Phytopathology最新文献

Deep Sequence Characterization of Tomato Spotted Wilt Virus (TSWV) in Cultivated and Wild-Derived Allotetraploid Peanuts in Georgia, USA. 美国乔治亚州栽培和野生异源四倍体花生中番茄斑点枯萎病毒（TSWV）的深度序列鉴定。

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-07 DOI: 10.1094/PHYTO-08-25-0271-R

Namrata Maharjan, Simone G Ribeiro, David J Bertioli, Soraya C M Leal-Bertioli

{"title":"Deep Sequence Characterization of Tomato Spotted Wilt Virus (TSWV) in Cultivated and Wild-Derived Allotetraploid Peanuts in Georgia, USA.","authors":"Namrata Maharjan, Simone G Ribeiro, David J Bertioli, Soraya C M Leal-Bertioli","doi":"10.1094/PHYTO-08-25-0271-R","DOIUrl":"https://doi.org/10.1094/PHYTO-08-25-0271-R","url":null,"abstract":"Tomato spotted wilt virus (TSWV) is a globally important pathogen of peanut, characterized by rapid evolution, broad host range, and capacity to overcome resistance. In this study, we performed deep sequencing of TSWV isolates from two peanut cultivars (GA-06G and Bailey II) and two induced allotetraploids derived from four wild Arachis species ([A. gregoryi × A. stenosperma]⁴ˣ and [A. vallsii × A. williamsii]⁴ˣ) grown in Georgia, USA. Complete genomes of TSWV and co-infecting peanut mottle virus (PMV) were assembled for all samples. Despite the phylogenetic distance among hosts, TSWV isolates showed high sequence identity and clustered tightly, suggesting limited host-driven divergence. However, segment-wise analysis revealed differential variation: conserved regions included RdRp, GN/GC, and N proteins, while NSm and NSs-linked to host adaptation and immune suppression-were more variable. This inference is based solely on the TSWV isolates used in this study. Phylogenetic comparison with 113 global isolates confirmed that clustering was driven more by geography than host, with southeastern USA isolates forming a distinct clade. Notably, peanut-associated TSWV isolates showed the highest nucleotide diversity among hosts, illustrating their potential to generate resistance-breaking variants. PMV, once thought nearly absent, was detected in all samples, raising new questions about low-level persistence or resurgence. This study reports the first full-genome sequences of TSWV and PMV from U.S. peanuts, including wild-derived genotypes, and highlights the need for sustained genomic surveillance. The results have direct implications for resistance breeding and disease management, particularly as wild genetic resources are increasingly integrated into peanut improvement.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Approaches for Predicting Fusarium Head Blight Epidemic Levels from Climatological Time Series Features. 从气候时间序列特征预测镰刀菌疫病流行水平的机器学习方法。

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-07 DOI: 10.1094/PHYTO-06-25-0221-FI

Rui Mao, Ying Shi, Tao Zhu, Jia Zhou, Junjuan Wang, Liangwei Zhao, Meiyan Liu, Xiangming Xu, Xiaoping Hu

{"title":"Machine Learning Approaches for Predicting Fusarium Head Blight Epidemic Levels from Climatological Time Series Features.","authors":"Rui Mao, Ying Shi, Tao Zhu, Jia Zhou, Junjuan Wang, Liangwei Zhao, Meiyan Liu, Xiangming Xu, Xiaoping Hu","doi":"10.1094/PHYTO-06-25-0221-FI","DOIUrl":"10.1094/PHYTO-06-25-0221-FI","url":null,"abstract":"Fusarium head blight (FHB), caused by the FHB species complex, is one of the most damaging diseases affecting wheat. Accurately predicting FHB occurrence prior to infection is crucial for preventing outbreaks, minimizing crop losses, and reducing the risks of mycotoxins entering the food chain. This study utilized 55 years of historical weather data and the level of the primary Fusarium inoculum in crop debris to predict FHB severity. Time series features were extracted from daily average temperature, relative humidity, precipitation, and sunshine hours recorded from 1 January to 31 March each year. Random forest (RF), statistical analysis, binary enumeration, and feature interpretability analysis were employed to identify features most strongly associated with FHB occurrence. Six machine learning (ML) models, including artificial neural networks (ANNs), logistic regression, K-nearest neighbors, support vector machine, RF, and extreme gradient boosting, were applied to the selected features to classify FHB epidemic levels. The results revealed that (i) the inclusion of inoculum strength did not significantly enhance the predictive accuracy of models based solely on climatological data; (ii) the nine complex time series features derived from the four types of climatological data were effective in classifying FHB epidemic levels through cross-regional validation by capturing critical conditions linked to the pathogen lifecycle; and (iii) ANNs outperformed the other five ML models in classifying observed FHB epidemic levels using the selected nine features. Further research should focus on applying this model to larger datasets to enhance its practical utility for FHB management.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":"PHYTO06250221FI"},"PeriodicalIF":3.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the Impact of Seasonal Weather Dynamics on Rice Disease Occurrence Using Neural Networks: A Case Study of Panicle Blast and Grain Rot. 利用神经网络了解季节天气动态对水稻病害发生的影响——以穗瘟病和谷粒腐病为例

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-06 DOI: 10.1094/PHYTO-01-25-0004-FI

Jaehan Shin, Wonjae Jeong, Hyeon-Ji Yang, Mun-Il Ahn, Kwang-Hyung Kim

{"title":"Understanding the Impact of Seasonal Weather Dynamics on Rice Disease Occurrence Using Neural Networks: A Case Study of Panicle Blast and Grain Rot.","authors":"Jaehan Shin, Wonjae Jeong, Hyeon-Ji Yang, Mun-Il Ahn, Kwang-Hyung Kim","doi":"10.1094/PHYTO-01-25-0004-FI","DOIUrl":"10.1094/PHYTO-01-25-0004-FI","url":null,"abstract":"Panicle blast (PB) and grain rot (GR) are two major rice diseases that directly affect panicles and result in severe yield losses worldwide. This study introduces a novel data-driven approach to understanding the impact of seasonal weather dynamics on the occurrence of these diseases using neural networks. By relying solely on meteorological data, the proposed method demonstrates the potential to elucidate hidden relationships between meteorological conditions and disease occurrence. In this study, time-series data comprising seven meteorological variables over 180 days until the peak incidence dates of each disease were used to train a long short-term memory-based model. By applying the holdout method, the prediction model achieved maximum test accuracies of 64.9 and 68.0% for the PB and GR, respectively. Subsequently, a gradient-based analysis further reinforced the reliability of the resulting models by showing consistency with previous findings, in which rainfall and wind speed were frequently identified as critical variables for disease prediction. The temporal dynamics of individual meteorological variables, contributing to disease occurrence, were also revealed from the gradient-based analysis. Overall, our results emphasize the reliability of deep learning models when predicting disease occurrence using only meteorological data, making a substantial contribution to the crop disease prediction system development, and the scalability of applying the same method to other crop diseases when sufficient data are available.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":"PHYTO01250004FI"},"PeriodicalIF":3.1,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144529349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chitin and Laminarin Trigger Plant Defense Responses Against Soybean Rust Caused by Phakopsora pachyrhizi. 几丁质和层粘连素引发植物对大黄豆锈病的防御反应

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-06 DOI: 10.1094/PHYTO-02-25-0079-R

Shuxian Li, Nicholas Rhoades, Jodi Scheffler, Guixia Hao

{"title":"Chitin and Laminarin Trigger Plant Defense Responses Against Soybean Rust Caused by Phakopsora pachyrhizi.","authors":"Shuxian Li, Nicholas Rhoades, Jodi Scheffler, Guixia Hao","doi":"10.1094/PHYTO-02-25-0079-R","DOIUrl":"10.1094/PHYTO-02-25-0079-R","url":null,"abstract":"Soybean (Glycine max) is one of the most economically important crops in the world. Production of soybean can be severely impacted by many diseases, including soybean rust. Elicitor treatments have been utilized to enhance plant resistance against multiple diseases. To investigate whether elicitor treatment can induce soybean resistance, pilot experiments were conducted to test the effects of elicitors (chitin, laminarin, and co-treated with both) on the reactive oxygen species (ROS) burst in five soybean genotypes. We discovered that all elicitor treatments induced an ROS burst with different levels. The expression of several plant defense genes was upregulated in soybean Williams 82 following elicitor treatments. GmCERK1, GmRbohD, GmPR1, GmPR2, GmPAL, and GmCHS exhibited the highest expression at 3 h post-elicitor treatments. Interestingly, co-treatment with chitin and laminarian significantly enhanced the expression of GmPAL and GmCHS. Soybean rust severity was evaluated on plants with elicitor treatment prior to Phakopsora pachyrhizi inoculation. A 5-point scale, with 5 as the highest, was used. With chitin treatment, the severities were reduced to 2.0 and 1.9 in Williams 82 and PI 200526, respectively. Controls without elicitor treatments had severities of 4.2 and 3.8, which were significantly (P < 0.001) higher than the severities in the genotypes with elicitor treatments. To the best of our knowledge, this is the first demonstration of the effects of elicitors chitin and laminarin on inducing resistance in soybean against P. pachyrhizi infection. The information from this research will be useful for development of an alternative method to control soybean rust or other diseases in crops.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":"PHYTO02250079R"},"PeriodicalIF":3.1,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Do Growers Respond to Host Resistance? A Conditional Gaussian Bayesian Network for Causal Inference of Fungicide Cost Savings. 种植者如何应对寄主的抗性？杀菌剂成本节约因果推理的条件高斯贝叶斯网络。

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-01 DOI: 10.1094/PHYTO-06-25-0199-R

Jae Young Hwang, Sharmodeep Bhattacharyya, Shirshendu Chatterjee, Thomas L Marsh, Joshua F Pedro, David H Gent

{"title":"How Do Growers Respond to Host Resistance? A Conditional Gaussian Bayesian Network for Causal Inference of Fungicide Cost Savings.","authors":"Jae Young Hwang, Sharmodeep Bhattacharyya, Shirshendu Chatterjee, Thomas L Marsh, Joshua F Pedro, David H Gent","doi":"10.1094/PHYTO-06-25-0199-R","DOIUrl":"https://doi.org/10.1094/PHYTO-06-25-0199-R","url":null,"abstract":"The economic value of cultivars resistant to disease is of great interest, but how growers change their fungicide use in response to host resistance may be nuanced. We draw upon a well-described data set of the incidence of hop plants with powdery mildew and associated production meta-data and demonstrate the utility of Bayesian networks as a framework for quantifying causal relationships for fungicides use and cost in response to host resistance. Conditional Gaussian Bayesian network models applied to cultivars differing in race-specific resistance to powdery mildew revealed cultivar resistance to powdery mildew influenced disease levels in early spring, which had causal effect on how often and what fungicides growers later applied. Annual costs depended not only on the number of applications made but the specific types of fungicides growers selected. Fungicide costs were little changed on cultivars that possessed race- specific resistance to only one of two extant strains of the pathogen. For cultivars with resistance to both pathogen strains, annual costs of fungicides were reduced commensurate with the level of resistance. Predicted values from the Bayesian networks and simulation indicate that growers apply a baseline level of fungicide, independent of cultivar resistance. Fungicide cost savings result from how fungicide inputs differentially scale with the incidence of powdery mildew and the type of fungicides used. Our analyses indicate that for a high value crop, deployment of disease resistance may cause complex and unexpected changes in growers' fungicide use patterns that may not be obvious in simplified randomized controlled trials.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoding Rhizosphere Synergies: Pseudomonas and Bacillus Enhance Microbiome-Mediated Suppression of Rhizoctonia solani in Sugar Beet. 解码根际协同作用：假单胞菌和芽孢杆菌增强微生物介导的甜菜根核孢子菌抑制。

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-01 DOI: 10.1094/PHYTO-05-25-0159-R

Kexin Li, Tai Li, Yonglong Liu, Bingchen Zou, Gui Geng, Yao Xu, Jiahui Liu, Yuguang Wang

{"title":"Decoding Rhizosphere Synergies: Pseudomonas and Bacillus Enhance Microbiome-Mediated Suppression of Rhizoctonia solani in Sugar Beet.","authors":"Kexin Li, Tai Li, Yonglong Liu, Bingchen Zou, Gui Geng, Yao Xu, Jiahui Liu, Yuguang Wang","doi":"10.1094/PHYTO-05-25-0159-R","DOIUrl":"https://doi.org/10.1094/PHYTO-05-25-0159-R","url":null,"abstract":"Sugar beet is a crucial sugar crop with substantial economic and nutritional value. The occurrence of damping-off disease severely impacts sugar beet quality and yield. Here, we successfully isolated two endophytes from sugar beet, and it follow as Bacillus albus SB-3 and Pseudomonas chlororaphis SB-35, via morphological observation and molecular identification. Both SB-3 and SB-35 exhibited nitrogen-fixing and potassium mobilization capabilities, with SB-35 demonstrating additional traits including phosphate solubilization, potassium mobilization. SB-3 and SB-35 promoted the growth of sugar beet, resulting in increased biomass, and improved soil available nutrient. Besides, SB-3 and SB-35 had also extracellular protease activities and inhibited the mycelium growth of Rhizoctonia solani. In independent pot experiments, SB-3 and SB-35 exhibited significantly controlling the damping-off of seedlings for sugar beet. Further analysis indicated that SB-3 and SB-35 may alter microbial community structure, reducing the abundance of Rhizoctonia solani, promoting the recruitment of beneficial microorganisms, such as Hypocrea, Peziza, and Talaromyces, to occupy ecological niches, thereby reducing the numbers of pathogen. The two bacterial strains modulated the diversity and community structure of rhizosphere microorganisms, suggesting a microbiome-mediated mechanism underlying their host-beneficial effects. This study advances our understanding of harnessing endophytes to enhance sugar beet productivity and suppressing sugar beet damping-off caused by Rhizoctonia solani.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New Genomes of Xanthomonas citri pv. bilvae from Modern and Historical Material Reveal the History and Genomics of a Neglected Crop Pathogen. 柑橘黄单胞菌新基因组。来自现代和历史材料的幼虫揭示了一种被忽视的作物病原体的历史和基因组学。

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-01 DOI: 10.1094/PHYTO-07-25-0255-SC

Claudine Boyer, Paola Campos, Nathalie Becker, Lionel Gagnevin, Karine Boyer, Timothy M A Utteridge, Olivier Pruvost, Adrien Rieux

{"title":"New Genomes of Xanthomonas citri pv. bilvae from Modern and Historical Material Reveal the History and Genomics of a Neglected Crop Pathogen.","authors":"Claudine Boyer, Paola Campos, Nathalie Becker, Lionel Gagnevin, Karine Boyer, Timothy M A Utteridge, Olivier Pruvost, Adrien Rieux","doi":"10.1094/PHYTO-07-25-0255-SC","DOIUrl":"https://doi.org/10.1094/PHYTO-07-25-0255-SC","url":null,"abstract":"In this study, we present novel genomic data for Xanthomonas citri pv. bilvae (Xcb), the causal agent of bacterial shot-hole disease in bael trees. Using a hybrid sequencing approach that combines short- and long-read technologies, we assembled high-quality genomes of the only two available contemporary Xcb strains. Furthermore, we reconstructed the first historical genome of Xcb from a herbarium specimen collected in 1848, thereby extending the documented presence of this overlooked disease in India by nearly 100 years. We then characterized the genomic features of these strains, with a particular emphasis on virulence factors and plasmid content, using a suite of specialized bioinformatics tools. The contemporary Xcb strains were found to carry between one and four plasmids, which varied in their mobility potential (conjugative, mobilizable, or non-mobile). A total of 30 to 32 type III effector (T3E) genes were identified across chromosomes and plasmids. Notably, one of the contemporary strains harbored four plasmid-borne transcription activator-like effectors (TALEs), which showed only distant similarity to TALEs found in X. citri pv. citri, a globally major pathogen with a partially overlapping host range. Comparative genomic analysis between the contemporary and historical strains revealed a remarkable conservation of effector gene content, indicating that key pathogenic traits may have been acquired early in Xcb's evolutionary history. Collectively, these new genomic resources provide valuable insights into the biology and evolution of this underexplored bacterial pathogen.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Population Structure of Phytophthora ×alni on a Local Scale and Its Temporal Development. 疫霉菌×alni在地方尺度上的种群结构及其时间发展。

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-01 DOI: 10.1094/PHYTO-03-25-0091-R

Štěpán Pecka, Ondřej Koukol, Gabriela Šrámková, Daniel Zahradník, Simone Prospero, Petra Štochlová, Karel Černý

{"title":"Population Structure of Phytophthora ×alni on a Local Scale and Its Temporal Development.","authors":"Štěpán Pecka, Ondřej Koukol, Gabriela Šrámková, Daniel Zahradník, Simone Prospero, Petra Štochlová, Karel Černý","doi":"10.1094/PHYTO-03-25-0091-R","DOIUrl":"https://doi.org/10.1094/PHYTO-03-25-0091-R","url":null,"abstract":"Within Phytophthora alni, an invasive pathogen of alders (Alnus spp.), three species have been identified. The most frequent and pathogenic species is P. ×alni. It has a variable intraspecific structure, with the dominance of the Pxa-1 genotype and the presence of dozens of rare genotypes (in most cases derived from Pxa-1). Its local populations are highly variable, and their population structure and development remain unknown. We compared two sets of strains isolated from identical sites during the epidemic (2005-2010) and post-epidemic (2020-2024) phases of the disease in the Vltava River basin (Czech Republic) and studied them using microsatellite marker analysis and fitness tests (sporangia production, growth, and virulence). We acquired 151 P. ×alni isolates of 23 multilocus genotypes. We found that during the post-epidemic phase, genetic diversity decreased, and the dominance and incidence of the Pxa-1 genotype increased. Only the dominant genotype (Pxa-1) was repeatedly isolated from the same sites, whereas the rare genotypes were replaced. During the post-epidemic phase, both the incidence of rare genotypes and the degree of their derivation from Pxa-1 decreased. The rare genotypes had lower fitness than Pxa-1 (the more changes there were, the worse the fitness was). These results allow us to hypothesize the evolution of local populations of P. ×alni in Europe, as the most pathogenic genotype, Pxa-1, will also prevail during the late phases of the disease and the risk of further damage to the surviving host populations will persist.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Factor Analysis and Prediction of Disease Risk Based on Large Ensembles of Models: Application to Virus Yellows in Sugar Beet. 基于模型大集合的因子分析与疾病风险预测：在甜菜病毒黄病中的应用。

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-01 DOI: 10.1094/PHYTO-01-25-0014-FI

D Chauvin, E Gabriel, D Martinetti, J Papaïx, C Martinez, G Geniaux, F Joudelat, S Soubeyrand

{"title":"Factor Analysis and Prediction of Disease Risk Based on Large Ensembles of Models: Application to Virus Yellows in Sugar Beet.","authors":"D Chauvin, E Gabriel, D Martinetti, J Papaïx, C Martinez, G Geniaux, F Joudelat, S Soubeyrand","doi":"10.1094/PHYTO-01-25-0014-FI","DOIUrl":"10.1094/PHYTO-01-25-0014-FI","url":null,"abstract":"Identifying disease risk factors, characterizing their effects, and forecasting disease risk across space and time are crucial tasks in human, animal, and plant epidemiology. Statistical and machine learning models have largely superseded purely descriptive analyses of data in handling these tasks. In addition, these models have demonstrated their full potential in the current era, characterized by an unprecedented abundance of data. However, applying these models to real-world, large-scale data sets raises critical questions: Which model should be used? Which explanatory variables should be selected? What data should be allocated for training and validation? The answers to these questions often have a significant impact on the analysis outcomes. One way to address some of these challenges is to analyze risk factors and predict risk by using an ensemble of models rather than relying on a single model. This approach is developed in this article and implemented in the case of virus yellows in sugar beet in France. Among the explanatory variables correlated with the severity of virus yellows, we identified winter and spring temperatures (positive correlation), spring humidity and precipitation (negative correlation), the proportion of cereal crops (positive correlation), the proportion of grasslands (negative correlation), and the distance to sugar beet seed production fields (negative correlation). Additionally, we found that predictions are generally more robust when using a spatial aggregation of models compared with relying on the best individual model. Our approach is highly versatile and can be applied to characterize and predict the spatiotemporal distributions of diverse diseases.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":"PHYTO01250014FI"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance and Stability of Winter Wheat Cultivars to Stagonospora Nodorum Blotch Epidemics in Multi-Environment Trials. 冬小麦品种对斑病病多环境试验的表现及稳定性

IF 3.1 2区农林科学

Phytopathology Pub Date : 2025-10-01 DOI: 10.1094/PHYTO-12-24-0398-R

Vinicius C Garnica, Mohammad Nasir Shalizi, Peter S Ojiambo

{"title":"Performance and Stability of Winter Wheat Cultivars to Stagonospora Nodorum Blotch Epidemics in Multi-Environment Trials.","authors":"Vinicius C Garnica, Mohammad Nasir Shalizi, Peter S Ojiambo","doi":"10.1094/PHYTO-12-24-0398-R","DOIUrl":"https://doi.org/10.1094/PHYTO-12-24-0398-R","url":null,"abstract":"Field performance of winter wheat genotypes with quantitative resistance to Stagonospora nodorum blotch is influenced by genotype-by-environment interactions (GEI). This phenomenon explains why cultivars may perform inconsistently across environments, affecting decisions on locally adapted varieties. Further, GEI can also impact risk assessment when cultivar reaction is used as a model predictor under the assumption of stability of responses across environments. Thus, this study investigated GEI effects on four disease metrics; final disease severity (SEV), relative area under disease progress stairs (rAUDPS), time to 50% disease incidence (T50) and the apparent rate of disease increase (ω), describing SNB epidemics of 18 commercial soft red winter wheat cultivars planted in 18 environments in North Carolina from 2021 to 2024. Linear mixed models with various variance-covariance structures for random effects were used to analyze the disease data and a 3rd order factor analytic model provided the best fit to the data across the metrics examined. Type B genetic correlation (rg), broad-sense heritability (Hb^2), overall cultivar performance (OP), and global stability (RMSD), were estimated using model outputs and the factor analytic selection tool method. For SEV, rAUDPS and T50, values of rg ranged from -0.15 to 0.99 with most environment pairs exhibiting high rg values indicating an agreement in cultivar rankings. Cultivar 'USG 3230' was the top-performing and most stable, while 'TURBO' and 'SH7200' were more unstable cultivars. Cultivar reaction classes derived from OP exhibited consistent class-level means of marginal predictions supporting their utility as stable indicators of SNB susceptibility in risk assessment models.","PeriodicalId":20410,"journal":{"name":"Phytopathology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0