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
Linking a whole farm model to the APSIM suite to predict N leaching on New Zealand dairy farms
Nitrogen excreted directly onto pasture on New Zealand dairy farms, particularly from urine, drives most nitrogen (N) leaching. Therefore, the representation of urine patches in different paddocks is important when modelling on-farm N leaching. This paper: 1. describes the Grid and Probabilistic methods in connection with DairyNZ's Whole Farm Model (WFM); and 2. compares N leaching predictions using the Grid and Probabilistic methods for a simulated Waikato (New Zealand) dairy farm on a Horotiu silt loam soil (Typic Orthic Allophanic soil). The Grid method divides the paddock into cells and allocates the urination events randomly for each grazing event and the Probabilistic method calculates the probability using the Poisson distribution of the different temporal urination patterns, estimates the N leaching from each pattern and then calculates the leaching at a paddock level. Different simplifications to each method are required to make the implementation feasible. The modelling in this study uses a new framework called the Urine Patch Framework (UPF) that post-processes the results of the WFM and runs the Agricultural Production System Simulator (APSIM) to simulate the urine patches. The WFM extracts the urine information for each grazing event on each paddock, accounting for the partitioning of the urine between walkways, milking parlour, and the stand-off. Only the fate of urinary N directly excreted onto pastures is analyzed in this study. The UPF breaks each paddock into either cells (Grid method), or areas with different urination patterns (i.e. dates of deposition and amount excreted on each date) (Probabilistic method) and saves the information into text files. The software reads these files and prepares input files (.xml) for APSIM model runs, which are used to invoke APSIM for each cell or pattern. Finally, the leaching data are collected to calculate N leaching for the whole paddock and subsequently the whole farm. The main simplification used in the Probabilistic method is based on the assumption that the effect of one urination event can be neglected after a number of months following deposition (months to remember). This implies that the full simulation period (i.e. the WFM run) can be subdivided into periods of time (months to remember + 1 month), which stops the exponential explosion in the number of possible urination patterns. A range from 6 to 12 months to remember was explored. For one period at a time, all the patterns are created, and later on simulated in APSIM. Nitrogen leaching results are collected only in the last month of the period, whereas the previous months are used to initialize and populate the soil model with relevant historical information (urination events, fertilization, defoliations and climate). Nitrogen leaching predictions from both methods converged when the number of months to remember in the Probabilistic method was approximately 9-10 months. Even with 10 months to remember, the Probabilistic method was computationally more efficient, taking less than half of the time for the 2 year runs compared to the Grid method. For a 2 year run, the processing time was similar for both methods with about 14 months to remember. Ten months to remember was used throughout the rest of the study. The average annual N leaching was similar for the Grid and Probabilistic method (37.0 +/- 14.5 vs. 38.1 +/- 10.9 kg N/ha/year, respectively). However, the Grid method, despite the long processing time, could be used to simulate dung patches to enable predictions of soil carbon balance.
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