Create Random or LHC Sample Plans (Zoning Tools)

There are a few places and methods for creating soil sampling plans in PCT Agcloud. Under the Soil Data tab in the View tool, Grid, Zone, and Composite plans can be created. You can also create sample plans within the Create Zones/Zone Locate tool and the k-Means Clustering tool using either manual, random, or Latin hypercube options. See the video or article below to learn how to do this. 

-Note: The example below uses the Create Zones tool (in View), but the same process applies to the Zone Locate tool and the k-Means Clustering tool.

After an existing zone layer has been selected or a new one has been created, sample points can be plotted in three ways: manual, random, and Latin hypercube. 

When using any of the methods, the location of the points can be adjusted by clicking and dragging. 

Delete points by clicking the 'X' at the bottom of the list. 

The point ID's can be edited under the "name" column, and duplicate ID's can be used to create a whole field or zone composite plan. 

When a plan is finished, click 'Save points'. 

After the plan has been saved, it will appear under the 'Sample Plans' tab in the View tool.

If the plan is for zone sampling, be sure to check the 'Process by Zones' box under the 'Soil Plan Properties' tab. 

Manual

To plot sample points manually, simply click within the map. 

Random

The 'Generate Random Sample Points' option utilizes the created management zone layer and plots the number of samples entered under the 'Samples per zone' line randomly within each zone. The buffer ensures that points are placed away from the field and zone boundaries.

LHC

The 'Generate LHC Sample Points' option considers data in the input layers to optimize where the sampling sites are located. The samples are positioned so that they capture the maximum amount of variability in the input layers from the given number of sampling locations. This minimizes the correlation between sampling points and increases the overall efficiency of the sampling plan. The LHC option is most suited to the k-Means Clustering tool. 

LHC will not necessarily put an equal number of samples in each zone. 

The number of samples can be set by field or per acre. And the buffer ensures that points are placed away from the field and zone boundaries.