ORCID Profile
0000-0002-9648-4606
Current Organisation
Queensland Department of Environment and Science
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Publisher: Elsevier BV
Date: 07-2018
Publisher: CSIRO Publishing
Date: 18-01-2023
DOI: 10.1071/RJ22047
Abstract: Regenerative grazing, which generally involves some form of rotational grazing with strategic rest, is increasingly seen as a profitable management approach that will accelerate landscape recovery. However, there is limited quantitative evidence supporting the benefits of this approach in northern Australia. This space-for-time study collected vegetation and soil data from a range of properties in the Burdekin catchment in Queensland that have implemented regenerative grazing strategies for between 5 and 20 years. Data were also collected at adjacent control sites that did not undergo regenerative grazing, but where more traditional continuous set-stocking grazing approaches were applied. Coincident data were also collected from several sites where grazing had been excluded for ~30 years. Data suggested that improvements in vegetation, soil and land condition can be obtained from implementing regenerative grazing principles, although it is likely to take at least 3–5 years, and up to 15–20 years for statistically significant improvements to be measurable at a site, particularly for areas that are moving from a degraded baseline condition. Vegetation attributes such as plant biomass and basal area and litter incorporation all appeared to be better surrogates than percentage ground cover for representing improved landscape condition and soil health. Sites that maintained remotely sensed percentage ground cover at or above the minimally disturbed reference benchmark levels for years, as well as having statistically higher biomass, basal area and litter, had significant increases in total nitrogen (TN) and soil organic carbon (SOC) relative to the local control site. Although there are indications that regenerative grazing can lead to improvements in land condition, this study does not enable us to conclude whether regenerative grazing will accelerate improvements compared with other best-practice grazing land management (GLM) approaches, and further research on the social and economic dimensions of regenerative grazing is needed.
Publisher: Wiley
Date: 19-12-2022
DOI: 10.1002/AGJ2.21223
Abstract: In large‐scale arable cropping systems, understanding within‐field yield variations and yield‐limiting factors are crucial for optimizing resource investments and financial returns, while avoiding adverse environmental effects. Sensing technologies can collect various crop and soil information, but there is a need to assess whether they reveal within‐field yield constraints. Spatial data regarding grain yields, proximal soil sensing data, and topographical and soil properties were collected from 26 maize ( Zea mays L.) growing fields in the U.S. Mid‐Atlantic. Apparent soil electrical conductivity (ECa) collected by an on‐the‐go sensor (Veris) was an effective method for estimating subsoil textural variation and water holding capacity in the Coastal Plain region, which was also the best predictor of spatial yield pattern when combined with surface pH and topographic wetness index in a Random Forest (RF) model. In the Piedmont Plateau region, proximal soil sensors showed a lower correlation to measured soil properties, while topographical properties (aspect and slope) were important estimators of spatial yield patterns in an RF model. In locations where the RF model failed to predict yield variation, soil compaction appeared to be limiting crop yields. In conclusion, the application of RF models using ECa sensors and topographical properties was effective in revealing within‐field yield constraints, especially in the Coastal Plain region. On the Piedmont Plateau, the calibration of proximal sensor information needs to be improved with a particular focus on soil compaction.
Publisher: Japan Society of Hydrology and Water Resources
Date: 2011
DOI: 10.3178/HRL.5.16
Publisher: Burleigh Dodds Science Publishing
Date: 25-07-2023
Abstract: In the past few decades, the Oceania region has experienced a general trend of rising temperature, increased rainfall variability, declining rainfall in parts of Australia and New Zealand, and increased rainfall, particularly extreme events in other parts of the Oceanian region. These changes pose challenges to agriculture, forcing agricultural management to adapt. Systems modelling has played a key role in understanding how climate variability and change impact on agricultural systems. This chapter summarises the climate change in the recent past and future projections for the Oceanian region, briefly reviews how agriculture has responded to past climate changes, and how systems modelling has helped to quantify impact of future climate change, evaluate adaptation strategies and influence policy. Case studies in Australia, New Zealand and Papua New Guinea are presented and future research needs for modelling are discussed. Web links for further information of relevant research and applications are provided at the end.
Publisher: Elsevier BV
Date: 02-2023
DOI: 10.1016/J.SCITOTENV.2022.160240
Abstract: Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder decision-making guided by those models. This paper explores whether the temporal variation of water quality drivers, such as season and flow, influence inconsistency in the classification, and whether variability is captured in spatial datasets that include original vegetation to represent the variability of biotic responses in areas mapped with the same land use. An Artificial Neural Network Pattern Recognition (ANN-PR) method is used to match catchments by Dissolved Inorganic Nitrogen (DIN) patterns in water quality datasets partitioned into Wet vs Dry Seasons and Increasing vs Retreating flows. Explainable artificial intelligence approaches are then used to classify catchments via spatial feature datasets for each catchment. Catchments matched for sharing patterns in both spatial data and DIN datasets were corroborated and the benefit of partitioning the observed DIN dataset evaluated using Kruskal Wallis method. The highest corroboration rates for spatial data classification with DIN classification were achieved with seasonal partitioning of water quality datasets and significant independence (p < 0.001 to 0.026) from non-partitioned datasets was achieved. This study demonstrated that DIN patterns fall into three categories suited to classification under differing temporal scales with corresponding vegetation types as the indicators. Categories 1 and 3 included dominance of woodlands in their datasets and catchments suited to classify together change depending on temporal scale of the data. Category 2 catchments were dominated by vineforest and classified catchments did not change under different temporal scales. This demonstrates that including original vegetation as a proxy for differences in DIN patterns will help guide future classification where only spatially mapped data is available for ungauged catchments and will better inform data needs for water modelling.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 06-2018
Publisher: Elsevier BV
Date: 02-2021
DOI: 10.1016/J.SCITOTENV.2021.151139
Abstract: In hydrological modelling, classification of catchments is a fundamental task for overcoming deficits in observational datasets. Most attention on this issue has focussed on identifying the catchments with similar hydrological responses for streamflow. Yet, effective methods for catchment classification are currently lacking in respect to Dissolved Inorganic Nitrogen (DIN), a water quality constituent that, at increasing concentrations, is threatening nutrient sensitive environments. Pattern recognition, using standard Artificial Neural Network algorithm is applied, as a novel approach to classify datasets that are considered to be suitable proxies for biological and anthropogenic drivers of observed DIN releases. Eleven gauged Great Barrier Reef (GBR) catchments within Queensland Australia are classified using spatial datasets extracted from ecosystem (e.g. original ecosystem responses to biogeographic, land zone, land form, and soil type attributes) and land use maps. To evaluate the performance of the examined spatial datasets as a proxy for deductive classification, the classification process is repeated inductively, using observed DIN and streamflow data from gauging stations. The ANN-PR method is seen to generate the same classification score format for the differing dataset types, and this facilitates a direct comparison for model output for observed data corroborations. The Kruskal-Wallis test for independence, at p > 0.05, identifies the deductive classification approach as a predictor for classification using DIN observations, which lacks an independence from each other at a p value of 0.01 and 0.02. This study concludes that an ANN-PR method can integrate the ecosystem and land use mapping data to deductively classify the GBR catchments into four regions that also have similar patterns of DIN concentrations. Due to the uniform availability of the mapping data, the findings provide a sound basis for further investigations into the transposing of knowledge from gauged catchments to ungauged areas.
Publisher: Informa UK Limited
Date: 04-04-2021
Publisher: MDPI AG
Date: 13-10-2021
DOI: 10.3390/SU132011267
Abstract: Dispersive spoil/soil management is a major environmental and economic challenge for active coal mines as well as sustainable mine closure across the globe. To explore and design a framework for managing dispersive spoil, considering the complexities as well as data availability, this paper has developed a Bayesian Belief Network (BBN)-a probabilistic predictive framework to support practical and cost-effective decisions for the management of dispersive spoil. This approach enabled incorporation of expert knowledge where data were insufficient for modelling purposes. The performance of the model was validated using field data from actively managed mine sites and found to be consistent in the prediction of soil erosion and ground cover. Agreement between predicted soil erosion probability and field observations was greater than 74%, and greater than 70% for ground cover protection. The model performance was further noticeably improved by calibration of Conditional Probability Tables (CPTs). This demonstrates the value of the BBN modelling approach, whereby the use of currently best-available data can provide a practical result, with the capacity for significant model improvement over time as more (targeted) data come to hand.
Publisher: Informa UK Limited
Date: 2019
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 12-2019
Publisher: Wiley
Date: 27-04-2011
DOI: 10.1002/HYP.8086
Publisher: Wiley
Date: 19-04-2013
DOI: 10.1002/HYP.9791
Publisher: Wiley
Date: 09-12-2011
DOI: 10.1002/ESP.2253
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.SCITOTENV.2022.154722
Abstract: Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks (CNN), bi-directional long-short term memory (BiLSTM), and ant colony optimisation (ACO) with a two-phase decomposition approach at the 7-day, 14-day, and 28-day forecast horizons. The newly developed CBILSTM method is coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods to extract the most significant features within predictor variables to build a hybrid CVMD-CBiLSTM model. We integrate three distinct datasets (satellite-derived, climate mode indices, and ground-based meteorological observations) to improve the forecasting capability of the CVMD-CBiLSTM model, applied at nineteen different gauging stations in the Australian Murray River system. This proposed model returns a significantly accurate performance with ~98% of all prediction errors within less than ±0.020 m and a low relative root mean square of ~0.08%, demonstrating its superiority over several benchmark models. The results show that using the new hybrid deep learning algorithm with ACO feature selection can significantly improve the accuracy of forecasted river water levels, and therefore, the method is attractive for adopting remote sensing data to the model ground-based river flow for strategic water savings planning initiatives and dealing with climate change-induced extreme events such as drought events.
Publisher: MDPI AG
Date: 03-06-2023
DOI: 10.3390/W15112128
Abstract: The aim of this paper is to assess the extent to which the Sad-Kalan watershed in Iran participates in floods and rank the Sad-Kalan sub-watersheds in terms of flooding potential by utilizing multi-criteria decision-making approaches. We employed the entropy of a drainage network, stream power index (SPI), slope, topographic control index (TCI), and compactness coefficient (Cc) in this investigation. After forming a decision matrix with 25 possibilities (sub-watersheds) and 5 evaluation indices, we used four MCDM approaches, including the analytic hierarchy process (AHP), best–worst method (BWM), interval rough numbers AHP (IRNAHP), picture fuzzy with AHP (PF-AHP), and picture fuzzy with linear assignment model (PF-LAM, hereafter PICALAM) algorithms, to rank the sub-watersheds. The study results demonstrated that PICALAM exhibited superior performance compared to the other methods due to its consideration of both local and global weights for each criterion. Additionally, among the methods used (AHP, BWM, and IRNAHP) that showed similar performances in ranking the sub-watersheds, the BWM method proved to be more time-efficient in the ranking process.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Springer Science and Business Media LLC
Date: 24-10-2023
Publisher: MDPI AG
Date: 02-08-2020
DOI: 10.3390/W12082179
Abstract: Study region: North Johnstone catchment, located in the north east of Australia. The catchment has wet tropical climate conditions and is one of the major sediment contributors to the Great Barrier Reef. Study focus: The purpose of this paper was to identify soil erosion hotspots through simulating hydrological processes, soil erosion and sediment transport using the Soil and Water Assessment Tool (SWAT). In particular, we focused on predictive uncertainty in the model evaluations and presentations—a major knowledge gap for hydrology and soil erosion modelling in the context of Great Barrier Reef catchments. We carried out calibration and validation along with uncertainty analysis for streamflow and sediment at catchment and sub-catchment scales and investigated details of water balance components, the impact of slope steepness and spatio-temporal variations on soil erosion. The model performance in simulating actual evapotranspiration was compared with those of the Australian Landscape Water Balance (AWRA-L) model to increase our confidence in simulating water balance components. New hydrological insights for the region: The spatial locations of soil erosion hotspots were identified and their responses to different climatic conditions were quantified. Furthermore, a set of land use scenarios were designed to evaluate the effect of reforestation on sediment transport. We anticipate that protecting high steep slopes areas, which cover a relatively small proportion of the catchment (4–9%), can annually reduce 15–26% sediment loads to the Great Barrier Reef.
Publisher: Elsevier
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 02-05-2021
Publisher: Elsevier BV
Date: 04-2022
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 11-2021
Publisher: Public Library of Science (PLoS)
Date: 05-06-2023
DOI: 10.1371/JOURNAL.PONE.0286748
Abstract: Incorporating cover crops into the rotation is a practice applied across many parts of the globe to enhance soil biological activities. In dryland farming, where crop production is highly dependent on rainfall and soil water storage, cover cropping can affect soil water, yet its effects on soil hydrological and biological health require further investigation. The objective of this study was to evaluate the effect of different timing of summer sorghum cover crop termination on soil water, total and labile organic carbon, arbuscular mycorrhizal fungi and their mediating effects on wheat yield. Through on-farm trial, soil characteristics along with wheat biomass, yield and grain quality were monitored. In comparison with the control (fallow), the early terminated cover crop was the most effective at retaining greater soil water at wheat sowing by 1~4% in 0–45cm soil profile. An increase in water use efficiency, yield and grain protein by 10%, 12% and 5% was observed under early termination. Under late terminated summer cover crop, there was 7% soil water depletion at wheat planting which resulted in 61% decline in yield. However, late-terminated cover crop achieved the greatest gain in soil total and particulate organic carbon by 17% and 72% and arbuscular mycorrhizal fungal Group A and B concentration by 356% and 251%. Summer cover crop incorporation resulted in a rapid gain in labile organic carbon, which constituted hotspots for arbuscular mycorrhizal fungi growth, conversely, fungal activities increased labile organic carbon availability. The combined effect of increased soil water at sowing and over the growing season, organic carbon, and microbial activities contributed to greater yield. The findings suggest that summer cover cropping with timely termination can have implications in managing soil water at sowing time and enhancing soil water storage during the season, soil carbon, and facilitating microbial activities while enhancing productivity in the dryland cropping system.
Publisher: Elsevier BV
Date: 03-1298
Publisher: Springer Science and Business Media LLC
Date: 12-01-2021
Publisher: Elsevier BV
Date: 08-2020
Publisher: Elsevier BV
Date: 11-2019
Publisher: CSIRO Publishing
Date: 2016
DOI: 10.1071/AN15327
Abstract: Greenhouse gas emissions (GHG) from broadacre sheep farms constitute ~16% of Australia’s total livestock emissions. To study the ersity of Australian sheep farming enterprises a combination of modelling packages was used to calculate GHG emissions from three sheep enterprises (Merino ewe production for wool and meat, Merino-cross ewes with an emphasis on lamb production, and Merino wethers for fine wool production) at 28 sites across eight climate zones in southern Australia. GHG emissions per ha, per dry sheep equivalents and emissions intensity (EI) per tonne of clean wool or liveweight sold under different pasture management or animal breeding options (that had been previously determined in interviews with farmers) were assessed relative to baseline farms in each zone (‘Nil’ option). Increasing soil phosphorus fertility or sowing 40% of the farm area to lucerne resulted in the smallest and largest changes in GHG/dry sheep equivalents, respectively (–66%, 113%), though both of these options had little influence on EI for either clean wool or liveweight sold. Breeding ewes with greater body size or genotypes with higher fleece weight resulted in 11% and 9% reductions, respectively, in EI. Enterprises specialising in lamb production (crossbred ewes) had 89% lower EI than enterprises specialising in fine wool production (Merino wethers). Thus, sheep producers aiming for lower EI could focus more on liveweight turnoff than wool production. Emissions intensities were typically highest in cool temperate regions with high rainfall and lowest in semiarid and arid regions with low aboveground net primary productivity. Overall, animal breeding options reduced EI more than feedbase interventions.
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 11-2020
Publisher: MDPI AG
Date: 04-02-2021
DOI: 10.3390/RS13040554
Abstract: Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.
Publisher: Elsevier BV
Date: 04-2011
Publisher: Elsevier BV
Date: 11-2020
Publisher: Springer Science and Business Media LLC
Date: 07-09-2022
Publisher: MDPI AG
Date: 16-12-2019
DOI: 10.3390/SU11247224
Abstract: Managed temperate grasslands occupy 25% of the world, which is 70% of global agricultural land. These lands are an important source of food for the global population. This review paper examines the impacts of climate change on managed temperate grasslands and grassland-based livestock and effectiveness of adaptation and mitigation options and their interactions. The paper clarifies that moderately elevated atmospheric CO2 (eCO2) enhances photosynthesis, however it may be restiricted by variations in rainfall and temperature, shifts in plant’s growing seasons, and nutrient availability. Different responses of plant functional types and their photosynthetic pathways to the combined effects of climatic change may result in compositional changes in plant communities, while more research is required to clarify the specific responses. We have also considered how other interacting factors, such as a progressive nitrogen limitation (PNL) of soils under eCO2, may affect interactions of the animal and the environment and the associated production. In addition to observed and modelled declines in grasslands productivity, changes in forage quality are expected. The health and productivity of grassland-based livestock are expected to decline through direct and indirect effects from climate change. Livestock enterprises are also significant cause of increased global greenhouse gas (GHG) emissions (about 14.5%), so climate risk-management is partly to develop and apply effective mitigation measures. Overall, our finding indicates complex impact that will vary by region, with more negative than positive impacts. This means that both wins and losses for grassland managers can be expected in different circumstances, thus the analysis of climate change impact required with potential adaptations and mitigation strategies to be developed at local and regional levels.
No related grants have been discovered for Afshin Ghahramani.