Population Genomics Provide Insights into the Global Genetic Structure of Colletotrichum graminicola, the Causal Agent of Maize Anthracnose.

Understanding the genetic diversity and mechanisms underlying genetic variation in pathogen populations is crucial to the development of effective control strategies. We investigated the genetic diversity and reproductive biology of Colletotrichum graminicola isolates which infect maize by sequencing the genomes of 108 isolates collected from 14 countries using restriction site-associated DNA sequencing (RAD-seq) and whole-genome sequencing (WGS). Clustering analyses based on single-nucleotide polymorphisms revealed three genetic groups delimited by continental origin, compatible with short-dispersal of the pathogen and geographic subdivision. Intra- and intercontinental migration was observed between Europe and South America, likely associated with the movement of contaminated germplasm. Low clonality, evidence of genetic recombination, and high phenotypic diversity were detected. We show evidence that, although it is rare (possibly due to losses of sexual reproduction- and meiosis-associated genes) C. graminicola can undergo sexual recombination. Our results support the hypotheses that intra- and intercontinental pathogen migration and genetic recombination have great impacts on the C. graminicola population structure. IMPORTANCE Plant pathogens cause significant reductions in yield and crop quality and cause enormous economic losses worldwide. Reducing these losses provides an obvious strategy to increase food production without further degrading natural ecosystems; however, this requires knowledge of the biology and evolution of the pathogens in agroecosystems. We employed a population genomics approach to investigate the genetic diversity and reproductive biology of the maize anthracnose pathogen (Colletotrichum graminicola) in 14 countries. We found that the populations are correlated with their geographical origin and that migration between countries is ongoing, possibly caused by the movement of infected plant material. This result has direct implications for disease management because migration can cause the movement of more virulent and/or fungicide-resistant genotypes. We conclude that genetic recombination is frequent (in contrast to the traditional view of C. graminicola being mainly asexual), which strongly impacts control measures and breeding programs aimed at controlling this disease.

Fusarium Head Blight of Small Grains in Pennsylvania: Unravelling Species Diversity, Toxin Types, Growth, and Triazole Sensitivity.

Fusarium graminearum is the main causal species of Fusarium head blight (FHB) globally. Recent changes in the trichothecene (toxin) types in the North American FHB pathogens support the need for continued surveillance. In this study, 461 isolates were obtained from symptomatic spikes of wheat, spelt, barley, and rye crops during 2018 and 2019. These were all identified to species and toxin types using molecular-based approaches. An additional set of 77 F. graminearum isolates obtained from overwintering crop residues during winter 2012 were molecularly identified to toxin types. A subset of 31 F. graminearum isolates (15 15-acetyl-deoxynivalenol [15ADON] and 16 3-acetyl-deoxynivalenol [3ADON]) were assessed for mycelial growth, macroconidia, perithecia, and ascospore production, and sensitivity to two triazoles. Ninety percent of isolates obtained from the symptomatic spikes (n = 418) belonged to F. graminearum, with four other species found at a lower frequency (n = 39). The F. graminearum isolates from symptomatic spikes were mainly of the 15ADON (95%), followed by 3ADON (4%), nivalenol (0.7%), and NX-2 (0.3%) toxin types. All F. graminearum isolates obtained from overwintering residue were of the 15ADON type. The toxin types could not be differentiated based on the multivariate analysis of growth and reproduction traits. All isolates were sensitive to tebuconazole and metconazole fungicides in vitro. This study confirms the dominance of F. graminearum and suggests ecological and environmental factors, to be further identified, that lead to similar composition of toxin types in the northern United States. Our results may be useful to assess the sustainability of FHB management practices and provide a baseline for future FHB surveys.

Fusarium graminearum Species Complex: A Bibliographic Analysis and Web-Accessible Database for Global Mapping of Species and Trichothecene Toxin Chemotypes.

Fusarium graminearum is ranked among the five most destructive fungal pathogens that affect agroecosystems. It causes floral diseases in small grain cereals including wheat, barley, and oats, as well as maize and rice. We conducted a systematic review of peer-reviewed studies reporting species within the F. graminearum species complex (FGSC) and created two main data tables. The first contained summarized data from the articles including bibliographic, geographic, methodological (ID methods), host of origin and species, while the second data table contains information about the described strains such as publication, isolate code(s), host/substrate, year of isolation, geographical coordinates, species and trichothecene genotype. Analyses of the bibliographic data obtained from 123 publications from 2000 to 2021 by 498 unique authors and published in 40 journals are summarized. We describe the frequency of species and chemotypes for 16,274 strains for which geographical information was available, either provided as raw data or extracted from the publications, and sampled across six continents and 32 countries. The database and interactive interface are publicly available, allowing for searches, summarization, and mapping of strains according to several criteria including article, country, host, species and trichothecene genotype. The database will be updated as new articles are published and should be useful for guiding future surveys and exploring factors associated with species distribution such as climate and land use. Authors are encouraged to submit data at the strain level to the database, which is accessible at https://fgsc.netlify.app.

Soybean Roots and Soil From High- and Low-Yielding Field Sites Have Different Microbiome Composition.

The occurrence of high- (H) and low- (L) yielding field sites within a farm is a commonly observed phenomenon in soybean cultivation. Site topography, soil physical and chemical attributes, and soil/root-associated microbial composition can contribute to this phenomenon. In order to better understand the microbial dynamics associated with each site type (H/L), we collected bulk soil (BS), rhizosphere soil (RS), and soybean root (R) samples from historically high and low yield sites across eight Pennsylvania farms at V1 (first trifoliate) and R8 (maturity) soybean growth stages (SGS). We extracted DNA extracted from collected samples and performed high-throughput sequencing of PCR amplicons from both the fungal ITS and prokaryotic 16S rRNA gene regions. Sequences were then grouped into amplicon sequence variants (ASVs) and subjected to network analysis. Based on both ITS and 16S rRNA gene data, a greater network size and edges were observed for all sample types from H-sites compared to L-sites at both SGS. Network analysis suggested that the number of potential microbial interactions/associations were greater in samples from H-sites compared to L-sites. Diversity analyses indicated that site-type was not a main driver of alpha and beta diversity in soybean-associated microbial communities. L-sites contained a greater percentage of fungal phytopathogens (ex: Fusarium, Macrophomina, Septoria), while H-sites contained a greater percentage of mycoparasitic (ex: Trichoderma) and entomopathogenic (ex: Metarhizium) fungal genera. Furthermore, roots from H-sites possessed a greater percentage of Bradyrhizobium and genera known to contain plant growth promoting bacteria (ex: Flavobacterium, Duganella). Overall, our results revealed that there were differences in microbial composition in soil and roots from H- and L-sites across a variety of soybean farms. Based on our findings, we hypothesize that differences in microbial composition could have a causative relationship with observed within-farm variability in soybean yield.

A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north-central United States.

Foliar fungicide usage in soybeans in the north-central United States increased steadily over the past two decades. An agronomically-interpretable machine learning framework was used to understand the importance of foliar fungicides relative to other factors associated with realized soybean yields, as reported by growers surveyed from 2014 to 2016. A database of 2738 spatially referenced fields (of which 30% had been sprayed with foliar fungicides) was fit to a random forest model explaining soybean yield. Latitude (a proxy for unmeasured agronomic factors) and sowing date were the two most important factors associated with yield. Foliar fungicides ranked 7th out of 20 factors in terms of relative importance. Pairwise interactions between latitude, sowing date and foliar fungicide use indicated more yield benefit to using foliar fungicides in late-planted fields and in lower latitudes. There was a greater yield response to foliar fungicides in higher-yield environments, but less than a 100 kg/ha yield penalty for not using foliar fungicides in such environments. Except in a few production environments, yield gains due to foliar fungicides sufficiently offset the associated costs of the intervention when soybean prices are near-to-above average but do not negate the importance of disease scouting and fungicide resistance management.

Advancing agricultural research using machine learning algorithms.

Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands.

Abiotic conditions outweigh microbial origin during bacterial assembly in soils.

Understanding the processes guiding microbial community assembly in soils is essential for predicting microbiome structure and function following soil disturbance events like agricultural soil fumigation. However, assembly outcomes are complex and variable, being affected by both selective abiotic forces and by the history of colonizing microorganisms. To untangle the interactions between these factors, we conducted a controlled microcosm study tracking bacterial assembly in cleared soils over 7 weeks. We used mesh bags to connect five unsterilized source soils, differing in land use history (forested, agricultural, or fallow), with four sterile recipient soil treatments, differing in abiotic conditions (no soil additives, salt addition, urea addition, or mixed salt/urea addition). We found that 59%-96% of bacterial colonizers after 1 week were Firmicutes, but by 7 weeks Actinobacteria and Bacteroidetes were also dominant. Salt and nitrogen additions reshaped bacterial assembly by constraining alpha diversity by up to half and biomass accumulation by up to an order of magnitude. Within-treatment dispersion was significantly lower for salt and nutrient addition microcosms, suggesting deterministic selective pressures. In contrast, source soil origin had little impact on assembly trajectories. These results suggest that abiotic conditions can overshadow microbial source history in shaping community assembly outcomes.

Modeling the relationship between estimated fungicide use and disease-associated yield losses of soybean in the United States II: Seed-applied fungicides vs seedling diseases.

Use of seed-applied fungicides has become commonplace in the United States soybean production systems. Although fungicides have the potential to protect seed/seedlings from critical early stage diseases such as damping-off and root/stem rots, results from previous studies are not consistent in terms of seed-applied fungicide's ability to mitigate yield losses. In the current study, the relationship between estimated soybean production losses due to seedling diseases and estimated seed-applied fungicide use was investigated using annual data from 28 soybean growing states in the U.S. over the period of 2006 to 2014. National, regional (northern and southern U.S.), state, and temporal scale trends were explored using mixed effects version of the regression analysis. Mixed modeling allowed computing generalized R2 values for conditional (R2GLMM(c); contains fixed and random effects) and marginal (R2GLMM(m); contains only fixed effects) models. Similar analyses were conducted to investigate how soybean production was related to fungicide use. National and regional scale modeling revealed that R2GLMM(c) values were significantly larger compared to R2GLMM(m) values, meaning fungicide use had limited utility in explaining the national/regional scale variation of yield loss and production. The state scale analysis revealed the usefulness of seed-applied fungicides to mitigate seedling diseases-associated soybean yield losses in Illinois, Indiana, North Carolina, and Ohio. Further, fungicide use positively influenced the soybean production and yield in Illinois and South Dakota. Taken together, use of seed-applied fungicide did not appear to be beneficial to many of the states. Our findings corroborate the observations made by a number of scientists through field scale seed-applied fungicide trials across the U.S and reiterate the importance of need base-use of seed-applied fungicides rather than being a routine practice in soybean production systems.

Modeling the relationship between estimated fungicide use and disease-associated yield losses of soybean in the United States I: Foliar fungicides vs foliar diseases.

Fungicide use in the United States to manage soybean diseases has increased in recent years. The ability of fungicides to reduce disease-associated yield losses varies greatly depending on multiple factors. Nonetheless, historical data are useful to understand the broad sense and long-term trends related to fungicide use practices. In the current study, the relationship between estimated soybean yield losses due to selected foliar diseases and foliar fungicide use was investigated using annual data from 28 soybean growing states over the period of 2005 to 2015. For national and regional (southern and northern United States) scale data, mixed effects modeling was performed considering fungicide use as a fixed and state and year as random factors to generate generalized R2 values for marginal (R2GLMM(m); contains only fixed effects) and conditional (R2GLMM(c); contains fixed and random effects) models. Similar analyses were performed considering soybean production data to see how fungicide use affected production. Analyses at both national and regional scales showed that R2GLMM(m) values were significantly smaller compared to R2GLMM(c) values. The large difference between R2 values for conditional and marginal models indicated that the variation of yield loss as well as production were predominantly explained by the state and year rather than the fungicide use, revealing the general lack of fit between fungicide use and yield loss/production at national and regional scales. Therefore, regression models were fitted across states and years to examine their importance in combination with fungicide use on yield loss or yield. In the majority of cases, the relationship was nonsignificant. However, the relationship between soybean yield and fungicide use was significant and positive for majority of the years in the study. Results suggest that foliar fungicides conferred yield benefits in most of the years in the study. Furthermore, the year-dependent usefulness of foliar fungicides in mitigating soybean yield losses suggested the possible influence of temporally fluctuating abiotic factors on the effectiveness of foliar fungicides and/or target disease occurrence and associated loss magnitudes.

Dissecting the economic impact of soybean diseases in the United States over two decades.

Soybean (Glycine max L. Merrill) is an economically important commodity for United States agriculture. Nonetheless, the profitability of soybean production has been negatively impacted by soybean diseases. The economic impacts of 23 common soybean diseases were estimated in 28 soybean-producing states in the U.S., from 1996 to 2016 (the entire data set consisted of 13,524 data points). Estimated losses were investigated using a variety of statistical approaches. The main effects of state, year, pre- and post-discovery of soybean rust, region, and zones based on yield, harvest area, and production, were significant on "total economic loss" as a function of diseases. Across states and years, the soybean cyst nematode, charcoal rot, and seedling diseases were the most economically damaging diseases while soybean rust, bacterial blight, and southern blight were the least economically damaging. A significantly greater mean loss (51%) was observed in states/years after the discovery of soybean rust (2004 to 2016) compared to the pre-discovery (1996 to 2003). From 1996 to 2016, the total estimated economic loss due to soybean diseases in the U.S. was $95.48 billion, with $80.89 billion and $14.59 billion accounting for the northern and southern U.S. losses, respectively. Over the entire time period, the average annual economic loss due to soybean diseases in the U.S. reached nearly $4.55 billion, with approximately 85% of the losses occurring in the northern U.S. Low yield/harvest/production zones had significantly lower mean economic losses due to diseases in comparison to high yield/harvest/production zones. This observation was further bolstered by the observed positive linear correlation of mean soybean yield loss (in each state, due to all diseases considered in this study, across 21 years) with the mean state wide soybean production (MT), mean soybean yield (kg ha-1), and mean soybean harvest area (ha). Results of this investigation provide useful insights into how research, policy, and educational efforts should be prioritized in soybean disease management.

Neonicotinoid seed treatments of soybean provide negligible benefits to US farmers.

Neonicotinoids are the most widely used insecticides worldwide and are typically deployed as seed treatments (hereafter NST) in many grain and oilseed crops, including soybeans. However, there is a surprising dearth of information regarding NST effectiveness in increasing soybean seed yield, and most published data suggest weak, or inconsistent yield benefit. The US is the key soybean-producing nation worldwide and this work includes soybean yield data from 194 randomized and replicated field studies conducted specifically to evaluate the effect of NSTs on soybean seed yield at sites within 14 states from 2006 through 2017. Here we show that across the principal soybean-growing region of the country, there are negligible and management-specific yield benefits attributed to NSTs. Across the entire region, the maximum observed yield benefits due to fungicide (FST = fungicide seed treatment) + neonicotinoid use (FST + NST) reached 0.13 Mg/ha. Across the entire region, combinations of management practices affected the effectiveness of FST + NST to increase yield but benefits were minimal ranging between 0.01 to 0.22 Mg/ha. Despite widespread use, this practice appears to have little benefit for most of soybean producers; across the entire region, a partial economic analysis further showed inconsistent evidence of a break-even cost of FST or FST + NST. These results demonstrate that the current widespread prophylactic use of NST in the key soybean-producing areas of the US should be re-evaluated by producers and regulators alike.

Effects of Pre- and Postanthesis Applications of Demethylation Inhibitor Fungicides on Fusarium Head Blight and Deoxynivalenol in Spring and Winter Wheat.

Anthesis is generally recommended as the optimum growth stage for applying a foliar fungicide to manage Fusarium head blight (FHB) and the Fusarium-associated toxin deoxynivalenol (DON) in wheat. However, because it is not always possible to treat fields at anthesis, studies were conducted to evaluate pre- and postanthesis treatment options for managing FHB and DON in spring and winter wheat. Network meta-analytical models were fitted to data from 19 years of fungicide trials, and log response ratio ([Formula: see text]) and approximate percent control ([Formula: see text]) relative to a nontreated check were estimated as measures of the effects of six treatments on FHB index (IND: mean percentage of diseased spikelets per spike) and DON. The evaluated treatments consisted of either Caramba (metconazole) applied early (at heading [CE]), at anthesis (CA), or late (5 to 7 days after anthesis; CL), or Prosaro (prothioconazole + tebuconazole) applied at the same three times and referred to as PE, PA, and PL, respectively. All treatments reduced mean IND and DON relative to the nontreated check, but the magnitude of the effect varied with timing and wheat type. CA and PA resulted in the highest [Formula: see text] values for IND, 52.2 and 51.5%, respectively, compared with 45.9% for CL, 41.3% for PL, and less than 33% for CE and PE. Anthesis and postanthesis treatments reduced mean IND by 14.9 to 29.7% relative to preanthesis treatments. The estimated effect size was also statistically significant for comparisons between CA and CL and PA and PL; CA reduced IND by 11.7% relative to CL, whereas PA reduced the disease by 17.4% relative to PL. Differences in efficacy against IND between pairs of prothioconazole + tebuconazole and metconazole treatments applied at the same timing (CE versus PE, CA versus PA, and CL versus PL) were not statistically significant. However, CA and CL outperformed PA and PL by 7 and 12.8%, respectively, in terms of efficacy against DON. All application programs had comparable efficacy against IND between spring and winter wheat types, but efficacy against DON was 10 to 16% greater for spring than winter wheat for applications made at or after anthesis. All programs led to an increase in mean grain yield and test weight relative to the nontreated check.

Statistical Power in Plant Pathology Research.

In null hypothesis testing, failure to reject a null hypothesis may have two potential interpretations. One interpretation is that the treatments being evaluated do not have a significant effect, and a correct conclusion was reached in the analysis. Alternatively, a treatment effect may have existed but the conclusion of the study was that there was none. This is termed a Type II error, which is most likely to occur when studies lack sufficient statistical power to detect a treatment effect. In basic terms, the power of a study is the ability to identify a true effect through a statistical test. The power of a statistical test is 1 - (the probability of Type II errors), and depends on the size of treatment effect (termed the effect size), variance, sample size, and significance criterion (the probability of a Type I error, α). Low statistical power is prevalent in scientific literature in general, including plant pathology. However, power is rarely reported, creating uncertainty in the interpretation of nonsignificant results and potentially underestimating small, yet biologically significant relationships. The appropriate level of power for a study depends on the impact of Type I versus Type II errors and no single level of power is acceptable for all purposes. Nonetheless, by convention 0.8 is often considered an acceptable threshold and studies with power less than 0.5 generally should not be conducted if the results are to be conclusive. The emphasis on power analysis should be in the planning stages of an experiment. Commonly employed strategies to increase power include increasing sample sizes, selecting a less stringent threshold probability for Type I errors, increasing the hypothesized or detectable effect size, including as few treatment groups as possible, reducing measurement variability, and including relevant covariates in analyses. Power analysis will lead to more efficient use of resources and more precisely structured hypotheses, and may even indicate some studies should not be undertaken. However, the conclusions of adequately powered studies are less prone to erroneous conclusions and inflated estimates of treatment effectiveness, especially when effect sizes are small.

Oomycete Species Associated with Soybean Seedlings in North America-Part I: Identification and Pathogenicity Characterization.

Oomycete pathogens are commonly associated with soybean root rot and have been estimated to reduce soybean yields in the United States by 1.5 million tons on an annual basis. Limited information exists regarding the frequency and diversity of oomycete species across the major soybean-producing regions in North America. A survey was conducted across 11 major soybean-producing states in the United States and the province of Ontario, Canada. In 2011, 2,378 oomycete cultures were isolated from soybean seedling roots on a semiselective medium (CMA-PARPB) and were identified by sequencing of the internal transcribed spacer region of rDNA. Sequence results distinguished a total of 51 Pythium spp., three Phytophthora spp., three Phytopythium spp., and one Aphanomyces sp. in 2011, with Pythium sylvaticum (16%) and P. oopapillum (13%) being the most prevalent. In 2012, the survey was repeated, but, due to drought conditions across the sampling area, fewer total isolates (n = 1,038) were collected. Additionally, in 2012, a second semiselective medium (V8-RPBH) was included, which increased the Phytophthora spp. isolated from 0.7 to 7% of the total isolates. In 2012, 54 Pythium spp., seven Phytophthora spp., six Phytopythium spp., and one Pythiogeton sp. were recovered, with P. sylvaticum (14%) and P. heterothallicum (12%) being recovered most frequently. Pathogenicity and virulence were evaluated with representative isolates of each of the 84 species on soybean cv. Sloan. A seed-rot assay identified 13 and 11 pathogenic species, respectively, at 13 and 20°C. A seedling-root assay conducted at 20°C identified 43 species as pathogenic, having a significantly detrimental effect on the seedling roots as compared with the noninoculated control. A total of 15 species were pathogenic in both the seed and seedling assays. This study provides a comprehensive characterization of oomycete species present in soybean seedling roots in the major production areas in the United States and Ontario, Canada and provides a basis for disease management and breeding programs.

Oomycete Species Associated with Soybean Seedlings in North America-Part II: Diversity and Ecology in Relation to Environmental and Edaphic Factors.

Soybean (Glycine max (L.) Merr.) is produced across a vast swath of North America, with the greatest concentration in the Midwest. Root rot diseases and damping-off are a major concern for production, and the primary causal agents include oomycetes and fungi. In this study, we focused on examination of oomycete species distribution in this soybean production system and how environmental and soil (edaphic) factors correlate with oomycete community composition at early plant growth stages. Using a culture-based approach, 3,418 oomycete isolates were collected from 11 major soybean-producing states and most were identified to genus and species using the internal transcribed spacer region of the ribosomal DNA. Pythium was the predominant genus isolated and investigated in this study. An ecology approach was taken to understand the diversity and distribution of oomycete species across geographical locations of soybean production. Metadata associated with field sample locations were collected using geographical information systems. Operational taxonomic units (OTU) were used in this study to investigate diversity by location, with OTU being defined as isolate sequences with 97% identity to one another. The mean number of OTU ranged from 2.5 to 14 per field at the state level. Most OTU in this study, classified as Pythium clades, were present in each field in every state; however, major differences were observed in the relative abundance of each clade, which resulted in clustering of states in close proximity. Because there was similar community composition (presence or absence) but differences in OTU abundance by state, the ordination analysis did not show strong patterns of aggregation. Incorporation of 37 environmental and edaphic factors using vector-fitting and Mantel tests identified 15 factors that correlate with the community composition in this survey. Further investigation using redundancy analysis identified latitude, longitude, precipitation, and temperature as factors that contribute to the variability observed in community composition. Soil parameters such as clay content and electrical conductivity also affected distribution of oomycete species. The present study suggests that oomycete species composition across geographical locations of soybean production is affected by a combination of environmental and edaphic conditions. This knowledge provides the basis to understand the ecology and distribution of oomycete species, especially those able to cause diseases in soybean, providing cues to develop management strategies.

Effect of Maize Hybrid and Foliar Fungicides on Yield Under Low Foliar Disease Severity Conditions.

Foliar fungicide use in the U.S. Corn Belt increased in the last decade; however, questions persist pertaining to its value and sustainability. Multistate field trials were established from 2010 to 2012 in Illinois, Iowa, Ohio, and Wisconsin to examine how hybrid and foliar fungicide influenced disease intensity and yield. The experimental design was in a split-split plot with main plots consisting of hybrids varying in resistance to gray leaf spot (caused by Cercospora zeae-maydis) and northern corn leaf blight (caused by Setosphaera turcica), subplots corresponding to four application timings of the fungicide pyraclostrobin, and sub-subplots represented by inoculations with either C. zeae-maydis, S. turcica, or both at two vegetative growth stages. Fungicide application (VT/R1) significantly reduced total disease severity relative to the control in five of eight site-years (P<0.05). Disease was reduced by approximately 30% at Wisconsin in 2011, 20% at Illinois in 2010, 29% at Iowa in 2010, and 32 and 30% at Ohio in 2010 and 2012, respectively. These disease severities ranged from 0.2 to 0.3% in Wisconsin in 2011 to 16.7 to 22.1% in Illinois in 2010. The untreated control had significantly lower yield (P<0.05) than the fungicide-treated in three site-years. Fungicide application increased the yield by approximately 6% at Ohio in 2010, 5% at Wisconsin in 2010 and 6% in 2011. Yield differences ranged from 8,403 to 8,890 kg/ha in Wisconsin 2011 to 11,362 to 11,919 kg/ha in Wisconsin 2010. Results suggest susceptibility to disease and prevailing environment are important drivers of observed differences. Yield increases as a result of the physiological benefits of plant health benefits under low disease were not consistent.

Effect of Glyphosate Application on Sudden Death Syndrome of Glyphosate-Resistant Soybean Under Field Conditions.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, is an important yield limiting disease of soybean. Glyphosate is used to control weeds in soybean; however, its effect on SDS is not clearly understood. The objective of this study was to examine the impact of glyphosate on SDS, yield, and plant nutrition under field conditions. Fourteen field experiments were conducted in Iowa, Illinois, Indiana, Michigan, Wisconsin, and Ontario, Canada during 2011 to 2013. The experiment consisted of six treatment combinations of glyphosate and herbicides not containing glyphosate. Disease index was significantly different across the location-years, ranging from 0 to 65. The highest disease was noted in locations with irrigation, indicating that high soil moisture favors development of SDS. There were no effects of herbicide treatments or interactions on disease. The foliar disease index among the treatments over all years ranged from 9 to 13. Glyphosate-treatments also tended to yield more than treatments of herbicides not containing glyphosate. There were no interactions between glyphosate-treatments and total manganese in plant tissue. The interaction of glyphosate with other nutrients in plant tissue was inconclusive. This 14 location-year study demonstrated that glyphosate application did not increase SDS severity or adversely affect soybean yield under field conditions.

Meta-analysis to determine the effects of plant disease management measures: review and case studies on soybean and apple.

The continuing exponential increase in scientific knowledge, the growing availability of large databases containing raw or partially annotated information, and the increased need to document impacts of large-scale research and funding programs provide a great incentive for integrating and adding value to previously published (or unpublished) research through quantitative synthesis. Meta-analysis has become the standard for quantitative evidence synthesis in many disciplines, offering a broadly accepted and statistically powerful framework for estimating the magnitude, consistency, and homogeneity of the effect of interest across studies. Here, we review previous and current uses of meta-analysis in plant pathology with a focus on applications in epidemiology and disease management. About a dozen formal meta-analyses have been published in the plant pathological literature in the past decade, and several more are currently in progress. Three broad research questions have been addressed, the most common being the comparative efficacy of chemical treatments for managing disease and reducing yield loss across environments. The second most common application has been the quantification of relationships between disease intensity and yield, or between different measures of disease, across studies. Lastly, meta-analysis has been applied to assess factors affecting pathogen-biocontrol agent interactions or the effectiveness of biological control of plant disease or weeds. In recent years, fixed-effects meta-analysis has been largely replaced by random- (or mixed-) effects analysis owing to the statistical benefits associated with the latter and the wider availability of computer software to conduct these analyses. Another recent trend has been the more common use of multivariate meta-analysis or meta-regression to analyze the impacts of study-level independent variables (moderator variables) on the response of interest. The application of meta-analysis to practical problems in epidemiology and disease management is illustrated with case studies from our work on Phakopsora pachyrhizi on soybean and Erwinia amylovora on apple. We show that although meta-analyses are often used to corroborate and validate general conclusions drawn from more traditional, qualitative reviews, they can also reveal new patterns and interpretations not obvious from individual studies.

Application of a Rank-Based Method for Improved Cultivar Selection in Soft Red Winter Wheat.

Both grain yield and disease performance are important factors to consider for winter wheat (Triticum aestivum) cultivar selection. However, disease severity and yield data are often presented separately, making it difficult to compare values across multiple environments. The objective of this study was to use a rank-based method to compare cultivars based on yield and disease performance combined across multiple environments. Thirty-six wheat cultivars were planted at each of four Wisconsin locations (Arlington, Chilton, Janesville, and Lancaster) in 2009 and 2010. Plots were assessed four times during the growing season for powdery mildew, Septoria/Stagonospora leaf blotch, and leaf rust. Incidence and severity of Fusarium head blight were assessed at Zadoks growth stage 85 (soft dough). Within each location-year, cultivars were ranked for severity of each disease and for grain yield. One-way analysis of variance was performed to calculate an overall rank value that incorporated data for all four diseases and yield across the eight location-years. There was an effect of cultivar on overall rank (P < 0.0001). Powdery mildew rank was strongly correlated with overall rank (Spearman's rho = 0.485, P = 0.005), as was yield rank (Spearman's rho = 0.674, P < 0.0001). Cultivars described as "best" or "worst" cultivars were generally more consistent in their rankings across different measures. The use of a rank-based method provides an approach that will allow growers to base cultivar selection on multiple performance measures across multiple environments.

Effect of Location, Cultivar, and Diseases on Grain Yield of Soft Red Winter Wheat in Wisconsin.

Knowledge is limited about the impact of foliar diseases on wheat yield in Wisconsin. The objective of this study was to compare yield and diseases of wheat cultivars in several locations in Wisconsin in 2009 and 2010. Thirty-six wheat cultivars were planted in a randomized complete block design at field sites near Arlington, Chilton, and Lancaster, WI. At a fourth location, Janesville, WI, the design was a split plot with foliar fungicide application at Zadoks growth stage (GS) 45 at the whole-plot level and cultivar at the subplot level. Disease assessments were made four times during the growing season for powdery mildew (PM), Septoria/Stagonospora leaf blotch (SLB), and leaf rust. Incidence and severity of Fusarium head blight were assessed on 100 heads per plot at GS 85. Linear mixed-model analyses were used to study the effects of location, cultivar, and disease on grain yield (α = 0.05). Overall, SLB and PM were the most prevalent diseases. SLB severity was uniform among locations and PM was most prevalent at Arlington and Chilton. In both years, yield was affected by location, cultivar, location-cultivar interaction, and location-SLB and location-PM interactions. Yield was also negatively affected by PM in 2010. No effect of fungicide on disease severity or yield was observed at Janesville in either year. These results suggest that cultivar selection and location strongly influence grain yield in Wisconsin and that powdery mildew is capable of reducing grain yield.