Abstract:
Efficiency of tillage depends largely on the nature of the field, soil type, spatial
distribution of soil properties, and the correct setting of the tillage implement.
However, current tillage practice is often implemented without full understanding
of machine design and capability leading to lowered efficiency and further
potential damage to the soil structure. By modifying the physical properties of
soil only where the tillage is needed for optimum crop growth, variable depth
tillage (VDT) has been shown to reduce costs, labour, fuel consumption and
energy requirements. To implement VDT it is necessary to determine and map
soil physical properties, spatially and with depth through the soil profile. Up until
now the measurement of soil compaction for VDT has been soil penetration
resistance, expressed as Cone Index (CI).
In this research a multi-sensor and data fusion approach was developed that
allowed augmenting data collected with an electromagnetic sensor, a standard
penetrometer, and conventional methods for the measurement of bulk density
(BD) and moisture content (MC). Packing density values were recorded for
eight soil layers of 0-5, 5-10, 10-15, 15-20, 20-25, 25-30 30-35 and 35-40 cm.
From the results only 62% of the site required the deepest tillage at 38 cm, 16%
required tillage at 33 cm and 22% required no tillage at all. The resultant maps
of packing density were shown to be a useful tool to guide VDT operations. The
results provided in this study indicate that the new multi0sensor and data fusion
approach introduced is a useful approach to map layered soil compaction to
guide VDT operations. The economic benefit analysis demonstrated fuel
savings of 48% by implementing the proposed system. Further work is needed
to implement the packing density map for VDT in larger numbers of field in
order to generalise the approach.
