Effect of fungicide and timing of application on management of Phoma black stem of cultivated sunflowers in the United States.

Phoma black stem (PBS), caused by Phoma macdonaldii Boerema (teleomorph Leptosphaeria lindquistii Frezzi), is the most common stem disease of sunflower (Helianthus annuus L.) in the Northern Great Plains (NGP) region of the United States (US). However, the impact of PBS on sunflower yield in the US is unclear, and a near complete absence of information on the impact of fungicides on disease management exists. The objectives of this study were to determine the impact of PBS on sunflower yield, the efficacy of available fungicides, the optimal fungicide application timing, and the economic viability of fungicides as a management tool. Fungicide timing efficacy was evaluated by applying single and/or sequential applications of pyraclostrobin fungicide at three sunflower growth stages in ten field experiments between 2017 and 2019. Efficacy of ten fungicides from FRAC groups 3, 7, and 11 were evaluated in four field experiments between 2018 and 2019. The impact of treatments on PBS were evaluated by determination of incidence, severity, maximum lesion height (MLH), disease severity index (DSI) and harvested yield. Nine of the ten fungicides evaluated, and all fungicide timings that included an early bud application, resulted in disease reductions when compared to the non-treated controls. The DSI was negatively correlated to sunflower yield in high-yield environments (p=0.0004; R2 = 0.3425), but not in low- or moderate- yield environments. Although FRAC 7 fungicides were generally most efficacious, the sufficient efficacy and lower cost of FRAC 11 fungicides make them more economically viable in high-yielding environments at current market conditions.

Evaluation of OptRx™ active optical sensor to monitor soybean response to nitrogen inputs.

BACKGROUND: Active optical crop sensors have been gaining importance to determine in-season nitrogen (N) fertilization requirements for on-the-go variable rate applications. Although most of these active in-field crop sensors have been evaluated in maize (Zea mays L.) and wheat (Triticum aestivum L. emend. Thell.), these sensors have not been evaluated in soybean [Glycine max (L.) Merr.] production systems in North Dakota, USA. Recent research from both South Dakota and North Dakota, USA indicate that in-season N application in soybean can increase soybean yield under certain conditions. RESULTS: The study revealed that OptRx™ sensor reading did not show any significant differences from early to midway through the growing season. The NDRE (normalized difference red edge) index data collected towards the end of the growing season showed significantly higher values for some of the N treatments as compared to others in both years. The NDRE values were strongly correlated to grain yield for both years under tiled (r = 0.923) and non-tiled (r = 0.901) drainage conditions. Certain soybean varieties displayed significantly higher NDRE values over both years. The three varieties tested across years, under both tiled and non-tiled conditions, showed a significant linear relationship between late August NDRE values and yield (R2  = 0.85 for tiled and R2  = 0.81 for non-tiled). CONCLUSION: In this research, the study results show that the OptRx™ sensor has the potential to work for soybean as well, though later in the crop growing season. Further investigation is needed to confirm the use of OptRx™ sensor for variable rate in-season N applications in soybeans. © 2019 Society of Chemical Industry.

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

Soybean [Glycine max (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. The goal of this synthesis-analysis was to identify the main effects of G, E, and M factors on seed composition (protein and oil concentration) and yield. The entire dataset (13,574 data points) consisted of 21 studies conducted across the United States (US) between 2002 and 2017 with varying treatments and all reporting seed yield and composition. Environment (E), defined as site-year, was the dominant factor accounting for more than 70% of the variation for both seed composition and yield. Of the crop management factors: (i) delayed planting date decreased oil concentration by 0.007 to 0.06% per delayed week (R 2∼0.70) and a 0.01 to 0.04 Mg ha-1 decline in seed yield per week, mainly in northern latitudes (40-45 N); (ii) crop rotation (corn-soybean) resulted in an overall positive impact for both seed composition and yield (1.60 Mg ha-1 positive yield difference relative to continuous soybean); and (iii) other management practices such as no-till, seed treatment, foliar nutrient application, and fungicide showed mixed results. Fertilizer N application in lower quantities (10-50 kg N ha-1) increased both oil and protein concentration, but seed yield was improved with rates above 100 kg N ha-1. At southern latitudes (30-35 N), trends of reduction in oil and increases in protein concentrations with later maturity groups (MG, from 3 to 7) was found. Continuing coordinated research is critical to advance our understanding of G × E × M interactions.

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. Although the influence of in-season weather trends on the yields of crops such as soybean, wheat and maize has been explored in several countries(2-6), the potentially confounding influence of genetic improvements on yields has been overlooked. Here we assess the effect of in-season weather trends on soybean yields in the United States between 1994 and 2013, using field trial data, meteorological data and information on crop management practices, including the adoption of new cultivars. We show that in-season temperature trends had a greater impact on soybean yields than in-season precipitation trends over the measurement period. Averaging across the United States, we show that soybean yields fell by around 2.4% for every 1 °C rise in growing season temperature. However, the response varied significantly among individual states, ranging from -22% to +9%, and also with the month of the year in which the warming occurred. We estimate that year-to-year changes in precipitation and temperature combined suppressed the US average yield gain by around 30% over the measurement period, leading to a loss of US$11 billion. Our data highlight the importance of developing location-specific adaptation strategies for climate change based on early-, mid- and late-growing season climate trends.