Grid Soil Mapping defines the variability in multiple soil properties.


Andrew Vincent

Enhance your

natural resource

Understanding the characteristics of your soil and how it varies across the farm is a fundamental starting place for any sustainable farming operation. Starting with soil, you can look to enhance your natural resource base and get more from every hectare.

With our Soil Management Plans, we help you set up a long term program including strategic soil sampling programs, zonal paddock management and variable rate gypsum application. EM38 soil mapping is used as a reliable option for zoning paddocks according to soil type and potential yields. It is a fast and cost effective way of measuring soil moisture content, salt levels, and soil texture (clay content).

A Precision Agriculture research project has highlighted the benefits of grid soil mapping in developing targeted strategies to address individual soil properties on your farming operation.

The project investigated the use of grid soil mapping to measure a variety of surface soil chemical properties, and to explore the relationships between soil pH, Cation Exchange Capacity (CEC), Exchangeable Sodium Percentage (ESP), and soil test phosphorus (P) observed in the grid mapped data.

Over a 12-month period in 2018, Precision Agriculture tested and mapped approximately 100,000 hectares of agricultural land across New South Wales, Victoria, South Australia, and Tasmania. Results from 289 paddocks – almost 10,000 grid soil samples -where multiple soil properties were measured, were used in this project.

Each paddock was divided into small grids for the soil sampling – generally a two-hectare grid – with 0-10cm soil cores collected within each grid on a diagonal transect that zig-zagged through the paddock. The soil samples were sent to a commercial, accredited laboratory for analysis of soil pHCaCl, exchangeable cations and soil test P (either Olsen or Colwell P).

Principal Scientist with Precision Agriculture, Dr Kirsten Barlow, said the results highlighted the variability within individual paddocks for these soil characteristics.

“Soil pH generally had the lowest Coefficients of Variation (CoV ) of the four soil tests, with a mean CoV of 5.2%,” Dr Barlow said. “This value corresponded with an average standard deviation of 0.28 pH units within a paddock and a range of 1 pH unit.

“Colwell P and Olsen P were comparably variable with an average CoV of 28% and average standard deviation of 15 and 5.6 mg/kg respectively.
“The analysis found CEC and ESP had the greatest spread in the variation observed with an average CoV of 25% for CEC and 36% for ESP, although this ranged up to 100%.”

The investigation into the relationships between the different soil tests also revealed wide variations.

The average correlation coefficient between pH and CEC was 0.54, with 46% of the paddocks having a strong correlation – greater than 0.7 – which means that as pH increased, so did the soils CEC.

In contrast, there was a very random distribution in the correlation coefficients observed for pH:soil P, soil P:CEC and CEC:ESP. In each of these paddocks, the individual correlations ranged from a strong positive correlation (>0.7) to a strong negative correlation (<-0.7), with between 56-76% of the paddocks having a poor correlation of between -0.5 to 0.5.

Dr Barlow said grid soil mapping provides a comprehensive measure of the variation in soil properties across the paddock.

“Where multiple soil properties are measured, variable rate strategies can be developed for each soil constraint to ameliorate individual soil properties,” Dr Barlow said.

”This variation is generally lost when sampling is based on the whole paddock or broad management zones, as the soil characteristics are averaged over a larger area, which may include both high and low soil test values.”

A Monash University Honours student has continued this research and has been directly investigating the relationships between extensive grid soil sampling data and management zones developed from other data, including NDVI and yield data.

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