Future Rice Research Articles

Genome-wide association study identifies key F-box genes linked to ethylene responsiveness and root growth in rice (Oryza sativa L.)

Rice (Oryza sativa L.) is a staple food for more than half of the world’s population, but its yields are increasingly threatened by environmental problems, including soil compaction. This problem limits root growth which limits water and nutrient foraging capacity thus reduces productivity due to, restricted diffusion of ethylene, a key plant hormone playing an important role in exacerbating these effects. Elevated ethylene levels in compacted soils can further inhibit root development. However, rice varieties that are less sensitive to ethylene may have an advantage as they exhibit better root growth and resource utilization under such conditions. In this study, 220 diverse rice accessions were analyzed to uncover the genetic factors that influence root length reduction (RLR) in response to ethylene. Genome-wide association studies (GWAS) identified a significant QTL on chromosome 10, named qRLR10, associated with ethylene response. Within this region, 20 candidate genes were identified, with three F-box genes namely Os10g0124700, Os10g0126600 and Os10g0128200 showing a strong correlation with RLR variations. These genes are involved in protein degradation, root development and hormone signaling, indicating their possible role in regulating ethylene sensitivity. The results suggest that rice varieties with lower ethylene sensitivity may have better root growth in compacted soils, making them ideal targets for breeding programs aimed at improving resilience to harsh environmental conditions. These results underscore the critical role of ethylene in rice root development and provide valuable insights for future rice improvement strategies aimed at mitigating the effects of soil compaction.

Advances in Understanding Drought Stress Responses in Rice: Molecular Mechanisms of ABA Signaling and Breeding Prospects

Drought stress is a pivotal environmental factor impacting rice production and presents a significant challenge to sustainable agriculture worldwide. This review synthesizes the latest research advancements in the regulatory mechanisms and signaling pathways that rice employs in response to drought stress. It elaborates on the adaptive changes and molecular regulatory mechanisms that occur in rice under drought conditions. The review highlights the perception and initial transmission of drought signals, key downstream signaling networks such as the MAPK and Ca2+ pathways, and their roles in modulating drought responses. Furthermore, the discussion extends to hormonal signaling, especially the crucial role of abscisic acid (ABA) in drought responses, alongside the identification of drought-resistant genes and the application of gene-editing technologies in enhancing rice drought resilience. Through an in-depth analysis of these drought stress regulatory signaling pathways, this review aims to offer valuable insights and guidance for future rice drought resistance breeding and agricultural production initiatives.

Evolution of Rice Cultivar Performance Across China: A Multi-Dimensional Study on Yield and Agronomic Characteristics over Three Decades

Rice (Oryza sativa L.) is a staple food crop for over half of the world’s population, with China being the largest producer. However, the growth rate of rice yield per hectare has slowed in recent years, emphasizing the need for in-depth studies on the evolution of rice cultivar performance. This study presents a comprehensive analysis of the yield and key agronomic traits of rice cultivars across China over three decades, utilizing data from 11,811 cultivar trials conducted between 1990 and 2023. We assessed the spatial distribution and temporal evolution of rice cultivar performance, exploring regional differences and the interplay between agronomic traits and environmental factors. Our results reveal significant variations in growth duration, plant height, grains per panicle, thousand-grain weight, effective panicle number, and seed setting rate across different regions. Temporal trends showed diverse patterns of improvement, with some regions experiencing rapid advancements (up to 1.42% annual yield increase in Jiangxi Province of Central China) and others nearing yield plateaus (0.16% in Jilin Province and 0.45% in Heilongjiang Province of Northeast China). Correlation analysis between agronomic traits and grain yield highlighted the complex relationships and potential for further genetic gains through targeted breeding. This study underscores the importance of region-specific breeding strategies to optimize rice production in the face of environmental challenges and yield ceilings. The insights gained provide a scientific basis for future rice cultivar development and regional agricultural policies aimed at enhancing sustainability and efficiency in China’s diverse rice-growing regions.

Transgressive segregation and generation mean analysis reveal the gene action underlying the inheritance of drought tolerance in rice

Climate change, an effective driver of unprecedented seasonal droughts, is greatly affecting rice production in Africa by threatening food security and safety. Rice, one of the major staple crops on the continent, can save the situation through the development of drought-tolerant cultivars, presenting a major challenge for future rice improvement programs as drought is regarded as a critical limitation in rain-fed ecosystems. This study sought to understand the genetic basis and inheritance behind the expression of tolerance of rice breeding lines to drought-stress through generation mean analysis. To achieve these objectives, two drought-sensitive genotypes (Jasmine 85 and CRI-Agrarice) were crossed with a drought-tolerant genotype (APO) to develop six populations (F1, F2, BC1, BC2, P1 and P2) under screenhouse drought-stress and non-stress evaluation. Data were collected on grain yield and yield-related traits among which the generation mean analysis was conducted. At least one transgressive phenotype was produced in the F2 population for each trait whether there is a significant difference or not among the parental lines under drought-stress. Under non-stress conditions, there was a significance for all six types of gene action for days to flowering in both crosses. Among both crosses and water-regimes, additive x additive gene interaction was significant for most of the traits even though the scaling tests were not significant indicating the effectiveness of selection in early generations. Therefore, either forward breeding or backcross breeding can be adopted as breeding strategies for rapid improvement for these lines to drought tolerance.

Exploring genetic variation and genetic components under salinity stress among Basmati and Non-Basmati rice (Oryza sativa L.) cultivars

Rice (Oryza sativa L.) is an essential cereal crop which feeds more than half of the world’s population. Rice is cultivated on one-tenth of the world's arable land (approximately 150 million hectares (ha)). Its production is critically affected by salinity stress, which harms photosynthesis and growth, causing biomass loss and partial sterility. The extent of salinization of cultivable soil is increasing by 10% annually. It is predicted that more than 50% of the world's land will be damaged by salinization by 2050. In Pakistan, approximately 6.30 Mha of land is severely affected by salt stress. To cope with this problem, this study was conducted to identify saline-tolerant rice cultivars, 14 rice cultivars (PK1121 aromatic, Nonabokra, Kalomonok, PK10683, PK8892 Tol-19, Shaheen Tol-19, Chenab Tol-19, Punjab Basmati, Super Basmati 2019, Basmati 2000, Basmati 370, Basmati 385, Super Basmati, and Basmati 515 Tol-19) were grown in a 5.5 electrical conductivity (EC) salinity block to investigate their tolerance to salinity stress. Seven rice cultivars (Nonabokra, Basmati 2000, Kalomonok, Basmati 370, Basmati 385, Super Basmati, and Basmati 515 Tol-19) exhibited tolerance to salinity. Basmati 370 was tolerant to a high salinity level of 5.5 EC and showed high yield than other cultivars, however, Nonabokra outperformed other cultivars in terms of all yield attributes investigated in this study. These results were also verified via molecular screening of these genotypes by using already reported DNA markers. Polymorphic SSR marker (RM7075) associated with the SaltTOl QTL to evaluate the tolerance of rice varieties to salinity. This study showed that Nonabokra, Basmati 2000, Kalomonok, Basmati 370, Basmati 385, Super Basmati, and Basmati 515 Tol-19 performed better against salt stress and may be used as salinity-tolerant lines for future rice breeding programs.

African Cultivated, Wild and Weedy Rice (Oryza spp.): Anticipating Further Genomic Studies.

Rice is a staple crop in sub-Saharan Africa, and it is mostly produced by Asian cultivars of Oryza sativa that were introduced to the continent around the fifteenth or sixteenth century. O. glaberrima, the native African rice, has also been planted due to its valuable traits of insect and drought tolerance. Due to competition and resistance evolution, weedy rice has evolved from O. sativa and O. glaberrima, posing an increasing threat to rice production. This paper provides an overview of current knowledge on the introduction and domestication history of cultivated rice in Africa, as well as the genetic properties of African weedy rice that invades paddy fields. Recent developments in genome sequencing have made it possible to uncover findings about O. glaberrima's population structure, stress resilience genes, and domestication bottleneck. Future rice genomic research in Africa should prioritize producing more high-quality reference genomes, quantifying the impact of crop-wild hybridization, elucidating weed adaptation mechanisms through resequencing, and establishing a connection between genomic variation and stress tolerance phenotypes to accelerate breeding efforts.

Game analysis of future rice yield changes in China based on explainable machine-learning and planting date optimization

ContextGlobal warming's escalating severity necessitates sophisticated approaches for predicting rice yield. Research QuestionCombining crop models with data-driven techniques, such as machine learning, can more effectively grasp the complex interplay of variables influencing crop growth. It remains a significant challenge to balance accuracy and interpretability in such hybrid models. MethodsThe research integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with statistical and machine learning models respectively, to assess rice yield changes in China under four future Shared Socio-economic Pathway (SSP). SSPs are scenarios that integrate socioeconomic trends with greenhouse gas emissions and radiative forcing pathways, which affect the phenology and yield of rice. The Shapley Additive Explanation (SHAP) method was employed to interpret the model, effectively determining the interplay among variables influenced rice yields. Mitigated the negative impacts of climate change on rice yield through the planting date optimization. ResultsProjections indicate significant rice yield losses in China without CO2, worsening with increased radiative forcing (p < 0.001). Considering rising CO2, single-season rice yields are projected to increase by 0.1–3.6 %, early rice by 4.6–9.5 %, while late rice yields are still decrease by 2.3–8.8 %. The rising CO2 can offset yield losses for single and early rice but not for late rice. The hybrid approach which combined the Random Forest (RF) with the DSSAT performed best in predicting rice yield. Studies showed that rising temperatures caused rice yield losses in China, yet we found that Growing Degree Days (GDD) exerted a more negative impact (p < 0.001). In high-precipitation regions, deep soil moisture is more influential than shallow soil moisture, whereas the reverse was true in drier areas (p < 0.001). Advancing planting dates for early and single rice and delaying for late rice can increase yields (p < 0.001). Adjusting to optimal planting dates, single-season rice yields increased by 3.3–6.3 %, early rice increased by 9.7–18.3 %, while late rice still decreased by 1.0–4.7 %. ConclusionsWithout considering the impact of CO2, significant rice yield losses in China are projected. Even with the fertilization effect of CO2, rice yields remain negatively impacted by climate change. However, implementing appropriate measures, such as optimizing planting dates, can help Chinese rice production benefit under changing climate. ImplicationsThis study offers insights into balancing accuracy and interpretability in hybrid models and provides guidance for local policymakers to address future climate change.

Analysis of Forecasting Methods on Rice Price Data at Milling Level According to Quality

Rice is a primary source of carbohydrates for many Indonesians, and its prices often surge due to uncontrolled demand. Therefore, the government is crucial in monitoring rice prices to maintain stability. Information technology, particularly data mining such as forecasting, is essential for providing accurate information on future rice prices. It will assist various stakeholders in making informed pricing policy decisions. This study employs Random Forest Regression and Gradient Boosting Regressor methods to predict rice prices using a dataset that includes monthly average rice prices at milling levels, categorized by quality (Premium and Medium), spanning from January 2013 to April 2024. The dataset consists of 136 rows, each representing a unique combination of year, month, and quality, and is stored in CSV format. Methodological steps include data collection, preprocessing, modeling, and model evaluation using monthly average rice prices at milling levels based on quality, including premium and medium grades. The results from Random Forest Regression indicate Root Mean Square Error (RMSE) values of 24.90 for premium rice and 25.47 for medium rice. The study reveals that Random Forest Regression outperforms Gradient Boosting Regressor in this context. Future research should explore additional prediction methods and consider other variables influencing rice prices to enhance model accuracy.

Allelic variation in rice blast resistance: a pathway to sustainable disease management.

Rice blast is a major problem in agriculture, affecting rice production and threatening food security worldwide. This disease, caused by the fungus Magnaporthe oryzae, has led to a lot of research since the discovery of the first resistance gene, pib, in 1999. Researchers have now identified more than 50 resistance genes on eight of the twelve chromosomes in rice, each targeting different strains of the pathogen.These genes are spread out across seventeen different loci. These genes, which primarily code for nucleotide-binding and leucine-rich repeat proteins, play an important part in the defense of rice against the pathogen, either alone or in combination with other genes. An important characteristic of these genes is the allelic or paralogous interactions that exist within these loci. These relationships contribute to the gene's increased capacity for evolutionary adaptation. The ability of resistance proteins to recognize and react to novel effectors is improved by the frequent occurrence of variations within the domains that are responsible for recognizing pathogen effectors. The purpose of this review is to summarize the progress that has been made in identifying these essential genes and to investigate the possibility of utilizing the allelic variants obtained from these genes in future rice breeding efforts to increase resistance to rice blast.

Rising atmospheric carbon dioxide concentrations increase gaps of rice yields between low- and middle-to-high-income countries.

The rising carbon dioxide concentrations are expected to increase future rice yields. However, variations in the CO2 fertilization effect (CFE) between rice subspecies and the influence of concurrent global warming introduce uncertainty in future global rice yield projections. Here we conducted a meta-analysis of rising carbon dioxide field experiments and employed crop modelling to assess future global rice yields for the top 14 rice producing countries. We found a robust parabolic relationship between rice CFE and temperature, with significant variations between rice subspecies. Our projections indicate that global rice production in the 2050s is expected to increase by 50.32 million tonnes (7.6%) due to CFE compared with historical production. Because low-income countries will experience higher temperatures, the gaps (difference of Δyield) between middle-to-high-income and low-income countries are projected to widen from the 2030s to the 2090s under elevated carbon dioxide. These findings underscore the critical role of CFE and emphasize the necessity to increase investments in research and technology for rice producing systems in low-income countries.

Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China

Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors, of which the most important one is the increasing carbon dioxide (CO2) concentrations. Estimates of CO2 fertilization effect (CFE) on rice, however, still had large uncertainties. Therefore, using the rice planting areas in East China as the study area, we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models, and found that the CFE predicted by these models had significant differences. We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province. Using CFE measurements from a field experiment as benchmark, we have developed an experiment–model integration approach aiming to reduce this variation. This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty, which is beneficial for the accurate prediction of future global rice yield in the context of climate change.

Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging.

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods.

Domestication of rice may have changed its arbuscular mycorrhizal properties by modifying phosphorus nutrition-related traits and decreasing symbiotic compatibility.

Modern cultivated rice (Oryza sativa) typically experiences limited growth benefits from arbuscular mycorrhizal (AM) symbiosis. This could be due to the long-term domestication of rice under favorable phosphorus conditions. However, there is limited understanding of whether and how the rice domestication has modified AM properties. This study compared AM properties between a collection of wild (Oryza rufipogon) and domesticated rice genotypes and investigated the mechanisms underlying their differences by analyzing physiological, genomic, transcriptomic, and metabolomic traits critical for AM symbiosis. The results revealed significantly lower mycorrhizal growth responses and colonization intensity in domesticated rice compared to wild rice, and this change of AM properties may be associated with the domestication modifications of plant phosphorus utilization efficiency at physiological and genomic levels. Domestication also resulted in a decrease in the activity of the mycorrhizal phosphorus acquisition pathway, which may be attributed to reduced mycorrhizal compatibility of rice roots by enhancing defense responses like root lignification and reducing carbon supply to AM fungi. In conclusion, rice domestication may have changed its AM properties by modifying P nutrition-related traits and reducing symbiotic compatibility. This study offers new insights for improving AM properties in future rice breeding programs to enhance sustainable agricultural production.

Genome-wide association analysis uncovers rice blast resistance alleles of Ptr and Pia

A critical step to maximize the usefulness of genome-wide association studies (GWAS) in plant breeding is the identification and validation of candidate genes underlying genetic associations. This is of particular importance in disease resistance breeding where allelic variants of resistance genes often confer resistance to distinct populations, or races, of a pathogen. Here, we perform a genome-wide association analysis of rice blast resistance in 500 genetically diverse rice accessions. To facilitate candidate gene identification, we produce de-novo genome assemblies of ten rice accessions with various rice blast resistance associations. These genome assemblies facilitate the identification and functional validation of novel alleles of the rice blast resistance genes Ptr and Pia. We uncover an allelic series for the unusual Ptr rice blast resistance gene, and additional alleles of the Pia resistance genes RGA4 and RGA5. By linking these associations to three thousand rice genomes we provide a useful tool to inform future rice blast breeding efforts. Our work shows that GWAS in combination with whole-genome sequencing is a powerful tool for gene cloning and to facilitate selection of specific resistance alleles for plant breeding.

OsLSC6 Regulates Leaf Sheath Color and Cold Tolerance in Rice Revealed by Metabolite Genome Wide Association Study

Plant metabolites including anthocyanins play an important role in the growth of plants, as well as in regulating biotic and abiotic stress responses to the environment. Here we report comprehensive profiling of 3315 metabolites and a further metabolic-based genome-wide association study (mGWAS) based on 292,485 SNPs obtained from 311 rice accessions, including 160 wild and 151 cultivars. We identified hundreds of common variants affecting a large number of secondary metabolites with large effects at high throughput. Finally, we identified a novel gene namely OsLSC6 (Oryza sativa leaf sheath color 6), which encoded a UDP 3-O-glucosyltransferase and involved in the anthocyanin biosynthesis of Cyanidin-3-Galc (sd1825) responsible for leaf sheath color, and resulted in significant different accumulation of sd1825 between wild (purple) and cultivars (green). The results of knockout transgenic experiments showed that OsLSC6 regulated the biosynthesis and accumulation of sd1825, controlled the purple leaf sheath. Our further research revealed that OsLSC6 also confers resistance to cold stress during the seedling stage in rice. And we identified that a SNP in OsLSC6 was responsible for the leaf sheath color and chilling tolerance, supporting the importance of OsLSC6 in plant adaption. Our study could not only demonstrate that OsLSC6 is a vital regulator during anthocyanin biosynthesis and abiotic stress responses, but also provide a powerful complementary tool based on metabolites-to-genes analysis by mGWAS for functional gene identification andpromising candidate in future rice breeding and improvement.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Preliminary Exploration of Physiology and Genetic Basis Underlying High Yield in Indica–Japonica Hybrid Rice

The utilization of heterosis is of great significance in improving rice yield. To explore the physiological and genetic basis for high yield in indica–japonica hybrid rice, Zheyou18 (z18) and Yongyou12 (y12) were used as materials and compared with indica hybrid rice, Zheyou12 (z12); japonica hybrid rice, Liangyoupeijiu (LYPJ); and the conventional lines zhe04B (04B) and zhehui818 (h818) under seedling growth vigor, functional leaf morphology, chlorophyll content, yield component, panicle trait, and InDel heterosis analysis. Z18 and y12 showed the largest increase in plant height 6 d and 9 d after germination; the root dry weight of z18 was 31.2% and 42.0% higher than its parents on the 12th d. The length of functional leaves ranked in the middle, while the width was the largest, resulting in z18 and y12 having the largest leaf area. Yield components showed that z18 and y12 had the highest number of primary branches, spikelets, and grains, and grain yield, which was 58.1 g in z18, increased by 29.8% and 8.7%, respectively, in comparison with h818 and LYPJ. The InDel genetic distance was significantly positively correlated with single spike weight, with r reaching 0.771, making it the only consistent and most correlated among the seven traits. Therefore, we speculated that as the InDel genetic distance expands, heterosis mainly manifests in the increase in single spike weight. This study comprehensively explored the physiological mechanism of yield improvement in indica–japonica-hybrid rice and used InDel genetic distances to study the genetic basis of heterosis, which will be helpful for future rice yield improvement.

A wild rice CSSL population facilitated identification of salt tolerance genes and rice germplasm innovation.

Salt stress is one of the major factors that limits rice production. Therefore, identification of salt-tolerant alleles from wild rice is important for rice breeding. In this study, we constructed a set of chromosome segment substitution lines (CSSLs) using wild rice as the donor parent and cultivated rice Nipponbare (Nip) as the recurrent parent. Salt tolerance germinability (STG) was evaluated, and its association with genotypes was determined using this CSSL population. We identified 17 QTLs related to STG. By integrating the transcriptome and genome data, four candidate genes were identified, including the previously reported AGO2 and WRKY53. Compared with Nip, wild rice AGO2 has a structure variation in its promoter region and the expression levels were upregulated under salt treatments; wild rice WRKY53 also has natural variation in its promoter region, and the expression levels were downregulated under salt treatments. Wild rice AGO2 and WRKY53 alleles have combined effects for improving salt tolerance at the germination stage. One CSSL line, CSSL118 that harbors these two alleles was selected. Compared with the background parent Nip, CSSL118 showed comprehensive salt tolerance and higher yield, with improved transcript levels of reactive oxygen species scavenging genes. Our results provided promising genes and germplasm resources for future rice salt tolerance breeding.

The effect of dataset construction and data pre-processing on the eXtreme Gradient Boosting algorithm applied to head rice yield prediction in Australia

Dataset quality heavily impacts the predictive performance of data-driven modelling. This issue can be exacerbated in the prediction of agricultural production due to the complex interactions between the climate, the environment and the way the plant is affected by these conditions during the season. This study aims to create an empirical model to predict Head Rice Yield (HRY), the primary quality metric for rice growers and millers globally. Model development focused on an industry-level dataset made available by SunRice, Australia's most prominent rice trading company. Using the SunRice data, two dataset construction methods were implemented to evaluate the effect of dataset construction and data pre-processing on model accuracy. The first dataset construction method was based on aggregating meteorological conditions using estimates of phenology, while the second method used aggregations based on defined lengths of time. Deviations of each construction method were generated to explore the impact of varying levels in aggregation stages and stage lengths. Each constructed dataset underwent feature selection prior to model training using the XGBoost algorithm with Leave-One-Year-Out Cross-Validation. The time-based dataset construction method proved to be the most accurate dataset construction method, producing the highest mean model accuracy scores across all pre-processing and model training configurations. The single most accurate model came from the two-week aggregation dataset, which yielded a 125% increase in Lin’s Concordance Correlation Coefficient compared to the worst-performing model produced in this study. Developing a highly accurate model that allows for crop stage knowledge discovery is critical for uncovering actionable insights to improve the management of future rice crops for HRY. The knowledge discovered in this study provides actionable insights to improve the management of future rice crops for HRY. The developed model demonstrates the potential for SunRice to predict HRY at the receival point to optimise post-harvest handling and milling. When matched to region-specific data, the dataset construction methods explored can be replicated in other rice-growing regions globally.

Hybrid Rice Production: A Worldwide Review of Floral Traits and Breeding Technology, with Special Emphasis on China.

Rice is an important diet source for the majority of the world's population, and meeting the growing need for rice requires significant improvements at the production level. Hybrid rice production has been a significant breakthrough in this regard, and the floral traits play a major role in the development of hybrid rice. In grass species, rice has structural units called florets and spikelets and contains different floret organs such as lemma, palea, style length, anther, and stigma exsertion. These floral organs are crucial in enhancing rice production and uplifting rice cultivation at a broader level. Recent advances in breeding techniques also provide knowledge about different floral organs and how they can be improved by using biotechnological techniques for better production of rice. The rice flower holds immense significance and is the primary focal point for researchers working on rice molecular biology. Furthermore, the unique genetics of rice play a significant role in maintaining its floral structure. However, to improve rice varieties further, we need to identify the genomic regions through mapping of QTLs (quantitative trait loci) or by using GWAS (genome-wide association studies) and their validation should be performed by developing user-friendly molecular markers, such as Kompetitive allele-specific PCR (KASP). This review outlines the role of different floral traits and the benefits of using modern biotechnological approaches to improve hybrid rice production. It focuses on how floral traits are interrelated and their possible contribution to hybrid rice production to satisfy future rice demand. We discuss the significance of different floral traits, techniques, and breeding approaches in hybrid rice production. We provide a historical perspective of hybrid rice production and its current status and outline the challenges and opportunities in this field.