Crop yields and the cost of fertilizer are major concerns for agricultural producers that rank right alongside weather. The difference is that, while producers still haven't found ways to control the weather, they now have better control over crop yields and how to fertilize because of precision agriculture.

Precision agriculture (PA) is a farming management concept based on observing, measuring and responding to inter- and intra-field variability in soils, pests and crop yields. Crop variability typically has both a spatial and a temporal component.

While precision agriculture is fundamentally a science, it can also become an art — especially when the geospatial characteristics of a field don't conform to a perfectly square or rectangular grid in a grower’s or consultant’s geographic information system (GIS), or when field topography is far from flat.

Through the years, efforts at attaining greater degrees of precision agriculture to optimize crop yields, reduce fertilization and other operational costs have resulted in modifications to farm equipment and in software-based tools. There have been many successes and breakthroughs with these techniques, but there is still room to learn more.

Challenges of Precision Ag Technology

“An immediate problem that agricultural producers encounter with precision agriculture software is that it is too complex,” says North Carolina State University (NCSU) associate professor of crop and soil sciences, Jeff White. “At the heart of this is not being able to translate documented spatial variability into effective spatially variable management. There is the same complexity with attempts to use sensing technologies. There is a lot of physics behind setting these capabilities up correctly, and new graduates, as well as producers in the field, aren’t always equipped to do this.”

Executing precise operations in the field isn't straightforward, either.

“Farmers are continually tasked with trying to determine the correct fertilizer and proportions of ingredients in the fertilizer to apply to their fields,” says Carl Crozier, professor of crop and soil science and extension specialist at NCSU. “The soils in every field are inherently variable, so there isn't really a uniform fertilizer prescription that works for every spot in the field. This is important to producers because fertilizer is very expensive. You don't want to use it incorrectly, and it is also vital to get maximum crop yield from the field.”

How Geospatial Technology Plays a Role

Clearly, viable scientific approaches are needed to assist producers with field yields and fertilizer prescriptions, and most are employing varying degrees of geospatial technology to assist in the process.

“To improve yields, producers use grid soil sampling to assist them in dealing with soil variability,” Crozier says. “They take various soil samples from different areas of a field, mark these samples to the locations they were taken from, and then send the samples into the lab for fertilizer and lime recommendations. Once these are available, a map of the field showing their spatial variability must be created to guide precision management.”

According to Crozier, there are two ways to do this.

“The first way is to create a grid of the field and subdivide the field into smaller areas, drawing a soil sample from each area, and then having the lab analyze these,” he says. “The second method is where you create a set of points in your field where you draw soil samples from, and then mathematically interpolate, based on historical data, what the changes in the soil are likely to be between the different points. Both methods offer ways for farmers to fertilize their fields with greater precision.”

Precision agriculture techniques help farmers identify spots in their fields where there might be a problem with soil moisture content, or where the depth of the soil is not as deep as it should be to sustain the crop.

“The traditional way to sample the soil in a 20-acre field is to take randomly about one sample per acre, mix them and send the mixture to the lab, so you would end up with one fertilizer or lime recommendation for the entire field,” White says. “But with the advances today in geospatial soil sampling, GPS (global positioning systems) and geographical information systems, we can take many more soil samples by pinpointing them on a geospatial grid so we can perform at a higher level of precision, with soil enrichment prescriptions for many more locations in these fields to improve crop yield.”

Producers take this information and, with the help of geospatial software, develop spatially variable prescriptions for the application of different rates and/or mixture of fertilizers, or other specific inputs for specific areas in their field. This prescription is stored in the software that controls how the application equipment spreads the variable rates of fertilizer appropriately to specific areas in the field.

“It took us a long time to get to this point,” White says. “Initially, using geospatial analysis and precision agriculture was expensive, and there were steep learning curves. At first, we weren’t sure if we could actually make it work in practice, and when GPS was first employed, the technology wasn’t always accurate. But we eventually got the technology aligned with what we needed to do, and today we have major agricultural equipment manufacturers that offer this software option on their equipment.”

Dealing with Field Topography

Assistance from the equipment itself couldn't come at a better time, because determining how to spread the fertilizer with greatest precision from the truck is more complicated than you think. For example, at a given location in the field, your fertilization might require more potassium and less phosphorus. At a second location, the situation could be the reverse.

“There are two ways for farmers to handle this variability,” Crozier says. “One is to go over the field multiple times with the truck, with a different spread of formulas for each ingredient for each pass. Another way is to use a truck equipped with software that can link into the geospatial coordinates of the different locations in the field and adjust the fertilizer mix to fit the individual prescriptions for these locations. A third variable producers must consider is the width of the truck itself, which must be matched with how the field must be spread. This is where some creativity comes in.”

There is also the issue of field topography that must be considered in fertilization planning.

“In eastern North Carolina, the fields tend to be poorly drained, so there are many open surface ditches to facilitate drainage and the fields tend to be long and narrow,” Crozier says. “Consequently, when planning out a grid for fertilization, these ditches have to be planned for as the fertilization driving pattern is mapped out. There is no single way of doing this, but when you lay out your geospatial grid, you can customize the grid to take this into account.”

Future Directions

In the future, agricultural producers are likely to make greater use of UAVs, since the technology is now widely available and dropping in cost.

“We can mount cameras and view fields from above with UAVs,” White says. “This will aid farmers in detecting anomalies in the fields, such as variances in moisture or color, like when they see a yellow spot in an otherwise green field. Spectral analysis of blue, green, red and near infrared bands can be employed for the detection of nitrogen content and fertilizer need. This will help farmers achieve more granular levels of precision agriculture so they can begin asking themselves whether they should add more nitrogen to certain areas of their fields.”

Crozier says he also expects to see more sensor- and LiDAR-based technology introduced into precision agriculture moving forward. “We can use sensors to help us visualize what is needed for land leveling and drainage development in fields. Optical sensors will also be deployed on drones and planes to detect color and/or greenness variations in crops; and LiDAR can be used to overview the entire crop canopy of a field, detecting both high and low spots, and then matching these with the producer’s combined yields to check harvesting efficiencies. We are also working with GIS systems to better isolate areas where soil types change, either abruptly or gradually.”

More granularity — and visibility — into what is going on in a field, in a crop and in the soil is being enabled with geospatial technologies. These technologies and their best use cases are still emerging, because learning curves have yet to be mastered, and there are also technologies that are overly complex and must be simplified.

A key driver will be cost. Will an investment in a new piece of equipment and software for precision agriculture provide an operational savings to recoup the investment?

“There is more research to do, we need more accurate equipment and soil sampling, and we need to fine-tune recommendations and best practices,” Crozier says. “But with the evolution of sensors, analytics, LiDAR, GPS and GIS, it’s reasonable to expect higher crop yields and greater levels of precision in agriculture in the future.”