Before the revision, nine Peronospora species had been validly described from Veronica, although about 500 Veronica species had been reported as hosts of downy mildew. Many earlier identifications assigned a single pathogen to numerous hosts within the plant family. The authors note that recent studies have instead shown that most Peronospora species are highly host-specific, making re-examination of host–pathogen relationships within Veronica necessary.

Material came from Eurasia, Oceania and South America, including Argentina, Australia, Austria, the Czech Republic, Germany, Greece, Palestine, South Korea, Spain, Sweden and Switzerland. The investigators examined conidiophores, conidia and, where available, oospores from dried herbarium material. They also sequenced the nuclear ITS and nrLSU regions and the mitochondrial COX1, COX2, COX2-1 spacer, NAD1 and RPS10 regions. The concatenated phylogenetic dataset comprised 104 sequences spanning 5,767 aligned sites.

Minimum Evolution, Maximum Likelihood and Bayesian analyses produced no significant topological conflicts. The reconstruction identified 17 Peronospora lineages on Veronica, comprising seven previously described species, one newly combined species and 10 previously undescribed species. One lineage was represented only by DNA sequences and lacked specimens for morphological analysis, so the researchers formally described nine rather than 10 new species. Based on phylogenetic results and morphological differences, they accepted 16 species: seven previously described taxa and nine new species.

The new taxa were Peronospora brevibrachia, P. chionodendron, P. fidelia, P. gracilis, P. microspora, P. obscura, P. petricosa, P. sagittaria and P. seminaria. Each was associated with a particular Veronica host in the specimens examined, including V. chamaedrys for P. brevibrachia, V. verna for P. chionodendron, V. triphyllos for P. fidelia and V. praecox for P. gracilis. The revision also showed that several pathogen–host records had previously been combined too broadly. P. agrestis was restricted in the examined material to V. polita and the closely related V. filiformis and V. persica, while the pathogen on V. arvensis was genetically distinct. P. arvensis was likewise found principally on its type host, V. hederifolia, whereas the pathogen on V. triphyllos was distinct.

Morphology and host identification were important because some Veronica species are difficult to distinguish, and several specimens previously assigned to different hosts or pathogens represented other taxa. DNA barcoding of the host plants helped resolve these ambiguities. For example, specimens labelled V. sublobata differed from V. hederifolia by only two ITS nucleotides and were identical to V. hederifolia in the matK barcode. In another case, a sequence assigned to P. aquatica from V. anagallis-aquatica clustered with the distinct pathogen P. sagittaria from V. catenata. Host ITS and matK sequences supported the conclusion that the original host identification was mistaken.

The study therefore combined pathogen and host molecular identification with oomycete morphology rather than relying on either plant names or a single barcode locus alone. Several newly recognised species were close relatives whose nuclear sequences alone did not provide sufficient resolution. P. fidelia and P. gracilis differed by two gaps in ITS and one base in LSU D1–3, with no variation in LSU D6–8, but substantial differences occurred in four mitochondrial loci. The pathogen on V. triloba and P. veronicae-cymbalariae showed a comparable pattern, supporting the use of multiple loci for taxonomic separation of closely related downy mildews.

The study also recorded what the authors interpreted as host shifts to ornamental plants. Peronospora palustris, previously reported only from V. scutellata, was found on Veronica armstrongii, formerly Hebe armstrongii, and V. diosmifolia, formerly H. diosmifolia. P. petricosa, found on V. arvensis in Europe, Asia, South America and Australia, was also detected on V. odora, formerly H. buxifolia, in Australia. Because the natural ranges of the Hebe species do not overlap with those of the pathogens’ principal hosts, and no native Peronospora species are confirmed from Oceania, the authors considered these associations evidence of recent host shifts rather than long-standing relationships.

The findings are bounded by the material available to the revision. One of the 17 detected lineages could not be described morphologically because no specimens were available, and the European record base was substantially larger than that from other regions. The study established genetic and morphological associations among available specimens; it did not experimentally test the host shifts or determine their effects on plant health, trade or disease spread. Even with those limitations, the revision shows that downy mildew diversity on Veronica is considerably greater than the previous count of nine recognised species and that many apparent broad host ranges reflected misidentification or lumping of distinct lineages.

Subject of Research: Taxonomic revision of Peronospora downy mildews infecting Veronica

Article Title: A revision of Peronospora species on Veronica unravels a species-rich group of downy mildew pathogens with host shifts to economically important ornamental plants

Article References: Mu, M., Choi, Y.-J., Brand, T., Ploch, S., Voglmayr, H., & Thines, M. (2026). A revision of Peronospora species on Veronica unravels a species-rich group of downy mildew pathogens with host shifts to economically important ornamental plants. IMA Fungus, 17, Article e186696. https://doi.org/10.3897/imafungus.17.186696

Image Credits: AI Generated

DOI: 10.3897/imafungus.17.186696

Keywords: Peronospora, Veronica, downy mildew, oomycetes, plant pathology, taxonomy, phylogenetics, host specificity, host shifts, ornamental plants, revision, species

The Gold Honor Medal is awarded annually and represents the pinnacle of recognition bestowed by the American Rose Society, the national organization dedicated to the appreciation, cultivation and study of roses. Selection is not taken lightly. Eight members of the society’s National Prizes and Awards Committee determine the recipient, and candidates must meet a demanding threshold: at least twelve years of service on a national level and a sustained record of diligently provided, outstanding meritorious service. Those criteria immediately distinguish lifetime contributors whose influence extends across the country rather than within a single region or institution, and they help explain why the medal has historically gone to individuals whose names are spoken with reverence among dedicated rose growers.

For Windham, the honor carried personal as well as professional significance. When he surveyed the list of distinguished rosarians who had received the medal before him, he described himself as beyond honored to join them in the distinction. He noted that working for the University of Tennessee Institute of Agriculture for as long as he did was a privilege, and he credited the countless colleagues, mentors and collaborators who made his achievements possible. That emphasis on collective effort reflects a career built as much on partnership and outreach as on laboratory science, a combination that ultimately defined his professional legacy.

Windham’s scientific portfolio reads like a catalog of the most damaging rose pathogens in North America. Over the course of his tenure, he studied and developed management strategies for black spot, Cercospora leaf spot and downy mildew, and more recently he turned his attention to rose rosette disease, in which he is now considered one of the nation’s leading authorities. Each of these diseases presents distinct challenges. Black spot, caused by the fungus Diplocarpon rosae, produces the familiar dark lesions and premature defoliation that weaken plants season after season. Cercospora leaf spot, driven by Cercospora rosicola, similarly mars foliage and can compromise plant vigor in humid climates. Downy mildew, caused by Peronospora sparsa, can defoliate roses rapidly under cool, moist conditions and is particularly feared in nursery production, where outbreaks can devastate inventory before symptoms are even visible.

Rose rosette disease, the most recent focus of Windham’s work, is arguably the most alarming threat facing roses in the United States today. The disease is associated with a virus transmitted by the eriophyid mite Phyllocoptes fructiplans, and it produces grotesque symptoms: excessive thorniness, distorted leaves, abnormal reddening of new growth and deformed, proliferating shoots that give the condition its name. There is no cure once a plant is infected, and affected roses typically decline and die within a few years. Because the microscopic mite vectors spread the pathogen efficiently, and because symptomatic plants can serve as reservoirs for new infections, management depends on early detection, rapid removal of infected plants and coordination across gardens, nurseries and landscapes. The disease has swept through rose plantings across much of the country, making authoritative expertise on its behavior and containment exceptionally valuable to both commercial growers and home gardeners.

That expertise did not remain confined to research plots. Windham joined the University of Tennessee Institute of Agriculture in 1985, and over the following decades he shared his research and expertise at thousands of events with producers, industry professionals and rose societies across the country. This kind of sustained engagement is the lifeblood of agricultural extension work, translating peer-reviewed findings into practical guidance that growers can apply in the field and gardeners can apply in their own backyards. For an ornamental crop like roses, which is cultivated by millions of hobbyists as well as by commercial nurseries, the gap between laboratory knowledge and everyday practice is bridged almost entirely by scientists willing to teach, listen and answer questions at meetings large and small.

Windham’s collaborative approach extended to the written record as well. In 2018, he partnered with Extension ornamental plant pathologist Alan Windham and other colleagues to write the e-book A Guide to Rose Diseases and Their Management, a resource designed to put diagnostic and control information directly into the hands of those who need it. Written guides of this kind serve a dual purpose: they consolidate scattered research findings into a coherent, accessible format, and they provide a durable reference that can be updated as new pathogens emerge and management recommendations evolve. For rose growers confronting an unfamiliar disease symptom, such a resource can mean the difference between an early, effective intervention and a lost planting.

Mark and Alan Windham retired from the University of Tennessee in 2022, but retirement has not diminished their advocacy. Both have continued to champion residential and commercial rose production across the Southeast and beyond, a region where heat, humidity and the continuing spread of rose rosette disease make disease management an ongoing concern. Their continued presence in the rose-growing community underscores a truth about plant pathology that extends well beyond a single institution: the relationships between researchers and the growers they serve do not end with a retirement ceremony, particularly when emerging threats still demand experienced eyes and hard-won judgment.

Institutional leaders were quick to connect the national honor to Windham’s long association with the University of Tennessee’s research infrastructure. Hongwei Xin, dean of UT AgResearch, noted that Windham conducted his solution-oriented research in Crossville, Tennessee, at the Plateau AgResearch and Education Center, and that his work helped establish the facility’s reputation at the forefront of rose disease research and outreach. Xin added that even after Windham’s retirement, the center considers it an honor to carry on the mission of providing data-driven information for those throughout the industry. Field research centers like the Plateau facility occupy a crucial position in plant pathology, offering the controlled outdoor conditions, disease nurseries and regional climate exposure that greenhouse work alone cannot replicate, and the diseases studied there reflect the real pressures growers face.

Praise from the institute’s top leadership accompanied the announcement as well. Keith Carver, UTIA senior vice chancellor and senior vice president, expressed gratitude on behalf of the institute for what he described as an exceptional research career, calling the award no small feat and stating that it honors a man who has elevated the university nationally for decades. Carver added that he could not think of anyone more deserving. The Gold Honor Medal is not, however, the only high distinction the American Rose Society has conferred on Windham. In 2023, the society awarded him the Joseph and Marion Kilma Medal for Excellence in Rose Education for his exceptional contributions to horticultural education. Receiving both medals during a single career places Windham among a small and select group of recipients worldwide, a dual recognition that mirrors the two halves of his professional life: rigorous research into the pathogens that plague roses, and tireless education of the people who grow them.

Subject of Research: Recognition of a plant pathologist's lifetime research on rose disease management

Article Title: Mark Windham receives Gold Honor Medal from the American Rose Society

Article References: Mark Windham receives Gold Honor Medal from the American Rose Society. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Mark Windham, American Rose Society, Gold Honor Medal, plant pathology, rose rosette disease, black spot, Cercospora leaf spot, downy mildew, University of Tennessee, horticulture, plant disease management, agricultural extension

The research, led by Marta Zaffaroni and colleagues, was carried out in partnership with a French wine cooperative. Rather than asking growers to react to a finished model, the team used an action-research approach: over four workshops, cooperative managers and a multidisciplinary group of researchers sat together to design deployment strategies that were not only scientifically interesting but genuinely feasible for a real business operating a real landscape. This participatory framing matters, because deployment strategies that look optimal on paper often fail when confronted with practical constraints such as market demands, varietal identity, labor and the patchwork of plots that make up a working vineyard.

Out of those workshops came six candidate strategies for planting resistant cultivars, abbreviated RC in the study. The scenarios shared a common logic: resistant vines would replace old vines on plots more than thirty years old, which are natural candidates for replanting. Within that shared frame, the strategies differed in pace and placement. Some involved planting resistant cultivars on a fixed share of the older plots each year, set at 3.3 percent, producing a gradual rollout over decades. Others concentrated resistant vines in no-treatment zones, areas where fungicide spraying is restricted or impossible because of proximity to watercourses or human dwellings. Some scenarios also imposed a maximum percentage of resistant vines allowed in the vineyard, capping the overall share of the landscape planted with the new varieties.

To evaluate these six strategies, the team turned to the landsepi model, a simulation platform designed to capture how pathogens spread and evolve across agricultural landscapes. Landsepi treats the landscape as a mosaic of fields and tracks both the epidemiological dynamics of disease and the evolutionary dynamics of the pathogen population, including the emergence and establishment of strains capable of overcoming host resistance. The researchers implemented the strategies within the actual landscape of the cooperative, so the simulations reflected the real spatial arrangement of plots rather than an idealized grid. This landscape-scale realism is a key strength, because the spatial distribution of resistant and susceptible hosts strongly influences how quickly a pathogen can adapt.

The assessment rested on four families of criteria. The first was disease control efficacy, meaning how well each strategy kept downy mildew damage below damaging thresholds. The second was resistance durability, essentially how long the resistant cultivars remained effective before virulent pathogen strains became established. The third was the reduction in fungicide use, a central motivation for planting resistant varieties in the first place. The fourth was economic performance, evaluated both for individual vineyards and for the cooperative as a whole. By combining epidemiological, evolutionary and economic indicators, the study captured trade-offs that any single metric would have missed.

When the results were presented back to the cooperative, the managers prioritized one strategy above the rest: planting resistant cultivars in the no-treatment zones. This choice illustrates the value of the participatory process. The no-treatment strategy was not necessarily the top performer on every simulated metric, but it fit the cooperative’s broader practical constraints, including regulatory restrictions on spraying near sensitive areas. Putting resistant vines exactly where fungicides cannot be used converts an agronomic liability into an opportunity, protecting those plots genetically while leaving the rest of the landscape under conventional management.

The exercise also opened two scientific questions that have received little attention in the literature on resistance deployment. The first concerns fungicides: should growers keep spraying, even on and around resistant vines? The simulations showed that fungicide application substantially decreases the risk of resistance breakdown. Fungicides complement the epidemiological control provided by resistant cultivars, and this protective effect is particularly pronounced when the fitness costs of virulence are low, meaning the pathogen pays little penalty for evolving the ability to attack resistant plants. In that situation, reducing the pathogen population through chemical control also shrinks the evolutionary opportunity for resistance-breaking strains to emerge and spread.

The second question concerns speed: is it better to plant resistant cultivars rapidly or gradually? The simulations generally found that the probability of pathogens adapted to resistant cultivars becoming established was lower with gradual deployment than with rapid deployment. Spreading the new varieties over time keeps the landscape mosaic diverse for longer, making it harder for a specialized pathogen strain to find enough compatible hosts. However, the study uncovered a subtler and somewhat counterintuitive pattern: when adaptation did occur, the time to establishment was shorter under gradual strategies once the mean cropping ratio of resistant cultivars reached fifty percent or more. In other words, a slow rollout lowers the odds of breakdown but can accelerate it in the scenarios where breakdown happens anyway, because the resistant share of the landscape keeps climbing over time.

Perhaps the most sobering finding is that these deployment choices, despite their clear individual effects, had only a marginal overall impact on disease control. The reason lies in compensatory processes that partly cancel each other out. Dilution effects, in which resistant hosts interrupt the transmission of the pathogen among susceptible ones, improve epidemiological control as the resistant share grows. At the same time, a growing resistant share raises both the probability that a resistance-breaking strain will appear and the speed at which it establishes once it does. Rapid versus gradual deployment, spraying versus not spraying, each shift the balance between these epidemiological and evolutionary forces, but the net effect on disease levels tends to even out. This does not mean deployment strategy is irrelevant; durability and the timing of breakdown still differ between scenarios, and those differences matter to growers whose livelihoods depend on a variety remaining effective for decades.

Beyond the specific findings for viticulture, the study is a demonstration of what participatory, landscape-scale modeling can achieve. By building the scenarios with the cooperative rather than for it, the researchers ensured that the strategies were implementable, and the cooperative gained a quantitative understanding of the trade-offs behind its replanting decisions. The approach is directly transferable to other crops and other diseases where resistant cultivars are a cornerstone of integrated disease management. As agriculture seeks to reduce its reliance on fungicides, pesticides and other chemical inputs, the lesson from this French wine cooperative is that sustainable disease control is not just a breeding problem or a modeling problem. It is a design problem, best solved when the models of scientists and the knowledge of farmers are combined from the very start.

Subject of Research: Participatory landscape-scale modeling of disease-resistant grapevine cultivar deployment strategies with a wine cooperative

Article Title: Co-designing deployment strategies for disease-resistant cultivars: Insights from a participatory landscape-scale modeling study with a wine cooperative

Article References: Zaffaroni, M., Alonso Ugaglia, A., Miclot, A.-S., Rimbaud, L., Papaïx, J., Rey, J.-F., & Fabre, F. (2026). Co-designing deployment strategies for disease-resistant cultivars: Insights from a participatory landscape-scale modeling study with a wine cooperative. PLOS Sustainability and Transformation, 5(10), e0000286. https://doi.org/10.1371/journal.pstr.0000286

Image Credits: AI Generated

DOI: 10.1371/journal.pstr.0000286

Keywords: grapevine, downy mildew, resistant cultivars, resistance durability, landscape modeling, participatory research, wine cooperative, fungicide reduction, pathogen evolution, sustainable agriculture, landsepi, France

The study, published in the International Journal of Data Science and Analytics, starts from a candid acknowledgment of a problem that plagues much of agricultural machine learning: the labels. True pathological observations of infection are rare and expensive, so the researchers instead derived their disease-risk annotations from fungicide-treatment records, the practical decisions growers made to protect their vines. This makes the labels management-oriented risk proxies rather than direct measurements of pathogen presence. The team is explicit about this distinction, and it matters, because a treatment record reflects a grower’s judgment as much as the biology of the disease itself. Any model trained on such data learns to predict management behavior conditioned on weather, not infection events in the strict epidemiological sense.

The proxy-label problem had a second wrinkle. The dataset contained separate annotations for downy mildew and powdery mildew, but the two agreed on 98.9 percent of observed days, and in the validation and test years they were identical. Rather than pretending the two diseases share an epidemiology, which they demonstrably do not, the researchers collapsed the labels into a single operational any-mildew target. This pragmatic choice reflects how growers actually experience the threat: either mildew is a concern today or it is not. But the authors are careful to note that the combined target cannot support claims about distinguishing the two diseases, and biological interpretation of model outputs must remain cautious.

With the target defined, the team built an event-based formulation. A new risk event is counted only after a minimum disease-free gap has elapsed, preventing a single prolonged risky period from fragmenting into dozens of trivial episodes. Models are then asked a genuinely anticipatory question: will a new event begin within the following three to seven days? This framing, borrowed from the early-prediction literature on time series, directly measures what daily classification cannot, namely whether a model can see an episode coming before it starts. The data were split chronologically, with 2020 and 2021 for training, 2022 for validation, and 2023 for testing, ensuring that models were always forecasting into the future rather than interpolating within a shuffled mixture of seasons.

The model comparison spanned the standard spectrum of tabular and sequence learners. Logistic regression provided a linear baseline, XGBoost represented gradient-boosted decision trees, and two deep sequence architectures, long short-term memory networks and temporal convolutional networks, captured temporal dependencies in the agro-meteorological streams. Crucially, the researchers compared three input representations: calendar-only features such as day-of-year, environmental-only variables from the vineyard sensors, and the two combined. This design allowed them to isolate how much predictive power genuinely comes from the weather, a question that many published disease-forecasting studies never ask.

The results were humbling for the environmental data. Calendar-only models were highly competitive, with calendar-based LSTM and TCN models detecting 7 of the 8 test events, while a simple monthly climatological baseline, essentially a lookup table of historical risk by month, still detected 6. Environmental-only models were substantially weaker across the board, and adding environmental variables to calendar information produced inconsistent gains that varied by model family rather than delivering a uniform improvement. In other words, much of what the sophisticated models learned may have been the seasonal rhythm of vineyard disease risk, a pattern that a calendar already encodes. The authors’ SHAP analysis, which attributes model predictions to input features, confirmed that the fitted models used both seasonal and meteorological information, but it did not reveal a single dominant or consistently interpretable environmental precursor, no silver-indicator variable that reliably signals an impending outbreak.

This seasonal confounding is arguably the study’s most important cautionary finding. Mildew risk in temperate vineyards follows the growing season, so any model with access to the date can achieve respectable performance without understanding humidity, leaf wetness, or temperature at all. Without explicit seasonal baselines, reported accuracies can flatter models that are merely reproducing climatology. The Skopje team’s insistence on comparing against calendar-only and climatological baselines offers a template for how disease-forecasting studies should be evaluated, and their results suggest that several celebrated successes in the literature may deserve a second look under this stricter light.

The event-based formulation also revealed trade-offs that daily classification obscures. When the researchers compared their event-based models against conventional daily-status classifiers, they found the two objectives capture different things and exhibit different profiles in point-wise accuracy, event coverage, and alert burden. A model can score well on daily labels while missing the onset of episodes entirely, or catch most onsets while flooding the grower with false alarms. Neither formulation proved uniformly superior, which means the choice between them should be driven by the operational goal: a grower deciding when to scout needs anticipation, while a system scheduling routine monitoring may prefer stable daily estimates. Sensitivity analyses reinforced this picture, showing that results depend materially on the historical window length, the prediction horizon, and the event-separation gap, parameters that previous studies often fixed arbitrarily.

For the wine industry, the practical implications are sobering but useful. The best models anticipated most risk episodes several days ahead, which is genuinely valuable lead time for scheduling sprays and scouting, and targeting interventions more precisely can reduce fungicide use, cost, and environmental impact. But the finding that environmental sensors added inconsistent value over the calendar suggests that in-vineyard IoT weather stations, increasingly common in precision-agriculture deployments, must justify their cost through demonstrable gains over seasonal baselines on each new dataset, not through generic claims. The study also underscores the value of the underlying agro-meteorological dataset introduced by Arvanitis and colleagues in 2025, whose IoT-derived environmental records made the analysis possible, and the Skopje team states that their processed data, event annotations, engineered features, and experimental scripts will be released in a public repository, with interim availability from the corresponding author.

More broadly, the work contributes to a growing movement in machine learning toward event-oriented evaluation, in which systems are judged not on per-instance accuracy but on their ability to anticipate discrete, consequential occurrences, whether disease outbreaks, forest fires, or equipment failures. By defining events explicitly, enforcing minimum gaps, and measuring event coverage against alert burden, the vineyard study offers a methodological blueprint that extends well beyond oenology. Its honest treatment of proxy labels, its insistence on seasonal baselines, and its refusal to overclaim interpretability from SHAP attributions model the kind of rigor that agricultural AI will need as it moves from papers to practice. The next mildew wave may still be hard to predict, but thanks to this work, we now know better how to ask the question, and better how to tell whether an answer is real.

Subject of Research: Event-based machine learning early warning of vineyard mildew disease risk from agro-meteorological time series

Article Title: Event-based early warning of vineyard disease risk from environmental time series

Article References: Dimitrovski, I., Kitanovski, I., Davcev, D., Kalajdziski, S., & Mitreski, K. (2026). Event-based early warning of vineyard disease risk from environmental time series. International Journal of Data Science and Analytics, 22(1), Article 317. https://doi.org/10.1007/s41060-026-01282-8

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01282-8

Keywords: machine learning, viticulture, downy mildew, powdery mildew, early warning systems, agro-meteorological time series, LSTM, temporal convolutional networks, XGBoost, SHAP, precision agriculture, disease forecasting