How we are using low-cost and open-source weather stations for decision support 

Dalyn McCauley

On-farm weather data can provide valuable information to growers including informing irrigation scheduling, tracking plant growth indices, and mitigating damaging events like frost, heat waves or disease. Weather can vary widely across landscapes, even across a single field, and we have found that there is value in having multiple distributed weather stations on-farm to capture variability across small spatial scales. To do this cost effectively, I developed a low-cost open-source weather station (LOCOS) for my M.S. thesis at the University of Idaho that uses low-cost sensors and an Arduino microcontroller for data logging. By distributing multiple LOCOS across a vineyard, we found that there were distinct micro-climates that had varying susceptibility to grape powdery mildew disease. From calculating a Powdery Mildew Risk Index at each station, we saw that some vineyard blocks could benefit from unique fungicide application schedules. You can read more about this project here.

Since then, the LOCOS have been adapted to study crop water stress. In the summer of 2021, we used LOCOS equipped with infrared thermometers to develop a crop water stress index (CWSI) for hazelnuts. The CWSI is based on leaf temperature and weather data (air temperature, relative humidity, wind speed, and solar radiation). Leaf temperature is a known indicator of plant stress. When a plant is actively transpiring the leaves will be cooler than the surrounding air because of the evaporative cooling effect of transpiration. Whereas a plant that is stressed and not transpiring will have a warmer canopy that is closer to the ambient air temperature. The CWSI varies from 0 to 1, where 1 indicates a stressed, non-transpiring plant, and 0 indicates a well-watered plant transpiring at max potential.  

We used the LOCOS to collect canopy temperature of the hazelnut trees from June to September, 2021. The trees were subject to three different irrigation treatments, over watered, moderate water, and no water (dryland) so we could get a range of canopy temperatures to incorporate into our model. We also collected data on leaf water potential, leaf transpiration and leaf conductance to validate the index against. We found that the CWSI we developed was closely correlated with leaf water potential (r2 = 0.84), leaf conductance (r2 = 0.75) and leaf transpiration (r2 = 0.72). These are exciting results because it shows that the LOCOS could provide continuous data on crop water stress that can be used to inform irrigation decision in near real-time. This summer, we will use the LOCOS in another study to develop a CWSI for red maples.