Sheng Zhou

Soil organic carbon (SOC) stabilization is largely governed by the accumulation of microbial necromass carbon, yet how stabilization is influenced by root exudation remains poorly understood, particularly under long-term nitrogen (N) fertilization. Here, we investigated SOC dynamics in paddy soil under long-term N fertilization (12 years, urea) at four application rates (0, 100, 200, and 300 kg N ha⁻¹). We quantified root exudate inputs, microbial community composition, microbial necromass, and soil physicochemical properties to identify the primary drivers of SOC accumulation. Our results showed that SOC content increased progressively with N application rate and these changes were attributable to N-induced increases in root exudate inputs, alterations to microbial community structure, and enhanced accumulation of microbial necromass. Structural equation modeling further revealed that root exudates served as a critical linkage between N fertilization, microbial necromass accrual, and SOC stabilization. Taken together, our findings imply that long-term N fertilization enhances SOC accumulation by stimulating root exudate-driven microbial necromass accrual, highlighting the pivotal role of plant–soil interactions in regulating carbon dynamics in rice agroecosystems.

Biochar amendment is increasingly recognized as a promising strategy for enhancing soil carbon sequestration in cropland systems; however, the mechanisms governing its effects on root exudate-induced decomposition of native soil organic carbon (SOC) remain poorly understood. We conducted a controlled laboratory incubation experiment using ¹³C-labeled glucose as a proxy for root exudates to quantify native SOC decomposition in wheat soil with or without long-term biochar amendment. Glucose addition induced a strong positive priming effect in unamended control soils, increasing native SOC decomposition by 354.4 μg CO₂ g⁻¹ soil over 30 days, whereas biochar amendment substantially suppressed this response by 75.7%. This suppression was attributed to biochar-enhanced phosphorus availability and a shift in microbial community composition toward greater fungal dominance, which collectively reduced microbial nutrient mining from native SOC pools. Our findings demonstrate that biochar can effectively mitigate the priming effect through coordinated alterations in nutrient dynamics and microbial community structure, thereby promoting long-term SOC stabilization. These results strengthen the scientific basis for biochar application as a climate change mitigation strategy in agricultural ecosystems.

Ridge tillage (RC) has been proposed as a water-saving irrigation technique to mitigate greenhouse gas (GHG) emissions from paddy fields. To evaluate its effectiveness under cold-region climatic conditions, a two-year field experiment (2023–2024) was conducted in Northeast China. The study assessed the effects of RC on rice yield, methane (CH₄), nitrous oxide (N₂O), and total GHG emissions (expressed as CO₂e). A no-puddling treatment (NP) was additionally included in 2024. The results showed that compared to conventional cultivation (CK), RC significantly increased the number of effective panicles in 2023 (p < 0.05) but did not significantly affect yield in either year. CH₄ emissions exhibited a double-peak pattern, with peaks at the heading and grain-filling stages; the heading stage contributed the largest part (53.1–69.0%). N₂O emissions showed no distinct seasonal pattern, although N fertilization events stimulated N₂O peak. RC consistently reduced CH₄ emissions, with reductions of 50.8% in 2023 and 71.0% in 2024. NP in 2024 reduced CH₄ emissions by 27.0%. N₂O emissions showed no significant differences among treatments; however, their contribution from fertilization events varied with treatment and year. Total GHG was dominated by CH₄ (>99%). RC significantly lowered GHG and GHGI by 50.7–70.1% and 57.9–73.2% compared to CK, respectively. In conclusion, ridge tillage is an effective practice to reduce CH₄ and GHG emissions while maintaining rice yield in cold-region paddy fields. The large inter-annual variability strongly affects baseline emissions and underscores the needs for multi-year assessments.

Rice production underpins food security but relies heavily on nitrogen (N) fertilization, much of which is lost as gaseous emissions. Dinitrogen (N ₂ ) represents the largest N loss, yet its sources remain poorly constrained because biological dinitrogen (N ₂ ) fluxes are difficult to quantify against the atmospheric background. Here, we apply an in situ ¹⁵ N tracing–membrane inlet mass spectrometry ( ¹⁵ N–MIMS) technique to simultaneously measure N ₂ , ammonia (NH ₃ ), and nitrous oxide (N ₂ O) emissions and partition their soil- versus fertilizer-derived origins across the growing season in conventional japonica rice and hybrid rice. We find that soil organic N (SON) accounts for most N ₂ emissions (72 to 75%), overturning the prevailing assumption that fertilizer dominates this loss pathway, which is independently confirmed by a 14-y fertilization experiment. In contrast, NH ₃ originates mainly from fertilizer (71 to 77%) and N ₂ O derives from both sources in near-equal proportions. We identify a previously unrecognized “microbial N pump”, in which rapid microbial assimilation of fertilizer-derived ammonium (NH ₄ ⁺ ) induces stoichiometric imbalance and stimulates SON mineralization, mobilizing soil-derived NH ₄ ⁺ that ultimately fuels N ₂ emissions, with depleted SON partially replenished through microbial N turnover. Neglecting SON contributions causes systematic overestimation of fertilizer-derived N ₂ and NH ₃ losses by ~35%. Hybrid rice increases yield by 59% and reduces yield-scaled gaseous N losses by 43% through enhanced fertilizer uptake and microbial N use efficiency. Together, these findings reveal an underappreciated pathway of fertilization-driven soil N losses, revise N budgets for flooded rice systems, and demonstrate that cultivar-informed management can simultaneously enhance rice productivity and environmental sustainability.

To assess the potential of biochar-based fertilizers, this study applied 200 kg N hm^–2, 180 kg P₂O₅ hm^–2, and 180 kg K₂O hm^–2 for maize straw cultivation using either organic or chemical fertilizers. The resulting biochar from both treatments was analyzed. Findings indicated an increase in total carbon (C) content, pH, and mean residence time (MRT) with rising pyrolysis temperatures (300–700 °C). Biochar derived from chemically fertilized maize demonstrated higher total C content and MRT (61.3–74.4 wt% and 232.5–1473.6 year, respectively) compared to that from organically fertilized maize (54.7–59.1 wt% and 126.7–714.5 year, respectively). Potassium (43.6–113.8 g kg^–1) and phosphorus (9.5–12.3 g kg^–1) contents were notably higher in biochar from organic fertilizer. All biochar samples were generally higher in the organic fertilizer biochar than in the chemical fertilizer biochar (14.5–49.6 g kg^–1 and 5.5–10.5 g kg^–1, respectively) and met industry standard of biochar-based organic fertilizer (NY/T 3618–2020). Biochar from both fertilization methods, pyrolyzed at 500 °C, can serve as nutrient carriers and facilitate C sequestration in soil.

Accurate photosynthetic parameters obtained from photosynthetic light-response curves (LRCs) are crucial for enhancing our comprehension of plant photosynthesis. However, the task of fitting LRCs is still demanding due to diverse variations in LRCs under different environmental conditions, as previous models were evaluated based on a limited number of leaf traits and a small number of LRCs. This study aimed to compare the performance of nine LRC models in fitting a set of 108 LRCs measured from paddy rice (Oryza sativa L.) grown in field across 3 years under different leaf positions, leaf ages, nitrogen levels, irrigation levels, and varieties. The shape of 108 LRCs varies significantly under a range of leaf traits, which can be typed into three leaf light-acclimation types—high-light leaves (HL-1 and HL-2), and low-light leaves (LL). The accuracy of these models was evaluated by (1) LRCs from three acclimation types: HL-1 and HL-2, and LL; and (2) LRCs across three irradiance stages: light-limited, light-saturated, and photoinhibition. Results indicate that the Ye model emerged as the top performance among the nine models, particularly in the photoinhibition stage of LL leaves, with median values of R², SSE, and AIC of 0.99, 2.39, and −14.03, respectively. Furthermore, the Ye model produced the most accurate predictions of key photosynthetic parameters, including dark respiration (R_D), light-compensation point (I_comp), maximum net photosynthetic rate (P_Nmax), and light-saturation point (I_sat). Results also suggest that P_NImax and I_max were the most appropriate parameters to describe photosynthetic activity at the light-saturation point. These findings have significant implications for improving the accuracy of fitting LRCs, and thus robust predictions of photosynthetic parameters in rice under different environmental conditions.

Accurate counting of the number of rice panicles per unit area is essential for rice yield estimation. However, intensive planting, complex growth environments, and the overlapping of rice panicles and leaves in paddy fields pose significant challenges for precise panicle detection. In this study, we propose YOLO-Rice, a rice panicle detection model based on the You Only Look Once version 8 nano (YOLOv8n). The model employs FasterNet, a lightweight backbone network, and incorporates a two-layer detection head to improve rice panicle detection performance while reducing the overall model size. Additionally, we integrate a Normalization-based Attention Module (NAM) and introduce a Minimum Point Distance-based IoU (MPDIoU) loss function to further improve the detection capability. The results demonstrate that the YOLO-Rice model achieved an object detection accuracy of 93.5% and a mean Average Precision (mAP) of 95.9%, with model parameters reduced to 32.6% of the original YOLOv8n model. When deployed on a Raspberry Pi 5, YOLO-Rice achieved 2.233 frames per second (FPS) on full-sized images, reducing the average detection time per image by 81.7% compared to YOLOv8n. By decreasing the input image size, the FPS increased to 11.36. Overall, the YOLO-Rice model demonstrates enhanced robustness and real-time detection capabilities, achieving higher accuracy and making it well-suited for deployment on low-cost portable devices. This model offers effective support for rice yield estimation, as well as for cultivation and breeding applications.