Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content
- Benke, Kurt, Norng, Sorn, Robinson, Nathan, Chia, K., Rees, David, Hopley, J.
- Authors: Benke, Kurt , Norng, Sorn , Robinson, Nathan , Chia, K. , Rees, David , Hopley, J.
- Date: 2020
- Type: Text , Journal article
- Relation: Geoderma Vol. 366, no. (2020), p.
- Full Text:
- Reviewed:
- Description: The pedotransfer function is a mathematical model used to convert direct soil measurements into known and unknown soil properties. It provides information for modelling and simulation in soil research, hydrology, environmental science and climate change impacts, including investigating the carbon cycle and the exchange of carbon between soils and the atmosphere to support carbon farming. In particular, the pedotransfer function can provide input parameters for landscape design, soil quality assessment and economic optimisation. The objective of the study was to investigate the feasibility of using a generalised pedotransfer function derived with a machine learning method to predict soil electrical conductivity (EC) and soil organic carbon content (OC) for different regional locations in the state of Victoria, Australia. This strategy supports a unified approach to the interpolation and population of a single regional soils database, in contrast to a range of pedotransfer functions derived from local databases with measurement sets that may have limited transferability. The pedotransfer function generation was based on a machine learning algorithm incorporating the Generalized Linear Mixed Model with interactions and nested terms, with Residual Maximum Likelihood estimation, and a predictor-frequency ranking system with step-wise reduction of predictors to evaluate the predictive errors in reduced models. The source of the data was the Victorian Soil Information System (VSIS), which is a database administered for soil information and mapping purposes. The database contains soil measurements and information from locations across Victoria and is a repository of historical data, including monitoring studies. In total, data from 93 projects were available for inputs to modelling and analysis, with 5158 samples used to derive predictors for EC and 1954 samples used to derive predictors for OC. Over 500 models were tested by systematically reducing the number of predictors from the full model. Five-fold cross-validation was used for estimation of model mean-squared prediction error (MSPE) and mean-absolute percentage error (MAPE). The results were statistically significant with only a gradual reduction in error for the top-ranked 50 models. The prediction errors (MSPE and MAPE) of the top ranked model for EC are 0.686 and 0.635, and 0.413 and 0.474 for OC respectively. The four most frequently occurring predictors both for EC and OC prediction across the full set of models were found to be soil depth, pH, particle size distribution and geomorphological mapping unit. The possible advantages and disadvantages of this approach were discussed with respect to other machine learning approaches. © 2020 Elsevier B.V.
- Authors: Benke, Kurt , Norng, Sorn , Robinson, Nathan , Chia, K. , Rees, David , Hopley, J.
- Date: 2020
- Type: Text , Journal article
- Relation: Geoderma Vol. 366, no. (2020), p.
- Full Text:
- Reviewed:
- Description: The pedotransfer function is a mathematical model used to convert direct soil measurements into known and unknown soil properties. It provides information for modelling and simulation in soil research, hydrology, environmental science and climate change impacts, including investigating the carbon cycle and the exchange of carbon between soils and the atmosphere to support carbon farming. In particular, the pedotransfer function can provide input parameters for landscape design, soil quality assessment and economic optimisation. The objective of the study was to investigate the feasibility of using a generalised pedotransfer function derived with a machine learning method to predict soil electrical conductivity (EC) and soil organic carbon content (OC) for different regional locations in the state of Victoria, Australia. This strategy supports a unified approach to the interpolation and population of a single regional soils database, in contrast to a range of pedotransfer functions derived from local databases with measurement sets that may have limited transferability. The pedotransfer function generation was based on a machine learning algorithm incorporating the Generalized Linear Mixed Model with interactions and nested terms, with Residual Maximum Likelihood estimation, and a predictor-frequency ranking system with step-wise reduction of predictors to evaluate the predictive errors in reduced models. The source of the data was the Victorian Soil Information System (VSIS), which is a database administered for soil information and mapping purposes. The database contains soil measurements and information from locations across Victoria and is a repository of historical data, including monitoring studies. In total, data from 93 projects were available for inputs to modelling and analysis, with 5158 samples used to derive predictors for EC and 1954 samples used to derive predictors for OC. Over 500 models were tested by systematically reducing the number of predictors from the full model. Five-fold cross-validation was used for estimation of model mean-squared prediction error (MSPE) and mean-absolute percentage error (MAPE). The results were statistically significant with only a gradual reduction in error for the top-ranked 50 models. The prediction errors (MSPE and MAPE) of the top ranked model for EC are 0.686 and 0.635, and 0.413 and 0.474 for OC respectively. The four most frequently occurring predictors both for EC and OC prediction across the full set of models were found to be soil depth, pH, particle size distribution and geomorphological mapping unit. The possible advantages and disadvantages of this approach were discussed with respect to other machine learning approaches. © 2020 Elsevier B.V.
- Dahlhaus, Peter, MacLeod, Andrew, Robinson, Nathan
- Authors: Dahlhaus, Peter , MacLeod, Andrew , Robinson, Nathan
- Type: Text , Dataset
- Full Text: false
- Description: DSM is a collaborative workspace for researchers working on digital soil mapping in Australia. This workspace is the initiative of the Advisory Group on Digital Soil Assessment, a working group of the National Committee on Soil & Terrain. The website is part of an interoperable web-GIS maintained by the Centre for eResearch and Digital Innovation (CeRDI) at Federation University Australia (FedUni). The International Union of Soil Sciences Digital Soil Mapping Working Group defines DSM as "creation and the population of a geographically referenced soil database, generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships." Digital Soil Mapping (DSM) utilises numerical methods and information technologies to produce predictive maps of soil types and their properties. DSM relies on traditional field mapping, observations and laboratory analyses for soil data but also utilises spatial models of landscape terrain and remotely-sensed properties. Typically numerical methods such as interpolation algorithms and data mining are used to create the maps. OzDSM in collaboration with CeRDI is developing a digital soil mapping tool, and a demonstrator version is displayed on the site. A range of data is included, such as nutrient budgets and soil pH, and with extra data available for the Corangamite Catchment Management Authority region including landslides, erosion and salinity.
- Robinson, Nathan, Benke, Kurt, Hopley, J., MacEwan, Richard, Clark, R., Rees, David, Kitching, Matt, Imhof, Mark, Bardos, David
- Authors: Robinson, Nathan , Benke, Kurt , Hopley, J. , MacEwan, Richard , Clark, R. , Rees, David , Kitching, Matt , Imhof, Mark , Bardos, David
- Date: 2014
- Type: Text , Conference paper
- Relation: GlobalSoilMap: Basis of the Global Spatial Soil Information System - Proceedings of the 1st GlobalSoilMap Conference p. 353-358
- Full Text: false
- Reviewed:
- Description: Production of a digital soil map for the state of Victoria in Australia is subject to various errors arising from the use of legacy data (as is often the case around the globe). Potential sources of uncertainty for inputs and methods undertaken in the creation of a Victorian DSM version 1.0 (VicDSMv1) are identified. These sources of uncertainty are recognised and issues discussed including their potential contribution to error propagation. Examples include possible errors associated with legacy soil maps, soil sites, laboratory analysis and predictive modelling by regression or spline approaches. Experiences in processing of legacy data in Victoria are described and some aspects of incorporating uncertainty in data discussed. As part of this initial DSM exercise these uncertainties and contextual information will be captured as associated metadata. A framework, as a five component process model for integrated assessment of uncertainty, is suggested based on uncertainty in the mapping process. © 2014 Taylor & Francis Group, London, UK.
Determination of munsell soil colour using smartphones
- Nodi, Sadia, Paul, Manoranjan, Robinson, Nathan, Wang, Liang, Rehman, Sabih
- Authors: Nodi, Sadia , Paul, Manoranjan , Robinson, Nathan , Wang, Liang , Rehman, Sabih
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 6 (2023), p.
- Full Text:
- Reviewed:
- Description: Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only (Formula presented.) for the top 5 predictions, the accuracy of the proposed method is (Formula presented.), which is significant. © 2023 by the authors.
- Authors: Nodi, Sadia , Paul, Manoranjan , Robinson, Nathan , Wang, Liang , Rehman, Sabih
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 6 (2023), p.
- Full Text:
- Reviewed:
- Description: Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only (Formula presented.) for the top 5 predictions, the accuracy of the proposed method is (Formula presented.), which is significant. © 2023 by the authors.
- Schefe, Cassandra, Barlow, Kirsten, Robinson, Nathan, Crawford, Douglas, McLaren, Timothy, Smernik, Ronald, Croatto, George, Walsh, Ronald, Kitching, Matt
- Authors: Schefe, Cassandra , Barlow, Kirsten , Robinson, Nathan , Crawford, Douglas , McLaren, Timothy , Smernik, Ronald , Croatto, George , Walsh, Ronald , Kitching, Matt
- Date: 2015
- Type: Text , Journal article
- Relation: Soil Research Vol. 53, no. 6 (2015), p. 662-676
- Full Text: false
- Reviewed:
- Description: Pasture-based animal production systems, which occupy a significant proportion of the landscape in Victoria, Australia, have historically been nutrient-limited, with phosphorus (P) often the most limiting nutrient. The Permanent Top-Dressed (PTD) pasture experiment was established in 1914 at the Rutherglen Research Station, Victoria, to investigate the management of this deficiency. The main objective of the PTD experiment was to demonstrate the value of adding P fertiliser at two rates to increase pasture productivity for lamb and wool production. We report on the status of the PTD soils after 100 years, investigating the long-term implications of continuous grazing and fertiliser management (0, 125 and 250kg/ha of superphosphate every second year) of non-disturbed pasture. We investigated the long-term effects of P fertiliser on the forms and distribution of P and other relevant soil parameters. In the fertilised treatments, P has accumulated in the surface soils (0-10cm) as both orthophosphate and organic P, with an Olsen P of 16-21mg P/kg, which is non-limiting for pasture production. In the treatment with 250kg superphosphate, there has also been movement of P down through the soil profile, probably due to the high sand content of the surface soil and the transfer through the profile of small quantities of water-soluble P and P bound to organic ligands. Over time, the site has continued to acidify (surface 0-10cm); the soil acidity combined with aluminium (Al) concentrations in the fertilised treatments approach a level that should impact on production and where broadcast lime would be recommended. After 100 years of non-disturbed pasture, the surface soils of these systems would be in a state of quasi-equilibrium, in which the fertilised systems have high levels of carbon (C), nitrogen, P and exchangeable Al. The continued stability of this system is likely dependent upon maintaining the high C status, which is important to nutrient cycling and the prevention of Al phytotoxicity. There are two risks to this system: (i) the declining pH; and (ii) soil disturbance, which may disrupt the equilibrium of these soils and the bio-chemical processes that maintain it. © CSIRO 2015.
Soil–water dynamics investigation at agricultural hillslope with high-precision weighing lysimeters and soil–water collection systems
- Krevh, Vedran, Groh, Jannis, Filipović, Lana, Gerke, Horst, Defterdarović, Jasmina, Thompson, Sally, Sraka, Mario, Bogunović, Igor, Kovač, Zoran, Robinson, Nathan, Baumgartl, Thomas, Filipović, Vilim
- Authors: Krevh, Vedran , Groh, Jannis , Filipović, Lana , Gerke, Horst , Defterdarović, Jasmina , Thompson, Sally , Sraka, Mario , Bogunović, Igor , Kovač, Zoran , Robinson, Nathan , Baumgartl, Thomas , Filipović, Vilim
- Date: 2023
- Type: Text , Journal article
- Relation: Water (Switzerland) Vol. 15, no. 13 (2023), p.
- Full Text:
- Reviewed:
- Description: A quantitative understanding of actual evapotranspiration (ETa) and soil–water dynamics in a hillslope agroecosystem is vital for sustainable water resource management and soil conservation; however, the complexity of processes and conditions involving lateral subsurface flow (LSF) can be a limiting factor in the full comprehension of hillslope soil–water dynamics. The research was carried out at SUPREHILL CZO located on a hillslope agroecosystem (vineyard) over a period of two years (2021–2022) by combining soil characterization and field hydrological measurements, including weighing lysimeters, sensor measurements, and LSF collection system measurements. Lysimeters were placed on the hilltop and the footslope, both having a dynamic controlled bottom boundary, which corresponded to field pressure head measurements, to mimic field soil–water dynamics. Water balance components between the two positions on the slope were compared with the goal of identifying differences that might reveal hydrologically driven differences due to LSF paths across the hillslope. The usually considered limitations of these lysimeters, or the borders preventing LSF through the domain, acted as an aid within this installation setup, as the lack of LSF was compensated for through the pumping system at the footslope. The findings from lysimeters were compared with LSF collection system measurements. Weighing lysimeter data indicated that LSF controlled ETa rates. The results suggest that the onset of LSF contributes to the spatial crop productivity distribution in hillslopes. The present approach may be useful for investigating the impact of LSF on water balance components for similar hillslope sites and crops or other soil surface covers. © 2023 by the authors.
- Authors: Krevh, Vedran , Groh, Jannis , Filipović, Lana , Gerke, Horst , Defterdarović, Jasmina , Thompson, Sally , Sraka, Mario , Bogunović, Igor , Kovač, Zoran , Robinson, Nathan , Baumgartl, Thomas , Filipović, Vilim
- Date: 2023
- Type: Text , Journal article
- Relation: Water (Switzerland) Vol. 15, no. 13 (2023), p.
- Full Text:
- Reviewed:
- Description: A quantitative understanding of actual evapotranspiration (ETa) and soil–water dynamics in a hillslope agroecosystem is vital for sustainable water resource management and soil conservation; however, the complexity of processes and conditions involving lateral subsurface flow (LSF) can be a limiting factor in the full comprehension of hillslope soil–water dynamics. The research was carried out at SUPREHILL CZO located on a hillslope agroecosystem (vineyard) over a period of two years (2021–2022) by combining soil characterization and field hydrological measurements, including weighing lysimeters, sensor measurements, and LSF collection system measurements. Lysimeters were placed on the hilltop and the footslope, both having a dynamic controlled bottom boundary, which corresponded to field pressure head measurements, to mimic field soil–water dynamics. Water balance components between the two positions on the slope were compared with the goal of identifying differences that might reveal hydrologically driven differences due to LSF paths across the hillslope. The usually considered limitations of these lysimeters, or the borders preventing LSF through the domain, acted as an aid within this installation setup, as the lack of LSF was compensated for through the pumping system at the footslope. The findings from lysimeters were compared with LSF collection system measurements. Weighing lysimeter data indicated that LSF controlled ETa rates. The results suggest that the onset of LSF contributes to the spatial crop productivity distribution in hillslopes. The present approach may be useful for investigating the impact of LSF on water balance components for similar hillslope sites and crops or other soil surface covers. © 2023 by the authors.
Online Farm Trials (OFT) – the past, present and future
- Robinson, Nathan, Dahlhaus, Peter, Feely, Paul, Light, Kate, MacLeod, Andrew
- Authors: Robinson, Nathan , Dahlhaus, Peter , Feely, Paul , Light, Kate , MacLeod, Andrew
- Type: Text , Conference paper
- Relation: Proceedings of the 19th Australian Society of Agronomy Conference,25-29 August 2019, Wagga Wagga, NSW, Australia
- Full Text:
- Reviewed:
- Description: Online Farm Trials (OFT) (www.farmtrials.com.au) is a free web-based resource and trial discovery system that contains more than 7,100 trials from 76 different organisations from across Australia. Since its inception in 2013, OFT has developed via a collaborative approach with grower groups, research organisations, agricultural experts and grains industry organisations. This ensures the outcomes are highly relevant, practical and beneficial for growers. Users can view, analyse and export grains research data as well as compare trials based upon historical, geographic and crop-specific search filters. Current developments include seasonally relevant collections of trials to highlight priority topics and aid on-farm decision making. To meet the future needs of industry stakeholders, system developments are planned to include expanded trial research information access, foster innovation through publishing and promoting active trials and enhance trial data standards and quality. **Please note that there are multiple Federation University authors for this article, including the name of the first 5 and also including “Rob Milne, Julie Parker, Helen Thompson, Judi Walters and Ben Wills" is provided in this record**
- Authors: Robinson, Nathan , Dahlhaus, Peter , Feely, Paul , Light, Kate , MacLeod, Andrew
- Type: Text , Conference paper
- Relation: Proceedings of the 19th Australian Society of Agronomy Conference,25-29 August 2019, Wagga Wagga, NSW, Australia
- Full Text:
- Reviewed:
- Description: Online Farm Trials (OFT) (www.farmtrials.com.au) is a free web-based resource and trial discovery system that contains more than 7,100 trials from 76 different organisations from across Australia. Since its inception in 2013, OFT has developed via a collaborative approach with grower groups, research organisations, agricultural experts and grains industry organisations. This ensures the outcomes are highly relevant, practical and beneficial for growers. Users can view, analyse and export grains research data as well as compare trials based upon historical, geographic and crop-specific search filters. Current developments include seasonally relevant collections of trials to highlight priority topics and aid on-farm decision making. To meet the future needs of industry stakeholders, system developments are planned to include expanded trial research information access, foster innovation through publishing and promoting active trials and enhance trial data standards and quality. **Please note that there are multiple Federation University authors for this article, including the name of the first 5 and also including “Rob Milne, Julie Parker, Helen Thompson, Judi Walters and Ben Wills" is provided in this record**
Assessing productive soil - landscapes in Victoria using digital soil mapping
- Authors: Robinson, Nathan
- Date: 2016
- Type: Text , Thesis , PhD
- Full Text:
- Description: Spatial soil information is used to support questions on agriculture and the environment from global to local scales. Historically, soil mapping has been used to inform and guide a multitude of land users with their decisions. Demand for specific spatial soil information is increasing in response from a wider range of users operating across agricultural and environmental domains. To satisfy these demands, users must be provided with practical and relevant spatial soil information. Novel approaches are required to deal with global deficiencies in available soil information. A major limitation to this is the plethora of incongruent legacy data with poor spatial and temporal coverage. This research study initially identifies the specific needs of users for spatial soil information with a focus on the requirements of biophysical modellers. Secondly, error sources that hamper Digital Soil Mapping (DSM) are identified, described and assessed using pH in practical and relevant examples. A final aim is to spatially predict soil properties (e.g. clay mineralogy) that underpin soil chemical behaviour. This is achieved by harmonising legacy data in combination with new spectroscopy techniques and a spatial inference approach. The spatial soil information needs of biophysical modellers in Victoria, Australia were found to be consistent with global needs for information including soil water characteristics, organic carbon and effective rooting depth. To accommodate stochastic and epistemic uncertainties in spatial soil information, uncertainty frameworks proved effective to deal with, and understand the limitations of legacy data in spatial inference models. Robust and reliable spectroscopic models for properties that are linked to functions and services delivered by soil were achieved and used in 3D spatial models. These findings will enable a tactical response through the delivery of pertinent spatial soil information that is contemporary, quality assured and sought by users. Learnings presented should enable producers of spatial soil information to be more comprehensive in their delivery of products that are easy to use, accessible and understood by a growing user community.
- Description: Doctor of Philosphy
- Authors: Robinson, Nathan
- Date: 2016
- Type: Text , Thesis , PhD
- Full Text:
- Description: Spatial soil information is used to support questions on agriculture and the environment from global to local scales. Historically, soil mapping has been used to inform and guide a multitude of land users with their decisions. Demand for specific spatial soil information is increasing in response from a wider range of users operating across agricultural and environmental domains. To satisfy these demands, users must be provided with practical and relevant spatial soil information. Novel approaches are required to deal with global deficiencies in available soil information. A major limitation to this is the plethora of incongruent legacy data with poor spatial and temporal coverage. This research study initially identifies the specific needs of users for spatial soil information with a focus on the requirements of biophysical modellers. Secondly, error sources that hamper Digital Soil Mapping (DSM) are identified, described and assessed using pH in practical and relevant examples. A final aim is to spatially predict soil properties (e.g. clay mineralogy) that underpin soil chemical behaviour. This is achieved by harmonising legacy data in combination with new spectroscopy techniques and a spatial inference approach. The spatial soil information needs of biophysical modellers in Victoria, Australia were found to be consistent with global needs for information including soil water characteristics, organic carbon and effective rooting depth. To accommodate stochastic and epistemic uncertainties in spatial soil information, uncertainty frameworks proved effective to deal with, and understand the limitations of legacy data in spatial inference models. Robust and reliable spectroscopic models for properties that are linked to functions and services delivered by soil were achieved and used in 3D spatial models. These findings will enable a tactical response through the delivery of pertinent spatial soil information that is contemporary, quality assured and sought by users. Learnings presented should enable producers of spatial soil information to be more comprehensive in their delivery of products that are easy to use, accessible and understood by a growing user community.
- Description: Doctor of Philosphy
Assessment of error sources in measurements of field pH : Effect of operator experience, test kit differences, and time-of-day
- Robinson, Nathan, Norng, Sorn, Rees, David, Benke, Kurt, Davey, Michelle
- Authors: Robinson, Nathan , Norng, Sorn , Rees, David , Benke, Kurt , Davey, Michelle
- Date: 2018
- Type: Text , Journal article
- Relation: Communications in Soil Science and Plant Analysis Vol. 49, no. 3 (2018), p. 269-285
- Full Text:
- Reviewed:
- Description: Various methods exist to measure soil pH, and while there is general agreement between the existing published laboratory and field-based methods, the latter are subject to uncertainties including test kit reliability, accuracy, precision, and environmental factors. The contribution of this study is to quantify three uncertainties that affect the conversion between field pH and laboratory pH measurements, namely operator experience, choice of test kit, and the time-of-day for measurement. Soil samples from western Victoria, representing the pH range 4.5–10.0, were used in a randomized complete block design with 10 assessors split into two groups representing experienced and inexperienced users. Statistical analysis of laboratory and field pH was based on using the Maximum Likelihood Functional Relationship (MLFR) to determine if there was any bias between the two methods. Significant differences were found between experienced and inexperienced users, and between test kits. © 2017 Taylor & Francis.
- Authors: Robinson, Nathan , Norng, Sorn , Rees, David , Benke, Kurt , Davey, Michelle
- Date: 2018
- Type: Text , Journal article
- Relation: Communications in Soil Science and Plant Analysis Vol. 49, no. 3 (2018), p. 269-285
- Full Text:
- Reviewed:
- Description: Various methods exist to measure soil pH, and while there is general agreement between the existing published laboratory and field-based methods, the latter are subject to uncertainties including test kit reliability, accuracy, precision, and environmental factors. The contribution of this study is to quantify three uncertainties that affect the conversion between field pH and laboratory pH measurements, namely operator experience, choice of test kit, and the time-of-day for measurement. Soil samples from western Victoria, representing the pH range 4.5–10.0, were used in a randomized complete block design with 10 assessors split into two groups representing experienced and inexperienced users. Statistical analysis of laboratory and field pH was based on using the Maximum Likelihood Functional Relationship (MLFR) to determine if there was any bias between the two methods. Significant differences were found between experienced and inexperienced users, and between test kits. © 2017 Taylor & Francis.
Soil data for biophysical models in Victoria, Australia : Current needs and future challenges
- Robinson, Nathan, Dahlhaus, Peter, MacEwan, Richard, Alexander, J. K.
- Authors: Robinson, Nathan , Dahlhaus, Peter , MacEwan, Richard , Alexander, J. K.
- Date: 2016
- Type: Text , Journal article
- Relation: Geoderma Regional Vol. 7, no. 3 (2016), p. 259-270
- Full Text: false
- Reviewed:
- Description: The use of biophysical models to support increased food production and environmental protection is on the rise. This paper reviews the demand for, and trends in, soil property data for various biophysical models being used in Victoria, Australia, over the 2009-2014 period. The study used surveys, workshops and interviews with public sector modellers to examine perceptions of the soil parameters that affect model sensitivity and error. Although the data requirements of models have remained similar over the 5 year period, the diversity of models used has decreased. There is evidence of increased application of models at point/site scale to support grains, dairy and livestock production industries in Victoria. Opportunities are identified to deliver finer scale soil data from digital soil mapping to better meet modelling requirements for agricultural industries in Victorian landscapes. © 2016 Elsevier B.V. All rights reserved.
Operationalising digital soil mapping – lessons from Australia
- Kidd, Darren, Searle, Ross, Grundy, Mike, McBratney, Alex, Robinson, Nathan, O'Brien, Lauren, Zund, Peter, Arrouays, Dominique, Thomas, Mark, Padarian, José, Jones, Edward, Bennett, John, Minasny, Budiman, Holmes, Karen, Malone, Brendan, Liddicoat, Craig, Meier, Elizabeth, Stockmann, Uta, Wilson, Peter, Wilford, John, Payne, Jim, Ringrose-Voase, Anthony, Slater, Brian, Odgers, Nathan, Gray, Jonathan, van Gool, Dennis, Andrews, Kaitlyn, Harms, Ben, Stower, Liz, Triantafilis, John
- Authors: Kidd, Darren , Searle, Ross , Grundy, Mike , McBratney, Alex , Robinson, Nathan , O'Brien, Lauren , Zund, Peter , Arrouays, Dominique , Thomas, Mark , Padarian, José , Jones, Edward , Bennett, John , Minasny, Budiman , Holmes, Karen , Malone, Brendan , Liddicoat, Craig , Meier, Elizabeth , Stockmann, Uta , Wilson, Peter , Wilford, John , Payne, Jim , Ringrose-Voase, Anthony , Slater, Brian , Odgers, Nathan , Gray, Jonathan , van Gool, Dennis , Andrews, Kaitlyn , Harms, Ben , Stower, Liz , Triantafilis, John
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Geoderma Regional Vol. 23, no. (2020), p.
- Full Text:
- Reviewed:
- Description: Australia has advanced the science and application of Digital Soil Mapping (DSM). Over the past decade, DSM in Australia has evolved from being purely research focused to become ‘operational’, where it is embedded into many soil-agency land resource assessment programs around the country. This has resulted from a series of ‘drivers’, such as an increased need for better quality and more complete soil information, and ‘enablers’, such as existing soil information systems, covariate development, serendipitous project funding, collaborations, and Australian DSM ‘champions’. However, these accomplishments were not met without some barriers along the way, such as a need to demonstrate and prove the science to the soil science community, and rapidly enable the various soil agencies' capacity to implement DSM. The long history of soil mapping in Australia has influenced the evolution and culmination of the operational DSM procedures, products and infrastructure in widespread use today, which is highlighted by several recent and significant Australian operational DSM case-studies at various extents. A set of operational DSM ‘workflows’ and ‘lessons learnt’ have also emerged from Australian DSM applications, which may provide some useful information and templates for other countries hoping to fast-track their own operational DSM capacity. However, some persistent themes were identified, such as applicable scale, and communicating uncertainty and map quality to end-users, which will need further development to progress operational DSM. © 2020 The Authors
- Authors: Kidd, Darren , Searle, Ross , Grundy, Mike , McBratney, Alex , Robinson, Nathan , O'Brien, Lauren , Zund, Peter , Arrouays, Dominique , Thomas, Mark , Padarian, José , Jones, Edward , Bennett, John , Minasny, Budiman , Holmes, Karen , Malone, Brendan , Liddicoat, Craig , Meier, Elizabeth , Stockmann, Uta , Wilson, Peter , Wilford, John , Payne, Jim , Ringrose-Voase, Anthony , Slater, Brian , Odgers, Nathan , Gray, Jonathan , van Gool, Dennis , Andrews, Kaitlyn , Harms, Ben , Stower, Liz , Triantafilis, John
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Geoderma Regional Vol. 23, no. (2020), p.
- Full Text:
- Reviewed:
- Description: Australia has advanced the science and application of Digital Soil Mapping (DSM). Over the past decade, DSM in Australia has evolved from being purely research focused to become ‘operational’, where it is embedded into many soil-agency land resource assessment programs around the country. This has resulted from a series of ‘drivers’, such as an increased need for better quality and more complete soil information, and ‘enablers’, such as existing soil information systems, covariate development, serendipitous project funding, collaborations, and Australian DSM ‘champions’. However, these accomplishments were not met without some barriers along the way, such as a need to demonstrate and prove the science to the soil science community, and rapidly enable the various soil agencies' capacity to implement DSM. The long history of soil mapping in Australia has influenced the evolution and culmination of the operational DSM procedures, products and infrastructure in widespread use today, which is highlighted by several recent and significant Australian operational DSM case-studies at various extents. A set of operational DSM ‘workflows’ and ‘lessons learnt’ have also emerged from Australian DSM applications, which may provide some useful information and templates for other countries hoping to fast-track their own operational DSM capacity. However, some persistent themes were identified, such as applicable scale, and communicating uncertainty and map quality to end-users, which will need further development to progress operational DSM. © 2020 The Authors
Improving the FAIRness of Australia’s grains research sector data
- Willis, Ben, Parker, Julie, Robinson, Nathan, Wong, Megan
- Authors: Willis, Ben , Parker, Julie , Robinson, Nathan , Wong, Megan
- Date: 2019
- Type: Text , Conference paper
- Relation: Proceedings of the 19th Australian Society of Agronomy Conference,25-29 August 2019, Wagga Wagga, NSW, Australia
- Full Text:
- Reviewed:
- Description: Across Australia’s arable landscapes, thousands of crop trials have been conducted to improve the profitability and sustainability of Australian grain production. Although there have been significant steps to make knowledge gained from trials available to users, there is the potential to further support the development of next generation data models and knowledge products by integrating trials from disparatei sources by adhering to FAIR principles of data management. That is, making data: findable, accessible, interoperable and reusable. This research explores whether Online Farm Trials increase the FAIRness of agricultural grains trial datasets through a comparison of the trial data capture and handling practices of organisations whose datasets are not discoverable through Online Farm Trials (OFT) (N = 50) with the FAIRness of the datasets discoverable through OFT. The findings demonstrate that OFT is helping to make the results of Australia’s grains trials more FAIR to the users of trial data, and suggests a number of improvements to the FAIRness of trial datasets, foremost through the use of machine-readable metadata.
- Authors: Willis, Ben , Parker, Julie , Robinson, Nathan , Wong, Megan
- Date: 2019
- Type: Text , Conference paper
- Relation: Proceedings of the 19th Australian Society of Agronomy Conference,25-29 August 2019, Wagga Wagga, NSW, Australia
- Full Text:
- Reviewed:
- Description: Across Australia’s arable landscapes, thousands of crop trials have been conducted to improve the profitability and sustainability of Australian grain production. Although there have been significant steps to make knowledge gained from trials available to users, there is the potential to further support the development of next generation data models and knowledge products by integrating trials from disparatei sources by adhering to FAIR principles of data management. That is, making data: findable, accessible, interoperable and reusable. This research explores whether Online Farm Trials increase the FAIRness of agricultural grains trial datasets through a comparison of the trial data capture and handling practices of organisations whose datasets are not discoverable through Online Farm Trials (OFT) (N = 50) with the FAIRness of the datasets discoverable through OFT. The findings demonstrate that OFT is helping to make the results of Australia’s grains trials more FAIR to the users of trial data, and suggests a number of improvements to the FAIRness of trial datasets, foremost through the use of machine-readable metadata.
The 3D distribution of phyllosilicate clay minerals in western Victoria
- Robinson, Nathan, Kitching, Matt
- Authors: Robinson, Nathan , Kitching, Matt
- Date: 2016
- Type: Text , Journal article
- Relation: Geoderma Vol. 284, no. (2016), p. 152-177
- Full Text: false
- Reviewed:
- Description: The mineralogy of the clay fraction of soils is a major determinant of the behavior of soil. Conventionally these clay minerals have been determined using techniques such as X-ray Diffraction (XRD), but new complementary methods such as infrared spectroscopy can be used to rapidly and economically predict these minerals. This paper presents a methodology to predict these clay minerals at high-resolution that adhere to GlobalSoilMap (GSM) standards. Mid-infrared (MIR) spectroscopic models were formulated for clay minerals kaolinite, illite and smectite using partial least squares regression (PLSR) and legacy quantitative XRD determinations. Very strong models were achieved for kaolinite, illite and smectite and the root mean square error of cross validation (RMSECV) were all < 12 wt.%. Spectroscopic models were applied to 11,500 samples from western Victoria and harmonized to the GSM specified depth intervals using equal area splines. Clay minerals were then mapped using data mining model trees with a 10-fold cross validation to derive a mean prediction estimate and 90% prediction interval. Spatial models accounted for 26 to 77% of the total variation with kaolinite predictions for all 6 GSM depths ≥ 65%. Kaolinite is the dominant soil clay mineral of western Victoria for uplands and weathered volcanic terrains. Illite concentrations are higher where associated with weathered granitic plutons and in aeolian deposits of semi-arid environments. Smectite tends to occur associated with depressions of plains (volcanic and sedimentary). Further supplementation of additional sites and samples for landscapes with relatively sparse observations should contribute to refined and improved maps of these clay minerals. © 2016 Elsevier B.V.
- Ollerenshaw, Alison, Murphy, Angela, Walters, Judi, Robinson, Nathan, Thompson, Helen
- Authors: Ollerenshaw, Alison , Murphy, Angela , Walters, Judi , Robinson, Nathan , Thompson, Helen
- Date: 2023
- Type: Text , Journal article
- Relation: Agricultural Systems Vol. 206, no. (2023), p.
- Full Text: false
- Reviewed:
- Description: CONTEXT: Agriculture is experiencing rapid change with the widespread availability of industry-specific technological and digital innovations. One example of this is Online Farm Trials (OFT), a user-facing web portal that systematises on-farm and field-based cropping research trial data for Australia's grains industry. The portal delivers access to research data, including legacy data, from thousands of grains trials projects that have been supplied by industry contributors. The portal is well established, having been informed by regular stakeholder input that has guided and informed the continued improvement of the portal from its development and implementation to continued operations. OBJECTIVE: Research was conducted across three time-points to assess the usage and application of OFT, and to examine its perceived impact on users to facilitate access to information supporting on-farm decision making and practice change within the Australian grains industry. METHODS: Quantitative and qualitative data (portal usage and website analytics, surveys, in-depth interviews) were collected at three time points over 6 years with the aim of examining the usage and application of the portal. Portal users, data contributors, and other stakeholders from the grains and agriculture industries participated in this research. RESULTS AND CONCLUSIONS: Over the three time points, a total of 89 surveys were completed and 49 interviews were conducted. Portal usage data confirms consistency in the number of visitors over time; most users of the portal were from Australia, with many accessing the portal on multiple occasions. Survey and interview data demonstrate that OFT is valued, widely used, and that the data on the portal are being broadly applied. Access to information and data through the portal (including legacy data) is used to support knowledge and to make sector-relevant decisions, and is assisting portal users in their workplace and work practices. The availability of data and information through the portal is increasing connections between industry and stakeholders across the grains sector. However, the trust and quality of contributor data has been consistently raised as a point of discussion by some portal users. SIGNIFICANCE: This research demonstrates the contribution that this data portal has on usage, adoption and application within the Australian grains industry. The insights and learnings about the application of digital technology for data and information access for the grains sector may be applicable to other agricultural sectors. © 2023 Elsevier Ltd
Using agricultural metadata : a novel investigation of trends in sowing date in on-farm research trials using the online farm trials database
- Walters, Judi, Light, Kate, Robinson, Nathan
- Authors: Walters, Judi , Light, Kate , Robinson, Nathan
- Date: 2021
- Type: Text , Journal article
- Relation: F1000Research Vol. 9, no. (2021), p.1305-1305
- Full Text:
- Reviewed:
- Description: Background: A growing ability to collect data, together with the development and adoption of the FAIR guiding principles, has increased the amount of data available in many disciplines. This has given rise to an urgent need for robust metadata. Within the Australian grains industry, data from thousands of on-farm research trials (Trial Projects) have been made available via the Online Farm Trials (OFT) website. OFT Trial Project metadata were developed as filters to refine front-end database searches, but could also be used as a dataset to investigate trends in metadata elements. Australian grains crops are being sown earlier, but whether on-farm research trials reflect this change is currently unknown. Methods: We investigated whether OFT Trial Project metadata could be used to detect trends in sowing dates of on-farm crop research trials across Australia, testing the hypothesis that research trials are being sown earlier in line with local farming practices. The investigation included 15 autumn-sown, winter crop species listed in the database, with trial records from 1993 to 2019. Results: Our analyses showed that (i) OFT Trial Project metadata can be used as a dataset to detect trends in sowing date; and (ii) cropping research trials are being sown earlier in Victoria and Western Australia, but no trend exists within the other states. Discussion/Conclusion: Our findings show that OFT Trial Project metadata can be used to detect trends in crop sowing date, suggesting that metadata could also be used to detect trends in other elements such as harvest date. Because OFT is a national database of research trials, further assessment of metadata may uncover important agronomic, cultural or economic trends within or across the Australian cropping regions. New information could then be used to lead practice change and increase productivity within the Australian grains industry. © 2021 Walters J et al.
- Authors: Walters, Judi , Light, Kate , Robinson, Nathan
- Date: 2021
- Type: Text , Journal article
- Relation: F1000Research Vol. 9, no. (2021), p.1305-1305
- Full Text:
- Reviewed:
- Description: Background: A growing ability to collect data, together with the development and adoption of the FAIR guiding principles, has increased the amount of data available in many disciplines. This has given rise to an urgent need for robust metadata. Within the Australian grains industry, data from thousands of on-farm research trials (Trial Projects) have been made available via the Online Farm Trials (OFT) website. OFT Trial Project metadata were developed as filters to refine front-end database searches, but could also be used as a dataset to investigate trends in metadata elements. Australian grains crops are being sown earlier, but whether on-farm research trials reflect this change is currently unknown. Methods: We investigated whether OFT Trial Project metadata could be used to detect trends in sowing dates of on-farm crop research trials across Australia, testing the hypothesis that research trials are being sown earlier in line with local farming practices. The investigation included 15 autumn-sown, winter crop species listed in the database, with trial records from 1993 to 2019. Results: Our analyses showed that (i) OFT Trial Project metadata can be used as a dataset to detect trends in sowing date; and (ii) cropping research trials are being sown earlier in Victoria and Western Australia, but no trend exists within the other states. Discussion/Conclusion: Our findings show that OFT Trial Project metadata can be used to detect trends in crop sowing date, suggesting that metadata could also be used to detect trends in other elements such as harvest date. Because OFT is a national database of research trials, further assessment of metadata may uncover important agronomic, cultural or economic trends within or across the Australian cropping regions. New information could then be used to lead practice change and increase productivity within the Australian grains industry. © 2021 Walters J et al.
Matching the model to the available data to predict wheat, barley, or canola yield : a review of recently published models and data
- Clark, Robert, Dahlhaus, Peter, Robinson, Nathan, Larkins, Jo-ann, Morse-McNabb, Elizabeth
- Authors: Clark, Robert , Dahlhaus, Peter , Robinson, Nathan , Larkins, Jo-ann , Morse-McNabb, Elizabeth
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Agricultural Systems Vol. 211, no. (2023), p.
- Full Text:
- Reviewed:
- Description: CONTEXT: Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. OBJECTIVE: This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. METHODS: A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. RESULTS AND CONCLUSIONS: The literature search produced 11, 908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. SIGNIFICANCE: The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. © 2023 The Authors
- Authors: Clark, Robert , Dahlhaus, Peter , Robinson, Nathan , Larkins, Jo-ann , Morse-McNabb, Elizabeth
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Agricultural Systems Vol. 211, no. (2023), p.
- Full Text:
- Reviewed:
- Description: CONTEXT: Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. OBJECTIVE: This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. METHODS: A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. RESULTS AND CONCLUSIONS: The literature search produced 11, 908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. SIGNIFICANCE: The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. © 2023 The Authors
Improving the information content in soil pH maps: a case study
- Robinson, Nathan, Benke, Kurt, Norng, Sorn, Kitching, Matt, Crawford, Deborah
- Authors: Robinson, Nathan , Benke, Kurt , Norng, Sorn , Kitching, Matt , Crawford, Deborah
- Date: 2017
- Type: Text , Journal article
- Relation: European Journal of Soil Science Vol. 68, no. 5 (2017), p. 592-604
- Full Text: false
- Reviewed:
- Description: Uncertainties associated with legacy data contribute to the spatial uncertainty of predictions for soil properties such as pH. Examples of potential sources of error in maps of soil pH include temporal variation and changes in land use over time. Prediction of soil pH can be improved with a linear mixed model (LMM) to analyse factors that contribute to uncertainty. Probabilities from conditional simulations in combination with agronomic critical thresholds for acid-sensitive species can be used to identify areas that are likely, or very likely, to be below these critical thresholds for plant production. Because of rapid changes in farming systems and management practices, there is a need to be vigilant in monitoring changes in soil acidification. This is because soil acidification is an important factor in primary production and soil sustainability. In this research, legacy data from south-western Victoria (Australia) were used with model-based geostatistics to produce a map of soil pH that accommodates a variety of error sources, such as the time of sampling, seasonal variation, differences in analytical method, effects of changes in land use and variable soil sample depth in legacy data. Spatial covariates that are representative of soil-forming factors were used to improve predictions. To transform spatial prediction and estimates of error in soil pH into more informative and usable maps with more information content, simulations from the conditional distribution were used to compute the probability of a soil's pH being less than critical agronomic production thresholds at each of the prediction locations. These probabilities were mapped to reveal areas of potential risk. Highlights: Can maps of soil pH be improved by accounting for temporal variation and change in land use? First example of taking account of temporal variability in sampling for pH in spatial models. Key factors for uncertainty in spatial prediction include time of sampling and sample depth. Accuracy improved by accounting for additional sources of error combined with conditional simulations. © 2017 British Society of Soil Science
Epistemic uncertainties in the assessment of regional soil acidification
- Benke, Kurt, Robinson, Nathan, Norng, Sorn, Rees, David, O’Leary, Garry
- Authors: Benke, Kurt , Robinson, Nathan , Norng, Sorn , Rees, David , O’Leary, Garry
- Date: 2022
- Type: Text , Journal article
- Relation: Environments - MDPI Vol. 9, no. 8 (2022), p.
- Full Text:
- Reviewed:
- Description: The increasing acidification of soil due to pollution and agricultural management practices is a growing problem worldwide, where food production is already under threat by climate change, more frequent droughts, and soil nutrient depletion. Soil acidification is quantified by pH measurements and is a primary metric for soil health. High soil acidity is a constraint on the production of grains and other crops because it decreases the bioavailability of important plant nutrients while increasing soil toxicity arising from an imbalance of essential soil elements. Field pH can be estimated by colour test kits which are very cost-effective and particularly suitable for developing countries where laboratory services are not available or fail to provide timely results. Because the pH test kit is based on visual colour matching between a colour card scale and a soil sample in solution, there are epistemic uncertainties, such as variability in expert opinion, differences in colour vision, measurement error, instrumentation, and changes in daylight spectral content. In this study, expert human observers were compared in experiments conducted using a standard pH test kit under a range of environmental conditions. A significant difference in uncertainty in colour discrimination was evident between male and female experts, whereas changes in daylight conditions had lower impact on the variance of pH estimates. In a group of subject matter experts, the male standard error (0.35 pH) was 57% higher on average over the range of pH values (pH = 4
- Authors: Benke, Kurt , Robinson, Nathan , Norng, Sorn , Rees, David , O’Leary, Garry
- Date: 2022
- Type: Text , Journal article
- Relation: Environments - MDPI Vol. 9, no. 8 (2022), p.
- Full Text:
- Reviewed:
- Description: The increasing acidification of soil due to pollution and agricultural management practices is a growing problem worldwide, where food production is already under threat by climate change, more frequent droughts, and soil nutrient depletion. Soil acidification is quantified by pH measurements and is a primary metric for soil health. High soil acidity is a constraint on the production of grains and other crops because it decreases the bioavailability of important plant nutrients while increasing soil toxicity arising from an imbalance of essential soil elements. Field pH can be estimated by colour test kits which are very cost-effective and particularly suitable for developing countries where laboratory services are not available or fail to provide timely results. Because the pH test kit is based on visual colour matching between a colour card scale and a soil sample in solution, there are epistemic uncertainties, such as variability in expert opinion, differences in colour vision, measurement error, instrumentation, and changes in daylight spectral content. In this study, expert human observers were compared in experiments conducted using a standard pH test kit under a range of environmental conditions. A significant difference in uncertainty in colour discrimination was evident between male and female experts, whereas changes in daylight conditions had lower impact on the variance of pH estimates. In a group of subject matter experts, the male standard error (0.35 pH) was 57% higher on average over the range of pH values (pH = 4
A digital soil map of Victoria-VicDSMv1
- Hopley, J., Rees, David, MaEwan, Richard, Clark, R., Benke, Kurt, Imhof, Mark, Robinson, Nathan, Bardos, David
- Authors: Hopley, J. , Rees, David , MaEwan, Richard , Clark, R. , Benke, Kurt , Imhof, Mark , Robinson, Nathan , Bardos, David
- Date: 2014
- Type: Text , Conference paper
- Relation: GlobalSoilMap: Basis of the Global Spatial Soil Information System - Proceedings of the 1st GlobalSoilMap Conference p. 185-189
- Full Text: false
- Reviewed:
- Description: This paper describes the production of the first version of a digital soil map for Victoria (VicDSMv1) which has combined existing soil point and polygon data. Four soil properties: pH, EC (electrical conductivity), clay percentage and Soil Organic Carbon (SOC) at the 6 depths specified by GlobalSoilMap.net have been mapped. The mapping has utilised data from 5,233 legacy sites collated from soil and land surveys conducted across Victoria since the 1930s and stored in the Victorian Soil Information System (VSIS). These sites were prepared by allocating property values to each of the 6 depths using equal area splines or depth weighted values. A land unit map for Victoria was derived from an overlay of map units from 32 surveys mapped at 100,000 scale or better. A dominant soil type at Suborder level in the Australian Soil Classification system (ASC) was assigned to each land unit. For each polygon a hierarchical grouping of sites from the VSIS was created using soil classification and location in relation to the polygon. A set of statistics for each soil property value at each set depth were calculated from the best available site cluster for each polygon. Metadata relating to property calculations have been collected. Creation of the VicDSMv1 has involved the preparation and entry of a large volume of legacy soil information into the VSIS. Consultation with current and retired soil surveyors during the process has enabled valuable expert knowledge to be captured into digital soil mapping. © 2014 Taylor & Francis Group, London, UK.
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