Wind, Rain and Winter: How Weather Spreads Crop Disease

Are the weather and plant disease connected? Very much so. As I said on the latest Around the Farm Podcast, a portion of my team’s work has been dedicated to building predictive models that connect the complex relationship between plant pathogens and relative humidity, temperature, wind speed and more. Here are a few overlooked factors that could silently be impacting the presence of disease in your fields.

The threat of disease can extend beyond the growing and even harvest season. As new crops emerge in Spring, certain pathogens will be waiting within this year’s crop residue. If the environmental conditions are favorable, the disease will spread and infect next year’s crop. Proper rotation and tillage can help reduce future disease risk by burying the pathogen into the soil and allow it to break down over time.

Fungi reproduce by releasing tiny spores. When a gust of wind moves through, these spores can catch a free ride to another plant. This process keeps repeating as long as the wind current is strong enough. Given wind has been forecasted for a long time, we can now predict how certain plant diseases will spread in the same way that we forecast pollen during allergy season. Southern Rust, for example, is driven by large-scale wind patterns, so if we can predict the wind, we can use machine learning to predict the likelihood that Southern Rust (in corn) will spread in your field. 


As rain falls from the sky, fungi land on crop leaves, which creates a splash effect or can even allow the leaf to bend and “fling” the fungi forward. Tiny fungal spores can then land on another leaf and infect more plants. And the process repeats itself.

This type of disease spread doesn’t occur in a meaningful way during every rainfall. It needs to pass a certain threshold. If you look at a soybean disease like Frogeye Leaf Spot, this pathogen relies on ground disturbance, which can be from machinery moving through the field, or the rainfall. This makes tracking those weather events even more important. 


Many inputs have an optimal window of time to apply them. But with fungicides in particular, the opportunity to add the right amount, at the right time, can be extremely difficult to calculate in your head. That’s why my team is focusing on how emerging technologies, like machine learning, that can comb through this endless list of factors. We look at past rainfall, current temperature, relative humidity, the rainfall that may occur in the next 24 hours or next week, windspeed, thunderstorms, etc. Using this elite level of computation, we’re working to deliver advice on fungicide applications that is both easy to understand, and backed by an immense database of information.

Deciding when and how much to spray will never be easy. But with advancements in neural networks and machine learning, we can make finding that answer easier and more effective. Because the weather will always change, so we need as many tools as possible to think ahead of it.