Update:
Objective 2: To identify when during germination and emergence are soybeans most susceptible to Pythium.
Data were analyzed and a manuscript is being drafted for submission to Plant Disease. At 18°C, soybeans were most susceptible to Pythium root rot at either GS1 (water imbibition [0-1 day after planting [DAP]), GS2 (radicle growth [1-3 DAP]), or GS3 (emergence [4 DAP]) depending on the species. With a period of cold stress (10°C) soon after planting, soybeans were most susceptible to Pythium root rot at either GS1 (water imbibition [0-1 day after planting DAP]) or GS2 (radicle growth [1-3 DAP]) depending on the species. At 7-10 DAP, little infection of soybean by Pythium was observed.
Objective 4. To develop a seedling disease risk assessment model that growers could use to schedule planting
A model was developed to predict emergence and seed loss as a function of temperature, planting depth, soil moisture, presence of inoculum of a soil borne pathogen (Pythium sylvaticum), and use of fungicide seed treatment. Experiments in 8oz foam cups filled with vermiculite under controlled conditions were conducted to study the effect of each factor on soybean emergence and seed loss. The number of emerged soybean plants were recorded daily. At least 5 replications (cups) were evaluated per treatment. Two independent runs were performed for each experiment.
Experiment 1: the seeds were planted at 10, 18, 27, 35 and 40 °C.
Experiment 2: the seeds were planted at 18 °C in cups at three different soil depth: 0 inch (0 mm), 1 inch (25.4 mm), 2 inch (50.8 mm) and 3 inch (76.2 mm).
Experiment 3: seeds were planted at 18 °C in a factorial experiment with naked seeds or fungicide treated seed, cups inoculated with Pythium sylvaticum or non-inoculated cups, and three different soil moistures (10, 22 and 40 %VWC) to simulate conditions of water deficit, optimal and saturated soil condition. Seed was treated with Intego Suite™ (clothianidin, ethaboxam, ipconazole and metalaxyl) according to label recommendations. Pythium sylvaticum inoculum was grown on millet seed. 5 ml of infested millet or sterile millet (non-inoculated control) were added to each cup. Soil moisture was monitored with a moisture sensor SM 100 and recorded with a data logger Watch Dog 1000 Series, Spectrum Technologies®.
Data analysis. Emergence curves over time were plotted for each treatment using ggplot2 package in R, version 1.2.1139 (© 2009-2018 RStudio, Inc.). Emergence rate was calculated dividing 1 by days to 50 % emergence (days required for 50% of the seeds to emerge). Days to 50% emergence were interpolated using the function aprox() for each emergence curve. Seed loss expressed as percentage was calculated for each curve by subtracting the maximum emergence to 100. Emergence rate and Seed loss (%) were plotted versus each individual factor (Temperature, Planting depth, Soil Moisture, and use of seed treatment). A linear regression was performed to describe the relationship observed in each graph.
Calibration and Validation data. Data from growth chamber experiments were used as a calibration data. To validate the model, we used a consolidated data base of Robertson’s Lab seed treatment field trials (2011 to 2016) with stand count data and also data from Archontoulis Lab (FACTS project).
Results
Experimental results and derivation of model parameters. The base temperature for soybean emergence rate was 5 °C, at temperatures below °C there is no emergence. The optimum temperature for emergence was 35 °C. Between base and optimum temperature range the rate of emergence linearly increases. Beyond the optimum temperature the rate of emergence sharply declines to no emergence at 40 °C. The emergence rate was zero at 40 °C because the amount of emerged seedlings were less than 50 %. Seed loss was between 18 °C and 35 °C and increased outside this temperature range.
Planting depth was the second most important variable explaining emergence date. Time to emergence increased when the seed is planted deeper. Seed loss was minimal when seed was planted between 0 and 1 inch depth. When the seed was planted between 1 to 2 inches, seed loss increased at a rate of 0.39 % for each 1 mm increase in depth; and when the seed was planted deeper than 2 inches , seed loss increased 1.8 % for each 1 mm increase in depth.
Soil moisture also affected the time of emergence. When seeds were planted at different soil moisture (10, 22 and 40 % Volumetric Water Content, VWC) representing wilting point, field capacity and saturation. The emergence rate was slightly higher at field capacity; and similarly, seed loss was slightly lower when soil was at field capacity.
When the seeds were planted in cups inoculated with Pythium sylvaticum, the use of a seed treatment increased emergence rate. Similarly, seed loss was substantially reduced with the use of seed treatment.
These data were used to develop a model in APSIM (Figure 2). Input variables include Sowing date, Plants (=Seeding rate), Planting depth, Seed treatment and Pathogen (= history of seedling disease). Examples of output scenarios are shown in Figure 3. In this scenario, a planting date of May 5 was used, and a planting depth of 2.0". Treated seed reached 50% emergence 1 day behind naked seed in optimum moisture conditions, however, 84% (32 of 38 seed per 10.8 square feet) of treated seed emerged compared to 58% of naked seed. In wet conditions, naked seed reached 50% emergence 26 days after planting (DAP) compared to treated seed that reached 50% emergence 22 DAP. The number of plants emerged for naked seed in wet conditions was considerably lower (8 of 38 seed planted) than for treated seed (29 or 38 seed planted).
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In Iowa, frequent rains and cool temperatures at planting favor seedling diseases of soybean caused by pathogens that live in the soil. These pathogens belong to the following genera: Pythium, Fusarium, Phytophthora and Rhizoctonia. To protect against seedling disease, soybean seed can be coated with a combination of fungicides called a seed treatment. Seed treatments are an extra input cost for farmers. If conditions are warm and dry after planting, it is possible that a farmer may not need to spend money on seed treatment.
The goal of our research was to improve our understanding of the factors that favor soybean seedling disease caused by Pythium species. Although farmers try to plant when conditions are good (warm and moist), it is not uncommon for cold fronts to pass through the state resulting in cold, wet (saturated) conditions soon after planting. Therefore we evaluated the effect of cold stress on seedling disease. We also did experiments to identify when during germination or emergence the soybean seedling was infected by Pythium. These data plus additional data on the effect of planting depth, soil moisture and seed treatment were used to develop a model for predicting stand loss due to soybean disease. This tool could be used by soybean farmers could use to schedule planting and make seed treatment decisions to ensure successful stand establishment. Lastly, we tested soybean varieties to see if there was a relationship between seedling disease caused by Pythium species and cold tolerance (early season vigor).
We demonstrated that when cold (<55F), wet soil conditions occurred 2 to 4 days after planting, there was more seedling disease that if soil temperatures were at 60F and increasing. Using a seed treatment mitigated the seedling disease and emergence of treated soybean in cold, wet conditions was equivalent to soybean planted in warm, moist conditions. Infection of soybean primarily occurred 1 to 4 days after planting (seed imbibition and emergence of the radicle). These data suggests that if a cold front occurs more than 4 days after planting, there will be minimal seedling disease.
We were unable to determine a relationship between cold tolerance and seedling disease. Since seed quality can affect early season vigor and seedling disease, it is possible seed quality may have confounded our results.
An emergence model is available in APSIM for farmers to compare various scenarios and make seed treatment (or simply planting) decisions. To compare scenarios, a farmer inputs the following data: planting date, seeding rate, seeding depth, soil moisture, whether the field has a history of stand establishment problems and if a seed treatment was used. Running the model estimates the time to 50% emergence and number of plants emerged for each scenario. Currently, the model can only be run in APSIM, but we hope to secure funding to make the model available as a web-based tool, and perhaps even a smartphone app.