ORCID Profile
0000-0002-5318-8161
Current Organisation
NSW Department of Planning, Industry and Environment
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 05-2009
Publisher: Wiley
Date: 07-11-2021
DOI: 10.1111/SUM.12766
Abstract: Digital soil maps of soil organic carbon (SOC) sequestration potential resulting from a hypothetical 10% relative increase in long‐term vegetation cover are presented at 100‐m resolution across the state of New South Wales (NSW) in southeast Australia. This land management outcome is considered realistically achievable for many land managers, using strategies such as revegetation, grazing management or crop residue management. A mean state‐wide potential increase of 5.4 Mg ha −1 over the 0‐ to 30‐cm depth interval was derived. Assuming a 20‐year period of re‐equilibration, this equates to an average SOC increase of 0.27 Mg ha −1 year −1 . Sequestration potential is systematically influenced by a combination of climate, soil parent material and current vegetation cover, for ex le only 1.6 Mg ha −1 SOC under dry conditions in sandy, infertile soil material with sparse vegetation cover, compared with 15.9 Mg ha −1 under wet conditions in clay‐rich, fertile soil material with moderate–high vegetation cover. The outputs could be used to identify locations of highest sequestration potential and thereby help prioritize areas and inform decisions on sequestration programmes. Future application of the method at field scale with high levels of accuracy, together with strategic s ling, may provide statistically reliable estimates of carbon sequestration, for application in carbon trading schemes such as Australia's Emissions Reduction Fund. The modelling involved a conceptually transparent ‘space‐for‐time substitution’ process. Multiple linear regression (MLR) and random forest (RF) modelling techniques were applied, but only MLR gave consistently meaningful results. The apparent failing of RF in this application warrants further examination.
Publisher: Wiley
Date: 17-03-2023
DOI: 10.1111/SUM.12876
Abstract: The monitoring of land management practices is vital for the protection of soil resource and the environment. Here, we present a new approach to monitor sustainable land management using widely available remotely sensed data, such as MODIS fractional vegetation cover data. The method is based on the concept of maintaining sufficient vegetation cover to prevent hillslope water erosion beyond tolerable soil erosion targets. The targets were based on long‐term natural erosion rates plus a small constant and are spatially and temporally variable, not static as in most reported studies to date. Where vegetation cover is more than that required to prevent nontolerable erosion under normal conditions for that calendar month, the site (pixel) is deemed to be managed sustainably. Monthly indices are then combined to form a yearly sustainable land management index (SLMI), presented as raster maps with a spatial resolution of 100 m. We explored this new method through case studies over New South Wales (NSW), Australia, over the period 2010 to 2021, with a particular examination of 2020. Results were further stratified by land uses and natural resource management regions, which revealed useful data and trends. The method is offered as an ex le of the potential use of readily available vegetation cover data to quantitatively assess and monitor levels of sustainable land management across landscapes. We believe it overcomes the limitations of previous methods to monitor vegetation cover and land management from remote‐sensed data alone. Other users are encouraged to adapt the broad approach to meet local requirements.
Publisher: Elsevier BV
Date: 12-2016
Publisher: CSIRO Publishing
Date: 2019
DOI: 10.1071/SR18139
Abstract: Climate change will lead to altered soil conditions that will impact on plant growth in both agricultural and native ecosystems. Additionally, changes in soil carbon storage will influence carbon accounting schemes that may play a role in climate change mitigation programs. We applied a digital soil mapping approach to examine and map (at 100-m resolution) potential changes in three important soil properties – soil organic carbon (SOC), pH and sum-of-bases (common macro-nutrients) – resulting from projected climate change over south-eastern Australia until ~2070. Four global climate models were downscaled with three regional models to give 12 climate models, which were used to derive changes for the three properties across the province, at 0–30 and 30–100 cm depth intervals. The SOC stocks were projected to decline over the province, while pH and sum-of-bases were projected to increase however, the extent of change varied throughout the province and with different climate models. The average changes primarily reflected the complex interplay of changing temperatures and rainfall throughout the province. The changes were also influenced by the operating environmental conditions, with a uniform pattern of change particularly demonstrated for SOC over 36 combinations of current climate, parent material and land use. For ex le, the mean decline of SOC predicted for the upper depth interval was 15.6 Mg ha–1 for wet–mafic–native vegetation regimes but only 3.1 Mg ha–1 for dry–highly siliceous–cropping regimes. The predicted changes reflected only those attributable to the projected climate change and did not consider the influence of ongoing and changing land management practices.
Publisher: Elsevier BV
Date: 11-2019
Publisher: CSIRO Publishing
Date: 2016
DOI: 10.1071/SR14307
Abstract: Digital soil models and maps have been developed for pre-European (pre-clearing) levels of soil organic carbon (SOC) over New South Wales, Australia. These provide a useful first estimate of natural, unaltered soil conditions before agricultural development, which are potentially important for many carbon-accounting schemes such as those prescribed by the Intergovernmental Panel on Climate Change, carbon-turnover models such as RothC, and soil-condition monitoring programs. The modelling approach adopted included multiple linear regression and Cubist piecewise linear decision trees. It used 1690 soil profiles from undisturbed or only lightly disturbed native vegetation sites across all of eastern Australia, together with a range of covariates representing key soil-forming factors. The digital soil maps of pre-clearing SOC (% and mass) over New South Wales provide a more sophisticated alternative to currently available, equivalent maps. Independent validation of the SOC mass predictions over the top 30 cm revealed a concordance correlation coefficient of 0.76, which was 13% higher than the currently used map. Total pre-clearing SOC stocks amount to 4.21 Gt in the top 30 cm, which compared with a current stock estimate of 3.68 Gt, suggesting a total SOC loss of ~0.53 Gt over the entire state. The extent of SOC decline in both absolute and relative terms was found to be highly dependent on the climate, parent material and land use regime, reaching a maximum decline of 44.3 t/ha or 50.0% relative loss in cooler (moist) conditions over mafic parent materials under regular cropping use. The models also provide valuable pedological insights into the factors controlling SOC levels under natural conditions.
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR13306
Abstract: To help meet the increasing need for knowledge and data on the spatial distribution of soils, readily applied multiple linear regression models were developed for key soil properties over eastern Australia. Selected covariates were used to represent the key soil-forming factors of climate (annual precipitation and maximum temperature), parent material (a lithological silica index) topography (new topo-slope and aspect indices) and biota (a modified land disturbance index). The models are presented at three depth intervals (0–10, 10–30 and 30–100 cm) and are of variable but generally moderate statistical strength, with concordance correlation coefficients in the order of 0.7 for organic carbon (OC) upper depth, pHca, sum of bases, cation exchange capacity (CEC) and sand, but somewhat lower (0.4–0.6) for OC lower depths, total phosphorous, clay and silt. The pragmatic models facilitate soil property predictions at in idual sites using only climate and field-collected data. They were also moderately effective for deriving digital soil maps over the state of New South Wales and a regional catchment. The models and derived maps compared well in predictive ability to those derived from more sophisticated techniques involving Cubist decision trees with remotely sensed covariates. The readily understood and interpreted nature of these products means they may provide a useful introduction to the more advanced digital soil modelling and mapping techniques. The models provide useful information and broader insights into the factors controlling soil distribution in eastern Australia and beyond, including the change in a soil property with a given unit change in a covariate.
Publisher: Elsevier BV
Date: 2011
Publisher: Wiley
Date: 11-2015
Location: Australia
No related grants have been discovered for Jonathan Gray.