Journal of Phytopathology最新文献

{"title":"Exploring Bacterial Endophytes as Potential Biocontrol Agents Against Aspergillus parasiticus Colonisation and Aflatoxin Contamination in Maize","authors":"Ganapathy Sivakaame,&nbsp;Theerthagiri Anand,&nbsp;Vaikuntavasan Paranidharan,&nbsp;Govindasamy Senthilraja,&nbsp;Chinnapaiyan Vanitha","doi":"10.1111/jph.70119","DOIUrl":"https://doi.org/10.1111/jph.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>Aflatoxin is one of the most deleterious types of mycotoxins commonly found in maize. Using native endophytic bacteria to control phytopathogens can be a successful strategy for managing plant diseases. A total of 26 endophytic bacterial strains were isolated from different parts of maize plants and their efficacy tested against the mycelial growth of highly toxigenic <i>Aspergillus parasiticus</i> Asp23. Among the endophytic isolates, EPL2 and EPL3 showed the highest inhibition (51.1% inhibition over control after 7 days of co-culture) followed by EPR2, EPR4 and EPC1. All effective bacterial isolates were tentatively identified by 16S rRNA gene sequence analysis and homology analysis with the NCBI nucleotide database using BLAST search. A detoxification assay indicated that endophytic bacterial isolates EPR2, EPL2 and EPL3 were superior in detoxifying both aflatoxin B1 and B2. Furthermore, we demonstrated the antifungal mechanisms of endophytic bacteria, such as the production of extracellular lytic enzymes, antifungal volatiles and siderophores. Finally, the effect of endophytic bacterial isolates EPL3, EPR2 and EPC2 was tested against <i>A. parasiticus</i> and aflatoxin contamination in maize grains in vivo. The results revealed that there was a significant reduction in colonisation of <i>A. parasiticus</i> and the lowest population was recorded in EPL3-treated maize grains, followed by EPR2. Similarly, the endophyte EPL3 showed the highest reduction in aflatoxin levels of 94.8% (to 23.05 ppb), followed by EPR2 (94.4% reduction to 24.71 ppb), whereas in the untreated control the aflatoxin level was 445.37 ppb. This study demonstrated that endophytic bacteria isolated from maize could be a successful biocontrol source to suppress <i>Aspergillus</i> growth and reduce aflatoxin contamination in maize grains.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 4","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phytophthora palmivora as a Causal Pathogen of Stem Canker and Fruit Rot of Durio zibethinus in China","authors":"Ganggang Dong,&nbsp;Mingshuo Xue,&nbsp;Shenghua Xie,&nbsp;Xuejie Feng,&nbsp;Xianghong Li,&nbsp;Zaifeng Fan","doi":"10.1111/jph.70089","DOIUrl":"https://doi.org/10.1111/jph.70089","url":null,"abstract":"<div>\u0000 \u0000 <p>Durian (<i>Durio zibethinus</i> L.) is a high-value tropical seasonal fruit and an important cash crop in Southeast Asia. Durian introduction trials have been conducted in Hainan Province, China, and the first durian plantations have been established in recent years. However, the high temperature and humidity in Hainan Province favour plant diseases, severely affecting durian yield and quality. From July 2023 to June 2024, a disease survey was conducted in 21 durian orchards across six regions on southern Hainan Island, revealing that the primary diseases affecting durian were stem canker (black-head), fruit rot, and root rot. The main symptoms are stem necrotic blotch, brown root rot, and water-soaked fruit rot, which in severe cases can cause poor growth or extensive dieback of the durian tree. The causal pathogens were identified through morphological, molecular, and pathogenicity analyses. A total of 40 isolates were isolated from diseased durian fruits (30 isolates) and stems (10 isolates), exhibiting morphological characteristics similar to <i>Phytophthora</i> species. Further identification of the isolates was conducted using a combined dataset of ITS, <i>tef-1α</i> and <i>CoxI</i>, revealing that all the isolates belonged to <i>Phytophthora palmivora</i>. Pathogenicity tests confirmed <i>P. palmivora</i> as the pathogenic agent causing durian stem canker and fruit rot in Hainan Province. This study demonstrates that <i>P. palmivora</i> is the major causal agent of durian stem canker and fruit rot in China, providing a basis for improved durian disease identification and management.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 4","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paddy Crop Leaf Disease Classification Using Dense Kronecker Network","authors":"S. Veluchamy,&nbsp;Pon Bharathi A,&nbsp;Siva Raja P. M,&nbsp;Shaji D. S","doi":"10.1111/jph.70087","DOIUrl":"https://doi.org/10.1111/jph.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>Agriculture plays a critical role in feeding populations worldwide, yet farmers often lack the specialised knowledge required to detect and treat diseases in crops, which can lead to delays in disease diagnosis. This challenge is particularly evident in the case of rice crops, where early detection of leaf diseases is essential for minimising losses. Although numerous methods for classifying rice leaf diseases have been proposed, many of them have shown limited effectiveness due to the complexity and diversity of the diseases. To address this gap, an advanced method for rice leaf disease classification named Dense Kronecker Net (DK-Net) is devised. Firstly, an input image is given into image preprocessing, which is done utilising a Wiener filter. Thereafter, image segmentation is conducted utilising M-segNet. Then, image augmentation takes place using flipping, cropping, and rotation techniques. After that, the segmented image is delivered to the feature extraction process and extracted features include Grey Level Co-occurrence Matrix (GLCM), entropy-based Complete Local Binary Pattern (CLBP), and Local Gabor Directional Pattern (LGDP). Finally, leaf disease classification is exhibited utilising DK-Net, which is a combination of DenseNet and Deep Kronecker Net. The DK-Net achieved outstanding performance with the highest accuracy of 91.3%, True positive rate (TPR) of 91.4%, and True negative rate (TNR) of 91.6%. These results demonstrate that DK-Net outperforms previous methods, offering a more accurate and robust solution for the early detection of rice leaf diseases.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leaf Spot and Fruit Rot on Achachairu: Novel Diseases Caused by Lasiodiplodia theobromae","authors":"Kevison Romulo da Silva França,&nbsp;Marcus Vinícius Santos da Silva,&nbsp;Mayra Machado de Medeiros Ferro,&nbsp;Denise de Santana Silva,&nbsp;Jaqueline Figueredo de Oliveira Costa,&nbsp;Gaus Silvestre de Andrade Lima,&nbsp;Iraildes Pereira Assunção","doi":"10.1111/jph.70094","DOIUrl":"https://doi.org/10.1111/jph.70094","url":null,"abstract":"<p>The achachairu (<i>Garcinia humilis</i>) is an exotic tropical fruit native to Bolivia that has recently gained commercial popularity in Brazil. In a producing region of Pernambuco, Brazil, spots and rot symptoms were observed on the leaves and fruit of <i>G. humilis</i>. Fungal isolates from the <i>Lasiodiplodia</i> genus were obtained from symptomatic tissue. Based on multigenic analyses (<i>TEF1-α</i>, ITS and <i>TUB2</i>) and morphological characteristics, the isolates were identified as <i>L. theobromae</i>, which were confirmed to be pathogenic on achachairu leaves and fruits. This report presents two new diseases in achachairu caused by <i>L. theobromae</i>. This study improves our understanding of the achachairu pathosystem and establishes a foundation for future disease management research.</p>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jph.70094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Leaf Spot Disease Caused by Alternaria jacinthicola on Durio zibethinus in China","authors":"Ganggang Dong,&nbsp;Shenghua Xie,&nbsp;Mingshuo Xue,&nbsp;Xuejie Feng,&nbsp;Xianghong Li,&nbsp;Zaifeng Fan","doi":"10.1111/jph.70112","DOIUrl":"https://doi.org/10.1111/jph.70112","url":null,"abstract":"<div>\u0000 \u0000 <p>A leaf spot disease with an incidence of approximately 50% and a severity rate of around 70% was found on Durian in Hainan Province, China, in July 2023. The early symptoms appeared as light grey to dark brown, irregular, small, necrotic lesions, distributed along the leaf veins. As the disease progresses, the lesions turned to rusty-coloured necrotic spots and pale yellow chlorotic halos or rings around the edges, even extensive leaf death and defoliation, severely impairing photosynthesis. <i>Alternaria jacinthicola</i> and <i>Alternaria tomato</i> were isolated from diseased durian leaves, and only <i>A. jacinthicola</i> was identified as the predominant pathogen through morphological and multi-gene phylogenetic analysis. Pathogenicity assays demonstrated its ability to infect both durian leaves and fruits. To our knowledge, this is the first report of <i>A. jacinthicola</i> causing leaf spot disease on durian. These findings lay the foundation for further research into the pathogenesis of durian diseases and the development of effective control measures.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Impact of Weather Parameters on Cotton Leaf Curl Disease Progression and Whitefly Population Dynamics: A Decadal Analysis (2011–2020)","authors":"N. K. Yadav,&nbsp;Yogesh Kumar,&nbsp;Navish Kumar Kamboj,&nbsp;Preeti Vashisht","doi":"10.1111/jph.70115","DOIUrl":"https://doi.org/10.1111/jph.70115","url":null,"abstract":"<div>\u0000 \u0000 <p>Cotton leaf curl disease (CLCuD) has become a potential menace to the production of cotton (<i>Gossypium hirsutum</i>) in Africa and South Asia. Whitefly (<i>Bemisia tabaci</i>) is the potential vector of this virus. Due to the dearth of resistant cultivars and effective management strategies of whitefly, yield loss in cotton is witnessd regularly. To ensure the timely application of management practices, there is a dire need for a reliable regression model that can forecast the CLCuD with high speed and accuracy. Keeping this in view, long-term studies were conducted during 2011–2020 at Cotton Research Station, Sirsa. The influence of weather parameters on the modulation of the disease progression and whitefly dynamics was recorded and analysed through correlation and regression. A prediction equation for disease incidence and vector population was developed through regression. Minimum temperature, maximum temperature and evening relative humidity (RH) significantly influenced the disease development, with the former two having negative significant effects. The coefficient of determination (<i>R</i>²) ranged from 0.19 to 0.90 for disease development with the weather parameters. The best fitted regression equation based on the decadal study for prediction of CLCuD incidence was <i>Y</i> = −12.913_Tmax + 2.489_Tmin + 0.242_RHm − 0.197_RHe − 0.890_Rf + 459.368 and for percent disease intensity (PDI) of CLCuD was <i>Y</i> = −8.962_Tmax + 2.608_Tmin + 0.232_RHm − 0.567_RHe − 0.570_Rf + 306.433. Additionally, a significant positive correlation between the CLCuD incidence, PDI and population of whiteflies was observed, as reflected by high values of the correlation coefficient (<i>r</i>) for most of the years during the period of study. The vector, whitefly (<i>B. tabaci</i>) population was also significantly influenced by the ambient weather conditions. High relative humidity favoured the whitefly population. Regression analysis revealed 25%–62% variability in whitefly population due to weather parameters, and the best fitted regression model for whitefly incidence is <i>Y</i> = −0.194_Tmax − 1.610_Tmin − 0.439_RHm + 0.911_RHe + 0.020_Rf + 44.733. On the basis of these equations, the main meteorological factors, such as temperature, relative humidity and rainfall, have a substantial impact on the emergence of CLCuD over the years.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual RNN Architecture for Crop Disease Detection: Improved Patch-Based CNN for Segmentation","authors":"M. Shereesha,&nbsp;G. K. Sandhia,&nbsp;R. Pitchai","doi":"10.1111/jph.70079","DOIUrl":"https://doi.org/10.1111/jph.70079","url":null,"abstract":"<div>\u0000 \u0000 <p>Crop diseases pose a significant threat to global food security, leading to substantial yield losses and economic repercussions. Timely intervention and efficient management of many disorders depend on their prompt and precise detection. Deep learning techniques have transformed computer vision in recent years and provided potential solutions for automated crop disease diagnosis. The proposed framework of dual RNN-based crop disease detection (D-RNN-based CDD) encompasses several crucial stages aimed at harnessing deep learning methods for precise and efficient disease identification in agricultural crops. First, preparation methods are used to improve the raw images quality. This involves the contrast transformation to enhance visibility and the application of a Gaussian filter to reduce noise, ensuring smoother images suitable for subsequent processing. After preprocessing, image segmentation is done using a hyper softmax patch-based convolutional neural network (HSP-CNN) approach, dividing the images into smaller patches for localised analysis. This segmentation method allows the model to focus on specific regions of interest, facilitating accurate identification of diseased areas. Following segmentation, the next step is feature extraction that captures pertinent characteristics indicative of crop diseases. Shape features, such as area, convexity, centroid and perimeter, are extracted to quantify the disease symptoms. Colour features capture RGB components in terms of mean, median and standard deviation. Moreover, texture features are extracted to analyse the patterns and structures associated with different diseases using modified texture orientation-based multitexon (MTOM) features enabling extraction at multiple scales. These extracted features offer comprehensive representations of the underlying characteristics of diseased regions within crop images. Finally, classification is done using a dual RNN (D-RNN) comprising two triple hidden layers assisted in RNN (THA-RNN) models. One THA-RNN model trains the segmented images, while the other captures the extracted feature set and trains on it. Leveraging the capabilities of THA-RNNs, adept at analysing sequential information (features), the classifier accurately predicts the presence or absence of crop diseases. For Dataset 1, the developed THA-RNN model achieved the maximum value of 0.955, outperforming other models, such as GoogLeNet (0.911), LeNet (0.921), CNN (0.894), Bi-LSTM (0.877), DNN (0.895), RNN (0.873), RESNET (0.886) and ANN (0.870).</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First Report of a Powdery Mildew-like Disease caused by Teratoramularia rumicicola on Rumex obtusifolius in Korea","authors":"Joon-Ho Choi,&nbsp;Young-Joon Choi,&nbsp;Lamiya Abasova,&nbsp;Hye-Ryeong Jang,&nbsp;Hyeon-Dong Shin,&nbsp;Ji-Hyun Park","doi":"10.1111/jph.70103","DOIUrl":"https://doi.org/10.1111/jph.70103","url":null,"abstract":"<p><i>Rumex obtusifolius</i>, a perennial plant native to Europe, has become an aggressive invasive weed across temperate regions worldwide. <i>Rumex obtusifolius</i> poses a significant threat to agricultural ecosystems, highlighting the need for effective management strategies. During the summer of 2022, powdery mildew-like symptoms were observed on the leaves of <i>R. obtusifolius</i> at four locations in Korea. The causal agent was identified as <i>Teratoramularia rumicicola</i> through morphological characterization and phylogenetic analysis based on the ITS and LSU genes of the rDNA. Artificial inoculation of healthy <i>R. obtusifolius</i> leaves with an isolate obtained from this pathogen led to symptom development after 6 days, thereby fulfilling Koch's postulates. This study is the first global report of <i>R. obtusifolius</i> infected with <i>T. rumicicola</i>.</p>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jph.70103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling the Genetic Basis of Fusarium Wilt Resistance and Abiotic Stress Tolerance in Cotton","authors":"Gulzira Narkizilova,&nbsp;O. S. Turaev,&nbsp;Sayfulla Boboyev,&nbsp;Mirvakhob Mirakhmedov,&nbsp;Muhammad Zafar,&nbsp;Salman Majeed,&nbsp;Mohamed Fawzy Ramadan,&nbsp;Trobjon Makhkamov,&nbsp;Muhammad Rizwan Khan,&nbsp;Aleena Gul,&nbsp;Ankeela Pńisyer","doi":"10.1111/jph.70107","DOIUrl":"https://doi.org/10.1111/jph.70107","url":null,"abstract":"<div>\u0000 \u0000 <p>Cotton (<i>Gossypium hirsutum</i> L.), often referred to as ‘white gold’, is a vital global crop, yet its productivity and fibre quality are significantly affected by abiotic stressors like drought and salinity, as well as biotic threats such as Fusarium wilt (<i>Fusarium oxysporum</i> f. sp. vasinfectum). To enhance stress tolerance, disease resistance and fibre quality, this study employs marker-assisted selection (MAS) as a more efficient alternative to conventional breeding. Genomic DNA from eight cotton genotypes, including hybrids and varieties, was analysed using polymerase chain reaction (PCR) with molecular markers (BNL1604, Gh247, BNL3255, JESPR220, <i>SOC1</i> and <i>CAT</i>) linked to economically significant traits. Results indicated that the BNL1604 marker (102 bp allele) was associated with fibre quality in most samples, while the Gh247 marker (125 bp allele) suggested additional genetic influences on fibre characteristics. The BNL3255 marker (225 bp allele) was consistently linked to Fusarium wilt resistance, whereas the JESPR220 marker showed allele variation. The <i>SOC1</i> gene, related to early maturation, exhibited polymorphism but failed to amplify in the Kamolot-79 variety. Stress tolerance variability was reflected in fragment size differences of the <i>CAT</i> gene, associated with abiotic stress resilience. These findings highlight the significance of molecular markers in accelerating cotton improvement through MAS, enabling the development of high-yield, disease-resistant and stress-tolerant cultivars. Future research should focus on validating these markers across broader genetic populations to optimise their use in cotton breeding programmes.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Planting Time on Severity of Alternaria Blight and Yield Losses in African Marigold (Tagetes erecta)","authors":"Rakesh Kumar,&nbsp;Ranjit Singh,&nbsp;Annie Khanna,&nbsp;Gaurav Singh,&nbsp;Pankaj Kumar","doi":"10.1111/jph.70101","DOIUrl":"https://doi.org/10.1111/jph.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>Alternaria blight, caused by <i>Alternaria tagetica</i>, poses a significant threat to marigold production, leading to substantial losses in flower yield. The present study aimed to evaluate the impact of different transplanting months on Alternaria leaf blight severity, flower blight incidence and yield losses in marigold. Results indicated that transplanting the crop in October month resulted in the highest leaf blight severity (89.00% ± 1.87%), flower blight incidence (59.30% ± 1.84%) and disease progression (AUDPC = 4259.50 ± 53.37), while February and March transplanted crops had the lowest disease severity (9.13% ± 1.11% and 15.13% ± 1.89%, respectively). Marketable flower yield varied significantly with transplanting months and highest marketable flower yield was observed in August (207.21 ± 1.17 q/ha), whereas the lowest was in December transplanting (17.60 ± 1.17 q/ha). The application of carbendazim effectively reduced disease severity and increased the marketable floral yield in protected plots compared to unprotected ones. Avoidable floral yield losses due to <i>Alternaria</i> infection ranged from 11.23% to 58.90% across different transplanting months, being highest in October. Correlation analysis revealed that disease severity was positively correlated with morning relative humidity (<i>r</i> = 0.93) and evening relative humidity (<i>r</i> = 0.59) while it was negatively correlated with maximum temperature (<i>r</i> = −0.76), wind speed (<i>r</i> = −0.94) and sunshine hours (<i>r</i> = −0.84). Principal component analysis indicated that high morning humidity and wind speed are the main drivers of Alternaria blight severity. These findings demonstrated that months with the highest yield potential are also congenial for the pathogen to cause infection. Marigold growing farmers can minimise financial losses by regularly monitoring their crops and transplanting crops to less favourable climatic conditions for disease development.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}