Ammonium and nitrate nitrogen are the two main forms of inorganic nitrogen (N) available to crops. However, it is not clear how mixtures of ammonium and nitrate N affect N uptake and partitioning in major rice cultivars in southern China. This study investigated the effects of different ammonium nitrogen and nitrate nitrogen mixture treatments (100:0, 75:25, 50:50, 25:75, and 0:100) on the growth, photosynthetic characteristics, nitrogen uptake, gene expression, and yield of different rice cultivars (Mei Xiang Zhan NO. 2: MXZ2; Nan Jing Xiang Zhan: NJXZ). Rice root biomass, tiller number, and yield were increased by 69.5%, 42.5%, and 46.8%, respectively, in the 75:25 ammonium-nitrate mixed treatment compared to the 100:0 ammonium-nitrate mixed treatment. The nitrogen content in rice roots, stems, leaves, and grains increased by 69.5%, 64.0%, 65.5%, and 17.5%, respectively. In addition, compared with MXZ2, NJXZ had a greater proportion of N allocated to leaves and grains. Analysis of root enzyme activities revealed that the 75:25 ammonium-nitrate mixed nutrient treatment increased rice root glutamine synthetase activity by an average of 35.0% and glutamate synthetase activity by an average of 52.0%. Transcriptome analysis revealed that the 75:25 mixed ammonium-nitrate nutrient treatment upregulated the expression of genes related to the nitrogen metabolism transporter pathway. Weighted correlation network analysis revealed that some differentially expressed genes (HISX and RPAB5) regulated the activities of nitrogen-metabolizing enzymes in rice and some (SAT2, CYSKP, SYIM, CHI1, and XIP1) modulated amino acid synthesis; greater expression of these genes was detected in the 75:25 ammonium-nitrate mixed nutrient treatment. The expression characteristics of the above genes were further confirmed by RT‒qPCR. Interestingly, the expression levels of the above genes were significantly correlated with the glutamate synthase activity, photosynthetic rate, and root volume. It is noteworthy that increasing the expression of the aforementioned genes coupled with nitrogen uptake was observed in the three main rice cultivars. These results suggest that the 75:25 ammonium-nitrate mixture may have increased nitrogen-metabolizing enzyme activities and promoted nitrogen uptake through the upregulated expression of nitrogen metabolism-related genes, thereby increasing tiller number and improving rice yield.

Background: Rapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics. Increasing the accuracy of estimation models for rice yield-related trait indicators (leaf nitrogen concentration, LNC; leaf area index, LAI; aboveground biomass, AGB; and grain yield, GY) through a strategy of "spectral data + texture data + dimensionality reduction + machine learning" is highly important. Methods: Between 2022 and 2023, hyperspectral canopy images, the LNC, LAI, AGB, and GY were collected synchronously. Then, dimensionality reduction was performed on the preprocessed spectral data using the Pearson correlation coefficient method, the successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) to select sensitive wavelengths. Estimation models were constructed using artificial neural networks (ANNs), support vector machine regression, one-dimensional convolutional neural networks, and long short-term memory networks. By extracting the texture features corresponding to sensitive wavelengths, high-precision estimation models were constructed using a "spectral data + texture data + dimensionality reduction + machine learning" method. Results: SPA-ANN provided the best prediction for LNC (R ² = 0.82, RMSE = 3.68 g/kg) and LAI (R ² = 0.75, RMSE = 0.47), while CARS-ANN was optimal for AGB (R ² = 0.90, RMSE = 79.05 g/m2) and GY (R ² = 0.63, RMSE = 0.59 t/ha). Adding texture features increased R ² by up to 9.9% and reduced RMSE by up to 27.2%. Conclusion: The optimized method can significantly increase the accuracy of estimation models. The results provide a scientific basis and technical data for the precise diagnosis of rice yield-related traits.

Introduction: Do indica rice cultivars prefer ammonium or nitrate? Understanding this preference is key to optimizing nitrogen use efficiency in rice production. Ammonium and nitrate are crucial for plant nitrogen nutrition, as rice cultivars exhibit varying preferences. However, few studies have classified ammonium and nitrate preferences within indica cultivars.Methods: For the first time, this study classifies indica rice cultivars based on their ammonium and nitrate preferences, revealing significant differences in biomass production under various nitrogen treatments. This study investigated the effects of ammonium-only nutrition (100:0), ammonium-nitrate mixed nutrition (75:25), and nitrate-only nutrition (0:100) on the maximum root length, shoot length, SPAD value, and biomass of 24 widely cultivated indica cultivars in South China.Result: Compared to ammonium-only nutrition, a mixed ammonium-nitrate treatment significantly boosted root and shoot growth, while nitrate-only nutrition led to a decline in chlorophyll content. Compared with the 100:0 treatment, the maximum root length, shoot length, root dry weight, shoot dry weight, and total dry weight in the 75:25 treatment significantly increased by 29.85%, 4.11%, 7.65%, 1.71% and 3.03% (p < 0.01), respectively; and the SPAD value in the 0:100 treatment significantly decreased by 4.22% (p < 0.01).Discussion: These results demonstrate distinct responses of rice cultivars to different nitrogen treatments. Through correlation, principal component, and cluster analyses, the rice cultivars were categorized into three types: ammonium-preferring type (APT), ammonium- and nitrate-preferring type (ANPT), and nitrate-preferring type (NPT). The APT, ANPT, and NPT showed the highest biomass in the 100:0, 75:25, and 0:100 treatments, respectively, with the biomass in the ANPT significantly exceeding that of the APT (p < 0.01). These insights provide a foundation for breeding high-yield indica rice, optimizing nitrogen fertilizer strategies, and improving nitrogen use efficiency in sustainable agriculture.

Seed development plays a critical role in determining both crop yield and grain quality in rice. As a key nutrient storage organ, the rice endosperm development not only contributes to grain filling but also plays an essential role during the early stages of seed germination. Amino acid metabolism is active during the process of seed development and seed germination. Asparagine is a primary amino acid responsible for long-distance organic nitrogen transport in plants. Asparagine synthetase catalyzes the synthesis of asparagine from aspartate and glutamine. In this study, CRISPR/Cas9-mediated knockout mutants of the OsASN2 gene of rice were generated. Homozygous mutants exhibited complete failure of seed germination, and heterozygotes could not produce homozygous offspring. Endosperm development of homozygous mutant seeds showed severe defects. Additionally, interacting protein screening combined with pull-down and co-immunoprecipitation (Co-IP) assays confirmed that OsASN2 physically interacted with pyruvate phosphate dikinase OsPPDKB, the mutants of which showed impaired endosperm development. These findings collectively indicate that OsASN2 plays a critical role in seed development and germination in rice.

Rice blast, caused by Magnaporthe oryzae, is a devastating disease that jeopardizes global rice production and food security. Precision agriculture demands timely and accurate monitoring tools to enable targeted intervention. This study introduces a novel deep learning framework that fuses time-series hyperspectral imaging with an advanced Autoformer model (AutoMSD) to dynamically track rice blast progression. The proposed AutoMSD model integrates multi-scale convolution and adaptive sequence decomposition, effectively decoding complex spatio-temporal patterns associated with disease development. When deployed on a 7-day hyperspectral dataset, AutoMSD achieved 86.67% prediction accuracy using only 3 days of historical data, surpassing conventional approaches. This accuracy at an early infection stage underscores the model’s strong potential for practical field deployment. Our work provides a scalable and robust decision-support tool that paves the way for site-specific disease management, reduced pesticide usage, and enhanced sustainability in rice cultivation systems.

Rice blast caused by Pyricularia oryzae progresses from symptom-free leaves to extensive tissue death, while neighboring severity grades often show similar spectra. This study developed XGBSVMRF-SA to integrate regional spectral-angle (SA) features with heterogeneous stacking of XGBoost 3.1.2, support vector machine, and random forest class probabilities. The analysis included 833 spectra from 40 recorded pot groups covering six severity grades (0–5). The 2151-band spectra underwent Savitzky–Golay smoothing and second-derivative preprocessing. Twelve near-infrared and shortwave-infrared SA features and 18 class probabilities formed a 30-dimensional logistic-regression input. Group-balanced grade centers and grouped out-of-fold predictions were generated within training partitions, without resampling or threshold adjustment. In matched grouped five-fold validation, XGBoost achieved 90.88% accuracy and 90.31% macro F1. Heterogeneous stacking increased these values to 91.34% and 90.61%; adding SA further increased them to 92.07% and 91.46%. The complete framework gained 1.19 and 1.16 percentage points over XGBoost, including SA-specific increments of 0.72 and 0.85 percentage points over the matched stack. Local perturbations of spectral-region boundaries produced a macro F1 span of 0.14 percentage points. These results support regional SA as a supplementary spectral-geometric input alongside class probabilities in heterogeneous stacking for quantitative rice blast grading.