Fukuoka, Japan—Researchers from Kyushu University have developed a new numerical model to explain the behavior of sunlight under various weather conditions. Unlike previous methods that focused on how plants perceive sunlight as a pure source of energy, this study introduces a metric to quantify sunlight intensity and its influence on plant growth and blooming patterns. Published in Ecological Informatics , the team hopes their research can benefit the agricultural industry and help farmers make better decisions about crop growth. This advancement is crucial for understanding the variations in plant photosynthesis in response to different sunlight and weather patterns, moreover it may also provide new insight into how changes in cloud cover due to climate change affect vegetation carbon dioxide uptake.
We know plants need the sun to live because it provides the energy for photosynthesis. And while conventional wisdom suggests that the more sun a plant gets the better, it is always not the case. In fact, because of how light is scattered, cloudy weather can greatly benefit a plant's growth. On cloudy days, sunlight is scattered out more evenly, allowing it to reach lower parts of the plant. In contrast, on sunny days, sunlight energy is stronger but comes from a single direction, causing the leaves on the lower parts of the plant remain shaded, and therefore do not absorb as much sunlight.
"Plants also respond to different wavelengths of sunlight. If you have ever seen a rainbow, you know that sunlight is made up of many wavelengths. Plants sense these different wavelengths of light and alter their growth responses," explains Amila Siriwardana, first author and PhD student at Kyushu University's Faculty of Agriculture.
Plants can detect the ratio of wavelengths to understand their surroundings. These ratios are affected by many factors, including atmospheric density, cloud cover, or the altitude of the sun. However, such information had not been categorized in the context of plant physiology and ecology. So, to see how daily sunlight changes during different weather conditions, the research team began a project collecting yearlong sunlight data.
"While many projects look at only the 'energy' produced by the sun, our methods set out to categorize both the 'energy' and 'quality' of light. By sorting sunlight into five categories, from clear skies to overcast, we can better understand how plants can adjust and respond to different light conditions," adds Professor Atsushi Kume, who led the team.
To record sunlight across different times of day and weather conditions the researchers used a device called a spectroradiometer that measures the full spectrum of sunlight. The spectroradiometer was placed on top of Kyushu University's Faculty of Agriculture building on Ito campus, where it collected data from sunrise to sunset every day in 2021.
Using the collected data, the researchers then developed a machine learning model to sort and predict how sunlight changes. The model works by spotting patterns in weather data such as how much sunlight reaches the ground, how much the light is scattered, how polluted or clear the weather is, and humidity. From these patterns, the model categorized sunlight into five groups on a sliding scale. For example, group one was categorized as a clear sunny day, while group five described overcast cloudy days.
The team noticed that on clear days, like those categorized as group one, more sunlight energy reaches the ground. Conversely, as the sky becomes more overcast, the sunlight energy decreased, but at the same time the scattering of light and the proportion of ultraviolet light increased. Additionally, the color of sunlight shifts slightly, moving from red tones on sunny days to blue tones under cloudy conditions.
Once the numerical model was trained, it could make highly accurate predictions of sunlight patterns using simpler weather data.
"Our method achieved 94% accuracy in predicting sunlight category without the need for expensive advanced equipment," says Siriwardana. "Our model is especially relevant for regions experiencing four distinct seasons similar to Japan's."
"We hope our model can be used to address the challenges in modern agriculture. Farmers can use this information to improve greenhouse conditions and planting schedules throughout the year," Kume explains. "For instance, during Japan's cloudy rainy season in June, farmers might adjust their greenhouse operations or crop spacing to maximize available light. Even in the fall and winter, when sunlight patterns change, farmers can adapt their strategies based on our model."
In the future, the team hopes to expand the model to cover more climate types, such as high-altitude or tropical regions, and further understand how sunlight affects the environment.
