Filling the agronomic data gap through a minimum data collection approach.

Context: Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist.

Objectives: Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas.

Integration of Soybean () Resistance Levels to Sclerotinia Stem Rot into Predictive Apothecial Models.

Sclerotinia stem rot (SSR) is a major disease of soybean across the Upper Midwest region of the United States. Management of this disease has relied on fungicide applications, but due to the environmental conditions necessary for SSR to develop, many of these applications are unnecessary. To mitigate this, predictive models have been developed using localized weather data for predicting the formation of apothecia, the inoculum source of SSR, and these models were integrated into a decision support system called Sporecaster.

Integration of Row Spacing, Seeding Rates, and Fungicide Applications for Control of Sclerotinia Stem Rot in .

Soybean () farmers in the Upper Midwest region of the United States often experience severe yield losses due to Sclerotinia stem rot (SSR). Previous studies have revealed benefits of individual management practices for SSR. This study examined the integration of multiple control practices on the development of SSR, yield, and the economic implications of these practices.

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.

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.

Host-Induced Gene Silencing of a Using Bean Pod Mottle Virus as a Vehicle Reduces Disease on Soybean.

A lack of complete resistance in the current germplasm complicates the management of Sclerotinia stem rot (SSR) caused by in soybean. In this study, we used bean pod mottle virus (BPMV) as a vehicle to down-regulate expression of a key enzyme in the production of an important virulence factor in , oxalic acid (OA). Specifically, we targeted a gene encoding oxaloacetate acetylhydrolase (), because deletion mutants are OA deficient and non-pathogenic on soybean.

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.

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.

Assessing Variation in US Soybean Seed Composition (Protein and Oil).

Soybean [ (L.) Merr.] seed composition and yield are a function of genetics (G), environment (E), and management (M) practices, but contribution of each factor to seed composition and yield are not well understood.

Meta-Analytic and Economic Approaches for Evaluation of Pesticide Impact on Sclerotinia Stem Rot Control and Soybean Yield in the North Central United States.

As complete host resistance in soybean has not been achieved, Sclerotinia stem rot (SSR) caused by continues to be of major economic concern for farmers. Thus, chemical control remains a prevalent disease management strategy. Pesticide evaluations were conducted in Illinois, Iowa, Michigan, Minnesota, New Jersey, and Wisconsin from 2009 to 2016, for a total of 25 site-years ( = 2,057 plot-level data points).

TBA, a fusion toxoid vaccine for protection and broad neutralization of staphylococcal superantigens.

Superantigens (SAgs) play a major role in the pathogenesis of Staphylococcus aureus and are associated with several diseases, including food poisoning, bacterial arthritis, and toxic shock syndrome. Monoclonal antibodies to these SAgs, primarily TSST-1, SEB and SEA have been shown to provide protection in animal studies and to reduce clinical severity in bacteremic patients. Here we quantify the pre-existing antibodies against SAgs in many human plasma and IVIG samples and demonstrate that in a major portion of the population these antibody titers are suboptimal and IVIG therapy only incrementally elevates the anti-SAg titers.

Defining Optimal Soybean Sowing Dates across the US.

Global crop demand is expected to increase by 60-110% by 2050. Climate change has already affected crop yields in some countries, and these effects are expected to continue. Identification of weather-related yield-limiting conditions and development of strategies for agricultural adaptation to climate change is essential to mitigate food security concerns.

Spectral Data Collection by Dual Field-Of-View System Under Changing Atmospheric Conditions-A Case Study of Estimating Early Season Soybean Populations.

There is an increasing interest in using hyperspectral data for phenotyping and crop management while overcoming the challenge of changing atmospheric conditions. The Piccolo dual field-of-view system collects up- and downwelling radiation nearly simultaneously with one spectrometer. Such systems offer great promise for crop monitoring under highly variable atmospheric conditions.

Benefits and Profitability of Fluopyram-Amended Seed Treatments for Suppressing Sudden Death Syndrome and Protecting Soybean Yield: A Meta-Analysis.

A meta-analytic approach was used to summarize data on the effects of fluopyram-amended seed treatment on sudden death syndrome (SDS) and yield of soybean (Glycine max L.) in over 200 field trials conducted in 12 U.S.

Resistance against Sclerotinia sclerotiorum in soybean involves a reprogramming of the phenylpropanoid pathway and up-regulation of antifungal activity targeting ergosterol biosynthesis.

Sclerotinia sclerotiorum, a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield-limiting disease of soybean called Sclerotinia stem rot. Resistance mechanisms against this pathogen in soybean are poorly understood, thus hindering the commercial deployment of resistant varieties. We used a multiomic approach utilizing RNA-sequencing, gas chromatography-mass spectrometry-based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance to S.

Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields.

In soybean, Sclerotinia sclerotiorum apothecia are the sources of primary inoculum (ascospores) critical for Sclerotinia stem rot (SSR) development. We recently developed logistic regression models to predict the presence of apothecia in irrigated and nonirrigated soybean fields. In 2017, small-plot trials were established to validate two weather-based models (one for irrigated fields and one for nonirrigated fields) to predict SSR development.

Revisiting Planting Date and Cultivar Effects on Soybean Sudden Death Syndrome Development and Yield Loss.

The impact of today's optimal planting dates on sudden death syndrome (SDS) (caused by Fusarium virguliforme) development and soybean yield loss are not yet well understood. Field trials established in Hancock, Wisconsin during 2013 and 2014 investigated interactions between planting date and cultivar on SDS development and soybean yield. In 2013, disease index (DX) levels differed among cultivars, but results showed no difference between the 6 May and 24 May planting dates.

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.

Climate-induced reduction in US-wide soybean yields underpinned by region- and in-season-specific responses.

The United States is one of the largest soybean exporters in the world. Production is concentrated in the upper Midwest(1). Much of this region is not irrigated, rendering soybean production systems in the area highly sensitive to in-season variations in weather.

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.