Digital soil mapping and assessment for Australia and beyond: A propitious future

Searle, Ross; McBratney, Alex B.; Grundy, Mike; Kidd, Darren; Malone, Brendan; Arrouays, Dominique; Stockman, Uta; Zund, Peter; Wilson, Peter; Wilford, John; Gool, Dennis van; Triantafilis, John; Thomas, Mark; Stower, Liz; Slater, Brian K.; Robinson, Nathan; Ringrose-Voase, A. J.; Padarian, José; Payne, J. E.; Orton, Thomas G.; Odgers, Nathan P.; O’Brien, Lauren; Minasny, Budiman; Bennett, John McLean; Liddicoat, Craig; Jones, E.V.; Holmes, Karen; Harms, Ben; Gray, Jonathan; Bui, Elisabeth N.; Andrews, Kaitlyn

doi:10.1016/j.geodrs.2021.e00359

Cited by 33 publications

(17 citation statements)

References 92 publications

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“…This may just be the perception of authors who have completed a very large amount of work; however, it is worth considering that perhaps a model skill ceiling has been reached or is near to being reached without a considerable new investment in collecting soil data where there are clear data gaps, or where prediction uncertainties persist. Searle et al (2021a) provides some practical options and examples of gap-filling that centre upon efforts to examine the current suite of soil legacy data with respect to environmental data coverage, in order to identify which parts of the landscape have adequate coverage of soil information and which parts do not.…”

Section: Discussionmentioning

confidence: 99%

Updating the Australian digital soil texture mapping (Part 2

Malone

Searle

2021

Soil Res.

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Malone and Searle (2021) described a new approach to convert field measured soil texture categories into quantitative estimates of the proportion of clay, silt and sand fractions. Converted data can seamlessly integrate with laboratory measured data into digital soil mapping workflow. Here, we describe updating the Australian national coverages of clay, sand and silt content. The approach, based on machine learning, predicts each soil texture fraction at 90 m grid cell resolution, at depths 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The approach accommodates uncertainty in converting field measurements to quantitative estimates of texture fractions. Existing methods of bootstrap resampling were exploited to predict uncertainties, which are expressed as 90% prediction intervals about the mean prediction at each grid cell. The models and the prediction uncertainties were assessed by an external validation dataset. Results were compared with Version 1 Soil and Landscape Grid of Australia (v1.SLGA) (Viscarra Rossel et al. 2015). All predictive and functional accuracy diagnostics demonstrate improvements compared with v1.SLGA. Improvements were noted for the sand and clay fraction mapping with average improvement of 3% and 2%, respectively, in the RMSE estimates. Marginal improvements were made for the silt fraction mapping, which was relatively difficult to predict. We also made comparisons with recently released World Soil Grid products (v2.WSG) and made similar conclusions. This work demonstrates the need to continually revisit and if necessary, update existing versions of digital soils maps when new methods and efficiencies evolve. This agility is a key feature of digital soil mapping. However, without a companion program of new data acquisition through strategic field campaigns, continued re-modelling of existing data does have its limits and an eventual model skill ceiling will be reached which may not meet expectations for delivery of accurate national scale digital soils information.

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Section: Discussionmentioning

confidence: 99%

Updating the Australian digital soil texture mapping (Part 2

Malone

Searle

2021

Soil Res.

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“…Digital soil assessment approaches have a direct impact on crop yield and performance by identifying zones that may cause low crop yield. Digital alternative methodologies for soil survey and identifying key soil characteristics could have the possibility to quantify the trend of agricultural soil conditions [52].…”

Section: Digital Twin In Soil and Irrigationmentioning

confidence: 99%

“…The recent development of digital soil mapping techniques may support digital twins by digital representation of knowledge obtained from the soil in virtual entrainment [53]. For instance, digital soil mapping could be used to describe soil variation in digital twins using information from complex soil variation at a specific depth, time, and special locations [52].…”

Section: Digital Twin In Soil and Irrigationmentioning

confidence: 99%

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Nasirahmadi

Hensel

2022

Sensors

116

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Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.

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“…Synthetic environments are being created for food production, under a digitally controlled environment where temperature, light, and humidity are regulated by algorithms and sensing devices to ensure optimal growth conditions. The population of the soil material with large number of microsensors coupled with wireless technologies and the IoT may also end up with the creation of a digital twin of the soil (as briefly discussed in Searle et al, 2021), or, as Haff (2014) puts it, a computerized soil.…”

Section: 3mentioning

confidence: 99%

Digital soil science and beyond

Wadoux

McBratney

2021

Soil Science Soc of Amer J

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Digital convergence is helping us to better understand and study the soil. Fixed and mobile sensors, and wireless communication systems aided by the internet produce cheap and abundant streams of digital soil data that can readily be used for modeling and information generation. Here, we explore the ways in which digital science and technology have affected soil science. We can call this digital soil science and define it as the study of the soil aided by the tools of the digital convergence. To some degree, all of our research and teaching had been enabled, enhanced, and expanded by the digital convergence. We outline how soil science has changed using illustrations of intellectual and technical developments enabled digitally. Digital soil sensors have been widely implemented, and new tools such as cell phones and applications, or metagenomics techniques are becoming available. There are also areas in soil science for which no major obstacles in the digital technologies exist, but which have not been thoroughly investigated-for example, to devise a truly digital soil field description or for building a formal digital quantitative system of soil classification. The soil science community will need to be alert to some of the dangers brought by digital convergence such as the lack of new theory and proprietary (black-box) soil prediction. Finally, we discuss a whole set of digital tools that will, or might, gain the stage in the immediate future and take a stab in the dark on what may lie over the horizon of digital soil science.

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Digital soil mapping and assessment for Australia and beyond: A propitious future

Cited by 33 publications

References 92 publications

Updating the Australian digital soil texture mapping (Part 2

Updating the Australian digital soil texture mapping (Part 2

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Digital soil science and beyond

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