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This chapter provides more details about the 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, and in-season or historical management zones. Two layers behave synchronously when you zoom in, zoom out or move a map for your convenience.
- 1.To enter the split view mode, select your field and click Compare Layers item in the top right corner.
- 2.Select the layers for analytics using the left tree and click Compare Layers button.
- 3.In the next step, you can compare and analyze data. Also, you can manage layers and add new ones or remove old ones.
- 4.To exit the split view mode, click Close button located at the top of the screen.
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 the Info icon on the map. The side panel will be opened.
The 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).
By combining GeoPard’sheterogeneity factor with multi-year analytics you can save the most on chemicals in 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 into crop development.
Relative variation factor or Relative Variation Index can be used to:
- locate spots with similar performance across 5-10-20 years and place the trials in areas with similar conditions to reduce the probability of mistake
- track the changes during the season and evaluate crop performance during the growth
- recognize the damaged areas after a weather disaster or a disease or a pest attack and calculate damaged areas
- detect the difference between the last two images and control the crop performance.
GeoPard’s Relative Variation Index (RVI) covers all those cases and many others. RVI will provide more insights into 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 of the 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. The 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 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:
Yield data Remote sensing data:
- Potential productivity map (single-year and multi-year)
- Stability/variation map
- Vegetation indices NDVI, EVI2, WDRVI, LAI, SAVI, OSAVI, GCI, GNDVI
- Digital elevation
- Wetness index
- CEC (cation exchange capacity)
- SOM (soil organic matter)
- K (potassium)
- Thin topsoil depth, lower available water holding capacity (drought-prone soil)
- EC (electrical conductivity)
- and other chemical attributes available in the uploaded dataset
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 the steps of the evolution of management zones while extending integration analytics with an additional data layer.