Remote-Sensed Data, Crop Modeling Helps African Farmers
Brad Peter, a Michigan State University doctoral candidate is focusing his research on improving agricultural systems by integrating remotely-sensed data with crop modeling. The primary site for his research is Malawi, though the models he’s developing can be applied more broadly to East Africa and the African continent.
“The overarching objective of my dissertation is to improve agricultural systems by integrating remotely-sensed data with crop modeling, ultimately to recommend improvement applications that are informed by spatiotemporal analytics of crop performance,” Peter said. His research proposes maps and methods that illuminate the scaling potential of various agricultural improvement strategies. At the center of this work is the smallholder farm.
“The reason for focusing on these areas is that population pressure, soil exhaustion, and climate change have led to food shortages across the African continent, particularly in Sub-Saharan Africa and Malawi,” Peter said. “Remote sensing data offer information in remote areas where fine resolution on-the-ground data are not collected regularly.”
In the field, Peter works with colleagues fromLilongwe University of Agriculture and Natural Resources (LUANAR) and Africa RISING (Research in Sustainable Intensification for the Next Generation). They provide valuable connections to local farmers and Extension Planning Area officers.
“These relationships are necessary to be effective in the field,” he said.
When Peter is in Malawi, he often talks with farmers about his research, but the real work is the relationship with the Extension Planning Area officers, who have deep knowledge of their respective agricultural areas.
“The data that the weather stations have been collecting over the last couple years are currently being used by agronomists and crop modelers to monitor agricultural response to climatic drivers,” Peter said. In the lab, the weather station data, collected in Malawi, are being linked with satellite measurements in order to develop more robust soil moisture models.