This chapter provides more details about advanced analytics that GeoPard offers for precision agriculture. That information can help you to make decisions for your field or even farm management with the aim to optimize your returns while saving on, for example, chemicals.
The Compare layers feature allows you to visually compare field analytics side by side in a split view. It is possible to select any type of layer for comparison: imagery with natural or infrared colors, imagery with vegetation views, in-season or historical management zones. Two layers behave synchronously when you zoom in, zoom out or move a map for your conveniences.
One of the features that GeoPard provides is the heterogeneity factor of your fields. To review it open the required field, select zones map and click on Info icon on the map. Side panel will be opened.
Heterogeneity factor is the number that shows the level of heterogeneity/variability of your fields. The more variability a field has, the more the need for precision agriculture technologies is. It is especially useful when used together with GeoPard’s multi-year analytics (30-year history).
If you have many fields, it can help you to understand which fields to target first with Variable Rate Applications (seeding, fertilizing, spraying).
By combining GeoPard’sheterogeneity factor with multi-year analytics you can save the most on chemicals on the most heterogeneous fields.
Detecting changes that happened in the field during the last one-two weeks or one-two months or even a couple of years helps to get insights about crop development.
Relative variation factor or Relative Variation Index can be used to:
GeoPard’s Relative Variation Index (RVI) covers all those cases and many others. RVI will provide more insights into the crop development when used together in combination with in-season and historical management zones.
Simply choose your field and satellite images to track the changes across them and get insights about every spot in your field.
Historical (multi-year) management zones provide insights about every spot in the field.
Historical (multi-year) management zones are built based on 30+ years archive of satellite imagery. Images with peak vegetation during every season are automatically selected as inputs for analytics. Otherwise, every such image represents a potential yield file for the related year.
The field crop development pattern helps to know the agricultural area better and to apply the right decision with the right input rates in the right spots. Historical management zones could be used as a blueprint for prescription (Rx) files for seeding, fertilization, zones based soil sampling.
Precision agriculture is capable of generating vast amounts of data in the form of yield data, satellite imagery, and soil fertility, among others. Lack of easy-to-use cloud precision software toolkits that assist crop producers in converting field data layers into useful knowledge and actionable recommendations limits the application of precision agricultural technologies. In precision agriculture, management zones are areas within a field that have similar yield potential based on soil type, slope position, soil chemistry, microclimate, and/or other factors that influence crop production. The producer’s knowledge of a field is a very important piece of the process. Management zones are thought of as a mechanism to optimize crop inputs and yield potential.
The big challenge is to build management zones that perfectly reflect field variability. A combination of different layers like satellite imagery, soil fertility, topography derivatives, and yield monitor data is the next logical step to generate more responsive management zones.
Multi-layer analytics (also known as integrated analysis) is becoming a part of the GeoPard geospatial analytics engine.
Classic combinations of integrated analysis parameters include one or more of yield data, NDVI map, elevation, and soil sensor physicochemical characteristics. GeoPard supports these parameters and in addition, allows the inclusion of other field data layers either already available in the system or uploaded directly by the user (soil sampling, yield datasets, etc.). As a result, you are free to operate with the complete set of parameters doing integrated analytics:
Remote sensing data:
It’s important to emphasize that custom factors are configured on top of every data layer to assign the desired layer weight. You are very welcome to share your integrated analytics use cases, and build management zones maps based on your knowledge of the field while selecting data sources and their weights in GeoPard.
Pictures contain a sample field with data layers (like a productivity map covering 18 years, digital elevation model, slope, hillshade, 2019 yield data) and various combinations of integration analytics maps. You can follow steps of evolution of management zones while extending integration analytics with an additional data layer.