Loading...

Current Issue

    28 November 2025, Volume 32 Issue 6 Previous Issue   

    Letters
    Reviews
    Research Papers

    For Selected: Toggle Thumbnails
    Letters
    Cross-Species Induction of Plant Immunity by Oryza-Specific Small Secreted Peptide, OsRALF26
    Oh-Kyu Kwon, A-Ram Jeong, Hyeran Moon, Ryoung Shin, Chang-Jin Park
    2025, 32(6): 747-750.  DOI: 10.1016/j.rsci.2025.04.016
    Abstract ( )   HTML ( )   PDF (1114KB) ( )  
    HS1 Enhances Rice Heat Tolerance Through Maintenance of Chloroplast Function and Reactive Oxygen Species Homeostasis
    Wang An, Shao Zhengji, Liu Ying, Zhang Guangheng, Zhu Li, Hu Jiang, Qian Qian, Ren Deyong
    2025, 32(6): 751-755.  DOI: 10.1016/j.rsci.2025.08.010
    Abstract ( )   HTML ( )   PDF (1057KB) ( )  
    Identification of Rice Leaf Width Gene FLW11 Through Genome-Wide Association Study and Functional Analysis
    Yang Yulu, Zhang Yanfang, Liu Xiong, Zhang Lihua, Huang Jingfen, Shen Lixing, Zhao Huibo, Shen Lan, Zhang Qiang, Zhu Li, Hu Jiang, Ren Deyong, Gao Zhenyu, Dong Guojun, Qiao Weihua, Qian Qian, Zhang Guangheng
    2025, 32(6): 756-760.  DOI: 10.1016/j.rsci.2025.06.004
    Abstract ( )   HTML ( )   PDF (951KB) ( )  
    Identification of Germplasm with High Ratooning Ability and Genetic Analysis of Ratooning Ability in Rice
    Yu Shouwu, XieYujun , Tang Gengsheng, Li Meizhen, Wang Linyou, Huang Yifeng, Bao Jinsong
    2025, 32(6): 761-765.  DOI: 10.1016/j.rsci.2025.06.006
    Abstract ( )   HTML ( )   PDF (715KB) ( )  
    OsABCG2 Controls Cadmium Accumulation in Rice Grains
    Yu Haipeng, Zhong Kaizhen, Zhang Zhen, Chen Mingxue, Zhang Weixing, Li Huijuan, Huang Guanrong, Huang Zengying, Tang Lu, Yang Pengfei, Zhong Zhengzheng, Hu Guocheng, Yu Guoping, Wu Dezhi, Tong Hanhua, Zhang Peng
    2025, 32(6): 766-771.  DOI: 10.1016/j.rsci.2025.06.008
    Abstract ( )   HTML ( )   PDF (908KB) ( )  
    Ecological Stoichiometric and Homeostatic Characteristics of Rice Soil under Different Ratios of Biochar Fertilizers
    Chen Yuqi, Wang Guanghua, Zhao Jinbiao, Yu Shilong, Jiang Min, Zhang Zujian, Huang Lifen
    2025, 32(6): 772-776.  DOI: 10.1016/j.rsci.2025.10.001
    Abstract ( )   HTML ( )   PDF (978KB) ( )  
    Reviews
    Functional and Nutraceutical Potential of Indian Rice Landraces: A Comprehensive Scientific Review
    D. Priyanga, K. Amudha, N. Sakthivel, P. Sivasakthivelan, S. Utharasu, D. Uma, M. Sudha
    2025, 32(6): 777-796.  DOI: 10.1016/j.rsci.2025.08.009
    Abstract ( )   HTML ( )   PDF (1713KB) ( )  

    In recent years, traditional rice landraces have gained increasing attention among consumers, scientists, and nutritionists because of their nutritional and therapeutic value. The diverse rice gene pool of the Indian subcontinent is bestowed with indigenous rice types augmented with nutrients and phytochemicals. Landraces high in resistant starch and dietary fiber contribute to gut health and help prevent gastrointestinal disorders, whereas those with high-quality protein contents, such as glutelin and lysine, all-trans retinoic acid, as well as iron and zinc contents (even in polished rice), play a vital role in the alleviation of malnutrition and hidden hunger. Metabolomic studies have revealed the presence of novel bioactive molecules, including tocols (e.g., gamma-tocotrienol and alpha-tocopherol), phytosterols (e.g., campestrol, beta-sitosterol, and stigmasterol), phenolic acids (e.g., 2-methoxy-4-vinylphenol, 4-vinylphenol, 3,5-di-tert-butylphenol, 2,4-di-tert-butylphenol, ionol, and 2,6-di-tert-butylphenol), flavonoids [e.g., flavonolignans tricin 4′-O-(threo-β-guaiacylglyceryl) ether and tricin 4′-O-(erythro-β-guaiacylglyceryl) ether], anthocyanins (e.g., delphinidin and cyanidin), carotenoids (e.g., 7,7′,8,8′-tetrahydrolycopene and 1-hydroxylycopene), diterpenoids (e.g., sugiol), vitamin D3 (a secosteroid), and bioactive vitamin D (e.g., calcitriol). These bioactive phytochemicals endow Indian rice landraces, rich in antioxidants, with antiphlogistic, antineoplastic, cardiac risk preventive, antiviral, and antitubercular activities, confirming their use in traditional Indian medicine. Furthermore, Indian landraces with a low glycemic index may benefit the Asian Indian phenotype, which is characterized by clinical anomalies such as insulin resistance, dyslipidemia (reduced high-density lipoprotein levels), and high dietary glycemic load. Therefore, the conservation of India’s traditional rice varieties is vital for both sustainable agriculture and improving global health.

    Genetic Regulation of Phytic Acid Biosynthesis in Rice: Pathways and Breeding Approaches for Low-Phytate Varieties
    Lishali Desingu, R. L. Visakh, R. P. Sah, Uday Chand Jha, R. V. Manju, Swapna Alex, Radha Beena
    2025, 32(6): 797-812.  DOI: 10.1016/j.rsci.2025.10.003
    Abstract ( )   HTML ( )   PDF (808KB) ( )  

    Phytic acid (PA), or myo-inositol 1,2,3,4,5,6-hexakisphosphate, is the main storage form of phosphorus (P) in seeds, accounting for 65% to 85% of their total P content. The negative charge of PA attracts metal cations, forming insoluble salts called phytates. These phytates, contain six negatively charged ions, can bind divalent cations such as Fe2+, Zn2+, Mg2+, and Ca2+, preventing their absorption in monogastric animals. To overcome P deficiency in non-ruminants, phytase is usually given as a supplement, which then results in excess P excretion, leading to environmental problems such as eutrophication. Improved fertilizer management, food processing techniques, and the development of low-PA crops through plant breeding are envisioned as effective ways to improve P-utilization and lessen the environmental impact while minimizing the effect of PA. A better understanding of the molecular and physiological basis of PA biosynthesis, grain PA distribution, the effects of genetic and environmental factors on PA accumulation, and methods to increase micronutrient bioavailability by lowering the effects of PA is essential for developing low-PA crops.

    Designing Climate-Resilient Rice Production Systems: Leveraging Genomics for Low-Emission Rice Varieties
    Kossi Lorimpo Adjah, Vimal Kumar Semwal, Nana Kofi Abaka Amoah, Isaac Tawiah, Negussie Zenna, Raafat Elnamaky, Koichi Futakuchi, Elliott Ronald Dossou-Yovo, Shailesh Yadav
    2025, 32(6): 813-830.  DOI: 10.1016/j.rsci.2025.08.003
    Abstract ( )   HTML ( )   PDF (1242KB) ( )  

    Rice cultivation contributes up to 12% of global anthropogenic methane (CH4) emissions, making it a significant climate concern. With rice demand projected to double by 2050, achieving the required 2.4% annual genetic gain must be balanced with emission reduction. This review synthesizes recent progress in three key areas: (1) mitigation strategies such as alternate wetting and drying and direct-seeded rice, which can reduce CH4 emissions by 30%-40%; (2) identification of physiological and molecular traits, such as short duration, high harvest index, improved nitrogen use efficiency, optimized root architecture, and stress tolerance with reduced greenhouse gas (GHG) footprints; and (3) the potential of genomics-assisted breeding and high-throughput phenotyping to accelerate the development of climate-resilient rice varieties with lower CH4 emissions. Specifically, we highlight how the synergistic integration of high-throughput phenotyping, genomic selection, and marker-assisted breeding can substantially improve the efficiency and precision of breeding programs targeting the development of climate-resilient rice varieties with reduced CH4 emissions. This is exemplified through successful case studies utilizing multi-omics approaches, including the development of Green Super Rice varieties (GSR 2 and GSR 8), which have demonstrated up to a 37% reduction in GHG emissions. Crucially, we propose a stratified trait profile for low-GHG rice development and provide guidelines and metrics for integrating these traits into mainstream breeding pipelines. We conclude by proposing a strategic framework integrating carbon-efficient breeding, climate-adapted agronomy, and policy support, which is essential for scaling low-GHG rice systems globally.

    Research Papers
    Enhanced Chlorophyll Accumulation is Early Response of Rice to Phosphorus Deficiency
    Pattanapong Jaisue, Chalongrat Daengngam, Panuwat Pengphorm, Surapa Nutthapornnitchakul, Sompop Pinit, Lompong Klinnawee
    2025, 32(6): 831-844.  DOI: 10.1016/j.rsci.2025.08.004
    Abstract ( )   HTML ( )   PDF (2679KB) ( )  

    Phosphorus (P) deficiency is a major constraint in rice production, causing significant reductions in growth and yield. While P deficiency typically decreases chlorophyll content in many plant species, our previous studies revealed an unexpected increase in chlorophyll content in P-deficient rice seedlings. Here, we investigated this phenomenon in KDML105 rice under various P regimes and analyzed the physiological mechanisms involved. We found that P-deficient rice seedlings significantly increased chlorophyll a, chlorophyll b, and carotenoid contents in young leaves while reducing photosystem II quantum yield and enhancing non-photochemical quenching. This response was specific to P deficiency and was not observed under other stress conditions such as salinity or copper toxicity, which induced oxidative stress. Time-course experiments revealed that increased chlorophyll accumulation was an early adaptive response that occurred primarily in young leaves, while older leaves eventually developed chlorosis under prolonged P deficiency. The increased chlorophyll content may be attributed to reduced leaf width and altered leaf morphology under P-limited conditions. Furthermore, using custom hyperspectral imaging analysis coupled with machine learning classification, we successfully differentiated P status in rice leaves with 98.96% accuracy in older leaves. This study demonstrates that enhanced chlorophyll accumulation is a characteristic early response to P deficiency in rice, rather than a typical general stress response observed in other conditions. Our findings highlight the limitations of relying solely on chlorophyll-based indices as indicators of plant health in precision agriculture, especially regarding phosphorus (P) nutrition management. This underscores the need for a more comprehensive approach and lays the groundwork for developing advanced remote sensing technologies aimed at accurately assessing P status in rice cultivation.

    Comparing Genotype and Climate Change Effects on Simulated Historical Rice Yields Using AquaCrop
    Fazli Hameed, Shah Fahad Rahim, Anis Ur Rehman Khalil, Ram L. Ray, Xu Junzeng, Alhaj Yousef Hamoud, Akhtar Ali, Ning Tangyuan
    2025, 32(6): 845-856.  DOI: 10.1016/j.rsci.2025.09.001
    Abstract ( )   HTML ( )   PDF (1521KB) ( )  

    Rice production, essential for global food security, is increasingly impacted by climate variability and genetic improvements. However, limited research has systematically quantified the individual contributions of climate change and genetic advancements to rice yield trends, particularly in high-latitude regions such as Harbin city, Heilongjiang Province, China. This study addresses this gap by using the AquaCrop model to partition the effects of climate change and genetic enhancements on rice yields over recent decades. The objectives were to evaluate the relative influences of climate and genotype on yield trends, assess irrigation efficiency under continuous flooding (CF) and alternate wetting and drying (AWD), and identify optimal transplanting dates for yield and water productivity. Four years of paddy field data were used to calibrate and validate AquaCrop for three rice varieties (V1, V2, and V3) under CF and AWD irrigation. Historical climate data were sourced for simulations. Key findings indicated that climate change accounts for 60%‒70% of yield improvements, while genotype contributes 30%‒40%. AWD achieved grain yields within 1% of CF, while improving water productivity by up to 7% in later (V2 and V3) varieties and with delayed transplanting dates. Additionally, 15 May was identified as the optimal transplanting date, yielding up to 7.53 t/hm2 under CF with biomass reaching 18.35 t/hm2. These findings highlight strategies for sustainable rice production in water-scarce regions and emphasize the role of genotype development in climate adaptation.

    Physical and Physicochemical Classification of Parboiled Rice Using VNIR-SWIR Spectroscopy and Machine Learning
    Nairiane dos Santos Bilhalva, Paulo Carteri Coradi, Rosana Santos de Moraes, Dthenifer Cordeiro Santana, Larissa Ribeiro Teodoro, Paulo Eduardo Teodoro, Marisa Menezes Leal
    2025, 32(6): 857-867.  DOI: 10.1016/j.rsci.2025.08.007
    Abstract ( )   HTML ( )   PDF (1214KB) ( )  

    The classification of parboiled rice into types can be optimized through the use of machine learning (ML) algorithms, resulting in greater speed and accuracy in data processing. The objectives of this study were: (i) to investigate the spectral behavior of different types of parboiled rice (Types 1-5 and Off-type); (ii) to identify the most effective ML algorithm for classifying parboiled rice types; (iii) to determine the best kernel configuration and preprocessing methods for spectral data; and (iv) to recommend a protocol for implementing this technique in the rice storage industry. Samples were selected based on the maximum defect limits tolerated for each type, according to the Technical Rice Regulation. Spectral data were acquired using a spectroradiometer in the range of 350-2500 nm and subsequently processed with different methods, including baseline correction, standard normal variate, multiplicative scattering correction, combinations of these techniques with Savitzky-Golay smoothing, and the application of the first derivative of Savitzky-Golay smoothing. The data were analyzed using six different ML algorithms: Artificial Neural Network, Decision Tree, Logistic Regression, REPTree, Random Forest, and Support Vector Machine. Rice types were treated as output variables, while spectral features served as input variables. Logistic Regression and Support Vector Machine algorithms showed the best classification performance, with accuracy rates above 97%, F-scores around 0.98, and Kappa values exceeding 0.97. Spectral preprocessing did not yield substantial improvements and incurred high computational costs; therefore, using raw data was a viable and efficient alternative. For practical implementation in the rice storage industry, we recommend acquiring a VNIR-SWIR (visible near-infrared and shortwave infrared) hyperspectral sensor (350-2500 nm) and developing a classification model based on the Support Vector Machine algorithm with a linear kernel trained on representative local samples. Additionally, we recommend implementing an automated real-time classification system, a representative sample collection protocol, and detailed reporting for inventory and logistics optimization.

    Intelligent Survey Method for Tiny Rice Pests and Their Natural Predators in Paddy Fields Using Augmented Reality (AR) Glasses
    Hong Chen, Luo Ju, Feng Zelin, Ling Heping, Li Lingyi, Wu Jian, Yao Qing, Liu Shuhua
    2025, 32(6): 868-884.  DOI: 10.1016/j.rsci.2025.08.005
    Abstract ( )   HTML ( )   PDF (2108KB) ( )  

    Rice crops are frequently threatened by pests such as rice planthoppers (Nilaparvata lugens, Sogatella furcifera, and Laodelphax striatellus) and leafhoppers (Cicadellidae), which cause significant yield losses. Accurate identification of both pest developmental stages and their natural predators is crucial for effective pest control and maintaining ecological balance. However, conventional field surveys are often subjective, inefficient, and lack traceability. To overcome these limitations, this study proposed RiceInsectID, a two-stage cascaded detection method designed to identify and count tiny rice pests and their natural predators from white flat plate images captured by head-worn AR glasses. The method recognizes 25 insect classes, including 17 instars of rice planthoppers, 2 instars of leafhoppers, 4 spider species (Araneae), as well as Miridae and rove beetles (Staphylinidae Latreille). At the first coarse-grained detection stage, 16 visually similar classes are consolidated into 6 broader categories and detected using an enhanced YOLOv6 model. To improve small object detection and address class imbalance, the full-region overlapping sliding slices and target pasting (FOSTP) algorithm was applied, increasing the mean average precision at a 50% IoU threshold (mAP50) by 35.46% over the baseline YOLOv6. Feature extraction and fusion were further improved by incorporating an efficient channel attention path aggregation feature pyramid network (ECA-PAFPN) and adaptive structure feature fusion (ASFF) modules, while the balanced classification mosaic (BCM) enhanced detection of minority classes. With test-time augmentation (TTA), mAP50 improved by an additional 2.06%, reaching 84.71%. At the second fine-grained classification stage, each of the six broad classes from the first stage is further classified using individual ResNet50 models. Online data augmentation and transfer learning were employed to significantly enhance generalization. Compared with the baseline YOLOv6, the two-stage cascaded method improved recall by 4.06%, precision by 3.79%, and the F1-score by 3.92%. Overall, RiceInsectID achieved 82.85% recall, 80.62% precision, and an F1-score of 81.72%, demonstrating an efficient and practical solution for monitoring tiny rice pests and their natural predators in paddy fields. This study provides valuable insights for ecosystem monitoring and supporting sustainable pest management in rice agriculture.