The talk presents a novel geospatial system approach that integrates different digital technologies to design and scale sustainable agricultural systems. This approach can be used to guide precision management of fields and nutrient inputs that could result in simultaneous improvement of environmental and economic performance of row crop production systems. The availability of Big-Data and sensing technologies have clearly shown improvements towards more effective management strategies but precisely matching crop N demand with supply remains a challenge.
A possible explanation for this standing challenge is to achieve more substantial and widespread environmental benefits is that thus far, the algorithm developers for precision management have lacked the data and computational tools needed to convert complex geospatial information on soil and plant health status into appropriate crop management actions, leading in some instances to misinterpretation or misuse.
Currently, many farmers utilize precision technology to apply more N fertilizer to low-yielding areas in the hope of increasing yields, rather than less N to avoid loss of nutrient that crops cannot use. As such, the technology can become counterproductive toward N conservation goals. The talk presents case studies with real farm data in addition to the introduction of new approaches to scale results from field to regions. Specifically, results showed that more than a third of the US Midwest cropland is unprofitable, but at the same time digital technologies can identify sustainable management practices that can revert these negative results.
In summary, the talk illustrates the critical need of a geospatial system approach to design strategies for precision plant health management and precision conservation to balance the economic, environmental, and social dimensions of sustainable food production.