Meta-Analysis of the Effects of QoI and DMI Fungicide Combinations on Fusarium Head Blight and Deoxynivalenol in Wheat.

Field trials were conducted in 17 U.S. states to evaluate the effects of quinone outside inhibitor (QoI) and demethylation inhibitor (DMI) fungicide programs on Fusarium head blight index (IND) and deoxynivalenol (DON) toxin in wheat. Four DMI-only treatments applied at Feekes 10.5.1, five QoI-only treatments applied between Feekes 9 or Feekes 10.5, three QoI+DMI mixtures applied at Feekes 10.5, and three treatments consisting of a QoI at Feekes 9 followed by a DMI at Feekes 10.5.1 were evaluated. Network meta-analytical models were fitted to log-transformed mean IND and DON data and estimated contrasts of log means were used to obtain estimates of mean percent controls relative to the nontreated check as measures of efficacy. Results from the meta-analyses were also used to assess the risk of DON increase in future trials. DMI at Feekes 10.5.1 were the most effective programs against IND and DON and the least likely to increase DON in future trials. QoI-only programs increased mean DON over the nontreated checks and were the most likely to do so in future trials, particularly when applied at Feekes 10.5. The effects of QoI+DMI combinations depended on the active ingredients and whether the two were applied as a mixture at heading or sequentially. Following a Feekes 9 QoI application with a Feekes 10.5.1 application of a DMI reduced the negative effect of the QoI on DON but was not sufficient to achieve the efficacy of the Feekes 10.5.1 DMI-only treatments. Our results suggest that one must be prudent when using QoI treatments under moderate to high risk of FHB, particularly where the QoI is used without an effective DMI applied in combination or in sequence.

Perceptions of Midwestern Crop Advisors and Growers on Foliar Fungicide Adoption and Use in Maize.

Foliar fungicide use in hybrid maize in the United States was rare before 2000. The decade from 2000 to 2010 saw foliar fungicides increasingly applied to maize in the absence of appreciable disease pressure, a practice seemingly at odds with integrated pest management philosophy. Yet, it is commonly believed that growers do not employ management strategies unless there are perceived benefits. Maize (corn) growers (CGs) and certified crop advisors (CCAs) across four Midwestern states (Iowa, Illinois, Ohio, and Wisconsin) were surveyed to better understand their practices, values and perceptions concerning the use of foliar fungicides during 2005 to 2009. The survey results demonstrated the rapid rise in maize foliar fungicide applications from 2000 through 2008, with 84% of CGs who sprayed having used a foliar fungicide in maize production for the very first time during 2005 to 2009. During 2005 to 2009, 73% of CCAs had recommended using a foliar fungicide, but only 35% of CGs sprayed. Perceived yield gains, conditional on having sprayed, were above the break-even point on average. However, negative yield responses were also observed by almost half of CCAs and a quarter of CGs. Hybrid disease resistance was a more important factor to economically successful maize production than foliar fungicides. Diseases as a yield-limiting factor were more important to CGs than CCAs. As a group, CGs were not as embracing of foliar fungicide as were CCAs, and remained more conservative about the perceived benefits to yield.

A White Paper on Global Wheat Health Based on Scenario Development and Analysis.

Scenario analysis constitutes a useful approach to synthesize knowledge and derive hypotheses in the case of complex systems that are documented with mainly qualitative or very diverse information. In this article, a framework for scenario analysis is designed and then, applied to global wheat health within a timeframe from today to 2050. Scenario analysis entails the choice of settings, the definition of scenarios of change, and the analysis of outcomes of these scenarios in the chosen settings. Three idealized agrosystems, representing a large fraction of the global diversity of wheat-based agrosystems, are considered, which represent the settings of the analysis. Several components of global changes are considered in their consequences on global wheat health: climate change and climate variability, nitrogen fertilizer use, tillage, crop rotation, pesticide use, and the deployment of host plant resistances. Each idealized agrosystem is associated with a scenario of change that considers first, a production situation and its dynamics, and second, the impacts of the evolving production situation on the evolution of crop health. Crop health is represented by six functional groups of wheat pathogens: the pathogens associated with Fusarium head blight; biotrophic fungi, Septoria-like fungi, necrotrophic fungi, soilborne pathogens, and insect-transmitted viruses. The analysis of scenario outcomes is conducted along a risk-analytical pattern, which involves risk probabilities represented by categorized probability levels of disease epidemics, and risk magnitudes represented by categorized levels of crop losses resulting from these levels of epidemics within each production situation. The results from this scenario analysis suggest an overall increase of risk probabilities and magnitudes in the three idealized agrosystems. Changes in risk probability or magnitude however vary with the agrosystem and the functional groups of pathogens. We discuss the effects of global changes on the six functional groups, in terms of their epidemiology and of the crop losses they cause. Scenario analysis enables qualitative analysis of complex systems, such as plant pathosystems that are evolving in response to global changes, including climate change and technology shifts. It also provides a useful framework for quantitative simulation modeling analysis for plant disease epidemiology.

Does the P Value Have a Future in Plant Pathology?

The P value (significance level) is possibly the mostly widely used, and also misused, quantity in data analysis. P has been heavily criticized on philosophical and theoretical grounds, especially from a Bayesian perspective. In contrast, a properly interpreted P has been strongly defended as a measure of evidence against the null hypothesis, H0. We discuss the meaning of P and null-hypothesis statistical testing, and present some key arguments concerning their use. P is the probability of observing data as extreme as, or more extreme than, the data actually observed, conditional on H0 being true. However, P is often mistakenly equated with the posterior probability that H0 is true conditional on the data, which can lead to exaggerated claims about the effect of a treatment, experimental factor or interaction. Fortunately, a lower bound for the posterior probability of H0 can be approximated using P and the prior probability that H0 is true. When one is completely uncertain about the truth of H0 before an experiment (i.e., when the prior probability of H0 is 0.5), the posterior probability of H0 is much higher than P, which means that one needs P values lower than typically accepted for statistical significance (e.g., P = 0.05) for strong evidence against H0. When properly interpreted, we support the continued use of P as one component of a data analysis that emphasizes data visualization and estimation of effect sizes (treatment effects).

Revisiting Fungicide-Based Management Guidelines for Leaf Blotch Diseases in Soft Red Winter Wheat.

Standard foliar fungicide applications in wheat are usually made between flag leaf emergence (Feekes [FK] 8) and heading (FK10.5) to minimize damage to the flag leaf. However, over the last few years, new fungicide programs such as applications prior to FK8 and split half-rate applications have been implemented, although there are few data pertaining to the efficacy of these programs. Eight experiments were conducted in Illinois, Indiana, Ohio, and Wisconsin from 2010 to 2012 to compare new programs to standard FK8 and FK10 programs in terms of disease control and yield response. The programs evaluated consisted of single full-rate applications of 19% tebuconazole + 19% prothioconazole (Prosaro) or 23.6% pyraclostrobin (Headline) at FK5 (pseudostem strongly erected), FK8, or FK10, or split half rates at FK5 and 8 (FK5+8), plus an untreated check (CK). Leaf blotch (LB) severity and yield data were collected and random effects meta-analytical models fitted to estimate the overall log odds ratio of disease reaching the flag leaf ( L¯OR ) and mean yield increase ( D¯ ) for each fungicide program relative to CK. For all programs, L¯OR was significantly different from zero (P < 0.05). Based on estimated odds ratios (OR = exp[ L¯OR ]), the two FK8 programs reduced the risk of LB reaching the flag leaf by 55 and 75%, compared with 62 and 69% and 67 and 70% for the two FK10 and FK5+8 programs, respectively, and only 32 and 37% for the two FK5 programs. D¯ was significantly different from zero (P ≤ 0.003) for all FK8, FK10, and FK5+8 programs, with values of 233 and 245, 175 and 220, and 175 and 187 kg ha-1 for the FK10, FK5+8, and FK8 programs, respectively. Differences in mean yield response between Headline and Prosaro were not statistically significant (P > 0.05). The probability of profitability was estimated for each program for a range of grain prices and fungicide application costs. All FK8, FK10, and FK5+8 programs had more than an 80% chance of resulting in a positive yield response, compared with 63 and 67% for the two FK5 programs. The chance of obtaining a yield increase of 200 kg ha-1, required to offset an application cost of $36 ha-1 at a grain price of $0.18 kg-1, ranged from 44 to 60% for FK8, FK10 and FK5+8 programs compared with 22 and 25% for the two FK5 programs. These findings could be used to help inform fungicide application decisions for LB diseases in soft red winter wheat.

Factors influencing aster leafhopper (Hemiptera: Cicadellidae) abundance and aster yellows phytoplasma infectivity in Wisconsin carrot fields.

In Wisconsin, vegetable crops are threatened annually by infection of the aster yellows phytoplasma (AYp), the causal agent of aster yellows (AY) disease, vectored by the aster leafhopper, Macrosteles quadrilineatus Forbes. Aster leafhopper abundance and infectivity are influenced by processes operating across different temporal and spatial scales. We applied a multilevel modeling approach to partition variance in multifield, multiyear, pest scouting data sets containing temporal and spatial covariates associated with aster leafhopper abundance and infectivity. Our intent was to evaluate the relative importance of temporal and spatial covariates to infer the relevant scale at which ecological processes are driving AY epidemics and identify periods of elevated risk for AYp spread. The relative amount of aster leafhopper variability among and within years (39%) exceeded estimates of variation among farm locations and fields (7%). Similarly, time covariates explained the largest amount of variation of aster leafhopper infectivity (50%). Leafhopper abundance has been decreasing since 2001 and reached its minimum in 2010. The average seasonal pattern indicated that periods of above average abundance occurred between 11 June and 1 August. Annual infectivity appears to oscillate around an average value of 2% and seasonal periods of above average infectivity occur between 19 May and 15 July. The coincidence of the expected periods of high leafhopper abundance and infectivity increases our knowledge of when the insect moves into susceptible crop fields and when it spreads the pathogen to susceptible crops, representing a seasonal interval during which management of the insect can be focused.

Seasonal patterns of aster leafhopper (Hemiptera: Cicadellidae) abundance and aster yellows phytoplasma infectivity in Wisconsin carrot fields.

In Wisconsin, vegetable crops are threatened annually by the aster yellows phytoplasma (AYp), which is obligately transmitted by the aster leafhopper. Using a multiyear, multilocation data set, seasonal patterns of leafhopper abundance and infectivity were modeled. A seasonal aster yellows index (AYI) was deduced from the model abundance and infectivity predictions to represent the expected seasonal risk of pathogen transmission by infectious aster leafhoppers. The primary goal of this study was to identify periods of time during the growing season when crop protection practices could be targeted to reduce the risk of AYp spread. Based on abundance and infectivity, the annual exposure of the carrot crop to infectious leafhoppers varied by 16- and 70-fold, respectively. Together, this corresponded to an estimated 1,000-fold difference in exposure to infectious leafhoppers. Within a season, exposure of the crop to infectious aster leafhoppers (Macrosteles quadrilineatus Forbes), varied threefold because of abundance and ninefold because of infectivity. Periods of above average aster leafhopper abundance occurred between 11 June and 2 August and above average infectivity occurred between 27 May and 13 July. A more comprehensive description of the temporal trends of aster leafhopper abundance and infectivity provides new information defining when the aster leafhopper moves into susceptible crop fields and when they transmit the pathogen to susceptible crops.

Efficacy and Stability of Integrating Fungicide and Cultivar Resistance to Manage Fusarium Head Blight and Deoxynivalenol in Wheat.

Integration of host resistance and prothioconazole + tebuconazole fungicide application at anthesis to manage Fusarium head blight (FHB) and deoxynivalenol (DON) in wheat was evaluated using data from over 40 trials in 12 U.S. states. Means of FHB index (index) and DON from up to six resistance class-fungicide management combinations per trial (susceptible treated [S_TR] and untreated [S_UT]; moderately susceptible treated [MS_TR] and untreated [MS_UT]; moderately resistant treated [MR_TR] and untreated [MR_UT]) were used in multivariate meta-analyses, and mean log response ratios across trials were estimated and transformed to estimate mean percent control ( ) due to the management combinations relative to S_UT. All combinations led to a significant reduction in index and DON (P < 0.001). MR_TR was the most effective combination, with a of 76% for index and 71% for DON, followed by MS_TR (71 and 58%, respectively), MR_UT (54 and 51%, respectively), S_TR (53 and 39%, respectively), and MS_UT (43 and 30%, respectively). Calculations based on the principle of treatment independence showed that the combination of fungicide application and resistance was additive in terms of percent control for index and DON. Management combinations were ranked based on percent control relative to S_UT within each trial, and nonparametric analyses were performed to determine management combination stability across environments (trials) using the Kendall coefficient of concordance (W). There was a significant concordance of management combinations for both index and DON (P < 0.001), indicating a nonrandom ranking across environments and relatively low variability in the within-environment ranking of management combinations. MR_TR had the highest mean rank (best control relative to S_UT) and was one of the most stable management combinations across environments, with low rank stability variance (0.99 for index and 0.67 for DON). MS_UT had the lowest mean rank (poorest control) but was also one of the most stable management combinations. Based on Piepho's nonparametric rank-based variance homogeneity U test, there was an interaction of management combination and environment for index (P = 0.011) but not for DON (P = 0.147), indicating that the rank ordering for index depended somewhat on environment. In conclusion, although the magnitude of percent control will likely vary among environments, integrating a single tebuconazole + prothioconazole application at anthesis with cultivar resistance will be a more effective and stable management practice for both index and DON than either approach used alone.

Risk factors for crop health under global change and agricultural shifts: a framework of analyses using rice in tropical and subtropical Asia as a model.

Plant disease epidemiology requires expansion of its current methodological and theoretical underpinnings in order to produce full contributions to global food security and global changes. Here, we outline a framework which we applied to farmers' field survey data set on rice diseases in the tropical and subtropical lowlands of Asia. Crop health risks arise from individual diseases, as well as their combinations in syndromes. Four key drivers of agricultural change were examined: labor, water, fertilizer, and land availability that translate into crop establishment method, water shortage, fertilizer input, and fallow period duration, respectively, as well as their combinations in production situations. Various statistical approaches, within a hierarchical structure, proceeding from higher levels of hierarchy (production situations and disease syndromes) to lower ones (individual components of production situations and individual diseases) were used. These analyses showed that (i) production situations, as wholes, represent very large risk factors (positive or negative) for occurrence of disease syndromes; (ii) production situations are strong risk factors for individual diseases; (iii) drivers of agricultural change represent strong risk factors of disease syndromes; and (iv) drivers of change, taken individually, represent small but significant risk factors for individual diseases. The latter analysis indicates that different diseases are positively or negatively associated with shifts in these drivers. We also report scenario analyses, in which drivers of agricultural change are varied in response to possible climate and global changes, generating predictions of shifts in rice health risks. The overall set of analyses emphasizes the need for large-scale ground data to define research priorities for plant protection in rapidly evolving contexts. They illustrate how a structured theoretical framework can be used to analyze emergent features of agronomic and socioecological systems. We suggest that the concept of "disease syndrome" can be borrowed in botanical epidemiology from public health to emphasize a holistic view of disease in shifting production situations in combination with the conventional, individual disease-centered perspective.

Seasonal phenology of Aphis glycines (Hemiptera: Aphididae) and other aphid species in cultivated bean and noncrop habitats in Wisconsin.

The occurrence of aphid-transmitted viruses in agricultural crops of the Midwest and northeastern United States has become more frequent since the arrival and establishment of the soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae). A. glycines is a competent vector of plant viruses and may be responsible for recent virus epidemics in Wisconsin snap bean, Phaseolus vulgaris L., fields. To determine whether vegetation surrounding crop fields could serve as sources of virus inocula, we examined the settling activity ofA. glycines and other aphid species in agricultural crops and noncrop field margins adjacent to snap bean fields. Noncrop field margins were made up of numerous virus-susceptible plant species within 10 m from snap bean field edges. During summers 2006 and 2007, horizontal pan traps were placed in commercial soybean [Glycine max (L.) Merr.], snap bean, and surrounding field margins to characterize aphid flight activity patterns in the different habitat types. Alate abundance and peak occurrence across years varied between crop and noncrop field margins and differed among patches of plants in field margins. Overall aphid activity peaked late in the season (21 August in 2006 and 28 July in 2007); with the majority (52%) of total aphids trapped in all habitats being A. glycines. Susceptibility to viral infection and confirmed visitation of A. glycines to these forage plants suggests the importance ofnoncrop habitats as potential sources of primary virus inoculum. Viral disease onset followed peak aphid flights and further implicates A. glycines as a likely vector of viruses in commercial bean and other crops in Wisconsin.

Beyond yield: plant disease in the context of ecosystem services.

The ecosystem services concept provides a means to define successful disease management more broadly, beyond short-term crop yield evaluations. Plant disease can affect ecosystem services directly, such as through removal of plants providing services, or indirectly through the effects of disease management activities, including pesticide applications, tillage, and other methods of plant removal. Increased plant biodiversity may reduce disease risk if susceptible host tissue becomes less common, or may increase risk if additional plant species are important in completing pathogen life cycles. Arthropod and microbial biodiversity may play similar roles. Distant ecosystems may provide a disservice as the setting for the evolution of pathogens that later invade a focal ecosystem, where plants have not evolved defenses. Conversely, distant ecosystems may provide a service as sources of genetic resources of great value to agriculture, including disease resistance genes. Good policies are needed to support conservation and optimal use of genetic resources, protect ecosystems from exotic pathogens, and limit the homogeneity of agricultural systems. Research is needed to provide policy makers, farmers, and consumers with the information required for evaluating trade-offs in the pursuit of the full range of ecosystem services desired from managed and native ecosystems.

Influence of Monocropping Brown Stem Rot-Resistant and -Susceptible Soybean Accessions on Soil and Stem Populations of Phialophora gregata f. sp. sojae.

Brown stem rot (BSR)-resistant and -susceptible soybean accessions were continuously cropped in an area never previously seeded to soybean to study the influence of monocultures on soil and stem populations of Phialophora gregata f. sp. sojae. P. gregata f. sp. sojae population size and genotype composition were determined by dilution plating, isolation of P. gregata f. sp. sojae and standard polymerase chain reaction (PCR), and by quantitative real-time PCR (q-PCR. In general, the sizes of P. gregata f. sp. sojae populations in soil were similar regardless of monoculture. The percentage of P. gregata f. sp. sojae genotype B was greater than A in soil following the monoculture of both BSR-susceptible and -resistant soybean accessions. Following the monoculture of a BSR-resistant accession, the percentage of P. gregata f. sp. sojae genotype B was greater than A. Overall, P. gregata f. sp. sojae populations in stems of a BSR-susceptible accession were greater than those in stems of a BSR-resistant accession. P. gregata f. sp. sojae genotype B was detected more often than A in stems of both resistant and susceptible accessions planted following a BSR-resistant monoculture. P. gregata f. sp. sojae genotype B was also detected more often than A in stems of a BSR-resistant accession planted following a BSR-susceptible monoculture. P. gregata f. sp. sojae genotypes A and B were isolated at similar frequencies from stems of a BSR-susceptible accession planted following a BSR-susceptible monoculture. However, q-PCR results indicate that the percentage of P. gregata f. sp. sojae genotype A was greater than B in stems of a BSR-susceptible accession planted following a BSR-susceptible monoculture. Among BSR-susceptible accessions, those with the soybean cyst nematode (SCN)-resistant cv. Peking in their parentage had the largest populations of P. gregata f. sp. sojae and a greater percentage of P. gregata f. sp. sojae genotype B. Similar results were observed for BSR-resistant accessions derived from SCN-resistant PI 88788.

Overwintering of Sclerotium rolfsii and S. rolfsii var. delphinii in Different Latitudes of the United States.

Previously known only from the southern United States, hosta petiole rot recently appeared in the northern United States. Sclerotium rolfsii var. delphinii is believed to be the predominant petiole rot pathogen in the northern United States, whereas S. rolfsii is most prevalent in the southern United States. In order to test the hypothesis that different tolerance to climate extremes affects the geographic distribution of these fungi, the survival of S. rolfsii and S. rolfsii var. delphinii in the northern and southeastern United States was investigated. At each of four locations, nylon screen bags containing sclerotia were placed on the surface of bare soil and at 20-cm depth. Sclerotia were recovered six times from November 2005 to July 2006 in North Dakota and Iowa, and from December 2005 to August 2006 in North Carolina and Georgia. Survival was estimated by quantifying percentage of sclerotium survival on carrot agar. Sclerotia of S. rolfsii var. delphinii survived until at least late July in all four states. In contrast, no S. rolfsii sclerotia survived until June in North Dakota or Iowa, whereas 18.5% survived until August in North Carolina and 10.3% survived in Georgia. The results suggest that inability to tolerate low temperature extremes limits the northern range of S. rolfsii.

Comparison of Models for Forecasting of Stewart's Disease of Corn in Iowa.

Three forecasting models for Stewart's disease (Pantoea stewartii subsp. stewartii) of corn (Zea mays) were examined for their ability to accurately predict the prevalence of Stewart's disease in Iowa at the county level. The Stevens Model, which is used as a predictor of the early wilt phase of Stewart's disease, the Stevens-Boewe Model, which predicts the late leaf blight phase of Stewart's disease, and the Iowa State Model that is used to predict the prevalence of Stewart's disease, all use mean air temperatures for December, January, and February for a preplant prediction of Stewart's disease risk in a subsequent season. Models were fitted using weighted binary logistic regression with Stewart's disease prevalence data and air temperature data for 1972 to 2003. For each model, the years 1972 to 1999 (n = 786 county-years) were used for model development to obtain parameter coefficients. All three models indicated an increased likelihood for Stewart's disease occurring in growing seasons preceded by warmer winters. Using internal bootstrap validation, the Stevens Model had a maximum error between predicted and calibrated probabilities of 10%, whereas the Stevens-Boewe and Iowa State models had maximum errors of 1% or less. External validation for each model, using air temperature and seed corn inspection data between 2000 and 2003 (n = 154 county-years), indicated that overall accuracy to predict Stewart's disease at the county level was between 62 and 66%. However, both the Stevens and Stevens-Boewe models were overly optimistic in predicting that Stewart's disease would not occur within specific counties, as the sensitivity for these two models was quite low (18 and 43%, respectively). The Iowa State Model was substantially more sensitive (67%). The results of this study suggest that the Iowa State Model has increased predictive ability beyond statewide predictions for estimating the risk of Stewart's disease at the county level in Iowa.

Quantifying the Feeding Periods Required by Corn Flea Beetles to Acquire and Transmit Pantoea stewartii.

The feeding periods required by corn flea beetles to acquire and transmit Pantoea stewartii were investigated in the Stewart's disease of corn pathosystem. To quantify the effect of acquisition feeding period on percentage of acquisition, field-collected corn beetles were allowed to feed for 6, 12, 24 36, 48, and 72 h on corn seedlings previously inoculated with a rifampicin- and nalidixic acid-restraint strain of P. stewartii. Acquisition of P. stewartii by corn flea beetles was considered positive if the rifampicin- and nalidixic acid-marked strain was recovered on selective media. To quantity the effect of transmission feeding period on percent transmission of P. stewartii by corn flea beetles, P. stewartii- infested corn flea beetles were allowed to feed on healthy corn seedlings for periods of 3, 6, 12, 24, 36, 48, and 72 h. After the appropriate transmission feeding period, leaf tissues surrounding the sites of feeding scars were cultured for the presence of the P. stewartii-marked strain. Transmission of P. stewartii was considered positive if the marked strain was recovered on selective media. Acquisition of P. stewartii occurred within 6 h and the percentage of corn flea beetles that had acquired P. stewartii after 72 h ranged from 68 to 94%. The change in P. stewartii acquisition by corn flea beetles (Y) with respect to acquisition feeding period (X) was best described by the Gompertz model, with R2 values ranging from 91 to 99%. The mean time for acquisition by 50% of the corn flea beetles was 36.5 ± 11.6 h. The minimum transmission feeding time required for corn flea beetles to transmit P. stewartii following a 48-h acquisition feeding period was less than 3 h. The percent transmission of P. stewartii by corn flea beetles was nearly 100% after a 48-h transmission feeding period and was 100% by 72 h. Among population growth models evaluated, the monomolecular model best described the relationship between percent transmission (Y) and transmission feeding periods (X), with R 2 values of up to 84%. However, a nonlinear form of the monomolecular model better quantified the relationship between percent transmission and transmission feeding period, because pseudo-R2 values ranged between 98.1 and 99.5%. The predicted transmission feeding time required for 50% of P. stewartii-infested corn flea beetles to transmit the pathogen was 7.6 ± 0.87 h. These results suggest that the corn flea beetle is a highly efficient vector that can quickly acquire and transmit P. stewartii, thereby requiring insecticide seed treatments and foliar insecticides that act quickly to prevent corn flea beetles from acquiring and transmitting P. stewartii to corn plants.

Population densities of corn flea beetle (Coleoptera: Chrysomelidae) and incidence of Stewart's wilt in sweet corn.

To quantify populations of the corn flea beetle, Chaetocnema pulicaria Melsheimer (Coleoptera: Chrysomelidae), and refine estimates of a threshold for its control to prevent Stewart's wilt caused by Erwinia stewartii, sequential plantings of 'Jubilee' sweet corn were made at 2-wk intervals from April or May through August or September 2001 and 2002 at four locations from southern to northern Illinois: Simpson, Brownstown, Champaign, and Mendota. Densities of C. pulicaria and incidence of Stewart's wilt were monitored weekly. At Mendota, where C. pulicaria populations were decimated by cold temperatures during winter 2000-2001, densities reached 33.3 beetles per 15-cm yellow sticky trap per day by September 2002, after a mild 2001-2002 winter. Maximum incidence of Stewart's wilt in single plots at Simpson, Brownstown, Champaign, and Mendota was 22, 36, 39, and 2%, respectively, in 2001, and 33, 47, 99, and 87%, respectively, in 2002. In 24 plots where beetle densities were < or =2 per trap per day, Stewart's wilt incidence was <5% in 20 plots. We propose that two corn flea beetles per trap per day be used as a threshold for insecticide application to seedlings to control C. pulicaria and minimize subsequent incidence of Stewart's wilt in processing sweet corn. Enzyme-linked immunosorbent assays indicated that E. stewartii incidence in C. pulicaria peaked at 67, 62, and 54%, respectively, at Simpson, Brownstown, and Champaign, in 2001, and at 71, 76, and 60%, respectively, in 2002. Further studies might allow the use of areawide or field-specific estimates of E. stewartii incidence in corn flea beetles for adjusting management decisions.

Trap height and orientation of yellow sticky traps affect capture of Chaetocnema pulicaria (Coleoptera: Chrysomelidae).

Field studies were conducted in Iowa during 2001 and 2002 to determine the optimal sampling height and orientation for using yellow sticky cards to monitor populations of Chaetocnema pulicaria Melsheimer, the vector of the bacterial pathogen Pantoea stewartii subsp, stewartii, the causal organism of Stewart's disease of corn, Zea mays L.. Sticky cards were placed at five different heights (0.15, 0.30, 0.45, 0.60, and 0.90 m) and three orientations (horizontal, vertical, and 30 degree angle) at three locations (Ames, Crawfordsville, and Sutherland) in 2001 and two locations (Crawfordsville and Johnston) in 2002. No statistical differences were observed among the placement combinations for individual sampling periods or for the total number of C. pulicaria captured in 2001. In 2002, the 0.30 m and vertical cards captured significantly (1.1-35 times) more C. pulicaria than any other placement combination during sampling throughout August at both Crawfordsville and Johnston. Also, the cumulative number of C. pulicaria captured by the 0.30 m and vertical cards was significantly higher than all other placement combinations. This information is important in the development of sampling protocols to aid growers in making management decisions. These management decisions include where and when to apply foliar insecticides during the corn growing season to control C. pulicaria populations, thereby reducing the risk for Stewart's disease of corn.

Temporal Dynamics of Corn Flea Beetle Populations Infested with Pantoea stewartii, Causal Agent of Stewart's Disease of Corn.

ABSTRACT In order to better understand the epidemiology of the Stewart's disease of corn pathosystem, quantitative information concerning the temporal dynamics of the amount of pathogen inoculum present in the form of Pantoea stewartii-infested corn flea beetles (Chaetocnema pulicaria) is needed. Temporal changes in the proportion of P. stewartii-infested corn flea beetle populations were monitored by testing individual corn flea beetles for the presence of P. stewartii using a peroxidase-labeled, enzyme-linked immunosorbent assay. Approximately 90 corn flea beetles were collected each week from seven locations in Iowa from September 1998 through October 2000 using sweep nets. The proportion of P. stewartii-infested beetles at the end of the 1998 growing season ranged from 0.04 to 0.19. In spring 1999, the proportion of overwintering adult corn flea beetles infested with P. stewartii ranged from 0.10 to 0.11 and did not differ significantly from the previous fall based on chi(2). During the 1999 corn-growing season, the proportion of infested corn flea beetles ranged from 0.04 to 0.86, with the highest proportions occurring in August. In fall 1999, the proportion of beetles infested with P. stewartii ranged from 0.20 to 0.77. In spring 2000, the proportion of overwintering adult corn flea beetles infested with P. stewartii ranged from 0.08 to 0.30; these proportions were significantly lower than the proportions observed in fall 1999 at Ames, Chariton, and Nashua. During the 2000 corn-growing season, the proportion of P. stewartii-infested corn flea beetles ranged from 0.08 to 0.53, and the highest observed proportions again occurred in August. Corn flea beetle populations sampled in late fall 2000 had proportions of infested beetles ranging from 0.08 to 0.20. This is the first study to quantify the temporal population dynamics of P. stewartii-infested C. pulicaria populations in hybrid corn and provides new quantitative information that should be useful in developing risk models to predict the seasonal and site-specific risks associated with Stewart's disease of corn.

Temporal distribution of Chaetocnema pulicaria (Coleoptera: Chrysomelidae) populations in Iowa.

In 1999 and 2000, yellow sticky cards and sweep net samples were used to document the occurrence of an overwintering adult generation of Chaetocnema pulicaria Melsheimer, corn flea beetle, followed by two distinct populations peaks during the growing season in Iowa Emergence of the overwintering adult generation started in mid-April and continued until early June in both years, with populations as high as 45 +/- 7.9 per 10 sweeps. Periods that ranged from 14 to 32 d were observed in 1999 and 2000 when C. pulicaria was not found following the overwintering generation. The first summer peak of C pulicaria was observed between the end of June into the middle of July, with the highest observed peak at 16.70 +/- 1.42 C. pulicaria per 10 sweeps in cornfields. The second summer peak of C pulicaria was observed between the middle into early September, with populations as high as 27.80 +/- 2.76 C. pulicaria per 10 sweeps. During the growing season, more C. pulicaria were caught on yellow sticky cards originating from soybean borders than from grass borders. There were significantly greater numbers of C. pulicaria on yellow sticky cards located in grass borders adjacent to cornfields at the end of the growing season, compared with yellow sticky cards located within cornfields, indicating the movement of C. pulicaria from the cornfield back into the grass borders at the end of the growing season. In 2000, from August to the end of the corn growing season, significantly more C. pulicaria were found in grass borders than in the cornfields. Based on this new quantitative information, planting time could be altered to avoid the emergence of the overwintering generation of C. pulicaria. In addition, knowledge concerning the seasonalities of the first and second population peaks of C pulicaria during the corn growing season could be used to recommend optimal timing for foliar-applied insecticide applications. This new knowledge concerning the seasonal dynamics of C pulicaria will help to improve management recommendations for Stewart's disease of corn, caused by the bacterium Pantoea stewartii, and that is vectored by C pulicaria.