Abstract:
This work investigates the application of new sensors to enable agronomists and farm
managers to make decisions for variable treatment strategies at key crop growth
stages. This is needed to improve the efficiency of crop production in the context of
precision farming.
Two non-invasive sensors were selected for investigation. These were:
1) The MGD-1 ion mobility gas detector made by Environics OY, Finland.
2) The EM38 electromagnetic induction (EMI) sensor made by Geonics Inc.,
Canada.
The gas detector was used to determine residual nitrogen and to measure carbon
dioxide gas as a surrogate indicator of soil quality. In the latter, increased microbial
carbon dioxide production was expected on soils with high organic matter content.
Overall, the results of gas detection were disappointing. The main problems inherent
in the system were; lack of control of the gas sampling, insufficient machine
resolution and cross contamination. This led to the decision to discontinue the gas
detection research. Instead, the application of electromagnetic induction (EMI) to
measure soil variation was investigated.
There were two principle advances in the research. Firstly the application of EMI to
the rapid assessment of soil textural class. Secondly the mapping of available water
content in the soil profile. These were achieved through the development of a new
calibration procedure based on EMI survey of the sites at field capacity, working with
field experiments from five sites over two years.
Maps of total available water holding capacity were produced. These were correlated
with yield maps from wet and dry seasons and used to explain some of the seasonal
influences on the spatial variation in yield.
A product development strategy for a new EMI sensor was considered which
produced a recommendation to design a new EMI sensor specifically for available
water content and soil texture mapping, that could be mounted on a tractor.
For the first time, this procedure enables routine monitoring of the spatial variation in
available water content. This enables the effects of seasonal and spatial variation to be
included in crop models, targeted irrigation and to aid decisions for the variable
application of inputs.
