Newly published Michigan State University research shows that incorporating in-season water deficit information into remote sensing-based crop models drastically improves corn yield predictions.
The findings were published in Remote Sensing of Environment, a leading journal in the field.
The project was led by Bruno Basso, an MSU Foundation Professor in the Departments of Earth and Environmental Sciences, and Plant, Soil and Microbial Sciences, as well as the W.K. Kellogg Biological Station. Alongside Basso was his graduate student Guanyuan Shuai.
Yield predictions are of great importance, from national and international food supply chains to the individual grower. In addition to ensuring food security, highly consequential financial decisions are made based on this information. Growers must decide how much fertilizer and other inputs to apply to their fields, for example, an area in which costs have soared for numerous reasons, including climate change and global conflict.
“An accurate knowledge of yield predictions before the end of the season is of paramount importance for grain prices, which affects profitability for farmers, as well as commodity traders and food companies,” Basso said.
To read the full story, visit canr.msu.edu
