It might be easier to pinpoint the timing of seasonal plant events, such as fruiting or leaf senescence, using time series images captured by PhenoCams, tower-mounted digital cameras operated by scientists participating in research on long-term agroecosystem (LTAR) network.

Led by the Agricultural Research Service (ARS), LTAR uses the experimentation and coordinated observations of 18 federal, non-government and academic research sites across the country to develop a national ‘roadmap’ for the sustainable scale-up of agriculture. The effort, which began in 2012, has two simultaneous objectives. The first is to provide food, fiber, animal feed and fuel to a growing global population likely to exceed 9 billion people by 2050. The second is to ensure that the soil, the water and other natural resources are not overexploited in the process, including important ecosystem services, such as water filtration and nutrient recycling.

In partnership with the PhenoCam network, ARS ecologist Dawn Browning and her LTAR team use data generated from time series images to measure the impact of climate change on plant phenology (seasonal synchronization of events plants) in three types of agroecosystems: cropland, pasture land and combinations of the two.

“Understanding how higher temperatures, more frequent droughts and floods, and changes in the timing and amount of rainfall influence the seasonal dynamics of forages and crops can guide decisions on which best practices to adopt or adapt to reduce risk of loss and maintain yield. Said Browning, of the ARS Range Management Research Unit in Las Cruces, New Mexico.

The Long-Term Agroecosystems Research Network (LTAR) helps scientists use time-series image data to measure the impact of climate change on seasonal events in plants, such as fruiting or leaf senescence.

Although several sensor technologies, including those based on satellites, are available to obtain measurements of plant phenology such as greenery and canopy area, there is little information on how the data generated. by each are continentally correlated, noted Browning, lead author of a paper on being published in the journal Ecological Indicators.

To find out, she and collaborators from more than a dozen ARS sites and state universities analyzed phenological data generated by three different sensor platforms: the Landsat satellite, the PhenoCam towers and the Foucault covariance. The latter measures the exchange of gases such as CO2 between the soil, air and vegetation within a defined area.

Based on the results, the team designed an assessment tool, called a ‘metric assessment framework’, which, for the first time, shows where and how the phenological estimates derived from the three sensors complement, diverge or intersect. duplicate. For example, Landsat-based estimates for the length of the growing season were significantly longer than those generated from PhenoCam and eddy covariance data, while the latter two sensors provided high ground detail and metrics. resolution not possible with an orbiting satellite.

Browning said use of the framework should improve the selection of sensing instruments used by producers, land managers and policy makers to model, monitor and forecast plant phenology in agroecosystems across the country, helping to ensure their maximum productivity and continued health and use for future generations. as well as the challenges of food security to come.