Mapping the unseen

Soil mapping is transforming how farmers understand and manage variability within their fields. By combining soil samples, GPS data, sensors, and digital modelling, modern soil mapping reveals differences in soil fertility, texture, and moisture that are invisible to the eye.

- Cobus du Plessis reports.

Officials and scientists often say: "You cannot manage what you cannot measure. Soil mapping makes the invisible visible."

For generations, farmers have relied on experience, observation, and instinct to guide production decisions. Yet one of the crucial factors influencing crop performance - the soil - often remains poorly understood at field level. While a stretch of land may appear uniform, soil properties can vary significantly within a single field, affecting yield potential, input efficiency, and profitability.

The challenge is that this variability is invisible. Differences in soil texture, fertility, and moisture cannot be seen from the tractor seat, and managing a field as a single unit often leads to over-application of inputs in some areas and under-application in others. Soil mapping technology is changing this. By combining field measurements with digital tools and predictive modelling, farmers can now visualise and manage soil variability with far greater precision.

What was once hidden beneath the surface is increasingly becoming a critical decision-making tool on the modern farm.

FROM SOIL SAMPLES TO DIGITAL MAPS

Soil mapping is the process of identifying and displaying how soil properties vary across a field. Traditional soil surveys provided useful information, but were often applied uniformly across entire lands, masking crucial differences.

Modern soil mapping, often referred to as digital soil mapping, takes a different approach. Instead of measuring soil everywhere, samples are collected at selected locations and combined with environmental data and modelling techniques to estimate soil properties across the entire field.

This process integrates:

• measured data from soil samples;

• environmental data such as terrain, vegetation, and climate;

• statistical or machine learning models to predict soil properties between sampling points.