Multi-environment evaluation for downy mildew disease incidence revealed the prevalence of multiple pathotypes and the need for region-specific breeding strategies in cucumber

Multi-environment evaluation for downy mildew disease incidence revealed the prevalence of multiple pathotypes and the need for region-specific breeding strategies in cucumber | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-environment evaluation for downy mildew disease incidence revealed the prevalence of multiple pathotypes and the need for region-specific breeding strategies in cucumber Vivek Hegde, Vidya Sagar, Vikrant Tomar, Swagata Nandi, Md. Yeasin, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8364842/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Downy mildew (DM), caused by Pseudoperonospora cubensis , is a major constraint in cucumber ( Cucumis sativus L. ) production, particularly in humid and subtropical agro-ecologies. To identify stable sources of resistance and understand genotype-by-environment interactions, a comprehensive evaluation of 131 cucumber accessions was conducted across three contrasting agro-climatic locations in India: New Delhi (IARI), Bengaluru (IIHR), and Varanasi (IIVR). Field evaluations under natural epiphytotic conditions and artificial inoculations were performed, and disease incidence was assessed using per cent disease index (PDI) and area under disease progress curve (AUDPC). Clustering analysis revealed significant genetic variability for DM resistance across environments. At IIHR, 29 genotypes, including IC523700, IC421752, and IC314341, exhibited high resistance (mean PDI ≤ 17.3; AUDPC < 700), while IIVR identified 35 highly resistant accessions with a mean PDI of 13.8 and AUDPC of 487.8. Several landraces, notably IC588146 and IC421752, demonstrated stable resistance across two or more locations, highlighting their potential as broad-spectrum resistance donors. The observed location-specific disease responses underline the prevalence of diverse DM pathotypes and the necessity for region-tailored breeding strategies. The study emphasises the value of indigenous germplasm in developing climate-resilient, disease-resistant cultivars through multi-location screening and genomic approaches. Identified resistant accessions provide a valuable foundation for marker-assisted selection and breeding programs targeting durable DM resistance in cucumber. Cucumber Downy mildew Resistance breeding multi-location evaluation Genetic diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1.Introduction One of the most extensively grown and consumed vegetables worldwide, cucumbers ( Cucumis sativus L.), a member of the Cucurbitaceae family, are valued for their economic, medicinal, and nutritional value. It is produced in more than 80 nations, with China, India, and Turkey leading the world in production, which surpassed 91 million metric tons in 2022 (FAOSTAT, 2023). With its abundance of native cucumber landraces and wild relatives, especially in the northeastern, eastern, and central Himalayan regions, India is the largest centre of cucumber diversity and these resources are crucial for crop improvement (Pandey et al., 2013 ). Downy mildew (DM), one of the most destructive foliar diseases affecting cucumbers worldwide, is a significant production constraint caused by the oomycete Pseudoperonospora cubensis . High humidity and moderate temperatures are ideal for the pathogen's growth, which negatively impacts photosynthesis and can cause sensitive cultivars to lose up to 70–90% of their yield (Thomas, 1996 ). The disease is common in India in both the rabi and kharif seasons, especially in humid subtropical agro-ecologies or under protected agriculture (Ghosh et al., 2015 ). Fungicide-based management is unsustainable from an economic and environmental standpoint, particularly for smallholder farmers. As a result, breeders opt for host plant resistance, which provides a financially and environmentally sound approach (Badr and Mohamed, 1998 ). The high pathogenic variability and quick evolution of P. cubensis races present another difficulty in breeding for DM resistance (Lebeda, 2001). Integrated omics methods and high-throughput phenotyping and genotyping techniques are revolutionising the identification of resistance loci. A number of genes linked to PR proteins, reactive oxygen species (ROS) detoxification, and hormone signalling have been shown to be differently expressed in transcriptome profiling of resistant cucumber lines infected with DM (Gao et al., 2021 ). These results are consistent with resistance mechanisms observed in squash and melon, as well as other cucurbit crops (Yadav et al., 2022 ). The potential for using Indian germplasm in genomic-assisted breeding is further enhanced by the availability of reference genomes and pan-genome resources for cucumber (Huang et al., 2009; Li et al., 2023). The underutilization of traditional Indian cucumber cultivars in mainstream breeding projects remains a significant obstacle, despite recent advancements. Despite having significant resistant features, their characteristics—such as late flowering, low yield, and bitterness—often cause them to be neglected (Pandey et al., 2013 ). Finding farmer-favoured, resistant lines appropriate for target agro-ecologies will be made easier by combining participatory varietal selection with multi-location testing. Disease pressure from downy mildew is anticipated to increase due to the impending threat of climate change, which is likely to increase humidity and temperature variability (Chakraborty and Newton, 2011 ). Therefore, Indian-origin cucumber germplasm presents a unique opportunity to identify climate-resilient resistance alleles, which are adaptable to a variety of agro-climatic stress zones. To utilise this latent genetic treasure, future initiatives should focus on creating core and mini-core collections of Indian cucumber varieties in addition to high-resolution phenotyping and predictive breeding models (Upadhyaya et al., 2008 ). Indian cucumber genetic resources offer a wealth of downy mildew resistance by combining traditional landraces with contemporary breeding possibilities. To fully realise their potential, a coordinated effort comprising field screening under multi-environmental evaluation is required. The purpose of this study is to determine stable, promising genotypes for use in breeding programs and to assess the level of downy mildew resistance in a large panel of Indian-originated cucumber genotypes across various agro-climatic regions. 2. Materials and methods 2.1 Plant materials A total of 131 cucumber ( Cucumis sativus L.) accessions were systematically documented and characterized based on passport data to assess the geographic and ecological breadth of genetic diversity across India. The germplasm predominantly comprised traditional landraces sourced from a wide range of agro-climatic zones, encompassing tropical, subtropical, humid, semi-arid, and temperate environments. Name and origin of accessions (state wise) is mentioned in germplasm passport data (supplementary table 1 ) . The highest number of accessions originated from West Bengal (22 genotypes), a region characterised by a humid subtropical climate with high annual precipitation and prolonged monsoon periods, which are conducive to the proliferation of biotic stresses and the selection for disease resistance. Jharkhand (18 genotypes) and Bihar (10), representing the eastern plateau and hot sub-humid agro-ecologies, contributed accessions adapted to rainfed agriculture and intermittent drought stress. Kerala (13 genotypes), part of the humid tropics with uniformly high temperatures and rainfall, contributed unique genotypes suited to year-round cultivation under high humidity. Uttar Pradesh (9 genotypes), within the Indo-Gangetic plains and characterized by a dry sub-humid climate with distinct summer and winter seasons, offered genotypes with adaptation to variable thermal and photoperiodic regimes. Contributions from Karnataka (7) and Tamil Nadu (5) reflected germplasm from semi-arid and dry sub-humid zones of peninsular India, while Odisha (5), located in a moist sub-humid tropical belt, contributed accessions adapted to moderate rainfall and high humidity. Accessions from Maharashtra (4) and Rajasthan (3) encompassed hot semi-arid to arid zones, indicating potential tolerance to heat and soil moisture stress. Representation from Himachal Pradesh (3) and Uttarakhand (2), situated in temperate to humid subtropical hill ecosystems, provided germplasm adapted to mid-altitude thermal and photoperiodic conditions. Additionally, Haryana (2), Tripura (1), Goa (2), and union territories including Delhi (2) and Lakshadweep (1) contributed to the ecological and geographical span of the collection. Twelve accessions of unknown or composite origin were classified as ‘Others’. This spatial and climatic representation underscores the adaptability of cucumber to diverse environmental niches and reflects evolutionary selection pressures imposed by regional agro-ecological constraints. The assembled germplasm constitutes a critical resource for pre-breeding, trait dissection, and crop improvement efforts aimed at enhancing climate resilience, biotic stress tolerance, and local adaptability through targeted genetic interventions. 2.2 Evaluation of the genotypes under natural epiphytotic conditions Twenty plants from each of the 130 genotypes were planted in the field from seeds. Two seeds per hill, spaced 60 cm apart, were sown on each side of the 1.5 m wide ridges. Among other cultural activities, fertilization and irrigation were practiced. During the Kharif season, when the natural load of DM was at its highest, the growing plants were exposed to field downy mildew infection without the application of fungicides to suppress this disease. The degree of disease signs on the top, middle, and bottom leaves of each plant was measured using a 0–9 scale to record the disease incidence (Bidalmali et al., 2024)(Supplementary Fig. 1). According to Jenkins and Wehner (1983), disease scoring started one month after planting and continued at 15 days intervals until 75 days after sowing (DAS). The plants were classified into four categories based on the PDI, which was determined using symptomatic leaf area data (S1): resistant (0–20%), moderately resistant (21–40%), susceptible (41–60%), and severely susceptible (> 60%) (4 clusters). $$\:\text{P}\text{D}\text{I}\:=\frac{\text{S}\text{u}\text{m}\:\text{o}\text{f}\:\text{n}\text{u}\text{m}\text{e}\text{r}\text{i}\text{c}\text{a}\text{l}\:\text{v}\text{a}\text{l}\text{u}\text{e}\text{s}\:}{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{l}\text{e}\text{a}\text{v}\text{e}\text{s}\:\text{g}\text{r}\text{a}\text{d}\text{e}\text{d}\:\times\:\:\text{M}\text{a}\text{x}\text{i}\text{m}\text{u}\text{m}\:\text{r}\text{a}\text{t}\text{i}\text{n}\text{g}\text{s}}\times\:\:100$$ 2.3 Artificial Screening for resistance to downy mildew To achieve a more precise assessment of the resistance inheritance, the six selected genotypes were evaluated under net-house conditions, which helped reduce environmental fluctuations and ensured consistently high humidity for uniform disease development. Twenty seedlings per genotype were raised in net houses and irrigated manually on alternate days. Once a week, the plants received a nutrient solution containing 150 mg L⁻¹ each of nitrogen, phosphorus, and potassium. When the seedlings reached 20–25 days of age, they were inoculated on the abaxial leaf surface using a 1-L sprayer filled with a sporangial suspension (10,000 sporangia mL⁻¹). Before inoculation, small pinholes were gently made on the abaxial surface with fine needles to facilitate pathogen entry. Three to four days after the first appearance of symptoms, the inoculated plants were shifted to a high-humidity chamber to promote disease establishment. Disease severity was assessed on the eighth day using a 0–4 linear rating scale and the percentage disease index (PDI) was calculated as described by Cohen et al. (2000) after 15 and 30 days of inoculation. 2.4 The area under disease progress curve (AUDPC): The AUDPC was calculated using the following formula based on the percent disease index (Shaner and Finney, 1977; Jeger and Viljanen-Rollinson, 2001). AUDPC=∑ [{Xi + 1 + Xi + 1/ 2} 𝑥 {ti + 1 − ti}] Where Xi = disease index expressed as a portion at ith observation, ti = age of the plant at i th observation, and n = total number of observations. 2.5 Statistical analysis: To assess the complex genotype-by-environment interactions and rank cucumber accessions for Downy Mildew (DM) resistance, a multivariate analytical framework was employed. The Augmented ANOVA was conducted on PDI) and AUDPC data collected for New Delhi (IARI), Bengaluru (IIHR) and Varanasi (IIVR) separately. Upon detecting significant genotypic differences, post hoc multiple comparison tests (Least Significant Difference) were conducted to determine the statistical significance of differences between genotypes and checks. To explore genetic diversity and patterns of disease response, hierarchical clustering was performed using Euclidean distance and Ward’s linkage which results resistance-based clusters. Further, Principal Component Analysis (PCA) was employed to reduce dimensionality and extract principal components that explained the majority of variance in disease traits. The extracted PCs were used for K-means clustering for integrating multiple traits. To prioritize genotypes with consistent and broad-spectrum resistance, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied. These multi-criteria decision-making method ranked genotypes based on their closeness to the ideal disease resistance profile by combining different criteria. All the analysis has been carried out using R (version 4.2.2) software. 3. Results 3.1 Clustering Pattern of Genotypes Based on Disease Progression Parameters at IARI A total of 130 cucumber genotypes were evaluated for their resistance to disease progression under field conditions at IARI and subsequently grouped into four distinct clusters based on their PDI at 30, 45, 60, and 75 days after sowing (DAS), as well as corresponding AUDPC values. Average PDI and AUDPC of downy mildew occurrence in IARI is shown via dendrogram (Fig. 1 , 2 ) Cluster I comprised 11 genotypes that exhibited moderate resistance, with average PDI values gradually increasing from 33.23 (30 DAS) to 73.16 (75 DAS), resulting in an overall mean PDI of 52.88 and an AUDPC of 2949.73. These genotypes maintained lower disease levels across all time points. In contrast, Cluster II comprised 42 highly susceptible genotypes, which reached maximum disease severity (100% PDI) by 60 DAS and maintained it through 75 DAS, with a mean PDI of 96.07 and the highest AUDPC value of 5,477.26. Cluster III consisted of 34 genotypes exhibiting intermediate disease progression, with an average PDI increasing from 45.67 (30 DAS) to 93.44 (75 DAS), averaging 72.04, and an AUDPC of 3918.24. Cluster IV, containing 43 genotypes, also showed high susceptibility with a mean PDI of 86.87 and AUDPC of 4772.87 (Table 1 ). Table 1 Clustering of the genotypes based on the PDI and AUDPC at IARI Cluster Genotypes No of Genotypes PDI 30 DAY MEAN PDI 45 DAY MEAN PDI 60 DAY MEAN PDI 75 DAY MEAN AVG PDI MEAN AUDPI I IC527413, IC523673, IC527400, IC541436, IC4110211, IC354750, C588145, IC572024, IC354787, IC523678,IC421752 11 33.23 45.73 59.38 73.16 52.88 2949.73 II IC410202, IC345264, IC344394, IC523694, IC448827, IC523680, IC344336, IC354806, IC410638, IC523681, IC248283, IC541234, IC410199, IC248274, IC344343, IC354793, IC523693, IC344357, IC248258, IC317485, IC344333, IC314341, IC523684, IC523701, IC523699, IC523695, IC523670, IC354782, IC523685, IC523675, IC527420, IC344371, IC527434, IC354819, IC523697 IC362446, IC317489, IC354801, IC354785, IC538130, IC538173, IC527391 42 87.7 96.61 100 100 96.07 5477.26 III IC538137, IC523676, IC527394, IC344358, IC523672, IC324476, IC353325, IC527418, IC264979, IC588144, IC354796, Pusa Barkha, IC538155, IC527419, IC410654, IC525746, IC523671, IC523698, IC523691, IC354749, IC588147, IC588146, IC331940, IC538145, IC371736, IC538121, IC410657, IC538158, IC523677, IC523687, IC557170, IC421146, IC255441, IC527404 34 45.67 65.63 81.40 93.44 72.04 3918.24 iv IC410682, IC371758, Japanese Long Green, IC335311, IC523683, IC527405, IC523688, IC410658, IC354797, IC527395, IC538147, IC354816, IC354815, EC1041438, IC331878, IC527412, IC527396, IC371705, IC410617, IC527402, IC523700, IC523689, IC527431, IC371751, Pusa Uday, Pusa Long Green, IC354828, IC354753, IC410215, IC264796, IC527410, IC538126, IC255800, IC276392, IC371738, IC354757, IC523674, IC527423, IC523679, IC523682, IC523690, IC523692, IC354788 43 65.79 84.33 96.78 99.42 86.87 4772.87 3.2 Clustering Pattern of Genotypes Based on Disease Progression Parameters at IIHR At the IIHR location, the same set of genotypes was clustered into four groups based on PDI and AUDPC to evaluate location-specific responses. Average PDI and AUDPC of downy mildew occurrence in IIHR is shown via dendrogram (Fig. 3 , 4 ) Cluster I comprised 7 genotypes that exhibited moderate resistance in the early stages (PDI 6.75 at 30 DAS) but showed substantial disease escalation by 75 DAS (PDI 90.05), resulting in a mean PDI of 44.56 and an AUDPC of 1971.25. Cluster II, comprising 34 genotypes, exhibited a progressive increase in disease incidence but remained moderately susceptible overall, with an average PDI of 37.51 and an AUDPC of 1651.98. Notably, Cluster III, with 29 genotypes, demonstrated the highest level of resistance, exhibiting negligible disease incidence at early stages (PDI = 0 and 0.43 at 30 and 45 DAS, respectively), and maintaining lower levels even at 75 DAS (PDI 50.66), with a mean PDI of 17.29 and AUDPC of 675.98. Cluster IV comprised the largest number of genotypes (60), which exhibited moderate susceptibility, characterized by a steady increase in disease severity, culminating in an average PDI of 28.03 and AUDPC of 1,155.69 (Table 2 ). These findings suggest that several genotypes, particularly those in Cluster III, hold considerable promise for developing resistant cultivars under the agro-climatic conditions of southern India. Table 2 Clustering of the genotypes based on the PDI and AUDPC at IIHR Cluster Genotypes No of Genotypes PDI 30 DAY MEAN PDI 45 DAY MEAN PDI 60 DAY MEAN PDI 75 DAY MEAN AVG PDI MEAN AUDPI I IC523683, IC331878, IC527434, IC538155, IC264796, IC354787, IC523674 7 6.75 31.84 49.60 90.05 44.56 1971.25 II IC448827, IC354750, IC527423, IC538130, IC371758, IC410682, IC371738, IC527402, IC523681, IC538121, IC410199, IC354819, IC354801, IC354749, IC410654, IC354806, IC354785, IC248274, IC354793, IC354797, IC354828, IC523693, IC523694, IC415530, IC354816, IC410202, IC588144, IC541234, IC588146, Pusa Long Green, IC353325, IC523682, IC354788, IC354753 34 0 4.97 56.22 88.86 37.51 1651.98 III IC523700, IC410215, IC421752, IC523676, IC523698, IC527431, IC527400, IC523701, IC523687, Japanese Long Green, IC255800, IC344394, IC421146, IC421769, IC588147, IC314341, IC572024, IC410211, IC541436, IC344336, IC538158, IC344357, IC324476, IC588145, IC527419, IC344343, IC344333, IC317485, IC371751 29 0 0.43 18.06 50.66 17.29 675.98 Iv IC317489, IC354757, IC523670, IC523684, IC345264, IC248283, IC538126, IC276392, IC354796, IC410617, IC255746, IC523690, IC255441, IC527412, IC523689, IC354782, IC523685, IC527395, IC538145, IC362446, IC527391, IC203853, IC523692, IC371736, IC523699, IC410638, IC523675, IC523678, IC557170, IC527394 IC527418, IC344358, IC523679, IC527410, IC527396, IC410657, IC527413, IC527404, IC527420, IC523677, IC527405, IC523673, IC538147, Pusa Uday, IC523697, IC523688, IC523671, IC331940, Pusa Barkha, IC248258, IC335311, IC523691, IC538137, IC344371, IC538173, IC523695, IC523680, IC410658, IC371705, IC354815 60 0 1.68 34.82 75.41 28.03 1155.69 3.3 Clustering Pattern of Genotypes Based on Disease Progression Parameters at IIVR The clustering of cucumber genotypes based on disease parameters at IIVR further validated the differential resistance observed across locations. Average PDI and AUDPC of downy mildew occurrence in IIVR is shown via dendrogram (Fig. 5 , 6 ) Cluster I, comprising 24 genotypes, exhibited moderate disease progression, with average PDI values ranging from 0.75 (30 DAS) to 93.85 (75 DAS), resulting in a mean PDI of 55.49 and an AUDPC of 2454.95. Cluster II, comprising 35 genotypes, was the most resistant group identified at IIVR, characterized by negligible early-stage infection (PDI = 0 at 30 DAS) and limited progression up to 75 DAS (PDI = 28.70), resulting in a mean PDI of 13.83 and AUDPC of 487.82. Cluster III comprised 30 genotypes exhibiting intermediate resistance, as indicated by a mean PDI of 28.13 and an AUDPC of 938.94. Cluster IV comprised 41 genotypes with higher susceptibility, as disease severity progressed to an average PDI of 41.10 and AUDPC of 1578.21. The clustering results at IIVR emphasize the location-specific performance of genotypes and the importance of multi-environment testing to identify genotypes with stable resistance (Table 3 ). The consistently low AUDPC values of Cluster II genotypes across locations suggest their potential utility as donors for resistance breeding and incorporation into integrated disease management strategies. Table 3 Clustering of the genotypes based on the PDI and AUDPC at IIVR Cluster Genotypes No of Genotypes PDI 30 DAY MEAN PDI 45 DAY MEAN PDI 60 DAY MEAN PDI 75 DAY MEAN AVG PDI MEAN AUDPI I IC588147, IC523700, IC538126, IC410215, IC264979 IC345264, IC344333, IC523694, IC354785, IC255441 IC523691, IC523675, IC344394, IC344343, IC335311 IC523689, IC527420, IC410682, IC354749, IC203853 IC541234, IC344336, IC523674, IC354753 24 0.75 52.36 75.01 93.85 55.49 2454.95 II IC527410, EC1041438, IC371705, IC527419, IC538121 IC371751, IC527400, IC527405, IC538173, IC527391 IC588146, IC523677, IC572024, IC527413, IC538147 IC523698, IC523680, IC527404, IC523682, IC527431, IC410658, IC538130, IC527412, IC353325, IC523683 IC354788, IC410657, IC248283, IC588144, IC354828 IC331940, IC538145, IC523678, IC264796, IC255746 35 0 9.18 17.44 28.70 13.83 487.82 III IC538158, IC557170, IC541436, IC371738, IC527402 IC354782, IC410617, IC354796, IC276392, IC538137 Ic527404, IC538155, IC448827, IC523681, IC410211 Pahari Harit, IC248258, IC421752, IC523687, IC354797 IC371736, IC523688, IC523671, IC331878, IC523701 IC410199, IC371758, IC523693, IC324476, IC344371 30 0 16.54 33.49 62.51 28.13 938.94 IV IC410202, IC248274, IC317489, IC354806, IC354801 IC523679, IC523699, IC410654, IC523690, IC362446 IC523685, IC588145, IC354757, IC527434, IC317485 IC527396, IC354819, IC354816, IC255800, IC523684 IC527394, Pusa Uday, IC527418, IC354815, IC344357 IC523695, IC410638, IC523697, IC523676, IC314341 IC527395, IC354750, IC354787, IC421146, IC344358 Pusa Long Green, IC523672, Pusa Barkha, IC354793 IC523670, IC523692, IC527423, IC523673 41 0 34.01 47.96 78.43 41.10 1578.21 3.4 Artificial inoculation Artificial inoculation was carried out on both resistant and susceptible genotypes in three sets. Based on the observations from artificial inoculation, the resistant genotype IC527413 maintained a very low average disease score of 0.80 (PDI 20) at 15 days after inoculation, and an average score of 1.10 (PDI 27.50) at 30 days after inoculation. But genotype IC523675 had high average disease score of 3.433 (PDI 85.83) after 15 days of inoculation and average disease score of 3.60 (PDI 90.00) after 30 days of inoculation (Table 4 ). Table 4 Average disease score and PDI of selected genotypes after artificial inoculation Genotype 15 Days post inoculation 30 Days post inoculation Average score PDI Average score PDI IC527413 0.800 ± 0.058 20.000 ± 1.443 1.100 ± 0.100 27.500 ± 2.500 IC523673 1.200 ± 0.058 30.000 ± 1.443 1.567 ± 0.088 39.167 ± 2.205 IC541436 1.133 ± 0.033 28.333 ± 0.833 1.700 ± 0.058 42.500 ± 1.443 IC354782 3.167 ± 0.067 79.167 ± 1.667 3.733 ± 0.088 93.333 ± 2.205 IC523685 3.167 ± 0.176 79.167 ± 4.410 3.500 ± 0.153 87.500 ± 3.819 IC523675 3.433 ± 0.203 85.833 ± 5.069 3.600 ± 0.153 90.000 ± 3.819 3.5 Correlation study The correlation matrix shows strong positive relationships among all PDI observations recorded at 30, 45, 60, and 75 DAS, with correlations increasing as the disease progresses. AVG PDI and AUDPC also exhibit very high correlations with all individual PDI time points, indicating consistent disease development patterns. Scatterplots show distinct clustering base on locations, suggesting location-specific disease pressure. Density plots further highlight differences in disease severity distribution across sites (Fig. 7 ). 4. Discussion Cucumis species are attacked by pathotype 3 (clade 2) of the pathogen, while Cucurbita species are attacked by pathotype 6 (clade 1). The comprehensive evaluation of 131 cucumber ( Cucumis sativus L.) accessions across three contrasting agro-ecological environments—New Delhi (IARI), Bengaluru (IIHR), and Mukteshwar (IIVR)—enabled a robust assessment of phenotypic diversity for resistance to downy mildew ( Pseudoperonospora cubensis ), a major foliar disease-causing significant yield and quality losses in cucurbits globally (Lebeda and Cohen, 2011 ). The use of natural epiphytotic conditions and standardized disease scoring at progressive crop growth stages allowed the capture of temporal disease dynamics and the identification of genotypes with early-stage tolerance, delayed disease onset, or reduced final disease severity. The clustering analysis based on percent disease index (PDI) and area under the disease progress curve (AUDPC) revealed substantial intra- and inter-location variation in disease progression. At IARI , four clusters were delineated, with Cluster I comprising 11 genotypes exhibiting moderate resistance (mean PDI: 52.88; AUDPC: 2949.73). Genotypes such as IC527413 , IC523673 , and IC541436 maintained relatively low PDI values through successive disease assessments. Notably, IC541436 , a landrace from a high-humidity zone, also showed lower disease pressure at IIVR, indicating partial stability. In contrast, Cluster II , comprising 42 genotypes, exhibited a rapid increase in PDI, reaching 100% by 60 DAS and remaining constant, indicative of systemic susceptibility under northern plains conditions (Supplementary Fig. 2). At IIHR , located in the humid tropics, disease progression was more gradual, reflecting the influence of diurnal humidity cycles and consistent temperatures on pathogen sporulation. Cluster III included 29 genotypes with remarkable resistance, registering minimal early-stage infection (PDI ≤ 0.5 at 30–45 DAS) and limited terminal severity (mean PDI: 17.29; AUDPC: 675.98). Among these, IC523700 , IC410215 , IC421752 , IC314341 , and IC523676 stood out for their sustained resistance. These genotypes originated from sub-humid regions and appear to possess traits such as reduced stomatal conductance or enhanced phytoalexin production, as proposed in previous resistance mechanisms against P. cubensis (Savory et al., 2012 ; Li et al., 2018 ). The presence of IC421752 in resistant clusters at both IIHR and IIVR further strengthens its candidature as a stable donor. The high-elevation temperate environment of IIVR provided a distinct disease pressure profile, where resistance was often expressed as delayed disease onset and lower terminal severity. Cluster II , with 35 genotypes, recorded the lowest average PDI (13.83) and AUDPC (487.82), indicative of strong field resistance under cool and moist conditions. Genotypes such as IC588146 , IC527419 , IC588144 , and IC523698 consistently ranked among the most resistant entries. Notably, IC588146 was grouped in resistant clusters at both IIHR and IIVR, exhibiting average AUDPC values below 700, signifying its broad adaptation and quantitative resistance. This level of consistency under different microclimates reflects durable resistance, which is often governed by additive gene effects and is less prone to pathogen race specificity (Thomas, 1996 ; Win et al., 2017 ). Several genotypes showed multi-environment stability in resistance expression, making them prime candidates for breeding programs. Specifically: IC588146 (Kerala): Resistant at both IIHR and IIVR; possibly carrying alleles selected under consistently humid tropical conditions. IC527419 : Moderate resistance at IARI and high resistance at IIVR, suggesting plasticity in resistance expression. IC523701, IC314341, and IC588144 : Repeatedly classified into resistant clusters, indicating broad-spectrum resistance potential. IC421752 and IC523676 : Displayed stable field resistance across IIHR and IIVR, validating their utility in different agro-ecosystems. The stability of these genotypes across multiple environments underlines their value as sources of quantitative resistance and reinforces the importance of multi-environment testing. This is particularly critical given the high G×E interactions typical for downy mildew, where resistance can be environmentally masked or enhanced depending on local pathogen pressure, temperature, humidity, and leaf wetness duration (Badr and Mohamed, 1998 ; Quesada-Ocampo et al., 2012 ). Furthermore, the results support earlier findings that resistance to P. cubensis in cucumber is governed by multiple QTLs, with major loci identified on chromosomes 4, 5, and 7 (Yoshioka et al., 2014 ; Win et al., 2017 ). The identified resistant genotypes in this study may carry novel alleles or favorable combinations of these loci, which could be further validated through fine mapping, marker-assisted backcrossing, or genomic selection strategies. Given that several of the resistant accessions are landraces from high-disease-pressure zones, they also represent important reservoirs of co-adapted gene complexes, including those conferring disease escape and tolerance under abiotic stress (Maxted et al., 2012 ). In conclusion, this study demonstrates that Indian cucumber landraces represent a rich and underutilized genetic resource for downy mildew resistance breeding. The identification of genotypes with consistent resistance across diverse environments—such as IC588146 , IC527419 , and IC421752 —provides a foundation for both pre-breeding and varietal development efforts. These accessions should be prioritized for molecular characterization, resistance pyramiding, and deployment in integrated disease management programs tailored to region-specific pathogen profiles and agro-ecological conditions. Declarations Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable Permissions to collect the plants/plant parts: All the relevant permissions for the collection of cucumber seeds from different parts of India were obtained and done under the aegis of ICAR-NBPGR, New Delhi Funding The research work was conducted with financial support from the CRP on Agrobiodiversity (Cucumber) of the Indian Council of Agricultural Research (ICAR). Author Contribution **Vivek Hegde** and **Shyam Sundar Dey** designed the study and coordinated the multi-location evaluations. **Vivek Hegde** , **Vidya Sagar** , **Vikrant** conducted the field experiments, disease scoring, and data collection across locations. **Yeasin** and **R. K. Paul** performed the statistical analyses, including clustering and AUDPC calculations. **Amrita Das** and **Pragya Ranjan** assisted with germplasm management, phenotyping, and laboratory support. **Chithra Devi Pandey** and **Sushil Pandey** contributed to field evaluation and phenotype validation. **T. K. Behera** and **G. P. Singh** provided supervision, resources, and critical manuscript revisions. **Vivek Hegde** prepared the original manuscript draft with inputs from **Swagata Nandi** and **Shyam Sundar Dey** . All authors reviewed and approved the final manuscript. Acknowledgements The authors are thankful to ICAR-IARI, New Delhi; ICAR-IIHR, Bangalore; ICAR-IIVR, Varanasi for providing the research facilities. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Call AD, Criswell AD, Wehner TC, Klosinska U, Kozik EU. Screening cucumber for resistance to downy mildew caused by Pseudoperonospora cubensis (Berk. and Curt.) Rostov. Crop Sci. 2012;52(2):577–92. Call AD, Criswell AD, Wehner TC, Ando K, Grumet R. Resistance of cucumber cultivars to a new strain of cucurbit downy mildew. HortScience. 2012;47(2):171–8. Ceccarelli S, Grando S. Decentralized-participatory plant breeding: an example of demand driven research. Euphytica. 2007;155(3):349–60. Chakraborty S, Newton AC. Climate change, plant diseases and food security: an overview. Plant Pathol. 2011;60(1):2–14. Li L, He H, Zou Z, Li Y. QTL analysis for downy mildew resistance in cucumber inbred line PI 197088. Plant Dis. 2018;102(7):1240–5. Badr LAA, Mohamed FG. Inheritance and nature of resistance to downy mildew disease in cucumber (Cucumis sativus L). Ann Agric Sci Moshtohor. 1998;36(4):2517–44. Ghosh D, Bhattacharya I, Dutta S, Saha A, Mazumdar D. Dependence of the weather on outbreak of cucumber downy mildew (Pseudoperonospora cubensis) in eastern India. J Agrometeorology. 2015;17(1):43. Lebeda A. (2001, September). Pathogenic variation of Pseudoperonospora cubensis in the Czech Republic and some other European countries. In II International Symposium on Cucurbits 588 (pp. 137–141). Lebeda A, Cohen Y. Cucurbit downy mildew ( Pseudoperonospora cubensis ) – biology, ecology, epidemiology, and control. Eur J Plant Pathol. 2011;129(2):157–92. https://doi.org/10.1007/s10658-010-9690-5 . Maxted N, Kell S, Ford-Lloyd B, Dulloo E, Toledo Á. Toward the systematic conservation of global crop wild relative diversity. Crop Sci. 2012;52(2):774–85. Pandey S, Ansari WA, Mishra VK, Singh AK, Singh M. Genetic diversity in Indian cucumber based on microsatellite and morphological markers. Biochem Syst Ecol. 2013;51:19–27. Savory EA, Adhikari BN, Hamilton JP, Vaillancourt B, Buell CR, Day B. (2012). mRNA-Seq analysis of the Pseudoperonospora cubensis transcriptome during cucumber (Cucumis sativus L.) infection. PLoS ONE, 7(4), e35796. Thomas CE. Downy mildew. Compendium of Cucurbit Diseases. APS; 1996. pp. 25–7. Gao X, Guo P, Wang Z, Chen C, Ren Z. Transcriptome profiling reveals response genes for downy mildew resistance in cucumber. Planta. 2021;253(5):112. Upadhyaya HD, Gowda CLL, Sastry DVSSR. Plant genetic resources management: collection, characterization, conservation and utilization. J SAT Agricultural Res. 2008;6:16. Win KT, Vegas J, Zhang C, Song K, Lee S. QTL mapping for downy mildew resistance in cucumber via bulked segregant analysis using next-generation sequencing and conventional methods. Theor Appl Genet. 2017;130(1):199–211. Li H, Wang S, Chai S, Yang Z, Zhang Q, Xin H, Zhang Z. Graph-based pan-genome reveals structural and sequence variations related to agronomic traits and domestication in cucumber. Nat Commun. 2022;13(1):682. Yadav V, Wang Z, Guo Y, Zhang X. Comparative transcriptome profiling reveals the role of phytohormones and phenylpropanoid pathway in early-stage resistance against powdery mildew in watermelon (Citrullus lanatus L). Front Plant Sci. 2022;13:1016822. Yoshioka Y, Sakata Y, Sugiyama M, Fukino N. Identification of quantitative trait loci for downy mildew resistance in cucumber (Cucumis sativus L). Euphytica. 2014;198(2):265–76. Quesada-Ocampo LM, Granke LL, Olsen J, Gutting HC, Runge F, Thines M, Hausbeck MK. The genetic structure of Pseudoperonospora cubensis populations. Plant Dis. 2012;96(10):1459–70. Petrov L, Boogert K, Sheck L, Baider A, Rubin E, Cohen Y. (2000, March). Resistance to downy mildew, Pseudoperonospora cubensis, in cucumbers. In VII Eucarpia Meeting on Cucurbit Genetics and Breeding 510 (pp. 203–210). Additional Declarations No competing interests reported. Supplementary Files GermplasmpassportdataSupplementarytable1.xlsx Supplementaryfig1.tif Supplementaryfig2.tif Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor invited by journal 13 Jan, 2026 Editor assigned by journal 06 Jan, 2026 Submission checks completed at journal 06 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8364842","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583396615,"identity":"174fe068-c3d4-47cc-9f48-cc3a1cde8cc2","order_by":0,"name":"Vivek Hegde","email":"","orcid":"","institution":"ICAR-Indian Institute of Horticultural Research","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Hegde","suffix":""},{"id":583396616,"identity":"1cb35224-2083-4d4d-8e5d-045ddde091fe","order_by":1,"name":"Vidya Sagar","email":"","orcid":"","institution":"ICAR-Indian Institute of Vegetable Research","correspondingAuthor":false,"prefix":"","firstName":"Vidya","middleName":"","lastName":"Sagar","suffix":""},{"id":583396617,"identity":"2c70fd0d-7221-4aee-bd61-72617c58a1bb","order_by":2,"name":"Vikrant Tomar","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Vikrant","middleName":"","lastName":"Tomar","suffix":""},{"id":583396618,"identity":"fed31fdc-82ba-4381-9eba-da068dce63de","order_by":3,"name":"Swagata Nandi","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Swagata","middleName":"","lastName":"Nandi","suffix":""},{"id":583396619,"identity":"fdf41812-6bee-4b97-a627-1f29cfabe365","order_by":4,"name":"Md. 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2","display":"","copyAsset":false,"role":"figure","size":382568,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of average AUDPC of downy mildew in IARI\u003c/p\u003e","description":"","filename":"Downymildewfigures2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/dd127048000ff3677e3d8af8.jpg"},{"id":101672374,"identity":"4be759c7-3c46-469d-a605-cab5f098caf2","added_by":"auto","created_at":"2026-02-02 12:59:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":386763,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of average PDI of downy mildew in IIHR\u003c/p\u003e","description":"","filename":"Downymildewfigures3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/e8f34c153c178c0877a84497.jpg"},{"id":101672375,"identity":"de52e69a-c0d3-4bf2-bdf1-4c379cdfbab7","added_by":"auto","created_at":"2026-02-02 12:59:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":382951,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of average AUDPC of downy mildew in IIHR\u003c/p\u003e","description":"","filename":"Downymildewfigures4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/266106317470d3a7ad5a7d41.jpg"},{"id":101753406,"identity":"2c86bec6-d079-4e7d-83aa-ec3bfa3fd902","added_by":"auto","created_at":"2026-02-03 10:39:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":383828,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of average PDI of downy mildew in IIVR\u003c/p\u003e","description":"","filename":"Downymildewfigures5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/31aca47836ceb02e3e935b37.jpg"},{"id":101672378,"identity":"119febe5-0f34-4bcd-ac06-b8ef7a86474a","added_by":"auto","created_at":"2026-02-02 12:59:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":375370,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of average AUDPC of downy mildew in IIVR\u003c/p\u003e","description":"","filename":"Downymildewfigures6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/c7965fa992aeda3207f4ddf7.jpg"},{"id":101753471,"identity":"07c503ca-9a4a-4f1b-a96d-45d26a47afb8","added_by":"auto","created_at":"2026-02-03 10:40:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":783043,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation study among PDI, AUPDC and location\u003c/p\u003e","description":"","filename":"Downymildewfigures7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/2213bce393e1b9189a45c555.jpg"},{"id":101755744,"identity":"25175240-dacf-4aa5-9526-17ea831505dc","added_by":"auto","created_at":"2026-02-03 10:54:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4437826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/ee78ef6f-6bc9-4553-8ca6-24fbc6db82e7.pdf"},{"id":101753431,"identity":"c8c285a0-4a33-49d8-98d0-d0778a0b3a12","added_by":"auto","created_at":"2026-02-03 10:40:02","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11903,"visible":true,"origin":"","legend":"","description":"","filename":"GermplasmpassportdataSupplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/0decfdac3a19553b222924f3.xlsx"},{"id":101672381,"identity":"540eb7c9-6c81-4aaf-926a-ad5a5e69defe","added_by":"auto","created_at":"2026-02-02 12:59:50","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":611454,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/4a4d4e692a81de690038bec7.tif"},{"id":101672380,"identity":"3312fe22-f79e-445f-b445-98ff0bf3e94b","added_by":"auto","created_at":"2026-02-02 12:59:50","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1235888,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8364842/v1/137a50e576ee0d5d3e25fb68.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-environment evaluation for downy mildew disease incidence revealed the prevalence of multiple pathotypes and the need for region-specific breeding strategies in cucumber","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eOne of the most extensively grown and consumed vegetables worldwide, cucumbers (\u003cem\u003eCucumis sativus\u003c/em\u003e L.), a member of the Cucurbitaceae family, are valued for their economic, medicinal, and nutritional value. It is produced in more than 80 nations, with China, India, and Turkey leading the world in production, which surpassed 91\u0026nbsp;million metric tons in 2022 (FAOSTAT, 2023). With its abundance of native cucumber landraces and wild relatives, especially in the northeastern, eastern, and central Himalayan regions, India is the largest centre of cucumber diversity and these resources are crucial for crop improvement (Pandey et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDowny mildew (DM), one of the most destructive foliar diseases affecting cucumbers worldwide, is a significant production constraint caused by the oomycete \u003cem\u003ePseudoperonospora cubensis\u003c/em\u003e. High humidity and moderate temperatures are ideal for the pathogen's growth, which negatively impacts photosynthesis and can cause sensitive cultivars to lose up to 70\u0026ndash;90% of their yield (Thomas, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The disease is common in India in both the rabi and kharif seasons, especially in humid subtropical agro-ecologies or under protected agriculture (Ghosh et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Fungicide-based management is unsustainable from an economic and environmental standpoint, particularly for smallholder farmers. As a result, breeders opt for host plant resistance, which provides a financially and environmentally sound approach (Badr and Mohamed, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high pathogenic variability and quick evolution of \u003cem\u003eP. cubensis\u003c/em\u003e races present another difficulty in breeding for DM resistance (Lebeda, 2001). Integrated omics methods and high-throughput phenotyping and genotyping techniques are revolutionising the identification of resistance loci. A number of genes linked to PR proteins, reactive oxygen species (ROS) detoxification, and hormone signalling have been shown to be differently expressed in transcriptome profiling of resistant cucumber lines infected with DM (Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These results are consistent with resistance mechanisms observed in squash and melon, as well as other cucurbit crops (Yadav et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The potential for using Indian germplasm in genomic-assisted breeding is further enhanced by the availability of reference genomes and pan-genome resources for cucumber (Huang et al., 2009; Li et al., 2023).\u003c/p\u003e \u003cp\u003eThe underutilization of traditional Indian cucumber cultivars in mainstream breeding projects remains a significant obstacle, despite recent advancements. Despite having significant resistant features, their characteristics\u0026mdash;such as late flowering, low yield, and bitterness\u0026mdash;often cause them to be neglected (Pandey et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Finding farmer-favoured, resistant lines appropriate for target agro-ecologies will be made easier by combining participatory varietal selection with multi-location testing. Disease pressure from downy mildew is anticipated to increase due to the impending threat of climate change, which is likely to increase humidity and temperature variability (Chakraborty and Newton, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, Indian-origin cucumber germplasm presents a unique opportunity to identify climate-resilient resistance alleles, which are adaptable to a variety of agro-climatic stress zones. To utilise this latent genetic treasure, future initiatives should focus on creating core and mini-core collections of Indian cucumber varieties in addition to high-resolution phenotyping and predictive breeding models (Upadhyaya et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndian cucumber genetic resources offer a wealth of downy mildew resistance by combining traditional landraces with contemporary breeding possibilities. To fully realise their potential, a coordinated effort comprising field screening under multi-environmental evaluation is required. The purpose of this study is to determine stable, promising genotypes for use in breeding programs and to assess the level of downy mildew resistance in a large panel of Indian-originated cucumber genotypes across various agro-climatic regions.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials\u003c/h2\u003e \u003cp\u003eA total of 131 cucumber (\u003cem\u003eCucumis sativus\u003c/em\u003e L.) accessions were systematically documented and characterized based on passport data to assess the geographic and ecological breadth of genetic diversity across India. The germplasm predominantly comprised traditional landraces sourced from a wide range of agro-climatic zones, encompassing tropical, subtropical, humid, semi-arid, and temperate environments. \u003cb\u003eName and origin of accessions (state wise) is mentioned in germplasm passport data (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/b\u003e. The highest number of accessions originated from West Bengal (22 genotypes), a region characterised by a humid subtropical climate with high annual precipitation and prolonged monsoon periods, which are conducive to the proliferation of biotic stresses and the selection for disease resistance. Jharkhand (18 genotypes) and Bihar (10), representing the eastern plateau and hot sub-humid agro-ecologies, contributed accessions adapted to rainfed agriculture and intermittent drought stress. Kerala (13 genotypes), part of the humid tropics with uniformly high temperatures and rainfall, contributed unique genotypes suited to year-round cultivation under high humidity. Uttar Pradesh (9 genotypes), within the Indo-Gangetic plains and characterized by a dry sub-humid climate with distinct summer and winter seasons, offered genotypes with adaptation to variable thermal and photoperiodic regimes. Contributions from Karnataka (7) and Tamil Nadu (5) reflected germplasm from semi-arid and dry sub-humid zones of peninsular India, while Odisha (5), located in a moist sub-humid tropical belt, contributed accessions adapted to moderate rainfall and high humidity. Accessions from Maharashtra (4) and Rajasthan (3) encompassed hot semi-arid to arid zones, indicating potential tolerance to heat and soil moisture stress. Representation from Himachal Pradesh (3) and Uttarakhand (2), situated in temperate to humid subtropical hill ecosystems, provided germplasm adapted to mid-altitude thermal and photoperiodic conditions. Additionally, Haryana (2), Tripura (1), Goa (2), and union territories including Delhi (2) and Lakshadweep (1) contributed to the ecological and geographical span of the collection. Twelve accessions of unknown or composite origin were classified as \u0026lsquo;Others\u0026rsquo;. This spatial and climatic representation underscores the adaptability of cucumber to diverse environmental niches and reflects evolutionary selection pressures imposed by regional agro-ecological constraints. The assembled germplasm constitutes a critical resource for pre-breeding, trait dissection, and crop improvement efforts aimed at enhancing climate resilience, biotic stress tolerance, and local adaptability through targeted genetic interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Evaluation of the genotypes under natural epiphytotic conditions\u003c/h2\u003e \u003cp\u003eTwenty plants from each of the 130 genotypes were planted in the field from seeds. Two seeds per hill, spaced 60 cm apart, were sown on each side of the 1.5 m wide ridges. Among other cultural activities, fertilization and irrigation were practiced. During the Kharif season, when the natural load of DM was at its highest, the growing plants were exposed to field downy mildew infection without the application of fungicides to suppress this disease. The degree of disease signs on the top, middle, and bottom leaves of each plant was measured using a 0\u0026ndash;9 scale to record the disease incidence (Bidalmali et al., 2024)(Supplementary Fig.\u0026nbsp;1). According to Jenkins and Wehner (1983), disease scoring started one month after planting and continued at 15 days intervals until 75 days after sowing (DAS). The plants were classified into four categories based on the PDI, which was determined using symptomatic leaf area data (S1): resistant (0\u0026ndash;20%), moderately resistant (21\u0026ndash;40%), susceptible (41\u0026ndash;60%), and severely susceptible (\u0026gt;\u0026thinsp;60%) (4 clusters).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{D}\\text{I}\\:=\\frac{\\text{S}\\text{u}\\text{m}\\:\\text{o}\\text{f}\\:\\text{n}\\text{u}\\text{m}\\text{e}\\text{r}\\text{i}\\text{c}\\text{a}\\text{l}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}\\text{s}\\:}{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{l}\\text{e}\\text{a}\\text{v}\\text{e}\\text{s}\\:\\text{g}\\text{r}\\text{a}\\text{d}\\text{e}\\text{d}\\:\\times\\:\\:\\text{M}\\text{a}\\text{x}\\text{i}\\text{m}\\text{u}\\text{m}\\:\\text{r}\\text{a}\\text{t}\\text{i}\\text{n}\\text{g}\\text{s}}\\times\\:\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Artificial Screening for resistance to downy mildew\u003c/h2\u003e \u003cp\u003eTo achieve a more precise assessment of the resistance inheritance, the six selected genotypes were evaluated under net-house conditions, which helped reduce environmental fluctuations and ensured consistently high humidity for uniform disease development. Twenty seedlings per genotype were raised in net houses and irrigated manually on alternate days. Once a week, the plants received a nutrient solution containing 150 mg L⁻\u0026sup1; each of nitrogen, phosphorus, and potassium. When the seedlings reached 20\u0026ndash;25 days of age, they were inoculated on the abaxial leaf surface using a 1-L sprayer filled with a sporangial suspension (10,000 sporangia mL⁻\u0026sup1;). Before inoculation, small pinholes were gently made on the abaxial surface with fine needles to facilitate pathogen entry. Three to four days after the first appearance of symptoms, the inoculated plants were shifted to a high-humidity chamber to promote disease establishment. Disease severity was assessed on the eighth day using a 0\u0026ndash;4 linear rating scale and the percentage disease index (PDI) was calculated as described by Cohen et al. (2000) after 15 and 30 days of inoculation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The area under disease progress curve (AUDPC):\u003c/h2\u003e \u003cp\u003eThe AUDPC was calculated using the following formula based on the percent disease index (Shaner and Finney, 1977; Jeger and Viljanen-Rollinson, 2001).\u003c/p\u003e \u003cp\u003eAUDPC=\u0026sum; [{Xi\u0026thinsp;+\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Xi\u0026thinsp;+\u0026thinsp;1/ 2} \u0026#119909; {ti\u0026thinsp;+\u0026thinsp;1\u0026thinsp;\u0026minus;\u0026thinsp;ti}]\u003c/p\u003e \u003cp\u003eWhere Xi\u0026thinsp;=\u0026thinsp;disease index expressed as a portion at ith observation, ti\u0026thinsp;=\u0026thinsp;age of the plant at i\u003csup\u003eth\u003c/sup\u003e observation, and n\u0026thinsp;=\u0026thinsp;total number of observations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis:\u003c/h2\u003e \u003cp\u003eTo assess the complex genotype-by-environment interactions and rank cucumber accessions for Downy Mildew (DM) resistance, a multivariate analytical framework was employed. The Augmented ANOVA was conducted on PDI) and AUDPC data collected for New Delhi (IARI), Bengaluru (IIHR) and Varanasi (IIVR) separately. Upon detecting significant genotypic differences, post hoc multiple comparison tests (Least Significant Difference) were conducted to determine the statistical significance of differences between genotypes and checks. To explore genetic diversity and patterns of disease response, hierarchical clustering was performed using Euclidean distance and Ward\u0026rsquo;s linkage which results resistance-based clusters. Further, Principal Component Analysis (PCA) was employed to reduce dimensionality and extract principal components that explained the majority of variance in disease traits. The extracted PCs were used for K-means clustering for integrating multiple traits. To prioritize genotypes with consistent and broad-spectrum resistance, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied. These multi-criteria decision-making method ranked genotypes based on their closeness to the ideal disease resistance profile by combining different criteria. All the analysis has been carried out using R (version 4.2.2) software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clustering Pattern of Genotypes Based on Disease Progression Parameters at IARI\u003c/h2\u003e \u003cp\u003eA total of 130 cucumber genotypes were evaluated for their resistance to disease progression under field conditions at IARI and subsequently grouped into four distinct clusters based on their PDI at 30, 45, 60, and 75 days after sowing (DAS), as well as corresponding AUDPC values. Average PDI and AUDPC of downy mildew occurrence in IARI is shown via dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster I comprised 11 genotypes that exhibited moderate resistance, with average PDI values gradually increasing from 33.23 (30 DAS) to 73.16 (75 DAS), resulting in an overall mean PDI of 52.88 and an AUDPC of 2949.73. These genotypes maintained lower disease levels across all time points. In contrast, Cluster II comprised 42 highly susceptible genotypes, which reached maximum disease severity (100% PDI) by 60 DAS and maintained it through 75 DAS, with a mean PDI of 96.07 and the highest AUDPC value of 5,477.26. Cluster III consisted of 34 genotypes exhibiting intermediate disease progression, with an average PDI increasing from 45.67 (30 DAS) to 93.44 (75 DAS), averaging 72.04, and an AUDPC of 3918.24. Cluster IV, containing 43 genotypes, also showed high susceptibility with a mean PDI of 86.87 and AUDPC of 4772.87 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClustering of the genotypes based on the PDI and AUDPC at IARI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of Genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePDI 30 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDI 45 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDI 60 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePDI 75 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVG PDI MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAUDPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC527413, IC523673, IC527400, IC541436, IC4110211, IC354750, C588145, IC572024, IC354787, IC523678,IC421752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e52.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2949.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC410202, IC345264, IC344394, IC523694, IC448827, IC523680, IC344336, IC354806, IC410638, IC523681, IC248283, IC541234, IC410199, IC248274, IC344343, IC354793, IC523693, IC344357, IC248258, IC317485, IC344333, IC314341, IC523684, IC523701, IC523699, IC523695, IC523670, IC354782, IC523685, IC523675, IC527420, IC344371, IC527434, IC354819, IC523697\u003c/p\u003e \u003cp\u003eIC362446, IC317489, IC354801, IC354785, IC538130, IC538173, IC527391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5477.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC538137, IC523676, IC527394, IC344358, IC523672, IC324476, IC353325, IC527418, IC264979, IC588144, IC354796, Pusa Barkha, IC538155, IC527419, IC410654, IC525746, IC523671, IC523698, IC523691, IC354749, IC588147, IC588146, IC331940, IC538145, IC371736, IC538121, IC410657, IC538158, IC523677, IC523687, IC557170, IC421146, IC255441, IC527404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3918.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC410682, IC371758, Japanese Long Green, IC335311, IC523683, IC527405, IC523688, IC410658, IC354797, IC527395, IC538147, IC354816, IC354815, EC1041438, IC331878, IC527412, IC527396, IC371705, IC410617, IC527402, IC523700, IC523689, IC527431, IC371751, Pusa Uday, Pusa Long Green, IC354828, IC354753, IC410215, IC264796, IC527410, IC538126, IC255800, IC276392, IC371738, IC354757, IC523674, IC527423, IC523679, IC523682, IC523690, IC523692, IC354788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4772.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Clustering Pattern of Genotypes Based on Disease Progression Parameters at IIHR\u003c/h2\u003e \u003cp\u003eAt the IIHR location, the same set of genotypes was clustered into four groups based on PDI and AUDPC to evaluate location-specific responses. Average PDI and AUDPC of downy mildew occurrence in IIHR is shown via dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster I comprised 7 genotypes that exhibited moderate resistance in the early stages (PDI 6.75 at 30 DAS) but showed substantial disease escalation by 75 DAS (PDI 90.05), resulting in a mean PDI of 44.56 and an AUDPC of 1971.25. Cluster II, comprising 34 genotypes, exhibited a progressive increase in disease incidence but remained moderately susceptible overall, with an average PDI of 37.51 and an AUDPC of 1651.98. Notably, Cluster III, with 29 genotypes, demonstrated the highest level of resistance, exhibiting negligible disease incidence at early stages (PDI\u0026thinsp;=\u0026thinsp;0 and 0.43 at 30 and 45 DAS, respectively), and maintaining lower levels even at 75 DAS (PDI 50.66), with a mean PDI of 17.29 and AUDPC of 675.98. Cluster IV comprised the largest number of genotypes (60), which exhibited moderate susceptibility, characterized by a steady increase in disease severity, culminating in an average PDI of 28.03 and AUDPC of 1,155.69 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings suggest that several genotypes, particularly those in Cluster III, hold considerable promise for developing resistant cultivars under the agro-climatic conditions of southern India.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClustering of the genotypes based on the PDI and AUDPC at IIHR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of Genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePDI 30 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDI 45 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDI 60 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePDI 75 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVG PDI MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAUDPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC523683, IC331878, IC527434, IC538155, IC264796, IC354787, IC523674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1971.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC448827, IC354750, IC527423, IC538130, IC371758, IC410682, IC371738, IC527402, IC523681, IC538121, IC410199, IC354819, IC354801, IC354749, IC410654, IC354806, IC354785, IC248274, IC354793, IC354797, IC354828, IC523693, IC523694, IC415530, IC354816, IC410202, IC588144, IC541234, IC588146, Pusa Long Green, IC353325, IC523682, IC354788, IC354753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1651.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC523700, IC410215, IC421752, IC523676, IC523698, IC527431, IC527400, IC523701, IC523687, Japanese Long Green, IC255800, IC344394, IC421146, IC421769, IC588147, IC314341, IC572024, IC410211, IC541436, IC344336, IC538158, IC344357, IC324476, IC588145, IC527419, IC344343, IC344333, IC317485, IC371751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e675.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC317489, IC354757, IC523670, IC523684, IC345264, IC248283, IC538126, IC276392, IC354796, IC410617, IC255746, IC523690, IC255441, IC527412, IC523689, IC354782, IC523685, IC527395, IC538145, IC362446, IC527391, IC203853, IC523692, IC371736, IC523699, IC410638, IC523675, IC523678, IC557170, IC527394\u003c/p\u003e \u003cp\u003eIC527418, IC344358, IC523679, IC527410, IC527396, IC410657, IC527413, IC527404, IC527420, IC523677, IC527405, IC523673, IC538147, Pusa Uday, IC523697, IC523688, IC523671, IC331940, Pusa Barkha, IC248258, IC335311, IC523691, IC538137, IC344371, IC538173, IC523695, IC523680, IC410658, IC371705, IC354815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1155.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Clustering Pattern of Genotypes Based on Disease Progression Parameters at IIVR\u003c/h2\u003e \u003cp\u003eThe clustering of cucumber genotypes based on disease parameters at IIVR further validated the differential resistance observed across locations. Average PDI and AUDPC of downy mildew occurrence in IIVR is shown via dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e,\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCluster I, comprising 24 genotypes, exhibited moderate disease progression, with average PDI values ranging from 0.75 (30 DAS) to 93.85 (75 DAS), resulting in a mean PDI of 55.49 and an AUDPC of 2454.95. Cluster II, comprising 35 genotypes, was the most resistant group identified at IIVR, characterized by negligible early-stage infection (PDI\u0026thinsp;=\u0026thinsp;0 at 30 DAS) and limited progression up to 75 DAS (PDI\u0026thinsp;=\u0026thinsp;28.70), resulting in a mean PDI of 13.83 and AUDPC of 487.82. Cluster III comprised 30 genotypes exhibiting intermediate resistance, as indicated by a mean PDI of 28.13 and an AUDPC of 938.94. Cluster IV comprised 41 genotypes with higher susceptibility, as disease severity progressed to an average PDI of 41.10 and AUDPC of 1578.21. The clustering results at IIVR emphasize the location-specific performance of genotypes and the importance of multi-environment testing to identify genotypes with stable resistance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The consistently low AUDPC values of Cluster II genotypes across locations suggest their potential utility as donors for resistance breeding and incorporation into integrated disease management strategies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClustering of the genotypes based on the PDI and AUDPC at IIVR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of Genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePDI 30 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDI 45 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDI 60 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePDI 75 DAY MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVG PDI MEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAUDPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC588147, IC523700, IC538126, IC410215, IC264979\u003c/p\u003e \u003cp\u003eIC345264, IC344333, IC523694, IC354785, IC255441\u003c/p\u003e \u003cp\u003eIC523691, IC523675, IC344394, IC344343, IC335311\u003c/p\u003e \u003cp\u003eIC523689, IC527420, IC410682, IC354749, IC203853\u003c/p\u003e \u003cp\u003eIC541234, IC344336, IC523674, IC354753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2454.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC527410, EC1041438, IC371705, IC527419, IC538121\u003c/p\u003e \u003cp\u003eIC371751, IC527400, IC527405, IC538173, IC527391\u003c/p\u003e \u003cp\u003eIC588146, IC523677, IC572024, IC527413, IC538147\u003c/p\u003e \u003cp\u003eIC523698, IC523680, IC527404, IC523682, IC527431, IC410658, IC538130, IC527412, IC353325, IC523683\u003c/p\u003e \u003cp\u003eIC354788, IC410657, IC248283, IC588144, IC354828\u003c/p\u003e \u003cp\u003eIC331940, IC538145, IC523678, IC264796, IC255746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e487.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC538158, IC557170, IC541436, IC371738, IC527402\u003c/p\u003e \u003cp\u003eIC354782, IC410617, IC354796, IC276392, IC538137\u003c/p\u003e \u003cp\u003eIc527404, IC538155, IC448827, IC523681, IC410211\u003c/p\u003e \u003cp\u003ePahari Harit, IC248258, IC421752, IC523687, IC354797\u003c/p\u003e \u003cp\u003eIC371736, IC523688, IC523671, IC331878, IC523701\u003c/p\u003e \u003cp\u003eIC410199, IC371758, IC523693, IC324476, IC344371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e938.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC410202, IC248274, IC317489, IC354806, IC354801\u003c/p\u003e \u003cp\u003eIC523679, IC523699, IC410654, IC523690, IC362446\u003c/p\u003e \u003cp\u003eIC523685, IC588145, IC354757, IC527434, IC317485\u003c/p\u003e \u003cp\u003eIC527396, IC354819, IC354816, IC255800, IC523684\u003c/p\u003e \u003cp\u003eIC527394, Pusa Uday, IC527418, IC354815, IC344357\u003c/p\u003e \u003cp\u003eIC523695, IC410638, IC523697, IC523676, IC314341\u003c/p\u003e \u003cp\u003eIC527395, IC354750, IC354787, IC421146, IC344358\u003c/p\u003e \u003cp\u003ePusa Long Green, IC523672, Pusa Barkha, IC354793\u003c/p\u003e \u003cp\u003eIC523670, IC523692, IC527423, IC523673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e78.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1578.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Artificial inoculation\u003c/h2\u003e \u003cp\u003eArtificial inoculation was carried out on both resistant and susceptible genotypes in three sets. Based on the observations from artificial inoculation, the resistant genotype IC527413 maintained a very low average disease score of 0.80 (PDI 20) at 15 days after inoculation, and an average score of 1.10 (PDI 27.50) at 30 days after inoculation. But genotype IC523675 had high average disease score of 3.433 (PDI 85.83) after 15 days of inoculation and average disease score of 3.60 (PDI 90.00) after 30 days of inoculation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage disease score and PDI of selected genotypes after artificial inoculation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15 Days post inoculation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e30 Days post inoculation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC527413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.800\u0026thinsp;\u0026plusmn;\u0026thinsp;0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.000\u0026thinsp;\u0026plusmn;\u0026thinsp;1.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e27.500\u0026thinsp;\u0026plusmn;\u0026thinsp;2.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC523673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.200\u0026thinsp;\u0026plusmn;\u0026thinsp;0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.000\u0026thinsp;\u0026plusmn;\u0026thinsp;1.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.567\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e39.167\u0026thinsp;\u0026plusmn;\u0026thinsp;2.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC541436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.133\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.333\u0026thinsp;\u0026plusmn;\u0026thinsp;0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.700\u0026thinsp;\u0026plusmn;\u0026thinsp;0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e42.500\u0026thinsp;\u0026plusmn;\u0026thinsp;1.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC354782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e79.167\u0026thinsp;\u0026plusmn;\u0026thinsp;1.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.733\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e93.333\u0026thinsp;\u0026plusmn;\u0026thinsp;2.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC523685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e79.167\u0026thinsp;\u0026plusmn;\u0026thinsp;4.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.500\u0026thinsp;\u0026plusmn;\u0026thinsp;0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e87.500\u0026thinsp;\u0026plusmn;\u0026thinsp;3.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC523675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.433\u0026thinsp;\u0026plusmn;\u0026thinsp;0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e85.833\u0026thinsp;\u0026plusmn;\u0026thinsp;5.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.600\u0026thinsp;\u0026plusmn;\u0026thinsp;0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e90.000\u0026thinsp;\u0026plusmn;\u0026thinsp;3.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation study\u003c/h2\u003e \u003cp\u003eThe correlation matrix shows strong positive relationships among all PDI observations recorded at 30, 45, 60, and 75 DAS, with correlations increasing as the disease progresses. AVG PDI and AUDPC also exhibit very high correlations with all individual PDI time points, indicating consistent disease development patterns. Scatterplots show distinct clustering base on locations, suggesting location-specific disease pressure. Density plots further highlight differences in disease severity distribution across sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCucumis species are attacked by pathotype 3 (clade 2) of the pathogen, while Cucurbita species are attacked by pathotype 6 (clade 1). The comprehensive evaluation of 131 cucumber (\u003cem\u003eCucumis sativus\u003c/em\u003e L.) accessions across three contrasting agro-ecological environments\u0026mdash;New Delhi (IARI), Bengaluru (IIHR), and Mukteshwar (IIVR)\u0026mdash;enabled a robust assessment of phenotypic diversity for resistance to downy mildew (\u003cem\u003ePseudoperonospora cubensis\u003c/em\u003e), a major foliar disease-causing significant yield and quality losses in cucurbits globally (Lebeda and Cohen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The use of natural epiphytotic conditions and standardized disease scoring at progressive crop growth stages allowed the capture of temporal disease dynamics and the identification of genotypes with early-stage tolerance, delayed disease onset, or reduced final disease severity.\u003c/p\u003e \u003cp\u003eThe clustering analysis based on percent disease index (PDI) and area under the disease progress curve (AUDPC) revealed substantial intra- and inter-location variation in disease progression. At \u003cb\u003eIARI\u003c/b\u003e, four clusters were delineated, with \u003cb\u003eCluster I\u003c/b\u003e comprising 11 genotypes exhibiting moderate resistance (mean PDI: 52.88; AUDPC: 2949.73). Genotypes such as \u003cb\u003eIC527413\u003c/b\u003e, \u003cb\u003eIC523673\u003c/b\u003e, and \u003cb\u003eIC541436\u003c/b\u003e maintained relatively low PDI values through successive disease assessments. Notably, \u003cb\u003eIC541436\u003c/b\u003e, a landrace from a high-humidity zone, also showed lower disease pressure at IIVR, indicating partial stability. In contrast, \u003cb\u003eCluster II\u003c/b\u003e, comprising 42 genotypes, exhibited a rapid increase in PDI, reaching 100% by 60 DAS and remaining constant, indicative of systemic susceptibility under northern plains conditions (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eAt \u003cb\u003eIIHR\u003c/b\u003e, located in the humid tropics, disease progression was more gradual, reflecting the influence of diurnal humidity cycles and consistent temperatures on pathogen sporulation. \u003cb\u003eCluster III\u003c/b\u003e included 29 genotypes with remarkable resistance, registering minimal early-stage infection (PDI\u0026thinsp;\u0026le;\u0026thinsp;0.5 at 30\u0026ndash;45 DAS) and limited terminal severity (mean PDI: 17.29; AUDPC: 675.98). Among these, \u003cb\u003eIC523700\u003c/b\u003e, \u003cb\u003eIC410215\u003c/b\u003e, \u003cb\u003eIC421752\u003c/b\u003e, \u003cb\u003eIC314341\u003c/b\u003e, and \u003cb\u003eIC523676\u003c/b\u003e stood out for their sustained resistance. These genotypes originated from sub-humid regions and appear to possess traits such as reduced stomatal conductance or enhanced phytoalexin production, as proposed in previous resistance mechanisms against \u003cem\u003eP. cubensis\u003c/em\u003e (Savory et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The presence of \u003cb\u003eIC421752\u003c/b\u003e in resistant clusters at both IIHR and IIVR further strengthens its candidature as a stable donor.\u003c/p\u003e \u003cp\u003eThe high-elevation temperate environment of \u003cb\u003eIIVR\u003c/b\u003e provided a distinct disease pressure profile, where resistance was often expressed as delayed disease onset and lower terminal severity. \u003cb\u003eCluster II\u003c/b\u003e, with 35 genotypes, recorded the lowest average PDI (13.83) and AUDPC (487.82), indicative of strong field resistance under cool and moist conditions. Genotypes such as \u003cb\u003eIC588146\u003c/b\u003e, \u003cb\u003eIC527419\u003c/b\u003e, \u003cb\u003eIC588144\u003c/b\u003e, and \u003cb\u003eIC523698\u003c/b\u003e consistently ranked among the most resistant entries. Notably, \u003cb\u003eIC588146\u003c/b\u003e was grouped in resistant clusters at both IIHR and IIVR, exhibiting average AUDPC values below 700, signifying its broad adaptation and quantitative resistance. This level of consistency under different microclimates reflects durable resistance, which is often governed by additive gene effects and is less prone to pathogen race specificity (Thomas, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Win et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral genotypes showed \u003cb\u003emulti-environment stability\u003c/b\u003e in resistance expression, making them prime candidates for breeding programs. Specifically: \u003cb\u003eIC588146\u003c/b\u003e (Kerala): Resistant at both IIHR and IIVR; possibly carrying alleles selected under consistently humid tropical conditions. \u003cb\u003eIC527419\u003c/b\u003e: Moderate resistance at IARI and high resistance at IIVR, suggesting plasticity in resistance expression. \u003cb\u003eIC523701, IC314341, and IC588144\u003c/b\u003e: Repeatedly classified into resistant clusters, indicating broad-spectrum resistance potential. \u003cb\u003eIC421752\u003c/b\u003e and \u003cb\u003eIC523676\u003c/b\u003e: Displayed stable field resistance across IIHR and IIVR, validating their utility in different agro-ecosystems.\u003c/p\u003e \u003cp\u003eThe stability of these genotypes across multiple environments underlines their value as sources of quantitative resistance and reinforces the importance of multi-environment testing. This is particularly critical given the high G\u0026times;E interactions typical for downy mildew, where resistance can be environmentally masked or enhanced depending on local pathogen pressure, temperature, humidity, and leaf wetness duration (Badr and Mohamed, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Quesada-Ocampo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the results support earlier findings that resistance to \u003cem\u003eP. cubensis\u003c/em\u003e in cucumber is governed by multiple QTLs, with major loci identified on chromosomes 4, 5, and 7 (Yoshioka et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Win et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The identified resistant genotypes in this study may carry novel alleles or favorable combinations of these loci, which could be further validated through fine mapping, marker-assisted backcrossing, or genomic selection strategies. Given that several of the resistant accessions are landraces from high-disease-pressure zones, they also represent important reservoirs of co-adapted gene complexes, including those conferring disease escape and tolerance under abiotic stress (Maxted et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates that Indian cucumber landraces represent a rich and underutilized genetic resource for downy mildew resistance breeding. The identification of genotypes with consistent resistance across diverse environments\u0026mdash;such as \u003cb\u003eIC588146\u003c/b\u003e, \u003cb\u003eIC527419\u003c/b\u003e, and \u003cb\u003eIC421752\u003c/b\u003e\u0026mdash;provides a foundation for both pre-breeding and varietal development efforts. These accessions should be prioritized for molecular characterization, resistance pyramiding, and deployment in integrated disease management programs tailored to region-specific pathogen profiles and agro-ecological conditions.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eClinical trial number:\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003ePermissions to collect the plants/plant parts:\u003c/h2\u003e\n\u003cp\u003eAll the relevant permissions for the collection of cucumber seeds from different parts of India were obtained and done under the aegis of ICAR-NBPGR, New Delhi\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe research work was conducted with financial support from the CRP on Agrobiodiversity (Cucumber) of the Indian Council of Agricultural Research (ICAR).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e**Vivek Hegde** and **Shyam Sundar Dey** designed the study and coordinated the multi-location evaluations. **Vivek Hegde** , **Vidya Sagar** , **Vikrant** conducted the field experiments, disease scoring, and data collection across locations. **Yeasin** and **R. K. Paul** performed the statistical analyses, including clustering and AUDPC calculations. **Amrita Das** and **Pragya Ranjan** assisted with germplasm management, phenotyping, and laboratory support. **Chithra Devi Pandey** and **Sushil Pandey** contributed to field evaluation and phenotype validation. **T. K. Behera** and **G. P. Singh** provided supervision, resources, and critical manuscript revisions. **Vivek Hegde** prepared the original manuscript draft with inputs from **Swagata Nandi** and **Shyam Sundar Dey** . All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors are thankful to ICAR-IARI, New Delhi; ICAR-IIHR, Bangalore; ICAR-IIVR, Varanasi for providing the research facilities.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCall AD, Criswell AD, Wehner TC, Klosinska U, Kozik EU. Screening cucumber for resistance to downy mildew caused by Pseudoperonospora cubensis (Berk. and Curt.) Rostov. Crop Sci. 2012;52(2):577\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCall AD, Criswell AD, Wehner TC, Ando K, Grumet R. Resistance of cucumber cultivars to a new strain of cucurbit downy mildew. 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Euphytica. 2014;198(2):265\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuesada-Ocampo LM, Granke LL, Olsen J, Gutting HC, Runge F, Thines M, Hausbeck MK. The genetic structure of Pseudoperonospora cubensis populations. Plant Dis. 2012;96(10):1459\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrov L, Boogert K, Sheck L, Baider A, Rubin E, Cohen Y. (2000, March). Resistance to downy mildew, Pseudoperonospora cubensis, in cucumbers. In \u003cem\u003eVII Eucarpia Meeting on Cucurbit Genetics and Breeding 510\u003c/em\u003e (pp. 203\u0026ndash;210).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cucumber, Downy mildew, Resistance breeding, multi-location evaluation, Genetic diversity","lastPublishedDoi":"10.21203/rs.3.rs-8364842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8364842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDowny mildew (DM), caused by \u003cem\u003ePseudoperonospora cubensis\u003c/em\u003e, is a major constraint in cucumber (\u003cem\u003eCucumis sativus L.\u003c/em\u003e) production, particularly in humid and subtropical agro-ecologies. To identify stable sources of resistance and understand genotype-by-environment interactions, a comprehensive evaluation of 131 cucumber accessions was conducted across three contrasting agro-climatic locations in India: New Delhi (IARI), Bengaluru (IIHR), and Varanasi (IIVR). Field evaluations under natural epiphytotic conditions and artificial inoculations were performed, and disease incidence was assessed using per cent disease index (PDI) and area under disease progress curve (AUDPC). Clustering analysis revealed significant genetic variability for DM resistance across environments. At IIHR, 29 genotypes, including IC523700, IC421752, and IC314341, exhibited high resistance (mean PDI\u0026thinsp;\u0026le;\u0026thinsp;17.3; AUDPC\u0026thinsp;\u0026lt;\u0026thinsp;700), while IIVR identified 35 highly resistant accessions with a mean PDI of 13.8 and AUDPC of 487.8. Several landraces, notably IC588146 and IC421752, demonstrated stable resistance across two or more locations, highlighting their potential as broad-spectrum resistance donors. The observed location-specific disease responses underline the prevalence of diverse DM pathotypes and the necessity for region-tailored breeding strategies. The study emphasises the value of indigenous germplasm in developing climate-resilient, disease-resistant cultivars through multi-location screening and genomic approaches. Identified resistant accessions provide a valuable foundation for marker-assisted selection and breeding programs targeting durable DM resistance in cucumber.\u003c/p\u003e","manuscriptTitle":"Multi-environment evaluation for downy mildew disease incidence revealed the prevalence of multiple pathotypes and the need for region-specific breeding strategies in cucumber","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 12:59:45","doi":"10.21203/rs.3.rs-8364842/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-25T13:18:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T05:25:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T04:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286769354321457196912183193097304079512","date":"2026-02-08T14:25:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1582098752431631190625517240801866262","date":"2026-01-31T06:37:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T07:52:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T05:28:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T03:46:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T05:47:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Plants","date":"2026-01-06T05:41:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d44eb87e-e904-4476-b730-1baebb4dc7c3","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T07:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 12:59:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8364842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8364842","identity":"rs-8364842","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}