Agriculture Communications最新文献

{"title":"Dynamic analysis of canopy coverage traits and their genetic basis in wheat under multiple environments","authors":"Lei Li,&nbsp;Jindong Liu,&nbsp;Guoliang Wan,&nbsp;Hongqin Wang,&nbsp;Mengjiao Yang,&nbsp;Shuaipeng Fei,&nbsp;Duoxia Wang,&nbsp;Yong Zhang,&nbsp;Xianchun Xia,&nbsp;Xin Ma,&nbsp;Yong He,&nbsp;Yonggui Xiao","doi":"10.1016/j.agrcom.2026.100161","DOIUrl":"10.1016/j.agrcom.2026.100161","url":null,"abstract":"<div><div>The extent of canopy coverage (CC) prior to the booting stage is a useful indicator of environmental adaptation and may help anticipate key developmental events such as heading and flowering. We used UAV-based high-throughput phenotyping to monitor CC in a 262-line F₈ recombinant inbred line population grown under four irrigation-year environments across two seasons. To quantify CC dynamics, we fitted regression models using either days after sowing (DAS) or accumulated active temperature (AT). These models showed a high goodness-of-fit (overall coefficient of determination (<em>R</em>²) &gt; 0.90 across environments; per-timepoint prediction <em>R</em>² = 0.60‒0.99 with root mean squared error (RMSE) = 0.00‒0.02) and were used to derive 31 CC-related traits. Principal component analysis (PCA) showed that the first two components explained 80.00% of total variance in CC traits, with PCA1 accounting for 48.88%‒62.51% of the variance for DAS-based traits and 50.26%‒54.93% for AT-based traits. PCA1 reflected early canopy vigor and rapid coverage increase, while PCA2 reflected canopy maintenance after jointing. Genotypes in the top 15% for PCA1 differed significantly in flowering time, heading time, and plant height from those in the bottom 15%. Cross-environment comparisons showed moderate to high consistency of CC traits (average correlation (<em>r</em>) = 0.41‒0.65 for DAS-standardized CC of 20‒160 DAS and 0.48‒0.67 for AT-standardized CC of 100‒1000 AT). Using CC features derived from both DAS and AT, we trained a random forest model to predict flowering time, highlighting the contribution of both temporal and thermal information to predictive accuracy. This model achieved an independent test set <em>R</em>² of 0.81 with an RMSE of 1.25 days within the current dataset. All 31 CC-derived traits were also used for QTL mapping. Inclusive composite interval mapping identified 156 QTL detection events across 15 chromosomes (0.80%‒27.70% phenotypic variance explained), which were consolidated into 26 QTL regions, including loci such as <em>QCC.caas.7A</em> (671.47‒680.09 Mb on 7A) and <em>QCC.caas.5D-2</em> (426.67‒459.42 Mb on 5D). Several of these regions co-localized with previously reported genes associated with tillering, flowering time, winter hardiness and plant height. These findings indicate that CC, characterized by DAS- and AT-based traits, is genetically tractable and predictive of flowering within the current dataset, supporting its potential as a phenology-related trait for wheat improvement. Further validation across broader environments, years, and genetic backgrounds will be needed before broader application.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 3","pages":"Article 100161"},"PeriodicalIF":11.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research progress on the differential distribution of polyphenols in dietary matrices and their mechanisms in the treatment of chronic noncommunicable diseases","authors":"Yousheng Shi,&nbsp;Lujia Zhang,&nbsp;Dan Liu,&nbsp;Weipeng Zhang,&nbsp;Hongyan Zhang,&nbsp;Cheng Zhang,&nbsp;Haiou Wang,&nbsp;Dongxing Yu,&nbsp;Jun Zhang,&nbsp;Mingqiang Xu,&nbsp;Fan Yang,&nbsp;Weiqiao Lv,&nbsp;Hongwei Xiao","doi":"10.1016/j.agrcom.2026.100162","DOIUrl":"10.1016/j.agrcom.2026.100162","url":null,"abstract":"<div><div>Chronic noncommunicable diseases (NCDs) are the leading cause of death worldwide, accounting for about 74% of all deaths and posing a significant public health challenge. Polyphenols, a class of plant chemicals with diverse biological activities, have shown significant potential in preventing and managing NCDs. This review summarizes the current status of polyphenol intake in diets across different regions worldwide. It systematically analyzes the content, types, and distribution of polyphenols in major dietary sources. Additionally, the review summarizes the latest research that reveals an association between polyphenol intake and a reduced risk of various NCDs. The paper focuses on the key biological activities of polyphenols and their regulatory mechanisms, including antioxidant, anti-inflammatory, antibacterial, anticancer, metabolic-regulatory, neuroprotective, and cardiovascular protective effects. These findings provide the scientific basis for the development of polyphenol-rich functional foods and the formulation of precise nutritional intervention strategies. Finally, the article discusses the main challenges currently faced in polyphenol research and outlines future research directions and application prospects.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 3","pages":"Article 100162"},"PeriodicalIF":11.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CropRowSeg: A lightweight model for seedling crop row image segmentation combining multi-scale CNN and transformer","authors":"Yonghao Tian,&nbsp;Long Xiao,&nbsp;Pengcheng Song,&nbsp;Wenjie Kang,&nbsp;Yao Ma,&nbsp;Yumeng Cui,&nbsp;Yuefeng Du,&nbsp;Zhongxiang Zhu,&nbsp;Zhenghe Song,&nbsp;Zhiqiang Zhai","doi":"10.1016/j.agrcom.2026.100163","DOIUrl":"10.1016/j.agrcom.2026.100163","url":null,"abstract":"<div><div>Crop row visual perception can provide navigation information for autonomous row-following equipment and its bottleneck is how to segment target crop rows from complex field images. Existing methods are often designed for specific scenarios or crops, suffering from complex hyperparameter tuning, large parameter sizes, high computational costs, poor convergence, and weak generalization. Departing from traditional row-by-row segmentation, we treat target crop rows aligned with the navigation path as foreground, while categorizing weeds, soil, and other rows as background, establishing an efficient annotation method. We propose CropRowSeg, a semantic segmentation model based on an encoder-decoder architecture. The encoder integrates parallel Convolutional Neural Network (CNN) and Vision Transformer (ViT) branches with hierarchical feature fusion, enabling local detail perception and global context modeling to address long-range dependencies. In the ViT branch, computational redundancy is reduced via feature dimensionality reduction and spatial-channel decoupled convolution, along with local feature enhancement. A hybrid attention adaptively calibrates feature importance across spatial and channel dimensions. The decoder employs a progressive architecture combining residual connections and coordinate attention to reconstruct details and mitigate information loss during upsampling. We introduce a special loss function to address foreground-background imbalance and spatial confusion between symmetrical rows. Experiments on a diverse crop row dataset demonstrate that CropRowSeg achieves accurate segmentation across environments, growth stages, and crop varieties, showing strong generalization. With only 6.91 M parameters and 12.06 Giga Floating-point Operations Per Second (GFLOPs), the model attains an mIoU of 82.55%, a Recall of 92.75%, and an F1-score of 90.26%. The improved model performance will support deployment on edge devices and promote practical applications.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 3","pages":"Article 100163"},"PeriodicalIF":11.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of marker genes for drought stress tolerance in wheat (Triticum aestivum) through carbon isotope composition","authors":"Sepideh Jafarian,&nbsp;Manuel Geyer,&nbsp;Franz Buegger,&nbsp;Barbro Winkler,&nbsp;Georg Gerl,&nbsp;Klaus F.X. Mayer,&nbsp;Nadia Kamal,&nbsp;Jörg-Peter Schnitzler,&nbsp;Lorenz Hartl,&nbsp;Manuel Spannagl","doi":"10.1016/j.agrcom.2026.100158","DOIUrl":"10.1016/j.agrcom.2026.100158","url":null,"abstract":"<div><div>Improving drought adaptation in wheat requires robust physiological traits that can be genetically dissected and translated into breeding tools. Carbon isotope composition (δ¹³C) is a stable proxy for intrinsic water-use efficiency (iWUE) and enables the identification of genetic factors underlying drought-stress responses. In this study, δ¹³C was measured in the Bavarian MAGIC Wheat (BMW) population, and quantitative trait locus (QTL) mapping at the drought-prone Morgenrot field site identified six significant loci on chromosomes 1D, 2B, 4B, 4D, 6A, and 6B. Based on phenotypic extremes at this site, eight genetically diverse lines with contrasting δ¹³C/iWUE values (four high δ¹³C and four low δ¹³C) were selected for detailed characterization. These extreme lines were evaluated under controlled greenhouse conditions using high-throughput phenotyping under both well-watered and drought-stress treatments.</div><div>Transcriptomic analyses revealed pronounced drought-induced expression changes, with enrichment of genes involved in abscisic acid signaling, stress perception, stomatal regulation, and energy metabolism. To identify robust molecular targets, QTL mapping results were integrated with differential gene expression (DGE) and weighted gene co-expression network analysis in a multi-layer framework. This approach prioritized 18 high-confidence candidate genes located mainly on chromosomes 1D, 4B, 4D, 6A, and 6B. These genes are embedded in regulatory networks dominated by transcription factors such as WRKY and NAC. Collectively, this study provides genetically anchored candidate genes for iWUE and drought adaptation, offering valuable resources for marker-assisted selection in wheat breeding.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 3","pages":"Article 100158"},"PeriodicalIF":11.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling xylem sap chemical traits mediating spittlebug-olive interaction through a metabolomics approach","authors":"Zeinab Hamouche,&nbsp;Maria Assunta Crescenzi,&nbsp;Giada Spadavecchia,&nbsp;Antonio Trani,&nbsp;Giuseppe Celano,&nbsp;Paola Montoro,&nbsp;Vincenzo Verrastro,&nbsp;Enrico de Lillo,&nbsp;Daniele Cornara","doi":"10.1016/j.agrcom.2026.100157","DOIUrl":"10.1016/j.agrcom.2026.100157","url":null,"abstract":"<div><div>Xylem sap chemistry is crucial for host plant acceptance and settling by xylem sap feeders such as the meadow spittlebug <em>Philaenus spumarius</em>, the main European vector of <em>Xylella fastidiosa</em> subsp. <em>pauca</em> to olive. Knowledge of specific metabolites underlying host plant suitability for vectors is limited. We combined spittlebug probing and feeding behavior characterization using Electropenetrography and UHPLC-Q-Exactive-MS/MS analysis of xylem sap of four olive varieties differing in their susceptibility to <em>X. fastidiosa</em> (Coratina, FS-17, Leccino and Ogliarola) to identify compounds putatively affecting host plant acceptance. Thereafter, we extended probing behavior analysis to 14 olive varieties (including the previously studied), calculated the xylem ingestion index, quantified the compounds selected in the first experiment (biomarkers), and inferred their role in shaping plant suitability. Partial least squares regression (PLSR) analysis revealed the compounds with the greatest influence on plant suitability for the spittlebug, namely glutamine, malic acid, and daucic acid acting as potential phagostimulants, and gluconic acid, salidroside, oleuropein diglucoside, and <span>d</span>-manno-2-heptulose discouraging feeding. Additionally, regardless of the effects of individual metabolites on plant suitability for spittlebugs, the least suitable olive varieties exhibited higher concentrations of all quantified organic acids selected as biomarkers. Our findings represent a crucial step for managing the spread of <em>X. fastidiosa</em> through either the selection of germplasm resistant to the vector, or agronomic practices that reduce plant suitability and vector activity by manipulating xylem sap chemistry.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 3","pages":"Article 100157"},"PeriodicalIF":11.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon deficit of a megaregion: Spatiotemporal dynamics and interpretable drivers of carbon loss in China's Greater Bay Area","authors":"Ya Li ,&nbsp;Zhao Chen ,&nbsp;Qi Wang ,&nbsp;Tongxu Liu","doi":"10.1016/j.agrcom.2026.100147","DOIUrl":"10.1016/j.agrcom.2026.100147","url":null,"abstract":"<div><div>Rapidly urbanizing megaregions are pivotal for achieving land-based climate-mitigation targets, yet the mechanisms governing their ecosystem carbon dynamics remain poorly understood. In this study, we quantify the spatiotemporal evolution of carbon storage in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a globally recognized example of rapid coastal urbanization, and diagnose the drivers behind an emerging regional “carbon deficit”. We couple a patch-generating land-use simulation (PLUS) model, a process-based carbon accounting model (InVEST-C), and interpretable machine learning (SHapley Additive exPlanations) to (i) reconstruct land-use and carbon changes from 2000 to 2020 and (ii) project carbon trajectories under three contrasting development pathways to 2030. Total ecosystem carbon storage decreased from 5.45 × 10⁸ Mg in 2000 to 5.11 × 10⁸ Mg in 2020, with the loss rate during 2010–2020 being 18 times higher than during 2000–2010, coinciding with the phase of megaregional integration. Scenario analysis shows that a business-<em>as</em>-usual “natural development” pathway, which extends 2010–2020 land-use trends without additional policy constraints, would lead to a further loss of 2.79 × 10⁷ Mg by 2030. In contrast, a policy-driven sustainable development pathway, which constrains construction-land expansion through urban growth boundaries and ecological conservation redlines and actively promotes ecological restoration, could avoid more than 83% of this potential decline. SHAP-based interpretation reveals that topography provides the essential natural capital for carbon storage, whereas population pressure is the decisive driver of loss. We further identify a strong non-linear interaction in which the ecological cost of urban expansion is greatly amplified on steep, ecologically fragile slopes. Our findings provide a transferable framework for diagnosing carbon deficits in rapidly urbanizing megaregions and deliver spatially explicit evidence to support strict ecological conservation redlines (ECRs) and urban growth boundaries (UGBs) that reconcile urban development with climate and ecological resilience.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 2","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in computer vision-based detection and management of greenhouse pests and diseases: A review","authors":"Jingxin Yu ,&nbsp;Hui Wang ,&nbsp;Huankang Cui ,&nbsp;Jiang Liu ,&nbsp;Congcong Sun ,&nbsp;Lujiale Guo ,&nbsp;Jiaqi Wang ,&nbsp;Wei Zhang ,&nbsp;Xiaoming Wei","doi":"10.1016/j.agrcom.2026.100146","DOIUrl":"10.1016/j.agrcom.2026.100146","url":null,"abstract":"<div><div>As pressure on global food security mounts, controlled-environment agriculture (CEA) demands innovations in monitoring and control. This review surveys computer-vision methods for greenhouse pests and diseases, covering foundations, core techniques, and applications, highlighting early detection, multimodal fusion, and decision support. We trace the evolution from traditional machine vision to AI-driven methods, spanning image acquisition and preprocessing, feature extraction, and semantic understanding. Deep learning-based pest and disease recognition improves accuracy under controlled conditions, and enables earlier detection of small-target pests and incipient diseases. Multimodal fusion and transfer learning further improve model generalization in complex greenhouse scenes. We also explore computer vision-driven intelligent decision-making systems, including disease and pest assessment, precision intervention, and optimization of integrated prevention and control strategies. Case studies indicate that AI-based vision systems can provide pest and disease warnings several days in advance, which reduces pesticide use and improves production efficiency. However, bottlenecks such as insufficient environmental adaptability, difficulties in learning from small samples, and limited computational resources still hinder broader implementation. Future research should prioritize multisource heterogeneous data fusion, lightweight model design, and the integration of agronomic knowledge. Together, these directions can support monitoring from phenotypes to mechanisms and from individual plants to populations.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 2","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ClURGT4 contributes to biomass accumulation in watermelon by influencing cell size and cell wall composition","authors":"Ge Gao ,&nbsp;Shengjin Liao ,&nbsp;Shouwei Tian ,&nbsp;Xiujing Feng ,&nbsp;Chen Zhang ,&nbsp;Jie Zhang ,&nbsp;Maoying Li ,&nbsp;Shaogui Guo ,&nbsp;Yongtao Yu ,&nbsp;Jinfang Wang ,&nbsp;Guoyi Gong ,&nbsp;Haiying Zhang ,&nbsp;Henrik V. Scheller ,&nbsp;Yong Xu ,&nbsp;Yi Ren","doi":"10.1016/j.agrcom.2026.100149","DOIUrl":"10.1016/j.agrcom.2026.100149","url":null,"abstract":"<div><div>Leaf and fruit size are the crucial fitness characters for plant evolution and the agronomic traits for crop yield and quality improvement in watermelon. However, the underlying genes and molecular mechanisms for regulating fruit size and biomass remain elusive. Here, we identified a <em>Citrullus lanatus</em> UDP-rhamnose (Rha)/UDP-galactose (Gal) transporter ClURGT4, which is localized in the Golgi apparatus and has the dual functions of transporting UDP-Gal and UDP-Rha. Loss-of-function <em>clurgt4</em> mutants resulted in decreased biomass and reduced Gal, Rha, and galacturonic acid (GalA) in cell wall components. Microscopical analysis showed that ClURGT4 promoted leaf and fruit size by modulating cell expansion. Proteomic analysis revealed that several cell wall metabolism-related proteins were changed in <em>clurgt4</em> mutants. Moreover, protein glycosylation was changed in the mutants, and several of the differentially glycosylated proteins were related to cell wall metabolism. These findings elucidated that ClURGT4 might control leaf and fruit size by affecting cell wall metabolism and provided a novel case for comprehensively revealing the regulatory network of watermelon biomass.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 2","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating ammonia, nitrous oxide and nitrate leaching losses and nitrogen use efficiency for spring wheat, grain corn and canola from 1981 to 2021 in Canada","authors":"Rong Jiang ,&nbsp;Jingyi Yang ,&nbsp;Craig F. Drury ,&nbsp;Devon E. Worth ,&nbsp;Jiangui Liu ,&nbsp;Ward N. Smith ,&nbsp;Brian B. Grant","doi":"10.1016/j.agrcom.2026.100148","DOIUrl":"10.1016/j.agrcom.2026.100148","url":null,"abstract":"<div><div>Reactive nitrogen (Nr) losses to the air (ammonia [NH₃], nitrous oxide [N₂O]) and to water (nitrate [NO₃⁻] leaching) have been reported for Canadian agricultural systems. However, there is very little information on N use efficiency (NUE) and reactive N losses for major crop types. The objectives of this study were to evaluate the NUE and reactive N losses for spring wheat, grain corn, and canola from 1981 to 2021 using the Canadian Agricultural Nitrogen Budget for Reactive N (CANBNr) model. The provincial averages of residual soil N (RSN) at harvest increased by over 3.8-fold for spring wheat, 2.7-fold for canola (grown in Alberta, Saskatchewan and Manitoba), but decreased by 10.8% for corn (mainly grown in Ontario and Quebec) from 1981 to 1985 to 2017–2021. The RSN was higher in Ontario and Quebec than in the Prairie provinces (Alberta, Saskatchewan and Manitoba). The NUE decreased from 1981 to 1985 to 2017–2021 for spring wheat and canola whereas it increased from 40.2% to 58.4% for corn. Over the same period, reactive N losses increased by 86.9% for spring wheat and 65.8% for canola, whereas it decreased by 6.8% for corn. Over the 41-year period, reactive N losses for corn were higher than for spring wheat and canola due in part to higher N inputs and greater precipitation in corn-production regions in Ontario and Quebec compared to spring wheat production in the Prairie provinces. Management practices should first target corn to reduce N losses to the environment while improving NUE and maintaining yields for all three crops.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 2","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brassica napus Panache: A graph pangenome representing presence absence variation across ten B. napus genomes","authors":"Tessa R. MacNish ,&nbsp;Connor McPhan ,&nbsp;Philipp E. Bayer ,&nbsp;Shengyi Liu ,&nbsp;Hawlader A. Al-Mamun ,&nbsp;Jacqueline Batley ,&nbsp;David Edwards","doi":"10.1016/j.agrcom.2026.100143","DOIUrl":"10.1016/j.agrcom.2026.100143","url":null,"abstract":"<div><div><em>Brassica napus</em> is an economically important oil crop with significant levels of gene presence absence variation (PAV) between individuals, providing a source of genetic diversity that can be applied for crop improvement. Graph pangenomes are a new format for representing pangenomes and can be used to identify and explore PAV for use in breeding to accelerate crop improvement. Here we present a graph pangenome constructed from ten high-quality <em>B. napus</em> genome assemblies. The pangenome is hosted and visualized online with the pangenome analyzer with chromosomal exploration (Panache) tool. This public resource provides a comprehensive map of genome variability in <em>B. napus</em> that can be applied to accelerate the breeding of this important crop.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"4 2","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}