Crop Journal最新文献

Bacterial leaf streak (BLS), caused by Xanthomonas oryzae pv. oryzicola (Xoc), is a bacterial disease affecting rice production in Asia and Africa, whose severity is expected to increase with climate change. Identification of new quantitative-trait loci (QTL) or resistance genes for BLS resistance is essential for developing resistant rice. A genome-wide association study to identify QTL associated with BLS resistance was conducted using phenotypic and genotypic data from 429 rice accessions. Of 47 QTL identified, 45 were novel and two co-localized with previously reported QTL or genes conferring BLS resistance. qBLS6.2 on chromosome 6 explained the greatest phenotypic variation. Combined analysis of differential expression and annotations of predicted genes near qBLS6.2 based on haplotype and disease phenotype identified OsBLS6.2 (LOC_Os06g02960) as a candidate gene for qBLS6.2. OsBLS6.2 knockout plants showed higher resistance to Xoc than wild-type plants. Many other candidate genes for resistance to Xoc were identified.

Cotton is a major crop that provides the most important renewable textile fibers in the world. Studies of the taxonomy and evolution of cotton species have received wide attentions, not only due to cotton’s economic value but also due to the fact that Gossypium is an ideal model system to study the origin, evolution, and cultivation of polyploid species. Previous studies suggested the involvement of mitochondrial genome editing sites and copy number as well as mitochondrial functions in cotton fiber elongation. Whereas, with only a few mitogenomes assembled in the cotton genus Gossypium, our knowledge about their roles in cotton evolution and speciation is still scarce. To close this gap, here we assembled 20 mitogenomes from 15 cotton species spanning all the cotton clades (A–G, K, and AD genomes) and 5 cotton relatives using short and long sequencing reads. Systematic analyses uncovered a high level of mitochondrial gene sequence conservation, abundant sequence repeats and many insertions of foreign sequences, as well as extensive structural variations in cotton mitogenomes. The sequence repeats and foreign sequences caused significant mitogenome size inflation in Gossypium and its close relative Kokia in general, while there is no significant difference between the lint and fuzz cotton mitogenomes in terms of gene content, RNA editing, and gene expression level. Interestingly, we further revealed the specific presence and expression of two novel mitochondrial open reading frames (ORFs) in lint-fiber cotton species. Finally, these structural features and novel ORFs help us gain valuable insights into the history of cotton evolution and polyploidization and the origin of species producing long lint fibers from a mitogenomic perspective.

Stem growth habit dictates plant architecture and influences flowering and podding (seed setting), making it an essential morphological and breeding agronomic trait of soybean (Glycine max). Stem growth habit in soybean is affected by photoperiod and environment and is determined by genetic variation at major genes. Classical genetic analysis identified two critical loci, designated Determinacy 1 (Dt1) and Determinacy 2 (Dt2). Dt1 is an ortholog of Arabidopsis thaliana TERMINAL FLOWER1 (TFL1) and specifies an indeterminate stem growth habit, whereas Dt2 specifies a semi-determinate growth habit. MADS-box proteins, including Dt2, SUPPRESSOR OF OVEREXPRESSION OF CO1 (GmSOC1) and MADS-box genes downregulated by E1 (GmMDE), repress Dt1 expression. Photoreceptors encoded by the E3 and E4 loci regulate the expression of soybean FLOWERING LOCUS T (GmFT) orthologs via circadian clock genes and E1, and GmFTs compete with Dt1 to regulate stem growth habit. Study of the molecular mechanism underlying the regulation of stem growth habit in soybean has focused on the repression of Dt1 expression. Here we provide an overview of progress made in elucidating the genetic and molecular bases of stem growth habit in soybean, with emphasis on the molecular components responsible for integrating photoperiodic flowering and stem growth habit.

Photosynthesis affects crop growth and yield. The roles of microRNAs (miRNAs) in photosynthesis are little known. In the present study, the role of the OsNF-YB7–OsMIR5810–OsMRLP6 regulatory module in photosynthesis was investigated. The malectin-like protein gene OsMRLP6 was identified as a target gene of osa-miR5810 (miR5810). Overexpression in rice of miR5810 or down-expression of OsMRLP6 resulted in reduced expression of genes involved in chloroplast development and photosynthesis and decreased net photosynthetic rate, finally leading to lower shoot biomass and grain yield. Down-expression of miR5810 and overexpression of OsMRLP6 showed the opposite effect. Overexpression of transcription factor OsNF-YB7 elevated expression of OsMIR5810 in rice seedlings by binding to its promoter. The OsNF-YB7–OsMIR5810–OsMRLP6 regulatory module affects photosynthesis to mediate growth and grain yield.

Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error (MAE), root mean squared error (RMSE), relative MAE (rMAE) and relative RMSE (rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively, for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.

Accurate estimation of crop evapotranspiration (ETc) and soil water balance, which is vital for optimizing water management strategy in crop production, can be performed by simulation. But existing software has many deficiencies, including complex operation, limited scalability, lack of batch processing, and a single ETc model. Here we present simET, an open-source software package written in the R programming language. Many concepts involved in crop ETc simulation are condensed into functions in the package. It includes three widely used crop ETc models built on these functions: the single-crop coefficient, double-crop coefficient, and Shuttleworth–Wallace models, along with tools for preparing model data and comparing estimates. SimET supports ETc simulation in crops with repeated growth cycles such as alfalfa, a perennial forage crop that is cut multiple times annually.