Web-based Weather Station Helps Validate Crop Disease Models

Market: 
Outdoor
Organization: 
Wisconsin State Extension
Summary: 
A state extension specialist at the University of Wisconsin uses a remote web-based weather monitoring system to help address real-world challenges in soybean and small grain production.

A conversation with Shawn Conley, Wisconsin State Extension Soybean and Small Grain Specialist

Shawn Conley, an Associate Professor and State Extension Specialist at the University of Wisconsin-Madison, uses Onset’s HOBO U30 Remote Monitoring Systems to help address real-world challenges in soybean and small grain production. We recently spoke with Shawn about how the web-based systems are helping him with his research.

Onset: How do data logging weather stations help you with your research?

Conley: I use HOBO® U30-GSM Remote Monitoring Systems at four of our winter wheat research test sites in Wisconsin to gather weather data to determine whether or not there is a risk for plant disease on regional soybean and wheat crops. We compare the collected data with information from the scab model to validate the model’s predictions.

The scab forecasting system was developed using 50 years of weather data. The model’s risk assessment is based on the number of days projected to have high precipitation and humidity levels, the optimal time for fungal spore development. During high humidity and precipitation, plants are more likely to develop disease.

In 2009, there was significant pressure on growers to apply a fungicide to their soybean crop. We analyzed the data collected from the HOBO U30 systems and discovered that weather conditions were not conducive for disease development, and it would not be in the grower’s best interest to spray fungicide on their crops, which can run upwards of $20 to $30 an acre for soybeans.

Onset: Do you need regional weather data? How did you deal with this in the past?

Conley: Right now, the in-field data indicates that the model is working. It takes the regional weather data and runs it in the background. We are trying to fine-tune the system to make sure that the predictions we receive match the data that we collect from the HOBO U30.

Presently, we don’t have yearly climate trends because we only purchased the HOBO U30 systems about one year ago.

Onset: What are the weather stations measuring?

Conley: We’ve been monitoring leaf wetness, soil moisture, temperature, and humidity. The leaf wetness data helps us determine if conditions are optimal for disease development.

Luckily, we had a situation where one of the soil moisture sensors set off an alarm notification alerting us that one of the areas we were monitoring was going to be affected by drought. We used that information to send to growers and crop consultants to help them make better management decisions.

Onset: How do you work with the data?

Conley: We typically download the data into HOBOware® Pro and export it into Excel. Sometimes we include the graphs on our blog, TheSoyReport.

Onset: Do you have any particular challenges with respect to collecting and retrieving data?

Conley: We haven’t come across any challenges collecting the data. We have taken advantage of the web-based platform and are able to view data at all of our sites, which are many miles away.

We typically download data first thing in the morning and are able to perform any system diagnostics that are necessary on the U30 right from our computer.

Onset: What are some of the product features you look for in a weather station?

Conley: We wanted something that was simple to install, user-friendly, and provided us with a quick and easy way to interpret the data.

Ideally, we hope to get more funding so that we can install additional systems across the state to get more information on conditions that affect the crops.

Onset: What were your funding sources for the HOBO U30s?

Conley: We received two grants. One grant was from the U.S. Scab Initiative and the other was from the Wisconsin Crop Improvement Association. We purchased the other two systems out-of-pocket.