PEER-REVIEW ARTICLE PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1136 Nano Fertilizer Application Under Different Establishment Techniques for Sustainable Paddy (Oryza sativa L.) Production Thangamuthu Theerthana ,a,* Shivalli Boregowda Yogananda ,a Salekoppal Sannegowda Prakash ,b Matadadoddi Nanjundegowda Thimmegowda ,c Hadivappa Marulappa Jayadeva ,c Avverahalli Puttegowda Mallikarjuna Gowda ,c and Rampura Shivappa Ramanji d Rice production in Asia is a cornerstone of global food security. Implementing innovative crop establishment practices and utilizing nano fertilizers can enhance rice yields and mitigate environmental concerns, thereby contributing to a resilient and sustainable food system. Therefore, a field experiment was conducted over 2020 and 2021 that included various methods of application (seed treatment, root dipping, soil and foliar application) of nano fertilizers (nano nitrogen and nano zinc) under different rice establishment methods (conventional paddy and SRI). Statistical analysis was performed using Fisher’s analysis of variance and Duncan’s multiple range test (p ≤ 0.05). The findings showed that the application of 75% N and two foliar sprays of nano-nitrogen and nano-zinc at 25 to 30 and 45 to 50 days after transplanting under System of Rice Intensification method (Treatment T14) was statistically superior in improving growth and yield parameters, grain and straw yield, and in enhancing the quality of rice over other treatments. Studies revealed strong positive correlations between all the measures, with the exception of the proportion of chaffiness and unfilled grains. The results of the stepwise regression analysis revealed the percentage dependence of grain and straw yield on growth, yield, and quality factors. DOI: 10.15376/biores.20.1.1136-1160 Keywords: Sustainable agriculture; Paddy; SRI; Nano fertilizers; Quality; Yield; Correlation; Regression Contact information: a: Department of Agronomy, College of Agriculture, V. C. Farm, Mandya, 571405, University of Agricultural Sciences, Bangalore, Karnataka, India; b: Department of Soil Science and Agricultural Chemistry, College of Agriculture, V. C. Farm, Mandya, 571405, University of Agricultural Sciences, Bangalore, Karnataka, India; c: College of Agriculture, GKVK, University of Agricultural Sciences, Bangalore-560065, Karnataka, India; d: Department of Agricultural Statistics, College of Agriculture, V. C. Farm, Mandya, University of Agricultural Sciences, Bangalore-560065, Karnataka, India; *Corresponding author: theerthumuthu@gmail.com INTRODUCTION The world’s population is predicted to surpass 9.7 billion by 2050, necessitating a 60% increase in food production (United Nations Department for Economic and Social Affairs 2019). The most contributing cereal crops, namely maize, rice, wheat, and their products in world, account for 140.43, 516.25 and 535.49 kcal/capita/day, respectively (FAO 2022). With 197 g/day and 71.9 kg/year, rice has the highest net availability per person of all the cereals in 2020 to 2021 (Directorate of Economics and Statistics 2021). https://orcid.org/0000-0003-1919-4063 https://orcid.org/0000-0003-2204-5333 https://orcid.org/0000-0002-0515-3706 https://orcid.org/0000-0001-7632-1036 https://orcid.org/0000-0002-6061-1386 https://orcid.org/0000-0003-1894-0837 PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1137 Rice provides about 700 calories day-1 person-1 for about 3000 million people living mostly in developing countries (Sangeetha and Baskar 2015). The success of rice production in Asia will determine the future stability of the world’s food supply. In addition to using between 24% and 30% of the global freshwater, rice consumes between 34% and 43% of the irrigation water on the global scale (Surendran et al. 2021). According to predictions, Asia’s 17 to 22 million hectares of irrigated rice land will experience water scarcity by 2025 (Tuong and Bouman 2002), prompting widespread use of water-saving techniques. While the total employment in agriculture dropped in India from 63.32% in 1991 to 42.6% in 2019 as a result of rapid economic growth in non-agricultural sectors and rising labor wages, manual rice transplanting requires 25 to 50 man-days ha-1 (Zhang et al. 2011; Singh and Sharma 2012). Crop establishment procedures can be changed to provide solutions to all of the aforementioned issues. However, transplanting machines are expensive, so poor farmers cannot afford them. Non-availability of herbicides, compulsory land leveling, and more quantity of seeds (8 to 10 kg acre-1) makes direct seeded rice disadvantageous. Aerobic rice is not appropriate for higher rainfall areas where water cannot be controlled and also requires relatively extra weed management (Alam et al. 2014; Alam et al. 2016; Chakraborty et al. 2017). System of Rice Intensification (SRI) is a renowned methodology that greatly enhances rice yield without requiring additional seeds, chemical fertilizer, or other external inputs (Devi and Ponnarasi 2009). The efficiency of nitrogen fertilizers in Asia is only 20% to 30%, compared to 45% globally. A proper and effective nutrient management could achieve 75% to 80% of potential yield (Sapkota et al. 2021). Management of nutrients helps to lower fertilizer losses and increase production (Ye et al. 2019). Most rice growing areas are nitrogen-poor, necessitating a strong concentration on nitrogen nutrition (Fageria and Baligar 2003). Consumption of nitrogenous fertilizers in India during 2019 to 2020 was 19,100 thousand tons while it was only 16,735 thousand tons during 2016 to 2017 (Department of Fertilizers, Ministry of Chemicals and Fertilizers 2020). Zinc deficiency is prevalent in many rice-growing regions (Impa and Johnson- Beebout 2012), with ca. 50% of soils in these areas exhibiting low zinc levels (Singh 2008). Submergence of the soil, which is prevalent in rice production, causes a Zn shortage. Zinc deficiency is also common in alkaline or calcareous soils (Prasad et al. 2014). Field studies have shown that seed treatment, foliar application, or a combination can effectively enhance zinc uptake and accumulation in grains (Nair et al. 2010). Nanotechnology is a strategy to enhance nutrient use efficiency. Nano fertilizers can be alternatives to conventional fertilizers for gradual and controlled supply of nutrients in the soil (Kottegoda et al. 2011; Shang et al. 2019). They could be a crucial development in the protection of the environment because they can be applied in smaller quantities compared to traditional fertilizers (Adisa et al. 2019), hence reducing leaching, runoff, and gas emissions to the atmosphere (Manjunatha et al. 2016). Given the recognized significance of these nano nitrogen and nano zinc in plant development and their common deficiencies in agricultural soils, this investigation was undertaken to explore their potential benefits on growth, yield, and quality parameters of rice. PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1138 EXPERIMENTAL Experimental Site The field experimentation was conducted at the A-block, College of Agriculture, Vishweshwaraiah Canal Farm, Mandya, situated in the Agro-Climatic Zone VI (Southern Dry Zone) of Karnataka at 12º 57' N latitude and 76º 83' E longitude at an altitude of 678 m above mean sea level. The details of the weather parameters recorded during the crop growth period are depicted in Fig. 1. The soil at the experiment site was sandy clay loam in texture with 57.3%, 14.0%, and 28.6% sand, silt, and clay, respectively. The soil was alkaline in reaction (pH 8.1) and low in soluble salts (0.45 dS m-1). a) 0 5 10 15 20 25 30 35 40 0 50 100 150 200 250 300 January February March April May June Month M e a n M a x .T e m p .( °C ) N , M e a n M a x .T e m p .( °C ) A , M e a n M in .T e m p .( °C ) N , M e a n M in .T e m p .( °C ) A , M e a n S u n s h in e h r. (h r d a y -1 ) N , M e a n S u n s h ii n e (h r d a y -1 ) A R a in fa ll ( m m ) N a n d A , M a x . R H (% ) N a n d A Rainfall (mm) N Rainfall (mm) A Max. Relative humidity (%) N Max. Relative humidity (%) A Maximum Mean temperature (ºC) N Maximum Mean temperature (ºC) A Minimum Mean temperature (ºC) N Minimum Mean temperature (ºC) A PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1139 b) Fig. 1. Meteorological data of the experimental area at College of Agriculture, V. C. Farm, Mandya during a) 2020 and b) 2021 The soil was in the medium range in organic carbon (0.52%), available nitrogen (318 kg ha-1), P2O5 (33.5 kg ha-1), K2O (226 kg ha-1), and S (15.3 mg kg-1). The exchangeable calcium and magnesium content of soil was 8.86 and 2.91 cmol (p+) kg-1, respectively. The DTPA extractable iron, zinc, manganese, copper, and hot water-soluble boron content was 34.9, 1.53, 11.2, and 3.11 mg kg-1, respectively. Bacterial, fungal, and actinomycetes population was 14.2 cfu × 105 g-1 of soil, 12.2 cfu × 104 g-1 of soil, and 5.28 cfu × 103 g-1 of soil, respectively. The dehydrogenase activity was 129 μg TPF g-1 soil hr- 1, urease activity was 10.7 μg NH4 +-N g-1 hr-1, acid and alkaline phosphatase activity was 17.9 and 13.0 μmol g-1 hr-1, respectively. Treatments and Layout The experiments were conducted during kharif 2020 and 2021. Considering the nature of factors under study and the convenience of agricultural operation, the experiment was laid out in randomized complete block design. The whole field was divided into three blocks each representing a replication. The experiment consisted of 14 treatments and was randomly allocated within the replications. A distance of 0.3 m between treatments and 0.50 m between replications was provided. Bunds with the height of 30 cm were raised in the space available between replications and treatments. The treatments included were as follows: T1: TP with recommended practice; T2: SRI with recommended practice; T3: TP with 50% RDN + ST; TP4 with 50% RDN + RD; T5: TP with 50% RDN + FS; T6: SRI with 50% RDN + ST; T7: SRI with 50% RDN + RD; T8: SRI with 50% RDN + FS; T9: TP with 75% RDN + ST; T10: TP with 75% RDN + RD; 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 M e a n M a x .T e m p .( °C ) N , M e a n M a x .T e m p .( °C ) A , M e a n M in .T e m p .( °C ) N , M e a n M in .T e m p .( °C ) A , M e a n S u n s h in e h r. (h r d a y -1 ) N , M e a n S u n s h ii n e (h r d a y -1 ) A R a in fa ll ( m m ) N a n d A , M a x . R H ( % ) N a n d A Month Rainfall (mm) N Rainfall (mm) A Max. Relative humidity (%) N Max. Relative humidity (%) A Maximum Mean temperature (ºC) N Maximum Mean temperature (ºC) A Minimum Mean temperature (ºC) N Minimum Mean temperature (ºC) A Mean Sunshine hours (hr day⁻¹) N Mean Sunshine hours (hr day⁻¹) A PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1140 T11: TP with 75% RDN + FS; T12: SRI with 75% RDN + ST; T13: SRI with 75% RDN + RD; T14: SRI with 75% RDN + FS (Note: TP: Transplanted paddy; SRI: System of Rice Intensification; RP: Recommended practice; ST: Seed treatment; RD: Root dipping; FS: Foliar sprays of both Nnano and Znnano; Recommended FYM, 100% P and K is common to all the treatments; Recommendations are as per package of practice of University of Agricultural Sciences, GKVK, Bangalore). ST: Seed treatment involved immersing the seeds in a nano-nutrient solution at a concentration of 1000 milliliters per hectare of seed material. This treatment involved soaking the seeds in a solution containing the nano-nutrients prior to sowing. This treatment aimed to enhance seed germination, early seedling vigor, and overall plant growth by delivering essential micronutrients directly to the germinating seeds. RD: Seedlings were dipped in a 1000 mL/ha nano nutrient solution to facilitate root uptake of nutrients. This technique is commonly used to enhance early plant growth and nutrient acquisition, particularly for micronutrients like zinc. FS: Two foliar applications of both Nnano and Znnano solutions were administered at two critical growth stages: 25-30 and 45-50 days after transplanting i.e. with 20 days interval. Each application utilized a 0.4% concentration solution, ensuring optimal nutrient delivery to the plants. A commercial nano-nitrogen and a nano-zinc product were sourced from IFFCO, a public sector company. Seeds were sown in the nursery beds and trays for manual transplanted paddy and SRI method, respectively. Fifteen days prior to transplanting, 10 t ha-1 FYM was applied to the experimental plots. The recommended doses of 100 kg N ha-1, 50 kg P2O5 ha-1, 50 kg K2O ha-1, and 20 kg ZnSO4 ha-1 fertilizers were applied for specific treatments through urea, single super phosphate (SSP), muriate of potash (MOP), and zinc sulphate (ZnSO4), respectively. A full dose of recommended phosphorus and potassium were applied at the time of transplanting to all the treatments along with 50% N as a basal dose. The remaining 50% N was applied in two splits at 30 and 60 DAT as top dressing according to the treatments. PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1141 Methods of Application of Nano Fertilizers Nano fertilizers were applied as seed treatment (before sowing), root dipping (before transplanting), soil application (mixing nano fertilizers with sand and applied as top dressing), and foliar application (sprayed directly onto the leaves). These are shown in the Fig. 2. a) Seed treatment b) Root dipping c) Soil application d) Foliar application Fig. 2. Methods of nano fertilizers application: a) Seed treatment; b) Root dipping; c) Soil application; d) Foliar application PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1142 Characterization of Nano Particles Dynamic light scattering (Zeta Sizer) for particle size analysis The average particle diameters of nano nitrogen and nano zinc particles were characterized from the intensity distribution analysis by using Zeta Sizer. The average particle diameters of nano nitrogen and nano zinc particles were found to be 57.45 nm and 65.2 nm, respectively. Similar results were confirmed with Gazulla et al. (2013) and Wazid et al. (2018). Scanning electron microscopy for surface morphology analysis The morphological features of nano nitrogen and nano zinc particles were characterized by scanning electron microscopy (SEM; EVO 18; Carle Zeiss India Pvt Ltd., Germany) and are shown in Fig. 3. The nano nitrogen particles formed were spherical shaped and zinc showed a spherical shape as well. The results are in agreement with the findings of Gazulla et al. (2013) and Alamdari et al. (2020). The SEM images of nano nitrogen and nano zinc particles on the nano fertilizer sprayed paddy leaves are shown in Fig. 4. Energy dispersive X-ray spectroscopy for elemental content Energy dispersive X-ray spectroscopy (EDX) (Oxford 80; Carle Zeiss India Pvt Ltd., Germany) is an elemental analysis technique, which is used in combination with SEM to determine the chemical composition in the sample and is shown in Fig. 3. The nano nitrogen particles formed were 45.4% weight basis N content in the sample whereas, nano zinc particles formed were 67.2% weight basis Zn content in the sample. Similar results were confirmed with (Gazulla et al. 2013). a) Energy-dispersive X-ray spectroscopy of nano nitrogen PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1143 b) Energy-dispersive X-ray spectroscopy of nano zinc c) Scanning electron microscope image of nano nitrogen d) Scanning electron microscope image of nano zinc Fig. 3. Characteristics of nano nitrogen and nano zinc particles: a) EDX of nano-N; b) EDX of nano-Zn; c) SEM of nano-N; d) SEM of nano-Zn 100 μm 100 μm 1 μm PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1144 a) SEM image of paddy leaves with foliar spray of nano nitrogen b) SEM image of paddy leaves with foliar spray nano zinc c) SEM image of paddy leaves with foliar spray nano nitrogen and nano zinc Fig. 4. SEM images of nano fertilizers on paddy leaves (nano N and nano Zn sprayed): a) SEM image of paddy leaves with nano-N; b) SEM image of paddy leaves with nano-Zn; c) SEM image of paddy leaves with nano-N and nano-Zn 0.2 μm 1 μm PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1145 Biochemical Analysis Carbohydrates The total carbohydrate content was estimated by the method of Hedge and Hofreiter (1962). Carbohydrate was first hydrolyzed into simple sugars using dilute hydrochloric acid. In hot acidic medium, glucose was dehydrated to hydroxmethyl furfural. This compound formed with anthrone a green-colored product with absorption maximum at 630 nm. Protein Total protein was estimated by modified Lowry’s method given by Hartree (1972). Determination of protein concentration by ultraviolet absorption depends on the presence of aromatic amino acids in the proteins. To the extracted samples, alkaline CuSO4 reagent was added and incubated at room temperature for 10 min followed by 0.5 mL of Folin’s phenol reagent. The contents were mixed well, and the absorbance was measured at 650 nm after 15 min in a spectrophotometer (Cary 60 UV-Vis; Agilent Technologies, India). From the standard graph, the amount of protein in the given unknown solution was calculated. Tryptophan content The tryptophan content in grain sample was estimated by colorimetric method (Sadasivam and Manickam 1992). The protein in the grain sample was hydrolyzed with a proteolytic enzyme, papain. Then, the hydrolyzed sample was incubated at 65 °C overnight. A total of 1.0 mL supernatant was taken after centrifugation. To this, 4 mL of ferric chloride was added and kept for incubation at 65 °C for 15 min. The indole ring of tryptophan gives an orange red color with ferric chloride under strongly acidic condition. The intensity was measured at 545 nm. The tryptophan content in sample was estimated by comparing with standard curve: 𝑇𝑟𝑝𝑡𝑜𝑝ℎ𝑎𝑛 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 = 𝑇𝑟𝑦𝑝𝑡𝑜𝑝ℎ𝑎𝑛 𝑣𝑎𝑙𝑢𝑒 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑔𝑟𝑎𝑝ℎ 𝑖𝑛 𝜇𝑔 × 0.096 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝑜𝑓 𝑁 𝑖𝑛 𝑡ℎ𝑒 𝑠𝑎𝑚𝑝𝑙𝑒 × 100 Statistical Analysis Observations recorded during different phenological phases of rice crop were analyzed statistically to find out the result and to draw a conclusion of the experiment conducted. Fisher’s method of analysis of variance (ANOVA) was used in the analysis, as given by Gomez and Gomez (1984). Significance between the treatments was tested by Duncan’s multiple range test at a significance level of p ≤ 0.05. The analysis was performed using IBM SPSS, version 22. Correlation and regression analysis were conducted using R4.2.0 software package. RESULTS AND DISCUSSION Plant Vegetative Growth Parameters Different growth parameters, such as plant height, number of tillers per hill, dry matter accumulation in leaves, stem, and panicles, were statistically influenced by the application of 75% N and two foliar sprays of nano nitrogen and nano zinc at 25 to 30 and PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1146 45 to 50 DAT under SRI method over rest of the treatments. Data pertaining to growth parameters are presented in Table 1. Benzon et al. (2015) revealed that plant height was more enhanced when nano fertilizer was combined with conventional fertilizers because nano fertilizer can either provide nutrients for the plant or aid in the transport or absorption of available nutrients, thereby resulting in better crop growth. The transplanting of younger seedlings with wider spacing helped for both direction weeding and the application of nano nitrogen and nano zinc as foliar spray improved the availability of nutrients throughout the crop growth period influencing the number of tillers per hill under the SRI method (Geethalakshmi et al. 2011; Ghafari and Jamshid 2013). The increase in dry matter accumulation may be due to the high reactivity of nano fertilizers, especially when they are applied as foliar spray because of more specific surface area in plant leaves (Dhoke et al. 2013). Large root volume, profuse tillering, and wider spacing of 25 cm × 25 cm sustained minimum injury while transplanting and established quickly due to the availability of nutrients (Hossain et al. 2003; Sathyanarayana and Babu 2004). Further, optimum utilization of resources leads to early tillering in SRI, which made the plants have more time for accumulation of photosynthates in panicles. Nano nitrogen and nano zinc fertilizers applied to the rice crop were readily available to the crop and that made the crop physiologically more active. As a result of better uptake and efficient utilization of nutrients, increased mobilization and accumulation of photosynthates in the reproductive parts of rice were observed. These results are in line with the findings of Armin et al. (2014) and Kumar et al. (2015a). The positive effect on plant growth of nano fertilizers was reported by Hassan et al. (2011), Morteza et al. (2013), Kannan et al. (2012), Prasad et al. (2012), Hedait and Salama (2012), and Tapan et al. (2013). PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1147 Table 1. Influence of Different Methods of Nano Nitrogen and Nano Zinc Applications on Growth Parameters of Paddy at Harvest During Kharif 2020 and 2021 Treatments Plant Height (cm) No. of Tillers Hill-1 Dry Matter Accumulation in Leaves (g hill-1) Dry Matter Accumulation in Stem (g hill-1) Dry Matter Accumulation in Panicles (g hill-1) 2020 2021 Pooled 2020 2021 Pooled 2020 2021 Pooled 2020 2021 Pooled 2020 2021 Pooled T1: TP+ Recommended practice 110.57c 125.20e 117.88d 11.03h 13.04h 12.03i 39.54d 47.83d 43.68d 61.18cd 69.43cd 65.31cd 61.01d 60.46d 60.74d T2: SRI+ Recommended practice 118.56bc 130.11de 124.33cd 18.21f 21.54e 19.88f 93.79b 113.47b 103.63b 97.19b 110.29b 103.74b 105.49b 104.55b 105.02b T3: TP+ 50% RDN + ST 121.74b 133.83cde 127.79cd 15.60fg 18.46efg 17.03gh 34.93d 42.26d 38.59d 47.98e 54.44e 51.21e 56.30d 55.79d 56.05d T4: TP+50% RDN + RD 124.98b 137.47bcde 131.23bc 13.23gh 15.65gh 14.44hi 35.98d 43.54d 39.76d 50.83de 57.69de 54.26de 58.15d 57.63d 57.89d T5: TP+50% RDN + FS 138.87a 150.77abc 144.82a 18.36f 21.71e 20.03f 37.18d 44.98d 41.08d 58.86cde 66.79cde 62.83cde 59.34d 58.81d 59.08d T6: SRI+ 50% RDN + ST 124.13b 136.63bcde 130.38bc 24.08de 28.48d 26.28e 40.03d 48.43d 44.23d 62.88cd 71.36cd 67.12c 61.25d 60.70d 60.97d T7: SRI+50% RDN + RD 123.18b 138.74bcde 130.96bc 27.71c 32.77c 30.24cd 55.56c 67.21c 61.38c 63.93c 72.54c 68.23c 74.93c 74.25c 74.59c T8: SRI+50% RDN + FS 138.07a 143.06abcd 140.57ab 30.86ab 38.51ab 34.68ab 93.57b 113.21b 103.39b 96.24b 109.22b 102.73b 103.26b 102.34b 102.80b T9: TP+ 75% RDN + ST 120.81b 136.63bcde 128.72bcd 16.82f 19.90ef 18.36fg 55.60c 67.27c 61.44c 65.24c 74.04c 69.64c 76.68c 75.99c 76.34c T10: TP+75% RDN + RD 124.21b 136.26bcde 130.23bc 15.45fg 18.27fg 16.86gh 92.23b 111.58b 101.90b 93.69b 106.32b 100.00b 100.50b 99.60b 100.05b T11: TP+75% RDN + FS 140.88a 152.55ab 146.72a 21.64e 25.59d 23.62e 94.44b 114.26b 104.35b 100.92b 114.53b 107.73b 112.67b 111.66b 112.16b T12: SRI+ 75% RDN + ST 127.40b 136.88bcde 132.14bc 26.66cd 31.53c 29.09d 94.99b 114.93b 104.96b 97.85b 111.04b 104.44b 110.99b 109.99b 110.49b T13: SRI+75% RDN + RD 128.16b 136.93bcde 132.55bc 29.02bc 35.90b 32.46bc 95.96b 116.09b 106.02b 98.07b 111.30b 104.69b 111.96b 110.96b 111.46b T14: SRI+75% RDN + FS 142.36a 154.92a 148.64a 32.39a 39.64a 36.01a 107.62a 130.20a 118.91a 113.12a 128.37a 120.75a 127.66a 126.51a 127.09a Values marked by a different letter differ significantly according to Duncan’s multiple range test (p ≤ 0.05) PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1148 Table 2. Influence of Different Methods of Nano Nitrogen and Nano Zinc Applications on Yield Parameters of Paddy at Harvest During Kharif 2020 and 2021 Treatments Panicle Length Panicle Weight Total Number of Unfilled Grains Panicle-1 Percent Chaffiness Test Weight 2020 2021 Pooled 2020 2021 Poole d 2020 2021 Pooled 2020 2021 Pooled 2020 2021 Pooled T1: TP+ Recommended practice 16.49c 18.60c 17.54cd 3.07d 3.35d 3.21d 61.44ef 63.96de 62.70ef 36.58g 30.14f 33.36g 17.58c 19.18bc 18.38cd T2: SRI+ Recommended practice 18.71bc 21.10bc 19.90bcd 3.34cd 3.64cd 3.49cd 54.15bcde 56.67bcd 55.41bcde 32.47d 26.62cd 29.55cd 19.41bc 21.18bc 20.29bcd T3: TP+ 50% RDN + ST 16.05c 18.10c 17.07d 2.80d 3.05d 2.93d 77.25h 79.77g 78.51h 43.36j 36.25i 39.80j 17.03c 18.58c 17.81d T4: TP+50% RDN + RD 16.22c 18.30c 17.26cd 2.90d 3.16d 3.03d 72.83gh 75.35fg 74.09gh 40.77i 33.85h 37.31i 17.43c 19.02bc 18.22cd T5: TP+50% RDN + FS 16.40c 18.50c 17.45cd 3.06d 3.34d 3.20d 66.43fg 68.95ef 67.69fg 38.47h 31.80g 35.13h 17.56c 19.16bc 18.36cd T6: SRI+ 50% RDN + ST 16.67c 18.80c 17.73cd 3.12cd 3.40cd 3.26d 61.22ef 63.74de 62.48ef 36.45g 30.03f 33.24g 18.09bc 19.74bc 18.91bcd T7: SRI+50% RDN + RD 17.11bc 19.30bc 18.21cd 3.14cd 3.42cd 3.28d 61.01def 63.53de 62.27def 36.30g 29.89f 33.10g 18.09bc 19.74bc 18.91bcd T8: SRI+50% RDN + FS 18.09bc 20.40bc 19.24bcd 3.20cd 3.49cd 3.34cd 54.96cde 57.48cd 56.22cde 32.85d 26.88cd 29.87de 18.35bc 20.02bc 19.19bcd T9: TP+ 75% RDN + ST 17.11bc 19.30bc 18.21cd 3.16cd 3.45cd 3.30cd 57.64de 60.16d 58.90de 34.97f 28.74ef 31.85f 18.11bc 19.76bc 18.94bcd T10: TP+75% RDN + RD 17.64bc 19.90bc 18.77bcd 3.19cd 3.48cd 3.34cd 57.34de 59.86d 58.60de 33.97e 27.84de 30.91ef 18.28bc 19.94bc 19.11bcd T11: TP+75% RDN + FS 19.68b 22.20b 20.94b 4.63b 5.05b 4.84b 46.13ab 48.65ab 47.39ab 28.32b 23.04b 25.68b 20.74ab 21.90b 21.32ab T12: SRI+ 75% RDN + ST 18.71bc 21.10bc 19.90bc 3.67c 4.00c 3.84c 52.65bcd 55.17bcd 53.91bcd 31.53c 25.75c 28.64c 19.56bc 21.34bc 20.45bcd T13: SRI+75% RDN + RD 18.71bc 21.10bc 19.90bc 4.59b 5.00b 4.80b 47.01abc 49.53abc 48.27abc 28.91b 23.50b 26.21b 19.70bc 21.49bc 20.59bc T14: SRI+75% RDN + FS 22.13a 24.97a 23.55a 5.35a 5.83a 5.59a 41.01a 43.53a 42.27a 23.94a 19.30a 21.62a 22.56a 24.62a 23.59a Values marked by a different letter differ significantly according to Duncan’s multiple range test (p ≤ 0.05) PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1149 Table 3. Influence of Different Methods of Nano Nitrogen and Nano Zinc Applications on Grain and Straw Yields of Paddy at Harvest During Kharif 2020 And 2021 Treatments Grain Yield Straw Yield 2020 2021 Pooled 2020 2021 Pooled T1: TP+ Recommended practice 5455cde 6118def 5787de 6501cd 7291cde 6896de T2: SRI+ Recommended practice 6155bcde 6903bcde 6529bcd 7335bcd 8226bcde 7780bcd T3: TP+ 50% RDN + ST 5245e 5882f 5563e 6250d 7009e 6629e T4: TP+50% RDN + RD 5373de 6026ef 5700de 6403cd 7181de 6792de T5: TP+50% RDN + FS 5395de 6050def 5723de 6429cd 7210de 6819de T6: SRI+ 50% RDN + ST 5474cde 6139def 5807de 6523cd 7316cde 6919cde T7: SRI+50% RDN + RD 5539cde 6212 cdef 5875cde 6600bcd 7402cde 7001cde T8: SRI+50% RDN + FS 5862bcde 6574bcdef 6218bcde 6985bcd 7834bcde 7410bcde T9: TP+ 75% RDN + ST 5811bcde 6517bcdef 6164bcde 6924bcd 7766bcde 7345bcde T10: TP+75% RDN + RD 5835bcde 6544bcdef 6189bcde 6953bcd 7798bcde 7375bcde T11: TP+75% RDN + FS 6478b 7265b 6871b 7719b 8657b 8188b T12: SRI+ 75% RDN + ST 6209bcd 6964bcd 6587bcd 7399bcd 8299bcd 7849bcd T13: SRI+75% RDN + RD 6347bc 7119bc 6733bc 7564bc 8483bc 8023bc T14: SRI+75% RDN + FS 7434a 8338a 7886a 8859a 9936a 9397a Values marked by a different letter differ significantly according to Duncan’s multiple range test (p ≤ 0.05) Yield Parameters Different yield parameters represented in Tables 2 and 3, such as panicle length, panicle weight, total number of unfilled grains per panicle, percent chaffiness, test weight, grain yield, and straw yield, were statistically influenced by the application of 75% N and two foliar sprays of nano nitrogen and nano zinc at 25 to 30 and 45 to 50 DAT under SRI method. Statistically higher panicle length and weight may be due to the enhanced availability of micronutrient by nano zinc application, which increased the photosynthesis and translocation of photosynthates to sink, synthesis of amino acid, chlorophyll, and better carbohydrates transformation along with the positive attributes of SRI. Stomata and base of the trichomes are the major ways for the nano particles to enter into the plant by foliar application, and then the nano particles are translocated to various tissues of the plants (Uzu et al. 2010). Similar results were reported by Safarined et al. (2013), Sirisena et al. (2013), Ruiqiang and Rattan (2014), and Eleyan et al. (2018). Because nano fertilizers are considered as the biological pump for the plants to absorb nutrients and water (Ma et al. 2009), more photosynthate accumulation was found in those treatments that received nano nitrogen and nano zinc as foliar spray. Hence, a lower number of unfilled grains and lesser percent chaffiness was recorded in those treatments. Similar results were reported by Harsini et al. (2014) and Kumar et al. (2015a). PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1150 Table 4. Influence of Different Methods of Nano Nitrogen and Nano Zinc Applications on Quality Parameters of Paddy at Harvest During Kharif 2020 and 2021 Treatments Carbohydrates Protein Tryptophan Lysine 2020 2021 Pooled 2020 2021 Pooled 2020 2021 Pooled 2020 2021 Pooled T1: TP+ Recommended practice 73.20bc 75.55b 74.38bc 6.61cde 6.91cd 6.76cde 0.68cd 0.71c 0.69c 4.01def 4.19de 4.10de T2: SRI+ Recommended practice 79.99bc 82.56b 81.28bc 7.30bc 7.64abc 7.47bc 0.71cd 0.74bc 0.72bc 3.47abcd 3.63abc 3.55abc T3: TP+ 50% RDN + ST 69.95c 72.21b 71.08c 5.87e 6.14d 6.01e 0.64d 0.67c 0.66c 4.16f 4.36e 4.26e T4: TP+50% RDN + RD 71.69bc 74.00b 72.84bc 6.24de 6.53cd 6.38de 0.67cd 0.70c 0.69c 4.13f 4.32e 4.22e T5: TP+50% RDN + FS 71.69bc 74.00b 72.84bc 6.61cde 6.91cd 6.76cde 0.68cd 0.71c 0.69c 4.09ef 4.28e 4.18e T6: SRI+ 50% RDN + ST 74.71bc 77.11b 75.91bc 6.61cde 6.91cd 6.76cde 0.69cd 0.72c 0.71c 3.93cdef 4.11cde 4.02cde T7: SRI+50% RDN + RD 76.21bc 78.67b 77.44bc 6.87cd 7.18cd 7.03cd 0.69cd 0.73c 0.71c 3.78bcdef 3.95bcde 3.87bcde T8: SRI+50% RDN + FS 77.72bc 80.23b 78.98bc 7.03bcd 7.35bc 7.19cd 0.71cd 0.74bc 0.72c 3.47abcd 3.63abc 3.55abc T9: TP+ 75% RDN + ST 77.27bc 79.76b 78.52bc 6.99cd 7.31bc 7.15cd 0.71cd 0.74bc 0.72bc 3.54abcde 3.71abcd 3.63abcd T10: TP+75% RDN + RD 77.60bc 80.10b 78.85bc 7.03bcd 7.35bc 7.19cd 0.71cd 0.74bc 0.72bc 3.47abcd 3.63abc 3.55abc T11: TP+75% RDN + FS 82.80b 85.47ab 84.14b 7.97ab 8.33ab 8.15ab 0.81ab 0.85ab 0.83ab 3.28ab 3.43ab 3.35ab T12: SRI+ 75% RDN + ST 80.74bc 83.34b 82.04bc 7.36bc 7.63abc 7.49bc 0.72cd 0.75bc 0.73bc 3.46abcd 3.61abc 3.54abc T13: SRI+75% RDN + RD 81.50bc 84.12b 82.81bc 7.28bc 7.62abc 7.45bc 0.75bc 0.77abc 0.76bc 3.36abc 3.52ab 3.44ab T14: SRI+75% RDN + FS 94.57a 96.91a 95.74a 8.26a 8.64a 8.45a 0.84a 0.88a 0.86a 3.20a 3.35a 3.27a Values marked by a different letter differ significantly according to Duncan’s multiple range test (p ≤ 0.05) PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1151 The increased seed weight upon nano nitrogen and nano zinc fertilization was attributed to efficient action of zinc in metabolic processes, like enhanced uptake, translocation of sugars, and higher carbohydrate accumulation in seeds. These results were in line with the findings of Abdoli et al. (2014). The lower yield in normal transplanted paddy with lesser nitrogen was due to lesser production of yield attributing characters because of competition by closer spacing. The results were in line with the findings of Hossain et al. (2003), Barison and Uphoff (2010), and Elamathi et al. (2012). Quality Parameters of Paddy Data pertaining to quality parameters of paddy grains viz., carbohydrates, protein, lysine, and tryptophan were recorded and represented based on pooled data of two successive kharif seasons in Table 4. Treatment with application of 75% N and two foliar sprays of nano nitrogen and nano zinc at 25 to 30 and 45 to 50 DAT under SRI method recorded statistically higher carbohydrates, protein, and tryptophan contents during kharif season. Whereas, statistically higher lysine content was recorded in the treatments in which tryptophan has been found lower, i.e., with seed treatment with nano nitrogen and nano zinc before sowing and application of 50% N under transplanted paddy. Carbohydrates (%) The availability of essential major and micro nutrients increased due to the nano fertilizers that influenced the amino acid accumulation, improvement in carbohydrate and crude fiber content in straw and grain during the various phenological stages of the crop (Nadi et al. 2013). Protein (%) Nano zinc enhances the cation-exchange capacity of the roots, which in turn enhances absorption of essential nutrients and foliar application of nano nitrogen, improved dry matter accumulation, and higher nitrogen uptake, which is responsible for higher protein content. Nano nitrogen and nano zinc plays a vital role in carbohydrate and proteins metabolism as well as it controls plant growth hormone, i.e., IAA. The results are in accordance with the findings of Satdev et al. (2021). Tryptophan and Lysine (%) Nano nitrogen and nano zinc enhance the quality by increasing absorption and allocation of other vital nutrients to the plant, thus enhancing the metabolic processes of the plant and playing an important role in many biochemical reactions within the plants. They also improve the protein content through amino acid accumulation due to increased nitrogen metabolism. They act as a stimulant factor that increases the production of indole acetic acid, thereby leading to an increase in amino acids such as tryptophan and decreased lysine content. It is mainly due to the antagonistic activity of tryptophan and lysine (Kisan et al. 2015). Correlation Matrix The degree of linear association of the grain yield with growth and yield variables (plant height, number of tillers, dry matter accumulation in leaves, stem and panicles, panicle length, panicle weight, chaffiness, and unfilled grains per panicle) is presented in a correlation matrix in Fig. 5. PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1152 Fig. 5. Pearson’s correlation matrix for growth and yield variables in paddy as influenced by different methods of nano nitrogen and nano zinc applications Fig. 6. Pearson’s correlation matrix for quality variables in paddy as influenced by different methods of nano nitrogen and nano zinc applications PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1153 The yield demonstrated a positive correlation with key vegetative growth parameters, including plant height, tiller number, and dry matter accumulation in leaves, stems, and panicles. Additionally, panicle length and weight were positively associated with yield. Conversely, a statistically significant negative correlation was observed between yield and grain quality parameters, such as chaffiness and the number of unfilled grains per panicle. These findings highlight the importance of these traits in determining the overall yield potential of the crop. The degree of linear association of the grain yield with quality parameter variables (carbohydrates, protein, tryptophan, and lysine) is presented in a correlation matrix in Fig. 6. The yield positively correlated with the carbohydrates, protein, and tryptophan, while statistically negative correlations were observed with the lysine. Table 5. Regression Coefficient Estimates of Pooled Data for Different Variables in Stepwise Regression Analysis Sl. No. Y (Dependent Variable) = a+b1x1+b2x2+………………..+e (Independent Variable) Multiple R2 Value 1 Grain yield = -3390.866 + 0.200 X1 - 5.114 X2 - 21.867 X3 + 8.559 X4 + 28.080 X5 + 195.670 X6 + 15.638 X7 + 156.951 X8 - 40.877 X9 + 123.380 X10 0.9601 2 Grain yield = 375.457 + 19.240 A1 + 0.677 A2 + 7532.109 A3 – 304.756 A4 0.9149 3 Straw yield = 6983.041 - 2.236 X1 - 6.618 X2 + 18.357 X3 – 23.273 X4 – 5.899 X5 + 96.351 X6 + 41.889 X7 + 169.483 X8 + 67.627 X9 – 234.228 X10 0.9850 4 Straw yield = 25.472 + 97.269 A1 – 236.992 A2 + 3526.563 A3 – 302.590 A4 0.9806 where X1 = Plant height, X2 = No. of tillers, X3 = Dry matter accumulation in leaves, X4 = Dry matter accumulation in stem, X5 = Dry matter accumulation in panicles, X6 = Panicle length, X7 = Panicle weight, X8 = Test weight, X9 = Unfilled grains, X10 = Chaffiness; A1 = Carbohydrates, A2 = Protein, A3 = Tryptophan, A4 = Lysine Stepwise Regression Analysis Stepwise regression analysis was performed using the grain yield (kg/ha) as a dependent variable and the remaining variables as independent variables. The correlation matrix (Figs. 5 and 6) showed a significant correlation among independent variables, which generates a multicollinearity problem. Stepwise regression analysis overcomes the problem of multicollinearity. The results of stepwise regression coefficients of pooled data for grain yield with growth/yield parameters revealed that out of the many independent variables, ten (Plant height, No. of tillers, Dry matter accumulation in leaves, Dry matter accumulation in stem, Dry matter accumulation in panicles, Panicle length, Panicle weight, test weight, unfilled grains, and chaffiness) were considered to explain the variable grain yield. The regression model was found to be highly significant, with F calculated to be 74.51 (p-value = < 2.2e-16). This statistical analysis revealed a highly significant regression model, indicating a strong association between the independent and dependent variables. This suggests that the model effectively captures the underlying relationship between the variables and provides a reliable prediction of the dependent variable based on the values of the independent variables. The regression coefficients for all variables are shown in Table 5. The ten variables were found to be significant, and can be used to predict the grain yield. The regression model is as follows: Grain Yield = -3390.8656 + 0.1998 Plant height - 5.1140 No. of tillers - 21.8672 Dry matter accumulation in leaves + 8.5588 Dry matter accumulation in stem + 28.0795 Dry matter accumulation in panicles + 195.6700 Panicle length + 15.6383 Panicle weight + 156.9508 Test weight - 40.8765 Unfilled grains + 123.3795 Chaffiness PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1154 The coefficient of determination (R²) of 0.9601 indicates that 96.01% of the variability in grain yield can be explained by the variations in the independent variables (growth and yield parameters) included in the model. This high R² value suggests that the model is a good fit for the data and that the growth and yield parameters are strong predictors of grain yield. The adjusted R2 value was 0.9472 (Fig. 7). Fig. 7. Stepwise regression coefficients of pooled data for grain yield with growth/yield parameters The results of stepwise regression coefficients of pooled data for grain yield with quality parameters revealed that four independent variables viz., carbohydrates, protein, tryptophan, and lysine were considered to explain the variable grain yield. The regression model was found to be highly significant, with F calculated to be 99.39 (p- value = < 2.2e-16). The highly significant F-statistic of 99.39 indicates that the regression model as a whole is a strong fit for the data. This suggests that at least one of the independent variables in the model is significantly associated with the dependent variable. The regression coefficients for all variables are shown in Table 5. The four variables were found to be significant, and can be used to predict the grain yield. The regression model is as follows: Grain Yield = 375.4571 + 19.2400 Carbohydrates + 0.6768 Protein + 7532.1086 Tryptophan – 304.7564 Lysine The coefficient of determination (R2) value was 0.9149, which means that 91.49% of the variation in the dependent variable (grain yield) is explained by the model. The adjusted R2 value was 0.9057 (Fig. 8). Fig. 8. Stepwise regression coefficients of pooled data for grain yield with quality parameters PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1155 The results of stepwise regression coefficients of pooled data for straw yield with growth/yield parameters revealed that out of the many independent variables, ten (Plant height, No. of tillers, Dry matter accumulation in leaves, Dry matter accumulation in stem, Dry matter accumulation in panicles, Panicle length, Panicle weight, test weight, unfilled grains. and chaffiness) were considered to explain the variable straw yield. The regression model was found to be highly significant, with F calculated to be 203.5 (p-value = < 2.2e-16). The regression coefficients for all variables are shown in Table 5. The ten variables were found to be significant, and can be used to predict the grain yield. The regression model is as follows: Straw Yield = 6983.041 - 2.236 Plant height - 6.618 No. of tillers + 18.357 Dry matter accumulation in leaves – 23.273 Dry matter accumulation in stem – 5.899 Dry matter accumulation in panicles + 96.351 Panicle length + 41.889 Panicle weight + 169.483 Test weight + 67.627 Unfilled grains – 234.228 Chaffiness The coefficient of determination (R2) value was 0.985, which means that 98.50% of the variation in the dependent variable (straw yield) is explained by the model and also indicates the extent of dependability on growth and yield variables. The adjusted R2 value was 0.9802 (Fig. 9). Fig. 9. Stepwise regression coefficients of pooled data for straw yield with growth/yield parameters The results of stepwise regression coefficients of pooled data for straw yield with quality parameters revealed that four independent variables viz., Carbohydrates, protein, tryptophan, and lysine, were considered to explain the variable straw yield. The regression model was found to be highly significant, with F calculated to be 517.9 (p- value = < 2.2e-16). The regression coefficients for all variables are shown in Table 5. The four variables were found to be significant, and can be used to predict the grain yield. The regression model is as follows: Straw Yield = 25.472 + 97.269 Carbohydrates – 236.992 Protein + 3526.563 Tryptophan – 302.590 Lysine The coefficient of determination (R2) value was 0.9825, which means that 98.25% of the variation in the dependent variable (straw yield) is explained by the model. The adjusted R2 value was 0.9806 (Fig. 10). PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1156 Fig. 10. Stepwise regression coefficients of pooled data for straw yield with quality parameters CONCLUSIONS 1. The treatment receiving 75% N and two foliar sprays of nano nitrogen and nano zinc at 25 to 30 and 45 to 50 DAT under SRI method (T14) was statistically superior in improving growth and yield parameters, grain and straw yields, and it was also superior in enhancing the quality of rice over rest of the treatments. 2. The lower yield in normal transplanted paddy with lesser nitrogen can be attributed to lesser production of yield attributing characters because of competition by closer spacing. 3. Correlation studies showed high positive correlation among all the parameters except for unfilled grains and chaffiness percentage, which showed high negative correlation with the grain yield. 4. The stepwise regression analysis showed the percentage dependability of grain and straw yields on the growth, yield, and quality parameters. It infers that the improvement in such variables is the key to enhance the yield of paddy in regions with similar agro-climatic conditions. ACKNOWLEDGEMENT The authors would like to thank the Head of the Department of Agronomy, College of Agriculture, V. C. Farm, Mandya, University of Agricultural Sciences, GKVK, Bangalore-560065, Karnataka for providing the resources and the laboratory for successful completion of research. CONFLICT OF INTEREST There are no relevant financial or non-financial competing interests to report. PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Theerthana et al. (2025). “Nano rice fertilizer,” BioResources 20(1), 1136-1160. 1157 REFERENCES CITED Abdoli, M., Esfandiari, E., Mousavi, S. B., and Sadeghzadeh, B. (2014). “Effects of foliar application of zinc sulfate at different phonological stages on yield formation and grain zinc content of bread wheat (cv. Kohdasht),” Azarian J. Agric. 10(1), 11-16. Adisa, I. O., Pullagurala, V. L. R., Peralta-Videa, J. R., Dimkpa, C. O., Elmer, W. H., and Gardea-Torresdey, J. L. 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