Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model.

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

Resistance to Fluopicolide and Propamocarb and Baseline Sensitivity to Ethaboxam Among Isolates of Pseudoperonospora cubensis From the Eastern United States.

Chemical control is currently the most effective method for controlling cucurbit downy mildew (CDM) caused by Pseudoperonospora cubensis. Most commercial cucurbit cultivars, with the exception of a few new cucumber cultivars, lack adequate disease resistance. Fluopicolide and propamocarb were among the most effective fungicides against CDM in the United States between 2006 and 2009. Since then, reduced efficacy of these two fungicides under field conditions was reported starting around 2013 but occurrence of resistance to fluopicolide and propamocarb in field isolates of P. cubensis had not been established. Thirty-one isolates collected from cucurbits in the eastern United States were tested for their sensitivity to fluopicolide and propamocarb using a leaf disc assay. This same set of isolates and four additional isolates (i.e., 35 isolates) were also used to establish the baseline sensitivity of P. cubensis to ethaboxam, an ethylamino-thiazole-carboxamide fungicide, which was recently granted registration to control CDM in the United States. About 65% of the isolates tested were resistant to fluopicolide with at least one resistant isolate being present in samples collected from 12 of the 13 states in the eastern United States. About 74% of the isolates tested were sensitive to propamocarb with at least one resistant isolate being among samples collected from 8 of the 12 states in the study. The frequency of resistance to fluopicolide and propamocarb was high among isolates collected from cucumber, while the frequency was low among isolates collected from other cucurbit host types. All isolates tested were found to be sensitive to ethaboxam and EC50 values ranged from 0.18 to 3.08 mg a.i./liter with a median of 1.55 mg a.i./liter. The ratio of EC50 values for the least sensitive and the most sensitive isolate was 17.1, indicating that P. cubensis isolates were highly sensitive to ethaboxam. The most sensitive isolates to ethaboxam were collected from New York, North Carolina, and Ohio, while the least sensitive isolates were collected from Georgia, Michigan, and New Jersey. These results show that ethaboxam could be a viable addition to fungicide programs used to control CDM in the United States.

Epidemiology: Past, Present, and Future Impacts on Understanding Disease Dynamics and Improving Plant Disease Management-A Summary of Focus Issue Articles.

Epidemiology has made significant contributions to plant pathology by elucidating the general principles underlying the development of disease epidemics. This has resulted in a greatly improved theoretical and empirical understanding of the dynamics of disease epidemics in time and space, predictions of disease outbreaks or the need for disease control in real-time basis, and tactical and strategic solutions to disease problems. Availability of high-resolution experimental data at multiple temporal and spatial scales has now provided a platform to test and validate theories on the spread of diseases at a wide range of spatial scales ranging from the local to the landscape level. Relatively new approaches in plant disease epidemiology, ranging from network to information theory, coupled with the availability of large-scale datasets and the rapid development of computer technology, are leading to revolutionary thinking about epidemics that can result in considerable improvement of strategic and tactical decision making in the control and management of plant diseases. Methods that were previously restricted to topics such as population biology or evolution are now being employed in epidemiology to enable a better understanding of the forces that drive the development of plant disease epidemics in space and time. This Focus Issue of Phytopathology features research articles that address broad themes in epidemiology including social and political consequences of disease epidemics, decision theory and support, pathogen dispersal and disease spread, disease assessment and pathogen biology and disease resistance. It is important to emphasize that these articles are just a sample of the types of research projects that are relevant to epidemiology. Below, we provide a succinct summary of the articles that are published in this Focus Issue .

A Model for Predicting Onset of Stagonospora nodorum Blotch in Winter Wheat Based on Preplanting and Weather Factors.

Stagonospora nodorum blotch (SNB) caused by Parastagonospora nodorum is a serious disease of wheat worldwide. In the United States, the disease is prevalent on winter wheat in many eastern states, and its management relies mainly on fungicide application after flag leaf emergence. Although SNB can occur prior to flag leaf emergence, the relationship between the time of disease onset and yield has not been determined. Such a relationship is useful in identifying a threshold to facilitate prediction of disease onset in the field. Disease occurred in 390 of 435 disease cases that were recorded across 11 counties in North Carolina from 2012 to 2014. Using cases with disease occurrence, the effect of disease onset on yield was analyzed to identify a disease onset threshold that related time of disease onset to yield. Regression analysis showed that disease onset explained 32% of the variation in yield (P < 0.0001) and from this relationship, day of year (DOY) 102 was identified as the disease onset threshold. Below-average yield occurred in 87% of the disease cases when disease onset occurred before DOY 102 but in only 28% of those cases when onset occurred on or after DOY 102. Subsequently, binary logistic regression models were developed to predict the occurrence and onset of SNB using preplanting factors and cumulative daily infection values (cDIV) starting 1 to 3 weeks prior to DOY 102. Logistic regression showed that previous crop, latitude, and cDIV accumulated 2 weeks prior to DOY 102 (cDIV.2) were significant (P < 0.0001) predictors of disease occurrence, and wheat residue, latitude, longitude, and cDIV.2 were significant (P < 0.0001) predictors of disease onset. The disease onset model had a correct classification rate of 0.94 and specificity and sensitivity rates >0.90. Performance of the disease onset model based on the area under the receiver operating characteristic curve (AUC), κ, and the true skill statistic (TSS) was excellent, with prediction accuracy values >0.88. Similarly, internal validation of the disease onset model based on AUC, κ, and TSS indicated good performance, with accuracy values >0.88. This disease onset prediction model could serve as a useful decision support tool to guide fungicide applications to manage SNB in wheat.

Using Next-Generation Sequencing to Develop Molecular Diagnostics for Pseudoperonospora cubensis, the Cucurbit Downy Mildew Pathogen.

Advances in next-generation sequencing (NGS) allow for rapid development of genomics resources needed to generate molecular diagnostics assays for infectious agents. NGS approaches are particularly helpful for organisms that cannot be cultured, such as the downy mildew pathogens, a group of biotrophic obligate oomycetes that infect crops of economic importance. Unlike most downy mildew pathogens that are highly host-specific, Pseudoperonospora cubensis causes disease on a broad range of crops belonging to the family Cucurbitaceae. In this study, we identified candidate diagnostic markers for P. cubensis by comparing NGS data from a diverse panel of P. cubensis and P. humuli isolates, two very closely related oomycete species. P. cubensis isolates from diverse hosts and geographical regions in the United States were selected for sequencing to ensure that candidates were conserved in P. cubensis isolates infecting different cucurbit hosts. Genomic regions unique to and conserved in P. cubensis isolates were identified through bioinformatics. These candidate regions were then validated using PCR against a larger collection of isolates from P. cubensis, P. humuli, and other oomycetes. Overall seven diagnostic markers were found to be specific to P. cubensis. These markers could be used for pathogen diagnostics on infected tissue, or adapted for monitoring airborne inoculum with real-time PCR and spore traps.

Quantifying the Effects of Wheat Residue on Severity of Stagonospora nodorum Blotch and Yield in Winter Wheat.

Stagonospora nodorum blotch (SNB), caused by the fungus Parastagonospora nodorum, is a major disease of wheat (Triticum aestivum). Residue from a previously infected wheat crop can be an important source of initial inoculum, but the effects of infected residue on disease severity and yield have not previously been quantified. Experiments were conducted in Raleigh and Salisbury, North Carolina, in 2012, 2013, and 2014 using the moderately susceptible winter wheat cultivar DG Shirley. In 2014, the highly susceptible cultivar DG 9012 was added to the experiment and the study was conducted at an additional site in Tyner, North Carolina. Four (2012) or six (2013 and 2014) wheat residue treatments were applied in the field in a randomized complete block design with five replicates. Treatments in 2012 were 0, 30, 60, and 90% residue coverage of the soil surface, while 10 and 20% residue treatments were added in 2013 and 2014. Across site-years, disease severity ranged from 0 to 50% and increased nonlinearly (P < 0.05) as residue level increased, with a rapid rise to an upper limit and showing little change in severity above 20 to 30% soil surface coverage. Residue coverage had a significant (P < 0.05) effect on disease severity in all site-years. The effect of residue coverage on yield was only significant (P < 0.05) for DG Shirley at Raleigh and Salisbury in 2012 and for DG 9012 at Salisbury in 2014. Similarly, residue coverage significantly (P < 0.05) affected thousand-kernel weight only of DG 9012 in 2014 at Raleigh and Salisbury. Our results showed that when wheat residue was sparse, small additions to residue density produced greater increases in SNB than when residue was abundant. SNB only led to effects on yield and test weight in the most disease-conducive environments, suggesting that the economic threshold for the disease may be higher than previously assumed and warrants review.

Use of Quantitative Traits to Assess Aggressiveness of Phakopsora pachyrhizi Isolates from Nigeria and the United States.

Soybean rust, caused by Phakopsora pachyrhizi, is one of the most important foliar diseases of soybean worldwide. The soybean-P. pachyrhizi interaction is often complex due to genetic variability in host and pathogen genotypes. In a compatible reaction, soybean genotypes produce tan-colored lesions, whereas in an incompatible reaction soybean genotypes produce an immune response (complete resistance) or reddish-brown lesions (incomplete resistance). In this study, in total, 116 and 72 isolates of P. pachyrhizi from Nigeria and the United States, respectively, were compared based on six quantitative traits to assess their aggressiveness on two soybean genotypes. All isolates produced reddish-brown lesions on plant introduction (PI) 462312 and tan lesions on TGx 1485-1D. The number of days after inoculation to first appearance of lesions, uredinia, and sporulation, along with the number of lesions and sporulating uredinia per square centimeter of leaf tissue, and the number of uredinia per lesion, were significantly (P < 0.001) different between the two soybean genotypes for all isolates from each country. The number of days to first appearance of lesions, uredinia, and sporulation were greater on PI 462312 than on TGx 1485-1D for all the test isolates. Similarly, the number of lesions and sporulating uredinia per square centimeter, and the number of uredinia per lesion were lower on PI 462312 than on TGx 1485-1D. For both soybean genotypes, the number of sporulating uredinia per square centimeter significantly (P = 0.0001) increased with an increase in the number of lesions per square centimeter. Although the slope of the regression of sporulating uredinia on number of lesions was greater (P < 0.0001) when TGx 1485-1D was inoculated with Nigerian isolates compared with U.S. isolates, slopes of the regression lines did not differ significantly (P > 0.0675) when PI 46312 was inoculated with Nigerian or U.S. isolates. This is the first study that used a large number of isolates from two continents to assess aggressiveness of P. pachyrhizi using multiple traits in soybean genotypes with contrasting types of disease reaction.

First Report of Pseudoperonospora cubensis Causing Downy Mildew on Momordica balsamina and M. charantia in North Carolina.

Momordica balsamina (balsam apple) and M. charantia L. (bitter melon/bitter gourd/balsam pear) commonly grow in the wild in Africa and Asia; bitter melon is also cultivated for food and medicinal purposes in Asia (1). In the United States, these cucurbits grow as weeds or ornamentals. Both species are found in southern states and bitter melon is also found in Pennsylvania and Connecticut (3). Cucurbit downy mildew (CDM), caused by the oomycete Pseudoperonospora cubensis, was observed on bitter melon and balsam apple between August and October of 2013 in six North Carolina sentinel plots belonging to the CDM ipmPIPE program (2). Plots were located at research stations in Johnston, Sampson, Lenoir, Henderson, Rowan, and Haywood counties, and contained six different commercial cucurbit species including cucumbers, melons, and squashes in addition to the Momordica spp. Leaves with symptoms typical of CDM were collected from the Momordica spp. and symptoms varied from irregular chlorotic lesions to circular lesions with chlorotic halos on the adaxial leaf surface. Sporulation on the abaxial side of the leaves was observed and a compound microscope revealed sporangiophores (180 to 200 µm height) bearing lemon-shaped, dark sporangia (20 to 35 × 10 to 20 µm diameter) with papilla on one end. Genomic DNA was extracted from lesions and regions of the NADH dehydrogynase subunit 1 (Nad1), NADH dehydrogynase subunit 5 (Nad5), and internal transcribed spacer (ITS) ribosomal RNA genes were amplified and sequenced (4). BLAST analysis revealed 100% identity to P. cubensis Nad1 (HQ636552.1, HQ636551.1), Nad5 (HQ636556.1), and ITS (HQ636491.1) sequences in GenBank. Sequences from a downy mildew isolate from each Momordica spp. were deposited in GenBank as accession nos. KJ496339 through 44. To further confirm host susceptibility, vein junctions on the abaxial leaf surface of five detached leaves of lab-grown balsam apple and bitter melon were either inoculated with a sporangia suspension (10 µl, 104 sporangia/ml) of a P. cubensis isolate from Cucumis sativus ('Vlaspik' cucumber), or with water as a control. Inoculated leaves were placed in humidity chambers to promote infection and incubated using a 12-h light (21°C) and dark (18°C) cycle. Seven days post inoculation, CDM symptoms and sporulation were observed on inoculated balsam apple and bitter melon leaves. P. cubensis has been reported as a pathogen of both hosts in Iowa (5). To our knowledge, this is the first report of P. cubensis infecting these Momordica spp. in NC in the field. Identifying these Momordica spp. as hosts for P. cubensis is important since these cucurbits may serve as a source of CDM inoculum and potentially an overwintering mechanism for P. cubensis. Further research is needed to establish the role of non-commercial cucurbits in the yearly CDM epidemic, which will aid the efforts of the CDM ipmPIPE to predict disease outbreaks. References: (1) L. K. Bharathi and K. J. John. Momordica Genus in Asia-An Overview. Springer, New Delhi, India, 2013. (2) P. S. Ojiambo et al. Plant Health Prog. doi:10.1094/PHP-2011-0411-01-RV, 2011. (3) PLANTS Database. Natural Resources Conservation Service, USDA. Retrieved from http://plants.usda.gov/ , 7 February 2014. (4) L. M. Quesada-Ocampo et al. Plant Dis. 96:1459, 2012. (5) USDA. Index of Plant Disease in the United States. Agricultural Handbook 165, 1960.

Modeling spatial frailties in survival analysis of cucurbit downy mildew epidemics.

Cucurbit downy mildew caused by Pseudoperonospora cubensis is economically the most important disease of cucurbits globally, and the pathogen is disseminated aerially over a large spatial scale. Spatio-temporal spread of the disease was characterized during phase I (low and sporadic disease outbreaks) and II (rapid increase in disease outbreaks) of the epidemic using records collected from sentinel plots from 2008 to 2009 in 23 states in the eastern United States as part of the United States Department of Agriculture Cucurbit Downy Mildew ipmPIPE network. A substantive goal of this study was to explain the pattern of time to disease outbreak using important covariates while accounting for spatially correlated differences in risk of disease outbreak among the states. Survival analyses that accounts for spatial dependence were performed on time to disease outbreak, and posterior median frailties (or random effects) were mapped to identify states with high or low risk for disease outbreak. From February to October, disease occurred in 195 and 172 out of 413 and 556 cases monitored in 2008 and 2009, respectively. Disease outbreaks were spatially aggregated, with a spatial dependence of up to ≈1,025 km where clustering of outbreaks in phase I and II of the epidemic were similar. However, unlike in phase I of the epidemic, space-time point pattern analysis was significant (P < 0.0001) for outbreaks in phase II, during which the highest risk window as estimated by the space-time function was within 1.5 months and 500 km of the initial outbreak. The risk of disease outbreak peaked around July and decreased thereafter until the end of the study period. Spatially correlated analysis of time to disease outbreak indicated the need to incorporate spatial frailties in standard survival analysis models. Evaluation of alternative formulations of the spatial models demonstrated that a Bayesian hierarchical spatially structured frailty model best described time to disease outbreak. This frailty model showed clustering of outbreaks at the state level and indicated that states in the mid-Atlantic region have high spatial frailties and a high risk of downy mildew outbreak.

Novel necrotrophic effectors from Stagonospora nodorum and corresponding host sensitivities in winter wheat germplasm in the southeastern United States.

Stagonospora nodorum blotch (SNB), caused by the necrotrophic fungus Stagonospora nodorum (teleomorph: Phaeosphaeria nodorum), is among the most common diseases of winter wheat in the United States. New opportunities in resistance breeding have arisen from the recent discovery of several necrotrophic effectors (NEs, also known as host-selective toxins) produced by S. nodorum, along with their corresponding host sensitivity (Snn) genes. Thirty-nine isolates of S. nodorum collected from wheat debris or grain from seven states in the southeastern United States were used to investigate the production of NEs in the region. Twenty-nine cultivars with varying levels of resistance to SNB, representing 10 eastern-U.S. breeding programs, were infiltrated with culture filtrates from the S. nodorum isolates in a randomized complete block design. Three single-NE Pichia pastoris controls, two S. nodorum isolate controls, and six Snn-differential wheat controls were also used. Cultivar-isolate interactions were visually evaluated for sensitivity at 7 days after infiltration. Production of NEs was detected in isolates originating in each sampled state except Maryland. Of the 39 isolates, 17 produced NEs different from those previously characterized in the upper Great Plains region. These novel NEs likely correspond to unidentified Snn genes in Southeastern wheat cultivars, because NEs are thought to arise under selection pressure from genes for resistance to biotrophic pathogens of wheat cultivars that differ by geographic region. Only 3, 0, and 23% of the 39 isolates produced SnToxA, SnTox1, and SnTox3, respectively, by the culture-filtrate test. A Southern dot-blot test showed that 15, 74, and 39% of the isolates carried the genes for those NEs, respectively; those percentages were lower than those found previously in larger international samples. Only two cultivars appeared to contain known Snn genes, although half of the cultivars displayed sensitivity to culture filtrates containing unknown NEs. Effector sensitivity was more frequent in SNB-susceptible cultivars than in moderately resistant (MR) cultivars (P = 0.008), although some susceptible cultivars did not exhibit sensitivity to NEs produced by isolates in this study and some MR cultivars were sensitive to NEs of multiple isolates. Our results suggest that NE sensitivities influence but may not be the only determinant of cultivar resistance to S. nodorum. Specific knowledge of NE and Snn gene frequencies in this region can be used by wheat breeding programs to improve SNB resistance.

Interactive Effects of Temperature and Leaf Wetness Duration on Sporangia Germination and Infection of Cucurbit Hosts by Pseudoperonospora cubensis.

Outbreaks of cucurbit downy mildew caused by Pseudoperonospora cubensis are dependent on the weather but effects of temperature and leaf wetness duration on infection have not been studied for different cucurbits. To determine the effects of these two weather variables on sporangia germination and infection of cucurbit host types by P. cubensis, three host types; cucumber ('Straight 8'), cantaloupe ('Kermit'), and acorn squash ('Table Queen'), were inoculated and exposed to leaf wetness durations of 2 to 24 h at six constant temperatures ranging from 5 to 30°C in growth-chamber experiments. Sporangia germination was assessed after each wetness period, and leaf area infected was assessed 5 and 7 days after inoculation. Germination of sporangia was highest on cantaloupe (16.5 to 85.7%) and lowest on squash (10.7 to 68.9%), while disease severity was highest and lowest on cucumber and cantaloupe, respectively. Host type, temperature, wetness duration and their interactions significantly (P < 0.0001) affected germination and disease severity. Germination and disease data for each host type were separately fitted to a modified form of a Weibull function that characterizes a unimodal response and monotonic increase of germination or infection with temperature and wetness duration, respectively. The effect of host type on germination and infection was characterized primarily by differences in the upper limit parameter in response to temperature. Differences among host types based on other parameters were either small or inconsistent. Temperature and wetness duration that supported a given level of germination or infection varied among host types. At 20°C, 15% leaf area infected was expected following 2, 4, and 8 h of wetness for cucumber, squash, and cantaloupe, respectively. When temperature was increased to 25°C, 15% disease severity was expected following 3, 7, and 15 h of wetness for cucumber, squash, and cantaloupe, respectively. Risk charts were constructed to estimate the potential risk of infection of cucurbit host types by P. cubensis based on prevailing or forecasted temperature and leaf wetness duration. These results will improve the timing and application of the initial fungicide spray for the control of cucurbit downy mildew.

A Degree-Day Model for the Latent Period of Stagonospora nodorum Blotch in Winter Wheat.

Stagonospora nodorum blotch (SNB), which is caused by Stagonospora nodorum, occurs frequently in the southeastern United States, and severe epidemics can lead to substantial yield losses. To develop a model for the progress of SNB based on the effects of temperature on the latent period of the pathogen, batches of two winter wheat cultivars, AGS 2000 and USG 3209, were inoculated with S. nodorum at weekly intervals for 16 weeks. After 72 h of incubation, inoculated plants were exposed to outdoor conditions where temperatures ranged from -6.6 to 35.8°C, with a mean batch temperature ranging from 9.7 to 24.7°C. Latent period, expressed as time from inoculation until the first visible lesions with pycnidia, ranged from 13 to 34 days. The relationship between the inverse of the latent period and mean temperature was best described by a linear model, and the estimated thermal time required for the completion of the latent period was 384.6 degree-days. A shifted cumulative gamma distribution model with a base temperature of 0.5°C significantly (P < 0.0001) described the relationship between increasing number of lesions with pycnidia and accumulated thermal time. When latent period was defined as time to 50% of the maximum number of lesions with pycnidia (L50), the model estimated L50 as 336 and 326 degree-days above 0.5°C for AGS 2000 and USG 3209, respectively. The relationship between 1/L50 and mean temperature was also best described using a linear model (r2 = 0.93, P < 0.001). This study provides data that link disease progress with wheat growth, which facilitates accurate identification of thresholds for timing of fungicide applications.

Dynamics of Soybean Rust Epidemics in Sequential Plantings of Soybean Cultivars in Nigeria.

Soybean rust, caused by Phakopsora pachyrhizi, is an important foliar disease of soybean. Disease severity is dependent on several environmental factors, although the precise nature of most of these factors under field conditions is not known. To help understand the environmental factors that affect disease development, soybean rust epidemics were studied in Nigeria by sequentially planting an early-maturing, highly susceptible cultivar, TGx 1485-1D, and a late-maturing, moderately susceptible cultivar, TGx 1448-2E, at 30- to 45-day intervals from August 2004 to September 2006. Within each planting date, disease onset occurred earlier on TGx 1485-1D than on TGx 1448-2E, and rust onset was at least 20 days earlier on soybean planted between August and October than on soybean planted between November and April. The logistic model provided a better description of the temporal increase in rust severity than the Gompertz model. Based on the logistic model, the highest absolute rates of disease increase were observed on soybean planted in September 2006 and October 2004 for TGx 1485-1D and TGx 1448-2E, respectively. Disease severity as measured by the relative area under disease progress curve (RAUDPC) was significantly (P < 0.05) negatively correlated with evaporation (r = -0.73), solar radiation (r = -0.59), and temperature (r = -0.64) but positively correlated with urediniospore concentration (r = 0.58). Planting date and soybean cultivar significantly (P < 0.05) affected disease severity, with severity being higher on soybean crops planted during the wet season than those planted in the dry season. Within the wet season, planting in May and July resulted in a significantly (P < 0.05) lower RAUDPC than planting between August and October. Yields were significantly (P < 0.001) related to RAUDPC during the wet season, whereby an increase in RAUDPC resulted in a linear decrease in yield. This study suggests that selection of planting date could be a useful cultural practice for reducing soybean rust.

Spatiotemporal spread of cucurbit downy mildew in the eastern United States.

The dynamics of cucurbit downy mildew, caused by Pseudoperonospora cubensis, in the eastern United States in 2008 and 2009 were investigated based on disease records collected in 24 states as part of the Cucurbit downy mildew ipmPIPE monitoring program. The mean season-long rate of temporal disease progress across the 2 years was 1.4 new cases per day. Although cucurbit downy mildew was detected in mid-February and early March in southern Florida, the disease progressed slowly during the spring and early summer and did not enter its exponential phase until mid-June. The median nearest-neighbor distance of spread of new disease cases was ≈110 km in both years, with ≈15% of the distances being >240 km. Considering disease epidemics on all cucurbits, the epidemic expanded at a rate of 9.2 and 10.5 km per day in 2008 and 2009, respectively. These rates of spatial spread are at the lower range of those reported for the annual spread of tobacco blue mold in the southeastern United States, a disease that is also aerially dispersed over long distances. These results suggest that regional spread of cucurbit downy mildew may be limited by opportunities for establishment in the first half of the year, when fewer cucurbit hosts are available for infection. The O-ring statistic was used to determine the spatial pattern of cucurbit downy mildew outbreaks using complete spatial randomness as the null model for hypothesis testing. Disease outbreaks in both years were spatially aggregated and the extent of spatial dependence was up to 1,000 km. Results from the spatial analysis suggests that disease outbreaks in the Great Lakes and mid-Atlantic regions may be due to the spread of P. cubensis sporangia from outbreaks of the disease near the Georgia/South Carolina/North Carolina border rather than from overwintering sites in southern Florida. Space-time point pattern analysis indicated strong (P < 0.001) evidence for a space-time interaction and a space-time risk window of ≈3 to 5 months after first disease outbreak and 300 to 600 km was detected in both years. Results of this study support the hypothesis that infection of cucurbits by P. cubensis appears to be an outcome of a contagion process, and the relative large space-time window suggests that factors occurring on a large spatial scale (≈1,000 km) facilitate the spread of cucurbit downy mildew in the eastern United States.

A quantitative review of fungicide efficacy for managing downy mildew in cucurbits.

A meta-analysis of the effect of fungicides on cucurbit downy mildew was conducted using data previously published in Fungicide & Nematicide Tests and Plant Disease Management Reports from 2000 to 2008. Standardized mean effect size (the difference in disease intensity expressed in standard deviation units between the fungicide treatment and its corresponding untreated control) was calculated for each of the 105 field studies evaluating the effects of fungicides on cucurbit downy mildew. Fixed- and random-effects meta-analyses were performed on the log-transformed standardized mean effect sizes to estimate the overall mean effect size (L+), and to determine the variability in the effect size among studies. Fungicides led to a significant (P<0.001) reduction in disease with an L+ value of 1.198, indicating that, overall, fungicides were highly effective against cucurbit downy mildew. Fixed- and random-effects meta-analyses were then conducted to determine the effects of moderator variables on L+. The fixed-effects model resulted in narrower 95% confidence intervals and generally lower estimates of moderator subgroup mean effect size (L) than the random-effects models. Fungicide efficacy was significantly (P<0.001) greater in cucumber, with L being 26.5% higher in cucumber than in other cucurbits. Year of study, number of sprays, and disease pressure in the control significantly (P<0.001) affected L. Fungicide efficacy was significantly lower during the 2004-05 season than prior to or after the 2004-05 season. Studies in which disease pressure was moderate had a significantly higher L than studies with either low or high disease pressure. Fungicide efficacy was ≥22% in studies that received 5 to 6 sprays than in studies that received 1 to 4 or >6 sprays. Fungicide products led to a significant (P<0.001) reduction in disease, although there was substantial between-study variability. The pyridinylmethyl-benzamide group of fungicides (fluopicolide) was the most effective, followed by the carbamate (propamocarb) and quinone inside inhibitors (cyazofamid) group of fungicides, while the carboxylic acid amide group (mandipropamid and dimethomorph) was the least effective. A combination of protectant and systemic fungicides resulted in a proportional increase in L compared with sole application of either protectant or systemic fungicides.

Quantitative models for germination and infection of Pseudoperonospora cubensis in response to temperature and duration of leaf wetness.

The influence of temperature and leaf wetness duration on germination of sporangia and infection of cantaloupe leaves by Pseudoperonospora cubensis was examined in three independent controlled-environment experiments by inoculating plants with a spore suspension and exposing them to a range of leaf wetness durations (2 to 24 h) at six fixed temperatures (5 to 30 degrees C). Germination of sporangia was assessed at the end of each wetness period and infection was evaluated from assessments of disease severity 5 days after inoculation. Three response surface models based on modified forms of the Weibull function were evaluated for their ability to describe germination of sporangia and infection in response to temperature and leaf wetness duration. The models estimated 15.7 to 17.3 and 19.5 to 21.7 degrees C as the optimum temperature (t) range for germination and infection, respectively, with little germination or infection at 5 or 30 degrees C. For wetness periods of 4 to 8 h, a distinct optimum for infection was observed at t = 20 degrees C but broader optimum curves resulted from wetness periods >8 h. Model 1 of the form f(w,t) = f(t) x (1 - exp{-[B x w](D)}) resulted in smaller asymptotic standard errors and yielded higher correlations between observed and predicted germination and infection data than either model 2 of the form f(w,t) = A{1 - exp[- f(t) x (w - C)](D)} or model 3 of the form f(w,t) = [1 - exp{-(B x w)(2)}]/cosh[(t - F)G/2]. Models 1 and 2 had nonsignificant lack-of-fit test statistics for both germination and infection data, whereas a lack-of-fit test was significant for model 3. The models accounted for approximately 87% (model 3) to 98% (model 1) of the total variation in the germination and infection data. In the validation of the models using data generated with a different isolate of P. cubensis, slopes of the regression line between observed and predicted germination and infection data were not significantly different (P > 0.2487) and correlation coefficients between observed and predicted values were high (r(2) > 0.81). Models 1 and 2 were used to construct risk threshold charts that can be used to estimate the potential risk for infection based on observed or forecasted temperature and leaf wetness duration.

Efficiency of adaptive cluster sampling for estimating plant disease incidence.

Conventional sampling designs such as simple random sampling (SRS) tend to be inefficient when assessing rare and highly clustered populations because most of the time is spent evaluating empty quadrats, leading to high error variances and high cost. In previous studies with rare plant and animal populations, adaptive cluster sampling, where sampling occurs preferentially in the neighborhood of quadrats in which the species of interest is detected during the sampling bout, has been shown to estimate population parameters with greater precision at an effort comparable to SRS. Here, we use computer simulations to evaluate the efficiency of adaptive cluster sampling for estimating low levels of disease incidence (0.1, 0.5, 1.0, and 5.0%) at various levels of aggregation of infected plants having variance-to-mean ratios (V/M) of approximately 1, 3, 5, and 10. For each simulation, an initial sample size of 50, 100, and 150 quadrats was evaluated, and the condition to adapt neighborhood sampling (CA), i.e., the minimum number of infected plants per quadrat that triggers a switch from random sampling to sampling in neighboring quadrats, was varied from 1 to 4 (corresponding to 7.7 to 30.8% incidence of infected plants per quadrat). The simulations showed that cluster sampling was consistently more precise than SRS at a field-level disease incidence of 0.1 and 0.5%, especially when diseased plants were highly aggregated (V/M = 5 or 10) and when the most liberal condition to adapt (CA = 1) was used. One drawback of adaptive cluster sampling is that the final sample size is unknown at the beginning of the sampling bout because it depends on how often neighborhood sampling is triggered. In our simulations, the final sample size was close to the initial sample size for disease incidence up to 1.0%, especially when a more conservative condition to adapt (CA > 1) was used. For these conditions, the effect of disease aggregation was minor. In summary, both precision and the sample size required with adaptive cluster sampling responded similarly to disease incidence and aggregation, i.e., both were most favorable at the lowest disease incidence with the highest levels of clustering. However, whereas relative precision was optimized with the most liberal condition to adapt, the ratio of final to initial sample size was best for more conservative CA values, indicating a tradeoff. In our simulations, precision and final sample size were both simultaneously favorable for disease incidence of up to 1.0%, but only when infected plants were most aggregated (V/M = 10).

Pathogenic variation of Phakopsora pachyrhizi infecting soybean in Nigeria.

Soybean rust, caused by Phakopsora pachyrhizi, is an important disease in Nigeria and many other soybean-producing countries worldwide. To determine the geographical distribution of soybean rust in Nigeria, soybean fields were surveyed in the Derived Savanna (DS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS) agroecological zones in Nigeria between 2004 and 2006. Disease severity in each zone was determined and analyzed using geostatistics. Prevalence of infected fields and disease severity in surveyed fields were significantly (P < 0.05) different between geographical zones with both variables being higher in the DS zone than in either NGS or SGS zones. Geostatistical analysis indicated that the spatial influence of disease severity at one location on severity at other locations was between 75 and 120 km. An exponential model best described the relationship between semivariance and lag distance when rust severity was high. Spatial interpolation of rust severity showed that locations in the DS zone were more conducive for the rust epidemic compared to areas in the NGS zone. In the 2005 survey, 116 purified isolates were established in culture on detached soybean leaves. To establish the nature of pathogenic variation in P. pachyrhizi, a set of four soybean accessions with Rpp(1), Rpp(2), Rpp(3), and Rpp(4) resistance genes, two highly resistant and two highly susceptible genotypes were inoculated with single uredinial isolates. Principal component analysis on the number of uredinia per square centimeter of leaf tissue for 116 isolates indicated that an adequate summary of pathogenic variation was obtained using only four genotypes. Of these four, PI 459025B (with Rpp(4) gene) and TGx 1485-1D had the lowest and highest number of uredinia per square centimeter, respectively. Based on cluster analysis of the number of uredinia per square centimeter, seven pathotype clusters were determined. Isolates in cluster III were the most virulent, while those in cluster IV were the least virulent. Shannon's index (H) revealed a more diverse pathogen population in the DS zone (H = 1.21) compared to the rust population in SGS and NGS with H values of 1.08 and 0.91, respectively. This work will be useful in breeding and management of soybean rust by facilitating identification of resistant genotypes and targeting cultivars with specific resistance to match prevailing P. pachyrhizi pathotypes in a given geographical zone.

Evaluation of atoxigenic isolates of Aspergillus flavus as potential biocontrol agents for aflatoxin in maize.

Aflatoxin contamination resulting from maize infection by Aspergillus flavus is both an economic and a public health concern. Therefore, strategies for controlling aflatoxin contamination in maize are being investigated. The abilities of eleven naturally occurring atoxigenic isolates in Nigeria to reduce aflatoxin contamination in maize were evaluated in grain competition experiments and in field studies during the 2005 and 2006 growing seasons. Treatments consisted of inoculation of either grains in vials or ears at mid-silking stage in field plots, with the toxigenic isolate (La3228) or atoxigenic isolate alone and co-inoculation of each atoxigenic isolate and La3328. Aflatoxin B(1) + B(2) concentrations were significantly (p < 0.05) lower in the co-inoculation treatments compared with the treatment in which the aflatoxin-producing isolate La3228 was inoculated alone. Relative levels of aflatoxin B(1) + B(2) reduction ranged from 70.1% to 99.9%. Among the atoxigenics, two isolates from Lafia, La3279 and La3303, were most effective at reducing aflatoxin B(1) + B(2) concentrations in both laboratory and field trials. These two isolates have potential value as agents for the biocontrol of aflatoxin contamination in maize. Because these isolates are endemic to West Africa, they are both more likely than introduced isolates to be well adapted to West African environments and to meet regulatory concerns over their use throughout that region.

Novel Sources of Resistance to Fusarium Stalk Rot of Maize in Tropical Africa.

Fusarium stalk rot is one of the most widespread and destructive diseases of maize, and deployment of resistant genotypes is one of the most effective strategies for controlling the disease. Fifty inbred lines and four checks from the breeding program of the International Institute of Tropical Agriculture were evaluated in field trials at Ikenne and Ibadan, Nigeria in 2003 and 2004 to identify new sources of resistance to stalk rot caused by Fusarium verticillioides. Evaluations were conducted under artificial inoculation and natural infection at Ibadan and Ikenne, respectively. Disease severity was recorded using a severity scale (SS) and direct estimation of stalk discoloration (SD). The two methods of disease assessment were compared and combined to classify genotypes into resistance groups using results from rank-sum analysis. In 2003, disease severity ranged from SS = 1 to 5 and SD = 1.3 to 33.8% at both locations. Both SS and SD were significantly (P < 0.01) higher in 2003 than in 2004 at the two locations. In both years, inbred lines significantly differed in SS (P < 0.02) and SD (P < 0.04) at Ibadan. Similarly, inbred lines significantly differed in SS (P < 0.04) and SD (P < 0.04) when genotypes were evaluated at Ikenne. Disease assessments based on SS and SD were significantly correlated (0.68 < r < 0.95, P < 0.01) in both years. Based on the results from rank-sum analysis, inbred lines were separated into highly resistant, resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible groups. At Ibadan, 6 (11.1%) and 8 (14.8%) were identified as highly resistant and resistant, respectively, whereas 11 (20.4%) were identified as resistant at Ikenne. Inbred lines 02C14609, 02C14643, 02C14654, and 02C14678 were consistently classified as either highly resistant or resistant to stalk rot across locations and years while the check genotypes were classified either as susceptible or moderately susceptible to stalk rot. These four inbred lines identified to have high levels of disease resistance may be used for breeding maize with resistance to Fusarium stalk rot.