Subsurface drip fertigation optimizes nitrogen distribution in soil under maize cultivation

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Singh, R.N. Sahoo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6870488/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Plant and Soil → Version 1 posted 5 You are reading this latest preprint version Abstract Background and Aims: Precise nitrogen (N) supply is vital to sustainable maize production amid food security challenges. This study aimed to evaluate subsurface drip fertigation (SSDF) for optimizing nitrogen distribution and enhancing maize productivity while reducing environmental risks. Methods The study was conducted at ICAR-IARI, New Delhi (2022–2023), using a split-plot design. SSDF was tested with 0–100% recommended dose of N (RDN) applied in 3–4 splits, combined with greengram residue incorporation. Soil mineral N (NH₄⁺-N, NO₃⁻-N) was measured at 0–50 cm depths and 0–20 cm from emitters, along with urease activity and grain yield. Results SSDF outperformed conventional methods, achieving peak NH₄⁺-N at 52.7 mg kg⁻¹ and NO₃⁻-N at 36.3 mg kg⁻¹ in the 20–30 cm layer under 100% RDN-4S treatment. Four splits-maintained N supply better than three splits. Residue incorporation improved soil N by 10–13% and urease activity by 11–13% (13.9 µg g⁻¹ h⁻¹). Yields from 75% RDN-4S (6.6 t ha⁻¹) equaled 100% RDN-4S (6.7 t ha⁻¹), achieving 25% N savings with significant N-yield correlations (r = 0.8–0.85). Conclusion SSDF optimized nitrogen distribution by concentrating mineral N in the 20–30 cm root zone and enabled 25% reduction in N application (75% RDN-4S) while maintaining equivalent maize yields. The integration of four-split applications with residue incorporation enhanced soil N availability and microbial activity, demonstrating SSDF's potential for sustainable maize production. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Maize ( Zea mays L.), a cornerstone of global cereal production, sustains over 1 billion people, demands heavy nitrogen (N) inputs–typically 150–200 kg ha⁻¹ to achieve optimal yields (Su et al., 2020 , Singh et al., 2024b ). Conventional fertilization, however, often delivers nitrogen-use efficiency (NUE) as low as 30–50%, resulting in economic losses and severe environmental pollution (Singh et al., 2024a ). Nitrate leaching pollutes groundwater, while greenhouse gas emissions–estimated at 5 kg CO 2 e per kg N produced–intensify climate change, threatening food security in regions like the Indo-Gangetic Plains (Ladha et al., 2020 ; IPCC, 2019 ). Such challenges underline the urgency of sustainable N management towards balancing productivity with ecological resilience. Subsurface drip fertigation (SSDF) offers a potential precision agriculture solution, can delivers N directly to the maize root zone (15–30 cm depth), enhancing NUE by 20–100% over surface methods through targeted and timely placement (Delbaz et al., 2023 ; Singh et al., 2024a ). Unlike conventional broadcasting, which disperses N shallowly and vulnerably, SSDF creates distinct soil N distribution patterns, yet their spatial and temporal evolution remains underexplored amid variable rainfall and soil conditions. Optimizing SSDF requires dissecting mineral N dynamics (NH₄⁺-N and NO₃⁻-N) under differing application schedules. Split N applications synchronize supply with maize’s peak demand (tasselling), but the ideal frequency and its interplay with soil processes are uncertain. Integrating N–rich legume residues, such as greengram ( Vigna radiata L. Wilczek), could amplify N availability–contributing 12–32 kg N ha⁻¹ via mineralization–while enhancing soil biological activity through urease-mediated urea hydrolysis (Sharma & Behera, 2009 ; Varatharajan et al., 2022 ). Yet, the synergistic effects of SSDF and residue on N distribution and transformation remain poorly studied compared to conventional systems, limiting their adoption potential. This study investigated how SSDF with reduced N rates (50–75% of recommended dose) and split applications (3 vs. 4 splits), combined with greengram residue incorporation, shapes soil mineral N distribution, urease activity, and maize productivity relative to conventional practices, N broadcasted in surface irrigated maize. By elucidating these dynamics, we aim to advance sustainable N management strategies that minimize environmental footprints, curb emissions, and sustain yields, offering scalable precision agriculture solutions for global cereal systems. Material and methods Experimental Site and Climate A field experiment was conducted during the rainy seasons of 2022 and 2023 at the ICAR-Indian Agricultural Research Institute research farm, New Delhi (28°38′N, 77°09′E, 228.6 m ASL), within the Indo-Gangetic Plains. The site characterised a semi-arid climate with annual rainfall of ~ 710 mm, predominantly from July–September monsoons, and a sandy loam soil ( Typic Ustochrepts ; pH 8.15, EC 0.43 dS m⁻¹, SOC 4.9 g kg⁻¹). Initial soil nutrient levels were 88.09 mg kg⁻¹ N (KMnO₄-oxidizable, Subbiah & Asija, 1956 ), 6.98 mg kg⁻¹ P (0.5 M NaHCO₃-extractable, Olsen et al., 1954 ), and 127 mg kg⁻¹ K (1 N NH₄OAc-extractable, Jackson, 1958 ). Weather data (Supplementary Figure S3) showed total rainfall of 593 mm (2022) and 375 mm (2023) during the maize growth period (July–November). Air temperatures during study period matched with ideal temperature requirement of maize crop. Experimental Design and Treatments The experiment used was a three-time replicated split-plot design, testing 16 treatment combinations over two years. Main-plot treatments comprised eight N fertigation levels via SSDF: 0%, 50%, 75%, and 100% of the recommended dose of N (RDN; 150 kg N ha⁻¹), applied in either three 20, 45, 65 days after sowing (DAS) or four splits, 20, 35, 50, 65 DAS. Subplot treatments included two residue management options: greengram residue incorporation (3 t ha⁻¹, air-dry) or no residue. A conventional nutrient management treatment (CNM-150 kg N ha⁻¹ surface-applied in three splits at sowing, knee-high, pre-tasselling stages) served as a benchmark. Urea (46% N) was the N source, while P and K were sourced from mono–potassium phosphate fertigated in SSDF plots or soil-incorporated in CNM plots (Supplementary Table S1 ). Subsurface Drip Fertigation System The SSDF system had 16 mm diameter lateral lines, each having inline dripper of 2 lph discharge capacity at 30cm interval installed 20 cm below the ground surface, laid at a uniform spacing of 45 cm (Supplementary Figure S2). Sub-main pipelines (63 mm and 40 mm) were buried 50 cm deep, connected to a 5 HP pump, filters, and venturi injector for fertigation, maintained at 1 kg cm⁻² pressure. Irrigation was scheduled twice weekly at 80% of cumulative crop evapotranspiration (ETc), computed using FAO ET₀ and daily weather data (ICAR-IARI Observatory), adjusted for rainfall. CNM plots received flood irrigation (5 cm) at 25% soil moisture depletion. Crop Management Maize (cv. Pusa HQPM 1-Improved) was sown on 19th July 2022 and 27th July 2023 using 20 kg ha⁻¹ seed, with a uniform interval of 45 cm × 30 cm both in SSDF and CNM plots. Greengram residue from the prior season was incorporated uniformly pre-sowing in designated plots. Harvest occurred on 10th November 2022 and 15th November 2023. Weeds, pests, and diseases were managed following the standard practices. Soil Sampling and Mineral N Analysis Soil samples were collected 25–30 DAS, 50–55 DAS, and at harvest from 0–50 cm depth (10 cm intervals) at 0, 10, and 20 cm radial distances from SSDF emitters or equivalent positions in CNM plots (Supplementary Fig. S1 ). Mineral N (NH₄⁺-N, NO₃⁻-N) was extracted with 2 M KCl (1:10 soil: solution) and quantified via steam distillation (Keeney & Nelson, 1982 ). NH₄⁺-N was liberated with MgO, trapped in 2% boric acid, and titrated with H₂SO₄. NO₃⁻-N was reduced with Devarda’s alloy and analyzed similarly. Urease Activity Assay Soil urease activity was assessed at 30 and 50 DAS from 0–30 cm depth (10 cm intervals) near emitters, using the method of Tabatabai and Bremner ( 1972 ). Air-dried, 2-mm sieved samples were incubated with urea and Tris buffer at 37°C, with NH₄⁺-N release quantified via distillation and expressed as µg NH₄⁺-N g⁻¹ soil h⁻¹. Spatial Analysis and Statistics Mineral N distribution was visualized using Surfer 13 (Golden Software Inc.) with kriging interpolation. Data were analysed in R (v4.1.0) via ANOVA with the ‘lme4’ package (Bates et al., 2015 ), treating N levels and residue as fixed effects and blocks as random. Means were separated using Duncan’s Multiple Range Test (p ≤ 0.05) via ‘agricolae’ (de Mendiburu, 2021 ). Results Spatial distribution of Ammonium-Nitrogen (NH₄⁺-N) Subsurface drip fertigation (SSDF) distinctly altered NH₄⁺-N distribution in soil compared to conventional surface application (CNM), targeting N to deeper, root-active zones. At 30 days after sowing (DAS), NH₄⁺-N under 100% recommended dose (RDN; 150 kg N ha⁻¹) in three splits (3S) peaked at 19.4 ± 1.9 mg kg⁻¹ on-emitter at 20–30 cm depth, significantly surpassing CNM’s surface maximum of 16.9 ± 1.5 mg kg⁻¹ at 0–10 cm (p < 0.05); (Fig. 1; Supplementary Table S4). Lateral movement was evident under SSDF, with 75% RDN-3S reaching 23.3 ± 2.2 mg kg⁻¹ at 10 cm distance and 100% RDN-3S hitting 27.4 ± 2.7 mg kg⁻¹ at 20 cm-levels, CNM failed to achieve (19.9 ± 1.6 mg kg⁻¹, uniform across 0–20 cm). By 55 DAS, coinciding with tasselling, 100% RDN-4S sustained elevated NH₄⁺-N concentrations–39.2 ± 2.1 mg kg⁻¹ at 0–10 cm, 47.5 ± 2.5 mg kg⁻¹ at 20–30 cm (10 cm distance), and a maximum of 60.8 ± 2.9 mg kg⁻¹ at 20–30 cm (20 cm)–outpacing 100% RDN-3S and CNM (20.8–22.8 mg kg⁻¹) (p < 0.01); (Supplementary Table S5; Fig. 2). This progression reflects four-split applications’ superior ability of maintaining N availability during peak demand, unlike the sharper declines under 3S and CNM. Contour maps highlighted SSDF’s precision: at 30 DAS, 100% RDN-3S formed a tight NH₄⁺-N zone (12–19 ppm) at 20–30 cm, expanding to 18–47 ppm by 55 DAS under 4S, driven by cumulative fertigation (Fig. 1). Residue incorporation (3 t ha⁻¹ greengram) consistently boosted NH₄⁺-N by 10–13% across positions (15.4 vs. 13.8 mg kg⁻¹ at 30 DAS, 20–30 cm; 31.4 vs. 29.9 mg kg⁻¹ at 55 DAS, 10 cm), enhancing spatial uniformity and suggesting organic N mineralization complemented urea inputs (Supplementary Table S5). Spatial distribution of Nitrate-Nitrogen (NO₃⁻-N) Nitrate-nitrogen (NO₃⁻-N) distribution under subsurface drip fertigation (SSDF) paralleled NH₄⁺-N, concentrating at 20–30 cm depth, reflecting the effectivity of SSDF in precisely targeting the maize root zone. At 30 days after sowing (DAS), 100% RDN with three splits (100%RDN-3S) recorded 29.9 ± 2.7 mg kg⁻¹ on-emitter, increasing laterally to 33.0 ± 3.0 mg kg⁻¹ at 10 cm and peaking at 36.3 ± 3.3 mg kg⁻¹ at 20 cm–significantly higher than CNM’s uniform 18.1 ± 1.0 mg kg⁻¹ across 0–20 cm (p < 0.05; Supplementary Table S6; Fig. 3). The 75% RDN-3S treatment showed comparable peaks (30.2 ± 2.8 mg kg⁻¹ at 10 cm), underscoring SSDF’s depth advantage even at reduced rates. By 55 DAS (tasselling stage), 100% RDN-4S sustained NO₃⁻-N at 25.8 ± 2.2 mg kg⁻¹ (20–30 cm, on-emitter) and 26.6 ± 0.6 mg kg⁻¹ (20 cm), exceeding 100% RDN-3S (23.9 ± 0.6 mg kg⁻¹) and CNM (14.1 ± 1.6 mg kg⁻¹) (p < 0.01; Supplementary Table S7; Fig. 4). This persistence highlights four-split applications’ ability to maintain a better N availability over time, contrasting with CNM’s sharper decline post-30 DAS. Contour maps illustrated SSDF’s spatial control: at 30 DAS, NO₃⁻-N zones spanned 10–30 ppm at 20–30 cm under 100% RDN-3S, narrowing to 15–26 ppm by 55 DAS with 4S, reflecting nitrification and uptake dynamics (Figs. 3–4). Residue incorporation (3 t ha⁻¹ greengram) boosted NO₃⁻-N by 10–12% across positions (22.9 vs. 21.8 mg kg⁻¹ at 20 cm, 55 DAS; 21.0 vs. 18.8 mg kg⁻¹ at 10 cm, 30 DAS), likely due to enhanced microbial activity converting organic N (Table S7). However, deep-layer NO₃⁻-N (30–50 cm) increased with N rate, reaching over 20.6 mg kg⁻¹ in 100% RDN-3S by 55 DAS, suggesting leaching potential in sandy loam under high rainfall. These findings, consistent across two kharif seasons, emphasize SSDF’s N placement efficacy, with residue amplifying supply, though deep N accumulation flags environmental risks requiring management. Mineral N at Harvest Stage At harvest, soil mineral N levels declined across all treatments, yet subsurface drip fertigation (SSDF) retained higher concentrations at deeper layers compared to conventional surface application (CNM). In four-split applied 100% recommended N (RDN-4S), NH₄⁺-N dropped from the peak value of 47.5 ± 2.5 mg kg⁻¹ at 20–30 cm (at 55 DAS) to 10–15 ppm (mg kg⁻¹) by harvest time, primarily concentrated at 20–30 cm depth near emitters (Supplementary Fig. S8). In contrast, CNM exhibited sharper declines, with NH₄⁺-N falling to 5–8 ppm, mostly in the 0–10 cm layer, reflecting faster depletion or losses via volatilization and runoff. NO₃⁻-N under SSDF showed greater persistence, ranging from 8–14 ppm at 30–45 cm across 75–100% RDN treatments, with 100% RDN-4S peaking at 13.8 ± 1.2 ppm at 40 cm (Supplementary Fig. S9). CNM, however, averaged 5–10 ppm, rarely exceeding 7 ppm beyond 20 cm depth, indicating a limited downward movement. Residue incorporation enhanced N-retention, increasing NH₄⁺-N and NO₃⁻-N by 5–10% at different depths (12.5 vs. 11.8 ppm NH₄⁺-N at 20–30 cm in 100% RDN-4S with residue vs. without). Overall Mineral N Distribution Total mineral N (NH₄⁺-N + NO₃⁻-N) across growth stages and depths ranged from 13 mg kg⁻¹ in the control (0% RDN) plots to 70 mg kg⁻¹ in 100% RDN-4S plots, reflecting SSDF’s superior N delivery compared to CNM (Fig. S10c). At 20–30 cm–the maize root zone–SSDF with 100% RDN-4S maintained a median of ~ 60 mg kg⁻¹, peaking at 65.3 ± 3.1 mg kg⁻¹ at 55 DAS (10 cm from emitters), while 75% RDN-4S followed closely at ~ 55 mg kg⁻¹ (e.g., 53.8 ± 2.9 mg kg⁻¹), doubling CNM’s median of ~ 30 mg kg⁻¹ (Fig. S10a,b). This disparity widened over time: at 30 DAS, SSDF treatments showed 40–50 mg kg⁻¹ vs. CNM’s 25–30 mg kg⁻¹, with four-split applications (4S) sustaining N longer than three-split (3S) by 5–10 mg kg⁻¹ at tasselling. Residue incorporation increased medians by ~ 10% across treatments, narrowing variability as seen in tighter interquartile ranges (8 vs. 12 mg kg⁻¹ for 4S vs. 3S). Spatially, SSDF concentrated N near emitters (0–10 cm), declining by 15–20% at 20 cm distance, unlike CNM’s uniform but lower distribution (20–35 mg kg⁻¹, 0–20 cm). By harvest, SSDF retained 20–25 mg kg⁻¹ at 30–50 cm vs. CNM’s 10–15 mg kg⁻¹, highlighting sustained availability. These trends suggest SSDF optimizes N placement and timing, with residue enhancing supply consistency, potentially reducing fertilizer needs while raising questions about deep N fate in sandy loams under variable rainfall. Correlations with Grain Yield Grain yield strongly correlated with mineral N at 20–30 cm depth at 55 DAS, aligning with maize’s tasselling stage (Fig. 5). NH₄⁺-N correlations peaked on-emitter (r = 0.85, p < 0.001) and at 10 cm (r = 0.82, p < 0.001), weakening to r = 0.75 (p < 0.01) at 20 cm, reflecting SSDF’s precise N delivery to active root zones. NO₃⁻-N followed similar trends, with r = 0.83 (p < 0.001) on-emitter and r = 0.80 (p < 0.001) at 10 cm, dropping sharply below 30 cm (r < 0.60), where root density declines. These robust relationships (n = 48, across treatments) underscore SSDF’s root-zone targeting as a key yield driver, explaining the equivalence of 75% RDN-4S (6.6 t ha⁻¹) to 100% RDN-4S (6.7 t ha⁻¹). Soil Urease Activity Soil urease activity, a key indicator of N transformation, increased with nitrogen (N) rate and split frequency under subsurface drip fertigation (SSDF). At 30 DAS, 100% RDN-3S recorded 12.4 ± 1.2 µg NH₄⁺-N g⁻¹ h⁻¹ at 0–10 cm, rising to 16.7 ± 2.0 µg g⁻¹ h⁻¹ by 50 DAS under 100% RDN-4S–a 183–221% surge over the 0% RDN control (5.9 ± 1.2 µg g⁻¹ h⁻¹) (Table 1 ). This peak, near emitters, reflected higher urea hydrolysis with four splits, sustaining N supply during tasselling. Conventional surface application (CNM) lagged at 12.2 ± 1.7 µg g⁻¹ h⁻¹ 50 DAS, limited to shallow depths (0–10 cm). Residue incorporation (3 t ha⁻¹ greengram) accelerated activity by 11–13% across treatments (13.9 vs. 13.2 µg g⁻¹ h⁻¹ at 50 DAS, 0–10 cm), enhancing microbial N cycling (Supplementary Fig. S11a, b). Activity declined with depth (10.5 µg g⁻¹ h⁻¹ at 20–30 cm, 100% RDN-4S), highlighting SSDF’s surface concentration and residue’s role in sustaining microbial processes. Maize Grain Yield Grain yield of maize was significantly influenced by nitrogen fertigation levels and residue management practices (Table 6, Supplementary Fig. S12). The highest grain yield was recorded in the 100% RDN-4S treatment in both years (2022: 6.7 ± 0.5 t ha⁻¹; 2023: 6.5 ± 0.5 t ha⁻¹), followed closely by 75% RDN-4S (2022: 6.6 ± 0.6 t ha⁻¹; 2023: 6.4 ± 0.7 t ha⁻¹), 100% RDN-3S, and 75% RDN-3S. Compared to the control (0% RDN), which yielded only 3.0 t ha⁻¹ in 2022 and 2.8 t ha⁻¹ in 2023, the best-performing treatments (100% RDN-4S and 75% RDN-4S) achieved yield increment of over 120%, emphasizing the critical role of both nitrogen rate and application timing in realizing yield potential under subsurface drip fertigation (SSDF). Notably, there was no statistically significant difference in yield between 75% RDN-4S and 100% RDN-4S in either of the years within fertigation treatments, revealing possibilities of 25% reduction in nitrogen input and increased fertigation frequency (four-time application) maintained yields identical to full RDN. Similarly, 100% RDN-3S (6.5 and 6.2 t ha⁻¹) and 75% RDN-3S (6.4 and 6.0 t ha⁻¹) were statistically at par with the 4-split variants. Yields from 50% RDN fertigated plots were though lower than 100% N fertigation, but these were similar to CNM (100% RDN). Fertigation of 50% RDN-4S yielded 5.5 t ha⁻¹ in 2022 and 5.2 t ha⁻¹ in 2023, depicting– an increase of ~ 83% over the control; –while, 50% RDN-3S yielded marginally lower than the former. Residue incorporation had a consistent positive effect on yield performance. Across all treatments, residue-retained plots produced significantly higher yields than their no-residue counterparts (2022: 5.8 ± 1.3 t ha⁻¹ vs. 5.5 ± 1.3; 2023: 5.6 ± 1.3 vs. 5.2 ± 1.2), with an average yield gain of 0.3–0.4 t ha⁻¹. The absolute difference between the residue applied and not applied treatments was particularly significant at higher nitrogen doses and 4-split fertigation. Discussion This study demonstrates that subsurface drip fertigation (SSDF) with split N application and greengram residue incorporation optimizes soil mineral nitrogen (N) distribution, enhancing maize productivity while advancing sustainable agriculture. Unlike conventional surface application (CNM), SSDF concentrates NH₄⁺-N and NO₃⁻-N at 20–30 cm depth near emitters (e.g., 47.5 ± 2.5 mg kg⁻¹ NH₄⁺-N and 25.8 ± 2.2 mg kg⁻¹ NO₃⁻-N at 55 DAS under 100% RDN-4S), aligning N with active root zones (Singh et al., 2024a ; Delbaz et al., 2023 ). This spatial precision, coupled with four-split applications and residue effects, reduces N inputs by 25% (75% RDN-4S yielding 6.6 t ha⁻¹ vs. 6.7 t ha⁻¹ for 100% RDN-4S) without compromising yields, offering a scalable strategy for resource-efficient cereal production. Enhanced N Distribution and Retention The ability of SSDF to target N at 20–30 cm depth contrasts sharply with CNM’s shallow, transient peaks (16.9 ± 1.5 mg kg⁻¹ NH₄⁺-N at 0–10 cm), which are prone to volatilization and runoff (Ladha et al., 2020 ). Contour maps (Figs. 1–6) reveal tight N zones (12–47 ppm NH₄⁺-N, 10–36 ppm NO₃⁻-N) under SSDF, broadening laterally with time and residue, unlike CNM’s diffuse patterns. This aligns with findings that subsurface delivery enhances N retention by 20–40% over surface methods (Delbaz et al., 2023 ; Barakat et al., 2016 ). Four-split applications sustained higher N levels at tasselling (55 DAS) than three-split (47.5 vs. 43.1 mg kg⁻¹ NH₄⁺-N), reflecting better synchrony with maize demand (Quemada & Gabriel, 2016 ). Residue incorporation further increased mineral N by 10–13% (15.4 vs. 13.8 mg kg⁻¹ NH₄⁺-N at 30 DAS), consistent with legume residues contributing 12–32 kg N ha⁻¹ via mineralization (Sharma & Behera, 2009 ; Varatharajan et al., 2022 ). This effect, evident across sampling positions (0–20 cm from emitters), likely stems from slower mineralization of organic N, as greengram residue (3 t ha⁻¹) continued releasing N post-tasselling. This synergy underlines SSDF’s potential to integrate organic and inorganic N sources, reducing reliance on synthetic fertilizers–-a key sustainability metric (Tilman et al., 2011 ). Yield Optimization with Reduced Inputs The equivalence of 75% RDN-4S (6.6 t ha⁻¹) to 100% RDN-4S (6.7 t ha⁻¹) highlights SSDF’s efficiency, cutting N inputs by 37.5 kg ha⁻¹ without yield loss. This mirrors findings where precision fertigation sustains cereal yields with 20–30% less N (Singh et al., 2024a ; Ghaffar et al., 2021 ). Strong correlations (r = 0.80–0.85, p < 0.001) between N at 20–30 cm and yield (Fig. 8) confirm that SSDF targets N to active root zones, a critical advantage over CNM’s lower yields (5.6 t ha⁻¹). Consistency of residue in enhancing yield (0.3–0.4 t ha⁻¹) aligns with studies showing organic amendments enhance soil fertility and water retention (Thierfelder et al., 2017 ). This dual approach––precision delivery and residue recycling––offers a practical pathway to sustainable intensification, particularly in resource-constrained regions like the Indo-Gangetic Plains. Environmental Implications While SSDF optimizes N placement, elevated NO₃⁻-N at 30–50 cm (e.g., 20.6 ± 1.8 mg kg⁻¹, 100% RDN-3S) and residual N at harvest (8–14 ppm) signal leaching risks, a concern in sandy loams under high rainfall (593 mm in 2022) (Ladha et al., 2020 ; Gheysari et al., 2009 ). This contrasts with CNM’s lower deep-layer N, likely due to surface losses (N₂O emissions), which SSDF may reduce by minimizing exposure (Shcherbak et al., 2014 ). Residue incorporation enhanced urease activity (13.9 vs. 13.2 µg NH₄⁺-N g⁻¹ h⁻¹ at 50 DAS), reflecting accelerated urea hydrolysis and microbial N transformation (Tabatabai & Bremner, 1972 ; Zhang et al., 2019 ). This suggests residue stimulates microbial activity, potentially retaining N as NH₄⁺-N longer and reducing NO₃⁻-N leaching, though direct immobilization requires further study (Wolińska et al., 2017 ; Grzyb et al., 2021 ). Integrating SSDF with real-time irrigation scheduling could further curb N losses, aligning with climate-smart agriculture goals (FAO, 2019 ). Scalability and Policy Relevance SSDF’s infrastructure (e.g., buried drippers) requires initial investment, but its water and N savings–coupled with residue’s low-cost, in-situ availability–make it viable for smallholders if supported by subsidies or extension services (Burney et al., 2010 ). The 25% N reduction potential could lower fertilizer demand by millions of tons annually in maize systems, cutting greenhouse gas emissions from N production (~ 5 kg CO₂e kg⁻¹ N) (IPCC, 2019 ). In India, where maize area exceeds 9 million ha, scaling SSDF could enhance food security while addressing soil degradation concerns. Policy incentives for precision technologies and residue management, as seen in China’s drip irrigation programs (Wang et al., 2021 ), could accelerate adoption. Limitations and Future Directions The study’s two-year scope limits long-term N cycling insights, particularly residue decomposition rates and microbial shifts. Elevated deep N suggests a need for dynamic irrigation models to match rainfall and crop uptake (Quemada & Gabriel, 2016 ). Future work should quantify N₂O emissions under SSDF vs. CNM and assess residue quality (e.g., C:N ratio) effects on N release. Multi-site trials could test SSDF’s adaptability across soil types and climates, enhancing its global relevance and wider adoption by producers. Conclusion This study reveals that subsurface drip fertigation (SSDF), combined with split nitrogen (N) applications and greengram residue incorporation, transforms soil mineral N dynamics favorably enhancing maize productivity and sustainable agriculture. By concentrating NH₄⁺-N and NO₃⁻-N at 20–30 cm depth (47.5 ± 2.5 mg kg⁻¹ and 25.8 ± 2.2 mg kg⁻¹ at 55 DAS under 100% RDN-4S), SSDF outperforms conventional surface application, aligning N with maize root zones and sustaining availability through tasseling. Four-split applications optimize this distribution compared to three-split, while residue augment soil N by 10–13% via enhanced microbial activity (urease: 13.9 vs. 13.2 µg g⁻¹ h⁻¹), reducing reliance on synthetic inputs. Remarkably, 75% RDN-4S (6.6 t ha⁻¹) matches 100% RDN-4S yields (6.7 t ha⁻¹), cutting-down N use by 25% without compromising output––a evidence to SSDF’s precision, corroborated by strong N-yield correlations (r = 0.80–0.85, p < 0.001). However, elevated NO₃⁻-N at 30–50 cm (20.6 ± 1.8 mg kg⁻¹) and residual N at harvest (8–14 ppm) highlight leaching risks, underscoring the need for tailored irrigation to balance efficiency and environmental stewardship. These findings offer a scalable blueprint for sustainable maize intensification, integrating precision technology and organic inputs (crop residue) to optimize N delivery, enhance soil health, and minimize ecological footprints. In regions like the Indo-Gangetic Plains, where maize supports millions, SSDF could reduce fertilizer demand, curb emissions from N production, and reinforce climate resilience if paired with policy support for adoption. Building on prior efficiency insights (Singh et al., 2024a ), this approach positions SSDF as a cornerstone of resource-efficient agriculture. Future research should refine irrigation strategies, quantify long-term N cycling, and test scalability across diverse agroecosystems, ensuring this innovation meets global sustainability imperatives with precision and purpose. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors gratefully acknowledge the financial support provided by the ICAR-NASF under grant no. NASF8016. References Barakat, M., Cheviron, B., & Angulo-Jaramillo, R. (2016). Influence of the irrigation technique and strategies on the nitrogen cycle and budget: A review. Agricultural Water Management, 178, 225–238. https://doi.org/10.1016/j.agwat.2016.09.027 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48. https://doi.org/10.18637/jss.v067.i01 Burney, J. A., Davis, S. J., & Lobell, D. B. (2010). Greenhouse gas mitigation by agricultural intensification. Proceedings of the National Academy of Sciences, 107(26), 12052–12057. https://doi.org/10.1073/pnas.0914216107 de Mendiburu, F. (2021). agricolae: Statistical procedures for agricultural research. R package version 1.3-5. https://CRAN.R-project.org/package=agricolae Delbaz, R., Ebrahimian, H., Abbasi, F., Ghameshlou, A. N., Liaghat, A., & Ranazadeh, D. (2023). A global meta-analysis on surface and drip fertigation for annual crops under different fertilization levels. Agricultural Water Management , 289 , 108504. doi: 10.1016/j.agwat.2023.108504 FAO. (2019). Climate-smart agriculture sourcebook. Food and Agriculture Organization of the United Nations. Ghaffar, A., Ali, S., & Khan, M. H. (2021). Precision nitrogen management in wheat using subsurface drip fertigation. Agricultural Water Management, 245, 106627. https://doi.org/10.1016/j.agwat.2020.106627 Gheysari, M., Mirlatifi, S. M., Homaee, M., Asadi, M. E., & Hoogenboom, G. (2009). Nitrate leaching in a silage maize field under different irrigation and nitrogen fertilizer rates. Agricultural Water Management, 96(6), 946-954. https://doi.org/10.1016/j.agwat.2009.01.005 Grzyb, A., Wolna-Maruwka, A., and Niewiadomska, A. (2021). The Significance of Microbial Transformation of Nitrogen Compounds in the Light of Integrated Crop Management. Agronomy, 11(7), 1415. https://doi.org/10.3390/agronomy11071415 IPCC. (2019). Climate change and land: An IPCC special report. Intergovernmental Panel on Climate Change. Jackson, M. L. (1958). Soil chemical analysis . Prentice-Hall. Keeney, D. R., & Nelson, D. W. (1982). Nitrogen—Inorganic forms. In A. L. Page (Ed.), Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (2nd ed., pp. 643–698). Madison, WI: ASA and SSSA. Ladha, J. K., Jat, M. L., Stirling, C. M., Chakraborty, D., Pradhan, P., Krupnik, T. J., Sapkota, T. B., Pathak, H., Rana, D. S., Tesfaye, K., & Gerard, B. (2020). Achieving the sustainable development goals in agriculture: The crucial role of nitrogen in cereal-based systems. In D. L. Sparks (Ed.), Advances in Agronomy 163, pp. 39–116. Academic Press. https://doi.org/10.1016/bs.agron.2020.05.001 Olsen, S. R., Cole, C. V., Watanabe, F. S., & Dean, L. A. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate (USDA Circular No. 939). U.S. Department of Agriculture. Quemada, M., & Gabriel, J. L. (2016). Approaches for increasing nitrogen use efficiency in cropping systems. Field Crops Research, 196, 76–85. https://doi.org/10.1016/j.fcr.2016.06.010 Sharma AR, Behera UK (2009) Recycling of legume residues for nitrogen economy and higher productivity in maize (Zea mays)–wheat (Triticum aestivum) cropping system. Nutr Cycl Agroecosyst 83(3):197–210. https:// doi. org/ 10. 1007/ s10705- 008- 9212-0. Shcherbak, I., Millar, N., & Robertson, G. P. (2014). Global meta-analysis of the nonlinear response of soil nitrous oxide (N₂O) emissions to fertilizer nitrogen. Proceedings of the National Academy of Sciences, 111(25), 9199–9204. https://doi.org/10.1073/pnas.1322434111 Singh A., Dass, A., Dhar, S., Sudhishri, S., Shekhawat, K., Meena, M. C., Nitinkumar, K. and Devi, A. D. (2024b). Sub-surface drip fertigation of nitrogen coupled with crop residue incorporation enhanced the growth and yield of maize in alluvial soils. Indian Journal of Agronomy , 69(2), 144–150. https://doi.org/10.59797/ija.v69i2.5499. Singh A.,Dass, A., Sudhishri, S., Singh,V.K., Shekhawat, K., Meena, M. C., Sahoo, R.N., Soora, N.K. Upadhyay, P.K., Dhar, S., Nitinkumar, K. (2024a). Sub-surface drip-fertigation of nitrogen and legume residue incorporation improved nitrogen-use efficiency and yield of maize. Nutrient Cycling in Agroecosystems . https://doi.org/10.1007/s10705-024-10371-8. Su, W., Ahmad, S., Ahmad, I., & Han, Q. (2020). Nitrogen fertilization affects maize grain yield through regulating nitrogen uptake, radiation and water use efficiency, photosynthesis and root distribution. PeerJ, 8 , e10291. https://doi.org/10.7717/peerj.10291 Subbiah, B. V., & Asija, G. L. (1956). A rapid procedure for the determination of available nitrogen in soils. Current Science, 25 (6), 259–260. Tabatabai, M. A., & Bremner, J. M. (1972). Assay of urease activity in soils. Soil Biology and Biochemistry, 4 (4), 479–487. https://doi.org/10.1016/0038-0717(72)90064-8 Thierfelder, C., Chivenge, P., Mupangwa, W., Rusinamhodzi, L., & Garrity, D. (2017). How climate-smart is conservation agriculture? Agriculture, Ecosystems & Environment, 238, 102–110. https://doi.org/10.1016/j.agee.2016.10.002 Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260–20264. https://doi.org/10.1073/pnas.1116437108 Varatharajan T, Dass A, Choudhary AK, Sudhishri S, Das TK, Rajanna GA, Prasad S, Swarnalakshmi K, Harish MN, Dhar S (2022) Integrated management enhances crop physiology and final yield in maize intercropped with blackgram in semiarid South Asia. Front Plant Sci. https:// doi. org/ 10. 3389/ fpls. 2022. 975569. Wang, J., Zhang, L., & Li, J. (2021). Adoption of drip irrigation in Chinese agriculture: Policy impacts and farmer perceptions. Sustainability, 13(8), 4567. https://doi.org/10.3390/su13084567 Wolińska, A., Stępniewska, Z., & Kuźniar, A. (2017). Urease activity as a soil quality indicator: A review. Acta Agriculturae Scandinavica, Section B – Soil & Plant Science, 67(4), 270–283. https://doi.org/10.1080/09064710.2016.1266243 Zhang, Y., Li, X., & Gregorich, E. G. (2019). Soil enzyme activities as affected by long-term fertilization and crop residue management. Soil Biology and Biochemistry, 135, 278–286. https://doi.org/10.1016/j.soilbio.2019.05.017 Table 1 Table 1 Effect of SSDF fertigation and residue management on soil urease activities and grain yield of maize. Treatment Urease (30 DAS) (μg urea hydrolyzed g −1 soil hr −1 at 37° C) Urease (50 DAS) (μg urea hydrolyzed g −1 soil hr −1 at 37° C) Grain yield (t ha -1 ) On emitter 0-10cm 10-20cm 20-30cm 0-10cm 10-20cm 20-30cm 2022 2023 Nitrogen fertigation levels 0%RDN 5.2±1.0 d 4.8±1.0 d 3.8±0.8 e 5.9±1.2 e 6.2±1.2 d 4.9±1.0 d 3.0±0.4 d 2.8±0.4 c 50%RDN-3S 10.1±1.8 bc 9.6±1.7b c 8.7±1.5b c 13.0±1.9c d 13.8±2.0b c 12.6±1.9 b 5.1±0.5 c 4.8±0.5 b 50%RDN-4S 8.7±1.2 c 7.9±1.1 c 7.1±1.0 d 13.5±1.5bc d 13.7±1.6b c 12.4±1.4 b 5.5±0.5b c 5.2±0.5 b 75%RDN-3S 11.7±0.9 ab 11.2±0.8a b 10.1±0.8a b 15.1±0.8ab c 16.2±0.9a b 14.6±0.8a b 6.4±0.4 a 6.0±0.5 a 75%RDN-4S 10.7±1.3 ab 9.8±1.2 b 8.7±1.0b c 15.8±1.9a b 16.8±2.0 a 15.0±1.8 a 6.6±0.6 a 6.4±0.7 a 100%RDN-3S 12.4±1.2 a 12.2±1.2 a 10.8±1.1 a 16.2±1.5 a 17.6±1.7 a 15.6±1.5 a 6.5±0.6 a 6.2±0.6 a 100%RDN-4S 11.3±1.3 ab 10.6±1.3a b 9.3±1.1 b 16.7±2.0 a 17.9±2.1 a 15.7±1.9 a 6.7±0.5 a 6.5±0.5 a Conventional 10.7±1.5 ab 9.9±1.4 b 7.7±1.1c d 12.2±1.7 d 12.7±1.7 c 9.9±1.4 c 5.6±0.3 b 5.1±0.3 b Residue management Residue 10.6±2.3 a 10.0±2.4 a 8.7±2.2 a 13.9±3.5 a 14.7±3.8 a 12.9±3.6 a 5.8±1.3 a 5.6±1.3 a No-Residue 9.6±2.5 b 9.0±2.5 b 7.9±2.3 b 13.2±3.8 a 14.0±4.2 a 12.3±3.9 a 5.5±1.3 b 5.2±1.2 b Supplementary Files 01SupplfilePS.docx Cite Share Download PDF Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Plant and Soil → Version 1 posted Reviewers agreed at journal 19 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor invited by journal 15 Jun, 2025 Editor assigned by journal 15 Jun, 2025 First submitted to journal 13 Jun, 2025 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. 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to achieve optimal yields (Su et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Singh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Conventional fertilization, however, often delivers nitrogen-use efficiency (NUE) as low as 30\u0026ndash;50%, resulting in economic losses and severe environmental pollution (Singh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Nitrate leaching pollutes groundwater, while greenhouse gas emissions\u0026ndash;estimated at 5 kg CO\u003csub\u003e2\u003c/sub\u003ee per kg N produced\u0026ndash;intensify climate change, threatening food security in regions like the Indo-Gangetic Plains (Ladha et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such challenges underline the urgency of sustainable N management towards balancing productivity with ecological resilience. Subsurface drip fertigation (SSDF) offers a potential precision agriculture solution, can delivers N directly to the maize root zone (15\u0026ndash;30 cm depth), enhancing NUE by 20\u0026ndash;100% over surface methods through targeted and timely placement (Delbaz et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Unlike conventional broadcasting, which disperses N shallowly and vulnerably, SSDF creates distinct soil N distribution patterns, yet their spatial and temporal evolution remains underexplored amid variable rainfall and soil conditions. Optimizing SSDF requires dissecting mineral N dynamics (NH₄⁺-N and NO₃⁻-N) under differing application schedules. Split N applications synchronize supply with maize\u0026rsquo;s peak demand (tasselling), but the ideal frequency and its interplay with soil processes are uncertain. Integrating N\u0026ndash;rich legume residues, such as greengram (\u003cem\u003eVigna radiata\u003c/em\u003e L. Wilczek), could amplify N availability\u0026ndash;contributing 12\u0026ndash;32 kg N ha⁻\u0026sup1; \u003cem\u003evia\u003c/em\u003e mineralization\u0026ndash;while enhancing soil biological activity through urease-mediated urea hydrolysis (Sharma \u0026amp; Behera, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Varatharajan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet, the synergistic effects of SSDF and residue on N distribution and transformation remain poorly studied compared to conventional systems, limiting their adoption potential.\u003c/p\u003e \u003cp\u003eThis study investigated how SSDF with reduced N rates (50\u0026ndash;75% of recommended dose) and split applications (3 vs. 4 splits), combined with greengram residue incorporation, shapes soil mineral N distribution, urease activity, and maize productivity relative to conventional practices, N broadcasted in surface irrigated maize. By elucidating these dynamics, we aim to advance sustainable N management strategies that minimize environmental footprints, curb emissions, and sustain yields, offering scalable precision agriculture solutions for global cereal systems.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Site and Climate\u003c/h2\u003e \u003cp\u003eA field experiment was conducted during the rainy seasons of 2022 and 2023 at the ICAR-Indian Agricultural Research Institute research farm, New Delhi (28\u0026deg;38\u0026prime;N, 77\u0026deg;09\u0026prime;E, 228.6 m ASL), within the Indo-Gangetic Plains. The site characterised a semi-arid climate with annual rainfall of ~\u0026thinsp;710 mm, predominantly from July\u0026ndash;September monsoons, and a sandy loam soil (\u003cem\u003eTypic Ustochrepts\u003c/em\u003e; pH 8.15, EC 0.43 dS m⁻\u0026sup1;, SOC 4.9 g kg⁻\u0026sup1;). Initial soil nutrient levels were 88.09 mg kg⁻\u0026sup1; N (KMnO₄-oxidizable, Subbiah \u0026amp; Asija, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), 6.98 mg kg⁻\u0026sup1; P (0.5 M NaHCO₃-extractable, Olsen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1954\u003c/span\u003e), and 127 mg kg⁻\u0026sup1; K (1 N NH₄OAc-extractable, Jackson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). Weather data (Supplementary Figure S3) showed total rainfall of 593 mm (2022) and 375 mm (2023) during the maize growth period (July\u0026ndash;November). Air temperatures during study period matched with ideal temperature requirement of maize crop.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental Design and Treatments\u003c/h3\u003e\n\u003cp\u003eThe experiment used was a three-time replicated split-plot design, testing 16 treatment combinations over two years. Main-plot treatments comprised eight N fertigation levels \u003cem\u003evia\u003c/em\u003e SSDF: 0%, 50%, 75%, and 100% of the recommended dose of N (RDN; 150 kg N ha⁻\u0026sup1;), applied in either three 20, 45, 65 days after sowing (DAS) or four splits, 20, 35, 50, 65 DAS. Subplot treatments included two residue management options: greengram residue incorporation (3 t ha⁻\u0026sup1;, air-dry) or no residue. A conventional nutrient management treatment (CNM-150 kg N ha⁻\u0026sup1; surface-applied in three splits at sowing, knee-high, pre-tasselling stages) served as a benchmark. Urea (46% N) was the N source, while P and K were sourced from mono\u0026ndash;potassium phosphate fertigated in SSDF plots or soil-incorporated in CNM plots (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSubsurface Drip Fertigation System\u003c/h3\u003e\n\u003cp\u003eThe SSDF system had 16 mm diameter lateral lines, each having inline dripper of 2 lph discharge capacity at 30cm interval installed 20 cm below the ground surface, laid at a uniform spacing of 45 cm (Supplementary Figure S2). Sub-main pipelines (63 mm and 40 mm) were buried 50 cm deep, connected to a 5 HP pump, filters, and venturi injector for fertigation, maintained at 1 kg cm⁻\u0026sup2; pressure. Irrigation was scheduled twice weekly at 80% of cumulative crop evapotranspiration (ETc), computed using FAO ET₀ and daily weather data (ICAR-IARI Observatory), adjusted for rainfall. CNM plots received flood irrigation (5 cm) at 25% soil moisture depletion.\u003c/p\u003e\n\u003ch3\u003eCrop Management\u003c/h3\u003e\n\u003cp\u003eMaize (cv. Pusa HQPM 1-Improved) was sown on 19th July 2022 and 27th July 2023 using 20 kg ha⁻\u0026sup1; seed, with a uniform interval of 45 cm \u0026times; 30 cm both in SSDF and CNM plots. Greengram residue from the prior season was incorporated uniformly pre-sowing in designated plots. Harvest occurred on 10th November 2022 and 15th November 2023. Weeds, pests, and diseases were managed following the standard practices.\u003c/p\u003e\n\u003ch3\u003eSoil Sampling and Mineral N Analysis\u003c/h3\u003e\n\u003cp\u003eSoil samples were collected 25\u0026ndash;30 DAS, 50\u0026ndash;55 DAS, and at harvest from 0\u0026ndash;50 cm depth (10 cm intervals) at 0, 10, and 20 cm radial distances from SSDF emitters or equivalent positions in CNM plots (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Mineral N (NH₄⁺-N, NO₃⁻-N) was extracted with 2 M KCl (1:10 soil: solution) and quantified \u003cem\u003evia\u003c/em\u003e steam distillation (Keeney \u0026amp; Nelson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). NH₄⁺-N was liberated with MgO, trapped in 2% boric acid, and titrated with H₂SO₄. NO₃⁻-N was reduced with Devarda\u0026rsquo;s alloy and analyzed similarly.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUrease Activity Assay\u003c/h2\u003e \u003cp\u003eSoil urease activity was assessed at 30 and 50 DAS from 0\u0026ndash;30 cm depth (10 cm intervals) near emitters, using the method of Tabatabai and Bremner (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Air-dried, 2-mm sieved samples were incubated with urea and Tris buffer at 37\u0026deg;C, with NH₄⁺-N release quantified \u003cem\u003evia\u003c/em\u003e distillation and expressed as \u0026micro;g NH₄⁺-N g⁻\u0026sup1; soil h⁻\u0026sup1;.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial Analysis and Statistics\u003c/h3\u003e\n\u003cp\u003eMineral N distribution was visualized using Surfer 13 (Golden Software Inc.) with kriging interpolation. Data were analysed in R (v4.1.0) \u003cem\u003evia\u003c/em\u003e ANOVA with the \u0026lsquo;lme4\u0026rsquo; package (Bates et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), treating N levels and residue as fixed effects and blocks as random. Means were separated using Duncan\u0026rsquo;s Multiple Range Test (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) \u003cem\u003evia\u003c/em\u003e \u0026lsquo;agricolae\u0026rsquo; (de Mendiburu, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpatial distribution of Ammonium-Nitrogen (NH₄⁺-N)\u003c/h2\u003e \u003cp\u003eSubsurface drip fertigation (SSDF) distinctly altered NH₄⁺-N distribution in soil compared to conventional surface application (CNM), targeting N to deeper, root-active zones. At 30 days after sowing (DAS), NH₄⁺-N under 100% recommended dose (RDN; 150 kg N ha⁻\u0026sup1;) in three splits (3S) peaked at 19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 mg kg⁻\u0026sup1; on-emitter at 20\u0026ndash;30 cm depth, significantly surpassing CNM\u0026rsquo;s surface maximum of 16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 mg kg⁻\u0026sup1; at 0\u0026ndash;10 cm (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); (Fig.\u0026nbsp;1; Supplementary Table S4). Lateral movement was evident under SSDF, with 75% RDN-3S reaching 23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 mg kg⁻\u0026sup1; at 10 cm distance and 100% RDN-3S hitting 27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 mg kg⁻\u0026sup1; at 20 cm-levels, CNM failed to achieve (19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 mg kg⁻\u0026sup1;, uniform across 0\u0026ndash;20 cm). By 55 DAS, coinciding with tasselling, 100% RDN-4S sustained elevated NH₄⁺-N concentrations\u0026ndash;39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 mg kg⁻\u0026sup1; at 0\u0026ndash;10 cm, 47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 mg kg⁻\u0026sup1; at 20\u0026ndash;30 cm (10 cm distance), and a maximum of 60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 mg kg⁻\u0026sup1; at 20\u0026ndash;30 cm (20 cm)\u0026ndash;outpacing 100% RDN-3S and CNM (20.8\u0026ndash;22.8 mg kg⁻\u0026sup1;) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); (Supplementary Table S5; Fig.\u0026nbsp;2). This progression reflects four-split applications\u0026rsquo; superior ability of maintaining N availability during peak demand, unlike the sharper declines under 3S and CNM. Contour maps highlighted SSDF\u0026rsquo;s precision: at 30 DAS, 100% RDN-3S formed a tight NH₄⁺-N zone (12\u0026ndash;19 ppm) at 20\u0026ndash;30 cm, expanding to 18\u0026ndash;47 ppm by 55 DAS under 4S, driven by cumulative fertigation (Fig.\u0026nbsp;1). Residue incorporation (3 t ha⁻\u0026sup1; greengram) consistently boosted NH₄⁺-N by 10\u0026ndash;13% across positions (15.4 vs. 13.8 mg kg⁻\u0026sup1; at 30 DAS, 20\u0026ndash;30 cm; 31.4 vs. 29.9 mg kg⁻\u0026sup1; at 55 DAS, 10 cm), enhancing spatial uniformity and suggesting organic N mineralization complemented urea inputs (Supplementary Table S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSpatial distribution of Nitrate-Nitrogen (NO₃⁻-N)\u003c/h2\u003e \u003cp\u003eNitrate-nitrogen (NO₃⁻-N) distribution under subsurface drip fertigation (SSDF) paralleled NH₄⁺-N, concentrating at 20\u0026ndash;30 cm depth, reflecting the effectivity of SSDF in precisely targeting the maize root zone. At 30 days after sowing (DAS), 100% RDN with three splits (100%RDN-3S) recorded 29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 mg kg⁻\u0026sup1; on-emitter, increasing laterally to 33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 mg kg⁻\u0026sup1; at 10 cm and peaking at 36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 mg kg⁻\u0026sup1; at 20 cm\u0026ndash;significantly higher than CNM\u0026rsquo;s uniform 18.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 mg kg⁻\u0026sup1; across 0\u0026ndash;20 cm (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Table S6; Fig.\u0026nbsp;3). The 75% RDN-3S treatment showed comparable peaks (30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 mg kg⁻\u0026sup1; at 10 cm), underscoring SSDF\u0026rsquo;s depth advantage even at reduced rates. By 55 DAS (tasselling stage), 100% RDN-4S sustained NO₃⁻-N at 25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 mg kg⁻\u0026sup1; (20\u0026ndash;30 cm, on-emitter) and 26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mg kg⁻\u0026sup1; (20 cm), exceeding 100% RDN-3S (23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mg kg⁻\u0026sup1;) and CNM (14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 mg kg⁻\u0026sup1;) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Supplementary Table S7; Fig.\u0026nbsp;4). This persistence highlights four-split applications\u0026rsquo; ability to maintain a better N availability over time, contrasting with CNM\u0026rsquo;s sharper decline post-30 DAS. Contour maps illustrated SSDF\u0026rsquo;s spatial control: at 30 DAS, NO₃⁻-N zones spanned 10\u0026ndash;30 ppm at 20\u0026ndash;30 cm under 100% RDN-3S, narrowing to 15\u0026ndash;26 ppm by 55 DAS with 4S, reflecting nitrification and uptake dynamics (Figs.\u0026nbsp;3\u0026ndash;4). Residue incorporation (3 t ha⁻\u0026sup1; greengram) boosted NO₃⁻-N by 10\u0026ndash;12% across positions (22.9 vs. 21.8 mg kg⁻\u0026sup1; at 20 cm, 55 DAS; 21.0 vs. 18.8 mg kg⁻\u0026sup1; at 10 cm, 30 DAS), likely due to enhanced microbial activity converting organic N (Table S7). However, deep-layer NO₃⁻-N (30\u0026ndash;50 cm) increased with N rate, reaching over 20.6 mg kg⁻\u0026sup1; in 100% RDN-3S by 55 DAS, suggesting leaching potential in sandy loam under high rainfall. These findings, consistent across two \u003cem\u003ekharif\u003c/em\u003e seasons, emphasize SSDF\u0026rsquo;s N placement efficacy, with residue amplifying supply, though deep N accumulation flags environmental risks requiring management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMineral N at Harvest Stage\u003c/h2\u003e \u003cp\u003eAt harvest, soil mineral N levels declined across all treatments, yet subsurface drip fertigation (SSDF) retained higher concentrations at deeper layers compared to conventional surface application (CNM). In four-split applied 100% recommended N (RDN-4S), NH₄⁺-N dropped from the peak value of 47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 mg kg⁻\u0026sup1; at 20\u0026ndash;30 cm (at 55 DAS) to 10\u0026ndash;15 ppm (mg kg⁻\u0026sup1;) by harvest time, primarily concentrated at 20\u0026ndash;30 cm depth near emitters (Supplementary Fig. S8). In contrast, CNM exhibited sharper declines, with NH₄⁺-N falling to 5\u0026ndash;8 ppm, mostly in the 0\u0026ndash;10 cm layer, reflecting faster depletion or losses via volatilization and runoff. NO₃⁻-N under SSDF showed greater persistence, ranging from 8\u0026ndash;14 ppm at 30\u0026ndash;45 cm across 75\u0026ndash;100% RDN treatments, with 100% RDN-4S peaking at 13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 ppm at 40 cm (Supplementary Fig. S9). CNM, however, averaged 5\u0026ndash;10 ppm, rarely exceeding 7 ppm beyond 20 cm depth, indicating a limited downward movement. Residue incorporation enhanced N-retention, increasing NH₄⁺-N and NO₃⁻-N by 5\u0026ndash;10% at different depths (12.5 vs. 11.8 ppm NH₄⁺-N at 20\u0026ndash;30 cm in 100% RDN-4S with residue vs. without).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOverall Mineral N Distribution\u003c/h2\u003e \u003cp\u003eTotal mineral N (NH₄⁺-N\u0026thinsp;+\u0026thinsp;NO₃⁻-N) across growth stages and depths ranged from 13 mg kg⁻\u0026sup1; in the control (0% RDN) plots to 70 mg kg⁻\u0026sup1; in 100% RDN-4S plots, reflecting SSDF\u0026rsquo;s superior N delivery compared to CNM (Fig. S10c). At 20\u0026ndash;30 cm\u0026ndash;the maize root zone\u0026ndash;SSDF with 100% RDN-4S maintained a median of ~\u0026thinsp;60 mg kg⁻\u0026sup1;, peaking at 65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 mg kg⁻\u0026sup1; at 55 DAS (10 cm from emitters), while 75% RDN-4S followed closely at ~\u0026thinsp;55 mg kg⁻\u0026sup1; (e.g., 53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 mg kg⁻\u0026sup1;), doubling CNM\u0026rsquo;s median of ~\u0026thinsp;30 mg kg⁻\u0026sup1; (Fig. S10a,b). This disparity widened over time: at 30 DAS, SSDF treatments showed 40\u0026ndash;50 mg kg⁻\u0026sup1; vs. CNM\u0026rsquo;s 25\u0026ndash;30 mg kg⁻\u0026sup1;, with four-split applications (4S) sustaining N longer than three-split (3S) by 5\u0026ndash;10 mg kg⁻\u0026sup1; at tasselling. Residue incorporation increased medians by ~\u0026thinsp;10% across treatments, narrowing variability as seen in tighter interquartile ranges (8 vs. 12 mg kg⁻\u0026sup1; for 4S vs. 3S). Spatially, SSDF concentrated N near emitters (0\u0026ndash;10 cm), declining by 15\u0026ndash;20% at 20 cm distance, unlike CNM\u0026rsquo;s uniform but lower distribution (20\u0026ndash;35 mg kg⁻\u0026sup1;, 0\u0026ndash;20 cm). By harvest, SSDF retained 20\u0026ndash;25 mg kg⁻\u0026sup1; at 30\u0026ndash;50 cm vs. CNM\u0026rsquo;s 10\u0026ndash;15 mg kg⁻\u0026sup1;, highlighting sustained availability. These trends suggest SSDF optimizes N placement and timing, with residue enhancing supply consistency, potentially reducing fertilizer needs while raising questions about deep N fate in sandy loams under variable rainfall.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations with Grain Yield\u003c/h2\u003e \u003cp\u003eGrain yield strongly correlated with mineral N at 20\u0026ndash;30 cm depth at 55 DAS, aligning with maize\u0026rsquo;s tasselling stage (Fig.\u0026nbsp;5). NH₄⁺-N correlations peaked on-emitter (r\u0026thinsp;=\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and at 10 cm (r\u0026thinsp;=\u0026thinsp;0.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), weakening to r\u0026thinsp;=\u0026thinsp;0.75 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) at 20 cm, reflecting SSDF\u0026rsquo;s precise N delivery to active root zones. NO₃⁻-N followed similar trends, with r\u0026thinsp;=\u0026thinsp;0.83 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on-emitter and r\u0026thinsp;=\u0026thinsp;0.80 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) at 10 cm, dropping sharply below 30 cm (r\u0026thinsp;\u0026lt;\u0026thinsp;0.60), where root density declines. These robust relationships (n\u0026thinsp;=\u0026thinsp;48, across treatments) underscore SSDF\u0026rsquo;s root-zone targeting as a key yield driver, explaining the equivalence of 75% RDN-4S (6.6 t ha⁻\u0026sup1;) to 100% RDN-4S (6.7 t ha⁻\u0026sup1;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSoil Urease Activity\u003c/h2\u003e \u003cp\u003eSoil urease activity, a key indicator of N transformation, increased with nitrogen (N) rate and split frequency under subsurface drip fertigation (SSDF). At 30 DAS, 100% RDN-3S recorded 12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 \u0026micro;g NH₄⁺-N g⁻\u0026sup1; h⁻\u0026sup1; at 0\u0026ndash;10 cm, rising to 16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 \u0026micro;g g⁻\u0026sup1; h⁻\u0026sup1; by 50 DAS under 100% RDN-4S\u0026ndash;a 183\u0026ndash;221% surge over the 0% RDN control (5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 \u0026micro;g g⁻\u0026sup1; h⁻\u0026sup1;) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This peak, near emitters, reflected higher urea hydrolysis with four splits, sustaining N supply during tasselling. Conventional surface application (CNM) lagged at 12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 \u0026micro;g g⁻\u0026sup1; h⁻\u0026sup1; 50 DAS, limited to shallow depths (0\u0026ndash;10 cm). Residue incorporation (3 t ha⁻\u0026sup1; greengram) accelerated activity by 11\u0026ndash;13% across treatments (13.9 vs. 13.2 \u0026micro;g g⁻\u0026sup1; h⁻\u0026sup1; at 50 DAS, 0\u0026ndash;10 cm), enhancing microbial N cycling (Supplementary Fig. S11a, b). Activity declined with depth (10.5 \u0026micro;g g⁻\u0026sup1; h⁻\u0026sup1; at 20\u0026ndash;30 cm, 100% RDN-4S), highlighting SSDF\u0026rsquo;s surface concentration and residue\u0026rsquo;s role in sustaining microbial processes.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eMaize Grain Yield\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003cp\u003eGrain yield of maize was significantly influenced by nitrogen fertigation levels and residue management practices (Table\u0026nbsp;6, Supplementary Fig. S12). The highest grain yield was recorded in the 100% RDN-4S treatment in both years (2022: 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 t ha⁻\u0026sup1;; 2023: 6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 t ha⁻\u0026sup1;), followed closely by 75% RDN-4S (2022: 6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 t ha⁻\u0026sup1;; 2023: 6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 t ha⁻\u0026sup1;), 100% RDN-3S, and 75% RDN-3S. Compared to the control (0% RDN), which yielded only 3.0 t ha⁻\u0026sup1; in 2022 and 2.8 t ha⁻\u0026sup1; in 2023, the best-performing treatments (100% RDN-4S and 75% RDN-4S) achieved yield increment of over 120%, emphasizing the critical role of both nitrogen rate and application timing in realizing yield potential under subsurface drip fertigation (SSDF). Notably, there was no statistically significant difference in yield between 75% RDN-4S and 100% RDN-4S in either of the years within fertigation treatments, revealing possibilities of 25% reduction in nitrogen input and increased fertigation frequency (four-time application) maintained yields identical to full RDN. Similarly, 100% RDN-3S (6.5 and 6.2 t ha⁻\u0026sup1;) and 75% RDN-3S (6.4 and 6.0 t ha⁻\u0026sup1;) were statistically at par with the 4-split variants. Yields from 50% RDN fertigated plots were though lower than 100% N fertigation, but these were similar to CNM (100% RDN). Fertigation of 50% RDN-4S yielded 5.5 t ha⁻\u0026sup1; in 2022 and 5.2 t ha⁻\u0026sup1; in 2023, depicting\u0026ndash; an increase of ~\u0026thinsp;83% over the control; \u0026ndash;while, 50% RDN-3S yielded marginally lower than the former. Residue incorporation had a consistent positive effect on yield performance. Across all treatments, residue-retained plots produced significantly higher yields than their no-residue counterparts (2022: 5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 t ha⁻\u0026sup1; vs. 5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3; 2023: 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 vs. 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2), with an average yield gain of 0.3\u0026ndash;0.4 t ha⁻\u0026sup1;. The absolute difference between the residue applied and not applied treatments was particularly significant at higher nitrogen doses and 4-split fertigation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that subsurface drip fertigation (SSDF) with split N application and greengram residue incorporation optimizes soil mineral nitrogen (N) distribution, enhancing maize productivity while advancing sustainable agriculture. Unlike conventional surface application (CNM), SSDF concentrates NH₄⁺-N and NO₃⁻-N at 20\u0026ndash;30 cm depth near emitters (e.g., 47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 mg kg⁻\u0026sup1; NH₄⁺-N and 25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 mg kg⁻\u0026sup1; NO₃⁻-N at 55 DAS under 100% RDN-4S), aligning N with active root zones (Singh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Delbaz et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This spatial precision, coupled with four-split applications and residue effects, reduces N inputs by 25% (75% RDN-4S yielding 6.6 t ha⁻\u0026sup1; vs. 6.7 t ha⁻\u0026sup1; for 100% RDN-4S) without compromising yields, offering a scalable strategy for resource-efficient cereal production.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEnhanced N Distribution and Retention\u003c/h2\u003e \u003cp\u003eThe ability of SSDF to target N at 20\u0026ndash;30 cm depth contrasts sharply with CNM\u0026rsquo;s shallow, transient peaks (16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 mg kg⁻\u0026sup1; NH₄⁺-N at 0\u0026ndash;10 cm), which are prone to volatilization and runoff (Ladha et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Contour maps (Figs.\u0026nbsp;1\u0026ndash;6) reveal tight N zones (12\u0026ndash;47 ppm NH₄⁺-N, 10\u0026ndash;36 ppm NO₃⁻-N) under SSDF, broadening laterally with time and residue, unlike CNM\u0026rsquo;s diffuse patterns. This aligns with findings that subsurface delivery enhances N retention by 20\u0026ndash;40% over surface methods (Delbaz et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barakat et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Four-split applications sustained higher N levels at tasselling (55 DAS) than three-split (47.5 vs. 43.1 mg kg⁻\u0026sup1; NH₄⁺-N), reflecting better synchrony with maize demand (Quemada \u0026amp; Gabriel, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Residue incorporation further increased mineral N by 10\u0026ndash;13% (15.4 vs. 13.8 mg kg⁻\u0026sup1; NH₄⁺-N at 30 DAS), consistent with legume residues contributing 12\u0026ndash;32 kg N ha⁻\u0026sup1; \u003cem\u003evia\u003c/em\u003e mineralization (Sharma \u0026amp; Behera, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Varatharajan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This effect, evident across sampling positions (0\u0026ndash;20 cm from emitters), likely stems from slower mineralization of organic N, as greengram residue (3 t ha⁻\u0026sup1;) continued releasing N post-tasselling. This synergy underlines SSDF\u0026rsquo;s potential to integrate organic and inorganic N sources, reducing reliance on synthetic fertilizers\u0026ndash;-a key sustainability metric (Tilman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eYield Optimization with Reduced Inputs\u003c/h2\u003e \u003cp\u003eThe equivalence of 75% RDN-4S (6.6 t ha⁻\u0026sup1;) to 100% RDN-4S (6.7 t ha⁻\u0026sup1;) highlights SSDF\u0026rsquo;s efficiency, cutting N inputs by 37.5 kg ha⁻\u0026sup1; without yield loss. This mirrors findings where precision fertigation sustains cereal yields with 20\u0026ndash;30% less N (Singh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Ghaffar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Strong correlations (r\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between N at 20\u0026ndash;30 cm and yield (Fig.\u0026nbsp;8) confirm that SSDF targets N to active root zones, a critical advantage over CNM\u0026rsquo;s lower yields (5.6 t ha⁻\u0026sup1;). Consistency of residue in enhancing yield (0.3\u0026ndash;0.4 t ha⁻\u0026sup1;) aligns with studies showing organic amendments enhance soil fertility and water retention (Thierfelder et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This dual approach\u0026ndash;\u0026ndash;precision delivery and residue recycling\u0026ndash;\u0026ndash;offers a practical pathway to sustainable intensification, particularly in resource-constrained regions like the Indo-Gangetic Plains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental Implications\u003c/h2\u003e \u003cp\u003eWhile SSDF optimizes N placement, elevated NO₃⁻-N at 30\u0026ndash;50 cm (e.g., 20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 mg kg⁻\u0026sup1;, 100% RDN-3S) and residual N at harvest (8\u0026ndash;14 ppm) signal leaching risks, a concern in sandy loams under high rainfall (593 mm in 2022) (Ladha et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gheysari et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This contrasts with CNM\u0026rsquo;s lower deep-layer N, likely due to surface losses (N₂O emissions), which SSDF may reduce by minimizing exposure (Shcherbak et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Residue incorporation enhanced urease activity (13.9 vs. 13.2 \u0026micro;g NH₄⁺-N g⁻\u0026sup1; h⁻\u0026sup1; at 50 DAS), reflecting accelerated urea hydrolysis and microbial N transformation (Tabatabai \u0026amp; Bremner, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This suggests residue stimulates microbial activity, potentially retaining N as NH₄⁺-N longer and reducing NO₃⁻-N leaching, though direct immobilization requires further study (Wolińska et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Grzyb et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Integrating SSDF with real-time irrigation scheduling could further curb N losses, aligning with climate-smart agriculture goals (FAO, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eScalability and Policy Relevance\u003c/h2\u003e \u003cp\u003eSSDF\u0026rsquo;s infrastructure (e.g., buried drippers) requires initial investment, but its water and N savings\u0026ndash;coupled with residue\u0026rsquo;s low-cost, in-situ availability\u0026ndash;make it viable for smallholders if supported by subsidies or extension services (Burney et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The 25% N reduction potential could lower fertilizer demand by millions of tons annually in maize systems, cutting greenhouse gas emissions from N production (~\u0026thinsp;5 kg CO₂e kg⁻\u0026sup1; N) (IPCC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In India, where maize area exceeds 9\u0026nbsp;million ha, scaling SSDF could enhance food security while addressing soil degradation concerns. Policy incentives for precision technologies and residue management, as seen in China\u0026rsquo;s drip irrigation programs (Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), could accelerate adoption.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s two-year scope limits long-term N cycling insights, particularly residue decomposition rates and microbial shifts. Elevated deep N suggests a need for dynamic irrigation models to match rainfall and crop uptake (Quemada \u0026amp; Gabriel, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Future work should quantify N₂O emissions under SSDF vs. CNM and assess residue quality (e.g., C:N ratio) effects on N release. Multi-site trials could test SSDF\u0026rsquo;s adaptability across soil types and climates, enhancing its global relevance and wider adoption by producers.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals that subsurface drip fertigation (SSDF), combined with split nitrogen (N) applications and greengram residue incorporation, transforms soil mineral N dynamics favorably enhancing maize productivity and sustainable agriculture. By concentrating NH₄⁺-N and NO₃⁻-N at 20\u0026ndash;30 cm depth (47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 mg kg⁻\u0026sup1; and 25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 mg kg⁻\u0026sup1; at 55 DAS under 100% RDN-4S), SSDF outperforms conventional surface application, aligning N with maize root zones and sustaining availability through tasseling. Four-split applications optimize this distribution compared to three-split, while residue augment soil N by 10\u0026ndash;13% \u003cem\u003evia\u003c/em\u003e enhanced microbial activity (urease: 13.9 vs. 13.2 \u0026micro;g g⁻\u0026sup1; h⁻\u0026sup1;), reducing reliance on synthetic inputs. Remarkably, 75% RDN-4S (6.6 t ha⁻\u0026sup1;) matches 100% RDN-4S yields (6.7 t ha⁻\u0026sup1;), cutting-down N use by 25% without compromising output\u0026ndash;\u0026ndash;a evidence to SSDF\u0026rsquo;s precision, corroborated by strong N-yield correlations (r\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, elevated NO₃⁻-N at 30\u0026ndash;50 cm (20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 mg kg⁻\u0026sup1;) and residual N at harvest (8\u0026ndash;14 ppm) highlight leaching risks, underscoring the need for tailored irrigation to balance efficiency and environmental stewardship.\u003c/p\u003e \u003cp\u003eThese findings offer a scalable blueprint for sustainable maize intensification, integrating precision technology and organic inputs (crop residue) to optimize N delivery, enhance soil health, and minimize ecological footprints. In regions like the Indo-Gangetic Plains, where maize supports millions, SSDF could reduce fertilizer demand, curb emissions from N production, and reinforce climate resilience if paired with policy support for adoption. Building on prior efficiency insights (Singh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e), this approach positions SSDF as a cornerstone of resource-efficient agriculture. Future research should refine irrigation strategies, quantify long-term N cycling, and test scalability across diverse agroecosystems, ensuring this innovation meets global sustainability imperatives with precision and purpose.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the financial support provided by the ICAR-NASF under grant no. NASF8016.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarakat, M., Cheviron, B., \u0026amp; Angulo-Jaramillo, R. (2016). Influence of the irrigation technique and strategies on the nitrogen cycle and budget: A review. Agricultural Water Management, 178, 225\u0026ndash;238. https://doi.org/10.1016/j.agwat.2016.09.027 \u003c/li\u003e\n\u003cli\u003eBates, D., M\u0026auml;chler, M., Bolker, B., \u0026amp; Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48. https://doi.org/10.18637/jss.v067.i01 \u003c/li\u003e\n\u003cli\u003eBurney, J. A., Davis, S. J., \u0026amp; Lobell, D. B. (2010). Greenhouse gas mitigation by agricultural intensification. Proceedings of the National Academy of Sciences, 107(26), 12052\u0026ndash;12057. https://doi.org/10.1073/pnas.0914216107 \u003c/li\u003e\n\u003cli\u003ede Mendiburu, F. (2021). agricolae: Statistical procedures for agricultural research. R package version 1.3-5. https://CRAN.R-project.org/package=agricolae\u003c/li\u003e\n\u003cli\u003eDelbaz, R., Ebrahimian, H., Abbasi, F., Ghameshlou, A. N., Liaghat, A., \u0026amp; Ranazadeh, D. (2023). A global meta-analysis on surface and drip fertigation for annual crops under different fertilization levels. \u003cem\u003eAgricultural Water Management\u003c/em\u003e, \u003cem\u003e289\u003c/em\u003e, 108504. doi: 10.1016/j.agwat.2023.108504 \u003c/li\u003e\n\u003cli\u003eFAO. (2019). Climate-smart agriculture sourcebook. Food and Agriculture Organization of the United Nations.\u003c/li\u003e\n\u003cli\u003eGhaffar, A., Ali, S., \u0026amp; Khan, M. H. (2021). Precision nitrogen management in wheat using subsurface drip fertigation. Agricultural Water Management, 245, 106627. https://doi.org/10.1016/j.agwat.2020.106627 \u003c/li\u003e\n\u003cli\u003eGheysari, M., Mirlatifi, S. M., Homaee, M., Asadi, M. E., \u0026amp; Hoogenboom, G. (2009). Nitrate leaching in a silage maize field under different irrigation and nitrogen fertilizer rates. 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Nitrogen fertilization affects maize grain yield through regulating nitrogen uptake, radiation and water use efficiency, photosynthesis and root distribution. \u003cem\u003ePeerJ, 8\u003c/em\u003e, e10291. https://doi.org/10.7717/peerj.10291 \u003c/li\u003e\n\u003cli\u003eSubbiah, B. V., \u0026amp; Asija, G. L. (1956). A rapid procedure for the determination of available nitrogen in soils. \u003cem\u003eCurrent Science, 25\u003c/em\u003e(6), 259\u0026ndash;260.\u003c/li\u003e\n\u003cli\u003eTabatabai, M. A., \u0026amp; Bremner, J. M. (1972). Assay of urease activity in soils. \u003cem\u003eSoil Biology and Biochemistry, 4\u003c/em\u003e(4), 479\u0026ndash;487. https://doi.org/10.1016/0038-0717(72)90064-8 \u003c/li\u003e\n\u003cli\u003eThierfelder, C., Chivenge, P., Mupangwa, W., Rusinamhodzi, L., \u0026amp; Garrity, D. (2017). How climate-smart is conservation agriculture? Agriculture, Ecosystems \u0026amp; Environment, 238, 102\u0026ndash;110. https://doi.org/10.1016/j.agee.2016.10.002 \u003c/li\u003e\n\u003cli\u003eTilman, D., Balzer, C., Hill, J., \u0026amp; Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260\u0026ndash;20264. https://doi.org/10.1073/pnas.1116437108\u003c/li\u003e\n\u003cli\u003eVaratharajan T, Dass A, Choudhary AK, Sudhishri S, Das TK, Rajanna GA, Prasad S, Swarnalakshmi K, Harish MN, Dhar S (2022) Integrated management enhances crop physiology and final yield in maize intercropped with blackgram in semiarid South Asia. Front Plant Sci. https:// doi. org/ 10. 3389/ fpls. 2022. 975569.\u003c/li\u003e\n\u003cli\u003eWang, J., Zhang, L., \u0026amp; Li, J. (2021). Adoption of drip irrigation in Chinese agriculture: Policy impacts and farmer perceptions. Sustainability, 13(8), 4567. https://doi.org/10.3390/su13084567\u003c/li\u003e\n\u003cli\u003eWolińska, A., Stępniewska, Z., \u0026amp; Kuźniar, A. (2017). Urease activity as a soil quality indicator: A review. Acta Agriculturae Scandinavica, Section B \u0026ndash; Soil \u0026amp; Plant Science, 67(4), 270\u0026ndash;283. https://doi.org/10.1080/09064710.2016.1266243 \u003c/li\u003e\n\u003cli\u003eZhang, Y., Li, X., \u0026amp; Gregorich, E. G. (2019). Soil enzyme activities as affected by long-term fertilization and crop residue management. Soil Biology and Biochemistry, 135, 278\u0026ndash;286. https://doi.org/10.1016/j.soilbio.2019.05.017\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Effect of SSDF fertigation and residue management on soil urease activities and grain yield of maize. \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 266px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrease (30 DAS)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026mu;g urea hydrolyzed g\u003csup\u003e\u0026minus;1\u0026nbsp;\u003c/sup\u003esoil hr\u003csup\u003e\u0026minus;1\u003c/sup\u003e at 37\u0026deg; C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrease (50 DAS)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026mu;g urea hydrolyzed g\u003csup\u003e\u0026minus;1\u003c/sup\u003e soil hr\u003csup\u003e\u0026minus;1\u003c/sup\u003e at 37\u0026deg; C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrain yield (t ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOn emitter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-10cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-20cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20-30cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-10cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-20cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20-30cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003eNitrogen fertigation levels\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0%RDN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.2\u0026plusmn;1.0\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.8\u0026plusmn;1.0\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.8\u0026plusmn;0.8\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.9\u0026plusmn;1.2\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.2\u0026plusmn;1.2\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.9\u0026plusmn;1.0\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.0\u0026plusmn;0.4\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2.8\u0026plusmn;0.4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e50%RDN-3S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e10.1\u0026plusmn;1.8\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.6\u0026plusmn;1.7b\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8.7\u0026plusmn;1.5b\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13.0\u0026plusmn;1.9c\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13.8\u0026plusmn;2.0b\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.6\u0026plusmn;1.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.1\u0026plusmn;0.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.8\u0026plusmn;0.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e50%RDN-4S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8.7\u0026plusmn;1.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.9\u0026plusmn;1.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.1\u0026plusmn;1.0\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13.5\u0026plusmn;1.5bc\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13.7\u0026plusmn;1.6b\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.4\u0026plusmn;1.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.5\u0026plusmn;0.5b\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.2\u0026plusmn;0.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e75%RDN-3S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e11.7\u0026plusmn;0.9\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11.2\u0026plusmn;0.8a\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.1\u0026plusmn;0.8a\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15.1\u0026plusmn;0.8ab\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16.2\u0026plusmn;0.9a\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e14.6\u0026plusmn;0.8a\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.4\u0026plusmn;0.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.0\u0026plusmn;0.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e75%RDN-4S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e10.7\u0026plusmn;1.3\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.8\u0026plusmn;1.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8.7\u0026plusmn;1.0b\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15.8\u0026plusmn;1.9a\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16.8\u0026plusmn;2.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.0\u0026plusmn;1.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.6\u0026plusmn;0.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.4\u0026plusmn;0.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e100%RDN-3S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e12.4\u0026plusmn;1.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.2\u0026plusmn;1.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.8\u0026plusmn;1.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16.2\u0026plusmn;1.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17.6\u0026plusmn;1.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.6\u0026plusmn;1.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.5\u0026plusmn;0.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.2\u0026plusmn;0.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e100%RDN-4S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e11.3\u0026plusmn;1.3\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.6\u0026plusmn;1.3a\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.3\u0026plusmn;1.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16.7\u0026plusmn;2.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17.9\u0026plusmn;2.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.7\u0026plusmn;1.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.7\u0026plusmn;0.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.5\u0026plusmn;0.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eConventional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e10.7\u0026plusmn;1.5\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.9\u0026plusmn;1.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.7\u0026plusmn;1.1c\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.2\u0026plusmn;1.7\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.7\u0026plusmn;1.7\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.9\u0026plusmn;1.4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.6\u0026plusmn;0.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.1\u0026plusmn;0.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003eResidue management\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6870488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6870488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims:\u003c/h2\u003e \u003cp\u003ePrecise nitrogen (N) supply is vital to sustainable maize production amid food security challenges. This study aimed to evaluate subsurface drip fertigation (SSDF) for optimizing nitrogen distribution and enhancing maize productivity while reducing environmental risks.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study was conducted at ICAR-IARI, New Delhi (2022\u0026ndash;2023), using a split-plot design. SSDF was tested with 0\u0026ndash;100% recommended dose of N (RDN) applied in 3\u0026ndash;4 splits, combined with greengram residue incorporation. Soil mineral N (NH₄⁺-N, NO₃⁻-N) was measured at 0\u0026ndash;50 cm depths and 0\u0026ndash;20 cm from emitters, along with urease activity and grain yield.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSSDF outperformed conventional methods, achieving peak NH₄⁺-N at 52.7 mg kg⁻\u0026sup1; and NO₃⁻-N at 36.3 mg kg⁻\u0026sup1; in the 20\u0026ndash;30 cm layer under 100% RDN-4S treatment. Four splits-maintained N supply better than three splits. Residue incorporation improved soil N by 10\u0026ndash;13% and urease activity by 11\u0026ndash;13% (13.9 \u0026micro;g g⁻\u0026sup1; h⁻\u0026sup1;). Yields from 75% RDN-4S (6.6 t ha⁻\u0026sup1;) equaled 100% RDN-4S (6.7 t ha⁻\u0026sup1;), achieving 25% N savings with significant N-yield correlations (r\u0026thinsp;=\u0026thinsp;0.8\u0026ndash;0.85).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSSDF optimized nitrogen distribution by concentrating mineral N in the 20\u0026ndash;30 cm root zone and enabled 25% reduction in N application (75% RDN-4S) while maintaining equivalent maize yields. The integration of four-split applications with residue incorporation enhanced soil N availability and microbial activity, demonstrating SSDF's potential for sustainable maize production.\u003c/p\u003e","manuscriptTitle":"Subsurface drip fertigation optimizes nitrogen distribution in soil under maize cultivation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-21 04:54:36","doi":"10.21203/rs.3.rs-6870488/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-06-19T08:29:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T15:55:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-06-16T01:23:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-16T00:57:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-06-13T07:20:44+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":"131b7a6d-266d-4f75-a040-f6e5fd78e025","owner":[],"postedDate":"June 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:04:21+00:00","versionOfRecord":{"articleIdentity":"rs-6870488","link":"https://doi.org/10.1007/s11104-026-08354-5","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2026-02-13 15:59:33","publishedOnDateReadable":"February 13th, 2026"},"versionCreatedAt":"2025-06-21 04:54:36","video":"","vorDoi":"10.1007/s11104-026-08354-5","vorDoiUrl":"https://doi.org/10.1007/s11104-026-08354-5","workflowStages":[]},"version":"v1","identity":"rs-6870488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6870488","identity":"rs-6870488","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}