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At AeroAg we're applying precision agriculture technology for farming and ranching operations to reduce cost and improve yields/forage quality. We use satellite imagery, drones, and variable rate spraying, to make sure that every area of a field gets exactly the inputs it needs for maximum yield with little to no waste. One of the key concepts we use to find savings is the inherent variability in a field. Traditional management practices don't account for in-field variability, so some areas will get more inputs than required, while other areas will not get enough than. In this article, Ill show you the three ways we use tech to reduce fertilizer cost.
Step 1: NDVI Imaging
The first thing that we do for our clients is an assessment service. We use drone and satellite imagery along with on-the-ground testing (soil test, weed scouting, tissue sampling) to find opportunities to save money using variable rate spraying. One of the four assessments we do through the year is a health analysis. We build maps using historical satellite data from past growing seasons, along with up-to-date drone data, then correlate that with information like the total amount of hay cut from a hay field to get a solid picture of the variability of growth within the field.
NDVI (Normalized Difference Vegetation Index) imagery allows for the assessment of plant health and can be used to predict overall biomass. It takes advantage of the fact that healthy plants reflect more of certain bands of light and uses that to show which areas are growing more quickly than others. We can then sample the field (using samples from good areas separate from poor health areas) and test it for dry matter, protein, etc. NDVI is more accurate than standard RGB imagery, and it can show differences in growth weeks before they become apparent to the naked eye.
We like to pull satellite imagery in two ways. We use Arcgis to build representative maps for the customer and run additional calculations to estimate the biomass in a field. We also use the built in functionality in Pix4D fields to pull satellite imagery to build variable rate application maps.
To pull satellite imagery using ArcGIS we do the following:
Open the program and open an existing map.
Click "add data," and "add data from path"
Go to this website https://www.arcgis.com/home/item.html?id=1f650908c00c42338aa3da3d654dfe59
Copy the URL on the bottom right side of the page https://sentinel.arcgis.com/arcgis/rest/services/Sentinel2/ImageServer
Paste that URL into the "data from path" box.
There are a number of processing options. The cleanest to view is "NDVI Colormap"
Select the layer named Sentinel2, Go to Data, "Processing Templates"
Choose NDVI Colormap from the dropdown
This gives you a colorized view of the NDVI values for the map.
To pull satellite imagery for Pix4D fields we do the following:
Open the program and an exisiting map
Click Import, and Satellite imagery
Choose the field boundaries the imagery will cover, note: you need boundaries created before this step.
Choose the date you want imagery for. I prefer to use values from a period of time where the target plant is
Click Create Layer.
Step 2: On Ground Testing
We use these maps to divide the field into management zones. Those zones allow us to target areas that were problematic in past years, or have underlying factors affecting soil nutrition (like erosion) that will be consistent across time. We then do on the ground testing within those zones. The way we like to track data and be sure we are inside the zone when sampling is by uploading the boundaries we create separating zones into ArcGIS online. We can view those maps in their "FieldMaps" app on mobile and see where our GPS location is in relation to the boundaries we created. When we sample within these zones, we can capture in-field variability with our testing. This lets us reduce inputs for areas that need less.
The types of testing we like to do for a pasture health analysis include:
Soil testing (if we haven't done it already in the same year)
Pasture Dry Matter
Pasture Forage Testing
Weed Identification and presence
Grazing Utilization (if recently grazed)
The main purpose of doing on-the-ground testing is to confirm our predictions and get accurate data for each zone in the field.
Step 3: Build Variable Rate Application Map
Once we've gathered all the data, we can create a variable rate application mission. The biggest benefit we offer to clients is reducing input cost. Using the data from our assessment phase we can determine the amount of nutrient each area of the field needs. We then build a variable rate application map based on those needs. To create the VRA map we do the following:
Open Pix4d Fields
Click the operation tab.
Choose if we want to manually draw in our rates or use our NDVI imagery to automatically create a VRA operation
To use NDVI we will select "Zonation," then choose the field boundary we want for the mission, adjust the grid size, and number off zones we want, then click create operation layer.
To manually draw in rates, we use "do not pre-fill cells" and then go through the same process.
We can use the drawing tools to manually adjust our map, and input a gallons per acre for each zone we created.
We then work to reduce cost further by developing a mix of products that increasese the effectiveness of fertilizers while reducing the amount we need per acre.
Slow Release Nitrogen: New fertilizer options include slow release nitrogen products that are 6-7 time more available to plants than traditional options. These allow us to use significantly less volume while getting the same or better effects.
Soil Surfactants: Soil surfactants improve water infiltration and distribution, these make it easier for fertilizer to spread uniformly in the top layer of soil and increase availability to the plant. These also hold moisture in that top layer, which improves uptake and reduces leaching. Using this type of product can reduce the amount of fertilizer needed by 25%.
Microbial Activators: By stimulating beneficial microbial activity in the soil, microbial activators can improve nutrient availability and plant uptake.