Insights from tuberose farmers: A survey study in Jashore District, Bangladesh.

To obtain information on the cultivation of tuberoses in Bangladesh's Jashore district, a study has been carried out to evaluate the economic and social standing of cultivators and examine the methods employed in production and farmers' attitudes towards field disease. The majority of surveyed farmers were men, aged between 30 and 40 years. Only 7.41% had a high level of education, and most had less than five years of experience in tuberose cultivation. On an annual basis, the total production cost amounted to Tk. 27,200 (bigha/year), yielding a net profit of Tk. 1,20,000 (bigha/year). 31.48% of farmers expressed the belief that diseases affecting tuberose originated from contaminated planting materials. A significant proportion (64.81%) of farmers used PGR to combat diseases affecting tuberose production. Government Agriculture Officers played a crucial role in assisting farmers with knowledge and guidance. Yet, most participants highlighted that the key to preventing diseases lies in the effective application of pesticides. The findings of this study can guide policymakers in implementing measures to enhance tuberose production and fortify the floriculture industry in Bangladesh.

Epidemiological and ecological consequences of virus manipulation of host and vector in plant virus transmission.

Many plant viruses are transmitted by insect vectors. Transmission can be described as persistent or non-persistent depending on rates of acquisition, retention, and inoculation of virus. Much experimental evidence has accumulated indicating vectors can prefer to settle and/or feed on infected versus noninfected host plants. For persistent transmission, vector preference can also be conditional, depending on the vector's own infection status. Since viruses can alter host plant quality as a resource for feeding, infection potentially also affects vector population dynamics. Here we use mathematical modelling to develop a theoretical framework addressing the effects of vector preferences for landing, settling and feeding-as well as potential effects of infection on vector population density-on plant virus epidemics. We explore the consequences of preferences that depend on the host (infected or healthy) and vector (viruliferous or nonviruliferous) phenotypes, and how this is affected by the form of transmission, persistent or non-persistent. We show how different components of vector preference have characteristic effects on both the basic reproduction number and the final incidence of disease. We also show how vector preference can induce bistability, in which the virus is able to persist even when it cannot invade from very low densities. Feedbacks between plant infection status, vector population dynamics and virus transmission potentially lead to very complex dynamics, including sustained oscillations. Our work is supported by an interactive interface https://plantdiseasevectorpreference.herokuapp.com/. Our model reiterates the importance of coupling virus infection to vector behaviour, life history and population dynamics to fully understand plant virus epidemics.

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model.

The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, susceptible/infected populations of host plant, susceptible/infected vectors population, and population of predator. The numerical performances through the LMBNN solver are observed for three different types of the nonlinear host-vector-predator model using the authentication, testing, sample data, and training. The proportions of these data are chosen as a larger part, i.e., 80% for training and 10% for validation and testing, respectively. The nonlinear host-vector-predator model is numerically treated through the LMBNNs, and comparative investigations have been performed using the reference solutions. The obtained results of the model are presented using the LMBNNs to reduce the mean square error (MSE). For the competence, exactness, consistency, and efficacy of the LMBNNs, the numerical results using the proportional measures through the MSE, error histograms (EHs), and regression/correlation are performed.

Global historic pandemics caused by the FAM-1 genotype of Phytophthora infestans on six continents.

The FAM-1 genotype of Phytophthora infestans caused late blight in the 1840s in the US and Europe and was responsible for the Irish famine. We sampled 140 herbarium specimens collected between 1845 and 1991 from six continents and used 12-plex microsatellite genotyping (SSR) to identify FAM-1 and the mtDNA lineage (Herb-1/Ia) present in historic samples. FAM-1 was detected in approximately 73% of the historic specimens and was found on six continents. The US-1 genotype was found later than FAM-1 on all continents except Australia/Oceania and in only 27% of the samples. FAM-1 was the first genotype detected in almost all the former British colonies from which samples were available. The data from historic outbreak samples suggest the FAM-1 genotype was widespread, diverse, and spread to Asia and Africa from European sources. The famine lineage spread to six continents over 144 years, remained widespread and likely spread during global colonization from Europe. In contrast, modern lineages of P. infestans are rapidly displaced and sexual recombination occurs in some regions.

The mitogenome of Phytophthora agathidicida: Evidence for a not so recent arrival of the "kauri killing" Phytophthora in New Zealand.

Phytophthora agathidicida is associated with a root rot that threatens the long-term survival of the iconic New Zealand kauri. Although it is widely assumed that this pathogen arrived in New Zealand post-1945, this hypothesis has yet to be formally tested. Here we describe evolutionary analyses aimed at evaluating this and two alternative hypotheses. As a basis for our analyses, we assembled complete mitochondrial genome sequences from 16 accessions representing the geographic range of P. agathidicida as well as those of five other members of Phytophthora clade 5. All 21 mitogenome sequences were very similar, differing little in size with all sharing the same gene content and arrangement. We first examined the temporal origins of genetic diversity using a pair of calibration schemes. Both resulted in similar age estimates; specifically, a mean age of 303.0-304.4 years and 95% HPDs of 206.9-414.6 years for the most recent common ancestor of the included isolates. We then used phylogenetic tree building and network analyses to investigate the geographic distribution of the genetic diversity. Four geographically distinct genetic groups were recognised within P. agathidicida. Taken together the inferred age and geographic distribution of the sampled mitogenome diversity suggests that this pathogen diversified following arrival in New Zealand several hundred to several thousand years ago. This conclusion is consistent with the emergence of kauri dieback disease being a consequence of recent changes in the relationship between the pathogen, host, and environment rather than a post-1945 introduction of the causal pathogen into New Zealand.

Study on the occurrence law and green control of grape gray mold from the perspective of ecological balance.

With the increase of grape planting years, the base number of pathogenic seedlings and insect population is gradually rising. In addition, the introduction, breeding system and control of seedlings are not standardized and other human factors, the occurrence of Botrytis cinerea(B.cinerea) on grape is becoming more and more serious, resulting in a prominent problem of yield decline. In this paper, the occurrence of B.cinerea was monitored and its control effect was tested from the perspective of ecological balance. Finally, the biological characteristics and control of B.cinerea were studied. The spore catcher was used to catch the pathogen spores of B. cinerea, and the amount of sporangium scattering reached its peak from August to September Spore scattering is affected by meteorological factors, and the temperature has reached a very significant level, and the low temperature and high humidity conditions are conducive to the disease; The results showed that the resistance frequency of 304 B.cinereastrains to carbendazim, boscalid, pyrimethanil was higher than 50%; the volatile compounds produced by yeast (Trichosproom sp.) YE-3-2 significantly inhibited the growth of B.cinerea (inhibition rate was 62.93%, according to the occurrence regularity of B.cinerea, the accurate and effective agricultural measures had a good control effect on B.cinerea, which could improve the quality of grape fruit and provide some help for the prevention of grape gray mold.

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models.

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.

A one-dimensional map to study multi-seasonal coffee infestation by the coffee berry borer.

The coffee berry borer (CBB, Hypothenemus hampei) is the most serious insect pest of coffee worldwide; understanding the dynamics of its reproduction is essential for pest management. The female CBB penetrates the coffee berry, eats the seed, and reproduces inside it. A mathematical model of the infestation progress of the coffee berry by the CBB during several coffee seasons is formulated. The model represents the interaction among five populations: uninfested, slightly infested, and severely infested coffee berries, and free and encapsulated CBBs. Coffee harvesting is also included in the model. A one-dimensional map is derived for tracking the population dynamics subject to certain coffee harvesting percentages over several seasons. Stability analysis of the map's fixed points shows that CBB infestation could be eliminated or controlled to a specific level over multiple seasons of coffee harvesting. However, the percent of coffee harvesting required is determined by the level of CBB infestation at the beginning of the first season and in some cases it is impossible to achieve that percentage.

Changes in global Orchidaceae disease geographical research trends: recent incidences, distributions, treatment, and challenges.

Many of the Orchidaceae species are threatened due to environmental changes and over exploitation for full fill global demands. The main objective of this article was critically analyzed the recent global distribution of Orchidaceae diversity, its disease patterns, microbial disease identification, detection, along with prevention and challenges. Critical analysis findings revealed that Orchidaceae growth and developments were affected indirectly or directly as a result of complex microbial ecological interactions. Studies have identified many species associated with orchids, some are pathogenic and cause symptoms such as soft rot, brown rot, brown spot, black rot, wilt, foliar, root rot, anthracnose, leaf spot. The review was provided the comprehensive data to evaluate the identification and detection of microbial disease, which is the most important challenge for sustainable cultivation of Orchidaceae diversity. Furthermore, this article is the foremost of disease triggering microbes, orchid relations, and assimilates various consequences that both promoted the considerate and facts of such disease multipart, and will permit the development of best operative disease management practices.

Real-time tracking of Tomato brown rugose fruit virus (ToBRFV) outbreaks in the Netherlands using Nextstrain.

Tomato brown rugose fruit virus (ToBRFV) is a Tobamovirus that was first observed in 2014 and 2015 on tomato plants in Israel and Jordan respectively. Since the first description, the virus has been reported from all continents except Oceania and Antarctica, and has been found infecting both tomato and pepper crops. In October 2019, the Dutch National Plant Protection Organization received a ToBRFV infected tomato sample as part of a generic survey targeting tomato pests. Presence of the virus was verified using Illumina sequencing. A follow-up survey was initiated to determine the extent of ToBRFV presence in the Dutch tomato horticulture and identify possible linkages between ToBRFV genotypes, companies and epidemiological traits. Nextstrain was used to visualize these potential connections. By November 2019, 68 companies had been visited of which 17 companies were found to be infected. The 50 ToBRFV genomes from these outbreak locations group in three main clusters, which are hypothesized to represent three original sources. No correlation was found between genotypes, companies and epidemiological traits, and the source(s) of the Dutch ToBRFV outbreak remain unknown. This paper describes a Nextstrain build containing ToBRFV genomes up to and including November 2019. Sharing data with this interactive online tool will enable the plant virology field to better understand and communicate the diversity and spread of this new virus. Organizations are invited to share data or materials for inclusion in the Nextstrain build, which can be accessed at https://nextstrain.nrcnvwa.nl/ToBRFV/20191231.

Can Winged Aphid Abundance Be a Predictor of Cucurbit Aphid-Borne Yellows Virus Epidemics in Melon Crop?

Aphid-borne viruses are frequent yield-limiting pathogens in open field vegetable crops. In the absence of curative methods, virus control relies exclusively on measures limiting virus introduction and spread. The efficiency of control measures may greatly benefit from an accurate knowledge of epidemic drivers, in particular those linked with aphid vectors. Field experiments were conducted in southeastern France between 2010 and 2019 to investigate the relationship between the epidemics of cucurbit aphid-borne yellows virus (CABYV) and aphid vector abundance. Winged aphids visiting melon crops were sampled daily to assess the abundance of CABYV vectors (Aphis gossypii, Macrosiphum euphorbiae and Myzus persicae) and CABYV was monitored weekly by DAS-ELISA. Epidemic temporal progress curves were successfully described by logistic models. A systematic search for correlations was undertaken between virus variables including parameters µ (inflection point of the logistic curve) and γ (maximum incidence) and aphid variables computed by aggregating abundances on periods relative either to the planting date, or to the epidemic peak. The abundance of A. gossypii during the first two weeks after planting was found to be a good predictor of CABYV dynamics, suggesting that an early control of this aphid species could mitigate the onset and progress of CABYV epidemics in melon crops.

Modelling the Effect of Incubation and Latent Periods on the Dynamics of Vector-Borne Plant Viral Diseases.

Most of the plant viral diseases spread through vectors. In case of the persistently transmitted disease, there is a latent time of infection inside the vector after acquisition of the virus from the infected plant. Again, the plant after getting infectious agent shows an incubation time after the interaction with an infected vector before it becomes diseased. The goal of this work is to study the effect of both incubation delay and latent time on the dynamics of plant disease, and accordingly a delayed model has been proposed. The existence of the equilibria, basic reproductive number ([Formula: see text]) and stability of equilibria have been studied. This study shows the relevance of the presence of two time delays, which may lead to system stabilization.

Computational models to improve surveillance for cassava brown streak disease and minimize yield loss.

Cassava brown streak disease (CBSD) is a rapidly spreading viral disease that affects a major food security crop in sub-Saharan Africa. Currently, there are several proposed management interventions to minimize loss in infected fields. Field-scale data comparing the effectiveness of these interventions individually and in combination are limited and expensive to collect. Using a stochastic epidemiological model for the spread and management of CBSD in individual fields, we simulate the effectiveness of a range of management interventions. Specifically we compare the removal of diseased plants by roguing, preferential selection of planting material, deployment of virus-free 'clean seed' and pesticide on crop yield and disease status of individual fields with varying levels of whitefly density crops under low and high disease pressure. We examine management interventions for sustainable production of planting material in clean seed systems and how to improve survey protocols to identify the presence of CBSD in a field or quantify the within-field prevalence of CBSD. We also propose guidelines for practical, actionable recommendations for the deployment of management strategies in regions of sub-Saharan Africa under different disease and whitefly pressure.

Effective Stochastic Algorithm in Disease Prediction.

Traditionally, the main process for olive fruit fly population monitoring is trap measurements. Although the above procedure is time-consuming, it gives important information about when there is an outbreak of the population and how the insect is spatially distributed in the olive grove. Most studies in the literature are based on the combination of trap and environmental data measurements. Strictly speaking, the dynamics of olive fruit fly population is a complex system affected by a variety of factors. However, the collection of environmental data is costly, and sensor data often require additional processing and cleaning. In order to study the volatility of correlation in trap counts and how it is connected with population outbreaks, a stochastic algorithm, based on a stochastic differential model, is experimentally applied. The results allow us to predict early population outbreaks allowing for more efficient and targeted spraying.

The spread of a wild plant pathogen is driven by the road network.

Spatial analyses of pathogen occurrence in their natural surroundings entail unique opportunities for assessing in vivo drivers of disease epidemiology. Such studies are however confronted by the complexity of the landscape driving epidemic spread and disease persistence. Since relevant information on how the landscape influences epidemiological dynamics is rarely available, simple spatial models of spread are often used. In the current study we demonstrate both how more complex transmission pathways could be incorpoted to epidemiological analyses and how this can offer novel insights into understanding disease spread across the landscape. Our study is focused on Podosphaera plantaginis, a powdery mildew pathogen that transmits from one host plant to another by wind-dispersed spores. Its host populations often reside next to roads and thus we hypothesize that the road network influences the epidemiology of P. plantaginis. To analyse the impact of roads on the transmission dynamics, we consider a spatial dataset on the presence-absence records on the pathogen collected from a fragmented landscape of host populations. Using both mechanistic transmission modeling and statistical modeling with road-network summary statistics as predictors, we conclude the evident role of the road network in the progression of the epidemics: a phenomena which is manifested both in the enhanced transmission along the roads and in infections typically occurring at the central hub locations of the road network. We also demonstrate how the road network affects the spread of the pathogen using simulations. Jointly our results highlight how human alteration of natural landscapes may increase disease spread.

Widespread occurrence of 'Candidatus Phytoplasma ulmi' in elm species in Germany.

BACKGROUND: 'Candidatus Phytoplasma ulmi' is the agent associated with elm yellows and has been categorised in the European Union as a quarantine pathogen. For central and northern European countries, information on the occurrence and distribution of the pathogen and its impact on elms is scarce, so a survey of native elm trees has been conducted in Germany. RESULTS: About 6500 samples from Ulmus minor, Ulmus laevis and Ulmus glabra, were collected nationwide. Phytoplasma detection was performed by applying a universal 16Sr DNA-based quantitative PCR (qPCR) assay and a novel 'Ca. P. ulmi' specific qPCR assay targeting the 16S-23S spacer region. Both assays revealed that 28% of the samples were infected by 'Ca. P. ulmi', but infection rates of the elm species and regional incidences differed. The phytoplasma presence in the trees was not correlated to disease-specific symptoms. The survey identified a regional disparity of infection which was high in east, south and central Germany, whereas only a few infected sites were found in the western and northern parts of the country. Monitoring the seasonal titre of 'Ca. P. ulmi' in an infected tree by qPCR revealed a high colonisation in all parts of the tree throughout the year. CONCLUSIONS: 'Ca. P. ulmi' is widely present in elms in Germany. The rare occurrence of symptoms indicates either a high degree of tolerance in elm populations or a low virulence of pathogen strains enabling high infection rates in a long-living host.

Pre-harvest climate and post-harvest acclimation to cold prevent from superficial scald development in Granny Smith apples.

Superficial scald is one of the most serious postharvest physiological disorders that can affect apples after a prolonged cold storage period. This study investigated the impact of pre- and post-harvest climatic variations on superficial scald in a susceptible apple cultivar. Fruit batches with contrasting phenotypes for superficial scald incidence were identified among several years of "Granny Smith" fruit production. The "low scald" year pre-harvest climate was characterised by a warm period followed by a sudden decrease in temperature, playing the part of an in vivo acclimation to cold storage. This was associated with many abiotic stress responsive genes which were induced in fruit peel. In particular 48 Heat Shock Proteins (HSPs) and 5 Heat Shock transcription Factors (HSFs) were strongly induced at harvest when scald incidence was low. For "high scald" year, a post-harvest acclimation of 1 week was efficient in reducing scald incidence. Expression profiles of stress related genes were affected by the acclimation treatment and indicate fruit physiological adaptations to cold storage. The identified stress-responsive genes, and in particular HSPs, could be useful indicators of the fruit physiological status to predict the risk of scald occurrence as early as harvest.

When a Palearctic bacterium meets a Nearctic insect vector: Genetic and ecological insights into the emergence of the grapevine Flavescence dorée epidemics in Europe.

Flavescence dorée (FD) is a European quarantine grapevine disease transmitted by the Deltocephalinae leafhopper Scaphoideus titanus. Whereas this vector had been introduced from North America, the possible European origin of FD phytoplasma needed to be challenged and correlated with ecological and genetic drivers of FD emergence. For that purpose, a survey of genetic diversity of these phytoplasmas in grapevines, S. titanus, black alders, alder leafhoppers and clematis were conducted in five European countries. Out of 132 map genotypes, only 11 were associated to FD outbreaks, three were detected in clematis, whereas 127 were detected in alder trees, alder leafhoppers or in grapevines out of FD outbreaks. Most of the alder trees were found infected, including 8% with FD genotypes M6, M38 and M50, also present in alders neighboring FD-free vineyards and vineyard-free areas. The Macropsinae Oncopsis alni could transmit genotypes unable to achieve transmission by S. titanus, while the Deltocephalinae Allygus spp. and Orientus ishidae transmitted M38 and M50 that proved to be compatible with S. titanus. Variability of vmpA and vmpB adhesin-like genes clearly discriminated 3 genetic clusters. Cluster Vmp-I grouped genotypes only transmitted by O. alni, while clusters Vmp-II and -III grouped genotypes transmitted by Deltocephalinae leafhoppers. Interestingly, adhesin repeated domains evolved independently in cluster Vmp-I, whereas in clusters Vmp-II and-III showed recent duplications. Latex beads coated with various ratio of VmpA of clusters II and I, showed that cluster II VmpA promoted enhanced adhesion to the Deltocephalinae Euscelidius variegatus epithelial cells and were better retained in both E. variegatus and S. titanus midguts. Our data demonstrate that most FD phytoplasmas are endemic to European alders. Their emergence as grapevine epidemic pathogens appeared restricted to some genetic variants pre-existing in alders, whose compatibility to S. titanus correlates with different vmp gene sequences and VmpA binding properties.

Estimating epidemiological parameters from experiments in vector access to host plants, the method of matching gradients.

Estimation of pathogenic life-history values, for instance the duration a pathogen is retained in an insect vector (i.e., retention period) is of particular importance for understanding plant disease epidemiology. How can we extract values for these epidemiological parameters from conventional small-scale laboratory experiments in which transmission success is measured in relation to durations of vector access to host plants? We provide a solution to this problem by deriving formulae for the empirical curves that these experiments produce, called access period response curves (i.e., transmission success vs access period). We do this by writing simple equations for the fundamental life-cycle components of insect vectors in the laboratory. We then infer values of epidemiological parameters by matching the theoretical and empirical gradients of access period response curves. Using the example of Cassava brown streak virus (CBSV), which has emerged in sub-Saharan Africa and now threatens regional food security, we illustrate the method of matching gradients. We show how applying the method to published data produces a new understanding of CBSV through the inference of retention period, acquisition period and inoculation period parameters. We found that CBSV is retained for a far shorter duration in its insect vector (Bemisia tabaci whitefly) than had previously been assumed. Our results shed light on a number of critical factors that may be responsible for the transition of CBSV from sub- to super-threshold R0 in sub-Saharan Africa. The method is applicable to plant pathogens in general, to supply epidemiological parameter estimates that are crucial for practical management of epidemics and prediction of pandemic risk.