ITHACA, N.Y. – Cornell University researchers and collaborators have developed a new framework that allows scientists to predict crop yield without the need for enormous amounts of high-quality data – which is often scarce in developing countries, especially those facing heightened food insecurity and climate risk.
In many parts of the world, crop yields are dropping, largely due to the effects of climate change. According to a recent Cornell study, over the last four decades, for every 1 degree Celsius of warming, net farm income decreased by 66%.
Farmers in developed countries can often rely on big datasets and risk management tools to help reduce the impacts of extreme heat on their yield and income. But in developing countries, data is scarce, and it is often difficult to accurately measure crop yield.
In a paper accepted in Environmental Research Letters in March, the scientists suggest using satellite photos to remotely measure solar-induced chlorophyll fluorescence (SIF) as a way of assessing and predicting crop yield. Using sample fields of corn in the U.S. and wheat in India, the scientists have hit upon an approach that should, in principle, work universally for any crop, according to Ying Sun, a co-author and associate professor of soil and crop sciences.
Chlorophyll fluorescence is the reddish light re-emitted by photosynthetic tissues and organisms, she said, a measurement that serves as a proxy of photosynthetic energy conversion in plants.
“It won’t tell you how many ears of corn are in a field,” she said, “but step one is to model photosynthesis from fluorescence. Crop yield depends on photosynthesis. Here we have a mechanistic model, which is very important.”
This approach could be valuable for making policy decisions, establishing crop insurance and even forecasting areas of poverty. It could be employed to help food assistance organizations and nongovernmental agencies be more fleet of foot in providing aid.
The strategy takes advantage of the growing availability of satellite data and is cheaper to use and faster to access than other yield-prediction methods.
Sun said she and her colleagues are working on further research that would allow this kind of tool to be used in real time to allow farmers to react, adjusting things like soil amendments or irrigation strategies to improve a current harvest’s health and productivity.
For additional information, see this Cornell Chronicle story.
ITHACA, N.Y. – Cornell University researchers and collaborators have developed a new framework that allows scientists to predict crop yield without the need for enormous amounts of high-quality data – which is often scarce in developing countries, especially those facing heightened food insecurity and climate risk.
In many parts of the world, crop yields are dropping, largely due to the effects of climate change. According to a recent Cornell study, over the last four decades, for every 1 degree Celsius of warming, net farm income decreased by 66%.
Farmers in developed countries can often rely on big datasets and risk management tools to help reduce the impacts of extreme heat on their yield and income. But in developing countries, data is scarce, and it is often difficult to accurately measure crop yield.
In a paper accepted in Environmental Research Letters in March, the scientists suggest using satellite photos to remotely measure solar-induced chlorophyll fluorescence (SIF) as a way of assessing and predicting crop yield. Using sample fields of corn in the U.S. and wheat in India, the scientists have hit upon an approach that should, in principle, work universally for any crop, according to Ying Sun, a co-author and associate professor of soil and crop sciences.
Chlorophyll fluorescence is the reddish light re-emitted by photosynthetic tissues and organisms, she said, a measurement that serves as a proxy of photosynthetic energy conversion in plants.
“It won’t tell you how many ears of corn are in a field,” she said, “but step one is to model photosynthesis from fluorescence. Crop yield depends on photosynthesis. Here we have a mechanistic model, which is very important.”
This approach could be valuable for making policy decisions, establishing crop insurance and even forecasting areas of poverty. It could be employed to help food assistance organizations and nongovernmental agencies be more fleet of foot in providing aid.
The strategy takes advantage of the growing availability of satellite data and is cheaper to use and faster to access than other yield-prediction methods.
Sun said she and her colleagues are working on further research that would allow this kind of tool to be used in real time to allow farmers to react, adjusting things like soil amendments or irrigation strategies to improve a current harvest’s health and productivity.
For additional information, see this Cornell Chronicle story.
Journal
Environmental Research Letters
Article Title
A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)
Article Publication Date
12-Apr-2024
