Floral scent is an important ornamental trait in garden plants.  Monoterpenes, a major class of terpenoids, constitute the primary volatile components of lily floral scents.  1-Deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) catalyzes the second enzymatic step in the MEP pathway, which supplies precursors for monoterpene biosynthesis.  However, the functional role of the DXR gene in floral monoterpene production in Lilium Oriental Hybrid ‘Sorbonne’ remains unclear.  In this study, ‘Sorbonne’ was used as the experimental material, and a differentially expressed LiDXR gene was identified from early transcriptomic data, showing high temporal correlation with the synthesis and emission dynamics of floral volatiles during flowering.  The LiDXR gene was cloned and subjected to bioinformatics analysis, revealing that it encodes a protein of 472 amino acids.  LiDXR expression peaked at the half-open floral stage and was significantly higher in petals than in other floral organs.  Subcellular localization analysis indicated that the LiDXR protein is targeted to chloroplasts in leaf epidermal cells.  VIGS of LiDXR reduced monoterpene levels by downregulating the expression of downstream TPS genes in the MEP pathway.  Consistently, headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) revealed a significant decrease in total volatile terpene content in silenced lilies.  Transgenic Arabidopsis thaliana and petunia plants overexpressing LiDXR exhibited enhanced growth vigor and accelerated flowering.  GC-Murashige and Skoog’s (MS) analysis of transgenic petunias showed a 78% increase in total volatile terpenes compared to wild-type plants.  Overexpression of LiDXR also modulated the expression of other MEP pathway genes, thereby influencing the biosynthesis of downstream terpenoids, including monoterpenes.  This study elucidates the functional role of LiDXR in terpenoid metabolism and provides a theoretical foundation for floral scent breeding in lily and other ornamental plants.

Fungi play crucial roles in nutrient acquisition, plant growth promotion, and the enhancement of resistance to both abiotic and biotic stresses.  However, studies on the fungal communities associated with peas (Pisum sativum L.) remain limited.  In this study, we systematically investigated the ecological effects of host niches (soil, root, stem, leaf, and pod) and genotypes on the diversity and composition of fungal communities in peas using a multi-level approach that encompassed pattern recognition (β-diversity decomposition), mechanism validation (neutral community model testing), and dynamic tracking methods (migration pathway source-tracking).  The results revealed that the dominant fungal phyla across niches and genotypes were Ascomycota, Basidiomycota, and Mortierellomycota, and the community structures of the soil–plant continuum were primarily determined by the pea niches rather than genotypes.  β-diversity decomposition was largely attributed to species replacement rather than richness differences, indicating strong niche specificity and microbial replacement across microhabitats.  Neutral model analysis revealed that stochastic processes influenced genotype-associated communities, while deterministic processes played a dominant role in niche-based community assembly.  Source-tracking analysis identified niche-to-niche fungal migration, with Erysiphe, Fusarium, Cephaliophora, Ascobolus, Alternaria, and Aspergillus as the key genera.  Migration rates from exogenous to endogenous niches were low (1.3–61.5%), whereas those within exogenous (64.4–83.7%) or endogenous (73.9–96.4%) compartments were much higher, suggesting that the pea epidermis acts as a selective barrier that filters and enriches microbial communities prior to internal colonization.  This study provides comprehensive insights into the mechanisms of host filtering, enrichment and microbial sourcing, which increases our understanding of the assembly rules of the pea-associated fungal microbiome.

Food security is a strategic priority for a country’s economic development.  In China, high-standard farmland construction (HSFC) is an important initiative to stabilize grain production and increase grain production capacity.  Based on panel data from 31 sample provinces, autonomous regions, and municipalities in China from 2005–2017, this study explored the impact of HSFC on grain yield using the difference-in-differences (DID) method.  The results showed that HSFC significantly increased total grain production, which is robust to various checks.  HSFC increased grain yield through three potential mechanisms.  First, it could increase the grain replanting index.  Second, it could effectively reduce yield loss due to droughts and floods.  Last, HSFC could strengthen the cultivated land by renovating the low- and medium-yielding fields.  Heterogeneity analysis found that the HSFC farmland showed a significant increase in grain yield only in the main grain-producing areas and balanced areas.  In addition, HSFC significantly increased the yields of rice, wheat, and maize while leading to a reduction in soybean yields.  The findings suggest the government should continue to promote HSFC, improve construction standards, and strictly control the “non-agriculturalization” and “non-coordination” of farmland to increase grain production further.  At the same time, market mechanisms should be used to incentivize soybean farming, improve returns and stabilize soybean yields.

Waterlogging poses a major challenge to Welsh onion (Allium fistulosum L.) production, exacerbated by climate change-induced extreme weather. Unraveling the molecular mechanisms of waterlogging tolerance is essential for breeding resilient cultivars. Here, we compared two Welsh onion varieties: BJQC (tolerant) and YZDC (sensitive). Waterlogging treatment revealed that YZDC exhibited higher accumulation of reactive oxygen species (ROS), including hydrogen peroxide (H₂O₂), superoxide ions (O₂⁻), and malondialdehyde (MDA), leading to increased mortality. In contrast, BJQC demonstrated enhanced waterlogging tolerance, attributed to its ability to upregulate flavonoid biosynthesis genes, resulting in higher flavonoid accumulation under waterlogging stress. Transcriptomic analysis identified that the activation of flavonoid pathway-related genes in BJQC was central to this response. In addition, genes associated with jasmonic acid and gibberellin signaling were also activated. Weighted gene co-expression network analysis (WGCNA) revealed that WRKY31 and MATE likely play critical roles in regulating flavonoid biosynthesis under waterlogging conditions. Genome-wide association study (GWAS) results from natural populations further supported the significance of these genes in waterlogging tolerance. Our comprehensive multi-omics analysis, including phenotypic, physiological, transcriptomic, and genomic approaches, provides new insights into the molecular mechanisms underlying Welsh onion responses to waterlogging. These findings highlight WRKY31 and MATE as key candidates for improving waterlogging tolerance in crop breeding programs. 

Accurate and nondestructive prediction of crop yield and yield categories are crucial for advancing precision. This study presented an integrated framework combining unmanned aerial vehicle (UAV) based hyperspectral imaging and machine learning to predict the early yield and yield categories of faba bean. Field experiments were conducted across three key growth stages—branching, early budding and mid budding—using high-resolution hyperspectral data. Full-band reflectance (FBR) and texture features (TF) were extracted and fused to enhance model sensitivity to canopy spectral-structural variations. The results revealed that FBR achieved higher coefficients of determination (R²>0.60) and lower root mean square errors (RMSE<0.93 t ha^-1) across all stages than vegetation indices (VIs), indicating its superior capacity to capture subtle physiological dynamics. The integration of TF with FBR further improved model accuracy, especially based on deep neural network in the mid budding stage (R²=0.8254, RMSE=0.4732 t ha^-1). Compared with the traditional method, the R² was increased by about 15-27%, and the RMSE was reduced by 38-49%, which highlighted the synergistic effect of spectral-spatial fusion. For predicting the yield categories, the XGBoost algorithm presented outstanding performance (F1-score>0.86). Spatial analyses confirmed strong consistency between measured and predicted distributions of yield and yield categories, validating the robustness of the proposed approach. In this study, the hyperspectral and machine learning prediction models were developed for early prediction of faba bean yield and yield categories, which provided a scalable data-driven tool for high-throughput phenotypic analysis and sustainable crop management.