Advanced Zone Analysis

Zoning spatial data can be achieved many ways and while there is no real right or wrong method the analysis, interpretation and results can be erratic. Often the techniques of automation and batching sound impressive, the results however are far from it.

Well its actually a very simple process – or can be kept simple. Base layers of data such as soil and terrain are typically stable – they don’t change. While there are differences from region to region they are generic in the way they are measured and managed.

Terrain data is one of the very few, if not the only, global generic layer. Elevation is elevation slope is slope and depression is a depression in any language.

Soils measured with a sensor like the Electromagnetic (EMI) sensor are influenced by common soil attributes such as Clay, Salt and Moisture. These attributes while variable, still have the same generic impact on the plant growth.

So now comes the variables. Crop type, variety, history, agronomic knowledge and practice, staff, to name a few. Then there is the big one ~ Climate. While many are attempting to predict the climate there is some reality to farming. You still need to plant on time, spray on time, irrigate on time, make agronomic and management decisions and of course marketing the crop. All this takes human input and most of it is actually requiring people to be in the field – still.

So what does it have to do with zone analysis and design. Unfortunately it has everything to do with it which is why it can be so difficult. People try to make it sound fancy by using the words ‘propitiatory algorithm’. So here is one you can use. Soil x Terrain = Zones.

Below is a simple analysis to explain. In the Compare module we overlay the yield on our EM based soil map. The results show not too much difference between soils. Zone one is a lighter clay soil and zone 4 is a higher clay soil. Therefore zone 4 may have a higher clay content, but may also have high salt loads. This combination can see a decrease in Plant Available Water (PAW) and therefore lower yield or be easily water logged.



The next image is overlaying Landscape change (higher landscape will shed water and lower landscape will accumulate or receive more water). In this case it appear the areas which are shedding water are yield higher.

If you think about the relationship of the soil and yield and landscape and yield we can summise that the heavier soil is water logged and reduced yield, and the lower areas in the field have water logged and reduced yield.



So does this mean that the heavier soils are where all the lower areas are. In this case there is a trend that suggest there is but the dotted line show 1 stand deviation in that data. So this means while there is a trend there is also a lot of variability in that trend.

comparelscand em

So we have established there are trend in both soil and terrain, where to now. Using the Pro Compare module we can intersect the soil and terrain zones and look at the yield for each of these zones with more knowledge of what is driving yield variability.

Zone one is light soil in a water shed area. Zone 4 and 7 are higher clay soils in a water shed but yield more because of the higher plant available water. Zone three is light soil again but is in a water accumulation area. Zone 6, 9 and 12 are soil increasing in clay and decreasing yield because the water logging has a greater impact in the heavier soils.

So the results are actually quite simple – depending on the soils ability to hold water and the surface of the soils ability to deliver water has a significant impact on yield. These can now be the basis of your future zone management. Everything else you do or happens is seasonal and will fluctuate – but at least you now what’s stable and where to start.

Now nutrition should be more easily match to the correct yield potential and monitored during the season appropriately to achieve and optimized yield.