. Applying controlled-release fertilizer significantly increased soil pH and reduced acidity compared to the control group. However, available phosphorus content decreased gradually within a specific range of fertilizer application.

Figure 1 visualizes how controlled-release fertilizer treatments reshape the sugarcane rhizosphere, showing shifts in bacterial and fungal community composition at the phylum level. The authors also map the top twenty metabolic pathways using KEGG annotations, where bar length indicates the count of metabolites assigned to each pathway.

This multi-layered view connects microbial diversity with functional metabolic output, revealing how different fertilizer rates influence soil biological activity. The ACE index showed significant differentiation between the D25 and D1 treatments, indicating changes in bacterial diversity.

Conversely, the alpha diversity of fungal communities remained largely unchanged despite alterations in fertilizer application rates. This figure uses Linear Discriminant Analysis to pinpoint specific bacterial and fungal genera that significantly distinguish between different controlled-release fertilizer treatments.

The authors then visualize the resulting metabolic shifts using radar charts, which map the fold change of key compounds like taurocholic acid and alpha-linolenic acid across various comparison groups. By filtering for high variable importance, these panels reveal exactly which microbial classes and secreted metabolites drive the differences in the sugarcane rhizosphere environment.

Comparing the D2 treatment against the control revealed the highest number of significantly altered metabolic markers. Over eighty percent of these markers showed an increase in expression, highlighting a strong metabolic response. In the comparison between D2 and D3, metabolites like taurocholic acid and GentamicinC1 showed significantly increased levels.

Conversely, Alpha-Linolenicacid and Prostaglandin F2alpha were found at significantly lower levels in the same comparison group. Figure 3 presents a redundancy analysis that maps the relationship between soil nutrients and the sugarcane rhizosphere community. The black arrows indicate environmental factors like pH and SOC, where their length reflects the strength of the correlation with microbial groups shown in orange.

By visualizing these associations for bacteria, fungi, and metabolites, the authors demonstrate how specific fertilizer conditions drive distinct shifts in the root zone ecosystem. A random forest algorithm screened for characteristic microorganisms, identifying Anaeromyxobacter as highly abundant under D1 and D25 conditions.

Characteristic fungi like Leotiomycetes and Cercospora increased in abundance with increasing fertilizer application. Figure 4 uses a randomized forest approach to identify the top twenty bacterial genera, fungal genera, and metabolites that contribute most significantly to variability across different CRF applications.

The horizontal axis displays mean importance, while adjacent heatmaps visualize how the abundance of these specific features fluctuates between treatments. This analysis effectively highlights key microbial groups like Firmicutes_bacterium and Sphingomonas as critical drivers of change in the sugarcane rhizosphere microcosm.

Figure 5 presents a heatmap analysis of the bacterial-fungal-metabolite triad, revealing intricate correlations among these key rhizosphere components. The authors utilized Spearman’s correlation coefficient to map relationships between the top 30 microbial genera and the top 20 metabolites with the highest degree of correlation.

In this visualization, green squares indicate positive associations while blue represents negative ones, helping researchers understand how specific dominant species like Sphingomonas interact with fungal counterparts such as Penicillium or Aspergillus. Dominant microbial genera regulate the increase or decrease in levels of characteristic metabolites within the rhizosphere.

For instance, Burkholderia showed a significant negative correlation with Xanthoxic acid, while Plectosphaerella showed a positive correlation. The research identifies a specific fertilizer rate that maximizes sugar yield by optimizing soil pH and triggering beneficial microbial shifts, offering a blueprint for sustainable farming.