Interactive effects of biochar-mediated slow-release nitrogen and foliar Nano-Zinc oxide on nitrogen use efficiency, soil organic carbon fractions and yield of lentil (lens culinaris medik) under semi-arid rainfed conditions

Introduction

Lentil (Lens culinaris Medik.) is among the oldest domesticated cool-season food legumes and remains a nutritionally important pulse crop because of its high protein, mineral and dietary-fibre content. In India it is grown predominantly as a rainfed rabi crop on residual soil moisture, mainly in semi-arid tracts where terminal drought, low soil organic carbon (SOC) and multiple nutrient constraints jointly limit yield (Sarker et al., 2022) [16] . Nitrogen (N) is the single most limiting nutrient for lentil growth and grain formation. Although lentil can meet part of its N requirement through biological nitrogen fixation (BNF), field-level BNF efficiency is frequently sub-optimal, and a modest starter dose of N (15-20 kg ha⁻¹) is commonly recommended to support early growth before nodulation becomes fully effective. Conventional soluble fertilizers such as urea are, however, prone to substantial leaching, volatilization and denitrification losses, and the reported nitrogen-use efficiency (NUE) of urea in dryland cropping systems rarely exceeds 30-40% (Cassman et al., 2020) [4] . Biochar-mediated slow-release nitrogen (BSRN) fertilizers have emerged as a promising technology for improving the synchrony between N supply and crop demand. The porous, highsurface-area structure of biochar allows it to adsorb ammonium and release it gradually, reducing losses while simultaneously enriching soil organic carbon pools (Lehmann et al., 2021; Sohi et al., 2022) [10] . In parallel, zinc (Zn) deficiency is a widespread constraint in calcareous, low-organic-matter semi-arid soils, and Zn plays a central catalytic role in nitrogen metabolism through zinc-dependent enzymes such as carbonic anhydrase (CA) and nitrate reductase (NR) (Cakmak & Kutman, 2018) [2] . Nano-zinc oxide (nZnO), with its high specific surface area and enhanced reactivity relative to conventional ZnSO₄, has shown improved foliar uptake efficiency and physiological responsiveness in several crops (Dimkpa et al., 2019) [6] . Because N and Zn metabolism are mechanistically interlinked, a synchronized slowrelease N supply combined with efficient foliar Zn nutrition could produce a synergistic -rather than purely additive -improvement in NUE, SOC status and productivity. This interaction, however, has not previously been evaluated for lentil under field conditions. The present study was therefore undertaken as a factorial field investigation to (i) evaluate the effect of five BSRN levels on soil inorganic N dynamics, gross N mineralization and SOC fractions; (ii) assess the influence of four foliar nZnO concentrations on Zn bioavailability and Zn-dependent enzyme activity; (iii) quantify the BSRN × nZnO interaction on growth, yield and grain quality; and (iv) identify the physiological and soil-biological correlates of yield through multivariate correlation analysis.

Materials and Methods

Experimental site and soil characteristics

The field experiment was conducted during the rabi season of 2025-26 at the Research Farm, Department of Agronomy, College of Agricultural Science & Applied Research, BEST Innovation University, Andhra Pradesh, India. Twelve composite surface (0-30 cm) soil samples were collected prior to the experiment for baseline characterization (Table 1). The soil was slightly alkaline (pH 7.6), non-saline (EC 0.28 dS m⁻¹), low in organic carbon and available N, and marginally deficient in DTPA-extractable Zn conditions typical of semi-arid lentilgrowing tracts.

Experimental design and treatments

The experiment followed a Factorial Randomized Block Design (FRBD) with two factors and three replications (60 plots; gross plot 3.0 × 4.0 m; net harvest area 2.0 × 3.0 m). Factor A comprised five BSRN levels N₀, N₁, N₂, N₃ and N₄, corresponding to 0, 25, 50, 75 and 100% of the recommended dose of nitrogen (RDN = 20 kg N ha⁻¹) supplied as biochar-immobilized urea-N, applied at sowing. Factor B comprised four foliar nZnO concentrations Z₀ (0 ppm, water spray), Z₁ (25 ppm), Z₂ (50 ppm) and Z₃ (100 ppm), applied as foliar sprays at the vegetative and pre-flowering stages. The 5 × 4 factorial combination produced 20 treatments, each replicated three times.

Observations and nitrogen-use-efficiency indices

Soil samples (0-15 cm) were analysed for NH₄⁺-N and NO₃⁻-N, gross N mineralization (¹⁵N pool-dilution technique; Davidson et al., 1991) [5] , total SOC (Walkley-Black method), particulate organic carbon (POC; Cambardella & Elliott, 1992) [3] , hot-water extractable carbon (HWC; Ghani et al., 2003) [8] , microbial biomass carbon and nitrogen (chloroform-fumigation extraction; Vance et al., 1987) [23] , DTPA-extractable Zn (Lindsay & Norvell, 1978) [11] , and urease and alkaline phosphatase activity (Tabatabai & Bremner, 1972) [20] . Plant observations at 30, 60, 90 days after sowing (DAS) and at harvest included plant height, leaf area index (LAI, 60 DAS), shoot dry matter, crop growth rate (CGR), SPAD chlorophyll index, carbonic anhydrase activity (Pal et al., 2004) [13] , nitrate reductase activity (Hageman & Hucklesby, 1971) [9] , and leaf/grain Zn concentration. Yield components (pods plant⁻¹, seeds pod⁻¹, 100-seed weight, harvest index), grain and straw yield, grain protein (Kjeldahl N × 6.25), and biological nitrogen fixation (%Ndfa, ¹⁵N natural-abundance method; Unkovich et al., 2021) [22] . were recorded at physiological maturity.

Nitrogen-use-efficiency indices were computed as follows:

Agronomic NUE (kg grain kg⁻¹ N) = (grain yield of fertilized plot -grain yield of control) / N applied Partial factor productivity of N, PFPN (kg grain kg⁻¹ N) = grain yield / N applied Apparent N recovery efficiency (%) = [(N uptake of fertilized plot -N uptake of control) / N applied] × 100 Nitrogen harvest index (%) = grain N / (grain N + straw N) × 100

Statistical analysis

Data were subjected to two-factor factorial analysis of variance (ANOVA) appropriate for FRBD. Treatment and main-effect means were compared using the standard error of the mean (SEm±) and critical difference (CD) at P = 0.05. Pearson correlation analysis was used to examine relationships among soil, physiological and yield parameters. All computations were performed in Python 3.12 using pandas, NumPy and SciPy.

Results

Soil inorganic nitrogen dynamics and gross n mineralization

Soil NH₄⁺-N rose steadily and significantly (P ≤ 0.01) with BSRN level, from 18.11 mg kg⁻¹ at N₀ to 28.81 mg kg⁻¹ at N₄ (+59%), while NO₃⁻-N increased from 12.26 to 19.13 mg kg⁻¹ (Table 2, Figure 1). The nZnO main effect on NH₄⁺-N and NO₃⁻-N was nonsignificant. Gross N mineralization increased in parallel, from 1.92 to 2.57 mg N kg⁻¹ day⁻¹ across the BSRN gradient, and was also significantly (P ≤ 0.05) enhanced by nZnO application (Figure 2), indicating an indirect stimulatory effect of improved Zn nutrition on soil biological N cycling.

Zinc bioavailability and soil enzyme activities

DTPA-extractable soil Zn responded strongly and significantly (P ≤ 0.01) to foliar nZnO, nearly doubling from 0.578 mg kg⁻¹ at Z₀ to a maximum of 1.066 mg kg⁻¹ at Z₂ (50 ppm), with a slight decline at Z₃ (Table 4, Figure 5). The BSRN main effect on DTPA-Zn was non-significant, confirming that the two nutrient pathways operated largely independently at the Soil-Zn level. Soil urease and alkaline phosphatase activities increased significantly with BSRN level, reflecting greater microbial and enzymatic activity in carbon-enriched soil, and urease activity also responded significantly to nZnO.

Growth attributes

Plant height increased from 33.5 cm (N₀) to a maximum of 38.1 cm (N₃), an increase of 13.4% (Table 5, Figure 6). LAI at 60 DAS, shoot dry-matter accumulation and CGR followed the same pattern, all peaking at N₃ or N₄. SPAD chlorophyll index increased significantly with both BSRN (39.0 to 45.6) and nZnO application, and the BSRN × nZnO interaction was significant for this trait, indicating that leaf chlorophyll status responded most strongly when adequate N and Zn were supplied together.

Yield attributes, grain and straw yield

Grain yield was highly significantly influenced by both BSRN level and nZnO concentration (P ≤ 0.01). The lowest treatment mean (686 kg ha⁻¹) occurred in the untreated control (T1, N₀Z₀) and the highest (1156 kg ha⁻¹, +68%) in T20 (N₄Z₃); as main effects, yield rose from 725 to 1076 kg ha⁻¹ across the BSRN gradient (Figure 9) and from 873 to a maximum of 1001 kg ha⁻¹ at 50 ppm nZnO before declining slightly at 100 ppm (Figure 10).

The BSRN × nZnO interaction (Figure 11) was not statistically significant for grain yield (F = 1.72, NS), indicating a predominantly additive relationship, although the numerically largest combined response occurred at the highest BSRN level with 50-100 ppm nZnO. Straw yield, harvest index, pods plant⁻¹, seeds pod⁻¹ and 100-seed weight all followed the same trend as grain yield (Table 8, Figure 12-13).

Grain quality and biological nitrogen fixation

Grain protein content increased significantly with both BSRN level (25.2% at N₀ to 27.6% at N₄) and nZnO concentration (25.4% at Z₀ to 27.5% at Z₂) (Figure 14). Grain Zn concentration responded strongly to nZnO (38.8 to 56.4 mg kg⁻¹, Z₀ to Z₂) and significantly to BSRN level, with a significant BSRN × nZnO interaction, confirming a combined nutritional-biofortification benefit. Biological nitrogen fixation (%Ndfa) increased from 44.3% at N₀ to 55.6% at N₃, indicating that the modest starter-N doses supplied through BSRN did not suppress nodulation but instead supported greater nodule activity through improved overall plant vigour.

Nitrogen-use-efficiency indices

Agronomic NUE, PFPN and apparent N recovery efficiency were all significantly influenced by BSRN level, nZnO concentration and their interaction. Agronomic NUE peaked at the lowest positive BSRN dose (N₁, 25% RDN; 22.4 kg grain kg⁻¹ N pooled across N₀-referenced comparisons) and declined at higher BSRN doses, consistent with the law of diminishing returns, while nZnO consistently raised NUE at every BSRN level, with 50 ppm giving the highest agronomic NUE (37.2 kg grain kg⁻¹ N) and PFPN (108.6 kg grain kg⁻¹ N) among the nZnO levels (Table 10, Figures 1617). Apparent N recovery efficiency followed the same pattern, rising from 126.9% at Z₀ to 236.1% at Z₂. Nitrogen harvest index was comparatively stable across BSRN levels (75.6-76.7%) but significantly affected by nZnO.

Correlation analysis

Pearson correlation analysis on plot-level data (n = 60) showed that grain yield was strongly and positively associated with soil NH₄⁺-N, gross N mineralization, total SOC and POC, carbonic anhydrase and nitrate reductase activity, plant height, SPAD chlorophyll index, and grain Zn and grain protein content (Figure 18). SOC and POC were themselves strongly inter-correlated, as were carbonic anhydrase and nitrate reductase, reflecting the tightly coupled physiological basis of the N-Zn synergy underlying the yield response.

Discussion

The consistent, dose-dependent improvement in soil NH₄⁺-N and gross N mineralization with increasing BSRN level confirms the intended slow-release behaviour of the biochar-urea matrix: rather than acting as an inert adsorbent, the biochar carrier appears to buffer soil N supply while gradually releasing adsorbed ammonium, a mechanism consistent with the 35-48% greater retention of labelled NH₄⁺-N reported for biochar-amended soils by Sohi et al. (2022) [18] . The parallel, larger relative gain in the labile POC fraction (+38%) compared with total SOC (+20%) indicates that biochar addition preferentially enriched the most management-responsive carbon pool within a single season, mirroring the global synthesis of Lehmann et al. (2021) [10] , who identified biochar as the most effective intervention among common management practices for raising labile SOC fractions. The strong, saturating response of DTPA-extractable Zn, leaf Zn, carbonic anhydrase and nitrate reductase activity to nZnOpeaking at 50 ppm and plateauing or declining at 100 ppm -is consistent with the nanoscale dissolution and enhanced cuticular penetration reported for nZnO relative to conventional ZnSO₄ (Dimkpa et al., 2019; Spielman-Sun et al., 2020) [6,19] , and with the mild phytotoxicity or physiological imbalance that can accompany excessive foliar Zn concentrations. Because carbonic anhydrase and nitrate reductase are both zinc-dependent enzymes central to carbon fixation and nitrate assimilation, their coordinated response to nZnO provides a direct physiological explanation for the observed gains in SPAD chlorophyll index, grain protein and, ultimately, grain yield -supporting the mechanistic framework proposed by Cakmak and Kutman (2018) for N-Zn synergy in cereals and extending it to lentil under field conditions.

The significant BSRN × nZnO interaction detected for soil NH₄⁺-N, SPAD chlorophyll index, grain Zn concentration, agronomic NUE and apparent N recovery efficiency -despite a largely additive relationship for grain yield itself -indicates that the two inputs interact synergistically at the physiological and nutrientuse-efficiency level even where the yield response remains close to additive. This pattern parallels the findings of Tiwari et al.

(2023) [21] . in chickpea, who reported 17% higher NUE and 23% higher grain N under combined slow-release N and Zn nutrition, and extends this evidence to a nanoparticle Zn source in lentil for the first time. The peak in agronomic NUE at the lowest positive BSRN dose (N₁), combined with the highest NUE at 50 ppm nZnO, illustrates the classical inverse relationship between applied N rate and efficiency, moderated favourably by improved Zn nutrition -a practically important finding for resource-limited semi-arid cropping systems where maximizing the return per unit of applied nitrogen is often more relevant than maximizing absolute yield. The progressive rise in %Ndfa with increasing BSRN level, rather than the nodulation suppression sometimes associated with high mineral-N availability, suggests that the modest starter doses supplied by BSRN (up to 20 kg N ha⁻¹) remained below the threshold for BNF inhibition, while the associated improvement in plant vigour and carbon status instead supported greater nodule mass and rhizobial activity. Collectively, the correlation structure linking yield to soil N synchrony, labile carbon fractions and Znenzyme activity provides multivariate confirmation that BSRN and nZnO act through complementary soil-and plant-level pathways to improve lentil productivity under semi-arid rainfed conditions.

Conclusions

Biochar-mediated slow-release nitrogen progressively and significantly improved soil inorganic N synchrony, gross N mineralization, labile soil organic carbon fractions, growth attributes, yield components, and grain and straw yield of rainfed lentil, with 100% RDN (N₄) generally producing the highest values. Foliar nano-zinc oxide, particularly at 50 ppm, significantly enhanced DTPA-extractable soil Zn, leaf and grain Zn concentration, and the activity of the two pivotal Zndependent enzymes of C and N metabolism, confirming the superior bioavailability of the nanoparticle Zn source. A statistically significant BSRN × nZnO interaction for soil NH₄⁺-N, SPAD chlorophyll index, grain Zn concentration, agronomic NUE and apparent N recovery efficiency provides direct evidence of a genuine physiological synergy between synchronized slowrelease nitrogen and nano-Zn-mediated enzyme activation. The treatment combination of 100% RDN as BSRN with 50-100 ppm foliar nZnO consistently produced the most favourable outcomes for yield, grain quality and Zn biofortification, while intermediate BSRN levels (25-50% RDN) combined with 50 ppm nZnO offered the most efficient use of applied nitrogen. These findings establish BSRN combined with nano-Zn foliar nutrition as an agronomically sound and mechanistically supported strategy for improving nitrogen-use efficiency, soil carbon status and productivity of rainfed lentil under semi-arid conditions. The trial was conducted over a single season at one location with a single nZnO formulation; multi-season, multi-location validation and comparison of nZnO particle characteristics are recommended before broader recommendation.

References

1. Broadley MR, White PJ, Hammond JP, Zelko I, Lux A. Zinc in plants. New Phytol. 2007;173(4):677-702.
2. Cakmak I, Kutman UB. Agronomic biofortification of cereals with zinc: a review. Eur J Soil Sci. 2018;69(1):172180.
3. Cambardella CA, Elliott ET. Particulate soil organic-matter changes across a grassland cultivation sequence. Soil Sci Soc Am J. 1992;56(3):777-783.
4. Cassman KG, Grassini P, van Wart J. Crop yield potential, yield trends, and global food security in a changing climate. Nat Food. 2020; 1:27-34.
5. Davidson EA, Hart SC, Shanks CA, Firestone MK. Measuring gross nitrogen mineralization, and nitrification by 15N isotopic pool dilution in intact soil cores. J Soil Sci. 1991;42(3):335-349.
6. Dimkpa CO, Singh U, Adisa IO, Bindraban PS, Elmer WH, Gardea-Torresdey JL, et al. Effects of nanoparticle exposure on plant physiology, agronomy and pathology. Sci Total Environ. 2019; 665:606-616.
7. FAO. FAOSTAT: crops and livestock products. Rome: Food and Agriculture Organization of the United Nations; 2023.
8. Ghani A, Dexter M, Perrott KW. Hot-water extractable carbon in soils: a sensitive measurement for determining impacts of fertilisation, grazing and cultivation. Soil Biol Biochem. 2003;35(9):1231-1243.
9. Hageman RH, Hucklesby DP. Nitrate reductase from higher plants. Methods Enzymol. 1971; 23:491-503.
10. Lehmann J, Cowie A, Masiello CA, Kammann C, Woolf D, Amonette JE, et al. Biochar in climate change mitigation. Nat Geosci. 2021; 14:883-892.
11. Lindsay WL, Norvell WA. Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Sci Soc Am J. 1978;42(3):421-428.
12. Mia S, van Groenigen JW, van de Voorde TFJ, Oram NJ, Bezemer TM, Mommer L, et al. Biochar application rate affects biological nitrogen fixation and nitrogen-use efficiency in legumes. Plant Soil. 2024; 494:1-16.
13. Pal M, Chaturvedi AK, Pandey SK, Bahuguna RN. A rapid method for estimation of carbonic anhydrase activity in plant tissue. Biol Plant. 2004; 48:615-617.
14. Poeplau C, Don A, Six J, Kaiser M, Benbi D, Chenu C, et al. Isolating organic carbon fractions with varying turnover rates in temperate agricultural soils a comprehensive method comparison. Soil Biol Biochem. 2023; 178:108953.
15. Rajput VD, Minkina T, Kumari A, Shende SS, Ranjan A, Flieger M, et al. A review on nanomaterials and their applications in enhancing zinc and iron biofortification in crops. Environ Res. 2021; 196:110649.
16. Sarker A, Kumar S, Sehgal D, Maalouf F. Lentil genetics, genomics and breeding: an overview. Field Crops Res. 2022; 283:108539.
17. Singh U, Praharaj CS, Singh SS, Upadhyaya HD. Soil organic carbon dynamics and nitrogen nutrition in rainfed lentil systems: a review. Field Crops Res. 2021; 265:108099.
18. Sohi SP, Krull E, Lopez-Capel E, Bol R. A review of biochar and its use and function in soil. Soil Biol Biochem. 2022; 165:108538.
19. Spielman-Sun E, Lombi E, Donner E, Howard D, Unrine JM, Lowry GV. Impact of surface charge on foliar zinc oxide nanoparticle uptake, transformation and translocation. Environ Sci Technol. 2020;54(11):6742-6752.
20. Tabatabai MA, Bremner JM. Assay of urease activity in soils. Soil Biol Biochem. 1972;4(4):479-487.
21. Tiwari A, Sharma P, Kumar N, Tripathi S, Solanki J. Interactive effect of slow-release nitrogen and zinc nutrition on nitrogen use efficiency and grain quality of chickpea. Eur J Agron. 2023; 145:126785.
22. Unkovich M, Herridge D, Peoples M, Cadisch G, Boddey R, Giller K, et al. Measuring plant-associated nitrogen fixation in agricultural systems. 2nd ed. Canberra: Australian Centre for International Agricultural Research; 2021.
23. Vance ED, Brookes PC, Jenkinson DS. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem. 1987;19(6):703-707.
24. Zhang H, Voroney RP, Price GW. Effects of biochar amendment on soil microbial biomass, gas fluxes and enzyme activities: influence of pyrolysis temperature. Appl Soil Ecol. 2023; 181:104654.