ZONES OPERATIONS ACROSS DIFFERENT LAYERS
In precision farming, field data collection and data-driven decision making are absolutely essential. As the next stage in the development of multi-layers analytics and finding dependencies across layers we introduce new module Zones Operations.
There you can search for dependencies between different ag zones maps such as historical vegetation, topography including its derivatives, data from yield monitors, soil data, scanners, stability maps, and so on. This is a step forward in defining the most influenced areas and understanding the reasons for field heterogeneity.
How can you identify the areas?
First of all select field maps, you want to cross-investigate. A layer comparison view is a good approach to define specific agriculture zones for analyzing.
You may want to compare low yield potential and sloppy areas, most unstable zones and low vegetation, low electrical conductivity and yield, as applied fertilization map and current vegetation, and others.
Secondly, mark specific agriculture zones on every map you want to compare in the Zones Operations module. And finally, obtain a zone of interest. Note that it is possible to use more than two maps in analyses.
How can you apply this knowledge?
In addition to finding relationships that can help explain yield, it is possible to set yield goals for defined agriculture zones; to scout interesting areas; to reduce investments in such localized zones or build the plan of mitigating limiting factors and pull up underperforming areas knowing the underlying causes; to build agronomy plan using VRA practices.
There are several examples of field insights on screenshots. Note that every field is unique and below-mentioned cases do not guarantee 100% same result for your field but it is a good way to start the investigation from.
You are very welcome to share your agronomic practices by commenting on this post, contact the GeoPard Agriculture team directly. We are open to the feedback because we build the solution for you for a better understanding of field variability and managing it.
Crop monitoring
