e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Using a whole farm model linked to the APSIM suite to predict production, profit and N leaching for next generation dairy systems in the Canterbury region of New Zealand


Management options were explored using DairyNZ's Whole Farm Model (WFM) to predict farm-scale milksolids (MS = fat + protein) production and operating profit. Predictions of urinary-N excretions from individual cows were linked to the Agricultural Production System Simulator (APSIM) suite via a Urine Patch Framework (UPF) to estimate N leaching on a farm scale while accounting for urine patches and patch overlaps. The UPF provides the inputs for soil, pasture and crop models in APSIM to simulate soil water, carbon and N dynamics and leaching on an individual urine patch scale. APSIM simulation outputs are then fed back into the UPF, which aggregates the patch-scale predictions up to paddock and farm level to give a mechanistic prediction of N leaching per hectare for the modelled farm. The paper focuses on the methodologies, and demonstrates utility of the WFM-UPF-APSIM linked model by presenting the results for a Canterbury irrigated milking enterprise. The Lincoln University Dairy Farm (LUDF) was used as the baseline farm for the region; it represents a high performing system typical of the Canterbury region. For the 2009/10 farming season, LUDF had a stocking rate of 4.15 cows/ha, with average genetic merit of the cows (Breeding Worth; BW) of NZ$92, used 200 kg fertilizer N/ha/yr, and imported 250 kg DM per cow of pasture silage. The WFM predicted production (1718 kg MS/ha) and operating profit ($4348/ha) with acceptable accuracy compared to the observed for LUDF for that season (1710 kg MS/ha and $4696/ha). From this baseline, two development pathways were investigated. The first pathway focused on intensification (More Milk = MM), whereby stocking rate increased to 5 cows/ha, average BW increased to 150, N fertilizer rate increased to 400 kg/ha, and imported grain was fed at 600-1000 kg DM/cow/yr. The second pathway (Better Efficiency = BE) was focused on reducing inputs and improving production efficiency by decreasing the stocking rate to 3.5 cows/ha, decreasing the N fertilizer rate to 150 kg/ha, and limiting purchased grain supplement to 100 kg DM/cow/yr. Simulations were conducted over 10 consecutive years from June 2000 - May 2010 using climate and economic inputs from the National Institute of Water and Atmospheric Research and the DairyNZ Economic Survey. Average MS and profit were higher in MM compared with LUDF and BE, but variability of profit ("risk") did not differ between scenarios. N leaching was substantially higher in MM, which means that it would only be a feasible option if a low cost technological solution was available, environmental regulations were lenient, or N leaching targets were set at a catchment rather than a farm scale. The models predicted that MS output per farm would decrease in BE, but operating profit could be maintained compared to LUDF. At a regional or catchment level it would still be possible to increase MS production providing extra land was available for expansion. Because of the low leaching losses in BE, approximately 30 kg N/ha/year, the total N leached on a regional basis could still be reduced. A new piece of software that links an existing whole farm model with mechanistic soil, carbon and N models (in the APSIM suite) provides the capability to predict N leaching from complex distributions of urine patches in space and time. The combined capabilities of the models enable the user to predict key outcomes for dairy systems i.e. production, profit and N leaching. Future work should focus on representing soil variability within and between paddocks, and distributed computing facilities to spread the high computing load.

  • NZ
  • DairyNZ_Ltd (NZ)
Data keywords
  • distributed computing
Agriculture keywords
  • farm
  • agriculture
  • farming
Data topic
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.