An experiential account with recommendations for the design, installation, operation and maintenance of a farm-scale soil moisture sensing and mapping system

Brendan Malone; David Biggins; Chris Sharman; Ross Searle; Mark Glover; Stuart Brown

doi:10.1071/SR24004

RESEARCH ARTICLE (Open Access)

Previous Contents Vol 62(5)

An experiential account with recommendations for the design, installation, operation and maintenance of a farm-scale soil moisture sensing and mapping system

Brendan Malone

^A ^* , David Biggins ^B , Chris Sharman ^B , Ross Searle

^C , Mark Glover

^A and Stuart Brown ^D

+ Author Affiliations

- Author Affiliations

^A CSIRO Agriculture and Food, Black Mountain, ACT, Australia.

^B CSIRO Data 61, Sandy Bay, Tas, Australia.

^C CSIRO Agriculture and Food, St Lucia, Qld, Australia.

^D CSIRO Agriculture and Food, Boorowa, NSW, Australia.

^* Correspondence to: brendan.malone@csiro.au

Handling Editor: Gavan McGrath

Soil Research 62, SR24004 https://doi.org/10.1071/SR24004

Submitted: 12 January 2024 Accepted: 4 July 2024 Published: 30 July 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

The research explores the benefits of real time tracking of soil moisture for various land management contexts and the importance of spatio-temporal modelling and mapping to gain clear and visual understanding of soil moisture fluxes across a farm.

Aims

This research aims to outline the key processes required for building an operational on-farm soil moisture monitoring system where the product is highly granular daily soil moisture maps depicting variations temporally, spatially and vertically.

Methods

We describe processes of capacitance soil moisture probe installation, data collection infrastructure, sensor calibration, spatio-temporal modelling, and mapping.

Key results

An out-of-bag soil moisture evaluation modelling system was tested for nearly 2 years. We found a model accuracy (RMSE) estimate of 0.002 cm⁻³ cm⁻³ and concordance of 0.96 were found. This result is averaged over this period but fluctuated daily, and related to rainfall patterns across the target farm, which were not directly incorporated into the modelling framework. As expected, incorporating prior estimates of soil moisture into the modelling framework contributed to very accurate estimates of real time available soil moisture.

Conclusions

This research promotes the importance of iterative improvements to the soil moisture monitoring system, particularly in areas of sensor recalibration and spatio-temporal modelling. We stress the need for a longer-term view and plan for ongoing maintenance and improvement of such systems in the emerging digital farming ecosystem.

Implications

The results of this research will be useful for researchers and practitioners involved in the design and implementation of on-farm soil monitoring systems.

Keywords: agriculture, digital agriculture, digital soil mapping, generalised additive models, IoT, sensor calibration, soil modelling, soil moisture, soil moisture sensing, soil monitoring.

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