Crop Science最新文献

Cercospora leaf spot (CLS) disease, caused by the fungus Cercospora beticola Sacc., and sugarbeet root maggot (SBRM, Tetanops myopaeformis [von Röder] [Diptera: Ulidiidae]) cause significant sugarbeet (Beta vulgaris L. [Caryophyllales: Amaranthaceae]) yield loss in the United States. Identification and utilization of resistance sources from wild sea beet (B. vulgaris ssp. maritima (L.) Arcang), the progenitor of all cultivated beet biotypes (sugarbeet, fodder beet, and table beet), will not only enhance sugarbeet resistance to pest and diseases but also broaden its genetic base for sustainable improvement. This research evaluated resistance/tolerance to CLS and SBRM feeding injury in 300 core Beta maritima accessions under multiple environments. The core accessions, obtained from publicly available collections in the United States, were selected based on a comprehensive phylogenetic analysis. A set of 42 accessions consistently exhibited resistance to CLS, of which 18 accessions belonged to sub-populations that are genetically distinct from cultivated sugarbeet. Another 32 accessions showed minor SBRM feeding injury to roots under varied environments and were considered tolerant. Nineteen of those accessions are genetically diverse from cultivated sugarbeet. A total of 11 accessions exhibited resistance/tolerance to both CLS and SBRM. The accessions identified as expressing host plant resistance to CLS and SBRM will be valuable sources with which to diversify commercial sugarbeet germplasm, and those showing distinct genetic distances from cultivated sugarbeet should have high potential to introduce novel genetic variations to broaden the sugarbeet genetic base.

Kodjovi, G. C., Coulon, M., Couturier, L., Morin, A., Goma-Louamba, I., Artault, C., Tcherkez, G., Vriet, C., La Camera, S., Pourtau, N., Moumen, B., & Doidy, J. (2025). A transcriptomic atlas facilitating systems biology approaches in pea. Crop Science, 65, e70194. https://doi.org/10.1002/csc2.70194

The Supplemental Material file that contained Supplemental Tables S1–S10 was inadvertently left out of the published paper. This has now been corrected. In addition, a citation for Table S10 has been placed in “Section 3.4.1 Validation of reference genes by RT-qPCR” in the sentence “The reference genes exhibited a mean Ct value ranging from 22.4 to 27.5 across all experiments, with Psat0s3790g0040 showing the highest expression and Psat5g227600 the lowest expression levels, respectively (Figure 4A; Table S10).”

The Supplemental Information section has also been updated with Table S10's caption: “Table S10: Raw Ct data of the top 10 reference genes and common housekeeping genes (TFIIA, PP2A, and β-tubulin) from qPCR performed on the three experiments (see details in Material & Methods section).”

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Flag leaf size is a determinant trait that influences plant architecture and yields potential in qingke (Hordeum vulgare var. coeleste L.). However, research on the map-based cloning of quantitative trait loci (QTL) for flag leaf length (FLL) and width (FLW) in qingke is limited. Therefore, a recombinant inbred line population derived from a cross between DZZ and KL10 was developed. A high-density genetic map was constructed using genotyping-by-sequencing data, followed by QTL analysis across five environments. The results revealed a total of 21 QTL for FLL and 28 for FLW, distributed on seven chromosomes of qingke. We defined 10 QTL that were detected in at least 2 environments as stable QTL, with average phenotypic variation explained (PVE) of 8.41%–26.36%. In addition, mate-QTL analysis revealed five QTL pairs regulating both FLL and FLW, which might be pleiotropic effect QTL co-regulating leaf size. Among them, SqFLL-6H.2 & SqFLW-6H.1 and SqFLL-7H.1 & SqFLW-7H.1 were co-localized major QTL stably detected in multi-environments, named SqFLS-6H and SqFLS-7H, with average PVE of 14.07% and 21.96%, respectively. Extreme individuals QTL and genetic effect analyses further confirmed the effective stability of SqFLS-6H and SqFLS-7H in regulating FLL and FLW. Our study identifies SqFLS-6H and SqFLS-7H as key loci stably regulating flag leaf size across multi-environments. These QTL provide a genetic foundation for marker-assisted selection to optimize leaf morphology and enhance yield potential in qingke breeding programs.

The contribution of cucurbit crops to global food and nutrition security is immense. They are economically and nutritionally important to smallholder farmers in Asia, who account for 81% of global cucurbit production. World Vegetable Center (WorldVeg) has been focused for 20 years on four species: bitter gourd (Momordica charantia), ridge gourd (Luffa acutangula), sponge gourd (Luffa cylindrica syn. L. aegyptica), and tropical pumpkin (Cucurbita moschata). Limited use of the available genetic diversity in seed industry cucurbit breeding programs resulted in reduced genetic gains for fruit yield and other key horticultural traits. WorldVeg's genebank stores cucurbit landraces collected from various parts of the world. WorldVeg has developed elite cucurbit lines and F1 hybrids by exploiting these landraces. This material is shared with seed industry partners to enable them to develop and release breakthrough F1 hybrids with enhanced fruit yield and resistance to diseases such as powdery mildew (Podosphaera xanthii), downy mildew (Pseudoperonospora cubensis), and multiple viruses, and also improved nutritional content, such as high carotenoid content in pumpkins. Molecular markers for virus and powdery mildew resistance and the gynoecious trait have been developed and validated. Future work on the discovery of new traits is emphasized, that is, gourds with enhanced fruit shelf life, rich intensity of antidiabetic compounds in bitter gourd, compact plant habit type loofahs, pumpkins with smaller or no seed cavity, and poleroviruses resistance. Bangladesh, Myanmar, Philippines, and China are important global centers for new germplasm accessions of these cucurbits, ensuring the global sustainability of cucurbit breeding and production.

Warsame, A. O. (2025). An optimized high-throughput colorimetric assay for phytic acid quantification. Crop Science, 65, e70195. https://doi.org/10.1002/csc2.70195

The third and fourth sentences in the abstract, “A previously reported colorimetric method for quantifying PA in soybean (Glycine max (L.) Merr.). However, the throughput of that method is relatively low.” have been updated to read, “A previously reported colorimetric method has been shown to be cost-effective and accurate for quantifying PA in soybean [Glycine max (L.) Merr.], but the throughput of this method is relatively low.”

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Synthesis of data from crop trait prioritization studies (CTPS) can provide insights to support decision-making, such as institutional funding allocation, and trait prioritization in crop improvement programs. This type of data synthesis is constrained by the lack of standardized crop trait terminology and suitable methods to deal with data heterogeneity. Crop trait ontologies provide terminology standardization, but annotating documents to link terms to ontology terms is time-consuming and may therefore miss trait terminology emerging from CTPS due to a data annotation bottleneck that constrains data synthesis. Natural language processing (NLP) techniques based on large language models (LLMs) can help in extracting information from unstructured text with no manual text annotation involved. This study applied NLP to synthesize unstructured text data extracted from CTPS by a recently published scoping review. Results show that (1) the trait vocabulary diversity used in CTPS varies per crop and by gender intentionality of CTPS, (2) crop trait preferences increasingly focus on food quality and climate adaptation traits, and (3) existing crop ontologies provide a good coverage of terms found in CTPS but might require the addition of terms, especially in crops such as cassava and sweet potato. This study demonstrates the utility of applying NLP and LLM to synthesize trait preference data across crops and timescales, potentially modeling an approach for broader utility to breeding programs and crop ontology curators alike.

Centipedegrass, Eremochloa ophiuroides [Munro] Hack., is a low-maintenance, warm-season turfgrass commonly grown in the southeastern United States. Limited information is known about the genomic regions that control centipedegrass traits, including stigma color. Stigma color can impact seed set and can have a role in insect pollination in other plant species. In this study, we used a genome-wide association study to detect a genomic region on the Hi-C genome assembler (HIC-ASM)-8 found to control stigma color. Examination of the most associated single-nucleotide polymorphic (SNP) markers revealed that plants with a homozygous C/C allele had mainly purple stigmas but could be white or a mixture of colors, whereas accessions that were T/T for these loci had only white stigmas. Two candidate genes, ctg780.162 and ctg780.158, with homologs involved in anthocyanin accumulation, were identified near the most significant SNPs. The entire ctg780.158 gene was sequenced from multiple accessions, and the white stigma accessions contained a large insertion before the start codon. Similarly, white accessions (TT) had three SNPs in the ctg780.162 coding region as compared to purple accessions (CC). This study identified candidate genes for stigma color and characterized the utilization of the ctg780.158 insertion.

Cotton (Gossypium hirsutum L.) fiber is a vital source of nature fibers in textile industry and an ideal model for studying plant cell development. Phospholipids and sphingolipids, which are derived from very long-chain fatty acids (, are essential for maintaining fiber cell membrane integrity and serving as fiber development signals. To elucidate the multifaceted roles of lipids in fiber development, this review synthesizes recent advances in understanding the lipid composition of fiber cells, their functional roles, and the regulatory mechanisms mediated by the interaction with phytohormones and proteins. Additionally, this review discusses strategies of modifying phospholipid metabolites to improve cotton fiber yield and quality.

Host resistance, using qualitative genes with major effects, such as resistance (R) genes, is one of the most effective disease control strategies. However, because major gene-derived resistance wanes over time, breeders must increasingly focus on quantitative trait loci and minor effect genes, which, when pyramided together, can confer stronger and longer lasting quantitative disease resistance (QDR). This review highlights the challenges of breeding for QDR in five case studies: blackleg (caused by Leptosphaeria maculans) in canola (Brassica napus), white mold (Sclerotinia sclerotiorum [Ss]) and common bacterial blight (Xanthomonas citri pv. fuscans and Xanthomonas phaseoli pv. phaseoli) in common bean (Phaseolus vulgaris), late leaf spot, early leaf spot, tomato spotted wilt virus, and southern stem rot in peanut (Arachis hypogaea), and stem, leaf, and stripe rusts (Puccinia spp.) and powdery mildew (Blumeria graminis) in wheat (Triticum aestivum). Five emerging approaches for accelerating QDR breeding are discussed: high-throughput phenotyping, phenomic selection, genomic selection, genome editing, and utilizing wild germplasm in pre-breeding. Lastly, we highlight the importance for breeders of QDR to consider the phenotypic, genetic, genomic, and pathogenicity gene variation within the pathogen population, using Ss in common bean as an example. By doing so, breeders will save time and resources and develop locally adapted cultivars.

It is essential to evaluate the genetic gain of lint yield in modern cotton (Gossypium hirsutum L.) cultivars planted in recent history and identify the trend of potential changes due to changes in breeding priorities in the United States. In National Cotton Variety Tests (NCVT) conducted since the 1960s, Upland cotton cultivars were tested annually at locations across the US Cotton Belt. The NCVT data from 1998 to 2022 after commercialization and inclusion of transgenic cotton cultivars were used to analyze lint yield trends during this period. The annual yield means were adjusted based on overlapped entries between testing years to minimize environmental influence during the long-term trials for genetic gain, which was estimated from regression of the adjusted annual means over testing years. The results showed that genetic gain of lint yield was 24.1 kg ha⁻¹ year⁻¹ during the 25-year period. When the long period was split into two segments, that is, 1998 to 2014 and 2015 to 2022, the genetic gains were 24.7 kg ha⁻¹ year⁻¹ and −1.3 kg ha⁻¹ year⁻¹, respectively. The yield trend of increasing before 2015 and plateauing after 2015 coincides with the trend of stacking technology advancement in development of transgenic cultivars. This coincidence reflects the early success of stacking technologies by seed companies in pyramiding stacked genes with cotton yield during the 2000s and the middle of 2010s. The yield plateau suggests the necessity of breakthroughs in breeding methods and biotechnologies in development of transgenic cotton for further increasing yield.