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Productivity Map

In this article you will find information on the use of Productivity Maps in agriculture and how it is implemented in Farm360.


You may be interested in browsing the following related articles: Productivity Map Report and Agricultural management layers.

What is a Productivity Map?

The productivity map reflects the variability of the field by analyzing the vegetation index NDVI.

It allows dividing the field in classes from very low to very high productivity, thus creating variability polygons that will later help the agronomic advisor to make the most convenient decisions for managing the field.




How is it generated?

The productivity maps are generated based on quantile or cluster analysis of images selected over time from the Sentinel and Landsat sensors. The selection of the images correspond to key dates, where the crops reflect a high level of activity, related to the phenological and maturation stages of the vegetation.

The selected images are normalized to be able to carry out the classification. Cluster analysis consists of grouping  pixels by similar values, seeking maximum homogeneity within each class and the maximum differences between classes. On the other hand, the classification by quantiles distributes equally in classses based on the values ​​of the pixels.

Both analysis methods lead us to obtain a synthesis of information represented in the productivity map, which allows comparing responses of the soil at different times in a single thematic map.

What is it for?

The Productivity Map is the ideal starting point to know the variability of the field and define the different productive environments of each management unit. Through them we can:

  • Relate other types of available relevant information (yield maps, soil variables, laboratory results, etc.) which will help us make different management decisions.
  • Guide  field visits to key locations, and validate the different environments
  • Delimit the productive zones.
  • Adjust the boundaries of each productive  area according to our own criteria (performance, climatic, economic, etc).
  • Establish georeferenced sampling stations or sampling grids by environment.
  • Apply variable rates of inputs in macro and micro environments.

All these options that are incorporated into the daily routine in the management by environments through the Productivity Map result in a deeper knowledge of the different areas of the field and thus, improve management of existing limitations.

Layer data structure

The productivity map has information associated with the layer and each class within the map.

  • Layer data, contains data from the creation and origin of the productivity map, such as:
  • Number of classes
    Date of the images and sensor, the acronym S2 and L8 are used for Sentinel2 and Landsat8.
  • Classification method used: Cluster or Quantile.

Class data, stores relative information for each of the classes:

  • Maximum, medium and minimum NDVI.
  • Area in hectares of the class.



The productivity map is not complete until it is validated in the field.


Each productivity environment must be compared with the real situation in the field, verifying the condition of low or high productivity.


Farm 360 has an app that allows targeted and efficient field visits. Positioning yourself at the points you want to travel, capturing photos and making annotations.

To carry out  targeted and efficient validation during a field visit, you can consult the following related articles: Farm360 y Farm360 app, send maps to Farm360 app and open maps for Farm360 app navigation.

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