https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41434 Wed 03 Aug 2022 14:19:25 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47677 2. This process is considered to be one of the largest global carbon fluxes and is affected by different physicochemical and biological properties of soil, land use, vegetation types and climate patterns. Soil respiration recently received much attention, and it could be measured in two states basal respiration (BR) and substrate induced respiration (SIR) which together gives a good representation of the general soil microbial activity. The aim of this study was to estimate the BR and SIR of 150 data points obtained from soil samples collected from the surface to 20 cm of depth under different land use categories using the Artificial Neural Network (ANN) and Linear Regression Methodology (LRM). This study is bringing data from an arid area, and there is little information on this issue. Soil samples were chosen from three provinces of Iran, with humid subtropical and semi-arid climate patterns. In each soil sample a variety of characteristics were measured: soil texture, pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), organic carbon (OC), OC fractionation data e.g. light fraction OC (LOC), heavy fraction OC (HOC), cold water extractable OC (COC) and warm water extractable OC (WOC), population of fungi, bacteria, actinomycete, BR and SIR. Our goal was to use the most efficient ANN-model to predict soil respiration with simple soil data and annual precipitation (AP) and mean annual temperature (MAT) and compare it with LRM. Our results indicated that for an ANN model containing all the measured soil parameters (14 variables), the R² and RMSE values for BR prediction were 0.64 and 0.05 while these statistical indicators for SIR obtained 0.58 and 0.15, respectively; whereas the addition of AP and MAT data to this model (16 variables) caused a decrease in statistical indicators. When the R² and RMSE values of the BR-ANN and SIR-ANN predicted using an ANN model with only 7 variables (including OC, pH, EC, CCE and soil texture) they were estimated to be 0.66, 0.043 and 0.52, 0.16, respectively. Overall, LRM in comparison to ANN had a lower R^2M. Therefore, the results show that ANN modeling is a reliable method for predicting soil respiration, even when based on easy to measure data. Our results revealed that highest and lowest BR and SIR were recorded in rice paddy soils and saline lands, respectively. In total, soil respiration (BR: 0.09 vs 0.06 and SIR: 0.46 vs 0.32 mg CO₂ g^-1 day^-1) was higher in agricultural land compared to natural covered land.]]> Tue 24 Jan 2023 16:15:52 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33126 Tue 03 Sep 2019 18:00:23 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18148 Sat 24 Mar 2018 08:04:41 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3401 Sat 24 Mar 2018 07:21:41 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23834 3 years. The results demonstrate that when correctly calibrated the landscape evolution model is able to reliably predict sediment output from these field plots. These results suggest that there is the potential to employ the bare waste rock dump parameters for the first 3-4 years then switch to vegetated parameters for the longer term modelling. Both the field plots and landscape evolution model simulations displayed considerable annual variability in total load. This variability is the result of different surface structure from imposed surface roughness (ripping by a bulldozer) and their unique topographic structure. Both initial DEM and model parameters have a large influence on predicted sediment load. The results here support the reliability of the model at the sub-metre grid scale.]]> Sat 24 Mar 2018 07:12:13 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:55799 Sat 22 Jun 2024 12:48:52 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43604 Mon 26 Sep 2022 15:33:49 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41042 Mon 18 Mar 2024 15:09:19 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:48236 Mon 01 Jul 2024 15:41:43 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45605 −2) was able to delay the time to ponding from 32 to 52 s and the time to runoff initiation from 57 to 129 s. Also, the mulching reduced the runoff coefficient from 65.6 to 50.5%. The effect on sediment transport was even more pronounced, as the straw mulch reduced the sediment concentration from 16.7 g l⁻¹ to 3.6 g l⁻¹ and the soil erosion rates from 439 g to 73 g. Our results indicated that mulching can be used as a useful management practice to control soil erosion rates due to the immediate effect on high soil detachment rate and runoff initiation reduction in conventional clementine orchards on sloping land, by slowing down runoff initiation and by reducing runoff generation and, especially, sediment losses. We indirectly concluded that straw mulch is also a sustainable solution in glyphosate-treated citrus plantations.]]> Fri 04 Nov 2022 14:46:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47855 Fri 03 Feb 2023 10:51:48 AEDT ]]>