Journal of Phytopathology最新文献

{"title":"Phyllosphere Yeast Meyerozyma caribbica YDP-27: Effective Biocontrol Against Post-Harvest Pathogens With Stress Resilience","authors":"Deewakar Baral,&nbsp;Jayanta Saha,&nbsp;Sukram Thapa,&nbsp;Ashok Kumar Koshariya,&nbsp;Sajeed Ali","doi":"10.1111/jph.70059","DOIUrl":"https://doi.org/10.1111/jph.70059","url":null,"abstract":"<div>\u0000 \u0000 <p>Post-harvest losses caused by the fungal pathogens constitute a serious issue, especially during the storage and transportation of perishable fruits and vegetables. This study assessed the potential of the phyllosphere yeast <i>Meyerozyma caribbica</i> YDP-27 in supressing post-harvest diseases caused by six fungal pathogens viz., <i>Colletotrichum musae</i>, <i>C. capsici</i>, <i>C. gloeosporioides</i>, <i>Alternaria alternata</i>, <i>Curvularia alcornii</i> and <i>Pestalotiopsis</i> sp. The yeast exhibited moderate biocontrol potential in vitro, with an average mycelial growth inhibition of 31.11% after 7 days of dual culture across all tested pathogens. The study also examined how environmental stresses, such as elevated temperature, oxidative stress and osmotic stress impacted the yeast viability and morphology. Results showed that high temperatures (40°C and 42°C) and elevated hydrogen peroxide concentration significantly reduced yeast populations. The addition of 5% sorbitol and ascorbic acid enhanced the yeast's oxidative stress tolerance and viability. Sorbitol-based YDP-27 formulations, particularly at 15%, were highly effective in reducing anthracnose lesions on banana and mango fruits, achieving up to 100% inhibition. While moderate sorbitol concentration maintained better yeast viability under osmotic stress, higher concentration impaired cell populations over time. Morphological analysis revealed that extreme stress conditions led to reduced cell size, rougher textures and the formation of pseudohyphae. These findings underscore the potential of <i>M. caribbica</i> YDP-27 as a biocontrol agent, with specific additives significantly enhancing its performance under stress, thereby improving its shelf life and effectiveness against post-harvest pathogens.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818724","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":"Population Dynamics and Virulence Patterns of Root-Knot Nematodes (Meloidogyne spp.) on Tomato in Poonch Highlands, Azad Jammu and Kashmir, Pakistan","authors":"Muhammad Tariq-Khan,&nbsp;Tanzeel Ur Rehman,&nbsp;Tariq Mukhtar,&nbsp;Basharat Mahmood,&nbsp; Attiq-Ur-Rahman,&nbsp;Raees Ahmed","doi":"10.1111/jph.70060","DOIUrl":"https://doi.org/10.1111/jph.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>Root-knot nematodes (RKNs) are root parasites of tomatoes. The population dynamics of RKNs, including their incidence and prevalence, need to be explored in tomato crops from the Poonch Highlands of Azad Jammu and Kashmir. Overall, 50.6% of the surveyed tomato crop was found to be infested, with disease severity (galling index) ranging from 1 to 9. The highest RKN prevalence (77.8%) was recorded in Haveli, followed by Poonch (64.2%), Sudhnuti (47.6%) and the lowest in Bagh (41.6%). The highest disease severity (galling index 2–9) was observed in Poonch district, followed by Bagh district (1–9), while it was lowest in Haveli district (4–6), followed by Sudhnuti district (2–7). Morphological and molecular diagnostics confirmed the presence of tropical RKN species affecting tomato. Three major tropical RKN species were identified. <i>Meloidogyne javanica</i> was the most prevalent, occurring at 27.6% of the surveyed sites, followed by <i>Meloidogyne incognita</i> (23.5%) and <i>Meloidogyne arenaria</i> (18.8%). Mixed populations were found in fewer than 5% of sites for each combination. The highest prevalence of <i>M. javanica</i> (66.7%) was recorded in Haveli, while the lowest (9.5%) was in Sudhnuti. <i>M. incognita</i> and <i>M. arenaria</i> exhibited similar distribution patterns across all districts, ranging from 20.2% to 33.3% and 17.0% to 44.4%, respectively. Mixed-population infestations were relatively low in Bagh and Sudhnuti compared to Poonch and Haveli. Ecological diversity was found to influence species virulence and distribution patterns. This study highlights the virulence patterns of tropical RKN species, likely representing an indigenous fauna impacting tomato crops under field conditions in the temperate highlands.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809811","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":"IoT-Enhanced Meta-Heuristic Hybrid Deep Learning Model for Predicting Cotton Leaf Diseases","authors":"Bhushan V. Patil,&nbsp;Pravin Sahebrao Patil","doi":"10.1111/jph.70058","DOIUrl":"https://doi.org/10.1111/jph.70058","url":null,"abstract":"<div>\u0000 \u0000 <p>In the textile industry, cotton serves as a crucial raw material; however, diseases affecting cotton leaves can result in substantial financial losses for farmers. Conventional illness detection techniques are frequently costly, time-consuming, and inaccurate. Existing deep learning models can detect and classify affected leaves, but they face several limitations, including high error rates, excessive time consumption, a tendency for overfitting, and suboptimal performance. To overcome these issues, this work proposes a hybrid deep learning model with meta-heuristic support integrated with Internet of Things applications to efficiently classify cotton plant diseases. This creative concept seeks to give the textile sector and farmers a more precise and efficient solution. The proposed approach consists of two phases: first, high-resolution images of cotton leaves are captured using a Canon EOS 450D digital camera, and potential diseases are identified through IoT sensors. In the second step, advanced techniques like pre-processing, segmentation, feature extraction, feature selection, and classification are implemented. Disease segmentation is accomplished via the modified dilated u-net (MDU-Net) model. Feature selection utilising the Binary Guided Whale-Dipper Throated Optimizer (BGW-DTO) helps to identify the most relevant properties. Using the Harris Whale Optimization Method, the best weight coefficients for every classifier are found; next, a stacking ensemble model using the most recent deep learning approaches performs classification. In a collection of photos of cotton plant leaves, the optimal ensemble model shows a 99.66% classification rate, thereby precisely diagnosing a range of illnesses comprising Army Worms, Powdery Mildew, Bacterial Blight, Aphids, and Target Spots.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801286","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":"Screening of Potential Endophytes Against Fungal Pathogens, Curvularia lunata, Bipolaris oryzae and Pyricularia setariae Using In Vitro and Computational Approaches","authors":"J. H. Ashwini,&nbsp;Shridhar Hiremath,&nbsp;Mantesh Muttappagol,&nbsp;M. Nandan,&nbsp;M. Bharath,&nbsp;K. S. Shankarappa,&nbsp;T. L. Mohan Kumar,&nbsp;C. R. Jahir Basha,&nbsp;C. N. Lakshminarayana Reddy","doi":"10.1111/jph.70050","DOIUrl":"https://doi.org/10.1111/jph.70050","url":null,"abstract":"<div>\u0000 \u0000 <p><i>Curvularia lunata</i>, <i>Bipolaris oryzae</i> and <i>Pyricularia setariae</i> are a group of dematiaceous fungi which pose significant challenges in agriculture. Extensive use of synthetic fungicides to control them has led to cross-kingdom transfer and the development of fungicide resistance. Hence, endophytes and their metabolites can be used as an eco-friendly option to mitigate them. Potential endophytes (<i>Macrophomina phaseolina</i>, <i>Macrophomina pseudophaseolina</i>, <i>Trichoderma asperellum</i> and <i>Polyporales</i> sp.) were tested for their bio-control efficacy through in vitro dual culture and double Petri dish assay. In the dual culture assay, all five endophytes exhibited significantly higher antagonistic activity against <i>C. lunata</i> than other plant pathogens. However, in the double Petri dish assay, which tested the efficacy of volatile organic compounds (VOCs) produced by endophytes for in vitro pathogen control, the growth of <i>P. setariae</i> was notably more inhibited than that of the other pathogens. Further, the individual metabolites produced by the fungal endophytes were characterised by LC–MS/MS and used for <i>in silico</i> docking analysis against specific target proteins: Polyketide synthase (PKS), beta-tubulin and trihydroxynapthalene reductase (THR) of the fungal pathogens. The docking analysis revealed that 3-beta-chloro-Imperialine, quinoline-6,8-disulfonic acid and bucladesine from CSR1 (<i>Macrophomina pseudophaseolina</i>) demonstrated superior binding affinities to β-tubulin of <i>C. lunata</i>, with dock scores of −7.45, −7.3 and −7.24 kcal/mol respectively, outperforming the commercially available fungicide carbendazim (−5.95 kcal/mol). Two metabolites, veratrosine and glimepiride present in <i>Polyporales</i> sp., interacted with THR with binding affinities of −12.4 and −10.76 kcal/mol. In interactions with PKS of <i>B. oryzae</i>, 3-methylcholanthrene and penitrem A from TPS2 (<i>Trichoderma asperellum</i>) showed outstanding dock scores of −11.22 and −10.08 kcal/mol, respectively. These findings highlight the potential of endophyte-derived metabolites as powerful inhibitors of fungal pathogens, offering promising leads for developing novel antifungal treatments.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787257","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":"Study on Secondary Metabolites of Spodoptera litura Gut-Associated Bacteria and Their Induction of Tomato Resistance to Pests","authors":"Chengzhen Gu,&nbsp;Chengxi Yan,&nbsp;Mengmeng Wang,&nbsp;Yangzheng Lin","doi":"10.1111/jph.70062","DOIUrl":"https://doi.org/10.1111/jph.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>Previous studies have found that the fermentation broth of gut-associated bacteria of the <i>Spodoptera litura</i> can induce resistance in tomatoes to the <i>Spodoptera litura</i> larva. In this paper, column chromatography and modern spectrometry techniques were utilised to isolate and identify the fermentation broth of gut-associated bacteria of <i>Spodoptera litura</i>, and five compounds were obtained, which were <i>N</i>-acetyltyramine (<b>1</b>), p-hydroxybenzeneacetic acid methyl ester (<b>2</b>), p-hydroxybenzenepropionic acid (<b>3</b>), 3-indolylcarboxaldehyde (<b>4</b>), and p-hydroxybenzeneethanol (<b>5</b>). These compounds were evaluated for their ability to induce insect resistance in tomato, revealing that all five increased the resistance of tomato to the <i>Spodoptera litura</i> to varying degrees. The strongest induced resistance was observed in tomatoes treated with compound <b>1</b> at 0.1 μg/mL, showing a 55.73% body weight inhibition against the <i>Spodoptera litura</i>. Compound <b>4</b>, at 1 μg/mL, induced a 42.39% inhibition of larval weight. Compound <b>5</b> at 0.1 μg/mL induced a 42.39% inhibition of growth in tomato against <i>Spodoptera litura</i> as well. Compounds <b>2</b> and <b>3</b> at 1 μg/mL resulted in 30.20% and 39.66% inhibition of the weight growth of tomato against <i>Spodoptera litura</i>, respectively. Among them, the induction of compound <b>2</b> at 0.1 <i>μ</i>g/mL even significantly promoted the feeding on tomato leaves by the <i>Spodoptera litura</i>.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143778219","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":"Pathogen Management in Glycyrrhiza glabra: Microbial Interactions and Phylogenetic Insights","authors":"Khabib Kushiev,&nbsp;Kuralova R,&nbsp;Tokhir Husanov,&nbsp;Bakhtiyor Rakhimov,&nbsp;Giyosiddin Khudayberdiyev,&nbsp;Kenjayev Shavkat,&nbsp;Eman Alhomaidi,&nbsp;Muhammad Zafar,&nbsp;Salman Majeed,&nbsp;Trobjon Makhkamov,&nbsp;Adnan Amin","doi":"10.1111/jph.70057","DOIUrl":"https://doi.org/10.1111/jph.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>The plant-associated microbial communities are crucial for understanding their roles in enhancing plant health and productivity. This research aimed to isolate and characterize bacterial strains from the roots of <i>Glycyrrhiza glabra</i> L. using 16S rRNA gene sequencing to explore their phylogenetic relationships and functional potential. Root samples were collected from the Gulistan phytogeographic region, and bacterial strains were isolated through serial dilution and cultured on nutrient agar. Genomic DNA was extracted, and 16S rRNA sequencing was performed to identify the isolates, followed by phylogenetic analysis using MEGA X software. The results revealed three dominant <i>Bacillus</i> species: <i>Bacillus licheniformis</i>, <i>Bacillus subtilis</i> and <i>Bacillus halotolerans</i>. The strains exhibited significant enzyme activities, including protease, amylase and cellulase production, suggesting their roles in organic matter degradation and nutrient cycling. Furthermore, greenhouse trials demonstrated enhanced plant growth parameters such as root length, plant height and biomass, reflecting their potential as plant growth-promoting rhizobacteria (PGPR). Antibiotic production assays indicated their capacity for biocontrol against pathogens, reinforcing their role in plant disease suppression. This study emphasizes the ecological significance of <i>Bacillus</i> spp. in sustainable agriculture and their potential application as biofertilizers and biocontrol agents. Future research should focus on field trials and metabolomic analysis to elucidate the molecular mechanisms underlying the beneficial interactions, further optimizing their use in crop improvement strategies.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749742","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":"Correction to “Identification of Plant Species Using Convolutional Neural Network with Transfer Learning”","authors":"","doi":"10.1111/jph.70054","DOIUrl":"https://doi.org/10.1111/jph.70054","url":null,"abstract":"<p>\u0000 <span>Arun, A.</span>, <span>S. Sharma</span>, <span>B. Singh</span>, and <span>T. Hazra</span>. <span>2025</span>. “ <span>Identification of Plant Species Using Convolutional Neural Network with Transfer Learning</span>.” <i>Journal of Phytopathology</i>, <span>173</span>, no. <span>1</span>: e70032. https://doi.org/10.1111/jph.70032\u0000 </p><p>In the above published article, several labeling errors were identified in figure captions and section references. These errors occurred in the Figures 14–18 and Sections 4.5.1 and 4.5.2 as follows:</p><p>In subsection 4.5.1 of “Performance Analysis of the Proposed Work” section, the reference to “<b>Figure 14”</b> was incorrect. This should refer to “<b>Figure 18”</b> instead.</p><p>We apologize for these errors.</p>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jph.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717138","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":"IoT-Enabled Plant Leaf Disease Detection Using HPJSO_SqueezeNet","authors":"Sridhar Manda,&nbsp;Arun Kumar Arigala,&nbsp;B. Krishna,&nbsp;Syed Asiya","doi":"10.1111/jph.70024","DOIUrl":"https://doi.org/10.1111/jph.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) has become a highly effective tool over the past few decades, finding relevance in real-time applications. In agriculture, the use of automated technologies for detecting plant diseases offers immense benefits but also poses significant challenges. To address existing challenges, a hybrid framework named Hunter Prey Jellyfish Search Optimization (HPJSO) enabled SqueezeNet (HPJSO_SqueezeNet) has been developed for multi-classification of plant leaf disease detection in IoT. Here, HPJSO combines Hunter Prey Optimization (HPO) and Jellyfish Search Optimization (JSO). The IoT nodes are simulated. Then, the Cluster Head (CH) is executed employing the Low-energy adaptive clustering hierarchy (LEACH) protocol. After that, the routing is executed using the selected CH and it is given to the Base Station (BS) utilising HPJSO. At BS, the pre-processing phase is performed using a Gaussian filter. Thereafter, plant leaf segmentation is carried out by a Psi-Net trained with HPJSO. Moreover, the classification process is done by Deep Convolutional Neural Network (Deep CNN), which is trained by HPJSO. Finally, the multi-classification of plant leaf disease detection is achieved using SqueezeNet trained with the proposed HPJSO. In addition, the overall performance of the proposed HPJSO_SqueezeNet method for multi-classification is compared with other existing methods such as sine cosine algorithm-based rider neural network (SCA based RideNN), IoT-based Fuzzy Based Function Network (IoT_FBFN), Taylor-Water Wave Optimization-based Generative Adversarial Network (Taylor-WWO-based GAN), Smart Farm Monitoring System (SFMS), Deep Learning, Improved Quantum Whale Optimization with Principal Component Analysis (IQWO-PCA), HPO-based SqueezeNet and JSO-based SqueezeNet. Additionally, the simulation outcomes of HPJSO are examined with Energy efficient routing, Secure and Scalable Routing protocol (SARP), Trust aware routing and Competitive Versatile Flower Pollination (CVFP) based routing. The HPJSO has achieved the highest energy of 73.80%, throughput of 77.60%, delay of 24.50% and distance of 10028.40%. As well as, the HPJSO_SqueezeNet attained the accuracy of 0.898 and sensitivity of 0.937. The proposed HPJSO model achieves higher energy compared to several other methods, with improvements of 52.30% over Energy efficient routing, 50.81% over SARP, 18.97% over Trust aware routing, and 20.87% over CVFP-based routing based on routing. Likewise, the proposed HPJSO_SqueezeNet model achieves higher accuracy compared to several other methods with improvements of 8.91% over SCA-based RideNN, 5.68% over IoT_FBFN, 3.67% over Taylor-WWO-based GAN, 3.23% over SFMS, 2.34% over Deep Learning, 1.00% over IQWO-PCA, 1.67% over HPO-based SqueezeNet, and 1.00% over JSO-based SqueezeNet. The code for the proposed approach is found at ‘https://github.com/SridharM87/HPJSO_SqueezeNet.git’.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717257","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":"Rice Sheath Rot: Targeted Approach for Studying the Efficacy of Fungicides and Biocontrol Agents Under Field Conditions of Northern India","authors":"Amritpal Mehta,&nbsp;S. K. Singh,&nbsp;Owais Ali Wani,&nbsp;Shafat Ahmad Ahanger,&nbsp;Umer Basu,&nbsp;Amrish Vaid,&nbsp;Sonali Sharma,&nbsp;Ashwani Kumar Basandrai","doi":"10.1111/jph.70055","DOIUrl":"https://doi.org/10.1111/jph.70055","url":null,"abstract":"<div>\u0000 \u0000 <p>Sheath rot, caused by <i>Sarocladium oryzae</i>, has emerged as a potential threat in the rice-growing areas of northern India where commercially available rice varieties are susceptible to this disease. Various fungicides and <i>Trichoderma</i> spp. were evaluated in vitro and in vivo against <i>S. oryzae</i> to develop effective management tools to reduce yield losses from the disease. In a 2-year field trial, different combinations of seed treatments and two foliar sprays of fungicide and/or <i>T. harzianum</i> Th-II were evaluated in conditions of artificial inoculation with the pathogen. Based on combined data from the 2 years, the minimum percent disease index (PDI) (11.2%) and the highest mean grain yield (3.63 t/ha) with maximum net profit of INR ₹43,350/ha were recorded for seed treatment with azoxystrobin 11% + tebuconazole 13.8% SC followed by two foliar sprays of azoxystrobin + tebuconazole. The next best treatment consisted of seed treatment with azoxystrobin + tebuconazole followed by two foliar sprays of tebuconazole 25.9% EC; seed treatment with azoxystrobin + tebuconazole followed by two foliar sprays of tebuconazole 50% + trifloxystrobin 25% WG; and seed treatment with Th-II followed by two foliar sprays of azoxystrobin + tebuconazole, which resulted in PDI values of 12.7%, 14.1% and 14.4%; grain yields of 3.56, 3.47 and 3.44 t/ha; and net profits per ha of INR ₹41,087, ₹36,341 and ₹35,769, respectively.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717258","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":"Potential Distribution of Fusarium Head Blight Under Climate Change Scenarios in Iran","authors":"Farid Houshyar,&nbsp;Behnam Pouzeshimiyab,&nbsp;Sevil Nematollahi,&nbsp;Khalil Valizadeh Kamran,&nbsp;Manizheh Jamshidi","doi":"10.1111/jph.70034","DOIUrl":"https://doi.org/10.1111/jph.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>Climate change is predicted to impact crop production and food security, with increased risks of pest infestations and crop failures. Using proximal climate variables and maximum entropy (MaxEnt) model, this research predicts the current and future potential distribution of Fusarium Head Blight (FHB), a significant disease affecting wheat and barley. The analysis considered two Climate Change Shared Socioeconomic Pathways (SSPs) over two distinct time periods across various agro-ecological zones (AEZs) in Iran. The results indicated that the climate conditions for FHB includes maximum temperature, precipitation and minimum temperature in March, contributing 47.85% ± 1.63%, 28.75% ± 3.20% and 23.40% ± 1.56% to its distribution, respectively. Currently, this disease was predicted predominantly to present in AEZs including North West, Caspian Coastal, Central Zagros and Southern Zagros, which collectively account for 49% of Iran's national crop production. However, projections suggested a future shift in these areas to Central, Khorasan, Arid Central and Southern Coastal AEZs, which currently contribute 36% to national crop production. This research emphasises the need for effective management strategies to mitigate the impacts of climate change on agricultural crops in Iran due to the shifting distribution of FHB.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690052","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}