ORCID Profile
0000-0002-9278-7941
Current Organisation
CSIRO
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: Wiley
Date: 03-2019
DOI: 10.1111/SUM.12467
Abstract: The impacts of a wildfire and subsequent rainfall event in 2013 in the Warrumbungle National Park in New South Wales, Australia were examined in a project designed to provide information on post‐fire recovery expectations and options to land managers. A coherent suite of sub‐projects was implemented, including soil mapping, and studies on soil organic carbon ( SOC ) and nitrogen (N), erosion rates, groundcover recovery and stream responses. It was found that the loss of SOC and N increased with fire severity, with the greatest losses from severely burnt sandstone ridges. Approximately 2.4 million t of SOC and ~74,000 t of N were lost from soil to a depth of 10 cm across the 56,290 ha affected. Soil loss from slopes during the subsequent rainfall event was modelled up to 25 t ha −1 , compared to a long‐term mean annual soil loss of 1.06 t ha −1 year −1 . Groundcover averages generally increased after the fire until spring 2015, by which time rates of soil loss returned to near pre‐fire levels. Streams were filled with sand to bank full levels after the fire and rainfall. Rainfall events in 2015–2016 shifted creek systems into a major erosive phase, with incision through the post‐fire sandy bedload deposits, an erosive phase likely related to loss of topsoils over much of the catchment. The effectiveness of the research was secured by a close engagement with park managers in issue identification and a communications programme. Management outcomes flowing from the research included installation of erosion control works, redesign of access and monitoring of key mass movement hazard areas.
Publisher: MDPI AG
Date: 02-06-2023
DOI: 10.3390/RS15112915
Abstract: One of the most valuable and nutritionally essential agricultural commodities worldwide is milk. The European Union and New Zealand are the second- and third-largest exporting regions of milk products and rely heavily on pasture-based production systems. They are comparable to the Australian systems investigated in this study. With projections of herd decline, increased milk yield must be obtained from a combination of animal genetics and feed efficiencies. Accurate pasture biomass estimation across all seasons will improve feed efficiency and increase the productivity of dairy farms however, the existing time-consuming and manual methods of pasture measurement limit improvements to utilisation. In this study, Sentinel-2 (S2) band and spectral index (SI) information were coupled with the broad season and management-derived datasets using a Random Forest (RF) machine learning (ML) framework to develop a perennial ryegrass (PRG) biomass prediction model accurate to +/−500 kg DM/ha, and that could predict pasture yield above 3000 kg DM/ha. Measurements of PRG biomass were taken from 11 working dairy farms across southeastern Australia over 2019–2021. Of the 68 possible variables investigated, multiple simulations identified 12 S2 bands and 9 SI, management and season as the most important variables, where Short-Wave Infrared (SWIR) bands were the most influential in predicting pasture biomass above 4000 kg DM/ha. Conditional Latin Hypercube S ling (cLHS) was used to split the dataset into 80% and 20% for model calibration and internal validation in addition to an entirely independent validation dataset. The combined internal model validation showed R2 = 0.90, LCCC = 0.72, RMSE = 439.49 kg DM/ha, NRMSE = 15.08, and the combined independent validation had R2 = 0.88, LCCC = 0.68, RMSE = 457.05 kg DM/ha, NRMSE = 19.83. The key findings of this study indicated that the data obtained from the S2 bands and SI were appropriate for making accurate estimations of PRG biomass. Furthermore, including SWIR bands significantly improved the model. Finally, by utilising an RF ML model, a single ‘global’ model can automate PRG biomass prediction with high accuracy across extensive regions of all seasons and types of farm management.
Publisher: Magnolia Press
Date: 11-10-2021
DOI: 10.11646/PHYTOTAXA.522.3.2
Abstract: Taxonomic revisions are the most reliable pathway in unfolding new species to the world. During such a revision of the genus Lagenandra in Sri Lankan, we came across two new species: Lagenandra kalugalensis and Lagenandra srilankensis from the Wet Zone of Sri Lanka. The two new species were studied in detail and compared with the morphology of the other species described in the genus, and based on field collected data conservation assessments were performed. A detailed description for the two new species and an updated taxonomic key to the Sri Lankan Lagenandra is presented here for easy identification. Recognizing two new endemic members enhances the number of Sri Lankan species of Lagenandra to eleven and global to nineteen. According to the IUCN red data category guidelines, L. kalugalensis qualifies for Critically Endangered category under Criterion B1ab (ii,iii,v) + B2ab (ii,iii,v) while L. srilankensis qualifies for Critically Endangered category under B1ab (iii, iv) + C2 (a) (i, ii). Hence, immediate conservation measures are imperative.
Publisher: CSIRO
Date: 2022
DOI: 10.25919/CH91-FA76
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 12-2015
Publisher: Elsevier BV
Date: 12-2018
Publisher: American Association for the Advancement of Science (AAAS)
Date: 07-04-2017
Abstract: Our findings indicate the importance of paleoclimatic information to improve quantitative predictions of global soil C stocks.
Publisher: Informa UK Limited
Date: 13-08-2019
Publisher: Elsevier BV
Date: 10-2023
Publisher: Pleiades Publishing Ltd
Date: 12-2019
Publisher: Elsevier BV
Date: 06-2016
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 10-2020
Publisher: MDPI AG
Date: 13-03-2020
DOI: 10.3390/RS12060928
Abstract: Nutritive value (NV) of forage is too time consuming and expensive to measure routinely in targeted breeding programs. Non-destructive spectroscopy has the potential to quickly and cheaply measure NV but requires an intermediate modelling step to interpret the spectral data. A novel machine learning technique for forage analysis, Cubist, was used to analyse canopy spectra to predict seven NV parameters, including dry matter (DM), acid detergent fibre (ADF), ash, neutral detergent fibre (NDF), in vivo dry matter digestibility (IVDMD), water soluble carbohydrates (WSC), and crude protein (CP). Perennial ryegrass (Lolium perenne) was used as the test crop. Independent validation of the developed models revealed prediction capabilities with R2 values and Lin’s concordance values reported between 0.49 and 0.82, and 0.68 and 0.89, respectively. Informative wavelengths for the creation of predictive models were identified for the seven NV parameters. These wavelengths included regions of the electromagnetic spectrum that are usually excluded due to high background variation, however, they contain important information and utilising them to obtain meaningful signals within the background variation is an advantage for accurate models. Non-destructive field spectroscopy along with the predictive models was deployed infield to measure NV of in idual ryegrass plants. A significant reduction in labour was observed. The associated increase in speed and reduction of cost makes targeting NV in commercial breeding programs now feasible.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier
Date: 2018
Publisher: MDPI AG
Date: 23-06-2020
DOI: 10.3390/RS12122017
Abstract: This study aimed to develop empirical pasture dry matter (DM) yield prediction models using an unmanned aerial vehicle (UAV)-borne sensor at four flying altitudes. Three empirical models were developed using features generated from the multispectral sensor: Structure from Motion only (SfM), vegetation indices only (VI), and in combination (SfM+VI) within a machine learning modelling framework. Four flying altitudes were tested (25 m, 50 m, 75 m and 100 m) and based on independent model validation, combining features from SfM+VI outperformed the other models at all heights. However, the importance of SfM-based features changed with altitude, with limited importance at 25 m but at all higher altitudes SfM-based features were included in the top 10 features in a variable importance plot. Based on the independent validation results, data generated at 25 m flying altitude reported the best model performances with model accuracy of 328 kg DM/ha. In contrast, at 100 m flying altitude, the model reported an accuracy of 402 kg DM/ha which demonstrates the potential of scaling up this technology at farm scale. The spatial-temporal maps provide valuable information on pasture DM yield and DM accumulation of herbage mass over the time, supporting on-farm management decisions.
Publisher: Elsevier BV
Date: 05-2014
Publisher: Springer Science and Business Media LLC
Date: 05-10-2015
DOI: 10.1038/NCOMMS9444
Abstract: The continuum hypothesis states that both deterministic and stochastic processes contribute to the assembly of ecological communities. However, the contextual dependency of these processes remains an open question that imposes strong limitations on predictions of community responses to environmental change. Here we measure community and habitat turnover across multiple vertical soil horizons at 183 sites across Scotland for bacteria and fungi, both dominant and functionally vital components of all soils but which differ substantially in their growth habit and dispersal capability. We find that habitat turnover is the primary driver of bacterial community turnover in general, although its importance decreases with increasing isolation and disturbance. Fungal communities, however, exhibit a highly stochastic assembly process, both neutral and non-neutral in nature, largely independent of disturbance. These findings suggest that increased focus on dispersal limitation and biotic interactions are necessary to manage and conserve the key ecosystem services provided by these assemblages.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 02-03-2018
Abstract: We discuss possible mechanisms to explain paleoclimate as a predictor of the current distribution of global soil C content.
Publisher: Magnolia Press
Date: 04-03-2021
DOI: 10.11646/PHYTOTAXA.489.2.10
Abstract: A new species of Lagenandra (Araceae), is described and illustrated from Walauwewaththa Wathurana freshwater sw forest, Bulathsinghala, Sri Lanka. Here we describe the new species as Lagenandra wayambae Madola, K. Yakandawala, D. Yakandawala and Karunaratne and provide a detailed description, drawing and colour photographs. We compare the morphology of L. wayambae with that of similar members of Lagenandra and conduct an assessment of its conservation status. A taxonomic key to the Sri Lankan Lagenandra is presented for easy identification. Recognizing a new endemic member enhances the number of Sri Lankan species to nine. According to the IUCN red data category guidelines L. wayambae qualifies for Critically Endangered category under Criterion B1ab (ii,iii,v) + B2ab (ii,iii,v).
Publisher: Elsevier BV
Date: 03-2015
Publisher: CSIRO Publishing
Date: 2014
DOI: 10.1071/SR13081
Abstract: The importance of soil organic carbon (SOC) in maintaining soil health is well understood. However, there is growing interest in studying SOC with an emphasis on quantifying its changes in space and time. This is because of the potential for soil to be used to sequester atmospheric C. There are many issues which make this difficult, for ex le shortcomings in s ling designs, and differences in vertical and lateral s ling supports between surveys, particularly if legacy data are used as the baseline survey. In this study, we systematically work through these issues and show how a protocol can be developed using design-based and model-based statistical approaches to estimate changes in SOC in space and time at different spatial supports. We demonstrate this protocol in a small subcatchment in the upper Namoi valley for estimating the change in SOC over time, whereby the baseline dataset was collected during 1999–2001 and is compared with a dataset from November 2010. The results from both design-based and model-based approaches revealed a drop in SOC across the catchment between the two survey periods. A 0.26% drop in SOC was reported globally across the catchment. Nevertheless, the change in SOC reported for both approaches was not statistically significant.
Publisher: Elsevier BV
Date: 11-2020
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR14178
Abstract: In this paper, we present a framework for a space–time observation system for soil organic carbon (STOS-SOC). We propose that the RothC model be embedded within the STOS-SOC, which is driven by satellite-derived inputs and readily available geospatial inputs, such as digital soil maps. In particular, advances in remote sensing have enabled the development of satellite products that represent key inputs into soil carbon models, ex les being evapotranspiration and biomass inputs to soil, which characterise space–time variations in management and land use. Starting from an initial calibrated base for prediction, as new observations are acquired, data assimilation techniques could be used to optimise calibration algorithms and predicted model outputs. We present initial results obtained from the implementation of the proposed STOS-SOC approach to the 1445-km2 Cox’s Creek catchment in northern New South Wales, Australia. Our results showed that use of satellite-derived biomass inputs with a MODIS satellite product (MOD17A3) improved the accuracy of simulations by 16% compared with carbon inputs derived through other methods normally adopted in the spatialisation of the RothC model. We further discuss the possibility of improving the capabilities of the STOS-SOC for future applications.
Location: Sri Lanka
Location: Australia
Location: Australia
Location: Australia
Location: Australia
Location: No location found
No related grants have been discovered for Senani Karunaratne.