Spatial Sampling Tool
A Decision Support Tool for managing the distribution of samples or sensors
Data Modelling
Sample4Crop
The Spatial Sampling Tool is based on the Sampling4Crop model and combines machine learning algorithms and spatial statistical methods to create sampling maps that represent the number and location of sensors or samples in an agricultural field or orchard. The tool outputs the numbers of samples, their location and their accuracy in predicting the sampled variable, for different possible numbers of samples. By combining spatial statistical methods and addressing spatial autocorrelation, the model accounts for the spatial structure, which enhances the accuracy of the sampling design in capturing the spatial structure of the sampled variable.
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Is not operational right now
FAQs
The output of the Sample4Crop model consists of a model validation graph which illustrates the root mean square error (RMSE) for each sampling configuration, which was tested.
Currently you have to insert the minimum required variables. We are working on future updates of the model and tool that will allow you to insert additional variables such as past yield and additional soil chemical properties.
Currently the tool can also sample Stem Water Potential (SWP) and Soil Water Content. We are working on future updates of the model and tool that will allow you to sample additional field and crop variables. Sign-up and we will keep you updated when new tools are released.
Using the tool is free of charge and accessible for all.
You can access the links to the published peer-reviewed publications that appear on the homepage in order to find out more information about the algorithms used in this model and the tool.
If you are interested in a workshop please drop us a note…
FAQs
