Combined controlled-release urea and organic fertilizer boost sustainable sugarcane productivity by optimizing soil C–N properties and microbial communities
Bibliographic record
Abstract
Long-term monocropping and excessive nitrogen (N) fertilization drive soil degradation, biodiversity loss, and low N use efficiency in Chinese sugarcane ( Saccharum officinarum L.) production. Organic substitution, while proposed to improve soil quality, faces trade-offs between short-term yield and soil quality due to slow mineralization. Controlled-release urea (CRU) provides sustained and stable N supply. It was hypothesized that integrating CRU and organic fertilizer will synergistically enhance crop productivity and soil multifunctionality (SMF) compared with single fertilization practices, thereby achieving sustainable productivity. Therefore, a two-year field experiment was conducted in an intensive sugarcane cropping system to evaluate five N management strategies: FP (conventional practice; 560 kg N ha −1 ), U (reduced urea-N; 300 kg N ha −1 ), CU (based on U, a 1:2 ratio of urea-N and controlled-release urea-N), MU (based on U, 30 % organic-N substitution), and MCU (integrated MU and CU). The effects on soil properties, microbial community characteristics, and microbial functions in rhizosphere and bulk soil were systematically evaluated. MCU optimized fertilizer C-N characteristics, reducing N input by 46.4 % while achieving the highest sugar yield (17.7 t ha⁻¹) and SMF. MCU produced greater improvements in soil C-N-P nutrients, enzyme activities, and 16 s/ITS-quantity. Regarding microbial community characteristics, MCU exhibited the highest bacterial ACE and network complexity, and enriched beneficial core species such as Bacillus and Mortierella . Regarding microbial functions, MCU enhanced bacterial C-N-P cycling genes (e.g., aerobic respiration, C-fixation, ammonification, N-fixation, organic-P mineralization) and reduced fungal pathogens. SEM identified soil properties and microbial functions as the primary and secondary drivers of both yield and SMF. Economically, MCU maintained net benefits comparable to FP and U, exceeded MU by 43.87 %, and although lower than CU, achieved optimal productivity and SMF. Overall, the MCU strategy offers an evidence-based approach for sustainable production of sugarcane in tropical and subtropical regions. • MCU reduced N input by 46.4 % while achieving highest sugar yield and SMF. • MCU produced greater improvements in soil C-N-P associated properties. • MCU improved soil microbial diversity, network complexity and microbial functions. • Soil properties and microbial functions as key drivers of yield and SMF. • MCU offers a scalable, cost-effective path to sustainable sugarcane productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".