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Case study: stratified soil sampling

Case study to show how SatAgro allows soil sampling to be planned based on biomass variability.
The state of your crop is both a result and an indicator of the underlying soil chemistry. SatAgro allows soil sampling to be planned based on biomass variability. The results will be more accurate and can be transformed into a prescription map (these will be covered in Step 2.11). For example, in a particularly wet year, parts of this hypothetical field experience wet zones that stop the crop from developing. These wet zones are visible on satellite imagery.

A

satellite map A showing a patchwork of fields an area of which is coloured red and divided into zones in a grid pattern (pale blue). The wet (dark blue) area looks like a long narrow pond with the grid superimposed.

B

satellite map B showing a patchwork of fields an area of which is coloured red and divided into zones in a grid pattern (pale blue). The wet (dark blue) area looks like a long narrow pond. In this case the dark blue area is outlined in pale blue and represents a separate zone from the rest of the grid.

Wet areas show up as dark blue in these vegetation index satellite images. The divisions into soil sampling zones are superimposed on the images in pale blue. When dividing the field up for soil sampling, designating a separate zone for the wet area (B) will lead to better, more representative results than a simple grid (A). © SatAgro

The field can therefore be divided into two areas, where:

  • the current crop has depleted the soil;

  • no crop developed, and the soil still contains nutrients.

Multiple samples can be taken from both areas but maintaining a border between the wet and ‘dry’ areas will result in more accurate soil chemistry maps as low lying, inundated areas will not be mixed in with dryer areas. The alternative approach of a simple grid division of the field captures much less of the variability. This method provides higher resolution results that can be converted into fertiliser prescription maps (see Step 2.11). Once you get the results from the lab, soil chemistry values will be assigned to the appropriate field zones, maintaining the boundary between areas affected by the moisture and the rest of the field. You’ll be able to adjust your actions to local conditions by taking the field’s variability into account.

© EIT Food
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Innovation in Arable Farming: Technologies for Sustainable Farming Systems

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