Fulvic acid accelerates functional restoration of “youth” paddy soils, outperforming organic and mineral amendments

Land reclamation for paddy expansion in mountainous regions creates highly degradation-prone “youth” soils characterised by poor structure, low fertility, and suppressed microbial activity. A two-year field experiment in subtropical China compared six fertilization-amendment strategies. Fulvic acid combined with chemical fertilizer induced the most rapid improvement, resulting in the highest microbial biomass carbon (+86 %), acid phosphatase activity (+112 %), cation exchange capacity (+18 %), and synchronized nutrient availability. This treatment consistently ranked first in both total dataset and minimal dataset soil quality indices across two seasons and achieved the second-highest rice grain yield. A parsimonious four-indicator SQI MDS explained 89% of yield variation in 2024 (R 2 = 0.889, P < 0.01), outperforming the total dataset SQI. These results demonstrate that fulvic acid combined with chemical fertilizer most effectively accelerates soil quality recovery and supports high productivity in newly constructed paddy fields, providing an efficient and practical strategy for sustainable rice production on marginal mountainous land. 1. Introduction Land reclamation for paddy expansion in mountainous regions is accelerating worldwide to meet rising food demand, yet it frequently triggers severe land degradation risks (Zhang et al., 2023; Qiu and Bao, 2023; Ding et al., 2025). Newly constructed terraces typically expose subsoils that are structurally unstable, organically impoverished, highly prone to nutrient fixation and leaching, and exhibit strongly suppressed microbial activity—collectively creating “youth” soils that are exceptionally vulnerable to long-term degradation and persistent low productivity (Chen et al., 2023; Huo et al., 2024). In southern Shaanxi, China, recent high-standard farmland programmes have converted thousands of hectares of hillside into paddy fields, but initial rice yields remain 30–50% below regional averages while conventional intensive fertilization exacerbates nutrient losses and degradation processes. Preventing early-stage degradation and achieving rapid functional restoration in these reclamation-derived soils is therefore a critical challenge for sustainable land management. Combining chemical fertilizers with organic and biological amendments has been shown to mitigate degradation and enhance soil resilience (Meng et al., 2017; Tao et al., 2020; Kuila & Ghosh, 2022; Zhang et al., 2022; Mi et al., 2023; Zhang et al., 2024). Among these, fulvic acid stands out for its capacity to chelate nutrients, stimulate microbial activity, and improve carbon status, yet its potential to counteract degradation and accelerate recovery in newly reclaimed mountainous paddies remains largely unexplored. Soil quality indices (SQI) integrating physical, chemical, and biological indicators, often simplified via minimum datasets (MDS), are powerful tools for diagnosing degradation and monitoring restoration success (de Paul Obade & Lal, 2016; Askari & Holden, 2016; Saha et al., 2024; Cui et al., 2024; Damiba et al., 2024; Mei et al., 2021; Peng et al., 2024). However, few studies have applied these approaches during the critical early-pedogenesis phase of reclamation-induced paddy soils under contrasting management regimes. Here, we conducted a two-year field experiment on newly reclaimed mountainous paddy soils in southern Shaanxi, China, to compare six fertilization strategies. We specifically tested the hypotheses that (1) co-application of fulvic acid with balanced chemical fertilization would most effectively prevent degradation and rapidly rebuild soil functions by alleviating nutrient fixation and microbial suppression; (2) these gains would translate into significantly higher and more stable rice yields from the earliest seasons; and (3) a parsimonious minimum dataset would reliably track restoration trajectories and productivity, providing a scalable diagnostic framework for degradation-neutral management of large-scale mountain paddy reclamation worldwide. 2. Materials and Methods 2.1 Overview of the Study Area The experimental site is located in Shenba Town, Hanbin District, Ankang City, Shaanxi Province (108°39’ E, 32°57’ N), at an average elevation of 700 meters. The region experiences a subtropical monsoon climate, characterized by concentrated summer rainfall and relatively dry winters. The annual mean temperature is 15°C, with a frost-free period of approximately 220 days and an average annual precipitation of 800 mm. A paddy-rapeseed rotation system is commonly practiced in the area. The experimental soil, newly established as high-standard farmland within the last two years, is classified as sandy loam. It is characterized by low microbial activity, significant evaporation, low organic matter content, poor water and fertilizer retention, and limited duration of fertilizer effectiveness. Fertilizer loss tends to occur later in the growing season. Table 1 summarizes the basic physical and chemical properties of the experimental soil. 2.2 Experimental Design The experiment was conducted from 2023 to 2024 to investigate which fertilization combination would most effectively enhance the fertility of the newly reclaimed soil. Conventional fertilization was used as the control treatment, and a total of six treatments were established: (1) chemical fertilizer alone (CK); (2) chemical fertilizer + bacterial agent (NB); (3) chemical fertilizer + acidic soil conditioner (NC); (4) chemical fertilizer + fulvic acid biomass nutrient solution (NF); (5) chemical fertilizer + alginate bio-organic fertilizer (NO); and (6) chemical fertilizer + silicon-calcium-magnesium-potassium fertilizer (NSi). The CK treatment followed the local high-standard farmland fertilization protocol, using urea (46% N) for nitrogen, single superphosphate (12% P 2 O 5 ) for phosphorus, and potassium chloride (60% K 2 O) for potassium. Each treatment was replicated three times, resulting in a total of 18 plots. Each plot measured 30 m² (5 × 6 m), with a 3 m buffer zone and 1 m protective row. A randomized block design was employed for the layout. The first season of paddy was sown on June 8, 2023, and harvested on October 7, 2023. The second season was planted on May 28, 2024, and harvested on September 4, 2024. Details of the treatment compositions and fertilizer application rates are presented in Table 2. Table 3 presents the types and application rates of additives used in different treatments. 2.3 Sample Collection and Measurement During the two-year period, at the ripening stage of paddy, representative healthy paddy plants were collected from a 1 m² area in each plot to assess overall growth performance. These plant samples were oven dried at 105°C before yield measurements were taken. In each plot, surface soil samples (0-20 cm) were collected at different stages using a soil auger to evaluate physical, chemical, and biological properties. Soil water content (SW), bulk density (BD), and soil porosity (SP) were measured using the ring knife method. Total nitrogen (TN) was determined by the concentrated sulfuric acid digestion-Kjeldahl method, while alkali-hydrolyzable nitrogen (AN) was assessed using the alkali solution diffusion method. Available phosphorus (AP) was determined by the NaHCO 3 extraction-molybdenum antimony colorimetric method, and available potassium (AK) was determined by the NH 4 OAc extraction followed by flame photometry. Soil organic matter (SOM) was determined by the potassium dichromate volumetric method (external heating method). Soil pH was determined by the potentiometric method (soil: water = 1:2.5), and cation exchange capacity (CEC) was determined by the ammonium acetate exchange method. Enzyme activities were measured as follows: urease (UE) using the phenol sodium-sodium hypochlorite colorimetric method; acid phosphatase (ACP) using the disodium phenyl phosphate colorimetric method; catalase (CAT) using the potassium permanganate titration, and sucrase (SA) using the 3,5-dinitrosalicylic acid colorimetric method. Microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) were determined using the chloroform fumigation-K 2 SO 4 extraction method, following established protocols (Jiao et al., 2023; Wu et al., 2017; Zhao et al., 2020; Ge et al., 2010; Tabatabai and Bremner, 1972; Sharma et al., 2014). 2.4 Data Analysis All data were compiled and organized using Microsoft Excel 2019. Statistical analyses, including one-way analysis of variance (ANOVA), least significant difference (LSD) tests, and Pearson correlation analysis, were performed using IBM SPSS 24.0. Results are presented as means ± standard deviations. Correlation analysis and principal component analysis (PCA) were employed to construct the TDS and MDS. Correlation heatmaps were generated using R, and other visualizations were created using Origin 2021. 3 Results 3.1 Soil Physical, Chemical, and Biological Properties 3.1.1 Soil physical properties Fulvic acid (NF) sustained the highest soil moisture content in both 2023 and 2024, followed by alginate bio-organic fertilizer (NO). By 2024, NO also recorded a slight year-on-year increase (Fig. 1). Bulk density remained stable at ≈1.2 g cm⁻³ across treatments, whereas soil porosity declined markedly under sole chemical fertilization (CK) in 2024. All amendment treatments (NB, NF, NO, NSi) significantly improved porosity compared with CK. 3.1.2 Soil pH and Cation Exchange Capacity Silicate-based fertilizer (NSi) raised soil pH in both years, whereas all other treatments induced acidification. The acidic conditioner (NC) caused the largest pH drop (−7.43% in 2024 vs 2023). Cation exchange capacity increased most under NF, reaching an 18.15% gain in 2024 and surpassing all treatments that year (Fig. 2). 3.1.3 Total Nitrogen, Organic Matter, and Carbon-to-Nitrogen Ratio Total nitrogen increased in all treatments relative to pre-experiment levels (Fig. 3). Fulvic acid (NF) and alginate bio-organic fertilizer (NO) delivered the highest final contents in both years, whereas the acidic conditioner (NC) exhibited the steepest single-year gain (+15.42%) despite remaining lower overall. Soil organic matter rose substantially under most amendments. The largest accumulations occurred with NO and NF; NC and silicate fertilizer (NSi) produced no significant increase, and sole chemical fertilization (CK) resulted in a 6.43% loss by 2024. The C/N ratio stayed below 20 across all treatments, signalling favourable conditions for organic matter decomposition (Weil and Brady, 2017). The slightly higher C/N under NO suggests moderate short-term N immobilisation that may support longer-term nitrogen retention. All treatments recorded lower C/N ratios in 2024 than in 2023. 3.1.4 Alkali-hydrolyzable Nitrogen, Available Phosphorus, and Available Potassium Alkali-hydrolysable nitrogen peaked at jointing under microbial (NB) and alginate bio-organic (NO) treatments, satisfying early-season demand (Fig. 4a). By ripening, however, fulvic acid (NF) sustained the highest residual concentrations, reflecting delayed release and reduced leaching. In 2024, NB further increased alkali-hydrolysable nitrogen by 15.06% compared with 2023. Available phosphorus declined gradually during the rice cycle but remained consistently highest under NF at every stage (Fig. 4b). The largest treatment difference occurred at ripening, driven by reduced P fixation and enhanced organic matter decomposition. Available potassium followed an early-peak pattern (Fig. 4c). Silicate-based fertilizer (NSi) supplied the greatest amounts throughout, especially at maturity, whereas NF maintained robust residual levels that far exceeded CK. Sole chemical fertilization (CK) consistently showed the lowest potassium availability, underscoring its limited contribution to long-term K supply in these immature soils. 3.1.5 Soil Enzyme Activity Enzyme activities exhibited clear amendment-specific patterns across growth stages and years (Figs 5–6). Urease (UE) activity peaked under microbial inoculants (NB) throughout the rice cycle, reflecting strong early stimulation of nitrogen metabolism (Fig. 5a). Fulvic acid (NF) started lower at jointing but rose progressively to maturity, consistent with delayed nitrogen release. Most other treatments showed declining UE activity as the season advanced. Acid phosphatase (ACP) responded most strongly to NF, which sustained the highest values at every stage in both years (Fig. 5b). Second-year activities were markedly higher than the first across all treatments, confirming cumulative enhancement in these newly reclaimed soils. Silicate fertilizer (NSi) consistently matched or fell below CK, indicating suppression of P-mobilising enzymes. Catalase (CAT) and sucrase (SA) were both dominated by NB in most stages, with the microbial inoculant driving the broadest early-season stimulation (Fig. 6). By harvest 2024, however, NF overtook NB for catalase and maintained high sucrase activity, highlighting fulvic acid’s increasing influence on carbon metabolism and oxidative protection at maturity. Overall, NF and NB emerged as the strongest drivers of enzymatic function, with NF excelling in P-cycling enzymes and NB in early C- and N-related activities. 3.1.6 Microbial Biomass Carbon and Nitrogen Microbial biomass responded rapidly and distinctly to the amendments (Fig. 7).Fulvic acid (NF) produced the highest microbial biomass carbon (MBC) at maturity in both 2023 and 2024, far exceeding all other treatments. The year-on-year increase from 2023 to 2024 was minimal, indicating that MBC approached an early plateau under fulvic acid stimulation. Microbial biomass nitrogen (MBN) was dominated by the microbial inoculant treatment (NB) in both seasons. Overall, MBN rose 8.2% from 2023 to 2024, confirming the capacity of inoculants to build nitrogen reserves. NF and NO also supported substantial MBN pools, whereas NSi and CK remained the lowest. The MBC/MBN ratio was consistently lower under amendment treatments than under CK, implying higher microbial nitrogen-use efficiency and a shift toward bacterial-dominated communities in restored soils. 3.1.7 Yield There were significant differences in rice yield at maturity among different treatments (P NF > NO > NB > NC > CK. The NSi treatment consistently exhibited the highest yield, while the NF treatment showed a significant yield increase. The yields for CK and NC treatments were relatively low, particularly for NC, which showed a weak effect and possibly even a suppressive effect. The yield in 2024 generally increased compared to 2023, indicating a cumulative effect of continuous application of additives. Among them, the NO treatment had the largest yield increase, with a 22.54% increase in 2024, significantly higher than the other treatments. The yield increases for the other treatments were as follows: CK 1.46%, NB 16.16%, NC 17.15%, NF 13.98%, NSi 14.49%. CK treatment showed the smallest yield increase, further confirming the limited effect of sole chemical fertilizers, while the combination of fertilizers with additives could better enhance paddy productivity (Fig. 8). 3.2 Soil Quality Index 3.2.1 Construction of Total Data Set (TDS), Minimal Data Set (MDS) Indicators significantly correlated with grain yield (|r| > 0.20, Pearson correlation; Evans, 1996) were selected for the total dataset (TDS), excluding weak associations (Fig. 9). This yielded 11 indicators in 2023 (CEC, pH, MBC/N, CAT, C/N, MBN, SOM, MBC, TN, AP, AK) and 14 indicators in 2024 (MBC/N, pH, CEC, AK, UE, C/N, SOM, TN, MBC, AN, AP, CAT, MBN, ACP). Shapiro-Wilk tests confirmed normality for all TDS indicators except MBC/MBN (2023) and C/N (2024). Principal component analysis (PCA) followed established protocols (Andrews et al., 2002; Liu et al., 2014; Deng Shaohuan et al., 2016). Kaiser–Meyer–Olkin (KMO) values were 0.786 (2023) and 0.757 (2024), indicating excellent sampling adequacy. The first three principal components had eigenvalues >1, explaining 87.51% (2023) and 76.56% (2024) of total variance. All communalities exceeded 0.55 (2023 >0.74), confirming robust representation. High-loading indicators (within 10% of maximum loading per component) were grouped, and correlation screening retained only weakly inter-correlated variables (r 0.6 retained the highest-loading indicator. The resulting four-indicator minimum datasets were: SOM, MBC, CEC, pH (2023); MBC, MBN, CEC, pH (2024). Complete factor loadings, weights, and communalities are provided in Supplementary Tables S1-S2. 3.1.2 Determination of Indicator Scores To eliminate the impact of dimensional differences on factor loadings, this study transformed the indicators in the MDS into dimensionless values (standardized) using membership functions. A membership function is a mathematical expression that represents the relationship between evaluation indicators and the crop growth effect curve, converting each indicator into a dimensionless value between 0 and 1. Three common types of standard scoring equations are used for soil indicator normalization: the positive S-type function, which is suitable for ”the more, the better” indicators (e.g., SOM, AK, AP, CEC, TN); the parabolic function, which is suitable for indicators with ”an optimal range” (e.g., bulk density, pH) (Qi et al., 2019). The calculation formula for the positive S-type function is: \(f\left(x\right)=\left\{\par\begin{matrix}0.1\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ x\leq L\\ 0.1+0.9(x-L)/(U-L)\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ L<X<U\\ 1.0\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ x\geq U\\ \end{matrix}\right.\ \) (1) In the formula: x is the monitoring value of the indicator; f(x) is the score of the indicator ranging between 0.1 and 1.0; and L and U are the lower and the upper threshold values of the indicator, respectively. The calculation formula for the parabolic function is: \(f\left(x\right)=\left\{\par\begin{matrix}0.1\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ x\leq L\ or\ x\geq U\\ 0.1+0.9(x-L)/(O_{1}-L)\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ L<X\leq O_{1}\\ 1.0\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ O_{1}<X\leq O_{2}\\ 1.0-0.9(x-O_{2})/(U-O_{2})\text{\ \ \ \ \ \ \ \ \ \ \ \ \ }O_{2}<X\leq L\ \\ \end{matrix}\right.\ \) (2) In the formula: x is the monitoring value of the indicator; f(x) is the score of the indicator ranging between 0.1 and 1.0; and L and U are the lower and the upper threshold values of the indicator, respectively. O 1 and O 2 represent the lower and the upper critical values of the optimal value, respectively. For soil AN, C/N, and various biological indicators, a simple linear scoring method is used, based on the principle of ”the higher, the better.” The highest measured value of an indicator is assigned a membership degree of 1, and the membership degree of other measured values is calculated as the ratio of their value to the highest value. The calculation formula is as follows (Liebig et al., 2001): \(f\left(x\right)=x/x_{\max}\) (3) In the formula: f(x) is the membership value, x is the measured value of the index, and x max is the highest measured value of the index. Normalised membership values of all TDS indicators were visualised using radar charts (Ye et al., 2013). Larger polygon areas indicate higher overall soil fertility, whereas scores closer to the centre reveal persistent limiting factors (Figs 10-11).In both 2023 and 2024, the CK treatment produced the smallest polygon area and the lowest scores for most indicators, reflecting its severe fertility limitation. In 2023, the NB, NO, and NF treatments generated markedly larger polygons and higher overall fertility. By 2024, the NF treatment exhibited the largest polygon area, confirming its superior performance across the two seasons. Available potassium (AK) and cation exchange capacity (CEC) consistently acted as the primary bottlenecks across all treatments. Even under the best-performing treatments, AK membership values remained below 0.36 (2023) and 0.48 (2024), while CEC values stayed below 0.51 (2023) and 0.58 (2024), demonstrating that K supply and surface-charge development are the dominant constraints on fertility improvement in these newly reclaimed mountainous paddy soils. 3.2.3 Soil Quality Index (SQI) under Different Fertilization Combinations Based on the standard scores and weights of the indicators derived above, the weighted additive Soil Quality Index (SQI) was calculated. A higher SQI value indicates better soil quality. The formula is as follows: \(SQI=\sum_{i=1}^{n}{N_{i}W_{i}}\) (4) Where N i represents the score for each indicator, and W i denotes the weight assigned to each indicator. Soil quality was evaluated using both the Total Data Set (TDS) and the Minimal Data Set (MDS), with results shown in Figure 12 (a) and Figure 12 (b). All five amendment treatments significantly increased the soil quality index compared with CK, and the NF treatment consistently exhibited the highest values over the two years. TDS and MDS results were largely consistent, with only minor differences. Treatment rankings at maturity were identical in 2023 and 2024: NF > NB > NO > NC > NSi > CK. Although slight differences appeared between TDS and MDS in 2024, the overall trend remained unchanged. In summary, the NF treatment consistently demonstrated the best soil quality across both harvest years, and the high consistency between the two datasets confirms the reliability of the minimal dataset. 3.2.4 Correlation Analysis between Yield and SQI The results indicate a highly significant positive correlation ( P < 0.01 ) between the Soil Quality Index based on the Minimal Data Set (SQI MDS ) and the Total Data Set (SQI TDS ), suggesting a high degree of consistency between the two in assessing soil fertility. In 2023, SQI MDS showed a significant correlation with rice yield ( P < 0.05 ), while SQI TDS did not exhibit a significant correlation. In 2024, SQI TDS demonstrated a significant correlation with yield ( P < 0.05 ), but SQI MDS showed an even stronger correlation ( P < 0.01 ), indicating that the minimal data set provides a stronger reflection of the relationship between soil fertility and yield. Overall, SQI MDS consistently exhibited higher correlations with yield than SQI TDS across both years, highlighting the superior relevance and explanatory power of the minimal data set in soil fertility characterization (Table 4). 4. Discussion Expanding rice production into mountainous regions through large-scale terracing and land reclamation is a widespread response to lowland scarcity and food security pressures worldwide. However, the resulting newly constructed paddy soils typically exhibit poor aggregation, low organic carbon, nutrient imbalances, and depressed microbial activity-constraints that can suppress yields for years and drive excessive synthetic fertilizer use. Rapid restoration of soil functions in these immature systems is therefore a critical challenge for sustainable intensification. Extensive literature indicates that long-term sole application of chemical fertilizers reduces soil organic matter, lowers fertilizer use efficiency, degrades soil quality, hinders scientific farming practices, and obstructs high-standard farmland development, ultimately undermining sustainable agriculture (Zhang et al., 2013; Piscitelli et al., 2020). Our two-year field positioning experiment confirms that integrating soil additives with chemical fertilizers improves soil properties and enhances crop productivity (Meng et al., 2017; Tao et al., 2020; Kuila and Ghosh, 2022). Specifically, these combinations significantly enhanced chemical and biological properties in newly reclaimed mountainous paddy soils: the NF treatment markedly increased total nitrogen, available phosphorus, and available potassium contents, while boosting acid phosphatase and urease activities; in turn, the NB treatment improved catalase and sucrase activities, underscoring the benefits of microbial inoculants on soil enzymes. In contrast to treatments with only chemical fertilizers, all treatments that included soil additives resulted in significant changes in most soil indicators, although some treatments had unintended negative effects on certain soil properties. One particularly noteworthy treatment was the combination of fertilizers with silicon-calcium-magnesium-potassium fertilizers, which significantly increased soil pH compared to the CK treatment. This effect occurs because silicates, which have high solubility, release SiO 3 2- into the soil, where it hydrolyzes to produce OH -, effectively neutralizing H + ions and raising the soil pH (Rheinheimer et al., 2018). Furthermore, silicates are highly mobile, and when applied in large quantities, they can transport basic salt ions from the surface into the subsoil, thereby raising the base ion content in the subsoil and further increasing soil pH. The application of silicon-calcium-magnesium-potassium fertilizers also suppressed certain soil enzyme activities, a finding that aligns with the research by Shi et al. (Shi et al., 2021). Additionally, the combination of fertilizers with fulvic acid biomass nutrient solution significantly increased the total nitrogen and available nutrient content in the soil at harvest. Research has shown that fulvic acid contains carboxyl groups, phenolic hydroxyl groups, and certain functional groups containing phosphorus, oxygen, nitrogen, and sulfur, which generally act as electron donors. These groups can coordinate with metal ions and organic compounds, forming coordination complexes that reduce nitrogen loss and improve nitrogen fertilizer utilization (Rekaby et al., 2020). In terms of phosphorus utilization, fulvic acid has also been shown to enhance the availability of phosphorus. It weakens the soil’s ability to fix water-soluble phosphorus, making it easier for crop roots to absorb this essential nutrient. This property effectively protects phosphorus fertilizers, increases phosphorus utilization efficiency, and promotes better phosphorus uptake by crops. Moreover, fulvic acid plays a crucial role in potassium fertilizer utilization. Its acidic functional groups are capable of adsorbing and storing potassium ions, reducing potassium loss due to water evaporation, and mitigating the fixation effect of clayey soils on potassium. This process results in an increase in exchangeable potassium content in the soil. Furthermore, fulvic acid can also dissolve potassium-containing minerals, gradually releasing potassium and raising the soil’s available potassium levels (Zhang et al., 2021a). These findings further validate the significant effects of fulvic acid on soil nutrient enhancement in this study, emphasizing its essential role in improving fertilizer efficiency, optimizing nutrient supply, and enhancing soil fertility. Moreover, the addition of microbial inoculants with fertilizers has been shown to significantly influence soil biological properties. As a biological soil amendment, microbial inoculants can secrete various active substances, such as organic acids, enzymes, and hormones, and help regulate the structure of soil microbial communities, which in turn increases soil enzyme activity. Among various soil quality evaluation methods, the Soil Quality Index (SQI) is the most widely applied, followed by the modified Nemerow Index (primarily for environmental quality assessments), whereas the composite index method adopted in this study is more suitable for fertility quality evaluation (Liebig et al., 2001; Chen et al., 2021; Kahsay et al., 2023). Selecting appropriate indicators to construct a dataset is critical for assessing soil fertility quality, as they must comprehensively reflect the soil’s physical, chemical, and biological characteristics while avoiding redundancy and ensuring representativeness (Liebig et al., 2001; Kahsay et al., 2023). Building on previous research methods, this study included indicators with a correlation coefficient greater than 0.2 with crop yield in the total dataset (TDS), then applied principal component analysis to select the minimal dataset (MDS) and determine weights, identifying the most representative metrics. The soil quality indices derived from TDS and MDS were largely consistent, with the high correlation between SQI TDS and SQIMDS (r = 0.920, P < 0.01 in 2024) and the stronger yield correlation of SQI MDS confirming that this streamlined MDS can effectively substitute for the total dataset-eliminating redundant indicators and reducing data noise to provide greater predictive accuracy, sensitivity to management effects, and advantages in cost and labor efficiency (Zhang et al., 2021b; Song et al., 2022; Yu et al., 2023). Compared to sole chemical fertilizer application, the combined use of fertilizers and soil additives significantly improved most soil fertility indicators and quality indices, aligning with prior research (Chen et al., 2021). Ultimately, co-application of fulvic acid with chemical fertilizers offers a practical, input-efficient pathway to overcome the “youth” constraints of newly constructed paddy soils. By accelerating soil maturation and closing early yield gaps, this approach supports sustainable rice intensification in the expanding frontier of global mountain agriculture. 5 Conclusion Integrating fulvic acid with balanced chemical fertilization rapidly overcomes the inherent physical, chemical, and biological limitations of newly reclaimed mountainous paddy soils, preventing early-stage land degradation while accelerating the transition from degradation-prone “youth” soils to productive and resilient conditions within just two cropping seasons. Among the amendments evaluated, fulvic acid consistently outperformed others in promoting soil functional restoration and sustaining rice yield. The streamlined four-indicator minimum dataset effectively tracks both soil recovery and crop performance, providing a practical, low-cost tool for monitoring and managing large-scale reclamation projects. These findings demonstrate that fulvic acid-assisted fertilization is an effective, scalable approach to mitigate degradation risks and achieve sustainable intensification in mountainous landscapes undergoing paddy expansion worldwide.

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Forests 11, 285. https://doi.org/10.3390/f11030285 Table 1 Physical and chemical properties of the study soil | 1.29 | 51.32 | 6.84 | 9.34 | 1.23 | 136.71 | 25.09 | 61.48 | 23.81 | Table 2 The source, composition and efficacy of different soil additives | Bacteria agent | Zhongke Chemical Products Co., Ltd., Zhengzhou, China | Extracted from various vegetables and fruit plants. | Bacillus subtilis, bacillus amyloliquefaciens. | Repair soil biological environment | | Alginate bio-organic fertilizer | Enbao Biotechnology Co., Ltd., China | Extracted from wild seaweed alginate. | Alginate, organic matter. | Improve soil fertility | | Fulvic acid biomass nutrient solution | Qinheng Ecological Technology Co., Ltd., China | Extracted from crop residues and animal manure. | Fulvic acid | Water and fertilizer conservation, improve soil nutrients availability | | Acidic soil conditioner | Maile Fertilizer Co., Ltd., China | Prepared by reacting alumina with sulfuric acid. | Mineral matter | Regulate soil pH | | Silicon-calcium- magnesium- potassium fertilizer | Akang Agricultural Technology Co., Ltd., China | Extracted from various organisms. | Effective silicon, effective calcium | Improve the resistance of crops | Table 3. Usage and dosage of chemical fertilizer and improver under different treatments | CK | N 154 kg/hm 2 ，P 2 O 5 82.9 kg/hm 2 ，K 2 O 189.2 kg/hm 2 | N(distributed as base fertilizer: tillering fertilizer: panicle fertilizer = 4:4:2), P 2 O 5 (entirely applied as base fertilizer), K 2 O (base fertilizer: panicle fertilizer = 6:4) | | NB | 154 kg/hm 2 of N, 82.9 kg/hm 2 of P 2 O 5 189.2, kg/hm 2 of pure K 2 O, 15 kg/hm 2 of bacterial agent | N, P 2 O 5, K 2 O are used in the same way as CK. Bacterial agent (diluted 50 times before irrigation pre-planting). | | NO | 154 kg/hm 2 of N, 82.9 kg/hm 2 of P 2 O 5 189.2, kg/hm 2 of pure K 2 O, 1200 kg/hm 2 of alginate bio-organic fertilizer | N, P 2 O 5, K 2 O are used in the same way as CK. Alginate bio-organic fertilizer (applied pre-planting and thoroughly mixed with other base fertilizers, with an additional application of 1200 kg/hm 2 during f lowering and grain filling stages) | | NF | 154 kg/hm 2 of N, 82.9 kg/hm 2 of P 2 O 5 189.2, kg/hm 2 of pure K 2 O, 750 kg/hm 2 of fulvic acid biomass nutrient solution | N, P 2 O 5, K 2 O are used in the same way as CK. Fulvic acid biomass nutrient solution (diluted 20 times before irrigation pre-planting) | | NC | 154 kg/hm 2 of N, 82.9 kg/hm 2 of P 2 O 5 189.2, kg/hm 2 of pure K 2 O, 750 kg/hm 2 of acid soil conditioner | N, P 2 O 5, K 2 O are used in the same way as CK. Acid soil conditioner (applied pre-planting and mixed uniformly with other base fertilizers) | | NSi | 154 kg/hm 2 of N, 82.9 kg/hm 2 of P 2 O 5 189.2, kg/hm 2 of pure K 2 O, 1200 kg/hm 2 of silicon-calcium-magnesium-potassium fertilizer | N, P 2 O 5, K 2 O are used in the same way as CK. Silicon-calcium-magnesium-potassium fertilizer (applied pre-planting and mixed evenly with other base fertilizers, with an additional application of 1200 kg/hm 2 during flowering and grain filling stages). | Note: CK—normal fertilization, NB—normal fertilization + bacteria agent, NO—normal fertilization + alginate bio organic fertilizer, NF—normal fertilization + fulvic acid biomass nutrient solution, NC—normal fertilization + acidic soil conditioner, NSi—normal fertilization + silicon-calcium-magnesium-potassium fertilizer. The abbreviations hereafter are consistent with those defined here. Table 4 Pearson correlation coefficient between paddy yield and fertility index | 2023 | Yield | 1.000 | || | SQI MDS | 0.482* | 1.000 | || | SQI TDS | 0.433 | 0.787** | 1.000 | | | 2024 | Yield | 1.000 | || | SQI MDS | 0.593** | 1.000 | || | SQI TDS | 0.561* | 0.920** | 1.000 | | | Note: * denotes a significant difference at the P < 0.05 level, ** denotes a significant difference at the P < 0.01 level. | Graphical Abstract Fig. 1 Water content (a), bulk density (b), and porosity (c) in the topsoil (0-20 cm) at ripening stage under different treatments Fig. 2 pH (a) and cation exchange capacity (b) in the topsoil (0-20 cm) at ripening stage under different treatments Fig. 3 Total nitrogen (a), organic matter (b), and carbon - nitrogen ratio (c) in the topsoil (0-20 cm) at ripening stage under different treatments Fig. 4 Alkali-hydrolyzable nitrogen (a), available phosphorus (b) and available potassium (c) in the topsoil (0-20 cm) at different growth stages under various treatments Fig. 5 Urease activity (a) and acid phosphatase activity (b) in the topsoil (0-20 cm) at different growth stages under different treatments Fig. 6 Catalase activity (a) and sucrose activity(b) in the topsoil (0-20 cm) at different growth stages under different treatments Fig. 7 Microbial biomass carbon (a)、microbial biomass nitrogen (b) and microbial biomass C/N (c) in the topsoil (0-20 cm) at ripening stage under different treatments Fig. 8 The yield of paddy at ripening stage in 2023 and 2024 under different treatments Fig. 9 Heatmap of correlation between soil indexes and yield in 2023(a) and 2024(b) (SW—soil water content, BD—soil bulk density, SP—soil porosity, CEC—cation exchange capacity, TN—total nitrogen, AN—alkaline nitrogen, AP—available phosphorus, AK—available potassium, SOM—soil organic matter, C/N—carbon–nitrogen ratio, UE— urease activity, ACP—acid phosphatase activity, CAT—catalase activity, SA—sucrose activity, MBC—microbial biomass carbon, MBN—microbial biomass nitrogen, MBC/MBN—microbial biomass carbon–nitrogen ratio, Yield—paddy yield. The abbreviations hereafter are consistent with those defined here.). Fig. 10 Radar plots of membership values of each index under different treatments in 2023. Fig. 11 Radar plots of membership values of each index under different treatments in 2024. Fig. 12 Soil quality index at maturity stage under different treatments in 2023(a) and 2024(b) Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 148views 93downloads Citations Download citation Nannan Wang, Chenyang Wei, Yu Cheng, et al. Fulvic acid accelerates functional restoration of “youth” paddy soils, outperforming organic and mineral amendments. Authorea. 27 November 2025. DOI: https://doi.org/10.22541/au.176422879.99168893/v1 DOI: https://doi.org/10.22541/au.176422879.99168893/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.