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.
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
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.
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.
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**
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.
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
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.
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.
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
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
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