Effects of Light Shading, Fertilization, and Cultivar Type on the Stable Isotope Distribution of Hybrid Rice

Abstract

The effect of fertilizer supply and light intensity on the distribution of elemental contents (%C and %N) and light stable isotopes (C, N, H, and O) in different rice fractions (rice husk, brown rice, and polished rice) of two hybrid rice cultivars (maintainer lines You-1B and Zhong-9B) were investigated. Significant variations were observed for δ¹³C (−31.3 to −28.3‰), δ¹⁵N (2.4 to 2.7‰), δ²H (−125.7 to −84.7‰), and δ¹⁸O (15.1‰ to 23.7‰) values in different rice fractions among different cultivars. Fertilizer treatments showed a strong association with %N, δ¹⁵N, δ²H, and δ¹⁸O values while it did not impart any significant variation for the %C and δ¹³C values. Light intensity levels also showed a significant influence on the isotopic values of different rice fractions. The δ¹³C values showed a positive correlation with irradiance. The δ²H and δ¹⁵N values decreased with an increase in the irradiance. The light intensity levels did not show any significant change for δ¹⁸O values in rice fractions. Multivariate ANOVA showed a significant interaction effect of different factors (light intensity, fertilizer concentration, and rice variety) on the isotopic composition of rice fractions. It is concluded that all environmental and cultivation factors mentioned above significantly influenced the isotopic values and should be considered when addressing the authenticity and origin of rice. Furthermore, care should be taken when selecting rice fractions for traceability and authenticity studies since isotopic signatures vary considerably among different rice fractions.

1. Introduction

Rice (Oryza sativa L.) is a major cereal crop consumed by half of the global population and widely planted in Asia, Africa, and parts of America. In 2020, the total area under rice cultivation exceeded 195 million hectares. The quality characteristics of rice are mainly associated with its growing conditions, and recently the traceability of rice back to its growing origins has gained increasing interest from consumers, producers, and related industries since it is vulnerable to economic fraud [1]. Many methods have been developed to address the authenticity of rice, including multi-element, spectroscopic, omic, and DNA-based analysis [2,3,4,5]. Most recently, stable isotope analysis (SIA) has been widely employed to authenticate organic rice, determine its geographic origin, and identify rice cultivars [6,7]. However, there may be unknown stable isotope effects on rice, caused by cultivar type, light intensity, environmental factors, and fertilizer treatments which may reduce the accurate determination of its geographical origin, especially when it is procured from nearby or adjoining localities. Therefore, identifying the range of stable isotope compositions (δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O) in different rice fractions according to the differences in light intensity level, fertilizer type and concentration, and cultivar/variety would improve the validity of traceability methods for rice and its products.

Different factors such as plant physiology, photosynthetic processes, climatic factors (temperature, sunshine, humidity, precipitation), and cultivation practices have been shown to induce stable isotope fractionation in plants [8]. Generally, the δ¹³C values of plants reflect plant photosynthetic processes and water use efficiency. Carbon found in mature rice grains originates from the assimilation of CO₂ during the grain filling period [9]. Solar irradiance is the main factor that affects the net CO₂ assimilation rate and high temperature is also associated with stomatal closure and a reduction in CO₂ assimilation. Conversely, if the temperature is below the optimum range, the net CO₂ assimilation rate will become light-limited and lead to reduced photosynthetic productivity [10]. In addition, plant photosynthesis is also affected internally by photozymes, hydrolase C3 reductase, and CO₂ fixation enzymes [11]. This internal physiological response is referred as the physiological index which mainly reflects stomatal conductance, transpiration rates, net photosynthetic rate, and intercellular CO₂ concentration. Plant δ²H and δ¹⁸O values reflect physical factors such as rainfall and evapotranspiration, and are also associated with plant physiological parameters such as transpiration and stomatal conductance [12]. Nitrogen isotopes are mostly associated with farming practices, crop types, and soil characteristics and also reflect significant correlations between plant growth, photosynthetic capacity, and respiration rate [8,12].

Many studies have reported the limitation of stable isotopes in addressing the authenticity of agro-products when the samples are procured from close geographical locations or from a region where the same agricultural practices (fertilizer, etc.) are adopted [13]. Therefore, it is very important to understand the effects of different factors (light intensity, fertilizer type and concentration, and cultivar type) on the composition of rice stable isotopes. To explore these effects, we conducted two field experiments with a split plot design using two commonly used Chinese hybrid rice cultivars (maintainer lines) Zhong-9B and You-1B. The objective of this study was to investigate the variability of %C, %N, δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values in different rice fractions (husk, polished rice, and brown rice) in response to different fertilizer regimes and light intensity levels, and to identify how light shading and fertilizer application control isotopic ratios in rice. The results from this study will contribute more insight into the localized climatic, environmental, and farming effects on the isotopic composition of rice and will allow us to better predict rice origin and authenticity using stable isotope-based traceability models.

2. Material and Methods

2.1. Field Experiment

Field trials were carried out in 2018 at an experimental field at the China National Rice Research Institute (CNRRI) in Fuyang, Zhejiang province. Two commonly used hybrid rice maintainer lines (Zhong9B and You1B, which are the most popular cultivars in China provided by CNRRI), were studied. In total, three nitrogen treatments (N0, N6, and N12) with three light intensities (shading; 50% and 75%, and non-shading; ambient light) were arranged in a split plot design. The size of each plot was 10 m² with 3 replicates for each treatment. Nitrogenous fertilizer was applied as 0 kg/ha (N0), 90 kg/ha (N6), and 180 kg/ha (N12), respectively, at different growing stages including basal planting, tillering, and heading stages which accounted for 50%, 30%, and 20% of the application, respectively. Phosphate fertilizers were used only as basal fertilizer, whereas potash was used as both basal and tillering fertilizers and the fertilizer proportion was N:P:K = 1:0.5:0.5, respectively.

In the case of N0, urea was not applied during the entire production period. In total, 450 kg/ha of super phosphate was applied as basal fertilizer and 150 kg/ha of potash as basal (75 kg/ha) and tillering stage (75 kg/ha), respectively. For N6 treatment, 195 kg/ha of urea was applied, including 97.5kg/ha as basal fertilizer, 58.5 kg/ha as tillering fertilizer (7 to 9 days after transplanting), and 39 kg/ha as heading fertilizer (30 days after transplanting), respectively. In the case of N12 treatment, a total of 390 kg/ha of urea was applied, of which 195 kg/ha was basal fertilizer, 117 kg/ha was tillering fertilizer, and 78 kg/ha was applied as heading fertilizer. The application rate of phosphate and potash fertilizers for both N6 and N12 treatments were the same as N0.

For the light intensity investigation, natural sunlight treatment, LS-0 (0% shading, no shading), and shaded treatments, LS-50 (50% shading) and LS-75 (75% shading) were applied. In the shaded treatments, plants were shaded with a shading screen/net. For LS-50, one layer was used and for LS-75 two layers of shading screen were applied over the top of the rice plants.

2.2. Elemental Content and Isotope Ratio Measurements

Rice grains (2 kg) from each plot were harvested at maturity and subsequently threshed by a hulling machine equipped with a rice polisher (PY-200, Hubei Pinyang Technology Co., Ltd., Xiaogan, China) to obtain different fractions including brown rice (BR), polished rice (PR), and rice husk (RH). All fractions were air-dried, then ground into a fine powder, and finally dried at 50 ± 2 °C for 24 h. The samples were stored in desiccators until further analysis. For the determination of elemental contents (% C, % N) and isotopes (δ¹³C and δ¹⁵N), dried powdered samples were weighed (4.5 to 5.5 mg) and packed into tin capsules (3 mm × 5 mm). The samples were combusted in an elemental analyzer (Vario Pyro Cube, Elementar, Hanau, Germany) and the combustion gases were analyzed using an isotope ratio mass spectrometer (IRMS) (IsoPrime100, Isoprime Ltd., Manchester, UK). Sample combustion was carried out in a combustion furnace at 1150 °C and reduction in the N₂O_x gases to N₂ over copper wire occurred at 850 °C. An inert gas (He) with a flow rate of 230 mL/min was passed through a CentrION prior to mass spectrometry. Acetanilide (Puriss. p.a., Sigma-Aldrich) was used to calibrate elemental % C and % N. For δ¹³C and δ¹⁵N analysis, multipoint calibration was applied using reference standard materials including B2155 (protein, δ¹³C = −27.0‰, δ¹⁵N = +6.0‰), IAEA-CH-6 (sucrose, δ¹³C = −10.4‰), USGS40 (L-glutamic acid, δ¹³C = −26.4‰, δ¹⁵N = −4.5‰), USGS64 (glycine, δ¹³C = −40.8‰, δ¹⁵N = +1.8‰), and IAEA-N-2 (ammonium sulfate, δ¹⁵N = +20.3‰). The δ¹³C and δ¹⁵N values were measured relative to V-PDB and AIR, respectively.

For δ²H and δ ¹⁸O isotopes, around 1.0 mg powdered sample of each fraction was weighed into silver capsules (6 mm × 4 mm) and analyzed using EA (Vario Pyro Cube, Elementar, Hanau, Germany) IRMS (IsoPrime100, Isoprime Ltd., Manchester, England). Reference materials USGS54 (Canadian lodgepole pine, δ²H = −150.4‰, δ¹⁸O = +17.8‰) and USGS55 (Mexican ziricote, δ²H = −28.2‰, δ¹⁸O = +19.1‰) were used to calibrate the δ²H and δ¹⁸O measurements. Samples and reference materials were freeze-dried at −60 °C for three days to remove all adsorbed water and subsequently equilibrated for five days in the laboratory and exposed to local atmospheric conditions prior to H and O analysis. Pyrolysis was performed at 1450 °C to convert organic H and O to gaseous H₂ and CO, respectively, and finally the analytes were transferred into the IRMS for isotope determination. The δ²H and δ¹⁸O values were measured relative to Vienna Standard Mean Ocean Water (V-SMOW). All the samples were analyzed in triplicate. Reference materials were sourced from the International Atomic Energy Agency (IAEA, Vienna, Austria) and the United States Geological Survey (USGS, Reston, Virginia, United States). B2155 was supplied by Elemental Microanalysis (Okehampton, United Kingdom). The analytical precision for δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O was less than ±0.1‰, ±0.2‰, ±2‰, and 0.5‰, respectively. The delta values (δ) were calculated as follows:

$$\delta \mathrm{E}=\left({\mathrm{R}}_{\mathrm{sample}}/{\mathrm{R}}_{\mathrm{standard}}\right)-1$$

(1)

where δE represents δ¹³C, δ¹⁵ N, δ²H, and δ¹⁸O whereas R_sample and R_standard represent the ¹³C/¹²C, ¹⁵N/¹⁴N, ²H/¹H, or ¹⁸O/¹⁶O ratios in samples and reference materials, respectively.

2.3. Data Analysis

The effect of light intensity, fertilizer type and concentration, cultivars, and their interaction were studied using %C, %N, δ¹³C, δ¹⁵ N, δ²H, and δ¹⁸O of different rice fractions with multivariate analysis of variance. Light shading level, fertilizer type and concentration, and cultivars were considered as fixed variables. Differences among the treatments were evaluated using Duncan’s test at a significance level of 0.05. All analyses were performed using R software (version 3.0.3).

3. Results and Discussion

3.1. Multivariate ANOVA for Elemental Content and Isotope Ratios

Multivariate analysis of variance was applied to evaluate the effect of different factors such as variety (vty), shading level, and fertilizer (nitrogen) concentration on the carbon (%C), nitrogen (%N), δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values of different rice fractions, including polished rice (PR), brown rice (BR), and rice husk (RH). The results are summarized in

Table 1. A combined analysis of variance for different influencing factors on the stable carbon (δ¹³C), nitrogen (δ¹⁵N), oxygen (δ¹⁸O), and hydrogen (δ²H) values among different rice fractions.

	Pr (>F)
	Factor	%C	%N	δ13C (‰)	δ15N (‰)	δ2H (‰)	δ18O (‰)
Polished Rice	Fertilizer (N)	0.300	0.168	0.241	0.007 **	0.003 **	0.903
	Light shading (LS)	0.624	0.000 ***	0.000 ***	0.001 **	0.000 ***	0.408
	Variety (vty)	0.960	0.011 *	0.000 ***	0.541	0.222	0.004 **
	N × LS	0.945	0.528	0.291	0.061	0.190	0.893
	Vty × N	0.289	0.072	0.007 **	0.043 *	0.028 *	0.367
	Vty × LS	0.889	0.033 *	0.016 *	0.000 ***	0.030 *	0.456
Brown Rice	Fertilizer (N)	0.061	0.007 **	0.775	0.014 *	0.005 **	0.848
	Light shading (LS)	0.208	0.000 ***	0.000 ***	0.001 **	0.000 ***	0.920
	Variety (vty)	0.398	0.339	0.000 ***	0.338	0.029 *	0.683
	N × LS	0.432	0.630	0.087	0.073	0.273	0.954
	Vty × N	0.662	0.354	0.439	0.583	0.187	0.287
	Vty × LS	0.876	0.602	0.258	0.000 ***	0.114	0.403
Rice Husk	Fertilizer (N)	0.326	0.165	0.344	0.002 *	0.974	0.000 ***
	Light shading (LS)	0.081	0.053	0.000 ***	0.002 **	0.829	0.008 **
	Variety (vty)	0.833	0.000 ***	0.000 ***	0.004 **	0.000 ***	0.000 ***
	N × LS	0.208	0.581	0.153	0.015 *	0.541	0.283
	Vty × N	0.476	0.217	0.279	0.695	0.253	0.000 ***
	Vty × LS	0.586	0.363	0.025 *	0.000 ***	0.708	0.000 ***

Note: *** Indicates highly significant at p < 0.001, ** p < 0.01, and * p < 0.05.

and shown in Figure 1. The results showed that the carbon content (%C) in different rice fractions was not affected by vty, light shading, fertilizer, or their interactions. For %N, light shading showed a significant influence on the total nitrogen content of PR and BR; however, no significant difference was observed in RH under different light shading levels. Moreover, an interaction (vty × light shading) effect was also observed for PR (Figure 1a). In the case of δ¹³C, light shading and variety significantly contributed to all rice fractions. The interaction (vty × fertilizer)/(vty × light shading) effect also showed significant influence for PR and (vty × light shading) for RH. No interaction effect was observed for BR (Figure 1b–d).

The δ¹⁵N values in RH, PR, and BR were significantly affected by fertilizer concentration, shading level, and interaction (vty × shading level) effects (Figure 1e–g). Significant interaction effects (vty × fertilizer) on δ¹⁵N were also observed for PR (Figure 1h) and (fertilizer × light) for RH (Figure 1i). The fertilizer concentration and shading levels were the major factors that contributed significant variation among all rice fractions. In the case of δ²H, the fertilizer concentration and shading level showed a significant difference for PR and BR, whereas no significant difference was observed for RH. The interaction (vty × fertilizer)/(vty × light shading) effect was only observed for PR (Figure 1j,k). Only cultivar imparted a significant variation for the δ²H values in RH. In the case of δ¹⁸O, different trends were observed. Almost all factors contributed to a significant variation for RH but no significant differences were observed for BR and PR. The interaction (vty × fertilizer)/(vty × light shading) effects for δ¹⁸O are shown in Figure 1l,m, respectively.

3.2. Elemental and Isotope Differences between the Rice Varieties

Differences in the %C, %N, δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values among the different rice cultivar fractions under different fertilizer concentrations and light intensities were determined. The results showed significant differences for all the analyzed parameters except for %C. Multiple comparisons were made between %N, δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values (Figure 2). The %N in RH showed a strong significant difference between the two rice varieties. The total %N of You-1B rice (1.8 ± 0.2%) was significantly higher than Zhong-9B rice (1.5 ± 0.4%). Similarly, the δ¹³C values of You-1B RH (−30.8‰), PR (−28.3‰), and BR (−29.1‰) were significantly higher than Zhong-9B (−31.3‰, −29.2‰, and −29.7‰), respectively. The lower δ¹³C values of Zhong-9B rice suggest higher water use efficiency. Different factors are responsible for genetic variations in the δ¹³C values, including diffusive conductance, water use efficiency, stomatal activity, etc. [14]. For the δ¹⁸O values, the two varieties also showed significant differences for PR and RH. Higher δ¹⁸O values of You-1B RH (23.7‰) indicate higher transpiration rates. Our results are consistent with previous findings where a similar trend was observed for δ¹⁸O variations among different rice cultivars [15]. Differences in the δ¹⁸O values are mainly due to the vegetative cycle of water [16].

A different pattern was observed for the δ²H values. You-1B BR (−88.6‰) and RH (−125.7‰) were significantly lower than Zhong-9B BR and RH (−84.7‰ and −117.0‰), respectively. This difference suggests that less water fractionation occurred in Zhong-9B rice. A previous study also reported a significant difference in the δ²H values among different rice cultivars [17].

The δ¹⁵N values also followed the same trend where Zhong-9B rice values were higher than You-1B, although the difference in traits between rice varieties was primarily reflected in RH. δ¹⁵N values of PR, BR, and RH in Zhong-9B and You-1B rice was (2.6‰, 2.5‰, 2.7‰) and (2.5‰, 2.4‰, and 2.4‰), respectively. Our results are consistent with previous findings where significant differences in the δ¹⁵N values among different rice cultivars were reported [18].

3.3. Effect of Fertilizer Regimes on Elemental and Isotope Values of Rice Fractions

The effect of different fertilizer treatments on the %C, %N, δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values was measured and the results are shown in Figure 3. %N did not show any significant effect among the rice fractions and the only interaction effect (variety × nitrogen) was detected in PR rice (Table 1). The %N and δ¹⁵N values among different rice fractions were significantly affected by nitrogen fertilizer application levels. The %N and δ¹⁵N values of different rice fractions under different fertilizer concentrations (N0, N6, and N12) followed different patterns of accumulation. Figure 3 shows that the %N of rice grains was higher when subjected to higher nitrogen application levels. The %N for BR (1.8 ± 0.2%) grown under N12 conditions was significantly higher than N6 (1.6 ± 0.26%) and N0 (1.6 ± 0.2%), respectively. However, the δ¹⁵N value for N0 was significantly higher than N6 and N12 indicating that the synthetic urea fertilizer application level was negatively correlated with the δ¹⁵N values in different rice fractions (Figure 3).

These results appear consistent with previous findings which suggest conventionally fertilized rice has lower δ¹⁵N values compared to control samples (without fertilizer) [19]. The interpretation of δ¹⁵N signatures in plant tissues is generally complex since it can differ from the nutrient source as ¹⁵N fractionation occurs during plant physiological processes, mainly during nitrogen uptake, nitrate assimilation, and reduction, as well as during remobilization to the rice grain [20,21]. Lower δ¹⁵N values in the fertilized plant fractions during pre-anthesis reflect the direct N availability from the fertilizer applied. Discrimination against ¹⁵N occurs during the assimilation of inorganic nitrogen within the plant, and the enzymes responsible for this isotope discrimination include nitrate reductase and glutamine synthase [22].

Higher N assimilation rates in fertilized plants enhance the uptake of inorganic fertilizer which results in the reduction in ¹⁵N in fertilized plants compared to non-fertilized plants [22]. In addition, the isotopic discrimination against ¹⁵N is marginal in non-fertilized rice ears (glumes, awns, grains) as compared to fertilized rice ears, probably because N limitation prevents discrimination and promotes a higher efficiency for N remobilization [23]. The higher amount of N carried over into the grain results in a higher ¹⁵N discrimination. This fact suggests a decrease in the δ¹⁵N values for the rice grain in fertilized plants compared to non-fertilized plants. Discrimination within the plant is lower in the non-fertilized grains (N0; lacking nitrogen fertilizer) because the ratio between the plant demand vs. available N is high [24].

Nitrogen fertilizer application had a significant effect on the δ²H values in PR and BR. The highest δ²H values in PR (−73.2‰) and BR (−83.2‰) were observed for the N12 treatment which indicates that the δ²H values increased with N fertilizer level. There is limited literature on this topic, which restricts the discussion of this mechanism, although a similar trend was observed for the δ²H values in a previous study, where it was reported that BS-fertilized (biogas slurry) rice had higher δ²H values than a control rice (without fertilizer) [19]. The effects of nitrogen application levels on the δ¹⁸O values contrasted with δ²H values. There was no significant effect in the PR and BR grains, probably due to a weak or no effect on grain morphological parameters, and a lack of N effect on stomatal conductance and transpiration rates [25].

A significant difference was observed in the δ¹⁸O values in RH. The mean δ¹⁸O values for N6 (21.3‰) and N12 (20.2‰) treatments were significantly lower than N0 treatments (22.9‰) suggesting that the δ¹⁸O values in RH decreased with increasing nitrogen fertilization. Lower δ¹⁸O values in RH than the rice grain may be associated with progressive enrichment in plant components from the root, to the stem, to the leaf, and finally to the grain, as well as physiological factors such as dehydration and plant tissue degradation that occurs during husk development [26].

3.4. Elemental and Isotopic Variations among Different Light Shading Treatments

Different shading treatments including LS-0, LS-50, and LS-75 were applied and the effect was observed on the %N, %C, δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values of different rice fractions. The experiment was performed using two shading treatments (LS-0 and LS-50) for rice plants grown with fertilizer treatment N0 and N6, respectively (Figure 4a), and three shading treatments (LS-0, LS-50, and LS-75) for rice plants cultivated using N12 fertilizer treatment (Figure 4b). The shading effect on the %N, %C, δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values of rice grown under different fertilizer treatments followed the same pattern. The light shading treatment showed a significant effect on the %N, δ¹⁵N, δ¹³C, and δ²H values in different rice fractions. The values of %N were significantly lower at LS-0 than that of LS-50 and LS-75 in all three rice grain fractions. In the case of PR, the highest %N value was observed LS-75 (2.3 ± 0.2%) followed by LS-50 (2.0 ± 0.2%) and the lowest was observed in LS-0 (1.6 ± 0.1%), respectively. The δ¹⁵N values followed a unique pattern. It can be seen in Figure 4b that the δ¹⁵N signature among different rice fractions was significantly higher in LS-50 compared to LS-0 (non-shaded), but it decreased in LS-75. No significant difference was found in the δ¹⁵N values for LS-0 and LS-75. Further investigation is required to explore this mechanism since no literature was found that explained this phenomenon in rice. However, previous research has shown that the effect of maize kernel shading (40%) on %N at different stages showed a decrease in the %N content (up to 60%) compared to the control (ambient sunlight), which is not consistent with our study [27]. This crop difference suggests that plant physiological characteristics can impart significant crop variation for N uptake, retention, utilization, and isotopic fractionation.

The δ¹³C values among different rice fractions also showed significant variations for different shading treatments. The %C values of LS-0 in different rice fractions including PR (−28.3‰), BR (−28.8‰), and RH (−30.5‰) were significantly higher than those of LS-50 (29.5‰, −30.0‰, −31.6‰), and LS-75 (−29.7‰, −30.1‰, −31.5‰), respectively, indicating that at greater light intensity, the ¹³C isotopic fractionation is higher. In previous studies, a similar trend in δ¹³C values for citrus, grapefruit, and banana plants were reported under different shading treatments [10,28,29]. The δ¹³C values are positively correlated with irradiance and a similar trend of increasing δ¹³C values with increasing irradiance has been found in banana plants [28]. The δ¹³C values have been shown to be negatively correlated to internal CO₂ concentration in plants during carbon uptake, so a decrease in δ¹³C values under shaded treatments indicates increased conductance during the time that the CO₂ was fixed and/or a decreased photosynthetic rate [10]. Another study argued that lower δ¹³C values in shaded treatments clearly indicated that shading disrupted the photosynthate metabolism, reducing the photosynthate accumulation in grains [30].

The δ²H values also showed significant variations for PR and BR. In PR, the highest δ²H value (−65.3‰) was found in LS-75 followed by LS-50 (−69.3‰), and the lowest was observed in LS-0 (−77.0‰), respectively. The same trend was observed for BR and there were significant differences between LS 50/75 and LS-0. Soil water (from groundwater, irrigation water, or precipitation) is the main source of δ²H in plants, and the present results indicate that different light intensities affect the δ²H values of soil water, causing the δ²H signature of rice grains to vary through transpiration. No significant difference was observed for the δ¹⁸O values among the different rice fractions at different light intensities.

4. Conclusions

In this study, the effect of cultivar type, fertilizer supply, and light intensity on the distribution of elemental contents and light stable isotopes (C, N, H, and O) in different rice fractions were investigated. Significant variations were observed for δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O values of different rice fractions among different cultivars. Fertilizer application rates showed a strong association with %N, δ¹⁵N, δ²H, and δ¹⁸O values, although there was no significant variation in the %C and δ¹³C values. The light intensity levels also showed a significant influence on isotopic contents among different rice fractions. The δ¹³C values showed a positive correlation with irradiance. The δ²H and δ¹⁵N values decreased with an increase in the irradiance. It is concluded that all factors mentioned above significantly influence isotopic values and should be considered when addressing the authenticity of rice. Furthermore, care should be taken when selecting the rice fraction for future studies as isotopic signatures vary considerably among different rice fractions. The findings from this study will have a significant impact on understanding different climatic trends (seasonal and annual) and fertilizer effects on rice and allow better understanding of isotopic variability for different geographical regions, farming practices, and environmental conditions.