Repurposing crop aerial parts to provide D-sorbitol for plant-specific growth

Repurposing crop aerial parts to provide D-sorbitol for plant-specific growth | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Repurposing crop aerial parts to provide D-sorbitol for plant-specific growth Xiaoyang Wan, Huixian Cheng, Jiefei Niu, Shufang Wang, Lanxin Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8409481/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Agricultural systems generate billions of tons of high-moisture plant residues annually, leading to soil degradation and replanting failure—a critical bottleneck for global sustainability. Here, using watermelon as a representative model, our survey of representative watermelon-producing regions in China identified unsustainable crop residue management as a key driver of this ecological bottleneck. We developed a Lactiplantibacillus plantarum WCFS1-mediated rapid fermentation system with the aim of repurposing watermelon aerial parts to alleviate continuous cropping obstacles and promote sustainable waste recycling. We found that the fermentation liquid promotes Brassica rapa growth through its key metabolite D-sorbitol. To date, D-sorbitol has been characterized primarily in Rosaceae plants as a sucrose-like energy source and signaling molecule, whereas studies in other plant families have focused predominantly on its roles in osmotic-stress responses. Thus, leveraging an unprecedented cross-lineage experimental framework spanning dozens of cultivation trials, we systematically evaluated the effects of exogenous D-sorbitol across 32 phylogenetically representative plant species, including bryophytes, ferns, gymnosperms, and angiosperms. Excitingly, we discovered a previously unrecognized light intensity–sorbitol–starch cascade that affects energy metabolism and growth in angiosperms, particularly in Brassicaceae and Crassulaceae, while having no effect on the Rosaceae and Plantaginaceae. This mechanism spans the evolutionary lineage of true dicots. Additionally, we found that rapid fermentation reduces the inhibitory effects of allelotoxins from fresh watermelon stem and leaf on the growth and yield of Brassica rapa and Zea mays by significantly reducing allelochemical content in fresh tissues and markedly improving the composition of rhizosphere soil bacterial communities. Our work establishes a closed-loop, waste-to-growth strategy that transforms an ecological burden into a targeted agricultural input, providing a scalable solution for sustainable crop production. Scientific community and society/Agriculture Biological sciences/Plant sciences/Plant physiology Sustainable agriculture D-sorbitol Lactiplantibacillus plantarum WCFS1 Brassica rapa Allelopathic compounds Continuous cropping obstacles Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Global agriculture annually generates over 5 billion tons of high-moisture plant residues ( 1-3 ) . Due to their rapid decomposition, handling challenges, and propensity to induce pest outbreaks and replanting disorders, these residues pose a critical ecological limitation on sustainable agriculture ( 3-7 ) . Among various crops, watermelon ( Citrullus lanatus ) ranks as the most widely cultivated horticultural species globally, with China alone accounting for over 60% of the total planting area ( 8, 9 ) . Its highly perishable aerial parts are produced in substantial quantities, making them a major contributor to replanting inhibition ( 10 ) . Thus, watermelon aerial parts represent both a core challenge in agricultural waste management and an ideal model for fundamental research on high-moisture plant residues. Although strategies such as composting, chemical degradation, and medicinal repurposing have been explored ( 11-13 ) , they are typically hindered by high costs, extended processing times, and limited scalability ( 4 ) . Meanwhile, microbial fermentation has emerged as a promising green alternative due to its operational efficiency and environmental compatibility ( 4, 14-16 ) . For example, strains like Lactiplantibacillus plantarum (formerly Lactobacillus plantarum ) effectively degrade plant biomass under defined conditions, yielding metabolites of agronomic importance ( 17 ) . However, conventional fermentation systems remain limited by prolonged durations, unstable microbial consortia, and inconsistent degradation performance ( 18-20 ) , thereby restricting their applicability in real-world agricultural settings. In this study, informed by agricultural surveys and biological analyses, we identified critical ecological bottlenecks in empirical practices of farmers for watermelon cultivation and plant waste management. To address these bottlenecks, we developed a rapid, Lactiplantibacillus plantarum WCFS1-mediated fermentation system that efficiently depolymerizes watermelon aerial parts biomass and markedly reduces its allelopathic toxicity within a short period of time. This system overcomes major limitations of conventional fermentation, including slow kinetics, unstable microbial communities, and limited process control. Notably, we identified a key fermentation-generated bioactive metabolite, D-sorbitol, which activates a previously unrecognized light intensity–sorbitol–starch cascade that affects energy metabolism and growth in angiosperms. This pathway is widespread in Brassicaceae and Crassulaceae plants, thus opening new directions for exploring sorbitol metabolism and function in plants. Overall, our findings provide mechanistic insights and a scalable strategy for the detoxification and sustainable use of high-moisture agricultural residues, offering both conceptual and technical foundations for transitioning toward science-based sustainable agriculture. Results Current cultivation practices and assessment of the utilization of aerial parts in representative watermelon-producing regions of China China is the largest watermelon producer in the world, accounting for over 60% of global production in 2024 (Extended Data Fig. 1A) ( 9 ) . To assess the sustainability of current cultivation and residue management practices, we conducted field-based surveys across major production regions in China. Henan Province (>10% of national production; Extended Data Fig. 1B), with its core production area in Shangqiu City (>1.6% of national production; Extended Data Fig. 1C), was selected as the primary study region, supplemented by responses from other provinces, including Shanxi and Shandong Provinces ( 21, 22 ) . In total, survey data ( n = 406) were collected to provide descriptive context and illustrate representative watermelon cultivation patterns (Extended Data Fig. 1D, E). Respondents were predominantly middle-aged (36–50 years, 47.29%) and lacked higher education (94.09%) (Extended Data Fig. 2A, B). Among the surveyed farmers, protected cultivation was predominant on medium to large holdings (>0.67 ha; >80%), with most also using high-density planting (>9,000 plants ha⁻¹; 91.38%) and single cultivars, primarily ‘Meidu’ (Extended Data Fig. 2C–H). With frequent fertilization (≥2 times; 94.33%) and input costs exceeding 7,500 CNY ha⁻¹, yields commonly exceeded 30 t ha⁻¹ (93.84%), resulting in high profits (>45,000 CNY ha⁻¹; 75.12%) (Extended Data Fig. 2I–N). However, the aerial parts biomass per watermelon plant exceeds 700 g ( 23 ) , this intensive system produces over 6 t/ha of stem-leaf residues annually. These high-moisture residues are bulky, difficult to handle, and prone to rapid decay, making recycling technically and economically challenging. Although the recycling of watermelon aerial parts has received broad support among farmers, most still opted to discard residues or return them directly to the soil, likely due to limited technical capacity and high costs (Fig. 1E, G; Extended Data Fig. 4A, B). Such practices not only waste biomass resources but also exacerbate soil-borne diseases. Continuous cropping was uncommon (11.57%) (Fig. 1B), and over 60% of farmers reported severe replanting obstacles (Fig. 1C), directly corroborating field experience and fragmented literature reports ( 24-26 ) . However, 88.42% of respondents had little understanding of the underlying causes (Extended Data Fig. 3A–D). Chinese cabbage ( Brassica rapa , 85.16%) and maize ( Zea mays , 84.38%) were the most commonly used rotation crops, reflecting the perception of the farmers—based on field experience—that these species were less affected by preceding watermelon cultivation (Fig. 1D). Similarly, faced with declining watermelon productivity and increasing economic pressure, many growers adopted a “nomadic farming” strategy, abandoning degraded fields in favor of new sites (Fig. 1F, Extended Data Fig. 4C). In general, our survey findings reveal a self-reinforcing cycle of excessive biomass accumulation, inefficient residue management, and progressive soil degradation that threatens the long-term sustainability of watermelon production (Fig. 1H). This highlights an urgent need for scalable strategies that promote efficient residue recycling and reduce replanting barriers in high-biomass cropping systems. Lactiplantibacillus plantarum WCFS1-mediated rapid fermentation system for watermelon aerial parts A rapid fermentation system for watermelon aerial parts biomass was developed using Lactiplantibacillus plantarum WCFS1 (LP - WCFS1), following a questionnaire-informed design strategy (Fig. 2A, Extended Data Fig. 5A). After 14 days of fermentation, full-length 16S rRNA gene sequencing identified LP - WCFS1 as the overwhelmingly dominant taxon in the rapid fermentation liquid (FL) system (relative abundance >98%; Extended Data Fig. 5, Supplementary Table 2), consistent with the acidic fermentation environment (Supplementary Table 1). Second-generation 16S rRNA gene sequencing further confirmed its predominance (>80%) but showed a notable reduction of LP - WCFS1 and increased microbial diversity in the long-term fermentation liquid (FLY) system (Extended Data Fig. 6A–E, Supplementary Tables S3–S4). Functional annotation indicated that fermentation in FL was primarily driven by LP - WCFS1-associated metabolic processes, whereas FLY was enriched in chemoheterotrophic functions (Extended Data Fig. 6F–H), suggesting a shift in community-level energy metabolism. Given that microbial composition directly influences metabolite production, we performed untargeted metabolomic profiling. A total of 329 differential metabolites were identified, with significant enrichment in aminobenzoate-, benzoate-, and furfural-degradation pathways (Fig. 2B, Extended Data Fig. 7A–D). Compared with the FL system, the FLY system accumulated higher levels of allelochemicals—including coumarin, ferulic acid, and caffeic acid (Fig. 2C, Extended Data Fig. 7E–G)—a pattern strongly correlated with its diversified microbial community (Extended Data Fig. 8A–M). The observed heterogeneity in abundance among the top 15 metabolites indicates that the metabolic state is not stable (Fig. 2D). The selective enrichment of allelopathic degradation pathways in the FL thus underscores its functional advantage for efficient detoxification and bioactive compound production. Functional validation of the fermented liquid from the rapid fermentation and identification of its active components To assess the application potential of the rapid fermentation system, we selected the primary rotation crop identified from regional surveys, namely B. rapa , for replicated trials across multiple regions in China. Foliar application of the diluted FL significantly enhanced shoot and root biomass in a concentration-dependent manner (Extended Data Fig. 9; Extended Data Fig. 10I–M). Repeated metabolomic analyses across independent batches showed that the common fermentation products D-sorbitol and L-phenylalanine ( 27-29 ) consistently accumulated in the FL system (Extended Data Fig. 10A–B), prompting the performance of compounding assays to evaluate their biological significance in plant growth experiments. Treatments with FL5 (5 mL/L FL), DS2 (2 g/L D-sorbitol), and DA (2 g/L D-sorbitol + 1 g/L L-phenylalanine) markedly increased net photosynthetic rate, shoot biomass, stomatal conductance, and transpiration, whereas L-phenylalanine alone showed no significant effect (Fig. 3A–C; Extended Data Fig. 10C–Q). Correlation analyses demonstrated that the growth-promoting effects of D-sorbitol closely mirrored those of FL5 (Fig. 3D), identifying D-sorbitol as the major bioactive component of the FL. To confirm this finding, dose–response foliar applications of DS1 and DS2 were conducted under multiple environmental conditions. Both treatments consistently enhanced photosynthetic activity and shoot biomass after 15 days (Fig. 3E). DS2 additionally increased root dry weight, while DS1 increased stomatal conductance and intercellular CO₂ concentration (Fig. 3F–G; Extended Data Fig. 11A–G). Across test locations, DS1 and DS2 improved shoot fresh weight by 3.38–23.01% and 8.4–36.35%, respectively (Fig. 3B, E, I; Extended Data Fig. 10I, J; Extended Data Fig. 11H, L, T–W), with parallel increases in photosynthesis and other growth indices (Extended Data Fig. 10C–Q; Extended Data Fig. 11B–S). D-sorbitol also significantly increased maximum leaf length and width (Extended Data Fig. 12A–D). Transmission electron microscopy analysis revealed that the 10-day D-sorbitol treatment increased thylakoid stacking and reduced starch granule accumulation in chloroplasts (Fig. 3J). This was accompanied by decreased total leaf starch content, increased D-sorbitol levels, and unchanged soluble sugar levels (Fig. 3G–H; Extended Data Fig. 13A–D). Chlorophyll content remained stable (Extended Data Fig. 15A–H), but chlorophyll fluorescence parameters (maximum fluorescence (Fm), minimum fluorescence (Fo), variable fluorescence (Fv), and near infrared (NIR)) and Ca 2+ accumulation were significantly increased (Extended Data Fig. 14A–F; Extended Data Fig. 16A–F). No oxidative stress symptoms were detected based on reactive oxygen species (ROS)-scavenging enzyme activity or chlorophyll fluorescence assays (Extended Data Fig. 16A–J; Extended Data Fig. 17A–F). To further explore the transcriptional and metabolic regulatory pathways underlying D-sorbitol-induced growth promotion, transcriptomic and metabolomic profiling were performed on B. rapa shoots at 2 h and 10 d post-treatment. RNA sequencing (RNA-seq) analysis identified 112 and 147 differentially expressed genes (DEGs) in DS1 and DS2 versus (vs.) controls, and 110 DEGs between DS1 and DS2 (Extended Data Fig. 18A–E). These DEGs were predominantly enriched in pathways associated with carbohydrate metabolism, lipid metabolism, and secondary metabolite biosynthesis, including wax biosynthesis and ABC transporter pathways (Extended Data Fig. 18F-K). Metabolomic profiling revealed 63, 25, and 99 differentially accumulated metabolites (DAMs) in DS1 vs. CK, DS2 vs . CK, and DS1 vs . DS2, respectively (Extended Data Fig. 19A–C). Notably, D-sorbitol was significantly upregulated in both DS1 and DS2, consistent with the measured contents (Fig. 3G; Extended Data Fig. 13A; Extended Data Fig. 19F). Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included galactose metabolism and ABC transporters (Extended Data Fig. 19G–I), and correlation network analysis further linked D-sorbitol content to these pathways (Extended Data Fig. 19J–L). Synergistic regulation of plant growth and energy metabolism by light intensity and D-sorbitol To elucidate the mechanism underlying D-sorbitol-induced plant growth, we systematically evaluated interspecies responses under controlled light conditions (PPFD: 300 μmol m⁻² s⁻¹) by performing 15-day foliar application of DS2 (2 g/L D-sorbitol) across a phylogenetically diverse panel of plants. Cultivation trials repeated across independent batches consistently showed no visible phenotypic changes in eight non-angiosperm species encompassing six families—including Marchantia polymorpha , Adiantum venustum , Lycopodium japonicum , and Podocarpus macrophyllus (Extended Data Fig. 20). In contrast, among 24 angiosperms encompassing 10 families (Extended Data Fig. 21A), six species, namely Lactuca sativa (9.16 - 9.83%), Sedum hispanicum (13.49 - 20.84%), B. rapa (13.88 - 26.32%), Brassica oleracea (13.97 - 29.09%), Rorippa aquatica (10.53 - 11.28%), and Brassica juncea (9.07 - 19.33%), exhibited significantly increased shoot biomass following D-sorbitol treatment (Extended Data Fig. 21B–C, T, W–AC). Conversely, three species, namely Raphanus sativus (-16.05 - -24.81%), Solanum tuberosum (-17.37 - -20.71%), and Zea mays (-13.19 - -16.90%), showed significant growth inhibition (Extended Data Fig. 21J, Q, S, Z–AC), while the remaining 15 species, including model and D-sorbitol-producing plants, such as Arabidopsis thaliana , Oryza sativa , Solanum lycopersicum , and Malus domestica , showed no significant response (Extended Data Fig. 21D–I, R, U–V, Z–AC). These results indicate that D-sorbitol has species- and lineage-specific growth-modulating effects in angiosperms, likely mediated by differential metabolic processing. Notably, light intensity exerted a profound effect on the family-specific D-sorbitol -induced phenotypes. At 300 μmol m⁻² s⁻¹, D-sorbitol treatment significantly increased shoot biomass in the four responsive species ( B. rapa , L. sativa , S. spectabile , B. oleracea ), whereas under a reduced half-light dose (150 μmol m⁻² s⁻¹), this growth-promoting effect was abolished (Fig. 4A–C, F–G, J–K, N–O; Extended Data Fig. 22A–B, E, H, K). Concurrently, D-sorbitol accumulation in leaf tissue was significantly increased under high-light conditions (Fig. 4D, H, L, P; Extended Data Fig. 22C, F, I, L), accompanied by species-specific changes in starch content: decreased in B. rapa and S. spectabile , and increased in L. sativa and B. oleracea (Fig. 4E, I, M, Q; Extended Data Fig. 22D, G, J, M), implying divergent carbon allocation strategies. Non-responsive species including Nicotiana benthamiana , Z. mays , A. thaliana , and M. domestica showed no consistent phenotypic or metabolic changes upon D-sorbitol treatment (Extended Data Fig. 23A–P), highlighting interspecific specificity. Interestingly, D-sorbitol showed a light-dependent growth-promoting pattern analogous to that of sucrose (Extended Data Fig. 24A–C), suggesting possible functional convergence. These results suggest that different angiosperms may facilitate D-sorbitol through distinct light intensity-D-sorbitol-starch-mediated metabolic pathways to promote growth. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis of two responsive species (across two non-Rosaceae families) revealed that expression of canonical D-sorbitol-metabolizing enzymes NADP‑dependent D‑sorbitol‑6‑phosphate dehydrogenase (S6PDH; BrS6PDH and LsS6PDH ) and sorbitol dehydrogenase (SORD; BrSORD‑X1 and LsSORD ) were affected by light intensity and D-sorbitol. The sucrose transporter BrSUC1-X1 in B. rapa was also upregulated synchronously under conditions of simultaneous exposure to light and D-sorbitol (Extended Data Fig. 25A-F, Tab. S5), showing a clear "light intensity-D-sorbitol dependence". Remarkably, although the sequences of S6PDH and SORD are conserved across phylogenetically diverse plant species and their predicted protein structures are similar (Extended Data Fig. 26 A-J, Tab. S6), these plants exhibit markedly different growth phenotypes in response to D-sorbitol. In summary, we hypothesize that D-sorbitol promotes plant growth by activating a specific energy metabolism pathway related to starch metabolism that is regulated by light, representing a potential and previously unrecognized carbon metabolism regulation pattern during differential evolution among plant lineages. LP-WCFS1-mediated rapid fermentation reduces the allelopathic effects of watermelon aerial parts Although direct residue discarding and field return are widely practiced and preferred by farmers in China (Fig. 1E), survey data revealed frequent land rotation and continuous cropping obstacles, indicating potential risks associated with the recycling of watermelon aerial part residues (Fig. 1C, F). Thus, we developed a rapid fermentation system to produce FL using LP-WCFS1, which markedly reduced fresh biomass and partially degraded dry matter (Tab. S7). In particular, untargeted metabolomics identified 451 DAMs between fresh watermelon aerial parts (FW) and fermented watermelon residues (FR) (Fig. 5A, Extended Data Fig. 27B, C), enriched in the degradation pathways of aminobenzoate, benzoate, and dioxins (Extended Data Fig. 27D–F). Notably, five major allelochemicals (ferulic acid, caffeic acid, etc .) were significantly decreased in FR (Fig. 5B), with reductions observed in half of the benzene and substituted derivatives, as well as in most cinnamic acid and coumarin derivatives (Extended Data Fig. 27G–I). These metabolic changes indicate that treatment with FL substantially reshapes allelochemical profiles and may mitigate phytotoxicity risks associated with residue return. To further validate the efficacy of the FL in rapidly reducing allelopathic effects, field experiments were conducted using B. rapa and Z. mays —the most common rotation crops identified in the survey—as representative models (Fig. 1D). In the repeated experiments in Zhangjiakou and Beijing, the fresh watermelon stem and leaf landfill treatment (FW: 4g/plant) significantly inhibited the above ground and underground dry weight and fresh weight of the two crops (Fig. 5 C-H, Extended Data Fig. 28 A-K, Extended Data Fig. 29 D, E, H-K), and reduced the plant height and stem diameter of Z. mays (Extended Data Fig. 29 F-G, L-M). Especially in the field experiment in Shangqiu City, the main survey area of the questionnaire, FW treatment significantly reduced the thousand grain weight and final yield of Z. mays kernels (Fig. 5I-K, Extended Data Fig. 29A-C), indicating that even empirically selected rotation crops with perceived low sensitivity still face substantial allelopathic risks (Fig. 1D). In contrast, the treatment with watermelon stem and leaf fermentation residues (FR: 2g/plant) not only significantly reduced the negative effects mentioned above, but also had no significant inhibitory effect on the biomass of the two crops and the plant type indicators of corn, and effectively mitigated the loss of corn yield (Fig. 5C-K, Extended Data Fig. 28, Extended Data Fig. 29). These findings indicate that the rapid fermentation system can effectively reduce the allelotoxicity of watermelon aerial parts, providing a practical path for their safe return to the field. Further analysis revealed marked differences in rhizosphere microbial community structures between FW- and FR-treated B. rapa sterilized substrates (Extended Data Fig. 30 A-F, Tab. S8, S9). After FR treatment, beneficial communities such as Pseudomonas and Flavobacterium were significantly enriched, while communities such as Brevundimonas was inhibited; However, FW treatment reversed the community structure, significantly inducing the enrichment of Brevundimonas and others, and the decrease of Pseudolabrys and others (Extended Data Fig. 31 A-C). Microbial function prediction showed that FW group rhizosphere microorganisms were significantly in enriched fermentation and microplastic degradation pathways (Extended Data Fig. 31 D), suggesting that they may induce abnormal fermentation and interfere with the ecological stability of rhizosphere microbes. Integrated analyses revealed that metabolite shifts in FR were positively correlated with changes in rhizosphere microbial community composition, a process mediated through the downregulation of key metabolites such as abscisic acid (Extended Data Fig. 32A–E). Moreover, the dynamic accumulation of the key effector metabolite D-sorbitol showed a strong positive correlation with the FR residue metabolome (Extended Data Fig. 33A–C), further supporting its role as a central functional mediator in this system. Finally, we proposed a rapid fermentation platform based on LP-WCFS1, which provides theoretical support and practical path for the utilization of watermelon waste residues and the construction of a closed-loop, sustainable planting mode (Fig. 5L). Discussion Continuous cropping obstacles have become a widely-recognized global challenge in watermelon production, driven by a variety of interacting factors, such as nutrient imbalance, soilborne diseases, and allelopathic metabolites ( 24-26 ) . Our survey revealed a self-reinforcing cycle of excessive biomass accumulation, inefficient residue management, and progressive soil degradation that collectively undermine the long-term sustainability of watermelon cultivation (Fig. 1H). Although farmers have empirically selected so-called “replant-tolerant” rotation crops, our experiments demonstrated that their normal growth was still markedly inhibited by allelochemicals derived from watermelon aerial parts (Fig. 5, Extended Data Fig. 28, Extended Data Fig. 29). This indicates that experience-based agricultural practices, in the absence of scientific validation, may carry hidden ecological and productivity risks ( 30-32 ) . Guided by the key issues identified in the survey, we developed an LP-WCFS1–mediated rapid fermentation system and confirmed its efficacy through crop rotation experiments (Fig. 2A, Fig. 5L), effectively redirecting the unsustainable feedback loop towards a resilient and sustainable watermelon production system. Integrating scientific guidance with on-farm practice is therefore essential to maximize both agricultural productivity and environmental benefits ( 33, 34 ) , providing a realistic framework for establishing future sustainable production systems. Fermentation has long served as a traditional and sustainable approach for managing plant-derived agricultural waste ( 15-17 ) . However, conventional fermentation typically requires 45–60 days and involves complex, unstable microbial consortia ( 18–20 ) . To overcome these limitations, we optimized the conventional long-term mixed fermentation process and established an LP-WCFS1-mediated rapid fermentation system (Fig. 2A). LP-WCFS1, widely used in food and pharmaceutical industries, is capable of depolymerizing plant residues and generating bioactive metabolites ( 17, 35, 36 ) . In this study, the LP-WCFS1-based rapid fermentation system outperformed traditional long-term fermentation in both metabolite composition and microbial community structure within a short period of time (Fig. 2C–D, Extended Data Fig. 5-7). Notably, prolonged fermentation led to the accumulation of allelopathic compounds, such as caffeic acid, and the emergence of a more complex microbial network, both of which posed potential risks to plant growth (Fig. 2B–D, Extended Data Fig. 5-7). The competitive dominance of LP-WCFS1 and the acidic, anaerobic microenvironment it establishes effectively suppresses most other harmful microorganisms ( 37–41 ) . The rapid fermentation system thus creates a weakly acidic, low-oxygen, and high-osmotic niche (pH = 3.34, EC = 3.8 mS cm⁻¹), favoring a highly sSupplementary Table ingle-strain community (Extended Data Fig. 5, Supplementary Table 1). Collectively, these results demonstrate that targeted microbial mediation can convert the traditional, slow, and variable fermentation into a rapid, controllable, and functionally stable process. This strategy offers a scalable biotechnological solution for the safe recycling and valorization of high-moisture agricultural residues, while reducing the ecological risks of residue mismanagement. Understanding the functional roles of fermentation-derived metabolites is crucial for advancing synthetic biology and developing sustainable agricultural systems ( 14–17 ) . D-sorbitol, a common bacterial fermentation product ( 27-28 ) , was identified as the primary metabolite generated in our system, which contributed directly to the observed bioactivity of the fermented liquid (Fig. 3 A-J). In plants, D-sorbitol is generally considered a primary photosynthetic product in Rosaceae and Plantaginacea e plants and is widely distributed across Brassicaceae, Solanaceae, and Poaceae families ( 42 ) . To date, its roles as a sucrose-like energy source and a signaling molecule have been established primarily in Rosaceae plants, with additional evidence suggesting potential functions in stress tolerance and osmotic regulation in plants ( 42-46 ) . However, the metabolism, functions, and evolutionary history of D-sorbitol in most plant groups have remained systematically unexplored This study systematically explored the ability of 32 plant species (including 16 families and 20 genera) to utilize and metabolize D-sorbitol. Surprisingly, significant growth differences were observed only in non-model plants, such as B. rapa , S. hispanicum , L. sativa , etc . (Extended Data Fig. 21 B, W-X, Z-AC). In contrast, we did not observe significant growth differences in model plants and plants that use D-sorbitol as a photosynthetic product, such as M. domestica and A. thaliana (Extended Data Fig. 21 J, M, R, Z-AC), consistent with previous studies ( 47-50 ) . Furthermore，we provide substantial physiological and molecular evidence to propose the hypothesis that sorbitol specifically promotes plant growth: (i) in plants such as B. rapa , S. hispanicum and L. sativa , the energy metabolism promoting phenotype of light intensity-D-sorbitol-starch axis is similar (Fig. 4, Extended Data Fig. 22); (ii) physiological phenotypes of non-responsive and inhibited model plants such as M. domestica , Plantago depressa Willd , A. thaliana , and other plants (Extended Data Fig. 20, Extended Data Fig. 21, Extended Data Fig. 23); (iii) The gene expression of S6PDH and SORD in B. rapa and L. sativa is regulated by both D-sorbitol and light intensity (Fig. 4 A-Q, Extended Data Fig. 22 A-M, Extended Data Fig. 25A-H). Therefore, we hypothesize that there is a broad and specific potential energy metabolism pathway involved in promoting growth mechanism in plants that is dependent on light intensity–D-sorbitol-starch. However, despite the high similarity in amino acid sequences and predicted protein structures between S6PDH and SORD in different closely related species (Extended Data Fig. 26 A-J), there are significant differences in physiological phenotypes. Although light has been reported to regulate S6PDH , SORD and appears to affect sorbitol and starch metabolism in M. domestica , it still cannot explain a broad and specific growth promoting mechanism ( 45, 48, 50 ) . Notably, under light conditions (300 μmol m –2 s –l .), B. rapa shared potential similarities in the metabolism of sorbitol and sucrose (Extended Data Fig. 24A–C; Extended Data Fig. 25C). For instance, sorbitol and sucrose share the same precursor, glucose-6-phosphate, and are linked through sugar transporter ( MdSTP13a ) and protein kinases ( MdSnRK1 ) in M. domestica ( 42, 45, 51-53 ) , suggesting a previously unrecognized connection between these pathways in plant energy metabolism (light-sugar/alcohol-starch). This study presents a hypothesis that provide new perspectives on the metabolic and evolutionary dynamics of the metabolism of sorbitol in plants. Although residue return is generally regarded as a direct and sustainable approach for managing plant-derived agricultural waste ( 2, 4–6 ) , allelopathic effects driven by plant metabolites are an important factor contributing to continuous cropping disorders ( 5, 6, 54-57 ) . In this study, we found that returning fresh watermelon aerial parts to farmland significantly reduced the growth and yield of maize and cabbage (Fig. 5C–K; Extended Data Fig. 28; Extended Data Fig. 29). Collectively, these findings suggest that fresh watermelon aerial parts are an important primary source of allelopathic toxicants in watermelon production systems, highlighting potential ecological and agronomic risks linked to their direct incorporation into farmland. In contrast, residues subjected to rapid fermentation markedly reduced these inhibitory effects (Fig. 5). During rapid fermentation, LP-WCFS1 preferentially changed the metabolite composition ( 59 ) . Metabolomic profiling revealed significant reductions in allelochemicals—including p-coumaric, ferulic, and caffeic acids—particularly within the cinnamic-acid and coumarin-derivative pathways (Fig. 5B; Extended Data Fig. 27G–I). These phenolic acids mediate strong allelopathic toxicity against various crops, such as Poaceae, Brassicaceae, Solanaceae, and Cucurbitaceae, and are closely associated with the enrichment of pathogens such as Fusarium oxysporum , jointly leading to severe continuous-cropping disorders ( 54-57, 59-64 ) . Their allelochemicals depletion therefore likely underpins the observed mitigation of phytotoxicity. These metabolic changes also substantially restructured the rhizosphere microbiome ( 57, 62-65 ) . In FR-treated soils, the root-associated microbiota showed reduced residue-decomposition activity and was markedly enriched in beneficial taxa, including Pseudomonas and Flavobacterium (Extended Data Fig. 30D; Extended Data Fig. 31A–D; Supplementary Table 8), which enhance plant resistance to soil-borne pathogens associated with replanting obstacles ( 66-69 ) . Notably, the combined effects of altered metabolite inputs and microbiome reassembly improved the rhizosphere conditions for subsequent crops, thereby promoting favorable growth ( 70, 71 ) . These findings demonstrate that controlled microbial fermentation effectively neutralizes the allelopathic risks of fresh plant residues while enhancing their ecological recycling potential. In summary, by bridging social and natural scientific perspectives, this study provides a practical and evidence-based foundation for advancing sustainable agricultural practices. Methods Questionnaire To investigate how Chinese farmers handle watermelon aerial parts, we conducted a nationwide survey among watermelon growers across 11 provinces, including Henan, Shandong, and Shanxi. The primary survey efforts were focused on Shangqiu City, Henan Province, China (115.69°E, 34.47°N), which is a major watermelon-producing region in the country, with a sample size of 336 respondents ( 9, 21, 22 ) . Additionally, the sample sizes from Shanxi and Shandong both exceeded 20. A total of 406 questionnaires were distributed between January and June 2024, yielding 406 valid responses. Subsequently, a random subset of 128 farmers from these 406 valid respondents was selected for supplementary surveys, with all 128 providing valid responses. Data collection was primarily carried out through on-site investigations, supplemented by electronic questionnaires completed by respondents who had access to digital devices. The questionnaire design is available in Academic ethics certificate; detailed results are presented in Source data Fig. 1 (Original questionnaire data). Preparation of fermentation liquid Under static conditions, the Lactobacillus plantarum WCFS1 was cultured in MRS medium (BD DIFCO, 288130) at 37°C for 24 hours ( 36 ) . For fermentation experiments, 1 mL of L. plantarum WCFS1 culture (10⁹ CFU), brown sugar, fresh watermelon aerial parts (cv. 8424 and Meidu), and water were added into 500-mL glass bottles at a mass ratio of 3:7:10 (brown sugar: plant material: water). After thorough mixing, the mixtures were subjected to sealed anaerobic fermentation under dark conditions at 15–28°C. Fresh watermelon aerial parts from 9 plants were pooled as one biological replicate. Samples were collected for analysis after 14 days (FL, Beijing, 2023 and 2024) and 365 days (FLY, Beijing, 2023–2024), respectively, with 3 biological replicates set up for each group. Characterization of microbial diversity For the 14-day fermented liquid samples (FL, Beijing, 2024), each sample was transferred to a 15-mL sterile centrifuge tube and stored at -80°C. Three biological replicates were prepared for each treatment. Full-length 16S rRNA gene sequencing (V4-V5 regions) was performed. Genomic DNA was extracted using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s protocol. The 16S rRNA gene V4 region was sequenced on an Illumina MiSeq PE 300 platform (Majorbio, Shanghai, China) ( 68 ) . The full-length 16S rRNA gene sequence data have been deposited in the National Genomics Data Center (NGDC) under accession number [to be CRA035035]. Following the same sampling procedure as used for full-length 16S rRNA gene sequencing, 14-day (FL) (June 5–19, 2023) and 365-day (FLY) (June 15, 2023–June 15, 2024) fermented liquid samples were collected for 16S rRNA gene (V4-V5 regions) sequencing. The bacterial 16S rRNA gene V4 region was sequenced using an Illumina NextSeq 2000 high-throughput sequencing platform (Majorbio, Shanghai, China) ( 68 ) . The 16S rRNA gene sequence data have been deposited in the National Genomics Data Center (NGDC) under accession number [to be CRA035038]. Rhizosphere samples of the cultivation substrate (peat:vermiculite:perlite = 2:1:1) were collected from cabbage plants under three treatments: no return (CK), fresh watermelon aerial parts return (FW), and fermented residue return (FR). Each rhizosphere substrate sample was placed in a sterile bag and stored at -80°C. Sample from three plants was pooled as one biological replicate, with three biological replicates per treatment. 16S rRNA gene (V4-V5 regions) sequencing was performed using the same protocol as applied to the fermented liquid samples. The 16S rRNA gene sequence data have been deposited in the National Genomics Data Center (NGDC) under accession number [to be CRA035036]. Metabolite characterization of fermentation liquid and fermentation residue For 14-day (FL, Beijing, 2023 and 2024), 365-day (FLY, Beijing, 2023–2024) and 14-day-Validation (FLV, Zhangjiakou, 2024) fermented liquid samples, each sample was transferred to a 15-mL sterile centrifuge tube and stored at -80°C, with three biological replicates set for each treatment. For metabolomic analysis, fresh watermelon aerial parts (FW) were sampled with five plants pooled as one biological replicate; fermented residue samples (FR, Beijing, 2024) were collected with each bottle serving as one biological replicate, with three biological replicates established for each group. Differential metabolites were identified using orthogonal partial least squares-discriminant analysis (OPLS-DA) with criteria of VIP > 1, p < 0.05, and BH-FDR < 0.05 ( 72 ) . Metabolome data have been deposited in OMIX under accession number, FR-FW [to be OMIX013593, https://ngdc.cncb.ac.cn/omix/preview/wHahAOfW], FL-FLY [to be OMIX013597, https://ngdc.cncb.ac.cn/omix/preview/jsQ2m1Yv], FLV [to be OMIX013596, https://ngdc.cncb.ac.cn/omix/preview/SxuyVtmc]. All analyses were conducted with support from Shanghai Majorbio Biopharmaceutical Technology Co., Ltd.. Plant materials and growth conditions The primary plant materials used in this study were Brassica rapa cv. Xiaqiuwang and Zea mays cv. Zhengdan 958. Other plant materials included Marchantia polymorpha , Funaria hygrometrica , Pteris ensiformis , Adiantum venustum , Selaginella tamariscina , Lycopodium japonicum , Nageia nagi , Podocarpus macrophyllus , Lactuca sativa cv. Luoma Shengcai (Roman lettuce), Sedum hispanicum , Coriandrum sativum cv. Shandong Daye Xiangcai, Plantago depressa Willd ., Citrullus lanatus cv. 8424, Cucumis sativus cv. Jinchun 4, Fragaria × ananassa cv. Zhenhong Meiling, Malus × robusta , Malus domestica , Sedum hispanicum cv. Green Sprite, Oryza sativa inbred line Nipponbare, Nicotiana benthamiana , Lycium ruthenicum , Solanum lycopersicum inbred line Condine Red, Solanum tuberosum cv. Desiree, Arabidopsis thaliana (accession: Col-0), Raphanus sativus cv. Powder Bird, Nasturtium officinale , Eruca sativa , Isatis indigotica , Brassica oleracea cv. Zhonggan 101, and Brassica juncea . Plants were grown in glass greenhouses and growth chambers at the College of Biology, Hebei North University (Zhangjiakou, Hebei, 40°48′29″N, 114°52′50″E; 18–30°C, 12-h light/12-h dark photoperiod, PPFD (Photosynthetic Photon Flux Density): 300μmol m –2 s –l ); and in glass greenhouses and growth chambers at the Institute of Botany, Chinese Academy of Sciences (CAS) (Beijing, 39°56′23″N, 116°20′31″E; 18–30°C, 12-h light/12-h dark photoperiod, PPFD: 300μmol m –2 s –l ). All laboratory-grown plants were cultivated in a substrate mixture (peat: vermiculite: perlite = 2:1:1). Growth chamber conditions were maintained at 26°C with a 12-h light/12-h dark photoperiod (PPFD: 300μmol m –2 s –l ), and 60% relative humidity. All plants were irrigated with 4 L of modified Hoagland’s nutrient solution ( 73 ) , which contained 2.6 mM Ca(NO₃)₂·4H₂O, 4.25 mM KNO₃, 1.96 mM KH₂PO₄, 0.25 mM (NH₄)₂SO₄, 1.75 mM MgSO₄·7H₂O, 2.28 mM K₂SO₄, 0.0134 mM EDTA-FeNa·3H₂O, 0.0101 mM MnSO₄·H₂O, 0.0052 mM ZnSO₄·7H₂O, 0.0054 mM Na₂B₈O₁₃·4H₂O, 0.0008 mM CuSO₄·5H₂O, and 0.00083 mM Na₂MoO₄·2H₂O. The nutrient solution was applied every 5 days. Uniformly growing plants were selected for the experiments. First, experiments investigating the growth-promoting effect of the fermentation liquid on Brassica rapa and the identification of its bioactive components were conducted as geographically replicated trials in Zhangjiakou, and Beijing with four concentration gradient treatments: 0 ml/L (CK),2.5ml/L (FL2.5), 5ml/L (FL5), 10 ml/L (FL10). Based on the experimental results (Fig 3), using the FL5 treatment as a reference, we compared the top 10 most abundant common metabolites from the untargeted metabolomics analysis of the fermentation liquid (Fig S10), and set up treatments: water (CK), 5 ml/L FL (FL5), 2g/L D-sorbitol (DS2), 0.2g/L Phenylalanine (AA), 2g/L D-sorbitol + 0.2g/L Phenylalanine (DA). Subsequently, we conducted geographically replicated trials in Zhangjiakou, and Beijing to investigate the mechanism by which D-sorbitol promotes the growth of Brassica rapa , with 0g/L D-sorbitol (CK),1g/L D-sorbitol (DS1) and 2g/L D-sorbitol (DS2) set as the experimental treatment. For experiments exploring the capacity of other plant species to utilize D-sorbitol, 0g/L DS (CK) and 2g/L D-sorbitol (DS2) was uniformly used as the experimental treatment. In experiments investigating D-sorbitol-induced plant energy metabolism (Fig. 4), we established the following treatments: CK: 0g/L D-sorbitol+ PPFD: 300μmol m –2 s –l , DS2: 2g/L D-sorbitol+ PPFD: 300μmol m –2 s –l , HCK: 0g/L D-sorbitol+ PPFD: 150μmol m –2 s –l , HDS2: 2g/L D-sorbitol+ PPFD: 150μmol m –2 s –l . Experiments were also conducted in Brassica rapa ， Sedum hispanicum 、 Solanum lycopersicum 、 Arabidopsis thaliana 、 Nicotiana benthamiana 、 Malus domestica 、 Brassica oleracea 、 Zea mays 、 Lactuca sativa . Additionally, we established treatments: CK: Water+ PPFD: 300μmol m –2 s –l , DS2: 2g/L D-sorbitol+ PPFD: 300μmol m –2 s –l , MA: 2g/L mannitol+ PPFD: 300μmol m –2 s –l , SU: 2g/L sucrose+ PPFD: 300μmol m –2 s –l , FU: 2g/L fructose+ PPFD: 300μmol m –2 s –l , GL: 2g/L glucose+ PPFD: 300μmol m –2 s –l , HCK: Water+ PPFD: 150μmol m –2 s –l , HDS2: 2g/L D-sorbitol+ PPFD: 150μmol m –2 s –l . HMA: 2g/L mannitol+ PPFD: 150μmol m –2 s –l , HSU: 2g/L sucrose+ PPFD: 150μmol m –2 s –l , HFU: 2g/L fructose+ PPFD: 150μmol m –2 s –l , HGL: 2g/L glucose+ PPFD: 150μmol m –2 s –l to investigate the effects of different sugars on Brassica rapa growth under varying light intensities. All foliar spray experiments were conducted with applications every three days, where the criterion for successful spraying was uniform coverage of both adaxial and abaxial leaf surfaces with fine droplets ( 74 ) . The experiments were terminated on the third day after the fifth application. Finally, we evaluated the quality of watermelon aerial parts return to fields based on the average number of watermelon plants reported in the questionnaire survey. The experimental treatments included: no return (CK), return of 4 g fresh watermelon aerial parts per plant (FW), and return of 2 g fermented residues per plant (FR) (with reference standards: soil tillage depth of 30 cm; 10,800 kg of fresh watermelon aerial parts per hectare, corresponding to an approximate content of 2 g/kg in soil; after fermentation, the fermented residues accounted for 1.1 g/kg, with 2 kg applied per pot of soil). Triplicated experiments on Brassica rapa and Zea mays were conducted in Zhangjiakou (Hebei), and Beijing in 2024, respectively, and all data were collected 15 days after treatment application. Additionally, from August to October 2024, a field comparison experiment on the incorporation of fresh watermelon aerial parts versus fermented residues was conducted in maize (Zea mays) fields in Shangqiu City, Henan Province (116.05°E, 34.07°N). Located in the central-eastern region of China, Shangqiu has a temperate climate with naturally occurring long-day conditions (daylength > 12 hours in summer). The field experiment adopted the same treatments as described above (CK, FW, FR), using Zea mays cv. Zhengdan 958 seeds. The growing season for the field experiment was from June to October 2024, with a planting configuration of 6 rows × 60 plants, at a row spacing of 25 m and plant spacing of 60 cm ( 75 ) . At least three plots were established as biological replicates, and all treatments within each plot were fully randomized. During sampling, measurement, and final yield analysis, edge plants in each plot were removed to avoid edge effects. Analysis of agronomic and physiological traits Shoot fresh weight (including leaves, buds, and stems) of all plants was recorded on a per-plant basis. For Brassica rapa and Zea mays , Shoot biomass (including leaves, buds, and stems) and Root biomass (roots) were collected. At harvest, Shoot and Root fresh weights were recorded, followed by determination of dry matter accumulation after oven-drying (30 minutes at 105 °C, then 3 days at 55 °C) ( 73 ) . The leaf length and width of the largest leaf in Brassica rapa , as well as the plant height and plant width of Zea mays , were measured using a ruler. We photographed the largest leaf of plants after 15 days of exogenous treatment. At the end of the field experiment, randomly selected maize plants were used for 1,000-grain weight determination. All Zea mays grains from the plots established in each field were collected to measure the actual yield ( 75 ) . Photosynthetic efficiency parameters Diurnal photosynthetic variations were measured in the field in Beijing and Zhangjiakou using a LI-COR 6400XT photosynthesis system ( 73, 76 ) . Photosynthetic rates of Brassica rapa were recorded from the largest fully expanded leaves during the growth stage under a photosynthetic photon flux density (PPFD) of 800 μmol m⁻² s⁻¹ and ambient CO₂ conditions. TEM images Transmission electron microscopy (TEM) samples were obtained from at least three distinct plants. Brassica rapa leaf samples were collected from the mid-section of the largest leaf. The samples were carefully excised into 1–3 mm³ blocks, rinsed three times with 0.1 M phosphate-buffered saline (PBS; pH 7.2), fixed in a solution containing 2.5% paraformaldehyde and 2% glutaraldehyde, post-fixed in osmium tetroxide, dehydrated through a graded ethanol series (30–100%), and embedded in Spurr’s resin (MilliporeSigma, Burlington, MA, USA), following established protocols ( 77 ) . Subsequently, three samples from different plants per treatment were sectioned using an LKB-V ultramicrotome (LKB Produkter AB, Bromma, Sweden), stained with 2% uranyl acetate and 0.5% lead citrate, and examined under a JEM-1230 transmission electron microscope (JEOL, Tokyo, Japan) at 80 kV ( 77 ) . Pigment measurements Brassica rapa leaf samples were harvested 15 days after treatment (Beijing, 2023; Zhangjiakou, 2024) and immediately frozen in liquid nitrogen. The ground leaf samples were weighed and extracted in 80% acetone, followed by spectrophotometric analysis to determine the contents of chlorophyll, carotenoids, and total chlorophyll ( 78 ) . Chlorophyll fluorimeter and reactive oxygen species (ROS)-scavenging enzyme activity measurements Chlorophyll fluorescence parameters, including Fv/Fm, Fo, Fm, Fv, Y(NPQ), and Y(NO), of the largest leaf in Brassica rapa were measured using an Imaging-PAM Chlorophyll Fluorimeter equipped with a computer-operated PAM-control unit (IMAG-MAXI; Heinz Walz, Effeltrich, Germany), following the method described previously ( 79 ) . The activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were assayed using commercial kits from Beijing Solarbio Science & Technology Co., Ltd. (Solarbio, China) according to the manufacturer’s protocols (77 , 80 ) . Plant suger composition and elemental content measurements For each treatment, 18 plants were divided into four biological replicates (4–5 plants per replicate). From each plant, the largest leaf of Brassica rapa was homogenized to analyze the nutritional composition of the tissue. Total sugar content was determined using the dinitrosalicylic acid (DNS) method ( 81 ) . The leaf D-sorbitol content and total starch content in Brassica rapa , Sedum hispanicum , Brassica oleracea , and Lactuca sativa were quantitatively determined via spectrophotometry using assay kits from Beijing Boxbio Science & Technology Co., Ltd ( 48, 81-82 ) . Electrical conductivity (EC) and pH values of the fermentation liquid were measured using an EC meter and a pH meter, respectively ( 84 ) . We quantified six photosynthesis-related elements using inductively coupled plasma optical emission spectrometry (ICP-OES). For elemental composition analysis, dried Shoot plant parts were analyzed using an Agilent ICP-OES 730 spectrometer (Agilent Technologies Inc., Santa Clara, CA, USA). Each treatment group, consisting of 18 plants in total, was divided into three biological replicates ( n = 6). Each biological replicate was ground into a powder, and equal aliquots from each plant were pooled to form a composite sample for each replicate. Three photosynthesis-related elements (K, Ca, Mg) were quantified using ICP-OES ( 77 ) . RNA-seq Analysis and metabolome of Brassica rapa leaves The experiment extracted RNA and characterized the transcriptome of Brassica rapa leaves two hours after the spraying sorbitol treatment. In each treatment group, five Brassica rapa plants with consistent growth were selected from each box as biological replicates, resulting in three replicates per group. The largest leaf from each plant were collected as experimental materials. Measurement details are described in a previous study25. Bulk transcriptome data processing included: (1) quality control with Trim Galore! (v.0.6.10) to remove adapters and low-quality bases; (2) alignment to the Brara_Chiifu_V3.5 genome using STAR; (3) gene-level quantification via feature Counts, followed by downstream analysis with Count. Transcripts per million (TPM) values were calculated for sample correlation analysis and PCA. DEGs were identified using DESeq2 with thresholds of P < 0.05, Benjamini-Hochberg-adjusted FDR (BH-FDR) 1. DEG expression patterns were clustered via Mfuzz using fuzzy C-means. KEGG pathway analysis was performed with cluster Profiler's enricher function and validated through KOBAS (http://kobas.cbi.pku.edu.cn/home.do) ( 77, 84 ) . The metabolome sampling protocol matched the transcriptome methodology. Differential metabolites were identified by orthogonal partial least squares-discriminant analysis (OPLS-DA) with VIP > 1, P < 0.05, and BH-FDR < 0.05. Transcriptome data are deposited in NGDC (accession: [subCRA057394]), metabolome data in OMIX ([OMIX013591, https://ngdc.cncb.ac.cn/omix/preview/EfMX1PAt]). Analyses were supported by Shanghai Majorbio Biopharmaceutical Technology Co., Ltd ( 72 ) . qRT‑PCR analysis The sampling method was consistent with that used for transcriptome analysis. Leaf tissues of Brassica rapa and Lactuca sativa were harvested and immediately flash-frozen in liquid nitrogen. Total RNA was extracted using the RN53 Total RNA Extraction Kit (TransGen Biotech, China), followed by reverse transcription with the One-Step gDNA Removal and cDNA Synthesis SuperMix (AG, China). Quantitative real-time PCR (qRT-PCR) was performed on a QuantStudio 5 instrument (Applied Biosystems, USA) using SYBR Green Mix (AG, China) in 384-well optical plates, following the manufacturer’s instructions. Detailed PCR conditions were as described in a published protocol ( 82 ) . Three independent biological replicates were analyzed. Real-time PCR data were generated and analyzed using the comparative Ct method to determine the relative mRNA expression levels in each tissue, as described in the iCycler manual (Bio-Rad, USA). Actin was used as the internal control, as its amplification efficiency was comparable to that of the target genes. Primer sequences are listed in Supplementary Table 5. Alignment and phylogenetic tree and structure prediction of NADP-dependent D-sorbitol-6-phosphate dehydrogenase and sorbitol dehydrogenase orthologs Amino acid sequences of canonical D-sorbitol-metabolizing enzymes (NADP-dependent D-sorbitol-6-phosphate dehydrogenase S6PDH and sorbitol dehydrogenase SORD ), and their orthologs from multiple species were downloaded from NCBI (https://www.ncbi.nlm.nih.gov/) and BRAD (http://www.brassicadb.cn/#/) (Tab S6). Sequence alignment was performed using ESPript 3.0 (http://espript.ibcp.fr/ESPript/ESPript/). A phylogenetic tree was constructed using 9 amino acid sequences retrieved from NCBI and BRAD databases. Evolutionary history was inferred using the Maximum Likelihood method with the JTT matrix-based model in MEGA (version 7; https://www.megasoftware.net) and visualized with iTOL (https://itol.embl.de/). Bootstrap values (with 1000 replicates) were calculated to assess the relative support for each branch, and those ≥50% are indicated on the tree. The protein structures of S6PDH and SORD were predicted using AlphaFold3 ( 84 ) , respectively. All structures were visualized using the PyMOL Molecular Graphics System. Statistical analysis The Student’s t-test was used to determine statistical significance between two groups using Microsoft Excel. For comparisons of multiple groups, the data were analyzed by one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test using IBM SPSS Statistics 25 (IBM Corp., U.S.A) was used for Pearson correlation analysis. P values smaller than 0.05 were considered statistically significant. No outliers were excluded in any statistical analysis. Figures were generated using GraphPad Prism (version 6.02, GraphPad, USA). Details and numbers of biological replicates are described in the respective figure legends. Declarations Acknowledgments This work was supported by grants from the National Natural Science Foundation of China (32201516). Part of the experimental expenses were borne by the authors themselves. We would like to thank Professor Jiqing Wang (Henan Agricultural University) for providing the foundation in fermentation technology, and Professor Jinxing Lin (Beijing Forestry University) for his valuable comments on the manuscript. We are also grateful to Associate Researcher Xu Cai (Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences) for providing the KEGG annotation data for Brassica rapa cv. Chiifu v3.5. Our sincere thanks go to all farmers and personnel involved in the survey. Special appreciation is extended to Ren Liu (watermelon grower), Yuan Zhao (Director of the Anhui Provincial Department of Agriculture and Rural Affairs), Pengyu Sun (Chairman of Fengtai Agricultural Products Co., Ltd., Lu’an City, Anhui Province), Jianwei Wan, Zixiao Sun, and other colleagues and friends who assisted in questionnaire collection. Author contributions X.Y.W. and H.Y.W. conceived and designed the research. X.Y.W., X.W.W., S.F.W. performed most of the experiments and analyses. H.X.C. contributed to the questionnaire analyses and all statistical analyses. J.F.N. contributed to the transcriptome and metabolome analysis. X.Y.W. wrote the manuscript. H.Y.W. and X.Y.W. coordinated the project. X.Y.W., H.X.C., J.F.N., S.F.W, L.X.L., X.W.W., and H.Y.W. discussed the findings online and revised the manuscript. H.Y.W., X.W.W., and X.Y.W., supervised the research. 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Supplementary Files ExtendeddataTable.xlsx Supplementary Data Table ExtendedDataFigure.docx Extended Data 1 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8409481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":569503196,"identity":"606710ab-bb89-4814-8715-7da0db3cc8b9","order_by":0,"name":"Xiaoyang Wan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYLCCBDirQkJOnkQtZyyMDRtIso6xrSKR4QABRQbHzx6TeFBjk9g/u/0CM+88iQTGBuaHj27g03ImL9kg4Vha4ow7ZwqYebdJ5LEzsBkb5+DRYnYgx/BBAtvhxIYbOQnMudskihkbeNik8Wo5/8bgQMK/w4nzwVrmSCQ2HCCk5QbQlsS2w4kbbqQfYM5tIEKL/Y03xgaJfWnGG2/kMDD/OSZhbNhMwC+S/Tlmkj++2cjOu5H+gHFGTZ2cPHvzw8f4tMCAYwMDj/kPMJOZCOVgBzIwsD8gUu0oGAWjYBSMNAAAKXpPyMwTqtsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0528-6445","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Wan","suffix":""},{"id":569503197,"identity":"b664d8bd-8340-46c6-8f1a-8bf66bd0ff9a","order_by":1,"name":"Huixian Cheng","email":"","orcid":"","institution":"College of Economics and Management, China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Huixian","middleName":"","lastName":"Cheng","suffix":""},{"id":569503198,"identity":"01f03d7d-68c4-47ea-9477-e362beec4ed7","order_by":2,"name":"Jiefei Niu","email":"","orcid":"","institution":"Research Unit of Molecular Epidemiology, Helmholtz Zentrum München","correspondingAuthor":false,"prefix":"","firstName":"Jiefei","middleName":"","lastName":"Niu","suffix":""},{"id":569503199,"identity":"7f50af91-33cc-4f95-b373-452e454e649b","order_by":3,"name":"Shufang Wang","email":"","orcid":"https://orcid.org/0009-0009-6364-6638","institution":"Institute of Botany, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shufang","middleName":"","lastName":"Wang","suffix":""},{"id":569503200,"identity":"f66edbc3-3fd9-47b8-9fdb-1c2fea2bba36","order_by":4,"name":"Lanxin Li","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lanxin","middleName":"","lastName":"Li","suffix":""},{"id":569503201,"identity":"92e1f4ee-d0d1-440e-a63b-ea0d9f1a2dfb","order_by":5,"name":"Xinwei Wang","email":"","orcid":"https://orcid.org/0000-0001-5012-8473","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Xinwei","middleName":"","lastName":"Wang","suffix":""},{"id":569503202,"identity":"124824fa-371a-4817-80dd-8c9ac4330e96","order_by":6,"name":"Hongyang Wu","email":"","orcid":"https://orcid.org/0009-0001-2271-2186","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hongyang","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-12-20 04:50:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8409481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8409481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99617905,"identity":"447de5d9-4c30-43d2-9cb0-6366272c123f","added_by":"auto","created_at":"2026-01-06 13:42:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4314177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCultivation patterns in representative watermelon-producing regions of China.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Watermelon planting frequency. Most farmers practiced annual planting as the dominant system (\u003cem\u003en\u003c/em\u003e = 406).\u003c/p\u003e\n\u003cp\u003eB. Cropping systems. Crop rotation was the primary cultivation approach adopted by farmers (\u003cem\u003en\u003c/em\u003e = 406).\u003c/p\u003e\n\u003cp\u003eC. Replanting obstacles. Over 60% of farmers reported problems associated with continuous cropping (\u003cem\u003en\u003c/em\u003e = 406).\u003c/p\u003e\n\u003cp\u003eD. Rotation crops. Maize and \u003cem\u003eB. rapa\u003c/em\u003e were the most commonly used rotation crops (multiple responses allowed; y-axis indicates percentage of respondents). Cucumber (\u003cem\u003eCucumis sativus\u003c/em\u003e), Watermelon (\u003cem\u003eCitrullus lanatus\u003c/em\u003e), Melon (\u003cem\u003eCucumis melo\u003c/em\u003e), Chinese cabbage (\u003cem\u003eBrassica rapa\u003c/em\u003e), Tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e), Corn (\u003cem\u003eZea mays\u003c/em\u003e), Wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e), Chili pepper (\u003cem\u003eCapsicum annuum\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eE. Residue disposal. Direct discarding and field return were the predominant residue management practices (multiple responses allowed; \u003cem\u003en\u003c/em\u003e = 406).\u003c/p\u003e\n\u003cp\u003eF. Field rotation intervals. Most farmers rotated watermelon fields within three years (\u003cem\u003en\u003c/em\u003e = 406).\u003c/p\u003e\n\u003cp\u003eG. Representative residue management in Shangqiu, Henan Province. Direct return (2018): residues left \u003cem\u003ein situ\u003c/em\u003e to decompose. Shredding and incorporation (2025): residues mechanically pulverized and tilled into soil. Discard (2024): residues piled and left to decay.\u003c/p\u003e\n\u003cp\u003eH. Summary schematic of dominant cultivation patterns. Major watermelon cultivation systems inferred from nationwide survey data.\u003c/p\u003e\n\u003cp\u003eThe survey covered 11 provinces, focusing on Henan Province (n \u0026gt; 300), and included 406 standard and 128 supplementary valid questionnaires.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/b883d628ef83cd3db29f03ca.png"},{"id":99617903,"identity":"277d6005-b745-4d1a-8f9c-97d2bcb71be3","added_by":"auto","created_at":"2026-01-06 13:42:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLactiplantibacillus plantarum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e (LP-WCFS1)-driven rapid fermentation platform for valorizing watermelon aerial parts biomass.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Schematic workflow of the LP-WCFS1-mediated rapid fermentation system. Fresh watermelon aerial parts are mixed with water and sucrose at defined ratios, followed by sealed fermentation for 14 days, yielding two products: a fermented liquid and fermentation residue.\u003c/p\u003e\n\u003cp\u003eB. Volcano plot of metabolite profiles comparing fermented liquids from the rapid (FL, 14 d) and long-term (FLY, 365 d) fermentations. A total of 329 differential metabolites (FDR \u0026lt; 0.05) were identified between the two fermentation durations.\u003c/p\u003e\n\u003cp\u003eC. Relative abundances of major allelochemicals in the FL and FLY. Color intensity (blue to red) indicates increasing relative abundance. There were significantly higher levels of key allelochemicals accumulated in the FLY.\u003c/p\u003e\n\u003cp\u003eD. Relative abundances of the top 15 metabolites in the FL and FLY. Color intensity (blue to red) indicates increasing abundance. No significant differences were detected among these top metabolites.\u003c/p\u003e\n\u003cp\u003eIn E–F, statistical significance was determined using two-tailed Student’s t-tests (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.001). Data are presented as the mean ± SD of three biological replicates (\u003cem\u003en\u003c/em\u003e = 3).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/a581058c89e966fb77abf8cf.png"},{"id":99617907,"identity":"7f234512-ac07-4774-a40c-da8c23a9559a","added_by":"auto","created_at":"2026-01-06 13:42:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1230965,"visible":true,"origin":"","legend":"\u003cp\u003eAll experiments were performed under natural conditions in a sunlit greenhouse in Beijing (2024). Statistical analyses in (B–H) were performed using one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.001). Data are presented as the mean ± SD. Biological replicates: \u003cem\u003en\u003c/em\u003e = 6 in (B), \u003cem\u003en\u003c/em\u003e = 9 in (F), \u003cem\u003en\u003c/em\u003e = 18 in (C–E), and n = 3 in (G–H).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional validation of the rapid fermentation liquid (FL) and identification of its active components.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCK: water, FL5: 5 mL/L FL, DS2: 2g/L D-sorbitol, AA: 0.2g/L Phenylalanine, DA: 2g/L D-sorbitol + 0.2g/L Phenylalanine. Photosynthetic Photon Flux Density (PPFD): 300 μmol m\u003csup\u003e–2\u003c/sup\u003e s\u003csup\u003e–l\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA. Growth phenotypes of \u003cem\u003eB. rapa\u003c/em\u003e under different combinations of component-defined treatments in a controlled environmental chamber. Scale bar, 10 cm.\u003c/p\u003e\n\u003cp\u003eB and C. Maximum leaf net photosynthetic rate (B) and shoot fresh weight (C) of \u003cem\u003eB. rapa\u003c/em\u003e under the same treatment combinations.\u003c/p\u003e\n\u003cp\u003eD. Correlation analysis linking component-defined treatments with photosynthetic rate and shoot biomass\u003c/p\u003e\n\u003cp\u003eE and F. Shoot fresh weight (E) and leaf net photosynthetic rate (F) of \u003cem\u003eB. rapa\u003c/em\u003e following foliar application of D-sorbitol. CK: 0g/L D-sorbitol, DS1: 1g/L D-sorbitol, DS2: 2g/L D-sorbitol.\u003c/p\u003e\n\u003cp\u003eG and H. Quantification of leaf D-sorbitol content (G) and Starch content (H) levels in \u003cem\u003eB. rapa\u003c/em\u003e under D-sorbitol treatment.\u003c/p\u003e\n\u003cp\u003eI. Growth phenotype of \u003cem\u003eB. rapa\u003c/em\u003e with foliar D-sorbitol application in a controlled environmental chamber. Scale bar, 8 cm.\u003c/p\u003e\n\u003cp\u003eJ. Transmission electron microscopy (TEM) images showing leaf microstructures (parenchyma, palisade tissue, and chloroplasts) of \u003cem\u003eB. rapa\u003c/em\u003e under D-sorbitol treatment.\u003c/p\u003e\n\u003cp\u003eIn A-J experiments were conducted under natural conditions in a sunlit greenhouse in Beijing (2024).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/c684e03a7398114d224dfea3.png"},{"id":99792780,"identity":"7ebe9f9d-65f8-4848-a22a-c72209797d18","added_by":"auto","created_at":"2026-01-08 13:26:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":237538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA synergy between light intensity and D-sorbitol drives plant growth and energy utilization.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCK: 0g/L D-sorbitol+ PPFD: 300 μmol m\u003csup\u003e–2\u003c/sup\u003e s\u003csup\u003e–l\u003c/sup\u003e, DS2: 2g/L D-sorbitol+ PPFD: 300 μmol m\u003csup\u003e–2\u003c/sup\u003e s\u003csup\u003e–l\u003c/sup\u003e, HCK: 0g/L D-sorbitol+ PPFD: 150 μmol m\u003csup\u003e–2\u003c/sup\u003e s\u003csup\u003e–l\u003c/sup\u003e, HDS2: 2g/L D-sorbitol+ PPFD: 150 μmol m\u003csup\u003e–2\u003c/sup\u003e s\u003csup\u003e–l\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA. Phylogenetic tree of growth-responsive plants. Three plant families are subject to light intensity and D-sorbitol synergistic regulation of growth and energy utilization.\u003c/p\u003e\n\u003cp\u003eB, C, D and E. Growth phenotype (B), Shoot fresh weight (C), Leaf D-sorbitol content (D) and Leaf starch content (E) of \u003cem\u003eL. sativa\u003c/em\u003e. Scale bar = 10 cm.\u003c/p\u003e\n\u003cp\u003eF, G, H and I. Growth phenotype (F), Shoot fresh weight (G), Leaf D-sorbitol content (H) and Leaf starch content (I) of \u003cem\u003eSedum hispanicum\u003c/em\u003e. Scale bar = 9 cm.\u003c/p\u003e\n\u003cp\u003eJ, K, L and M. Growth phenotype (J), Shoot fresh weight (K), Leaf D-sorbitol content (L) and Leaf starch content (M) of \u003cem\u003eB. rapa\u003c/em\u003e. Scale bar = 10 cm.\u003c/p\u003e\n\u003cp\u003eN, O, P and Q. Growth phenotype (N), Shoot fresh weight (O), Leaf D-sorbitol content (P) and Leaf starch content (Q) of \u003cem\u003eB. oleracea\u003c/em\u003e. Scale bar = 15 cm.\u003c/p\u003e\n\u003cp\u003eDifferent colors indicate the taxonomic families of the respective species. Panels B–Q show results from light intensity and D-sorbitol treatments performed in a controlled environmental chamber (Beijing, 2025). In panels C, G, K, and O, data represent 18 biological replicates. Statistical significance was determined using one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.001). In panels D, E, H, I, L, M, P, and Q, data represent three biological replicates. A two-tailed Student’s t-test was used to assess significance (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.001). All values are presented as the mean ± SD.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/bd90eadbd28cf308e6193bd9.png"},{"id":99617908,"identity":"a6e0b5ac-b9ef-4e07-98d9-a7705a84c77e","added_by":"auto","created_at":"2026-01-06 13:42:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1700192,"visible":true,"origin":"","legend":"","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/71a984e976f20c0dc2715b58.png"},{"id":100372068,"identity":"c9faf037-7683-4ca9-832a-4daac26e373d","added_by":"auto","created_at":"2026-01-16 08:11:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6712484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/38ae589e-df76-41d4-a317-ac4ca5eb9418.pdf"},{"id":99794530,"identity":"90de7219-df3b-4c83-86df-69074c4b9520","added_by":"auto","created_at":"2026-01-08 13:35:16","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26057,"visible":true,"origin":"","legend":"Supplementary Data Table","description":"","filename":"ExtendeddataTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/298afefdb94bf9ad44aafbae.xlsx"},{"id":99617909,"identity":"8f100cd8-6c60-418c-b2c7-1eed0f57572b","added_by":"auto","created_at":"2026-01-06 13:42:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10550088,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data 1\u003c/p\u003e","description":"","filename":"ExtendedDataFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8409481/v1/18b49fc72dc4091aefea7065.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Repurposing crop aerial parts to provide D-sorbitol for plant-specific growth","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal agriculture annually generates over 5 billion tons of high-moisture plant residues \u003csup\u003e(\u003cem\u003e1-3\u003c/em\u003e)\u003c/sup\u003e. Due to their rapid decomposition, handling challenges, and propensity to induce pest outbreaks and replanting disorders, these residues pose a critical ecological limitation on sustainable agriculture \u003csup\u003e(\u003cem\u003e3-7\u003c/em\u003e)\u003c/sup\u003e. Among various crops, watermelon (\u003cem\u003eCitrullus lanatus\u003c/em\u003e) ranks as the most widely cultivated horticultural species globally, with China alone accounting for over 60% of the total planting area \u003csup\u003e(\u003cem\u003e8, 9\u003c/em\u003e)\u003c/sup\u003e. Its highly perishable aerial parts are produced in substantial quantities, making them a major contributor to replanting inhibition \u003csup\u003e(\u003cem\u003e10\u003c/em\u003e)\u003c/sup\u003e. Thus, watermelon aerial parts represent both a core challenge in agricultural waste management and an ideal model for fundamental research on high-moisture plant residues.\u003c/p\u003e\n\u003cp\u003eAlthough strategies such as composting, chemical degradation, and medicinal repurposing have been explored \u003csup\u003e(\u003cem\u003e11-13\u003c/em\u003e)\u003c/sup\u003e, they are typically hindered by high costs, extended processing times, and limited scalability \u003csup\u003e(\u003cem\u003e4\u003c/em\u003e)\u003c/sup\u003e.\u0026nbsp;Meanwhile, microbial fermentation has emerged as a promising green alternative due to its operational efficiency and environmental compatibility \u003csup\u003e(\u003cem\u003e4, 14-16\u003c/em\u003e)\u003c/sup\u003e. For example, strains like\u003cem\u003e\u0026nbsp;Lactiplantibacillus plantarum\u003c/em\u003e (formerly \u003cem\u003eLactobacillus plantarum\u003c/em\u003e) effectively degrade plant biomass under defined conditions, yielding metabolites of agronomic importance \u003csup\u003e(\u003cem\u003e17\u003c/em\u003e)\u003c/sup\u003e. However, conventional fermentation systems remain limited by prolonged durations, unstable microbial consortia, and inconsistent degradation performance \u003csup\u003e(\u003cem\u003e18-20\u003c/em\u003e)\u003c/sup\u003e, thereby restricting their applicability in real-world agricultural settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, informed by agricultural surveys and biological analyses, we identified critical ecological bottlenecks in empirical practices of farmers for watermelon cultivation and plant waste management. To address these bottlenecks, we developed a rapid, \u003cem\u003eLactiplantibacillus plantarum\u003c/em\u003e WCFS1-mediated fermentation system that efficiently depolymerizes watermelon aerial parts biomass and markedly reduces its allelopathic toxicity within a short period of time. This system overcomes major limitations of conventional fermentation, including slow kinetics, unstable microbial communities, and limited process control. Notably, we identified a key fermentation-generated bioactive metabolite, D-sorbitol, which activates a previously unrecognized light intensity\u0026ndash;sorbitol\u0026ndash;starch cascade that affects energy metabolism and growth in angiosperms. This pathway is widespread in Brassicaceae and Crassulaceae plants, thus opening new directions for exploring sorbitol metabolism and function in plants. Overall, our findings provide mechanistic insights and a scalable strategy for the detoxification and sustainable use of high-moisture agricultural residues, offering both conceptual and technical foundations for transitioning toward science-based sustainable agriculture.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCurrent cultivation practices and assessment of the utilization of aerial parts in representative watermelon-producing regions of China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChina is the largest watermelon producer in the world, accounting for over 60% of global production in 2024 (Extended Data Fig. 1A) \u003csup\u003e(\u003cem\u003e9\u003c/em\u003e)\u003c/sup\u003e. To assess the sustainability of current cultivation and residue management practices, we conducted field-based surveys across major production regions in China. Henan Province (\u0026gt;10% of national production; Extended Data Fig. 1B), with its core production area in Shangqiu City (\u0026gt;1.6% of national production; Extended Data Fig. 1C), was selected as the primary study region, supplemented by responses from other provinces, including Shanxi and Shandong Provinces \u003csup\u003e(\u003cem\u003e21, 22\u003c/em\u003e)\u003c/sup\u003e. In total, survey data (\u003cem\u003en\u003c/em\u003e = 406) were collected to provide descriptive context and illustrate representative watermelon cultivation patterns (Extended Data Fig. 1D, E).\u003c/p\u003e\n\u003cp\u003eRespondents were predominantly middle-aged (36\u0026ndash;50 years, 47.29%) and lacked higher education (94.09%) (Extended Data Fig. 2A, B). Among the surveyed farmers, protected cultivation was predominant on medium to large holdings (\u0026gt;0.67 ha; \u0026gt;80%), with most also using high-density planting (\u0026gt;9,000 plants ha⁻\u0026sup1;; 91.38%) and single cultivars, primarily \u0026lsquo;Meidu\u0026rsquo; (Extended Data Fig. 2C\u0026ndash;H). With frequent fertilization (\u0026ge;2 times; 94.33%) and input costs exceeding 7,500 CNY ha⁻\u0026sup1;, yields commonly exceeded 30 t ha⁻\u0026sup1; (93.84%), resulting in high profits (\u0026gt;45,000 CNY ha⁻\u0026sup1;; 75.12%) (Extended Data Fig. 2I\u0026ndash;N). However, the aerial parts biomass per watermelon plant exceeds 700 g \u003csup\u003e(\u003cem\u003e23\u003c/em\u003e)\u003c/sup\u003e, this intensive system produces over 6 t/ha of stem-leaf residues annually. These high-moisture residues are bulky, difficult to handle, and prone to rapid decay, making recycling technically and economically challenging. Although the recycling of watermelon aerial parts has received broad support among farmers, most still opted to discard residues or return them directly to the soil, likely due to limited technical capacity and high costs (Fig. 1E, G; Extended Data Fig. 4A, B). Such practices not only waste biomass resources but also exacerbate soil-borne diseases. Continuous cropping was uncommon (11.57%) (Fig. 1B), and over 60% of farmers reported severe replanting obstacles (Fig. 1C), directly corroborating field experience and fragmented literature reports \u003csup\u003e(\u003cem\u003e24-26\u003c/em\u003e)\u003c/sup\u003e. However, 88.42% of respondents had little understanding of the underlying causes (Extended Data Fig. 3A\u0026ndash;D). Chinese cabbage (\u003cem\u003eBrassica rapa\u003c/em\u003e, 85.16%) and maize (\u003cem\u003eZea mays\u003c/em\u003e, 84.38%) were the most commonly used rotation crops, reflecting the perception of the farmers\u0026mdash;based on field experience\u0026mdash;that these species were less affected by preceding watermelon cultivation (Fig. 1D). Similarly, faced with declining watermelon productivity and increasing economic pressure, many growers adopted a \u0026ldquo;nomadic farming\u0026rdquo; strategy, abandoning degraded fields in favor of new sites (Fig. 1F, Extended Data Fig. 4C).\u003c/p\u003e\n\u003cp\u003eIn general, our survey findings reveal a self-reinforcing cycle of excessive biomass accumulation, inefficient residue management, and progressive soil degradation that threatens the long-term sustainability of watermelon production (Fig. 1H). This highlights an urgent need for scalable strategies that promote efficient residue recycling and reduce replanting barriers in high-biomass cropping systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLactiplantibacillus plantarum\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;WCFS1-mediated rapid fermentation system for watermelon aerial parts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA rapid fermentation system for watermelon aerial parts biomass was developed using \u003cem\u003eLactiplantibacillus plantarum\u003c/em\u003e WCFS1 (LP\u003cem\u003e-\u003c/em\u003eWCFS1), following a questionnaire-informed design strategy (Fig. 2A, Extended Data Fig. 5A). After 14 days of fermentation, full-length 16S rRNA gene sequencing identified LP\u003cem\u003e-\u003c/em\u003eWCFS1 as the overwhelmingly dominant taxon in the rapid fermentation liquid (FL) system (relative abundance \u0026gt;98%; Extended Data Fig. 5, Supplementary Table 2), consistent with the acidic fermentation environment (Supplementary Table 1). Second-generation 16S rRNA gene sequencing further confirmed its predominance (\u0026gt;80%) but showed a notable reduction of LP\u003cem\u003e-\u003c/em\u003eWCFS1 and increased microbial diversity in the long-term fermentation liquid (FLY) system (Extended Data Fig. 6A\u0026ndash;E, Supplementary Tables S3\u0026ndash;S4). Functional annotation indicated that fermentation in FL was primarily driven by LP\u003cem\u003e-\u003c/em\u003eWCFS1-associated metabolic processes, whereas FLY was enriched in chemoheterotrophic functions (Extended Data Fig. 6F\u0026ndash;H), suggesting a shift in community-level energy metabolism.\u003c/p\u003e\n\u003cp\u003eGiven that microbial composition directly influences metabolite production, we performed untargeted metabolomic profiling. A total of 329 differential metabolites were identified, with significant enrichment in aminobenzoate-, benzoate-, and furfural-degradation pathways (Fig. 2B, Extended Data Fig. 7A\u0026ndash;D). Compared with the FL system, the FLY system accumulated higher levels of allelochemicals\u0026mdash;including coumarin, ferulic acid, and caffeic acid (Fig. 2C, Extended Data Fig. 7E\u0026ndash;G)\u0026mdash;a pattern strongly correlated with its diversified microbial community (Extended Data Fig. 8A\u0026ndash;M). The observed heterogeneity in abundance among the top 15 metabolites indicates that the metabolic state is not stable (Fig. 2D). The selective enrichment of allelopathic degradation pathways in the FL thus underscores its functional advantage for efficient detoxification and bioactive compound production.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional validation of the fermented liquid from the rapid fermentation and identification of its active components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the application potential of the rapid fermentation system, we selected the primary rotation crop identified from regional surveys, namely \u003cem\u003eB. rapa\u003c/em\u003e, for replicated trials across multiple regions in China. Foliar application of the diluted FL significantly enhanced shoot and root biomass in a concentration-dependent manner (Extended Data Fig. 9; Extended Data Fig. 10I\u0026ndash;M).\u003c/p\u003e\n\u003cp\u003eRepeated metabolomic analyses across independent batches showed that the common fermentation products D-sorbitol and L-phenylalanine \u003csup\u003e(\u003cem\u003e27-29\u003c/em\u003e)\u003c/sup\u003e consistently accumulated in the FL system (Extended Data Fig. 10A\u0026ndash;B), prompting the performance of compounding assays to evaluate their biological significance in plant growth experiments. Treatments with FL5 (5 mL/L FL), DS2 (2 g/L D-sorbitol), and DA (2 g/L D-sorbitol + 1 g/L L-phenylalanine) markedly increased net photosynthetic rate, shoot biomass, stomatal conductance, and transpiration, whereas L-phenylalanine alone showed no significant effect (Fig. 3A\u0026ndash;C; Extended Data Fig. 10C\u0026ndash;Q). Correlation analyses demonstrated that the growth-promoting effects of D-sorbitol closely mirrored those of FL5 (Fig. 3D), identifying D-sorbitol as the major bioactive component of the FL.\u003c/p\u003e\n\u003cp\u003eTo confirm this finding, dose\u0026ndash;response foliar applications of DS1 and DS2 were conducted under multiple environmental conditions. Both treatments consistently enhanced photosynthetic activity and shoot biomass after 15 days (Fig. 3E). DS2 additionally increased root dry weight, while DS1 increased stomatal conductance and intercellular CO₂ concentration (Fig. 3F\u0026ndash;G; Extended Data Fig. 11A\u0026ndash;G). Across test locations, DS1 and DS2 improved shoot fresh weight by 3.38\u0026ndash;23.01% and 8.4\u0026ndash;36.35%, respectively (Fig. 3B, E, I; Extended Data Fig. 10I, J; Extended Data Fig. 11H, L, T\u0026ndash;W), with parallel increases in photosynthesis and other growth indices (Extended Data Fig. 10C\u0026ndash;Q; Extended Data Fig. 11B\u0026ndash;S). D-sorbitol also significantly increased maximum leaf length and width (Extended Data Fig. 12A\u0026ndash;D).\u003c/p\u003e\n\u003cp\u003eTransmission electron microscopy analysis revealed that the 10-day D-sorbitol treatment increased thylakoid stacking and reduced starch granule accumulation in chloroplasts (Fig. 3J). This was accompanied by decreased total leaf starch content, increased D-sorbitol levels, and unchanged soluble sugar levels (Fig. 3G\u0026ndash;H; Extended Data Fig. 13A\u0026ndash;D). Chlorophyll content remained stable (Extended Data Fig. 15A\u0026ndash;H), but chlorophyll fluorescence parameters (maximum fluorescence (Fm), minimum fluorescence (Fo), variable fluorescence (Fv), and near infrared (NIR)) and Ca\u003csup\u003e2+\u003c/sup\u003e accumulation were significantly increased (Extended Data Fig. 14A\u0026ndash;F; Extended Data Fig. 16A\u0026ndash;F). No oxidative stress symptoms were detected based on reactive oxygen species (ROS)-scavenging enzyme activity or chlorophyll fluorescence assays (Extended Data Fig. 16A\u0026ndash;J; Extended Data Fig. 17A\u0026ndash;F).\u003c/p\u003e\n\u003cp\u003eTo further explore the transcriptional and metabolic regulatory pathways underlying D-sorbitol-induced growth promotion, transcriptomic and metabolomic profiling were performed on \u003cem\u003eB. rapa\u003c/em\u003e shoots at 2 h and 10 d post-treatment. RNA sequencing (RNA-seq) analysis identified 112 and 147 differentially expressed genes (DEGs) in DS1 and DS2 \u003cem\u003eversus (vs.)\u003c/em\u003e controls, and 110 DEGs between DS1 and DS2 (Extended Data Fig. 18A\u0026ndash;E). These DEGs were predominantly enriched in pathways associated with carbohydrate metabolism, lipid metabolism, and secondary metabolite biosynthesis, including wax biosynthesis and ABC transporter pathways (Extended Data Fig. 18F-K). Metabolomic profiling revealed 63, 25, and 99 differentially accumulated metabolites (DAMs) in DS1 vs. CK, DS2 \u003cem\u003evs\u003c/em\u003e. CK, and DS1 \u003cem\u003evs\u003c/em\u003e. DS2, respectively (Extended Data Fig. 19A\u0026ndash;C). Notably, D-sorbitol was significantly upregulated in both DS1 and DS2, consistent with the measured contents (Fig. 3G; Extended Data Fig. 13A; Extended Data Fig. 19F). Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included galactose metabolism and ABC transporters (Extended Data Fig. 19G\u0026ndash;I), and correlation network analysis further linked D-sorbitol content to these pathways (Extended Data Fig. 19J\u0026ndash;L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynergistic regulation of plant growth and energy metabolism by light intensity and D-sorbitol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the mechanism underlying D-sorbitol-induced plant growth, we systematically evaluated interspecies responses under controlled light conditions (PPFD: 300 \u0026mu;mol m⁻\u0026sup2; s⁻\u0026sup1;) by performing 15-day foliar application of DS2 (2 g/L D-sorbitol) across a phylogenetically diverse panel of plants. Cultivation trials repeated across independent batches consistently showed no visible phenotypic changes in eight non-angiosperm species encompassing six families\u0026mdash;including \u003cem\u003eMarchantia polymorpha\u003c/em\u003e, \u003cem\u003eAdiantum venustum\u003c/em\u003e, \u003cem\u003eLycopodium japonicum\u003c/em\u003e, and \u003cem\u003ePodocarpus macrophyllus\u003c/em\u003e (Extended Data Fig. 20). In contrast, among 24 angiosperms encompassing 10 families (Extended Data Fig. 21A), six species, namely \u003cem\u003eLactuca sativa\u0026nbsp;\u003c/em\u003e(9.16 - 9.83%), \u003cem\u003eSedum hispanicum\u0026nbsp;\u003c/em\u003e(13.49 - 20.84%), \u003cem\u003eB. rapa\u003c/em\u003e (13.88 - 26.32%), \u003cem\u003eBrassica oleracea\u003c/em\u003e (13.97 - 29.09%), \u003cem\u003eRorippa aquatica\u003c/em\u003e (10.53 - 11.28%), and \u003cem\u003eBrassica juncea\u0026nbsp;\u003c/em\u003e(9.07 - 19.33%), exhibited significantly increased shoot biomass following D-sorbitol treatment (Extended Data Fig. 21B\u0026ndash;C, T, W\u0026ndash;AC). Conversely, three species, namely \u003cem\u003eRaphanus sativus\u003c/em\u003e (-16.05 - -24.81%), \u003cem\u003eSolanum tuberosum\u003c/em\u003e (-17.37 - -20.71%), and \u003cem\u003eZea mays\u003c/em\u003e (-13.19 - -16.90%), showed significant growth inhibition (Extended Data Fig. 21J, Q, S, Z\u0026ndash;AC), while the remaining 15 species, including model and D-sorbitol-producing plants, such as \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, \u003cem\u003eSolanum lycopersicum\u003c/em\u003e, and \u003cem\u003eMalus domestica\u003c/em\u003e, showed no significant response (Extended Data Fig. 21D\u0026ndash;I, R, U\u0026ndash;V, Z\u0026ndash;AC). These results indicate that D-sorbitol has species- and lineage-specific growth-modulating effects in angiosperms, likely mediated by differential metabolic processing.\u003c/p\u003e\n\u003cp\u003eNotably, light intensity exerted a profound effect on the family-specific D-sorbitol -induced phenotypes. At 300 \u0026mu;mol m⁻\u0026sup2; s⁻\u0026sup1;, D-sorbitol treatment significantly increased shoot biomass in the four responsive species (\u003cem\u003eB. rapa\u003c/em\u003e,\u003cem\u003e\u0026nbsp;L. sativa\u003c/em\u003e, \u003cem\u003eS. spectabile\u003c/em\u003e,\u003cem\u003e\u0026nbsp;B. oleracea\u003c/em\u003e), whereas under a reduced half-light dose (150 \u0026mu;mol m⁻\u0026sup2; s⁻\u0026sup1;), this growth-promoting effect was abolished (Fig. 4A\u0026ndash;C, F\u0026ndash;G, J\u0026ndash;K, N\u0026ndash;O; Extended Data Fig. 22A\u0026ndash;B, E, H, K). Concurrently, D-sorbitol accumulation in leaf tissue was significantly increased under high-light conditions (Fig. 4D, H, L, P; Extended Data Fig. 22C, F, I, L), accompanied by species-specific changes in starch content: decreased in \u003cem\u003eB. rapa\u003c/em\u003e and \u003cem\u003eS. spectabile\u003c/em\u003e, and increased in \u003cem\u003eL. sativa\u0026nbsp;\u003c/em\u003eand \u003cem\u003eB. oleracea\u003c/em\u003e (Fig. 4E, I, M, Q; Extended Data Fig. 22D, G, J, M), implying divergent carbon allocation strategies. Non-responsive species including \u003cem\u003eNicotiana benthamiana\u003c/em\u003e, \u003cem\u003eZ. mays\u003c/em\u003e, \u003cem\u003eA. thaliana\u003c/em\u003e, and \u003cem\u003eM. domestica\u003c/em\u003e showed no consistent phenotypic or metabolic changes upon D-sorbitol treatment (Extended Data Fig. 23A\u0026ndash;P), highlighting interspecific specificity. Interestingly, D-sorbitol showed a light-dependent growth-promoting pattern analogous to that of sucrose (Extended Data Fig. 24A\u0026ndash;C), suggesting possible functional convergence. These results suggest that different angiosperms may facilitate D-sorbitol through distinct light intensity-D-sorbitol-starch-mediated metabolic pathways to promote growth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuantitative real-time polymerase chain reaction (qRT-PCR) analysis of two responsive species (across two non-Rosaceae families) revealed that expression of canonical D-sorbitol-metabolizing enzymes NADP‑dependent D‑sorbitol‑6‑phosphate dehydrogenase (S6PDH; \u003cem\u003eBrS6PDH\u003c/em\u003e and \u003cem\u003eLsS6PDH\u003c/em\u003e) and sorbitol dehydrogenase (SORD; \u003cem\u003eBrSORD‑X1\u003c/em\u003e and \u003cem\u003eLsSORD\u003c/em\u003e) were affected by light intensity and D-sorbitol. The sucrose transporter \u003cem\u003eBrSUC1-X1\u003c/em\u003e in \u003cem\u003eB. rapa\u003c/em\u003e was also upregulated synchronously under conditions of simultaneous exposure to light and D-sorbitol (Extended Data Fig. 25A-F, Tab. S5), showing a clear \u0026quot;light intensity-D-sorbitol dependence\u0026quot;. Remarkably, although the sequences of\u003cem\u003e\u0026nbsp;\u003c/em\u003eS6PDH and SORD are conserved across phylogenetically diverse plant species and their predicted protein structures are similar (Extended Data Fig. 26 A-J, Tab. S6), these plants exhibit markedly different growth phenotypes in response to D-sorbitol. In summary, we hypothesize that D-sorbitol promotes plant growth by activating a specific energy metabolism pathway related to starch metabolism that is regulated by light, representing a potential and previously unrecognized carbon metabolism regulation pattern during differential evolution among plant lineages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLP-WCFS1-mediated rapid fermentation reduces the allelopathic effects of watermelon aerial parts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough direct residue discarding and field return are widely practiced and preferred by farmers in China (Fig. 1E), survey data revealed frequent land rotation and continuous cropping obstacles, indicating potential risks associated with the recycling of watermelon aerial part residues (Fig. 1C, F). Thus, we developed a rapid fermentation system to produce FL using LP-WCFS1, which markedly reduced fresh biomass and partially degraded dry matter (Tab. S7). In particular, untargeted metabolomics identified 451 DAMs between fresh watermelon aerial parts (FW) and fermented watermelon residues (FR) (Fig. 5A, Extended Data Fig. 27B, C), enriched in the degradation pathways of aminobenzoate, benzoate, and dioxins (Extended Data Fig. 27D\u0026ndash;F). Notably, five major allelochemicals (ferulic acid, caffeic acid, \u003cem\u003eetc\u003c/em\u003e.) were significantly decreased in FR (Fig. 5B), with reductions observed in half of the benzene and substituted derivatives, as well as in most cinnamic acid and coumarin derivatives (Extended Data Fig. 27G\u0026ndash;I). These metabolic changes indicate that treatment with FL substantially reshapes allelochemical profiles and may mitigate phytotoxicity risks associated with residue return.\u003c/p\u003e\n\u003cp\u003eTo further validate the efficacy of the FL in rapidly reducing allelopathic effects, field experiments were conducted using \u003cem\u003eB. rapa\u003c/em\u003e and \u003cem\u003eZ. mays\u003c/em\u003e\u0026mdash;the most common rotation crops identified in the survey\u0026mdash;as representative models (Fig. 1D). In the repeated experiments in Zhangjiakou and Beijing, the fresh watermelon stem and leaf landfill treatment (FW: 4g/plant) significantly inhibited the above ground and underground dry weight and fresh weight of the two crops (Fig. 5 C-H, Extended Data Fig. 28 A-K, Extended Data Fig. 29 D, E, H-K), and reduced the plant height and stem diameter of \u003cem\u003eZ. mays\u003c/em\u003e (Extended Data Fig. 29 F-G, L-M). Especially in the field experiment in Shangqiu City, the main survey area of the questionnaire, FW treatment significantly reduced the thousand grain weight and final yield of \u003cem\u003eZ. mays\u003c/em\u003e kernels (Fig. 5I-K, Extended Data Fig. 29A-C), indicating that even empirically selected rotation crops with perceived low sensitivity still face substantial allelopathic risks (Fig. 1D). In contrast, the treatment with watermelon stem and leaf fermentation residues (FR: 2g/plant) not only significantly reduced the negative effects mentioned above, but also had no significant inhibitory effect on the biomass of the two crops and the plant type indicators of corn, and effectively mitigated the loss of corn yield (Fig. 5C-K, Extended Data Fig. 28, Extended Data Fig. 29). These findings indicate that the rapid fermentation system can effectively reduce the allelotoxicity of watermelon aerial parts, providing a practical path for their safe return to the field.\u003c/p\u003e\n\u003cp\u003eFurther analysis revealed marked differences in rhizosphere microbial community structures between FW- and FR-treated \u003cem\u003eB. rapa\u003c/em\u003e sterilized substrates (Extended Data Fig. 30 A-F, Tab. S8, S9). After\u0026nbsp;FR treatment, beneficial communities such as \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eFlavobacterium\u003c/em\u003e were significantly enriched, while communities such as \u003cem\u003eBrevundimonas\u003c/em\u003e was inhibited; However, FW treatment reversed the community structure, significantly inducing the enrichment of \u003cem\u003eBrevundimonas\u003c/em\u003e and others, and the decrease of \u003cem\u003ePseudolabrys\u003c/em\u003e and others (Extended Data Fig. 31 A-C). Microbial function prediction showed that FW group rhizosphere microorganisms were significantly in enriched fermentation and microplastic degradation pathways (Extended Data Fig. 31 D), suggesting that they may induce abnormal fermentation and interfere with the ecological stability of rhizosphere microbes. Integrated analyses revealed that metabolite shifts in FR were positively correlated with changes in rhizosphere microbial community composition, a process mediated through the downregulation of key metabolites such as abscisic acid (Extended Data Fig. 32A\u0026ndash;E). Moreover, the dynamic accumulation of the key effector metabolite D-sorbitol showed a strong positive correlation with the FR residue metabolome (Extended Data Fig. 33A\u0026ndash;C), further supporting its role as a central functional mediator in this system. Finally, we proposed a rapid fermentation platform based on LP-WCFS1, which provides theoretical support and practical path for the utilization of watermelon waste residues and the construction of a closed-loop, sustainable planting mode (Fig. 5L).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eContinuous cropping obstacles have become a widely-recognized global challenge in watermelon production, driven by a variety of interacting factors, such as nutrient imbalance, soilborne diseases, and allelopathic metabolites \u003csup\u003e(\u003cem\u003e24-26\u003c/em\u003e)\u003c/sup\u003e. Our survey revealed a self-reinforcing cycle of excessive biomass accumulation, inefficient residue management, and progressive soil degradation that collectively undermine the long-term sustainability of watermelon cultivation (Fig. 1H). Although farmers have empirically selected so-called \u0026ldquo;replant-tolerant\u0026rdquo; rotation crops, our experiments demonstrated that their normal growth was still markedly inhibited by allelochemicals derived from watermelon aerial parts (Fig. 5, Extended Data Fig. 28, Extended Data Fig. 29). This indicates that experience-based agricultural practices, in the absence of scientific validation, may carry hidden ecological and productivity risks \u003csup\u003e(\u003cem\u003e30-32\u003c/em\u003e)\u003c/sup\u003e. Guided by the key issues identified in the survey, we developed an LP-WCFS1\u0026ndash;mediated rapid fermentation system and confirmed its efficacy through crop rotation experiments (Fig. 2A, Fig. 5L), effectively redirecting the unsustainable feedback loop towards a resilient and sustainable watermelon production system. Integrating scientific guidance with on-farm practice is therefore essential to maximize both agricultural productivity and environmental benefits \u003csup\u003e(\u003cem\u003e33, 34\u003c/em\u003e)\u003c/sup\u003e, providing a realistic framework for establishing future sustainable production systems.\u003c/p\u003e\n\u003cp\u003eFermentation has long served as a traditional and sustainable approach for managing plant-derived agricultural waste \u003csup\u003e(\u003cem\u003e15-17\u003c/em\u003e)\u003c/sup\u003e. However, conventional fermentation typically requires 45\u0026ndash;60 days and involves complex, unstable microbial consortia \u003csup\u003e(\u003cem\u003e18\u0026ndash;20\u003c/em\u003e)\u003c/sup\u003e. To overcome these limitations, we optimized the conventional long-term mixed fermentation process and established an LP-WCFS1-mediated rapid fermentation system (Fig. 2A). LP-WCFS1, widely used in food and pharmaceutical industries, is capable of depolymerizing plant residues and generating bioactive metabolites \u003csup\u003e(\u003cem\u003e17, 35, 36\u003c/em\u003e)\u003c/sup\u003e. In this study, the LP-WCFS1-based rapid fermentation system outperformed traditional long-term fermentation in both metabolite composition and microbial community structure within a short period of time (Fig. 2C\u0026ndash;D, Extended Data Fig. 5-7). Notably, prolonged fermentation led to the accumulation of allelopathic compounds, such as caffeic acid, and the emergence of a more complex microbial network, both of which posed potential risks to plant growth (Fig. 2B\u0026ndash;D, Extended Data Fig. 5-7). The competitive dominance of LP-WCFS1 and the acidic, anaerobic microenvironment it establishes effectively suppresses most other harmful microorganisms \u003csup\u003e(\u003cem\u003e37\u0026ndash;41\u003c/em\u003e)\u003c/sup\u003e. The rapid fermentation system thus creates a weakly acidic, low-oxygen, and high-osmotic niche (pH = 3.34, EC = 3.8 mS cm⁻\u0026sup1;), favoring a highly sSupplementary Table ingle-strain community (Extended Data Fig. 5, Supplementary Table 1). Collectively, these results demonstrate that targeted microbial mediation can convert the traditional, slow, and variable fermentation into a rapid, controllable, and functionally stable process. This strategy offers a scalable biotechnological solution for the safe recycling and valorization of high-moisture agricultural residues, while reducing the ecological risks of residue mismanagement.\u003c/p\u003e\n\u003cp\u003eUnderstanding the functional roles of fermentation-derived metabolites is crucial for advancing synthetic biology and developing sustainable agricultural systems \u003csup\u003e(\u003cem\u003e14\u0026ndash;17\u003c/em\u003e)\u003c/sup\u003e. D-sorbitol, a common bacterial fermentation product \u003csup\u003e(\u003cem\u003e27-28\u003c/em\u003e)\u003c/sup\u003e, was identified as the primary metabolite generated in our system, which contributed directly to the observed bioactivity of the fermented liquid (Fig. 3 A-J). In plants, D-sorbitol is generally considered a primary photosynthetic product in\u003cem\u003e\u0026nbsp;\u003c/em\u003eRosaceae and Plantaginacea\u003cem\u003ee\u003c/em\u003e plants and is widely distributed across Brassicaceae, Solanaceae, and Poaceae families \u003csup\u003e(\u003cem\u003e42\u003c/em\u003e)\u003c/sup\u003e. To date, its roles as a sucrose-like energy source and a signaling molecule have been established primarily in Rosaceae plants, with additional evidence suggesting potential functions in stress tolerance and osmotic regulation in plants\u0026nbsp;\u003csup\u003e(\u003cem\u003e42-46\u003c/em\u003e)\u003c/sup\u003e. However, the metabolism, functions, and evolutionary history of D-sorbitol in most plant groups have remained systematically unexplored This study systematically explored the ability of 32 plant species (including 16 families and 20 genera) to utilize and metabolize D-sorbitol. Surprisingly, significant growth differences were observed only in non-model plants, such as\u003cem\u003e\u0026nbsp;B. rapa\u003c/em\u003e,\u0026nbsp;\u003cem\u003eS. hispanicum\u003c/em\u003e,\u0026nbsp;\u003cem\u003eL. sativa\u003c/em\u003e, \u003cem\u003eetc\u003c/em\u003e. (Extended Data Fig. 21 B, W-X, Z-AC). In contrast, we did not observe significant growth differences in model plants and plants that use D-sorbitol as a photosynthetic product, such as \u003cem\u003eM. domestica\u003c/em\u003e and \u003cem\u003eA. thaliana\u003c/em\u003e (Extended Data Fig. 21 J, M, R, Z-AC), consistent with previous studies \u003csup\u003e(\u003cem\u003e47-50\u003c/em\u003e)\u003c/sup\u003e.\u0026nbsp;Furthermore，we provide substantial physiological and molecular evidence to propose the hypothesis that sorbitol specifically promotes plant growth: (i) in plants such as\u003cem\u003e\u0026nbsp;B. rapa\u003c/em\u003e,\u0026nbsp;\u003cem\u003eS. hispanicum\u003c/em\u003e and\u003cem\u003e\u0026nbsp;L. sativa\u003c/em\u003e,\u0026nbsp;the energy metabolism promoting phenotype of light intensity-D-sorbitol-starch axis is similar (Fig. 4, Extended Data Fig. 22); (ii) physiological phenotypes of non-responsive and inhibited model plants such as \u003cem\u003eM.\u003c/em\u003e \u003cem\u003edomestica\u003c/em\u003e, \u003cem\u003ePlantago depressa Willd\u003c/em\u003e, \u003cem\u003eA. thaliana\u003c/em\u003e, and other plants (Extended Data Fig. 20, Extended Data Fig. 21, Extended Data Fig. 23); (iii) The gene expression of \u003cem\u003eS6PDH\u003c/em\u003e and \u003cem\u003eSORD\u003c/em\u003e in\u0026nbsp;\u003cem\u003eB. rapa\u003c/em\u003e and\u003cem\u003e\u0026nbsp;L. sativa\u003c/em\u003e is regulated by both D-sorbitol and light intensity (Fig. 4 A-Q, Extended Data Fig. 22 A-M, Extended Data Fig. 25A-H).\u0026nbsp;Therefore, we hypothesize that there is a broad and specific potential energy metabolism pathway involved in promoting growth mechanism in plants that is dependent on light intensity\u0026ndash;D-sorbitol-starch. However, despite the high similarity in amino acid sequences and predicted protein structures between \u003cem\u003eS6PDH\u003c/em\u003e and \u003cem\u003eSORD\u003c/em\u003e in different closely related species (Extended Data Fig. 26 A-J), there are significant differences in physiological phenotypes. Although light has been reported to regulate \u003cem\u003eS6PDH\u003c/em\u003e, \u003cem\u003eSORD\u003c/em\u003e and appears to affect sorbitol and starch metabolism in\u0026nbsp;\u003cem\u003eM. domestica\u003c/em\u003e, it still cannot explain a broad and specific growth promoting mechanism \u003csup\u003e(\u003cem\u003e45, 48, 50\u003c/em\u003e)\u003c/sup\u003e. Notably, under light conditions (300 \u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e.),\u0026nbsp;\u003cem\u003eB. rapa\u003c/em\u003e shared potential similarities in the metabolism of sorbitol and sucrose (Extended Data Fig. 24A\u0026ndash;C; Extended Data Fig. 25C). For instance, sorbitol and sucrose share the same precursor, glucose-6-phosphate, and are linked through sugar transporter (\u003cem\u003eMdSTP13a\u003c/em\u003e)\u0026nbsp;and protein kinases (\u003cem\u003eMdSnRK1\u003c/em\u003e) in\u0026nbsp;\u003cem\u003eM. domestica\u003c/em\u003e \u003csup\u003e(\u003cem\u003e42, 45, 51-53\u003c/em\u003e)\u003c/sup\u003e, suggesting a previously unrecognized connection between these pathways in plant energy metabolism (light-sugar/alcohol-starch). This study presents a hypothesis that provide new perspectives on the metabolic and evolutionary dynamics of the metabolism of sorbitol in plants.\u003c/p\u003e\n\u003cp\u003eAlthough residue return is generally regarded as a direct and sustainable approach for managing plant-derived agricultural waste \u003csup\u003e(\u003cem\u003e2, 4\u0026ndash;6\u003c/em\u003e)\u003c/sup\u003e, allelopathic effects driven by plant metabolites are an important factor contributing to continuous cropping disorders \u003csup\u003e(\u003cem\u003e5, 6, 54-57\u003c/em\u003e)\u003c/sup\u003e. In this study, we found that returning fresh watermelon aerial parts to farmland significantly reduced the growth and yield of maize and cabbage (Fig. 5C\u0026ndash;K; Extended Data Fig. 28; Extended Data Fig. 29). Collectively, these findings suggest that fresh watermelon aerial parts are an important primary source of allelopathic toxicants in watermelon production systems, highlighting potential ecological and agronomic risks linked to their direct incorporation into farmland. In contrast, residues subjected to rapid fermentation markedly reduced these inhibitory effects (Fig. 5). During rapid fermentation, LP-WCFS1 preferentially changed the metabolite composition \u003csup\u003e(\u003cem\u003e59\u003c/em\u003e)\u003c/sup\u003e. Metabolomic profiling revealed significant reductions in allelochemicals\u0026mdash;including p-coumaric, ferulic, and caffeic acids\u0026mdash;particularly within the cinnamic-acid and coumarin-derivative pathways (Fig. 5B; Extended Data Fig. 27G\u0026ndash;I). These phenolic acids mediate strong allelopathic toxicity against various crops, such as Poaceae, Brassicaceae, Solanaceae, and Cucurbitaceae, and are closely associated with the enrichment of pathogens such as \u003cem\u003eFusarium oxysporum\u003c/em\u003e, jointly leading to severe continuous-cropping disorders \u003csup\u003e(\u003cem\u003e54-57, 59-64\u003c/em\u003e)\u003c/sup\u003e. Their\u0026nbsp;allelochemicals depletion therefore likely underpins the observed mitigation of phytotoxicity.\u0026nbsp;These metabolic changes also substantially restructured the rhizosphere microbiome \u003csup\u003e(\u003cem\u003e57, 62-65\u003c/em\u003e)\u003c/sup\u003e. In FR-treated soils, the root-associated microbiota showed reduced residue-decomposition activity and was markedly enriched in beneficial taxa, including \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eFlavobacterium\u0026nbsp;\u003c/em\u003e(Extended Data Fig. 30D; Extended Data Fig. 31A\u0026ndash;D; Supplementary Table 8), which enhance plant resistance to soil-borne pathogens associated with replanting obstacles \u003csup\u003e(\u003cem\u003e66-69\u003c/em\u003e)\u003c/sup\u003e. Notably, the combined effects of altered metabolite inputs and microbiome reassembly improved the rhizosphere conditions for subsequent crops, thereby promoting favorable growth \u003csup\u003e(\u003cem\u003e70, 71\u003c/em\u003e)\u003c/sup\u003e. These findings demonstrate that controlled microbial fermentation effectively neutralizes the allelopathic risks of fresh plant residues while enhancing their ecological recycling potential. In summary, by bridging social and natural scientific perspectives, this study provides a practical and evidence-based foundation for advancing sustainable agricultural practices.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eQuestionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate how Chinese farmers handle watermelon aerial parts, we conducted a nationwide survey among watermelon growers across 11 provinces, including Henan, Shandong, and Shanxi. The primary survey efforts were focused on Shangqiu City, Henan Province, China (115.69\u0026deg;E, 34.47\u0026deg;N), which is a major watermelon-producing region in the country, with a sample size of 336 respondents \u003csup\u003e(\u003cem\u003e9, 21, 22\u003c/em\u003e)\u003c/sup\u003e. Additionally, the sample sizes from Shanxi and Shandong both exceeded 20. A total of 406 questionnaires were distributed between January and June 2024, yielding 406 valid responses. Subsequently, a random subset of 128 farmers from these 406 valid respondents was selected for supplementary surveys, with all 128 providing valid responses. Data collection was primarily carried out through on-site investigations, supplemented by electronic questionnaires completed by respondents who had access to digital devices. The questionnaire design is available in Academic ethics certificate; detailed results are presented in Source data Fig. 1 (Original questionnaire data).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePreparation of fermentation liquid\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnder static conditions, the \u003cem\u003eLactobacillus plantarum\u003c/em\u003e WCFS1 was cultured in MRS medium (BD DIFCO, 288130) at 37\u0026deg;C for 24 hours \u003csup\u003e(\u003cem\u003e36\u003c/em\u003e)\u003c/sup\u003e. For fermentation experiments, 1 mL of L. plantarum WCFS1 culture (10⁹ CFU), brown sugar, fresh watermelon aerial parts (cv. 8424 and Meidu), and water were added into 500-mL glass bottles at a mass ratio of 3:7:10 (brown sugar: plant material: water). After thorough mixing, the mixtures were subjected to sealed anaerobic fermentation under dark conditions at 15\u0026ndash;28\u0026deg;C. Fresh watermelon aerial parts from 9 plants were pooled as one biological replicate. Samples were collected for analysis after 14 days (FL, Beijing, 2023 and 2024) and 365 days (FLY, Beijing, 2023\u0026ndash;2024), respectively, with 3 biological replicates set up for each group.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCharacterization of microbial diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the 14-day fermented liquid samples (FL, Beijing, 2024), each sample was transferred to a 15-mL sterile centrifuge tube and stored at -80\u0026deg;C. Three biological replicates were prepared for each treatment. Full-length 16S rRNA gene sequencing (V4-V5 regions) was performed. Genomic DNA was extracted using the E.Z.N.A.\u0026reg; Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer\u0026rsquo;s protocol. The 16S rRNA gene V4 region was sequenced on an Illumina MiSeq PE 300 platform (Majorbio, Shanghai, China) \u003csup\u003e(\u003cem\u003e68\u003c/em\u003e)\u003c/sup\u003e. The full-length 16S rRNA gene sequence data have been deposited in the National Genomics Data Center (NGDC) under accession number [to be CRA035035].\u003c/p\u003e\n\u003cp\u003eFollowing the same sampling procedure as used for full-length 16S rRNA gene sequencing, 14-day (FL) (June 5\u0026ndash;19, 2023) and 365-day (FLY) (June 15, 2023\u0026ndash;June 15, 2024) fermented liquid samples were collected for 16S rRNA gene (V4-V5 regions) sequencing. The bacterial 16S rRNA gene V4 region was sequenced using an Illumina NextSeq 2000 high-throughput sequencing platform (Majorbio, Shanghai, China) \u003csup\u003e(\u003cem\u003e68\u003c/em\u003e)\u003c/sup\u003e. The 16S rRNA gene sequence data have been deposited in the National Genomics Data Center (NGDC) under accession number [to be CRA035038].\u003c/p\u003e\n\u003cp\u003eRhizosphere samples of the cultivation substrate (peat:vermiculite:perlite = 2:1:1) were collected from cabbage plants under three treatments: no return (CK), fresh watermelon aerial parts return (FW), and fermented residue return (FR). Each rhizosphere substrate sample was placed in a sterile bag and stored at -80\u0026deg;C. Sample from three plants was pooled as one biological replicate, with three biological replicates per treatment. 16S rRNA gene (V4-V5 regions) sequencing was performed using the same protocol as applied to the fermented liquid samples. The 16S rRNA gene sequence data have been deposited in the National Genomics Data Center (NGDC) under accession number [to be CRA035036].\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMetabolite characterization of fermentation liquid and fermentation residue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor 14-day (FL, Beijing, 2023 and 2024), 365-day (FLY, Beijing, 2023\u0026ndash;2024) and 14-day-Validation (FLV, Zhangjiakou, 2024) fermented liquid samples, each sample was transferred to a 15-mL sterile centrifuge tube and stored at -80\u0026deg;C, with three biological replicates set for each treatment. For metabolomic analysis, fresh watermelon aerial parts (FW) were sampled with five plants pooled as one biological replicate; fermented residue samples (FR, Beijing, 2024) were collected with each bottle serving as one biological replicate, with three biological replicates established for each group.\u003c/p\u003e\n\u003cp\u003eDifferential metabolites were identified using orthogonal partial least squares-discriminant analysis (OPLS-DA) with criteria of VIP \u0026gt; 1, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, and BH-FDR \u0026lt; 0.05 \u003csup\u003e(\u003cem\u003e72\u003c/em\u003e)\u003c/sup\u003e. Metabolome data have been deposited in OMIX under accession number, FR-FW [to be OMIX013593, https://ngdc.cncb.ac.cn/omix/preview/wHahAOfW], FL-FLY [to be OMIX013597, https://ngdc.cncb.ac.cn/omix/preview/jsQ2m1Yv], FLV [to be OMIX013596, https://ngdc.cncb.ac.cn/omix/preview/SxuyVtmc]. All analyses were conducted with support from Shanghai Majorbio Biopharmaceutical Technology Co., Ltd.. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePlant materials and growth conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary plant materials used in this study were \u003cem\u003eBrassica rapa\u003c/em\u003e cv. Xiaqiuwang and \u003cem\u003eZea mays\u003c/em\u003e cv. Zhengdan 958. Other plant materials included \u003cem\u003eMarchantia polymorpha\u003c/em\u003e, \u003cem\u003eFunaria hygrometrica\u003c/em\u003e, \u003cem\u003ePteris ensiformis\u003c/em\u003e, \u003cem\u003eAdiantum venustum\u003c/em\u003e, \u003cem\u003eSelaginella tamariscina\u003c/em\u003e, \u003cem\u003eLycopodium japonicum\u003c/em\u003e, \u003cem\u003eNageia nagi\u003c/em\u003e, \u003cem\u003ePodocarpus macrophyllus\u003c/em\u003e, \u003cem\u003eLactuca sativa\u003c/em\u003e cv. Luoma Shengcai (Roman lettuce), \u003cem\u003eSedum hispanicum\u003c/em\u003e, \u003cem\u003eCoriandrum sativum\u003c/em\u003e cv. Shandong Daye Xiangcai, \u003cem\u003ePlantago depressa Willd\u003c/em\u003e., \u003cem\u003eCitrullus lanatus\u003c/em\u003e cv. 8424, \u003cem\u003eCucumis sativus\u003c/em\u003e cv. Jinchun 4, \u003cem\u003eFragaria \u0026times; ananassa\u003c/em\u003e cv. Zhenhong Meiling, \u003cem\u003eMalus \u0026times; robusta\u003c/em\u003e, \u003cem\u003eMalus domestica\u003c/em\u003e, \u003cem\u003eSedum hispanicum\u003c/em\u003e cv. Green Sprite, \u003cem\u003eOryza sativa\u003c/em\u003e inbred line Nipponbare, \u003cem\u003eNicotiana benthamiana\u003c/em\u003e, \u003cem\u003eLycium ruthenicum\u003c/em\u003e, \u003cem\u003eSolanum lycopersicum\u003c/em\u003e inbred line Condine Red, \u003cem\u003eSolanum tuberosum\u003c/em\u003e cv. Desiree, \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (accession: Col-0), \u003cem\u003eRaphanus sativus\u003c/em\u003e cv. Powder Bird, \u003cem\u003eNasturtium officinale\u003c/em\u003e, \u003cem\u003eEruca sativa\u003c/em\u003e, \u003cem\u003eIsatis indigotica\u003c/em\u003e, \u003cem\u003eBrassica oleracea\u003c/em\u003e cv. Zhonggan 101, and \u003cem\u003eBrassica juncea\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ePlants were grown in glass greenhouses and growth chambers at the College of Biology, Hebei North University (Zhangjiakou, Hebei, 40\u0026deg;48\u0026prime;29\u0026Prime;N, 114\u0026deg;52\u0026prime;50\u0026Prime;E; 18\u0026ndash;30\u0026deg;C, 12-h light/12-h dark photoperiod, PPFD (Photosynthetic Photon Flux Density): 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e); and in glass greenhouses and growth chambers at the Institute of Botany, Chinese Academy of Sciences (CAS) (Beijing, 39\u0026deg;56\u0026prime;23\u0026Prime;N, 116\u0026deg;20\u0026prime;31\u0026Prime;E; 18\u0026ndash;30\u0026deg;C, 12-h light/12-h dark photoperiod, PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eAll laboratory-grown plants were cultivated in a substrate mixture (peat: vermiculite: perlite = 2:1:1). Growth chamber conditions were maintained at 26\u0026deg;C with a 12-h light/12-h dark photoperiod (PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e), and 60% relative humidity. All plants were irrigated with 4 L of modified Hoagland\u0026rsquo;s nutrient solution \u003csup\u003e(\u003cem\u003e73\u003c/em\u003e)\u003c/sup\u003e, which contained 2.6 mM Ca(NO₃)₂\u0026middot;4H₂O, 4.25 mM KNO₃, 1.96 mM KH₂PO₄, 0.25 mM (NH₄)₂SO₄, 1.75 mM MgSO₄\u0026middot;7H₂O, 2.28 mM K₂SO₄, 0.0134 mM EDTA-FeNa\u0026middot;3H₂O, 0.0101 mM MnSO₄\u0026middot;H₂O, 0.0052 mM ZnSO₄\u0026middot;7H₂O, 0.0054 mM Na₂B₈O₁₃\u0026middot;4H₂O, 0.0008 mM CuSO₄\u0026middot;5H₂O, and 0.00083 mM Na₂MoO₄\u0026middot;2H₂O. The nutrient solution was applied every 5 days. Uniformly growing plants were selected for the experiments.\u003c/p\u003e\n\u003cp\u003eFirst, experiments investigating the growth-promoting effect of the fermentation liquid on \u003cem\u003eBrassica rapa\u003c/em\u003e and the identification of its bioactive components were conducted as geographically replicated trials in Zhangjiakou, and Beijing with four concentration gradient treatments: 0 ml/L (CK),2.5ml/L (FL2.5), 5ml/L (FL5), 10 ml/L (FL10). Based on the experimental results (Fig 3), using the FL5 treatment as a reference, we compared the top 10 most abundant common metabolites from the untargeted metabolomics analysis of the fermentation liquid (Fig S10), and set up treatments: water (CK), 5 ml/L FL (FL5), 2g/L D-sorbitol (DS2), 0.2g/L Phenylalanine (AA), 2g/L D-sorbitol + 0.2g/L Phenylalanine (DA).\u003c/p\u003e\n\u003cp\u003eSubsequently, we conducted geographically replicated trials in Zhangjiakou, and Beijing to investigate the mechanism by which D-sorbitol promotes the growth of \u003cem\u003eBrassica rapa\u003c/em\u003e, with 0g/L D-sorbitol (CK),1g/L D-sorbitol (DS1) and 2g/L D-sorbitol (DS2) set as the experimental treatment. For experiments exploring the capacity of other plant species to utilize D-sorbitol, 0g/L DS (CK) and 2g/L D-sorbitol (DS2) was uniformly used as the experimental treatment. In experiments investigating D-sorbitol-induced plant energy metabolism (Fig. 4), we established the following treatments: CK: 0g/L D-sorbitol+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, DS2: 2g/L D-sorbitol+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, HCK: 0g/L D-sorbitol+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, HDS2: 2g/L D-sorbitol+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e. Experiments were also conducted in \u003cem\u003eBrassica rapa\u003c/em\u003e，\u003cem\u003eSedum hispanicum\u003c/em\u003e、\u003cem\u003eSolanum lycopersicum\u003c/em\u003e、\u003cem\u003eArabidopsis thaliana\u003c/em\u003e、\u003cem\u003eNicotiana benthamiana\u003c/em\u003e、\u003cem\u003eMalus domestica\u003c/em\u003e、\u003cem\u003eBrassica oleracea\u003c/em\u003e、\u003cem\u003eZea mays\u003c/em\u003e、\u003cem\u003eLactuca sativa\u003c/em\u003e. Additionally, we established treatments: CK: Water+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, DS2: 2g/L D-sorbitol+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, MA: 2g/L mannitol+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, SU: 2g/L sucrose+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, FU: 2g/L fructose+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, GL: 2g/L glucose+ PPFD: 300\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, HCK: Water+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, HDS2: 2g/L D-sorbitol+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e. HMA: 2g/L mannitol+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, HSU: 2g/L sucrose+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, HFU: 2g/L fructose+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e, HGL: 2g/L glucose+ PPFD: 150\u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;l\u003c/sup\u003e to investigate the effects of different sugars on \u003cem\u003eBrassica rapa\u003c/em\u003e growth under varying light intensities. All foliar spray experiments were conducted with applications every three days, where the criterion for successful spraying was uniform coverage of both adaxial and abaxial leaf surfaces with fine droplets \u003csup\u003e(\u003cem\u003e74\u003c/em\u003e)\u003c/sup\u003e. The experiments were terminated on the third day after the fifth application.\u003c/p\u003e\n\u003cp\u003eFinally, we evaluated the quality of watermelon aerial parts return to fields based on the average number of watermelon plants reported in the questionnaire survey. The experimental treatments included: no return (CK), return of 4 g fresh watermelon aerial parts per plant (FW), and return of 2 g fermented residues per plant (FR) (with reference standards: soil tillage depth of 30 cm; 10,800 kg of fresh watermelon aerial parts per hectare, corresponding to an approximate content of 2 g/kg in soil; after fermentation, the fermented residues accounted for 1.1 g/kg, with 2 kg applied per pot of soil). Triplicated experiments on \u003cem\u003eBrassica rapa\u003c/em\u003e and \u003cem\u003eZea mays\u003c/em\u003e were conducted in Zhangjiakou (Hebei), and Beijing in 2024, respectively, and all data were collected 15 days after treatment application.\u003c/p\u003e\n\u003cp\u003eAdditionally, from August to October 2024, a field comparison experiment on the incorporation of fresh watermelon aerial parts versus fermented residues was conducted in maize (Zea mays) fields in Shangqiu City, Henan Province (116.05\u0026deg;E, 34.07\u0026deg;N). Located in the central-eastern region of China, Shangqiu has a temperate climate with naturally occurring long-day conditions (daylength \u0026gt; 12 hours in summer). The field experiment adopted the same treatments as described above (CK, FW, FR), using \u003cem\u003eZea mays\u003c/em\u003e cv. Zhengdan 958 seeds. The growing season for the field experiment was from June to October 2024, with a planting configuration of 6 rows \u0026times; 60 plants, at a row spacing of 25 m and plant spacing of 60 cm\u003csup\u003e (\u003cem\u003e75\u003c/em\u003e)\u003c/sup\u003e. At least three plots were established as biological replicates, and all treatments within each plot were fully randomized. During sampling, measurement, and final yield analysis, edge plants in each plot were removed to avoid edge effects. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAnalysis of agronomic and physiological traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShoot fresh weight (including leaves, buds, and stems) of all plants was recorded on a per-plant basis. For \u003cem\u003eBrassica rapa\u003c/em\u003e and \u003cem\u003eZea mays\u003c/em\u003e, Shoot biomass (including leaves, buds, and stems) and Root biomass (roots) were collected. At harvest, Shoot and Root fresh weights were recorded, followed by determination of dry matter accumulation after oven-drying (30 minutes at 105 \u0026deg;C, then 3 days at 55 \u0026deg;C) \u003csup\u003e(\u003cem\u003e73\u003c/em\u003e)\u003c/sup\u003e. The leaf length and width of the largest leaf in \u003cem\u003eBrassica rapa\u003c/em\u003e, as well as the plant height and plant width of \u003cem\u003eZea mays\u003c/em\u003e, were measured using a ruler. We photographed the largest leaf of plants after 15 days of exogenous treatment. At the end of the field experiment, randomly selected maize plants were used for 1,000-grain weight determination. All \u003cem\u003eZea mays\u003c/em\u003e grains from the plots established in each field were collected to measure the actual yield \u003csup\u003e(\u003cem\u003e75\u003c/em\u003e)\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePhotosynthetic efficiency parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiurnal photosynthetic variations were measured in the field in Beijing and Zhangjiakou using a LI-COR 6400XT photosynthesis system \u003csup\u003e(\u003cem\u003e73,\u003c/em\u003e \u003cem\u003e76\u003c/em\u003e)\u003c/sup\u003e. Photosynthetic rates of \u003cem\u003eBrassica rapa\u003c/em\u003e were recorded from the largest fully expanded leaves during the growth stage under a photosynthetic photon flux density (PPFD) of 800 \u0026mu;mol m⁻\u0026sup2; s⁻\u0026sup1; and ambient CO₂ conditions.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTEM images\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransmission electron microscopy (TEM) samples were obtained from at least three distinct plants. \u003cem\u003eBrassica rapa\u003c/em\u003e leaf samples were collected from the mid-section of the largest leaf. The samples were carefully excised into 1\u0026ndash;3 mm\u0026sup3; blocks, rinsed three times with 0.1 M phosphate-buffered saline (PBS; pH 7.2), fixed in a solution containing 2.5% paraformaldehyde and 2% glutaraldehyde, post-fixed in osmium tetroxide, dehydrated through a graded ethanol series (30\u0026ndash;100%), and embedded in Spurr\u0026rsquo;s resin (MilliporeSigma, Burlington, MA, USA), following established protocols \u003csup\u003e(\u003cem\u003e77\u003c/em\u003e)\u003c/sup\u003e. Subsequently, three samples from different plants per treatment were sectioned using an LKB-V ultramicrotome (LKB Produkter AB, Bromma, Sweden), stained with 2% uranyl acetate and 0.5% lead citrate, and examined under a JEM-1230 transmission electron microscope (JEOL, Tokyo, Japan) at 80 kV \u003csup\u003e(\u003cem\u003e77\u003c/em\u003e)\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePigment measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBrassica rapa\u003c/em\u003e leaf samples were harvested 15 days after treatment (Beijing, 2023; Zhangjiakou, 2024) and immediately frozen in liquid nitrogen. The ground leaf samples were weighed and extracted in 80% acetone, followed by spectrophotometric analysis to determine the contents of chlorophyll, carotenoids, and total chlorophyll \u003csup\u003e(\u003cem\u003e78\u003c/em\u003e)\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eChlorophyll fluorimeter and reactive oxygen species (ROS)-scavenging enzyme activity measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChlorophyll fluorescence parameters, including Fv/Fm, Fo, Fm, Fv, Y(NPQ), and Y(NO), of the largest leaf in \u003cem\u003eBrassica rapa\u003c/em\u003e were measured using an Imaging-PAM Chlorophyll Fluorimeter equipped with a computer-operated PAM-control unit (IMAG-MAXI; Heinz Walz, Effeltrich, Germany), following the method described previously \u003csup\u003e(\u003cem\u003e79\u003c/em\u003e)\u003c/sup\u003e. The activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were assayed using commercial kits from Beijing Solarbio Science \u0026amp; Technology Co., Ltd. (Solarbio, China) according to the manufacturer\u0026rsquo;s protocols \u003csup\u003e(77\u003cem\u003e, 80\u003c/em\u003e)\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePlant suger composition and elemental content measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each treatment, 18 plants were divided into four biological replicates (4\u0026ndash;5 plants per replicate). From each plant, the largest leaf of \u003cem\u003eBrassica rapa\u003c/em\u003e was homogenized to analyze the nutritional composition of the tissue. Total sugar content was determined using the dinitrosalicylic acid (DNS) method \u003csup\u003e(\u003cem\u003e81\u003c/em\u003e)\u003c/sup\u003e. The leaf D-sorbitol content and total starch content in \u003cem\u003eBrassica rapa\u003c/em\u003e, \u003cem\u003eSedum hispanicum\u003c/em\u003e, \u003cem\u003eBrassica oleracea\u003c/em\u003e, and \u003cem\u003eLactuca sativa\u003c/em\u003e were quantitatively determined via spectrophotometry using assay kits from Beijing Boxbio Science \u0026amp; Technology Co., Ltd \u003csup\u003e(\u003cem\u003e48, 81-82\u003c/em\u003e)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eElectrical conductivity (EC) and pH values of the fermentation liquid were measured using an EC meter and a pH meter, respectively \u003csup\u003e(\u003cem\u003e84\u003c/em\u003e)\u003c/sup\u003e. We quantified six photosynthesis-related elements using inductively coupled plasma optical emission spectrometry (ICP-OES). For elemental composition analysis, dried Shoot plant parts were analyzed using an Agilent ICP-OES 730 spectrometer (Agilent Technologies Inc., Santa Clara, CA, USA). Each treatment group, consisting of 18 plants in total, was divided into three biological replicates (\u003cem\u003en \u003c/em\u003e= 6). Each biological replicate was ground into a powder, and equal aliquots from each plant were pooled to form a composite sample for each replicate. Three photosynthesis-related elements (K, Ca, Mg) were quantified using ICP-OES \u003csup\u003e(\u003cem\u003e77\u003c/em\u003e)\u003c/sup\u003e. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRNA-seq Analysis and metabolome of \u003cem\u003eBrassica rapa\u003c/em\u003e leaves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment extracted RNA and characterized the transcriptome of \u003cem\u003eBrassica rapa\u003c/em\u003e leaves two hours after the spraying sorbitol treatment. In each treatment group, five \u003cem\u003eBrassica rapa\u003c/em\u003e plants with consistent growth were selected from each box as biological replicates, resulting in three replicates per group. The largest leaf from each plant were collected as experimental materials. Measurement details are described in a previous study25. \u003c/p\u003e\n\u003cp\u003eBulk transcriptome data processing included: (1) quality control with Trim Galore! (v.0.6.10) to remove adapters and low-quality bases; (2) alignment to the Brara_Chiifu_V3.5 genome using STAR; (3) gene-level quantification via feature Counts, followed by downstream analysis with Count. Transcripts per million (TPM) values were calculated for sample correlation analysis and PCA. DEGs were identified using DESeq2 with thresholds of P \u0026lt; 0.05, Benjamini-Hochberg-adjusted FDR (BH-FDR) \u0026lt; 0.05, and |log2FoldChange| \u0026gt; 1. DEG expression patterns were clustered via Mfuzz using fuzzy C-means. KEGG pathway analysis was performed with cluster Profiler\u0026apos;s enricher function and validated through KOBAS (http://kobas.cbi.pku.edu.cn/home.do) \u003csup\u003e(\u003cem\u003e77, 84\u003c/em\u003e)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe metabolome sampling protocol matched the transcriptome methodology. Differential metabolites were identified by orthogonal partial least squares-discriminant analysis (OPLS-DA) with VIP \u0026gt; 1, P \u0026lt; 0.05, and BH-FDR \u0026lt; 0.05. Transcriptome data are deposited in NGDC (accession: [subCRA057394]), metabolome data in OMIX ([OMIX013591, https://ngdc.cncb.ac.cn/omix/preview/EfMX1PAt]). Analyses were supported by Shanghai Majorbio Biopharmaceutical Technology Co., Ltd \u003csup\u003e(\u003cem\u003e72\u003c/em\u003e)\u003c/sup\u003e. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eqRT‑PCR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sampling method was consistent with that used for transcriptome analysis. Leaf tissues of \u003cem\u003eBrassica rapa\u003c/em\u003e and \u003cem\u003eLactuca sativa\u003c/em\u003e were harvested and immediately flash-frozen in liquid nitrogen. Total RNA was extracted using the RN53 Total RNA Extraction Kit (TransGen Biotech, China), followed by reverse transcription with the One-Step gDNA Removal and cDNA Synthesis SuperMix (AG, China). Quantitative real-time PCR (qRT-PCR) was performed on a QuantStudio 5 instrument (Applied Biosystems, USA) using SYBR Green Mix (AG, China) in 384-well optical plates, following the manufacturer\u0026rsquo;s instructions. Detailed PCR conditions were as described in a published protocol \u003csup\u003e(\u003cem\u003e82\u003c/em\u003e)\u003c/sup\u003e. Three independent biological replicates were analyzed. Real-time PCR data were generated and analyzed using the comparative Ct method to determine the relative mRNA expression levels in each tissue, as described in the iCycler manual (Bio-Rad, USA). Actin was used as the internal control, as its amplification efficiency was comparable to that of the target genes. Primer sequences are listed in Supplementary Table 5.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAlignment and phylogenetic tree and structure prediction of NADP-dependent D-sorbitol-6-phosphate dehydrogenase and sorbitol dehydrogenase orthologs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmino acid sequences of canonical D-sorbitol-metabolizing enzymes (NADP-dependent D-sorbitol-6-phosphate dehydrogenase\u003cem\u003e S6PDH \u003c/em\u003eand sorbitol dehydrogenase \u003cem\u003eSORD\u003c/em\u003e), and their orthologs from multiple species were downloaded from NCBI (https://www.ncbi.nlm.nih.gov/) and BRAD (http://www.brassicadb.cn/#/) (Tab S6). Sequence alignment was performed using ESPript 3.0 (http://espript.ibcp.fr/ESPript/ESPript/). A phylogenetic tree was constructed using 9 amino acid sequences retrieved from NCBI and BRAD databases. Evolutionary history was inferred using the Maximum Likelihood method with the JTT matrix-based model in MEGA (version 7; https://www.megasoftware.net) and visualized with iTOL (https://itol.embl.de/). Bootstrap values (with 1000 replicates) were calculated to assess the relative support for each branch, and those \u0026ge;50% are indicated on the tree. The protein structures of \u003cem\u003eS6PDH\u003c/em\u003e and \u003cem\u003eSORD\u003c/em\u003e were predicted using AlphaFold3 \u003csup\u003e(\u003cem\u003e84\u003c/em\u003e)\u003c/sup\u003e, respectively. All structures were visualized using the PyMOL Molecular Graphics System.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Student\u0026rsquo;s t-test was used to determine statistical significance between two groups using Microsoft Excel. For comparisons of multiple groups, the data were analyzed by one-way analysis of variance (ANOVA) followed by Tukey\u0026rsquo;s multiple comparison test using IBM SPSS Statistics 25 (IBM Corp., U.S.A) was used for Pearson correlation analysis. P values smaller than 0.05 were considered statistically significant. No outliers were excluded in any statistical analysis. Figures were generated using GraphPad Prism (version 6.02, GraphPad, USA). Details and numbers of biological replicates are described in the respective figure legends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (32201516). Part of the experimental expenses were borne by the authors themselves.\u003c/p\u003e\n\u003cp\u003eWe would like to thank Professor Jiqing Wang (Henan Agricultural University) for providing the foundation in fermentation technology, and Professor Jinxing Lin (Beijing Forestry University) for his valuable comments on the manuscript. We are also grateful to Associate Researcher Xu Cai (Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences) for providing the KEGG annotation data for \u003cem\u003eBrassica rapa\u003c/em\u003e cv. Chiifu v3.5.\u003c/p\u003e\n\u003cp\u003eOur sincere thanks go to all farmers and personnel involved in the survey. Special appreciation is extended to Ren Liu (watermelon grower), Yuan Zhao (Director of the Anhui Provincial Department of Agriculture and Rural Affairs), Pengyu Sun (Chairman of Fengtai Agricultural Products Co., Ltd., Lu\u0026rsquo;an City, Anhui Province), Jianwei Wan, Zixiao Sun, and other colleagues and friends who assisted in questionnaire collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.Y.W. and H.Y.W. conceived and designed the research. X.Y.W., X.W.W., S.F.W. performed most of the experiments and analyses. H.X.C. contributed to the questionnaire analyses and all statistical analyses. J.F.N. contributed to the transcriptome and metabolome analysis. X.Y.W. wrote the manuscript. H.Y.W. and X.Y.W. coordinated the project. X.Y.W., H.X.C., J.F.N., S.F.W, L.X.L., X.W.W., and H.Y.W. discussed the findings online and revised the manuscript. H.Y.W., X.W.W., and X.Y.W., supervised the research. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRaut, N. A. et al. Introduction: fundamentals of waste removal technologies. In \u003cem\u003e360-Degree Waste Management \u003c/em\u003e1\u0026ndash;16\u003cem\u003e \u003c/em\u003e(Elsevier, 2023). \u003c/li\u003e\n\u003cli\u003eTuck, C. O. et al. Valorization of biomass: deriving more value from waste. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e337\u003c/strong\u003e, 695\u0026ndash;699 (2012).\u003c/li\u003e\n\u003cli\u003eZhang, C. et al. 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A widespread plant defense compound disarms bacterial type III injectisome assembly. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e387\u003c/strong\u003e, eads0377 (2025)\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustainable agriculture, D-sorbitol, Lactiplantibacillus plantarum WCFS1, Brassica rapa, Allelopathic compounds, Continuous cropping obstacles","lastPublishedDoi":"10.21203/rs.3.rs-8409481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8409481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Agricultural systems generate billions of tons of high-moisture plant residues annually, leading to soil degradation and replanting failure—a critical bottleneck for global sustainability. Here, using watermelon as a representative model, our survey of representative watermelon-producing regions in China identified unsustainable crop residue management as a key driver of this ecological bottleneck. We developed a Lactiplantibacillus plantarum WCFS1-mediated rapid fermentation system with the aim of repurposing watermelon aerial parts to alleviate continuous cropping obstacles and promote sustainable waste recycling. We found that the fermentation liquid promotes Brassica rapa growth through its key metabolite D-sorbitol. To date, D-sorbitol has been characterized primarily in Rosaceae plants as a sucrose-like energy source and signaling molecule, whereas studies in other plant families have focused predominantly on its roles in osmotic-stress responses. Thus, leveraging an unprecedented cross-lineage experimental framework spanning dozens of cultivation trials, we systematically evaluated the effects of exogenous D-sorbitol across 32 phylogenetically representative plant species, including bryophytes, ferns, gymnosperms, and angiosperms. Excitingly, we discovered a previously unrecognized light intensity–sorbitol–starch cascade that affects energy metabolism and growth in angiosperms, particularly in Brassicaceae and Crassulaceae, while having no effect on the Rosaceae and Plantaginaceae. This mechanism spans the evolutionary lineage of true dicots. Additionally, we found that rapid fermentation reduces the inhibitory effects of allelotoxins from fresh watermelon stem and leaf on the growth and yield of Brassica rapa and Zea mays by significantly reducing allelochemical content in fresh tissues and markedly improving the composition of rhizosphere soil bacterial communities. Our work establishes a closed-loop, waste-to-growth strategy that transforms an ecological burden into a targeted agricultural input, providing a scalable solution for sustainable crop production.","manuscriptTitle":"Repurposing crop aerial parts to provide D-sorbitol for plant-specific growth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 13:42:51","doi":"10.21203/rs.3.rs-8409481/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abed37cd-18ff-4ed8-a07c-73e633039110","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60595944,"name":"Scientific community and society/Agriculture"},{"id":60595945,"name":"Biological sciences/Plant sciences/Plant physiology"}],"tags":[],"updatedAt":"2026-01-14T17:45:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 13:42:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8409481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8409481","identity":"rs-8409481","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}