Long-term isotopic model study for ecofriendly rice (Oryza sativa L.) authentication: Updating a case study in South Korea

Chinese organic rice transition spatial econometrics empirical analysis

Based on the integrated model of Super-SBM model, spatial Durbin model (SDM) and Grey neural network model, this paper analyzes the panel data of various provinces in China from multiple angles and dimensions. It was found that there were significant differences in eco-efficiency between organic rice production and conventional rice production. The response of organic rice to climate change, the spatial distribution of ecological and economic benefits and the impact on carbon emission were analyzed. The results showed that organic rice planting not only had higher economic benefits, but also showed a rising trend of ecological benefits and a positive feedback effect. This finding highlights the importance of organic rice farming in reducing carbon emissions. Organic rice farming effectively reduces greenhouse gas emissions, especially carbon dioxide and methane, by improving soil management and reducing the use of fertilizers and pesticides. This has important implications for mitigating climate change and promoting soil health and biodiversity. With the acceleration of urbanization, the increase of organic rice planting area shows the trend of organic rice gradually replacing traditional rice cultivation, further highlighting the potential of organic agriculture in emission reduction, environmental protection and sustainable agricultural production. To this end, it is recommended that the Government implement a diversified support strategy to encourage technological innovation, provide guidance and training, and raise public awareness and demand for organic products. At the same time, private sector participation is stimulated to support the development of organic rice cultivation through a public-private partnership model. Through these measures, further promote organic rice cultivation, achieve the dual goals of economic benefits and environmental benefits, and effectively promote the realization of double carbon emission reduction targets.

Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state

This study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δ13C, δ15N, δ18O, and δ34S in organic, pesticide-free, and conventional rice were assessed across different milling states (brown, milled, and bran). Individual stable isotope ratio alone such as δ15N demonstrated limited capacity to correctly differentiate organic, pesticide-free, and conventional rice. A support vector machine model-incorporating δ13C, δ15N, δ18O, and δ34S in milled rice-yielded overall predictability (95%) in distinguishing organic, pesticide-free, and conventional rice, where δ18O emerged as the pivotal variable based on the feature weights in the SVM model. These findings suggest the potential of multi-isotope and advanced statistical approaches in combating organic fraud and ensuring authenticity in the food supply chain.

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

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.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.