By Blake Jackson
Cornell University scientists have created a method to predict crop yields using satellite data, aiding areas with limited resources. This is particularly beneficial for developing countries facing food insecurity and the harsh effects of climate change.
Traditionally, large datasets are needed for accurate yield predictions. However, such data is scarce in developing regions. This new approach utilizes satellite technology to remotely measure a plant's chlorophyll fluorescence (SIF), which indicates photosynthetic activity.
"Chlorophyll fluorescence won't tell you the exact number of crops," explains Ying Sun, a co-author and Cornell professor, "but it reflects how efficiently plants convert sunlight into energy, which is crucial for yield."
This method offers several advantages. It's cheaper and faster than existing techniques, making it ideal for resource-constrained areas. Additionally, unlike traditional models that rely on historical data, this approach can account for variations in climate.
Co-author Chris Barrett highlights the potential applications: "This could be valuable for predicting food insecurity, establishing crop insurance, and allocating resources for maximum impact."
The tool can also assist organizations in delivering critical aid. Imagine pinpointing areas with potential crop failure and proactively providing assistance, says Barrett.
Researchers are currently exploring real-time applications, allowing farmers to make adjustments during the growing season, potentially improving harvest health and productivity.
This new framework offers a promising solution for predicting crop yields in developing countries, paving the way for improved food security and climate resilience.
Photo Credit: cornell-university
Categories: New York, Education