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Researchers at Cornell University, along with their collaborators, have introduced a novel framework designed to predict agricultural yields using minimal data, a boon particularly for developing regions grappling with food insecurity and the impacts of climate change.
Globally, the stability of crop yields has been undermined significantly by climate change, with a notable statistic from by Clarence Oxford
Los Angeles CA (SPX) May 02, 2024
Researchers at Cornell University, along with their collaborators, have introduced a novel framework designed to predict agricultural yields using minimal data, a boon particularly for developing regions grappling with food insecurity and the impacts of climate change.
Globally, the stability of crop yields has been undermined significantly by climate change, with a notable statistic from a Cornell study highlighting a 66% decline in net farm income with each 1-degree Celsius rise in temperature over the past forty years.
While farmers in developed nations have access to extensive datasets and advanced risk management tools to mitigate the adverse effects of extreme temperatures on crop yields and financial returns, such resources are scarce in less developed areas, complicating the accurate assessment of agricultural outputs.
A recent study published in Environmental Research Letters in March advocates the use of satellite imagery to track solar-induced chlorophyll fluorescence (SIF) as a reliable predictor of crop yields. This technique has been tested in fields of corn in the U.S. and wheat in India and is posited as a universally applicable method for any type of crop, explains Ying Sun, an associate professor of soil and crop sciences and co-author of the study.
Chlorophyll fluorescence, which appears as a reddish light emitted by photosynthetic tissues, indicates the efficiency of photosynthesis, the foundational process for crop yield, Sun notes. While this method cannot directly count the produce, it facilitates the modeling of photosynthesis, which is crucial for estimating yields.
Employing this method can significantly aid in policy-making, the creation of crop insurance schemes, and even in forecasting poverty zones. It offers a rapid, cost-effective alternative to traditional yield prediction techniques, enabling aid organizations and NGOs to respond more quickly and effectively in providing necessary support.
The research team, led by Sun, is currently enhancing this framework to enable real-time applications that could allow farmers to adjust agricultural practices like soil treatment and irrigation to optimize the health and output of their crops.
Research Report:A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)
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