The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
How can data from headwater catchments be used to improve runoff and nutrient predictions at larger scales?
Intuitively there should be a difference in the runoff and nutrient generation from catchment areas of varying sizes. The total runoff and constituent load can be modelled using one of the many hydrological models. However, how well a model performs can depend on the size of the catchment that it is applied to. In this study the modelling system Source Catchments is examined, to see if it can be improved when applied to different sized subcatchments to generate the total runoff and constituent concentrations. Within Source Catchments the AWBM model for predicting runoff on a daily timestep, is coupled with the EMC/DWC constituent generation model to predict sediment and nutrient concentrations in the study catchment. The catchment used for this study is the Lang Lang Catchment (LLC) in West Gippsland about 100km southeast of Melbourne. The Lang Lang River runs into the Western Port Bay taking with it nutrients from the surrounding lands. The catchment area can be broken up into several sub-catchments. These were chosen due to the availability of existing data and the different geology of the headwater subcatchments. Monitoring of runoff, sediments and nutrients has taken place at several points since the early 1980s. Within the area there are two dominant types of land use, dryland agriculture (mostly dairy farming with some beef) and forestry. Each of these land uses have their event mean concentrations (EMC) and dry weather concentrations (DWC) initially assigned from previous studies and then re-calibrated using the nutrient data obtained from the Victorian Data Warehouse from 1982-1994. From this the runoff and total constituent load (in this case the total loading into the bay) are calculated through modelling. The model is validated using recent water quality data from 2000 to 2010 from three points (allowing the effect of differing landforms and geology on water quality to also be investigated). Since 2009, monitoring has been taking place on a small farm in Poowong East, which lies within the southeastern headwaters of the LLC. The creeks on the property have three sub-catchments (the PECs) from where runoff and nutrient concentration data have been collected at scales from 1.2 to 4.4 km(2). The land is almost entirely used for dryland dairy and beef farming with some small areas of woodland along riparian zones. This data is used to validate the LLC Source Catchments model again, at this scale. A comparison between results from the PECs and LLC indicates how well the model can work at the different catchment scales. Initially the EMC & DWC parameters are the same for both simulations. It is known from existing monitoring data that there are lower nutrient concentrations (hence there would be lower EMC & DWC values if these data were used to fit the model) in the LLC compared to the PECs. Given that the measured concentrations of nitrogen and phosphorous are greater from the PECs it is expected therefore that the model will underpredict nutrient concentrations there. The model can be re-calibrated using the data from the PECs although this process should highlight some limitations of the EMC/DWC model approach, when nutrient concentrations (particularly EMCs) are variable over time and space due to local effects such as grazing and point sources of nutrients. Moreover, applying EMC & DWC parameters fitted to the PECs, to the LLC may cause it to overpredict concentrations and loads discharging into Western Port Bay. Therefore, suggestions are made of methods to improve on the EMC/DWC constituent model used to estimate loads from the LLC. A model that defines functional units to take into account geology and landform, as well as land use could improve the accuracy of runoff and nutrient load predictions. The LLC model probably cannot account for the generation effects in a small headwater subcatchment through which the water moves quite rapidly. The model could be improved by dividing the river into reaches between additional water quality monitoring sites. The losses could be modelled using an in-stream (link-based) sink term. Additional monitoring data from the monitoring agency could also enable the model to be improved. If compliance with Victorian Government State Environmental Protection Policy (SEPP) standards is to be met throughout the catchment the monitoring agency needs to be able to predict water quality in all reaches, not just at the outlet into Western Port Bay.
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