Search Results (1,820)

Nitrogen fertilizer application rate and storage duration are critical agronomic and environmental factors affecting rice quality stability. The milling appearance, eating and nutritional quality, physicochemical properties, microstructure, and volatile metabolic profiles during long-term storage were investigated using three indica-japonica hybrid cultivars at four nitrogen fertilizer application levels. High nitrogen fertilizer application (300 kg hm⁻²) promoted an over-filled protein matrix and induced structural defects such as micropores in starch granules, which acted as “trigger points” for accelerated aging. Specifically, storage duration was the dominant factor reshaping volatile profiles and lipid degradation, but high nitrogen amplified these effects by promoting lipid oxidation and the accumulation of off-flavor compounds. Correlation analysis revealed that gel consistency (GC) is a core determinant of eating quality, exhibiting significant negative correlations with amylose content, setback, hardness and fatty acid values, while showing positive correlations with peak viscosity, breakdown value, and adhesiveness. All correlation patterns collectively contributed to the deterioration of rice eating quality after storage, indicating GC might be served as an indirect indicator for evaluating rice deterioration and applied in the breeding of rice varieties with improved storage tolerance. Microstructural analysis via SEM high nitrogen induced distinct cultivar-specific deterioration characteristics after 12 months storage. Full article

Cadmium (Cd) contamination in paddy soils threatens food security by accumulating in rice grains. This study aimed to elucidate Cd-accumulation mechanisms using major japonica cultivars from Liaoning Province, a key northern Chinese rice-producing region where systematic comparisons remain limited. Four Liaoning japonica varieties (low-Cd: YF47, SN9903; high-Cd: QTXT, TJ) were analyzed for Cd accumulation, physiological responses, including malondialdehyde (MDA), superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), and expression of Cd-related transporter genes under Cd stress. Cd distribution in rice plants followed the following order: root > stems and leaves > grain. Varietal differences were attributed to root-to-shoot transport rather than root uptake, as low-Cd varieties exhibited lower transport coefficients and higher root Cd retention. Low-Cd varieties showed smaller MDA increases and significantly higher SOD and CAT activities under Cd stress. Notably, OsLCD was significantly down-regulated in low-Cd varieties but up-regulated in high-Cd varieties, an opposite regulation pattern that clearly distinguishes the two groups. The root-to-shoot translocation process and the OsLCD expression pattern are key determinants differentiating low- from high-Cd japonica varieties. These findings provide region-specific mechanistic insights and screening indicators for breeding low-Cd rice in northern China. Full article

Accurate in-season rice (Oryza sativa L.) yield prediction is crucial for improved nitrogen management and climate-smart decision making, yet rigorous comparative benchmarking of machine learning (ML) models using multi-temporal UAV spectral data with independent temporal validation remains limited. This study systematically evaluated four ML algorithms (Random Forest, XGBoost, Neural Network, and Linear Regression) and two Bayesian model averaging ensembles for rice yield prediction using UAV multispectral imagery. Field experiments spanning three growing seasons (2023–2025) at Louisiana State University comprised 9–10 varieties and six nitrogen rates (0–235 kg N ha⁻¹; 576 plots). Vegetation indices and spectral bands from three growth stages were extracted as predictors. Models were compared using 300 random train–test iterations with systematic hyperparameter optimization, followed by independent validation on 2025 data. Among the individual models, XGBoost achieved the highest internal accuracy (R² = 0.87, RMSE = 0.85 t ha⁻¹), substantially outperforming Linear Regression (R² = 0.66, RMSE = 1.32 t ha⁻¹), while reduced BMA reached R² = 0.89. XGBoost demonstrated robust temporal generalization (R² = 0.62, NRMSE = 8.47%) despite environmental variation. The Enhanced Vegetation Index and Normalized Difference Red Edge at 90 days after planting (reproductive stage) were the most stable predictors across 300 iterations. Tree-based ML models substantially outperform traditional linear approaches, providing reliable pre-harvest yield forecasting for operational precision rice production. Full article

Maize (Zea mays L.), a typical thermophilic crop originating from tropical regions, exhibits an inherent sensitivity to low-temperature stress. Cold stress severely restricts maize seed germination, seedling growth, the physiological metabolism, and the final grain yield, which greatly limits its geographical cultivation range and sustainable industrial development. Elucidating the molecular regulatory mechanisms underlying maize cold tolerance and excavating cold-resistant functional genes are essential for the molecular breeding of cold-tolerant maize varieties and expanding maize planting areas in high-latitude and low-temperature-prone regions. In this study, using the strongly cold-tolerant maize inbred line B144 as the experimental material, we cloned the ZmMAPKKKA gene (NCBI accession: LOC103651289) and systematically screened and verified its cold-stress-specific interacting proteins via multiple molecular biological assays. The full-length coding sequence (CDS) of ZmMAPKKKA is 1134 bp, encoding a 377-amino-acid protein with a predicted molecular weight of 40.37 kDa. The quantitative real-time PCR (qRT-PCR) results demonstrated that the ZmMAPKKKA expression was significantly upregulated by 16.56-fold in maize roots after 12 h of low-temperature treatment, indicating a tissue-specific and robust cold response in root tissues. A total of 25 interacting proteins were identified through yeast two-hybrid screening, among which three stress-responsive proteins, including a protein kinase (LOC100286253), a protein phosphatase 2C (PP2C) (LOC542176), and a NAC transcription factor (LOC118474710), were selected for subsequent verification. The Pull-Down, Co-immunoprecipitation (Co-IP), and bimolecular fluorescence complementation (BiFC) assays consistently confirmed that ZmMAPKKKA specifically interacts with these three proteins both in vitro and in vivo under cold stress conditions. This study is the first to construct a ZmMAPKKKA-centered protein interaction module in the maize mitogen-activated protein kinase (MAPK) cascade under cold stress, establishing a novel kinase–phosphatase–transcription factor regulatory cascade that improves the current understanding of cold signal transduction mechanisms in maize. Homologous genes of ZmMAPKKKA in gramineous crops including rice (Oryza sativa) and sorghum (Sorghum bicolor) have been proven to participate in diverse abiotic stress responses, suggesting the conserved functional roles of MAPKKK family genes across gramineous species. Collectively, our findings provide comprehensive insights into the molecular mechanism of the maize MAPK signaling pathway mediating cold stress adaptation and supply valuable functional gene resources for cold-tolerant maize germplasm innovation and molecular breeding. Full article

Gene-editing technology provides innovative strategies for coping with crop stress, enhancing resistance to biotic stresses (fungal, bacterial, viral infections) and abiotic stresses (salinity, drought, heavy metals, temperature extremes). The CRISPR/Cas9 system is widely used to knock out susceptibility genes, activate resistance genes, or modulate stress-response genes, yielding many stress-resistant crop varieties. However, off-target effects, chimeric effects, and the complexity of multi-gene synergistic editing limit its application. By optimizing and integrating with other cutting-edge technologies, gene editing is expected to yield highly stress-resistant and high-yielding crop varieties, contributing significantly to sustainable agricultural development and ensuring global food security. Rice, a key staple and model plant, has been extensively studied in gene-editing-based research on stress resistance. The practical potential of gene editing for agricultural improvement has been demonstrated by the effective modification of many genes linked to drought, salinity, temperature extremes, and disease resistance using CRISPR/Cas9 and related technologies. This review discusses gene-editing applications in crop stress research, examining the effects of various stresses on crops and the use of gene editing to develop stress-tolerant varieties. It offers substantial guidance for improving crop stress tolerance through gene editing, creating highly resilient cultivars with greater adaptation to complex, variable environments. Full article

Rice eating quality is a core determinant of consumer preference and commercial value. Although it is chemically determined by the accumulation and distribution of the substances in rice seeds, the comprehensive physicochemical basis underlying this trait in japonica rice remains insufficiently clarified. To identify the key physicochemical indicators that predict and regulate japonica rice eating quality, the taste value of 59 japonica rice varieties was evaluated, and the protein content (PC), apparent amylose content (AAC), starch pasting properties, gelatinization characteristics, and textural attributes were systematically measured. The results indicated that japonica rice has an average taste value of 72.0 with a range between 54.0 and 87.8. The taste value was significantly negatively correlated with PC, onset (To), peak (Tp) and conclusion (Tc) gelatinization temperatures, but was significantly positively correlated with appearance score, mouthfeel score, hot paste viscosity (HPV), and cool paste viscosity (CPV). PCA further indicated that AAC, HPV, CPV, peak viscosity (PV), and setback value (SB) were the major contributors to the first principal component, explaining 38.5% of the total variation. Stepwise regression analysis showed that the best regression equation for predicting taste value was: Taste value = 142.526 − 5.226 PC − 0.425 To (R² = 0.455; p < 0.001), confirming PC and To as the core parameters accounting for 45.5% of the taste value variation. Path analysis further indicated that PC and To affected japonica rice eating quality through direct and indirect pathways. These findings suggest that low PC, low gelatinization temperature, high HPV, and high CPV can serve as good physicochemical indicators for the breeding of high-eating-quality japonica rice. Full article

(1) Background: We investigated the effects of nutrient levels on rice yield and nitrogen uptake, with the aim of improving rice yield and nitrogen use efficiency. (2) Methods: A 3-year field experiment was conducted using the rice variety Changliangyou Fuxiangzhan, with six treatments: no nitrogen application (CK), conventional fertilization (FP), single basal application of 60-day slow-release urea (CRU1), single basal application of urea combined with 40-day and 90-day slow-release urea (CRU2), partial substitution of chemical fertilizer with bio-organic manure (FPM), and conventional fertilization combined with straw return (FPS). (3) Results: Different nutrient management regimes significantly affected rice yield and nitrogen uptake and use, as well as soil nitrogen content. CRU2 achieved the highest performance across most indicators, with grain yield averaging 9.6% higher than that of FP and 36.4% higher than that of CK, alongside consistently greater effective panicle numbers. It also significantly enhanced nitrogen uptake, with higher grain and straw N accumulation, and showed the best nitrogen use efficiencies. Soil responses varied by treatment: FPS and FPM increased total nitrogen, while CRU2 and CRU1 had the highest inorganic nitrogen, and microbial biomass nitrogen peaked under FPM, CRU2, and FPS. Despite these benefits, CRU2 showed the largest negative nitrogen balance, averaging −33.0 kg ha⁻¹ over 3 years. (4) Conclusions: The CRU2 treatment achieved efficient synchronization between nitrogen supply and demand, with the highest yield, nitrogen uptake, and soil nitrogen levels. Full article

Precise mapping of rice cultivars is of great significance for crop management and food security evaluation. Nevertheless, differentiating between Indica and Japonica rice remains a formidable task, mainly due to subtle discrepancies in spectral characteristics and scattered planting distributions. This study evaluated the synergistic effect of spatial resolution and red edge information in rice variety classification using PlanetScope (PS) and Sentinel-2 (S2) images from the Tillering and Jointing stage, Heading and Flowering stage in Huai’an, Jiangsu Province. Multiple feature schemes were constructed, including spectral bands, vegetation indices, and texture features, with and without red-edge variables. A total of eight feature schemes have been constructed, including spectral bands, vegetation index, texture features, and red edge features. The feature scheme division is based on the participation of different sensors, growth periods, and red edges. We fine-tune three classification models, Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and TabNet, to enhance classification performance. Additionally, we employ Shapley Additive Explanations (SHAP) to quantitatively measure the contribution of each feature to the prediction of distinct rice varieties. Results demonstrate that classification accuracy of different sensors reach the highest at the Heading and Flowering stage. The overall accuracy of PS scheme is 98.14%, the F1 scores of Japonica and Indica rice are 97.67% and 98.41%, the overall accuracy of S2 scheme is 97.87%, and the F1 scores of Japonica and Indica rice are 98.62% and 98.68, respectively. Incorporating red-edge features leads to a notable improvement in F1-scores for both Indica and Japonica rice under all experimental configurations. Although PS only has one red edge band set, its classification performance is similar to S2, and the boundaries between different rice variety recognition results and between non rice and rice plots are more refined compared to S2. Feature attribution analysis reveals that red-edge indices exert a dominant influence on the decision-making process of the models, especially during the Heading–Flowering period. These findings suggest that high-accuracy discrimination of rice varieties relies heavily on the synergistic optimization of phenological timing, red-edge spectral information, and spatial resolution, rather than merely increasing spectral dimensionality. The optimization direction for high-precision rice variety mapping in the future should prioritize the collaborative mechanism of phenological period, red edge data, and spatial resolution, rather than being limited to simple stacking in the spectral dimension. Full article

Rice fungal blast, one of the most devastating diseases caused by Magnaporthe oryzae, poses a severe threat to global rice production. For the breeding and deployment of rice varieties with blast resistance, it is critical to elucidate the frequencies and genetic variations in avirulence genes among M. oryzae populations. In this study, a total of 294 M. oryzae isolates were collected in 2022 from central Jilin Province, China. Pathogenicity assays on 24 monogenic rice lines revealed extensive virulence variations among the 294 isolates, with highly pathogenic strains being dominant and clear geographic differences in pathogenicity profiles. Resistance frequencies differed markedly among 24 monogenic lines, with Pi3, Pit, Pi7, Pikh, Pik, and Pia showing resistance rates over 50% and Pish exhibiting the lowest efficacy. Moreover, resistance profiles varied significantly across four sampling regions in central Jilin Province, with Pit being the most effective in Changchun and Jilin, Pi3 in Tonghua, and Pikm in Liaoyuan. In addition, the Avr genotypes of the isolates were postulated based on phenotypic data from the monogenic rice lines. Among the postulated Avr genotypes, the frequencies of Avr-Pi11 and Avr-Pish were the lowest at 29.25%. Furthermore, molecular characterization of three Avr genes (Avr-Pi9, Avr-Pita2, and Avr-Pizt) was performed by sequencing a subsample of 50 randomly selected isolates. Natural mutation sites were identified in Avr-Pita2 and Avr-Pizt, which were located within the coding sequence regions, leading to non-synonymous mutations and nonsense mutations that cause premature termination. Notably, no mutation was detected within the coding sequences of Avr-Pi9. Collectively, the findings provide a theoretical basis for breeding blast-resistant rice varieties that can be deployed in central Jilin Province, China. Full article

The agriculture sector in Nepal is highly vulnerable to climate change due to its traditional practices, limited technological intervention, and low adaptive capacity. Owing to the country’s complex topography, the impacts of climate change are spatially heterogeneous, making local-level climate change assessments highly relevant. This study focuses on the impact of climate change on three major crops (rice, wheat, and maize), in the Doti district of Nepal, based on meteorological records, crop yield data, questionnaire surveys, and focus group discussions. Climate records from 1982 to 2022 show a trend in annual rainfall at a rate of −3.28 mm per year, with a particularly pronounced decline during the monsoon season. Both maximum and minimum temperatures exhibit statistically significant increasing trends of 0.01 °C and 0.03 °C per year, respectively. The most significant warming for maximum temperature occurs during the monsoon season, while minimum temperature shows the highest increase during the pre-monsoon season. During the same period, annual yields of paddy, maize, and wheat show statistically significant increasing trends. These trends in climate variables and crop yields align with the perceptions of local communities. Linear correlation analysis indicates that maximum and minimum temperatures have a positive influence on crop yields, whereas precipitation and diurnal temperature range have negative effects. Among these, minimum temperature has the greatest impact on crop yields, followed by maximum temperature and rainfall. Multiple linear regression analysis reveals that climate variables better explain long-term trends in crop yields rather than year-to-year variability. The impact of climate is most pronounced in wheat where climate variables account for approximately 55% of the yield variability, followed by paddy (R²~49%) and maize (R²~20%). Despite the overall increase in crop yields, interannual variability has grown, consistent with increased variability in climate parameters. To cope with this uncertainty, local communities have adopted various adaptation strategies, including the use of improved seed varieties, green manure, and changes in crop types. Other key practices include the use of inorganic fertilizers, selection of short-duration crops, crop rotation, minimum tillage farming, and river conservation. Full article

Asian countries, which account for more than 60% of global rice consumption, are expanding the adoption of precision agriculture technology using image sensors to increase the profitability of rice production. This requires the development of technology to process 2D images that can be obtained by individual farmers instead of expensive 3D scanners. This study aims to quantitatively extract grain-level shape information necessary for yield prediction using 2D rice panicle images. To achieve this, a framework for predicting rice panicle yield from 2D images that uses a convolutional neural network (CNN) to detect grains is developed. Unlike existing approaches that measure grain length, width, and thickness using vernier calipers or 3D scanners to reconstruct 3D volume and estimate yield factors through volume-weight relationships, this methodology utilizes panicle length and projected grain area, which are relatively stable shape indices derived from 2D panicle images, to accurately describe weight variation within the same variety (e.g., Huaidao, Sidao, Suxiu, Jingjing). Experiments are conducted using panicle image data of Chinese Japonica rice varieties collected in Jiangsu Province, China. The proposed methodology demonstrates high prediction accuracy, with coefficients of determination ranging from 0.89 to 0.96, by combining panicle length and projected grain area information. Full article

Salinity stress is a major constraint affecting rice establishment and productivity in many coastal and salt-affected regions of the world, as well as in Bangladesh. Seed priming has emerged as an effective technique to enhance seed germination, seedling vigor and growth, and stress tolerance. To address this challenge, the present study investigated the potential of four different seed-priming agents (non-, hydro-(H₂O), osmo-(Polyethylene glycol, 30%), nano-(Zinc EDTA (12%), and 170 ppm) applied to two rice varieties (Binadhan-10 and BINA dhan25) under four levels of salinity stress (0, 5, 8, and 11 dS m⁻¹), with the aim of enhancing germination, improving the seedling vigor index, and promoting early growth performance in a completely randomized design with four replications. Nano-priming with Zinc EDTA (12%, at 170 ppm) involves soaking seeds in a solution containing this concentration of zinc chelate, which can improve seedling vigor and stress resilience, especially under challenging conditions like salinity. The results indicated that salinity significantly reduced germination and seedling growth, whereas seed priming improved seed performance under stress conditions. Among the treatments, nano-priming showed the most pronounced improvement in germination and seedling vigor. Binadhan-10 exhibited a greater tolerance to salinity compared with BINA dhan25. Multivariate analyses, including principal component analysis, correlation analysis, and heatmap, revealed strong positive relationships among germination, vigor index, and seedling biomass traits. The findings demonstrate that seed priming, particularly nano-priming, can effectively enhance rice seed germination, the vigor index, and different seedling traits under saline conditions, providing a promising strategy for improving rice production in salt-affected areas in Bangladesh. Full article

Effective weed control is essential for sustainable and safe rice production, particularly under the long-term and widespread use of chemical herbicides. Oilseed rape (Brassica napus L.) is one of the most important oil crops worldwide, and the oilseed rape–rice rotation system is widely practiced in China. It has been reported to exhibit strong allelopathy on various plants, but the feasibility of using its straw incorporation for weed control in transplanted rice fields remains unclear. In this study, a natural weed management strategy based on shallow tillage of oilseed rape straw (ORS) was evaluated through laboratory bioassays, greenhouse experiments, and field trials. The results indicated that soil decomposition liquids (SDLs) of ORS exhibited strong dose- and decomposition time-dependent allelopathic effects on seven paddy weed species, while rice showed markedly lower sensitivity. ORS incorporated at 700–1100 g/m² generally exhibited high integrated allelopathic inhibition (in lab) and population control effects (in greenhouse) on paddy weeds, especially Leptochloa chinensis (L.) Nees, Cyperus iria L., and Cyperus difformis L. Among the growth parameters of ORS allelopathic stress, root growth was the most sensitive indicator, followed by shoot growth and seed germination. Greenhouse experiments displayed variety-dependent impact on the transplanted rice seedlings, with Xiushui134 and Yongyou1540 showing relatively high tolerance. Field trials revealed that ORS incorporation at 1100 g/m² for 10 d achieved a satisfactory control of population (77.7–84.9%) and fresh weight (80.7–95.6%) across Gramineae, Cyperaceae and Broadleaf weeds, without adverse impact on the growth of transplanted rice seedlings (Yongyou1540). This treatment also significantly promoted theoretical grain yield by 13.4–19.4%. Overall, shallow tillage of oilseed rape straw provides a feasible and environmentally friendly weed control strategy for transplanted rice systems. Full article

Lodging is a major constraint limiting grain yield in dry direct seeding rice (DDSR), yet the key traits and phenotypic relationships governing lodging resistance in japonica varieties adapted to this system remain poorly understood. This study evaluated 79 japonica accessions over two years in Shenyang, Northeast China, to dissect phenotypic variation in lodging index and identify ideotypes for breeding. Based on hierarchical clustering, varieties were classified into strong lodging resistance (SLR), medium lodging resistance (MLR), and weak lodging resistance (WLR) types, with SLR varieties achieving lodging indices 27.4–31.8% lower than those of MLR and 63.2–83.8% lower than those of WLR varieties. SLR varieties reduced lodging risk by coordinately balancing whole-plant bending moment and stem breaking resistance: plant height and center-of-gravity height were 5.2–10.7% lower, while basal internode bending stress was 27.9–81.9% higher than in other types. Structural equation modeling identified culm dry weight, inner diameter, and culm phenotype index as primary determinants of lodging variation. Notably, despite 11.0–13.7% fewer spikelets per panicle, SLR varieties maintained grain yields comparable to those of WLR varieties through compensatory increases in grain-filling rate (6.7–7.3%) and 1000-grain weight (8.1–8.7%). These findings demonstrate that optimizing basal internode structure and enhancing culm tissue density can simultaneously improve lodging resistance and preserve yield potential, providing a practical framework for breeding lodging-resistant, high-yielding japonica varieties for DDSR systems in Northeast China. Full article

Heat stress, particularly during the reproductive stage, poses a major challenge to rice production, as pollen development is highly sensitive to elevated temperatures. Accurate assessment of heat tolerance during this period is crucial for improving rice heat-stress tolerance but is hindered by asynchronous panicle development and imprecise staging. In this study, we identified a pair of near-isogenic lines, ZP15 and ZP17, which exhibited contrasting seed-setting rates under heat stress. We demonstrated that this divergence arises from differential tolerance during the pollen developmental stage, corresponding to a critical window (9–16 days before heading). Taking these lines as references, we established a reliable system that synchronizes developmental staging and quantitatively assesses heat-induced fertility loss. Validated using heat-tolerant N22 and heat-sensitive Wushansimiao, this system was applied to assess four conventional varieties and eight hybrids. Huanghuazhan and self-bred hybrids (Yangxianyou 912, Yangxianyou 903, and Yangxian 9A/P119-8) displayed high tolerance comparable to control varieties, whereas Yangdao 6 and multiple hybrids showed pronounced sensitivity. Collectively, this work provides a precise and reproducible framework for evaluating heat tolerance during pollen development, offering a valuable tool for accelerating the breeding of heat-resilient rice varieties. Full article