Nano fertilizer application under different establishment techniques for sustainable paddy (Oryza sativa L.) production

Abstract

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.

Full Article

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). 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.

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)

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; 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 N_nano and Zn_nano; 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 N_nano and Zn_nano 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.

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 Fig. 2.

Fig. 2. Methods of nano fertilizers application: a) Seed treatment; b) Root dipping; c) Soil application; d) Foliar application

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).

1. Energy-dispersive X-ray spectroscopy of nano nitrogen

a) Energy-dispersive X-ray spectroscopy of nano zinc

b) Scanning electron microscope image of nano nitrogen

c) 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

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

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:

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 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).

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

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

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

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).

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

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.

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

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

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

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

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).

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.

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Article submitted: September 27, 2024; Peer review completed: November 9, 2024; Revised version received: November 22, 2024; Accepted: November 24, 2024; Published: December 6, 2024.

DOI: 10.15376/biores.20.1.1136-1160