Soil compaction mediates root–nutrient coupling associated with wheat yield response to depth-specific fertilization under contrasting long-term tillage systems

Soil compaction mediates root–nutrient coupling associated with wheat yield response to depth-specific fertilization under contrasting long-term tillage systems | 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 Research Article Soil compaction mediates root–nutrient coupling associated with wheat yield response to depth-specific fertilization under contrasting long-term tillage systems Ameet Kumar, Wenxu Dong, Xiuwei Liu, Chunsheng Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8727231/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Aims Depth-specific fertilization (DSF) has been proposed to reduce nutrient stratification in conservation tillage by relocating fertilizer; however, long-term no-tillage often develops subsurface compaction that restricts rooting and nutrient capture. We investigated whether DSF responses depend on tillage legacy and examined soil physical, biological, and root mechanisms regulating winter wheat yield. Methods A long-term split-plot experiment compared moldboard ploughing (MC) and no-tillage (NC) with fertilizer placements: conventional surface inorganic fertilizer, shallow placement at 0–10 cm (MC-10, NC-10), and deep placement at 15–25 cm (MC-25, NC-25; 50% IF + 50% pig manure). Soil properties (0–40 cm), root distribution (0–60 cm), antioxidant enzyme activities, and wheat yield were evaluated. Results NC exhibited higher bulk density and penetration resistance than MC, leading to strong nutrient stratification and restricted root penetration into deeper soil layers. Nutrient stratification remained higher under NC-10 and NC-25 than under MC-10 and MC-25 despite depth-specific fertilization. Although NC increased surface (0–10 cm) biological activity, indicated by higher microbial biomass C and dissolved organic C, these gains did not improve root–nutrient coupling or grain yield. In contrast, MC created a more root-permissive soil environment, promoted greater root proliferation across the soil profile, and enhanced root antioxidant enzyme activities. As a result, MC-10 achieved the highest grain yield (7909 kg ha⁻¹). Multivariate analyses showed stronger coupling among nutrients, roots, and yield under MC than under NC. Conclusions DSF must be combined with soil compaction–alleviation practices to achieve stable yield benefits under long-term conservation tillage systems. soil physical constraints root distribution root antioxidant enzymatic activities soil bulk density fertilizer placement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction No-tillage technology has gained extensive use to enhance the health of soil, curb soil erosion, and enhance the sustainability of the cropping systems of cereals over the long term. Nevertheless, the productivity of wheat in the case of no-till is not always constant and usually surpasses that of tilled systems (Shakoor et al., 2024 ). Meta-analyses also reveal that this decrease in yield can be largely attributed to the historical impact of long-term retention of residues and low intensity of soil disturbance, which may result in vertical stratification of nutrients and soil strength in deeper soil layers (Blanco-Canqui & Lal, 2008 ; Pittelkow et al., 2014 ). It has been proposed that depth-specific fertilization (DSF) can be used to relocate nutrients to the active root zones, and the research on its efficacy in systems with a long history of dissimilar tillage practices has not been properly researched. Long-term conservation methods are also getting encouraged in intensive wheat rotation to minimize the environmental impact without compromising the yields (Mandal et al., 2025 ). The physical and chemical legacies of various management systems, including moldboard plowing with residue incorporation or no-tillage with surface residue retention, determine different physical and chemical soils, which affect the nutrient distribution, root development, and yield patterns over the course of time (Sommer et al., 2011 ). Although no-till can improve the soil organic matter and soil aggregation of the upper soil, it might also lead to a rise in bulk density and the concentration of nutrients such as nitrogen and phosphorus in upper soils (Franzluebbers, 2002 ; Reichert et al., 2015 ). These depth-related soil processes are highly crucial since the crop performance can only be dependent on the availability of nutrients as well as their location in the accessible soil layers. The depth of placement of the fertilizer provides an effective means of determining the nutrient supply that accommodates the root activity (Nkebiwe et al., 2016 ). As opposed to surface broadcasting, which may enhance stratification of nutrients in no-till systems, placed or split-depth placement has the potential to redistribute the nutrients to deeper layers, where roots can access them (Zhang et al., 2020 ). Nevertheless, physical properties of soil like high penetration resistance and low pore connectivity may inhibit at least root growth with depth (Lipiec et al., 2012 ). The placement of nutrients deeper does not, therefore, necessarily enhance uptake and yield in cases where the soil continues to be compacted. Physical limitations of soils are highly intertwined with biological activities that affect nutrient cycling and the activities of roots (Xing et al., 2025 ). Moldboard plowing normally decreases the bulk density and stimulates early root development by increasing aeration and decreasing mechanical resistance (Hamza & Anderson, 2004 ). On the other hand, the early development of roots and the alteration of root system design can be constrained by long-term no-till when the soil is still strong (Lynch, 2013 ). The responses to these plastics encompass the alteration of the root diameter, biomass allocation, and spatial distribution, which are the manifestations of trade-offs between the acquisition of resources and mechanical adaptation (Mu et al., 2016 ; Chen et al., 2024 ; Wei et al., 2025 ). These types of structural changes have an effect on nutrient acquisition, rhizosphere dynamics, and microbial associations. The residue distribution, disturbance, and root-microbe interactions also affect the microbial biomass carbon (MBC) and dissolved organic carbon (DOC) because of tillage and fertilizer placement (Helgason et al., 2010 ; Man et al., 2022 ). Despite the observed enhanced biological activity at and close to the surface, no-till systems are not always able to give more yields because root development can be hampered at depths below the shallow soils (Mbuthia et al., 2015 ; Wen et al., 2022 ). Organic amendments can enhance the level of microbial activities as well as the nutrient cycling, but such amendments are dependent on the depth of placement and access of roots to active zones (Shu et al., 2022 ). In general, the interaction between nutrient stratification, physical limitations of soils, and root reactions has a critical knowledge gap, as it remains uncertain in general how these factors impact crop production. These factors have been studied individually, paying attention to soil stratification, root characteristics, or productivity (Bescansa et al., 2005 ; Qin et al., 2004 ; Zhao et al., 2015 ). Although the no-till system increases the levels of soil organic carbon and the biomass of microorganisms, the yield response is irregular, probably because the root access to nutrients at critical levels remains low due to continued subsurface compaction (Shao et al., 2016 ). There is little information on whether in-depth placement of fertilizers can address these interrelated restrictions under various long-term tillage regimes, in particular, the situation with integrated measurements of soil profiles, rooted characteristics, microbial pools, and crop production in a single experiment (Souza et al., 2023 ; Wang et al., 2021 ; H. Huang et al., 2024 ). Our novel integration of these components quantifies how DSF reshapes root–nutrient coupling in a mature no-till system and reveals trade-offs that may not be apparent in younger experiments. Using a 23-year field experiment, we evaluated how fertilizer placement depth (0–10 vs. 15–25 cm) interacts with long-term moldboard plowing and no-tillage to regulate: (i) soil nutrient availability (N, P, K), moisture, and bulk density; (ii) root system characteristics; and (iii) wheat grain yield and yield components, thereby mechanistically linking soil and root responses to crop performance. We hypothesized that pronounced nutrient stratification in long-term no-till restricts deep root growth and wheat yield under shallow fertilization and that deep placement at 15–25 cm would partially alleviate these constraints by enhancing subsurface nutrient availability and promoting deeper rooting. However, yield benefits would be strongest where soil physical constraints do not prevent roots from exploiting the placement zone. Materials and Methods Long-term experimental site, design, and crop management The long-term tillage experiment was established in October 2001 at the Luancheng Agroecosystem Experimental Station, Chinese Academy of Sciences, on the North China Plain under a winter wheat–summer maize double-cropping system (Fig. 1 A). We compared two tillage systems: (MC) moldboard ploughing with crushed maize residues incorporated to 25 cm and (NC) no-tillage with crushed maize residues retained on the soil surface. After the wheat harvest, wheat straw was chopped into 5–10 cm pieces and evenly mulched on all plots. The experiment followed a split-plot design with three blocks (replicates). The tillage system was the main-plot factor (8 m × 70 m; 560 m²) and was established in fixed positions in 2001 and maintained thereafter. Fertilizer placement was the subplot factor (5 m × 5 m; 25 m²). Within each main plot in each block, fertilizer-placement treatments were randomized. Under MC, subplots included MC-10 and MC-25; under NC, subplots included NC-10, NC-25, and NCB (Fig. 1 A). Winter wheat (2024–2025 season) was sown on 21 October 2024 and harvested on 8 June 2025. All plots were irrigated to maintain soil water content near ~ 65% of field capacity, with two additional irrigations (40–50 mm each, depending on in-season rainfall) applied at critical stages. Urea was top-dressed before jointing at 135 kg N ha⁻¹. Treatments and fertilizer placement Depth-specific fertilization (DSF) treatments were implemented within each tillage system. Under MC, MC-10 received inorganic fertilizer (IF) placed at 0–10 cm at 110 kg ha⁻¹. MC-25 received 50% IF + 50% pig manure (PM) placed at 15–25 cm (PM rate: 1.6 kg m⁻²) (Li et al., 2024 ). Under NC, NC-10 and NC-25 matched the placement depths and rates of MC-10 and MC-25, respectively (Table 1 ). NCB served as a no-till disturbance control: the applicator was passed as in DSF treatments to disturb the soil (approximately 0–10 cm and the 15–25 cm zone), but fertilizer was applied conventionally at the surface (IF only; no PM and no subsurface band placement). Fertilizers were applied immediately after tillage and before sowing using a hand-driven fertilizer applicator operated between wheat rows. Winter wheat was sown at 12 cm row spacing, and fertilizer was applied in every other inter-row (application-row spacing 24 cm). Fertilizer was delivered through discrete subsurface holes spaced at approximately 25 cm along each application row; the same application pattern was used for both placement depths (0–10 cm and 15–25 cm), with only the placement depth adjusted. Equipment schematics are provided in Fig. 1 B. Table 1 Experimental treatments and fertilizer placement depths/rates under long-term moldboard ploughing (MC) and no-tillage (NC). MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, subsurface-banded at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM subsurface-banded at 15–25 cm), NC (conventional surface IF), and NCB (NC with soil disturbed by the applicator without DSF or manure) Tillage system Treatment Fertilizer type Placement method Placement depth (cm) Application rate MC MC-10 Inorganic fertilizer (IF) Subsurface band (DSF) 0–10 110 kg ha⁻¹ MC-25 50% IF + 50% pig manure (PM) Subsurface band (DSF) 15–25 PM: 1.6 kg m⁻² + IF (50% of 110 kg ha⁻¹) MC IF Surface broadcast 0 (surface) 110 kg ha⁻¹ NC NC-10 IF Subsurface band (DSF) 0–10 110 kg ha⁻¹ NC-25 50% IF + 50% PM Subsurface band (DSF) 15–25 PM: 1.6 kg m⁻² + IF (50% of 110 kg ha⁻¹) NC (IF) Surface broadcast 0 (surface) 110 kg ha⁻¹ NCB (IF) Surface broadcast (soil disturbed) 0 (surface) 110 kg ha⁻¹ Soil sampling for nutrients and stratification ratio (SR) To quantify nutrient distributions and stratification, we collected soil using a tubular auger (5 cm inner diameter) at 0–10, 10–20, and 20–40 cm. On 20 May 2025 (peak flowering stage), we collected three replicate cores per subplot, sampling both the crop row and adjacent inter-row to capture within-plot spatial heterogeneity in the main nutrient uptake zone. Samples were air-dried, ground, and sieved (< 2 mm). We determined available N by alkaline hydrolysis/diffusion (with Kjeldahl digestion used where required by the adopted protocol), available P by the Olsen method, and exchangeable K by 1 M NH₄OAc extraction, following standard procedures (Blume, 1985 ; Sparks et al., 1996 ). Because fertilizers were placed at 0–10 cm (shallow) or as a subsurface band at 15–25 cm, and soils were sampled by fixed layers (0–10, 10–20, and 20–40 cm), the 15–25 cm band is captured primarily within the 10–20 cm layer and the upper portion of the 20–40 cm layer. Accordingly, we interpret DSF effects mainly as changes in nutrient availability in 10–20 cm (band layer) and 20–40 cm (below-band layer). We quantified nutrient stratification using the stratification ratio (SR) (Franzluebbers, 2002 ), calculated for N, P, and K across the 0–10/10–20 cm and 10–20/20–40 cm intervals. SR > 1 indicates surface enrichment, SR ≈ 1 indicates a relatively uniform profile, and SR < 1 indicates depletion in the upper layer (Nthebere et al., 2025 ; Slepetiene et al., 2024 ; Q. Zhang et al., 2024 ). $$\:SR=\frac{{C}_{i}}{{C}_{j}}$$ 1 where \(\:{C}_{i}\) is the nutrient concentration in the upper layer and \(\:{C}_{j}\) is the concentration in the immediately deeper layer. Root sampling, antioxidant enzymes, and root distribution We measured root antioxidant enzyme activities at tillering and jointing. Fine roots were excavated from the rhizosphere, gently washed, blotted dry, and stored at − 80°C. For each treatment, three whole plants per plot were pooled as one biological replicate, with three replicates. We prepared extracts by homogenizing 0.5 g fresh root in ice-cold 50 mM phosphate buffer (pH 7.8) containing 1% PVP, followed by centrifugation (10,000 × g, 20 min, 4°C); we used the supernatant for enzyme assays (Chen et al., 2024 ). We assayed SOD by the nitroblue tetrazolium (NBT) photoreduction method (560 nm; one unit defined as 50% inhibition) (Beauchamp & Fridovich, 1971 ), POD by the guaiacol–H₂O₂ method (470 nm), CAT by the decline in H₂O₂ absorbance (240 nm; one unit decomposing 1 µmol H₂O₂ min⁻¹ g⁻¹ FW) (Aebi, 1984 ; Giannopolitis & Ries, 1977 ), and DHA by TTC reduction with formazan quantified at 485 nm (Casida, 1977 ). We expressed activities on a fresh-weight basis. We assessed root distribution at peak flowering because this stage captures high crop nutrient demand, and the functional overlap between root distribution and nutrient availability is most relevant for yield formation. Roots were sampled within crop rows (12 cm spacing) at 24 cm intervals along the row (Fig. 1 C). We collected adjacent soil cores and sectioned them into 0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm using a 5 × 5 cm sampling frame. Roots were washed on a 0.5 mm sieve, and debris was removed manually. We scanned clean roots and analyzed them using WinRHIZO (Regent Instruments, Canada) to quantify RLD, RSA, RV, RWD, and mean root diameter, following established protocols for cereal root profiling (Burridge et al., 2020 ; Zhou et al., 2021 ; Arnhold et al., 2024 ). We determined root dry biomass after oven drying at 65°C to constant weight (48 h). Soil moisture, penetration resistance, and bulk density We measured soil moisture content (SMC) at tillering, flowering, and maturity from 10–60 cm using a 5 cm inner-diameter auger. Fresh samples were weighed, oven-dried at 105°C to constant mass, and SMC was determined gravimetrically. We measured soil penetration resistance in situ at the same stages using a hand-held cone penetrometer across 0–40 cm to characterize soil mechanical impedance among treatments. After harvest, we measured soil bulk density (BD) using undisturbed cores (5 cm diameter × 5 cm height) collected at 0–10, 10–20, 20–40, and 40–60 cm (three cores per depth and plot) and oven-dried at 105°C to constant mass. Microbial biomass carbon (MBC) and dissolved organic carbon (DOC) After the wheat harvest, we collected fresh soil samples at 0–10, 10–20, and 20–40 cm, stored samples in sealed bags, and kept them at − 20°C before analysis. We determined MBC by chloroform-fumigation extraction using an efficiency factor of 0.45 (Vance et al., 1987 ). We extracted DOC with distilled water (1:5 w/v) and quantified it using a TOC analyzer; instrument details are provided in Supplementary Methods. Grain yield and yield components At maturity, we hand-harvested winter wheat from each plot to determine grain yield (adjusted to 13% moisture), straw yield, and 1000-grain weight. We measured spike density using a 1.5 m² quadrat per plot and sampled 20 representative plants from this area to record spikes per plant and grains per spike. Data analysis and reproducibility Before any statistical analysis was performed, the Shapiro and Levene tests of residual normality and homogeneity of variances, respectively, were conducted. When models needed variables to be transformed, log-/square-root-transformed variables were used. The mixed-effects split-plot model that was used in the analysis of treatment effects was run in OriginPro 2025b (Learning Edition). Tillage system (MC; NC) was considered as the main-plot fixed factor, the fertilizer placement treatments were considered as subplot fixed factors (nested within tillage MC, MC-10, MC-25; and nested within NC (NC-10, NC-25, and NCB), and block (replicate) was treated as a random effect. They compared the main plot (tillage) effects to the correct main-plot error term and compared the subplot (fertilizer treatment) effects to the within main-plot error term. Where the variables are measured on soil depths and/or stages of growth, depth (and stage, where appropriate) was accepted as another fixed factor, and interaction terms (e.g., tillage × treatment × depth) were tested. In cases where significant interactions were found (p < 0.05), simple-effects analyses were run, and mean separation within each tillage system and at each depth or growth stage was carried out by means of the Tukey HSD test at p < 0.05. The contour plots created in OriginPro were used to visualize the spatial distributions of the available N, P, and K. The relationship between soil physicochemical characteristics, microbial indicators, root characteristics, and yield components was investigated using Pearson correlation analysis and principal component analysis (PCA; OriginPro extended statistics module). Results Weather conditions Monthly precipitation and air temperatures during the winter wheat growing season are shown in Fig. 2 . Precipitation peaked in May (69 mm) and was lowest in December (0.3 mm), while minimum and maximum temperatures were highest in June and lowest in January (Fig. 2 ). These conditions provide a seasonal context for interpreting treatment effects on soil moisture and mechanical impedance. Spatial nutrient distribution and stratification ratio Soil available nutrient profiles differed markedly between tillage systems (Fig. 3 ), revealing contrasting nutrient distribution outcomes under long-term moldboard ploughing (MC) versus no-till (NC). In the surface layer (0–10 cm), NC-25 showed higher available N than MC treatments (e.g., + 42% vs. MC-25; +28% vs. MC-10). MC treatments exhibited a comparatively flatter vertical N decline from the surface to deeper layers, whereas NC and NCB showed steeper decreases with depth, consistent with stronger surface accumulation under long-term no-till. Available P and K showed the same directional pattern. NC-25 exceeded MC treatments at the surface (e.g., + 45% P and + 38% K vs. MC-25). Still, concentrations declined more sharply below the surface in NC and NCB than in MC treatments, indicating stronger vertical gradients under no-till. Overall, these profiles show that long-term no-till maintained pronounced surface enrichment, while moldboard tillage produced a more vertically even nutrient distribution. Across the 0–40 cm profile, averaged available N, P, and K differed significantly among treatments (p < 0.0001), with NC-25 showing the highest nutrient levels (N = 170.94 mg kg⁻¹; P = 19.44 mg kg⁻¹; K = 309.13 mg kg⁻¹), whereas NC had the lowest values (N = 104.70 mg kg⁻¹; P = 5.89 mg kg⁻¹; K = 128.23 mg kg⁻¹) (Table 2 ). Table 2 Effects of long-term tillage legacy and depth-specific fertilization on available N, P, and K, microbial biomass carbon (MBC), and dissolved organic carbon (DOC), averaged across the 0–40 cm (mg kg⁻¹) soil profile, under different treatments. Capital letters indicate significant differences among treatments at p < 0.05. Lowercase letters indicate significant differences among fertilizer placement treatments within the same tillage system at p < 0.05. Treatment codes are defined in Table 1 . Treatment Depth (cm) N P K MBC DOC Mean Mean Mean Mean Mean MC-10 0–40 151.98 B 15.11 B 174.03 C 413.90 B 81.81 B MC-25 155.96 B 21.59 A 255.41 B 475.70 AB 99.69 AB MC 110.82 CD 7.72 CD 132.44 D 435.23 AB 105.03 AB NC-10 119.61 C 14.06 B 176.64 C 506.84 A 113.39 A NC-25 170.94 A 19.44 A 309.13 A 484.50 AB 126.32 A NCB 112.01 CD 10.02 C 140.66 D 507.48 A 124.63 A NC 104.70 D 5.89 D 128.23 D 480.10 AB 124.48 A P values Treatments < 0.0001 < 0.0001 < 0.0001 0.31615 0.00015 Stratification ratios (SR) further quantified these contrasts (Fig. 4 ). Surface-to-subsurface SRs were generally highest under no-till with shallow placement (NC-10) for available P and K, while deeper-layer SRs indicated a pronounced decline beneath the subsurface zone in NC treatments (notably NC-25 and NC for N and P). Together, the profile maps and SR results indicate that DSF can reposition nutrient enrichment downward; however, the persistence of steep gradients under long-term no-till suggests that realizing the benefit depends on whether roots can effectively access the targeted layers. DSF reduced surface stratification (0–10/10–20) relative to shallow placement but increased stratification below the band (10–20/20–40), indicating vertical redistribution of nutrient hotspots rather than profile homogenization.” Root antioxidant enzymes and root distribution by depth Root antioxidant enzyme activities (SOD, POD, CAT, DHA) responded significantly to tillage and fertilizer placement at both tillering and jointing (p < 0.05; Fig. 5 ). Across enzymes and stages, activities were highest under MC-10, followed by MC-25 and MC; NC-10 and NC-25 were intermediate, while NC and especially NCB showed the lowest activities. Enzyme activities were generally higher at tillering than at jointing, but treatment rankings were consistent between stages. Notably, placement effects were clearer under MC (MC-10 > MC-25 > MC) than under NC, suggesting that the physiological response to DSF was more strongly expressed when the soil environment supported root function. Root traits showed a clear tillage × placement interaction (p < 0.05; Fig. 6 A–B). Under MC, shallow banding (MC-10) produced the greatest RLD, RWD, RSA, and RV across depths, with MC-25 and MC intermediate. Under NC, deep placement (NC-25) improved rooting relative to NC-10 and NC and produced RLD and RSA at 20–60 cm that were comparable to those of MC treatments. However, total root biomass across 0–60 cm remained highest under MC-10, highlighting an important trade-off: in long-term no-till, DSF shifted rooting deeper, but whole-profile root development remained constrained relative to moldboard systems. Profile-averaged MBC did not differ among treatments (p = 0.316), whereas DOC showed a significant treatment effect (p = 0.00015), with higher DOC under no-till treatments (e.g., NC-25/NCB/NC 124–126 mg kg⁻¹) than under MC-10 (81.81 mg kg⁻¹) (Table 2 ). Soil moisture, soil strength, bulk density, and labile C pools Soil moisture content (SMC) differed significantly among treatments and depths (Fig. 7 ). Across stages, MC-10 and MC-25 maintained higher SMC in the 0–20 cm layer, while NC and NCB were consistently lowest. Differences narrowed with depth, but MC treatments tended to preserve higher SMC down to 40 cm, indicating a more favorable water environment in much of the active rooting zone during this season. Soil penetration resistance (SPR) increased with depth in all treatments (p < 0.05; Fig. 8 ). NC and NCB had the greatest SPR across the 0–45 cm profile, indicating persistent compaction, whereas MC-10 and MC-25 had the lowest SPR, particularly between 10 and 30 cm; MC and NC-25 were intermediate. Bulk density (BD) followed the same pattern: NC and NCB were highest throughout 0–30 cm, while MC-10 and MC-25 were significantly lower; MC-25 showed the lowest BD at 10–20 and 20–40 cm. Collectively, these results indicate stronger physical constraints under long-term no-till (higher SPR/BD), which likely reduce how efficiently roots can exploit subsurface nutrient placement. Microbial biomass carbon (MBC) and dissolved organic carbon (DOC) also varied by treatment and depth (p < 0.05, Fig. 9 and Table 2 ). In the 0–10 cm layer, both were highest under no-till treatments (NC-10, NC-25, NCB, NC) and lower under MC treatments, while differences diminished with depth. Thus, no-till enhanced labile C pools near the surface, but this biological advantage coincided with higher mechanical impedance, consistent with partial decoupling between surface biological improvement and deeper root access in compacted profiles. Grain yield and yield components Grain yield and yield components differed significantly among treatments (p < 0.05; Table 3 ). MC-10 produced the highest grain yield (7909 kg ha⁻¹) and the strongest yield components (1000-grain weight, biomass, spike number, straw yield). MC-25 and MC were intermediate. All no-till treatments (NC-10, NC-25, NCB, NC) yielded significantly less than MC-10, mainly due to reduced biomass and fewer grains per spike, while harvest index varied little. Importantly, the improved nutrient profiles and deeper rooting tendency under NC-25 did not close the yield gap with MC systems, consistent with the stronger physical constraints (SPR/BD) observed under long-term no-till. Table 3 Grain yield and yield components of winter wheat under long-term tillage legacy and depth-specific fertilization treatments (2024–2025 season). GY = grain yield, 1000‒GW = 1000-grain weight, BM = biomass, SN = spike number, SY = straw yield, NGS = grains per spike, and HI = harvest index. Different lowercase letters indicate significant differences among treatments (p < 0.05). Treatment codes are defined in Table 1 . Treatments GY (kg/ha) 1000‒GW (g) BM (kg/ha) x 100 NGS SN/ m 2 SY (kg/ha) HI (%) MC-10 7909 a 44.19 a 171.2 a 39.47 a 571 a 9207 a 46.2 abc MC-25 6963 ab 44.06 ab 148.8 ab 39.28 a 583 a 7919 a 46.9 a MC 6898 ab 41.91 c 146.2 ab 36.33 ab 475 a 7744 a 46.5 ab NC-10 5704 b 42.92 bc 124.3 b 34.25 b 486 a 6722 a 43.2 c NC-25 6450 b 43.14 ab 138.1 ab 34.93 b 458 a 7364 a 46.7 ab NCB 6442 b 41.99 c 138.6 ab 35.10 b 568 a 7417 a 46.5 ab NC 6451 b 39.79 d 142.6 ab 33.97 b 530a 7090 a 43.6 bc Multiscale coupling among root traits, soil conditions, nutrient availability, and grain yield Correlation matrices (Fig. 10 A–B) showed stronger and more yield-relevant coupling among nutrients, root traits, and grain yield in MC than in NC. In MC, grain yield correlated positively with surface-layer nutrient availability (N, P, K) and with multiple surface-layer root traits (RLD, RWD, RSA, RV; p < 0.05), whereas relationships involving deeper layers were weaker or non-significant. In NC, some surface-root nutrient relationships remained strong, but yield associations were less consistent, and below-surface linkages were generally weaker and more variable. Overall, the correlation structure indicates tighter root–nutrient–yield coupling under MC and a more decoupled structure under long-term NC.RLD was closely associated with both root physiological activity and soil physical conditions (Fig. 11 A–F). Linear regression analysis showed significant positive relationships between RLD and root antioxidant enzyme activities, including SOD, POD, catalase CAT, and DHA. Increases in enzyme activities were consistently accompanied by higher RLD, indicating enhanced root proliferation under greater physiological activity ( P < 0.01 for all regressions). In contrast, RLD declined significantly with increasing SBD and SPR. Linear regressions revealed strong negative relationships between RLD and both SBD and SPR, demonstrating that greater soil compaction and mechanical impedance restricted root distribution across treatments ( P < 0.01). Together, these relationships indicate that RLD responded positively to improved root metabolic activity while being simultaneously constrained by adverse soil physical conditions. PCA ordinations (Fig. 12 A–B) reinforced these contrasts. In the MC group, PC1 and PC2 explained 72.61% and 27.39% of the variance (cumulative 100%), with grain yield and yield components clustering with most root traits and nutrient variables, consistent with an integrated “root–nutrient–yield” axis. SPR and DOC projected away from this cluster, aligning with their role as constraints rather than co-benefits in the yield response. In the NC group, the lower PC1 contribution (58.13%) and weaker clustering indicate a less coherent multivariate coupling among roots, nutrients, and yield, consistent with the correlation results. Discussion Fertilizer placement depth regulates nutrient accessibility and vertical rooting under contrasting tillage systems Our novel integration of depth-resolved nutrients, soil physical constraints, root traits, and yield within a 23-year tillage legacy shows that DSF can improve subsurface nutrient accessibility and reshape rooting, but that yield benefits remain strongly tillage-dependent. Across treatments, nutrient redistribution followed the long-term disturbance template: nutrient availability was more surface-enriched under no-till, whereas moldboard ploughing reduced vertical gradients and broadened the nutrient-accessible zone through mixing (He et al., 2011 ; Lv et al., 2023). This matters because wheat yield responds most strongly where nutrients and active roots overlap, and upper layers often contribute disproportionately due to denser rooting and higher uptake activity (Wang et al., 2021 ; Ruis et al., 2024). In this context, DSF primarily repositioned nutrient hotspots within the profile. Subsurface banding at 15–25 cm can improve nutrient-use efficiency by placing relatively immobile nutrients (especially P) into more consistently moist zones and stimulating localized root proliferation around nutrient bands—responses widely reported across crops and placement strategies (Alam et al., 2018 ; Chen, 2023; Chen et al., 2022 ; Chen et al., 2016 ). Consistent with this mechanism, deeper placement shifted nutrient enrichment downward and increased root presence at depth under no-till relative to shallow placement, improving access to subsurface nutrients that would otherwise remain poorly connected to the effective rooting zone. However, our long-term dataset clarifies an important nuance that short-duration trials often cannot resolve: repositioning nutrients is not the same as capturing them. Under moldboard ploughing, mixing and lower impedance increase root–nutrient overlap even with shallow placement, reducing the marginal benefit of deeper placement. Under no-till, placement depth becomes more consequential, but only if roots can reliably proliferate in the target layer. This dual control—chemical opportunity (nutrient location) and physical accessibility (root reach)—helps explain why the coupling among nutrients, roots, and yield was more coherent under MC than under NC. Soil physical constraints modulate root system development and nutrient capture in long-term no-till Although measurements were made in one season, the 23-year management history means the observed soil and root responses reflect mature system states rather than transient effects. This is among the few long-term datasets showing that DSF benefits in no-till can be capped by a physical ceiling on rooting. Long-term no-till can enhance near-surface aggregation and residue-derived enrichment, but it may also sustain higher bulk density and penetration resistance in the subsurface, restricting root penetration and limiting access to deeper nutrient pools unless roots can exploit the placement zone (Tian et al., 2022 ; Sun et al., 2023 ). By contrast, moldboard ploughing lowers mechanical impedance and mixes residues and fertilizers, increasing overlap between roots and nutrients across the upper profile (He et al., 2011 ; Tian et al., 2022 ). These patterns align with mechanistic evidence that soil strength strongly controls root architecture and capture, and that constraints can intensify as soils dry (Bengough et al., 2011; Colombi et al., 2018 ; Liu et al., 2022 ). In wheat systems, tillage tends to reduce impedance and improve overlap between roots and accessible nutrients, whereas no-till often concentrates roots and nutrient cycling near the surface and increases sensitivity to subsurface constraints (Mu et al., 2016 ; Ruis et al., 2024). Accordingly, where penetration resistance and bulk density were higher, roots were more surface-concentrated, and deeper-layer nutrient-yield linkages were weaker and less consistent. This helps explain why measurable subsurface nutrients in long-term no-till do not automatically translate into yield gains unless roots can access and exploit those layers efficiently (Sun et al., 2023 ; Li et al., 2025 ). Linking back to our hypothesis, deeper DSF did enhance subsurface nutrient accessibility and supported deeper root presence, but it did not fully offset long-term no-till constraints. In mature no-till, DSF improved where nutrients were available, but not always how effectively the crop could capture them. This distinction helps reconcile inconsistent yield responses reported in the literature: short-term studies often show stronger DSF effects because legacy constraints are less pronounced, whereas mature no-till systems can develop persistent structural barriers. Practically, DSF is most likely to succeed in long-term no-till when paired with strategies that reduce subsurface impedance (e.g., strategic subsoiling), which can restore rooting depth and alleviate yield stagnation (Izumi et al., 2009 ). Integrated soil–root–nutrient interactions determine wheat grain yield responses to tillage legacy A key contribution is that we connect soil nutrients, soil physics, root morphology/physiology, and yield within one mature long-term experiment, showing that wheat yield responds to profile-scale coupling rather than any single factor alone. Under moldboard ploughing, correlation and PCA indicated tighter alignment of yield (and yield components) with root traits and nutrient variables, reflecting coordinated soil–root–nutrient functioning. Similar integration has been reported in wheat–maize rotations where tillage reshapes root distribution and yield tracks, coupling between roots and available nutrients in functionally active layers (Kan et al., 2020 ; Ye et al., 2019 ). Thus, MC converted nutrient availability into root growth and grain production more efficiently when the physical environment supported exploration. Conversely, the no-till group exhibited a more diffuse multivariate composition, with them having less strong and stable associations between yield, root traits, and nutrient variables. It means that it is semi-decoupled: it can be present but is limited in accessibility and uptake, and in line with syntheses that indicate no-till yield responses to be more heterogeneous and location-specific and conditioned by physical factors affecting the soil (Yan et al., 2024; Liu et al., 2025 ). This is corroborated by the fact that no-till alters the soil-profile distribution of roots and perceived rooting zone as compared to tilled systems (Ruis et al., 2024). Most importantly, we can use our long-term findings to explain short-term DSF research differences. Although short-term experiments frequently provide more information about responses to deeper placement or banding, we find that, at maturity, the benefits of DSF are less obvious in no-till due to the changing balance between nutrient positioning and physical accessibility, and root development across the entire profile. In contrast to short-term trials, our 23 years of evidence point to legacy accumulation as one of the factors behind lower returns to DSF in mature no-till. Generally, the optimum approach to rooting was determined, with placement in a permissive (MC) or a restrictive (NC) tillage legacy. Overall, our results demonstrate that wheat yield responses to fertilizer placement are governed by integrated soil physical conditions, nutrient distribution, and root functional traits rather than nutrient availability alone. Long-term tillage legacy determines whether fertilizer inputs are effectively translated into root activity and grain production by enabling or constraining root access to functionally active soil layers. Practical implications, limitations, and future directions Application-wise, the results indicate two recommendations. To start with, DSF can continue to be an effective method of enhancing accessibility of nutrients in the subsurface, especially when operated in no-till systems, although it must not be applied as a nutrient-only method, but as a method of coupling. Second, in conditions where long-term no-till is associated with high subsurface soil strength, DSF is most probably associated with giving yield advantages when joined with actions that lighten mechanical constraints (e.g., strategic subsoiling or controlled traffic), which are in agreement with the results that found that diminishing mechanical impedance restored to rooting depth and yield performance (Izumi et al., 2009 ). Moving deeper under moldboard ploughing can offer smaller yield advantages since mixing already leads to overlapping root-nutrient, meaning that efficacy of nutrient use, and the practicality of operations can be considered. This research is not without limitations as well. One season of measurements was taken, and therefore, it was not directly tested if there was interannual variability in the dynamics of rainfall and soil moisture, which can strengthen or weaken mechanical constraints. We also measured processes of capturing depth-wise co-occurrences and multivariate linkages as opposed to directly measuring nutrient uptake fluxes. The future research must determine whether the patterns of coupling can be applied through different seasons and should also examine integrative packages (DSF × compaction alleviation) under multi-year experimentation to determine when deeper placement would be translated into consistent yield. Conclusions A combination of nutrient profiles, soil physical constraints, root characteristics, and yield of a 23-year field experiment indicates that long-term tillage history is a strong regulator of the efficacy of fertilizer positioning. No-tillage created strong vertical stratification of nutrients and compaction of the subsurface, while moldboard ploughing retained a more homogeneous distribution of nutrients and a root-permissive physical environment. Deep placement shifted the spatial distribution of roots in no-till systems, but failed to counteract the total root constraints of long-term no-till, which validates the hypothesis that nutrient accessibility of fertilizer depth can be spatially changed. Nevertheless, the benefits of yield were not observed in mature no-till, which suggests that a better nutrient placement is not sufficient to overcome physical constraints created by legacy. Higher bulk density and penetration resistance weakened root–nutrient–yield coupling, while moldboard systems showed tighter integration among these components. Therefore, depth-specific fertilization can be treated as an approach to coupling and not accepted as a nutrient-only intervention. In no-till farming systems, DSF in combination with compaction-alleviation practices is crucial in the long term to accumulate stable yield gains and robust wheat production in the face of rising climate variability. Declarations Funding This study was funded by the National Key Research and Development Program of China (2023YFD1902605 and 2022YFD1500604). The first author was financially supported by the Chinese Government Scholarship (CGS). Author Contributions Ameet Kumar: Conceptualization; Methodology; Investigation; Data curation; Formal analysis; Statistical analysis; Visualization; Writing – original draft. Wenxu Dong: Conceptualization; Methodology; Resources; Supervision; Project administration; Funding acquisition; Writing – review & editing. Xiuwei Liu: Formal analysis; Validation; Writing – review & editing. Chunsheng Hu: Conceptualization; Supervision; Project administration; Resources; Funding acquisition; Writing – review & editing. 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J Exp Bot 72:747–756. https://doi.org/10.1093/jxb/eraa475 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 03 Feb, 2026 Editor assigned by journal 03 Feb, 2026 First submitted to journal 28 Jan, 2026 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-8727231","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586940306,"identity":"eea7574e-ec57-4ea6-9c0a-91b778681ec5","order_by":0,"name":"Ameet Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYNCCAyCC+eCDjw0gBmPjAcI6wErYkg1nNjBIALU0EKuFx0yaF6wFaisuwD/t8MHHH84cjuaffcBA2naHTZ1u+2GgLTU20bi0SNxOSzY4cONw7oxzCQnGuWfSJMzOJAK1HEvLbcCl53aOmcSBD4dzG84wHEjObTssYXYAqIWx4TBOLfK387//AGmZfwaozBKk5fxD/FoMbuewMYActuEMM2MzI0jLDQK2GN5OM5Y4cyY9d+MZNmbG3rY0yW03gLYk4PGL3O3khx8qjlnnzjvD//3HzzYbfrPz6Q8ffKixwe197CCBNOWjYBSMglEwCtAAAGE6byfOhyqVAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7067-9931","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ameet","middleName":"","lastName":"Kumar","suffix":""},{"id":586940307,"identity":"125e320e-7977-4378-8dee-491030543890","order_by":1,"name":"Wenxu Dong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenxu","middleName":"","lastName":"Dong","suffix":""},{"id":586940308,"identity":"6ed90bbe-3ce8-4450-b3e8-3ef0d53c586f","order_by":2,"name":"Xiuwei Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiuwei","middleName":"","lastName":"Liu","suffix":""},{"id":586940309,"identity":"6a9ae1eb-8a3b-474e-a995-c2512941c8a6","order_by":3,"name":"Chunsheng Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chunsheng","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2026-01-29 05:16:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8727231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8727231/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102745668,"identity":"ad68427b-ce86-4114-9d4c-f9884021dba0","added_by":"auto","created_at":"2026-02-16 08:53:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 1.\u003c/strong\u003e(A) Schematic layout of the long-term split-plot field experiment showing main plots under moldboard ploughing (MC) and no-tillage (NC) and fertilizer-placement subplots. Subplots included MC-10/NC-10 (IF placed at 0–10 cm), MC-25/NC-25 (50% IF + 50% pig manure placed at 15–25 cm), MC/NC (conventional surface IF), and NCB (no-till with conventional surface IF, but disturbed by the fertilizer applicator; disturbance control without DSF banding or manure). (B) fertilizer applicator, and (C) root sampling scheme along the crop row across the 0–60 cm soil profile\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/498d0dcc31fed3ea4817e04d.jpg"},{"id":102423060,"identity":"36723188-8aa2-481b-b678-64f502ddcd5d","added_by":"auto","created_at":"2026-02-11 13:59:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 2. \u003c/strong\u003eMonthly precipitation and mean air temperature (minimum, maximum, and average) during the winter wheat growing season (2024–2025) at the experimental site.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/4cbc04e6bb4b356bf32cf675.jpg"},{"id":102423048,"identity":"336d19d0-1173-409d-a896-4b01485b051b","added_by":"auto","created_at":"2026-02-11 13:59:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 3\u003c/strong\u003e. Spatial distribution (contour maps) of available N, P, and K across the 0–40 cm soil profile under long-term moldboard ploughing (MC) and no-tillage (NC) with depth-specific fertilization treatments. Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, subsurface-banded at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM subsurface-banded at 15–25 cm), NC (conventional surface IF), and NCB (NC with soil disturbed by the applicator without DSF or manure).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/6d90a588e61a8b5e23936078.jpg"},{"id":102745816,"identity":"90492410-8ddc-46af-9c6f-fc3fda70095d","added_by":"auto","created_at":"2026-02-16 08:54:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 4.\u003c/strong\u003e Effects of long-term tillage legacy and fertilizer placement strategy on nutrient stratification ratios (SR) for available N, P, and K. SR was calculated for the 0–10/10–20 cm and 10–20/20–40 cm intervals. Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, placed at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM placed at 15–25 cm), NC (conventional surface IF), and NCB (no-till receiving conventional surface IF but disturbed by the applicator; no subsurface banding and no manure). Different lowercase letters indicate significant differences among fertilizer treatments within the same tillage system (Tukey’s HSD, p \u0026lt; 0.05) following the mixed-model split-plot analysis.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/a6a8729c1a22258b38159c75.jpg"},{"id":102423049,"identity":"6561cbb6-f225-44bb-b18f-5272b5fee4d5","added_by":"auto","created_at":"2026-02-11 13:59:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 5. \u003c/strong\u003eEffects of long-term tillage legacy and depth-specific fertilization on root enzyme activities at tillering and jointing: superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and dehydrogenase (DHA). Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, placed at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM placed at 15–25 cm), NC (conventional surface IF), and NCB (no-till with conventional surface IF, but disturbed by the applicator; no DSF banding and no manure). Different lowercase letters indicate significant differences among fertilizer treatments within the same tillage system (Tukey’s HSD, p \u0026lt; 0.05) following the mixed-model split-plot analysis.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/9c765905396df73d1b1543a4.jpg"},{"id":102745745,"identity":"14ce5cc8-9c29-4c7f-b5ff-5870a6b205d0","added_by":"auto","created_at":"2026-02-16 08:53:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":563734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 06 A. \u003c/strong\u003eVertical distribution of root traits across the 0–60 cm soil profile: (a) root length density (RLD), (b) root weight density (RWD), (c) root surface area (RSA), (d) root volume (RV), and (e) mean root diameter (RD). Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, placed at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM placed at 15–25 cm), NC (conventional surface IF), and NCB (no-till with conventional surface IF, but disturbed by the applicator; no DSF banding and no manure). Different lowercase letters indicate significant differences among fertilizer treatments within the same tillage system at a given soil depth (simple-effects comparisons following the mixed-model split-plot analysis; Tukey’s HSD, p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/dab3b217432a498055224dba.jpg"},{"id":102423054,"identity":"0250b56d-c90e-458b-b6af-9076bfe7f5bd","added_by":"auto","created_at":"2026-02-11 13:59:12","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":727375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 06 B. \u003c/strong\u003eEffects of long-term tillage legacy and depth-specific fertilization on whole-profile root traits (right panels; 0–60 cm total) and their proportional distribution by depth (left panels; 0–10, 10–20, 20–40, and 40–60 cm): (a) root length density (RLD), (b) root weight density (RWD), (c) root surface area (RSA), (d) root volume (RV), and (e) mean root diameter (RD). Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, placed at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM placed at 15–25 cm), NC (conventional surface IF), and NCB (no-till with conventional surface IF, but disturbed by the applicator; no DSF banding and no manure). Different lowercase letters (right side) indicate significant differences among fertilizer treatments within the same tillage system at a given soil depth (simple-effects comparisons following the mixed-model split-plot analysis; Tukey’s HSD, p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/33e94cbeba76877f1a7602c4.jpg"},{"id":102423051,"identity":"03b4ee06-1a36-4385-bfe8-7e9dfb7779cc","added_by":"auto","created_at":"2026-02-11 13:59:12","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":76432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 7. \u003c/strong\u003eSoil moisture content (SMC) across growth stages (tillering, flowering, and maturity) measured at 10, 20, 30, 40, and 60 cm soil depths under long-term tillage legacy and depth-specific fertilization treatments. Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, placed at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM placed at 15–25 cm), NC (conventional surface IF), and NCB (no-till with conventional surface IF, but disturbed by the applicator; no DSF banding and no manure). Different lowercase letters indicate significant differences among fertilizer treatments within the same tillage system at a given soil depth (simple-effects comparisons following the mixed-model split-plot analysis; Tukey’s HSD, p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/5e108f04ba8223af23dda403.jpg"},{"id":102423055,"identity":"ec65d2b4-719f-44df-b2a1-05a410291d03","added_by":"auto","created_at":"2026-02-11 13:59:12","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":556836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 8. \u003c/strong\u003eSoil penetration resistance (SPR, values represent the mean of measurements taken at tillering, flowering, and maturity) and bulk density (BD) across soil depth under long-term tillage legacy and depth-specific fertilization treatments. Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, placed at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM placed at 15–25 cm), NC (conventional surface IF), and NCB (no-till with conventional surface IF, but disturbed by the applicator; no DSF banding and no manure). Different lowercase letters indicate significant differences among fertilizer treatments within the same tillage system at a given soil depth (simple-effects comparisons following the mixed-model split-plot analysis; Tukey’s HSD, p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/affdb2a79e97ee4e3a4cd4e1.jpg"},{"id":102423058,"identity":"3eab7ec7-ef1f-42c7-92c2-9f9c3c6b883f","added_by":"auto","created_at":"2026-02-11 13:59:12","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":62228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 09. \u003c/strong\u003eMicrobial biomass carbon (MBC) and dissolved organic carbon (DOC) across soil depths (0–10, 10–20, and 20–40 cm) under long-term tillage legacy and depth-specific fertilization treatments. Treatments were MC-10 (inorganic fertilizer, IF, placed at 0–10 cm), MC-25 (50% IF + 50% pig manure, PM, placed at 15–25 cm), MC (conventional surface IF), NC-10 (IF placed at 0–10 cm), NC-25 (50% IF + 50% PM placed at 15–25 cm), NC (conventional surface IF), and NCB (no-till with conventional surface IF, but disturbed by the applicator; no DSF banding and no manure). Different lowercase letters indicate significant differences among fertilizer treatments within the same tillage system at a given soil depth (simple-effects comparisons following the mixed-model split-plot analysis; Tukey’s HSD, p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/8d14ba4348d659f28320b9cf.jpg"},{"id":102745478,"identity":"538efeae-c92f-4e65-9a3b-bbb7eec0ead2","added_by":"auto","created_at":"2026-02-16 08:51:02","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":762313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 10 (A and B)\u003c/strong\u003e. Pearson correlation matrices for (A) the moldboard ploughing (MC) group (MC-10, MC-25, MC) and (B) the no-tillage (NC) group (NC-10, NC-25, NCB, NC). Summarized by soil layers (0–10, 10–20, and 20–40 cm). Variables include root length density (RLD), root weight density (RWD), available N, P, and K, and grain yield, Asterisks (*) indicate significant correlations (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/b1b68486fc1391b9a3a10729.jpg"},{"id":102745669,"identity":"c554f676-f067-4383-acae-afea828088ac","added_by":"auto","created_at":"2026-02-16 08:53:13","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":229871,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 11 (A–F).\u003c/strong\u003e Linear relationships among root physiological activity (mean values), soil physical constraints, and root length density (RLD). A–D show relationships between RLD and root enzyme activities: superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and dehydrogenase activity (DHA). E–F show relationships between RLD and soil physical properties: soil bulk density (SBD, mean values of soil depht from 0–50 cm) and soil penetration resistance (SPR, mean values of soil depth from 0–45 cm). Symbols represent individual observations, solid red lines indicate fitted linear regressions, and shaded areas represent 95% confidence intervals. Asterisks (*, **, and ***) indicate significant correlations (p \u0026lt; 0.05, 0.01, and 0.001, respectively).\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/13eff45e9e51f5f4b0ef7fd2.jpg"},{"id":102423057,"identity":"f6bfc135-fe85-4e5e-a6d1-456c92f69081","added_by":"auto","created_at":"2026-02-11 13:59:12","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":46773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 12 (A and B). \u003c/strong\u003ePrincipal component analysis (PCA) biplots showing multivariate relationships among soil properties, nutrient availability, root traits, root enzyme activities, and yield components for (A) the moldboard ploughing (MC) group and (B) the no-tillage (NC) group. Variables include root length density (RLD), root weight density (RWD), superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), dehydrogenase activity (DHA), available N, P, and K, soil bulk density (BD), soil penetration resistance (SPR), soil moisture content (SMC), grain yield (GY), 1000-grain weight (1000GW), biomass (BM), spike number (SN), straw yield (SY), grains per spike (NGS), and harvest index (HI). Treatment codes are defined in Table 1.\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/f86f0734573dd870e01be7fa.jpg"},{"id":102751597,"identity":"98cf4d64-6a21-4f67-930e-9e902adbf2af","added_by":"auto","created_at":"2026-02-16 09:26:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4502939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8727231/v1/30ad4187-be9f-4316-94bc-5be7b7003219.pdf"}],"financialInterests":"","formattedTitle":"Soil compaction mediates root–nutrient coupling associated with wheat yield response to depth-specific fertilization under contrasting long-term tillage systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNo-tillage technology has gained extensive use to enhance the health of soil, curb soil erosion, and enhance the sustainability of the cropping systems of cereals over the long term. Nevertheless, the productivity of wheat in the case of no-till is not always constant and usually surpasses that of tilled systems (Shakoor et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meta-analyses also reveal that this decrease in yield can be largely attributed to the historical impact of long-term retention of residues and low intensity of soil disturbance, which may result in vertical stratification of nutrients and soil strength in deeper soil layers (Blanco-Canqui \u0026amp; Lal, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pittelkow et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt has been proposed that depth-specific fertilization (DSF) can be used to relocate nutrients to the active root zones, and the research on its efficacy in systems with a long history of dissimilar tillage practices has not been properly researched. Long-term conservation methods are also getting encouraged in intensive wheat rotation to minimize the environmental impact without compromising the yields (Mandal et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The physical and chemical legacies of various management systems, including moldboard plowing with residue incorporation or no-tillage with surface residue retention, determine different physical and chemical soils, which affect the nutrient distribution, root development, and yield patterns over the course of time (Sommer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough no-till can improve the soil organic matter and soil aggregation of the upper soil, it might also lead to a rise in bulk density and the concentration of nutrients such as nitrogen and phosphorus in upper soils (Franzluebbers, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Reichert et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These depth-related soil processes are highly crucial since the crop performance can only be dependent on the availability of nutrients as well as their location in the accessible soil layers. The depth of placement of the fertilizer provides an effective means of determining the nutrient supply that accommodates the root activity (Nkebiwe et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As opposed to surface broadcasting, which may enhance stratification of nutrients in no-till systems, placed or split-depth placement has the potential to redistribute the nutrients to deeper layers, where roots can access them (Zhang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, physical properties of soil like high penetration resistance and low pore connectivity may inhibit at least root growth with depth (Lipiec et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe placement of nutrients deeper does not, therefore, necessarily enhance uptake and yield in cases where the soil continues to be compacted. Physical limitations of soils are highly intertwined with biological activities that affect nutrient cycling and the activities of roots (Xing et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moldboard plowing normally decreases the bulk density and stimulates early root development by increasing aeration and decreasing mechanical resistance (Hamza \u0026amp; Anderson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). On the other hand, the early development of roots and the alteration of root system design can be constrained by long-term no-till when the soil is still strong (Lynch, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The responses to these plastics encompass the alteration of the root diameter, biomass allocation, and spatial distribution, which are the manifestations of trade-offs between the acquisition of resources and mechanical adaptation (Mu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These types of structural changes have an effect on nutrient acquisition, rhizosphere dynamics, and microbial associations. The residue distribution, disturbance, and root-microbe interactions also affect the microbial biomass carbon (MBC) and dissolved organic carbon (DOC) because of tillage and fertilizer placement (Helgason et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Man et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the observed enhanced biological activity at and close to the surface, no-till systems are not always able to give more yields because root development can be hampered at depths below the shallow soils (Mbuthia et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Organic amendments can enhance the level of microbial activities as well as the nutrient cycling, but such amendments are dependent on the depth of placement and access of roots to active zones (Shu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In general, the interaction between nutrient stratification, physical limitations of soils, and root reactions has a critical knowledge gap, as it remains uncertain in general how these factors impact crop production. These factors have been studied individually, paying attention to soil stratification, root characteristics, or productivity (Bescansa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Qin et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the no-till system increases the levels of soil organic carbon and the biomass of microorganisms, the yield response is irregular, probably because the root access to nutrients at critical levels remains low due to continued subsurface compaction (Shao et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). There is little information on whether in-depth placement of fertilizers can address these interrelated restrictions under various long-term tillage regimes, in particular, the situation with integrated measurements of soil profiles, rooted characteristics, microbial pools, and crop production in a single experiment (Souza et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; H. Huang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur novel integration of these components quantifies how DSF reshapes root\u0026ndash;nutrient coupling in a mature no-till system and reveals trade-offs that may not be apparent in younger experiments. Using a 23-year field experiment, we evaluated how fertilizer placement depth (0\u0026ndash;10 vs. 15\u0026ndash;25 cm) interacts with long-term moldboard plowing and no-tillage to regulate: (i) soil nutrient availability (N, P, K), moisture, and bulk density; (ii) root system characteristics; and (iii) wheat grain yield and yield components, thereby mechanistically linking soil and root responses to crop performance. We hypothesized that pronounced nutrient stratification in long-term no-till restricts deep root growth and wheat yield under shallow fertilization and that deep placement at 15\u0026ndash;25 cm would partially alleviate these constraints by enhancing subsurface nutrient availability and promoting deeper rooting. However, yield benefits would be strongest where soil physical constraints do not prevent roots from exploiting the placement zone.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLong-term experimental site, design, and crop management\u003c/h2\u003e \u003cp\u003eThe long-term tillage experiment was established in October 2001 at the Luancheng Agroecosystem Experimental Station, Chinese Academy of Sciences, on the North China Plain under a winter wheat\u0026ndash;summer maize double-cropping system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We compared two tillage systems: (MC) moldboard ploughing with crushed maize residues incorporated to 25 cm and (NC) no-tillage with crushed maize residues retained on the soil surface. After the wheat harvest, wheat straw was chopped into 5\u0026ndash;10 cm pieces and evenly mulched on all plots. The experiment followed a split-plot design with three blocks (replicates). The tillage system was the main-plot factor (8 m \u0026times; 70 m; 560 m\u0026sup2;) and was established in fixed positions in 2001 and maintained thereafter. Fertilizer placement was the subplot factor (5 m \u0026times; 5 m; 25 m\u0026sup2;). Within each main plot in each block, fertilizer-placement treatments were randomized. Under MC, subplots included MC-10 and MC-25; under NC, subplots included NC-10, NC-25, and NCB (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Winter wheat (2024\u0026ndash;2025 season) was sown on 21 October 2024 and harvested on 8 June 2025. All plots were irrigated to maintain soil water content near ~\u0026thinsp;65% of field capacity, with two additional irrigations (40\u0026ndash;50 mm each, depending on in-season rainfall) applied at critical stages. Urea was top-dressed before jointing at 135 kg N ha⁻\u0026sup1;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTreatments and fertilizer placement\u003c/h3\u003e\n\u003cp\u003eDepth-specific fertilization (DSF) treatments were implemented within each tillage system. Under MC, MC-10 received inorganic fertilizer (IF) placed at 0\u0026ndash;10 cm at 110 kg ha⁻\u0026sup1;. MC-25 received 50% IF\u0026thinsp;+\u0026thinsp;50% pig manure (PM) placed at 15\u0026ndash;25 cm (PM rate: 1.6 kg m⁻\u0026sup2;) (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Under NC, NC-10 and NC-25 matched the placement depths and rates of MC-10 and MC-25, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). NCB served as a no-till disturbance control: the applicator was passed as in DSF treatments to disturb the soil (approximately 0\u0026ndash;10 cm and the 15\u0026ndash;25 cm zone), but fertilizer was applied conventionally at the surface (IF only; no PM and no subsurface band placement). Fertilizers were applied immediately after tillage and before sowing using a hand-driven fertilizer applicator operated between wheat rows. Winter wheat was sown at 12 cm row spacing, and fertilizer was applied in every other inter-row (application-row spacing 24 cm). Fertilizer was delivered through discrete subsurface holes spaced at approximately 25 cm along each application row; the same application pattern was used for both placement depths (0\u0026ndash;10 cm and 15\u0026ndash;25 cm), with only the placement depth adjusted. Equipment schematics are provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental treatments and fertilizer placement depths/rates under long-term moldboard ploughing (MC) and no-tillage (NC). MC-10 (inorganic fertilizer, IF, placed at 0\u0026ndash;10 cm), MC-25 (50% IF\u0026thinsp;+\u0026thinsp;50% pig manure, PM, subsurface-banded at 15\u0026ndash;25 cm), MC (conventional surface IF), NC-10 (IF placed at 0\u0026ndash;10 cm), NC-25 (50% IF\u0026thinsp;+\u0026thinsp;50% PM subsurface-banded at 15\u0026ndash;25 cm), NC (conventional surface IF), and NCB (NC with soil disturbed by the applicator without DSF or manure)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTillage system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilizer type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlacement method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlacement depth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApplication rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMC-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInorganic fertilizer (IF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubsurface band (DSF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110 kg ha⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMC-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50% IF\u0026thinsp;+\u0026thinsp;50% pig manure (PM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubsurface band (DSF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePM: 1.6 kg m⁻\u0026sup2; + IF (50% of 110 kg ha⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface broadcast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (surface)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110 kg ha⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNC-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubsurface band (DSF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110 kg ha⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNC-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50% IF\u0026thinsp;+\u0026thinsp;50% PM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubsurface band (DSF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePM: 1.6 kg m⁻\u0026sup2; + IF (50% of 110 kg ha⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(IF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface broadcast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (surface)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110 kg ha⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(IF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface broadcast (soil disturbed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (surface)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110 kg ha⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSoil sampling for nutrients and stratification ratio (SR)\u003c/h3\u003e\n\u003cp\u003eTo quantify nutrient distributions and stratification, we collected soil using a tubular auger (5 cm inner diameter) at 0\u0026ndash;10, 10\u0026ndash;20, and 20\u0026ndash;40 cm. On 20 May 2025 (peak flowering stage), we collected three replicate cores per subplot, sampling both the crop row and adjacent inter-row to capture within-plot spatial heterogeneity in the main nutrient uptake zone. Samples were air-dried, ground, and sieved (\u0026lt;\u0026thinsp;2 mm). We determined available N by alkaline hydrolysis/diffusion (with Kjeldahl digestion used where required by the adopted protocol), available P by the Olsen method, and exchangeable K by 1 M NH₄OAc extraction, following standard procedures (Blume, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Sparks et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Because fertilizers were placed at 0\u0026ndash;10 cm (shallow) or as a subsurface band at 15\u0026ndash;25 cm, and soils were sampled by fixed layers (0\u0026ndash;10, 10\u0026ndash;20, and 20\u0026ndash;40 cm), the 15\u0026ndash;25 cm band is captured primarily within the 10\u0026ndash;20 cm layer and the upper portion of the 20\u0026ndash;40 cm layer. Accordingly, we interpret DSF effects mainly as changes in nutrient availability in 10\u0026ndash;20 cm (band layer) and 20\u0026ndash;40 cm (below-band layer). We quantified nutrient stratification using the stratification ratio (SR) (Franzluebbers, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), calculated for N, P, and K across the 0\u0026ndash;10/10\u0026ndash;20 cm and 10\u0026ndash;20/20\u0026ndash;40 cm intervals. SR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates surface enrichment, SR\u0026thinsp;\u0026asymp;\u0026thinsp;1 indicates a relatively uniform profile, and SR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates depletion in the upper layer (Nthebere et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Slepetiene et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Q. Zhang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:SR=\\frac{{C}_{i}}{{C}_{j}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the nutrient concentration in the upper layer and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{j}\\)\u003c/span\u003e\u003c/span\u003eis the concentration in the immediately deeper layer.\u003c/p\u003e\n\u003ch3\u003eRoot sampling, antioxidant enzymes, and root distribution\u003c/h3\u003e\n\u003cp\u003eWe measured root antioxidant enzyme activities at tillering and jointing. Fine roots were excavated from the rhizosphere, gently washed, blotted dry, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. For each treatment, three whole plants per plot were pooled as one biological replicate, with three replicates. We prepared extracts by homogenizing 0.5 g fresh root in ice-cold 50 mM phosphate buffer (pH 7.8) containing 1% PVP, followed by centrifugation (10,000 \u0026times; g, 20 min, 4\u0026deg;C); we used the supernatant for enzyme assays (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We assayed SOD by the nitroblue tetrazolium (NBT) photoreduction method (560 nm; one unit defined as 50% inhibition) (Beauchamp \u0026amp; Fridovich, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), POD by the guaiacol\u0026ndash;H₂O₂ method (470 nm), CAT by the decline in H₂O₂ absorbance (240 nm; one unit decomposing 1 \u0026micro;mol H₂O₂ min⁻\u0026sup1; g⁻\u0026sup1; FW) (Aebi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Giannopolitis \u0026amp; Ries, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), and DHA by TTC reduction with formazan quantified at 485 nm (Casida, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). We expressed activities on a fresh-weight basis.\u003c/p\u003e \u003cp\u003eWe assessed root distribution at peak flowering because this stage captures high crop nutrient demand, and the functional overlap between root distribution and nutrient availability is most relevant for yield formation. Roots were sampled within crop rows (12 cm spacing) at 24 cm intervals along the row (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We collected adjacent soil cores and sectioned them into 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, 20\u0026ndash;40 cm, and 40\u0026ndash;60 cm using a 5 \u0026times; 5 cm sampling frame. Roots were washed on a 0.5 mm sieve, and debris was removed manually. We scanned clean roots and analyzed them using WinRHIZO (Regent Instruments, Canada) to quantify RLD, RSA, RV, RWD, and mean root diameter, following established protocols for cereal root profiling (Burridge et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Arnhold et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We determined root dry biomass after oven drying at 65\u0026deg;C to constant weight (48 h).\u003c/p\u003e\n\u003ch3\u003eSoil moisture, penetration resistance, and bulk density\u003c/h3\u003e\n\u003cp\u003eWe measured soil moisture content (SMC) at tillering, flowering, and maturity from 10\u0026ndash;60 cm using a 5 cm inner-diameter auger. Fresh samples were weighed, oven-dried at 105\u0026deg;C to constant mass, and SMC was determined gravimetrically. We measured soil penetration resistance in situ at the same stages using a hand-held cone penetrometer across 0\u0026ndash;40 cm to characterize soil mechanical impedance among treatments. After harvest, we measured soil bulk density (BD) using undisturbed cores (5 cm diameter \u0026times; 5 cm height) collected at 0\u0026ndash;10, 10\u0026ndash;20, 20\u0026ndash;40, and 40\u0026ndash;60 cm (three cores per depth and plot) and oven-dried at 105\u0026deg;C to constant mass.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial biomass carbon (MBC) and dissolved organic carbon (DOC)\u003c/h2\u003e \u003cp\u003eAfter the wheat harvest, we collected fresh soil samples at 0\u0026ndash;10, 10\u0026ndash;20, and 20\u0026ndash;40 cm, stored samples in sealed bags, and kept them at \u0026minus;\u0026thinsp;20\u0026deg;C before analysis. We determined MBC by chloroform-fumigation extraction using an efficiency factor of 0.45 (Vance et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). We extracted DOC with distilled water (1:5 w/v) and quantified it using a TOC analyzer; instrument details are provided in Supplementary Methods.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGrain yield and yield components\u003c/h3\u003e\n\u003cp\u003eAt maturity, we hand-harvested winter wheat from each plot to determine grain yield (adjusted to 13% moisture), straw yield, and 1000-grain weight. We measured spike density using a 1.5 m\u0026sup2; quadrat per plot and sampled 20 representative plants from this area to record spikes per plant and grains per spike.\u003c/p\u003e\n\u003ch3\u003eData analysis and reproducibility\u003c/h3\u003e\n\u003cp\u003eBefore any statistical analysis was performed, the Shapiro and Levene tests of residual normality and homogeneity of variances, respectively, were conducted. When models needed variables to be transformed, log-/square-root-transformed variables were used. The mixed-effects split-plot model that was used in the analysis of treatment effects was run in OriginPro 2025b (Learning Edition). Tillage system (MC; NC) was considered as the main-plot fixed factor, the fertilizer placement treatments were considered as subplot fixed factors (nested within tillage MC, MC-10, MC-25; and nested within NC (NC-10, NC-25, and NCB), and block (replicate) was treated as a random effect. They compared the main plot (tillage) effects to the correct main-plot error term and compared the subplot (fertilizer treatment) effects to the within main-plot error term. Where the variables are measured on soil depths and/or stages of growth, depth (and stage, where appropriate) was accepted as another fixed factor, and interaction terms (e.g., tillage \u0026times; treatment \u0026times; depth) were tested. In cases where significant interactions were found (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), simple-effects analyses were run, and mean separation within each tillage system and at each depth or growth stage was carried out by means of the Tukey HSD test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The contour plots created in OriginPro were used to visualize the spatial distributions of the available N, P, and K. The relationship between soil physicochemical characteristics, microbial indicators, root characteristics, and yield components was investigated using Pearson correlation analysis and principal component analysis (PCA; OriginPro extended statistics module).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWeather conditions\u003c/h2\u003e \u003cp\u003eMonthly precipitation and air temperatures during the winter wheat growing season are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Precipitation peaked in May (69 mm) and was lowest in December (0.3 mm), while minimum and maximum temperatures were highest in June and lowest in January (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These conditions provide a seasonal context for interpreting treatment effects on soil moisture and mechanical impedance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSpatial nutrient distribution and stratification ratio\u003c/h2\u003e \u003cp\u003eSoil available nutrient profiles differed markedly between tillage systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), revealing contrasting nutrient distribution outcomes under long-term moldboard ploughing (MC) versus no-till (NC). In the surface layer (0\u0026ndash;10 cm), NC-25 showed higher available N than MC treatments (e.g., +\u0026thinsp;42% vs. MC-25; +28% vs. MC-10). MC treatments exhibited a comparatively flatter vertical N decline from the surface to deeper layers, whereas NC and NCB showed steeper decreases with depth, consistent with stronger surface accumulation under long-term no-till.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAvailable P and K showed the same directional pattern. NC-25 exceeded MC treatments at the surface (e.g., +\u0026thinsp;45% P and +\u0026thinsp;38% K vs. MC-25). Still, concentrations declined more sharply below the surface in NC and NCB than in MC treatments, indicating stronger vertical gradients under no-till. Overall, these profiles show that long-term no-till maintained pronounced surface enrichment, while moldboard tillage produced a more vertically even nutrient distribution. Across the 0\u0026ndash;40 cm profile, averaged available N, P, and K differed significantly among treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with NC-25 showing the highest nutrient levels (N\u0026thinsp;=\u0026thinsp;170.94 mg kg⁻\u0026sup1;; P\u0026thinsp;=\u0026thinsp;19.44 mg kg⁻\u0026sup1;; K\u0026thinsp;=\u0026thinsp;309.13 mg kg⁻\u0026sup1;), whereas NC had the lowest values (N\u0026thinsp;=\u0026thinsp;104.70 mg kg⁻\u0026sup1;; P\u0026thinsp;=\u0026thinsp;5.89 mg kg⁻\u0026sup1;; K\u0026thinsp;=\u0026thinsp;128.23 mg kg⁻\u0026sup1;) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of long-term tillage legacy and depth-specific fertilization on available N, P, and K, microbial biomass carbon (MBC), and dissolved organic carbon (DOC), averaged across the 0\u0026ndash;40 cm (mg kg⁻\u0026sup1;) soil profile, under different treatments. Capital letters indicate significant differences among treatments at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Lowercase letters indicate significant differences among fertilizer placement treatments within the same tillage system at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Treatment codes are defined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDOC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151.98 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.11 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e174.03 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e413.90 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.81 B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.96 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.59 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255.41 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e475.70 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.69 AB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.82 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.72 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.44 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e435.23 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105.03 AB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.61 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.06 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176.64 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e506.84 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e113.39 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.94 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.44 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e309.13 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e484.50 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e126.32 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.01 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.02 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.66 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e507.48 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124.63 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.70 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.89 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128.23 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e480.10 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124.48 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStratification ratios (SR) further quantified these contrasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Surface-to-subsurface SRs were generally highest under no-till with shallow placement (NC-10) for available P and K, while deeper-layer SRs indicated a pronounced decline beneath the subsurface zone in NC treatments (notably NC-25 and NC for N and P). Together, the profile maps and SR results indicate that DSF can reposition nutrient enrichment downward; however, the persistence of steep gradients under long-term no-till suggests that realizing the benefit depends on whether roots can effectively access the targeted layers. DSF reduced surface stratification (0\u0026ndash;10/10\u0026ndash;20) relative to shallow placement but increased stratification below the band (10\u0026ndash;20/20\u0026ndash;40), indicating vertical redistribution of nutrient hotspots rather than profile homogenization.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRoot antioxidant enzymes and root distribution by depth\u003c/h2\u003e \u003cp\u003eRoot antioxidant enzyme activities (SOD, POD, CAT, DHA) responded significantly to tillage and fertilizer placement at both tillering and jointing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Across enzymes and stages, activities were highest under MC-10, followed by MC-25 and MC; NC-10 and NC-25 were intermediate, while NC and especially NCB showed the lowest activities. Enzyme activities were generally higher at tillering than at jointing, but treatment rankings were consistent between stages. Notably, placement effects were clearer under MC (MC-10\u0026thinsp;\u0026gt;\u0026thinsp;MC-25\u0026thinsp;\u0026gt;\u0026thinsp;MC) than under NC, suggesting that the physiological response to DSF was more strongly expressed when the soil environment supported root function.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRoot traits showed a clear tillage \u0026times; placement interaction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B). Under MC, shallow banding (MC-10) produced the greatest RLD, RWD, RSA, and RV across depths, with MC-25 and MC intermediate. Under NC, deep placement (NC-25) improved rooting relative to NC-10 and NC and produced RLD and RSA at 20\u0026ndash;60 cm that were comparable to those of MC treatments. However, total root biomass across 0\u0026ndash;60 cm remained highest under MC-10, highlighting an important trade-off: in long-term no-till, DSF shifted rooting deeper, but whole-profile root development remained constrained relative to moldboard systems. Profile-averaged MBC did not differ among treatments (p\u0026thinsp;=\u0026thinsp;0.316), whereas DOC showed a significant treatment effect (p\u0026thinsp;=\u0026thinsp;0.00015), with higher DOC under no-till treatments (e.g., NC-25/NCB/NC 124\u0026ndash;126 mg kg⁻\u0026sup1;) than under MC-10 (81.81 mg kg⁻\u0026sup1;) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSoil moisture, soil strength, bulk density, and labile C pools\u003c/h2\u003e \u003cp\u003eSoil moisture content (SMC) differed significantly among treatments and depths (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Across stages, MC-10 and MC-25 maintained higher SMC in the 0\u0026ndash;20 cm layer, while NC and NCB were consistently lowest. Differences narrowed with depth, but MC treatments tended to preserve higher SMC down to 40 cm, indicating a more favorable water environment in much of the active rooting zone during this season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSoil penetration resistance (SPR) increased with depth in all treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). NC and NCB had the greatest SPR across the 0\u0026ndash;45 cm profile, indicating persistent compaction, whereas MC-10 and MC-25 had the lowest SPR, particularly between 10 and 30 cm; MC and NC-25 were intermediate. Bulk density (BD) followed the same pattern: NC and NCB were highest throughout 0\u0026ndash;30 cm, while MC-10 and MC-25 were significantly lower; MC-25 showed the lowest BD at 10\u0026ndash;20 and 20\u0026ndash;40 cm. Collectively, these results indicate stronger physical constraints under long-term no-till (higher SPR/BD), which likely reduce how efficiently roots can exploit subsurface nutrient placement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMicrobial biomass carbon (MBC) and dissolved organic carbon (DOC) also varied by treatment and depth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the 0\u0026ndash;10 cm layer, both were highest under no-till treatments (NC-10, NC-25, NCB, NC) and lower under MC treatments, while differences diminished with depth. Thus, no-till enhanced labile C pools near the surface, but this biological advantage coincided with higher mechanical impedance, consistent with partial decoupling between surface biological improvement and deeper root access in compacted profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGrain yield and yield components\u003c/h2\u003e \u003cp\u003eGrain yield and yield components differed significantly among treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). MC-10 produced the highest grain yield (7909 kg ha⁻\u0026sup1;) and the strongest yield components (1000-grain weight, biomass, spike number, straw yield). MC-25 and MC were intermediate. All no-till treatments (NC-10, NC-25, NCB, NC) yielded significantly less than MC-10, mainly due to reduced biomass and fewer grains per spike, while harvest index varied little. Importantly, the improved nutrient profiles and deeper rooting tendency under NC-25 did not close the yield gap with MC systems, consistent with the stronger physical constraints (SPR/BD) observed under long-term no-till.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrain yield and yield components of winter wheat under long-term tillage legacy and depth-specific fertilization treatments (2024\u0026ndash;2025 season). GY\u0026thinsp;=\u0026thinsp;grain yield, 1000‒GW\u0026thinsp;=\u0026thinsp;1000-grain weight, BM\u0026thinsp;=\u0026thinsp;biomass, SN\u0026thinsp;=\u0026thinsp;spike number, SY\u0026thinsp;=\u0026thinsp;straw yield, NGS\u0026thinsp;=\u0026thinsp;grains per spike, and HI\u0026thinsp;=\u0026thinsp;harvest index. Different lowercase letters indicate significant differences among treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Treatment codes are defined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGY (kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000‒GW\u003c/p\u003e \u003cp\u003e(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBM\u003c/p\u003e \u003cp\u003e(kg/ha) x 100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSN/ m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003cp\u003e(kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7909 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.19 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171.2 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.47 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e571 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9207 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.2 abc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6963 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.06 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.8 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.28 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e583 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7919 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.9 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6898 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.91 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.2 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.33 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e475 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7744 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.5 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5704 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.92 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.3 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.25 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e486 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6722 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.2 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6450 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.14 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.1 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.93 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e458 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7364 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.7 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6442 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.99 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.6 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.10 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e568 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7417 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.5 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6451 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.79 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142.6 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.97 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e530a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7090 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.6 bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMultiscale coupling among root traits, soil conditions, nutrient availability, and grain yield\u003c/h2\u003e \u003cp\u003eCorrelation matrices (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA\u0026ndash;B) showed stronger and more yield-relevant coupling among nutrients, root traits, and grain yield in MC than in NC. In MC, grain yield correlated positively with surface-layer nutrient availability (N, P, K) and with multiple surface-layer root traits (RLD, RWD, RSA, RV; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas relationships involving deeper layers were weaker or non-significant. In NC, some surface-root nutrient relationships remained strong, but yield associations were less consistent, and below-surface linkages were generally weaker and more variable. Overall, the correlation structure indicates tighter root\u0026ndash;nutrient\u0026ndash;yield coupling under MC and a more decoupled structure under long-term NC.RLD was closely associated with both root physiological activity and soil physical conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA\u0026ndash;F). Linear regression analysis showed significant positive relationships between RLD and root antioxidant enzyme activities, including SOD, POD, catalase CAT, and DHA. Increases in enzyme activities were consistently accompanied by higher RLD, indicating enhanced root proliferation under greater physiological activity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all regressions). In contrast, RLD declined significantly with increasing SBD and SPR. Linear regressions revealed strong negative relationships between RLD and both SBD and SPR, demonstrating that greater soil compaction and mechanical impedance restricted root distribution across treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Together, these relationships indicate that RLD responded positively to improved root metabolic activity while being simultaneously constrained by adverse soil physical conditions. PCA ordinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA\u0026ndash;B) reinforced these contrasts. In the MC group, PC1 and PC2 explained 72.61% and 27.39% of the variance (cumulative 100%), with grain yield and yield components clustering with most root traits and nutrient variables, consistent with an integrated \u0026ldquo;root\u0026ndash;nutrient\u0026ndash;yield\u0026rdquo; axis. SPR and DOC projected away from this cluster, aligning with their role as constraints rather than co-benefits in the yield response. In the NC group, the lower PC1 contribution (58.13%) and weaker clustering indicate a less coherent multivariate coupling among roots, nutrients, and yield, consistent with the correlation results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFertilizer placement depth regulates nutrient accessibility and vertical rooting under contrasting tillage systems\u003c/h2\u003e \u003cp\u003eOur novel integration of depth-resolved nutrients, soil physical constraints, root traits, and yield within a 23-year tillage legacy shows that DSF can improve subsurface nutrient accessibility and reshape rooting, but that yield benefits remain strongly tillage-dependent. Across treatments, nutrient redistribution followed the long-term disturbance template: nutrient availability was more surface-enriched under no-till, whereas moldboard ploughing reduced vertical gradients and broadened the nutrient-accessible zone through mixing (He et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lv et al., 2023). This matters because wheat yield responds most strongly where nutrients and active roots overlap, and upper layers often contribute disproportionately due to denser rooting and higher uptake activity (Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ruis et al., 2024).\u003c/p\u003e \u003cp\u003eIn this context, DSF primarily repositioned nutrient hotspots within the profile. Subsurface banding at 15\u0026ndash;25 cm can improve nutrient-use efficiency by placing relatively immobile nutrients (especially P) into more consistently moist zones and stimulating localized root proliferation around nutrient bands\u0026mdash;responses widely reported across crops and placement strategies (Alam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen, 2023; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consistent with this mechanism, deeper placement shifted nutrient enrichment downward and increased root presence at depth under no-till relative to shallow placement, improving access to subsurface nutrients that would otherwise remain poorly connected to the effective rooting zone.\u003c/p\u003e \u003cp\u003eHowever, our long-term dataset clarifies an important nuance that short-duration trials often cannot resolve: repositioning nutrients is not the same as capturing them. Under moldboard ploughing, mixing and lower impedance increase root\u0026ndash;nutrient overlap even with shallow placement, reducing the marginal benefit of deeper placement. Under no-till, placement depth becomes more consequential, but only if roots can reliably proliferate in the target layer. This dual control\u0026mdash;chemical opportunity (nutrient location) and physical accessibility (root reach)\u0026mdash;helps explain why the coupling among nutrients, roots, and yield was more coherent under MC than under NC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSoil physical constraints modulate root system development and nutrient capture in long-term no-till\u003c/h2\u003e \u003cp\u003eAlthough measurements were made in one season, the 23-year management history means the observed soil and root responses reflect mature system states rather than transient effects. This is among the few long-term datasets showing that DSF benefits in no-till can be capped by a physical ceiling on rooting. Long-term no-till can enhance near-surface aggregation and residue-derived enrichment, but it may also sustain higher bulk density and penetration resistance in the subsurface, restricting root penetration and limiting access to deeper nutrient pools unless roots can exploit the placement zone (Tian et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By contrast, moldboard ploughing lowers mechanical impedance and mixes residues and fertilizers, increasing overlap between roots and nutrients across the upper profile (He et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese patterns align with mechanistic evidence that soil strength strongly controls root architecture and capture, and that constraints can intensify as soils dry (Bengough et al., 2011; Colombi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In wheat systems, tillage tends to reduce impedance and improve overlap between roots and accessible nutrients, whereas no-till often concentrates roots and nutrient cycling near the surface and increases sensitivity to subsurface constraints (Mu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ruis et al., 2024). Accordingly, where penetration resistance and bulk density were higher, roots were more surface-concentrated, and deeper-layer nutrient-yield linkages were weaker and less consistent. This helps explain why measurable subsurface nutrients in long-term no-till do not automatically translate into yield gains unless roots can access and exploit those layers efficiently (Sun et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLinking back to our hypothesis, deeper DSF did enhance subsurface nutrient accessibility and supported deeper root presence, but it did not fully offset long-term no-till constraints. In mature no-till, DSF improved where nutrients were available, but not always how effectively the crop could capture them. This distinction helps reconcile inconsistent yield responses reported in the literature: short-term studies often show stronger DSF effects because legacy constraints are less pronounced, whereas mature no-till systems can develop persistent structural barriers. Practically, DSF is most likely to succeed in long-term no-till when paired with strategies that reduce subsurface impedance (e.g., strategic subsoiling), which can restore rooting depth and alleviate yield stagnation (Izumi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated soil\u0026ndash;root\u0026ndash;nutrient interactions determine wheat grain yield responses to tillage legacy\u003c/h2\u003e \u003cp\u003eA key contribution is that we connect soil nutrients, soil physics, root morphology/physiology, and yield within one mature long-term experiment, showing that wheat yield responds to profile-scale coupling rather than any single factor alone. Under moldboard ploughing, correlation and PCA indicated tighter alignment of yield (and yield components) with root traits and nutrient variables, reflecting coordinated soil\u0026ndash;root\u0026ndash;nutrient functioning. Similar integration has been reported in wheat\u0026ndash;maize rotations where tillage reshapes root distribution and yield tracks, coupling between roots and available nutrients in functionally active layers (Kan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ye et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, MC converted nutrient availability into root growth and grain production more efficiently when the physical environment supported exploration.\u003c/p\u003e \u003cp\u003eConversely, the no-till group exhibited a more diffuse multivariate composition, with them having less strong and stable associations between yield, root traits, and nutrient variables. It means that it is semi-decoupled: it can be present but is limited in accessibility and uptake, and in line with syntheses that indicate no-till yield responses to be more heterogeneous and location-specific and conditioned by physical factors affecting the soil (Yan et al., 2024; Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This is corroborated by the fact that no-till alters the soil-profile distribution of roots and perceived rooting zone as compared to tilled systems (Ruis et al., 2024). Most importantly, we can use our long-term findings to explain short-term DSF research differences. Although short-term experiments frequently provide more information about responses to deeper placement or banding, we find that, at maturity, the benefits of DSF are less obvious in no-till due to the changing balance between nutrient positioning and physical accessibility, and root development across the entire profile. In contrast to short-term trials, our 23 years of evidence point to legacy accumulation as one of the factors behind lower returns to DSF in mature no-till. Generally, the optimum approach to rooting was determined, with placement in a permissive (MC) or a restrictive (NC) tillage legacy. Overall, our results demonstrate that wheat yield responses to fertilizer placement are governed by integrated soil physical conditions, nutrient distribution, and root functional traits rather than nutrient availability alone. Long-term tillage legacy determines whether fertilizer inputs are effectively translated into root activity and grain production by enabling or constraining root access to functionally active soil layers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePractical implications, limitations, and future directions\u003c/h2\u003e \u003cp\u003eApplication-wise, the results indicate two recommendations. To start with, DSF can continue to be an effective method of enhancing accessibility of nutrients in the subsurface, especially when operated in no-till systems, although it must not be applied as a nutrient-only method, but as a method of coupling. Second, in conditions where long-term no-till is associated with high subsurface soil strength, DSF is most probably associated with giving yield advantages when joined with actions that lighten mechanical constraints (e.g., strategic subsoiling or controlled traffic), which are in agreement with the results that found that diminishing mechanical impedance restored to rooting depth and yield performance (Izumi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moving deeper under moldboard ploughing can offer smaller yield advantages since mixing already leads to overlapping root-nutrient, meaning that efficacy of nutrient use, and the practicality of operations can be considered.\u003c/p\u003e \u003cp\u003eThis research is not without limitations as well. One season of measurements was taken, and therefore, it was not directly tested if there was interannual variability in the dynamics of rainfall and soil moisture, which can strengthen or weaken mechanical constraints. We also measured processes of capturing depth-wise co-occurrences and multivariate linkages as opposed to directly measuring nutrient uptake fluxes. The future research must determine whether the patterns of coupling can be applied through different seasons and should also examine integrative packages (DSF \u0026times; compaction alleviation) under multi-year experimentation to determine when deeper placement would be translated into consistent yield.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA combination of nutrient profiles, soil physical constraints, root characteristics, and yield of a 23-year field experiment indicates that long-term tillage history is a strong regulator of the efficacy of fertilizer positioning. No-tillage created strong vertical stratification of nutrients and compaction of the subsurface, while moldboard ploughing retained a more homogeneous distribution of nutrients and a root-permissive physical environment. Deep placement shifted the spatial distribution of roots in no-till systems, but failed to counteract the total root constraints of long-term no-till, which validates the hypothesis that nutrient accessibility of fertilizer depth can be spatially changed. Nevertheless, the benefits of yield were not observed in mature no-till, which suggests that a better nutrient placement is not sufficient to overcome physical constraints created by legacy. Higher bulk density and penetration resistance weakened root\u0026ndash;nutrient\u0026ndash;yield coupling, while moldboard systems showed tighter integration among these components. Therefore, depth-specific fertilization can be treated as an approach to coupling and not accepted as a nutrient-only intervention. In no-till farming systems, DSF in combination with compaction-alleviation practices is crucial in the long term to accumulate stable yield gains and robust wheat production in the face of rising climate variability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the National Key Research and Development Program of China (2023YFD1902605 and 2022YFD1500604). The first author was financially supported by the Chinese Government Scholarship (CGS).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eAmeet Kumar: Conceptualization; Methodology; Investigation; Data curation; Formal analysis; Statistical analysis; Visualization; Writing \u0026ndash; original draft. Wenxu Dong: Conceptualization; Methodology; Resources; Supervision; Project administration; Funding acquisition; Writing \u0026ndash; review \u0026amp; editing. 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J Exp Bot 72:747\u0026ndash;756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/eraa475\u003c/span\u003e\u003cspan address=\"10.1093/jxb/eraa475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"soil physical constraints, root distribution, root antioxidant enzymatic activities, soil bulk density, fertilizer placement","lastPublishedDoi":"10.21203/rs.3.rs-8727231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8727231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eDepth-specific fertilization (DSF) has been proposed to reduce nutrient stratification in conservation tillage by relocating fertilizer; however, long-term no-tillage often develops subsurface compaction that restricts rooting and nutrient capture. We investigated whether DSF responses depend on tillage legacy and examined soil physical, biological, and root mechanisms regulating winter wheat yield.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA long-term split-plot experiment compared moldboard ploughing (MC) and no-tillage (NC) with fertilizer placements: conventional surface inorganic fertilizer, shallow placement at 0\u0026ndash;10 cm (MC-10, NC-10), and deep placement at 15\u0026ndash;25 cm (MC-25, NC-25; 50% IF\u0026thinsp;+\u0026thinsp;50% pig manure). Soil properties (0\u0026ndash;40 cm), root distribution (0\u0026ndash;60 cm), antioxidant enzyme activities, and wheat yield were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNC exhibited higher bulk density and penetration resistance than MC, leading to strong nutrient stratification and restricted root penetration into deeper soil layers. Nutrient stratification remained higher under NC-10 and NC-25 than under MC-10 and MC-25 despite depth-specific fertilization. Although NC increased surface (0\u0026ndash;10 cm) biological activity, indicated by higher microbial biomass C and dissolved organic C, these gains did not improve root\u0026ndash;nutrient coupling or grain yield. In contrast, MC created a more root-permissive soil environment, promoted greater root proliferation across the soil profile, and enhanced root antioxidant enzyme activities. As a result, MC-10 achieved the highest grain yield (7909 kg ha⁻\u0026sup1;). Multivariate analyses showed stronger coupling among nutrients, roots, and yield under MC than under NC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDSF must be combined with soil compaction\u0026ndash;alleviation practices to achieve stable yield benefits under long-term conservation tillage systems.\u003c/p\u003e","manuscriptTitle":"Soil compaction mediates root–nutrient coupling associated with wheat yield response to depth-specific fertilization under contrasting long-term tillage systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 13:59:07","doi":"10.21203/rs.3.rs-8727231/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-03-23T17:15:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T11:50:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2026-02-04T02:35:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T02:05:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2026-01-29T00:16:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"589575d9-183a-49aa-a59f-8d1f5a43405e","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T13:59:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 13:59:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8727231","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8727231","identity":"rs-8727231","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}