One hundred and forty-three F_2:7recombinant inbred lines (RILs) developed from the cross of soybean cultivars ‘Charleston’ and ‘Dongnong 594’ were analyzed for the quantitative trait loci (QTLs) underlying protein or oil content at 6 different developmental stages by composite interval mapping with a mixed genetic model. The genotype × environment (G × E) interactions of the QTLs were also evaluated. Nineteen (2004) and 33 (2005) unconditional QTLs underlying seed protein or oil content at the different developmental stages were mapped onto 8 and 9 linkage groups, respectively. The proportion of phenotypic variation explained by these QTLs ranged from 6.26% to 30.52% and from 5.38% to 28.47%, respectively. Fourteen (2004) and 21 (2005) conditional QTLs underlying seed protein or oil content were mapped onto 5 and 8 linkage groups, respectively. The proportion of phenotypic variation explained by these QTLs ranged from 2.97% to 29.68% and from 5.42% to 31.96%, respectively. The numbers and types of QTLs and the genetic effect for the two traits were different at each developmental stage. However, several genomic regions that simultaneously control the development of both traits were detected. The genetic effect on protein content and oil content was opposite for loci in the marker interval Satt335–SSatt334, reflecting a negative correlation of protein content and oil content. A G × E interaction effect of some QTLs underlying protein or oil content at different growth periods was observed.

The mechanisms of plant defense against pathogen attack in plant leaves have been extensively studied. However, our understanding of plant defense mechanisms in plant roots is still limited. In this study, a semi-dominant mutant nrtp1-D (necrotic root tip 1), with a short-root phenotype, was characterized in rice. Map-based cloning revealed that NRTP1 encoded a typical coiled-coil nucleotide binding leucine rich repeat (CC-NB-LRR) type protein and the mutation caused an amino acid substitution in the Nucleotide-Binding adaptor shared by Apaf1, certain R genes and CED4 (NB-ARC) domain, which may cause constitutive auto-activation of the NRTP1 protein. Gene expression analysis revealed that NRTP1 was preferentially expressed in rice roots. Expression of mutant nrtp1-D in tobacco leaves induced necrotic lesions, which indicated a common mechanism of plant defense response between leaves and roots. Transcriptome analysis revealed that many typical defense-response genes were differentially expressed in homozygous nrtp1-D. In addition, we also found differential expression of genes in pathways which had not previously been described as being associated with pathogen response. Histochemical analysis showed that the level of nitric oxide (NO), but not reactive oxygen species (ROS), was increased in homozygous nrtp1-D mutant roots. These results indicate that, in addition to the mechanism of defense response common to both roots and shoots, a novel pathway may also exist in rice roots, which does not operate in shoots.

This study aims to develop an effective and reliable method for estimating rice quality indices and yield, addressing the growing need for rapid, non-destructive, and accurate predictions in modern agriculture. Field experiments were conducted in 2018 at the Suiling Water Conservancy Comprehensive Experimental Station (47°27′ N, 127°06′ E), using Longqingdao 3 as the test variety. Measurements included the leaf area index (LAI), chlorophyll content (SPAD), leaf nitrogen content (LNC), and leaf spectral reflectance during the tillering, jointing, and maturity stages. Based on these parameters, spectral indicators were calculated, and univariate linear regression models were developed to predict key rice quality indices. The results demonstrated that the optimal R² values for brown rice rate, moisture content, and taste value were 0.866, 0.913, and 0.651, with corresponding RMSE values of 0.122, 0.081, and 1.167. After optimizing the models, the R² values for the brown rice rate and taste value improved significantly to 0.95 (RMSE: 0.075) and 0.992 (RMSE: 0.179), respectively. Notably, the spectral index GM2 during the jointing stage achieved the highest accuracy for yield prediction, with an R² value of 0.822. These findings confirm that integrating multiple indicators across different growth periods enhances the accuracy of rice quality and yield predictions, offering a robust and intelligent solution for practical agricultural applications.

The objective of this study was to develop a low-cost method for rice growth information obtained quickly using digital images taken with smartphone. A new canopy parameter, namely, the canopy volume parameter (CVP), was proposed and developed for rice using the leaf area index (LAI) and plant height (PH). Among these parameters, the CVP was selected as an optimal parameter to characterize rice yields during the growth period. Rice canopy images were acquired with a smartphone. Image feature parameters were extracted, including the canopy cover (CC) and numerous vegetation indices (VIs), before and after image segmentation. A rice CVP prediction model in which the CC and VIs served as independent variables was established using a random forest (RF) regression algorithm. The results revealed the following. The CVP was better than the LAI and PH for predicting the final yield. And a CVP prediction model constructed according to a local modelling method for distinguishing different types of rice varieties was the most accurate (coefficient of determination (R²) = 0.92; root mean square error (RMSE) = 0.44). These findings indicate that digital images can be used to track the growth of crops over time and provide technical support for estimating rice yields.

Submergence during germination (SG) is a major constraint during sowing, severely limiting the promotion and application of direct-seeded rice. Recent studies have revealed the adaptive mechanisms by which rice responds to SG. At the physiological level, flood-tolerant varieties effectively maintain energy supply and cellular homeostasis by enhancing amylase activity, improving glycolysis and ethanolic fermentation efficiency, promoting embryo sheath elongation, and activating antioxidant enzyme systems; at the molecular level, multiple key genes and signalling pathways have been identified, including SUB1A, OsTPP7, OsGF14h, etc., participating in hypoxia perception, metabolic reprogramming, and hormone signal integration to regulate SG under flooding. In addition, the interactions among plant hormones, such as ethylene, gibberellin, abscisic acid, and cytokinin, also play key roles in the SG process. Future research should prioritize breeding strategies that pyramid multiple genes by integrating gene editing, whole-genome selection, and high-throughput phenotyping to improve seed germination under flood stress.