University of Sydney and University of Tasmania

Camden MC Franklin Lab

Faculty of Veterinary Science, The University of Sydney, Camden, NSW, Australia

Tasmanian Institute of Agriculture, University of Tasmania, Burnie, TAS, Australia

Period

From October to December 2013

Activities

As an APSIM trainee, I was a visiting scholar at the Dairy group of The University of Sydney in Camden and at the Tasmanian Institute of Agriculture, University of Tasmania. We used experimental field data collected in several locations of Argentina and Australia to evaluate the ability of APSIM Classic to simulate the growth patterns of annual and perennial forage crops and to predict dry matter yields in these regions. We also evaluated the APSIM ability to predict forage dry matter yield and water productivity of multiple continuous forage crop sequences.

Achievements

Two research collaborative papers published in high impact scientific journals.

  1. Ojeda JJ, Pembleton KG, Islam MR, Agnusdei MG, Garcia SC (2016) Evaluation of the agricultural production systems simulator simulating Lucerne and annual ryegrass dry matter yield in the Argentine Pampas and south-eastern Australia. Agricultural Systems. 143, 61-75. Publication derived from my PhD thesis. doi:10.1016/j.agsy.2015.12.005

  2. Ojeda JJ, Pembleton KG, Islam MR, Caviglia OP, Agnusdei MG, Garcia SC (2018) Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas. European Journal of Agronomy. 92, 84-96. Publication derived from PhD thesis. doi: 10.1016/j.eja.2017.10.004

Host Researchers

  • Dr Keith Pembleton (Tasmanian Institute of Agriculture)
  • Dr Rafiq Islam and Dr Sergio (Yani) Garcia (University of Sydney)

Funded by

Funded by INNOVA-T Grant and funding from INTA, The University of Sydney and University of Tasmania.

Grant amount

AUD $15,000

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Jonathan Ojeda (Jony)
Crop Ecophysiologist - Cropping Systems Modeller - Data Scientist

I use crop models to understand GxExM interactions and quantify sources of uncertainties in agricultural predictions.