Consortium partner David Lobell and colleagues at Stanford University including George Azzari (Atlas AI) and Christopher Seifert collaborated with Allegra Beal Cohen (University of Florida) to publish their work on Rotation Effects on Corn and Soybean Yield Inferred from Satellite and Field-level Data. This research explores the use of satellite data in evaluating crop rotation practices without the use of ground data.
It may often be the case that data at the field-scale is unavailable either due to privacy rights or lack of research scope. Satellite technology has improved vastly in accuracy and availability over the last decade in particular, allowing for agricultural analysis of crop yields. Furthermore, when satellite imagery is evaluated along with field data, the effects of crop management methods can be assessed.
It is Harvest’s mission to promote the collaborative use of Earth observation data in agricultural monitoring and while the use of this data is expanding, it has been used only sparsely on its own in determining management practices’ effects. With this fact in mind, the team of researchers set out to determine if satellite data could accurately mirror existing field data on the yield benefits of rotation practices. They focused on crop rotation of corn and soybean, adjusting for environmental differences between rotational and non-rotational fields. Ultimately their findings showed that satellite data is a viable option to evaluation these rotational practices, even without the use of ground data.
Read the full publication in The Agronomy Journal for an in-depth look at their results.