Proximal sensing technologies on soils and plants on Eyre Peninsula

2019

Research organisatons
Funding source

Trial details

Researcher(s) Fabio Arsego (SARDI)
Helena Oakey (Uni of Adelaide)
Andrew Ware (SARDI)
Year(s) 2019
Contributor SARDI Minnipa Agricultural Centre
Trial location(s) Condada, SA
Cungena, SA
Streaky Bay, SA
Related trials
Proximal sensing technologies on soils and plants on Eyre Peninsula locations
Aims

This research was done to develop predictive formulas that can be used by growers to estimate in-season soil nutrients from soil samples taken at different depths and crop nutrient content from proximal sensing (PS) data.

The upper Eyre Peninsula (UEP) is a challenging environment for growers, due to the irregular rainfall patterns which are coupled with lower soil fertility. Additionally, calcareous soils with poor structure and low water holding capacity provide additional restrictions for plant growth, as growers currently use granular fertilisers which require good soil moisture conditions to enable the uptake of nutrients. Topsoils from calcareous soils may dry quickly after rain events, which may explain poor water use and nutrient extraction efficiency.

 

PS technologies have the potential to support grower’s nutrient management decisions by monitoring in-season soil and crop water and nutrient content (Allen et al. 2017, Arsego et al. 2017). PS uses a wide range of wavelengths to predict soil and crop nutritional status in a non-destructive, quick, and inexpensive way. PS technology is mostly limited to laboratory use. The development of small, portable PS devices may allow the use of this technology in farm paddocks in the near future. In this study, we combined different UEP trials to develop predictive models for PS for crop nitrogen, crop nutrient content and soil moisture.

Key messages
  • Proximal sensing reflectance data predicts soil moisture with reasonable accuracy from samples taken at depths (0-10, 10-30, 30-60, 60-100 cm) across 46 Eyre Peninsula locations.
  • Moderate relationships were found between % organic carbon, pH(water) and soil spectral data.
  • Reflectance data have been proven useful for predicting the amount of crop macronutrients, including nitrogen, phosphorus, potassium and sulphur.
  • Further experimental data is required to test the reliability of the existing predictive models of soil absorbance and crop reflectance as a means to predict nutrient content.
Lead research organisation South Australian Research and Development Institute
Host research organisation N/A
Trial funding source GRDC DAS00165
Related program N/A
Acknowledgments

This project was part of the bilateral investment initiative between SARDI and GRDC (scope study DAS00165). Special thanks to Douglas Green, Stuart Modra, Ian Burrows, John Montgomerie, Tim Howard, Phil Wheaton, Matthew Cook, Myles Tomney, Matthew Cook, MAC and families for providing the location of field trials. Thank you to all growers who are part of the EPARF soil moisture probe network for letting us sample soils and crops. Thank you to Katrina Brands and Steve Jeffs for their collaboration with field activities. Thank you to Amanda Cook and Nigel Wilhelm for feedback and suggestions throughout the season.


Other trial partners Not specified
Download the trial report to view additional trial information

Method

Crop type Cereal (Grain): Wheat
Treatment type(s)
  • Crop: Type
  • Crop: Variety
  • Technology: Modelling
Trial type Experimental
Trial design Randomised,Replicated,Blocked

Condada 2019

Sow date Not specified
Harvest date Not specified
Plot size Not specified
Plot replication Not specified
Plot blocking Not specified
Plot randomisation Not specified
Fertiliser Not specified

Cungena 2019

Sow date Not specified
Harvest date Not specified
Plot size Not specified
Plot replication Not specified
Plot blocking Not specified
Plot randomisation Not specified
Fertiliser Not specified

Streaky Bay 2019

Sow date 6 May 2019 Multiple - please see report
Harvest date Unknown
Plot size 12m x 2m
Plot replication 3
Plot blocking Random
Plot randomisation Random blocks
Fertiliser

DAP

MAP

Urea 

Download the trial report to view additional method/treatment information
Trial source data and summary not available
Check the trial report PDF for trial results.
Observed trial site soil information
Trial site soil testing
Not specified
Soil conditions
Trial site Soil texture
Condada, SA Not specified
Cungena, SA Not specified
Streaky Bay, SA Not specified
Derived trial site soil information
Soil Moisture Source: BOM/ANU
Average amount of water stored in the soil profile during the year, estimated by the OzWALD model-data fusion system.
Year Condada SA Cungena SA Streaky Bay SA
2019 123.2mm129.1mm94.7mm
2018 122.2mm148.0mm105.3mm
2017 151.9mm148.0mm110.0mm
2016 148.4mm146.9mm123.8mm
2015 122.0mm109.6mm94.2mm
2014 174.1mm146.9mm119.5mm
2013 121.3mm126.1mm104.9mm
2012 124.8mm118.2mm99.4mm
2011 182.7mm147.6mm122.5mm
2010 167.2mm142.4mm108.5mm
2009 202.4mm158.1mm120.8mm
2008 135.9mm126.1mm103.0mm
2007 122.3mm109.4mm82.1mm
2006 151.9mm141.9mm94.0mm
2005 141.3mm133.9mm105.3mm
2004 144.3mm134.2mm107.8mm
2003 141.7mm132.8mm111.2mm
2002 129.3mm106.0mm96.4mm
2001 154.2mm124.7mm101.9mm
2000 155.5mm129.5mm105.1mm
National soil grid Source: CSIRO/TERN
NOTE: National Soil Grid data is aggregated information for background information on the wider area
Actual soil values can vary significantly in a small area and the trial soil tests are the most relevant data where available

Soil properties

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Climate

Streaky Bay SA 2019


Observed climate information

Rainfall avg ann (mm) 325mm
Rainfall avg gsr (mm) 241mm
Rainfall trial total (mm) 269mm
Rainfall trial gsr (mm) 208mm

Derived climate information

Condada SA

Cungena SA

Streaky Bay SA

Condada SA

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Cungena SA

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Streaky Bay SA

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Some data on this site is sourced from the Bureau of Meteorology

SILO weather estimates sourced from https://www.longpaddock.qld.gov.au/silo/
Jeffrey, S.J., Carter, J.O., Moodie, K.B. and Beswick, A.R. (2001). Using spatial interpolation to construct a comprehensive archive of Australian climate data , Environmental Modelling and Software, Vol 16/4, pp 309-330. DOI: 10.1016/S1364-8152(01)00008-1.

Trial report and links

2019 trial report



Trial last modified: 19-05-2023 14:52pm AEST