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November 1998

On The Road to Precision Agriculture - Assessing Soil Variability

So far in this series, we have looked at yield monitors, and the GPS / DGPS technologies that are important for associating yield data with specific field locations. This is indeed one way to look at soil variability. However, we must recognize that yield data represents a plant response not only to soil characteristics, but also to soil moisture and to weather.

Before we proceed, lets think a minute about what types of data represent soil characteristics that are of interest in crop production. From a soil fertility standpoint, the "big Three" are: nitrogen (N), phosphorous (P) and potassium (K). There are secondary nutrients, whose importance varies by crop. For potatoes, these might include: calcium (Ca) and magnesium (Mg). Other micronutrients may be important, depending on crop and soil conditions. Another important characteristic is soil pH, which will affect the uptake of nutrients by the crop. There are also several other soil factors that can and do influence crop yields: organic matter (OM), cation exchange capacity (CEC), texture (sand and clay content), structure (density and porosity), topography, tillage, compaction, drainage, competition (trees) and topsoil depth.

Soil variability as we currently experience it has a multitude of sources. Some variability originated from geological process, and is generally indicated on existing soil maps. This is particularly true for much of Michigan with our glacial activity. Previous management practices may have an effect on soil structure (such as compaction) or on nutrient contents or pH (poorly adjusted application equipment; over application of animal manure), so that soils in the same series have different, and posssibly contrasting, characteristics.

When analyzing soils for crop production, soil fertility usually receives the most attention. This has also been true for the early efforts of the precision agriculture movement. Soil fertility in this sense refers to the level of nutrients "available" in the soil.

I have found it useful for me to categorize these characteristics into three groups:

I also sub-divide these into characteristics that we can (readily) control and those that are uncontrollable. From my engineering perspective, I create a table like the following.

In the past, farmers estimated the conditions of an entire field by averaging the results from analysis of soil samples randomly gathered around the field. Then the entire field was treated based on the average analysis. Precision agriculture technology allows us to easily sample and treat any field as a number of arbitrary size and shape sections, each with its own yield response, fertility needs and management recommendations. If we direct sampling timing based on our concept of data persistence (fixed vs. variable) and whether or not that characteristic is controllable. Then there is always the question of whether that soil characteristic varies in a describable fashion between sections, or appears random.

This page was last updated on 10/08/01.