Precision Farming in Practice – Spot Spraying And Variable Rate Nitrogen

The approach starts with a simple principle: fields do not behave evenly, so inputs should not be applied evenly either. Implementation follows a clear, consistent workflow: first, understand variability, select the right tool, and finally, apply inputs only where they deliver measurable returns. Crucially, this means avoiding the assumption that “poor area = more nitrogen.” Crop growth depends on multiple interacting factors, and nitrogen is not always the limiting one. This is a central point emphasised by Jim Willson of SoilEssentials: the technology matters, but the diagnosis matters more.

Field variability: what can be fixed vs what must be managed

The starting point is understanding why parts of a field perform differently. Where constraints are fixable (for example, pH, drainage, or other soil chemistry issues), the aim is to correct them over time and lift the field average. Where constraints are inherent (such as slope, aspect, or shading), the focus shifts to managing them by tailoring inputs, especially nitrogen, so spend matches likely crop response.

This distinction matters because not all poor-performing zones are nitrogen-limited. Nitrogen is a key yield driver, but crops can be limited by many interacting factors. If something else is restricting growth (pH, drainage, aspect), adding more nitrogen will not increase yield, it simply increases waste and the risk of losses to water.

Variable Rate Nitrogen: “right rate, right place, right time” 

At its simplest, VRN means varying nitrogen rates within a field to match crop potential and likely response. Farms typically build VRN decisions using one or more sources of evidence, such as crop sensing, satellite imagery, yield maps, soil maps, and local knowledge of known problem areas. Two common delivery routes are:

• Real‑time sensing, where a tractor‑mounted sensor reads crop condition as the machine moves through the field and adjusts fertiliser rate on the go.
• Map‑based VRN, where imagery (often satellite-based) is used to create an application map in advance, then loaded into the spreader/sprayer for variable application.

In Scotland, the approach is often built around three nitrogen passes in winter crops. The first pass tends to be uniform (commonly linked with sulphur), the second may direct more nitrogen to weaker areas to encourage growth and tillering, and the third (late April/early May) often reverses the emphasis, reducing rates in areas that remain poor and increasing rates in stronger zones to keep them greener longer and maximise yield where there is still response. This staged approach avoids a common pitfall: throwing nitrogen at zones that cannot use it, which reduces gross margin and increases environmental risk.

A key practical takeaway from SoilEssentials is that VRN is most effective when it is used alongside good diagnosis: if an area is limited by something other than nitrogen, VRN helps avoid spending fertiliser (and carbon) there and redirects investment to where it will pay back.

Targeted (spot) spraying: the general approach and the SoilEssentials SKAi example

Alongside fertiliser efficiency, spot spraying is a broader category of precision application where the aim is to treat only the target plants (typically weeds), rather than spraying the whole field. Different systems achieve this in different ways. Some rely on mapped approaches (using scouting, imagery, or historic hotspots to build prescription maps). Others use real‑time detection, where sensors or cameras identify target weeds and trigger nozzles automatically. “Green‑on‑green” refers to detecting weeds within a green crop or sward (as distinct from “green‑on‑brown,” which targets weeds on bare soil).

An example of this technology is SoilEssentials’ SKAi / “Sky” system, who recently presented at the latest FAS Conference. Their system is designed to retrofit precision capability onto a conventional crop sprayer. In this setup, the sprayer is fitted with boom‑mounted cameras, onboard computing, and individual nozzle control. Cameras capture frequent images (around 15 frames per second in the example described), an AI model identifies target weeds in real time, and GPS timing ensures only the minimum number of nozzles activate to treat the weed at normal working speeds, around 9–12 km/h.

This retrofit description applies to the SKAi / “Sky” example, not to every spot spraying system. What it illustrates, however, is the core principle shared by many targeted spraying approaches: replacing blanket coverage with precision treatment, which reduces chemical use, avoids unnecessary impact on grass/clover, and lowers the risk of drift and off‑target contamination