Jonathan Ojeda (Jony)

Agro-ecosystems Modeler with focus on CDR - Scientific Founder Terradot

[email protected]


I am leading the Science Team at Terradot developing a team and a MRV platform to measure & simulate C sequestration & yield compliant with global standards (e.g. Cascade, Isometric, VERRA). At Terradot, I worked in close collaboration with high-level Stanford & world-renowned Reactive Transport Modelers focused on CDR projects. I also hold an academic positions as Adjunct Senior Research Fellow at the Centre for Sustainable Agricultural Systems (University of Southern Queensland) in Australia. Currently, I built a team from scratch to >10 people in just 6 months, fostering a robust & efficient workforce. I lead my team to innovate, iterate, and excel, always keeping the end-goal in sight: delivering impactful solutions swiftly to stay ahead in the ever-evolving digital landscape. My career is anchored in a relentless pursuit of excellence, where delivering V0 products swiftly isn’t just a goal, it’s my standard.

I apply data analytics, statistics and programming to understand soil-crop-climate interactions behind coupled models. My interests are in quantifying the effects of crop management & climate/soil variability on CO2 removal and yield through mechanistic models ( APSIM & MIN3P) at different spatio-temporal scales. I have a passionate interest in disentangling sources of uncertainties/variability to better predict yield and C (organic and inorganic) sequestration trough ERW field deployments. This requires a broad understanding of how the environment influences crop growth; how rainfall/irrigation, & applied rock influence soil status; how crops obtain water/nutrients from the soil; how soil processes contribute to the loss of C, N; and how all these processes interact.

Over 15 years of experience managing large soil-crop-climate datasets (8+ years using Python and geo-statistics), developing models (APSIM, 12+ years & MIN3P) & applying data pipelining and optimization towards solving large-scale ERW and C deployments. I collected (experiments) & analyzed data including >18 crop species (annual, perennials). I have 50+ published articles in high-impact journals (13 as first author). I received 18 research grants (70% as CI) with a cash value of ~USD5.5M.

I mentored graduate/ 5 PhD students who are currently leading global research in academia/industry. As part of Regrow Ag, I led a multicultural global team (15 people including data scientists, software developers and engineers) under the Niche project (USD4M) funded by B&MGF which focuses on the analysis of GxExM in Sub-Saharan Africa.

During my career, I work with multi-cultural & multi-disciplinary teams (hybrid approach - Academia & Industry) & have ground experience working in 9 countries (Argentina, Brazil, Australia, NZ, USA, The Netherlands, Germany, Rwanda & Kenya).


  • Crop modelling uncertainty quantification
  • Model up-scaling
  • Decision support tools development
  • Environmental data analysis
  • Climate change and sustainability assessments
  • Data analysis automation


  • PhD in Agricultural Sciences, 2017

    National University of Mar del Plata, Argentina

  • Agricultural Engineer, 2011

    National University of Entre Ríos, Argentina




Remote Sensing







International Networking


Team work


PhD in Agricultural Sciences (2017)

National University of Mar del Plata, Argentina (duration = 5 years; 422 credit hours)

GPA: 8.8 (out of 10) No. failed: 0 (none)

Dissertation: Precipitation use efficiency in annual forage crop sequences and perennial pastures (in Spanish)


  • Crop Eco-physiology
  • Advanced Crop Eco-physiology
  • Applied Eco-physiology to Pasture Management
  • Statistical Methods I
  • Statistical Methods II
  • Experimental Design I
  • Experimental Design II
  • Calculation Techniques and Agricultural Estimations in Extensive Crops
  • Use of DSSAT Models
  • Scientific Writing Methodologies
  • Neotyphodium (Festucosis)

About the PhD project

The growing demand for beef and dairy products requires technological options to improve the productivity and resource use efficiency of forages crops with less environmental impact. Livestock production systems based on forage crop sequences (FCS) could be more productive and efficient than those based on perennial pastures (PP). However, there are many questions about the FCS implementation related to the system stability in the long term and about the root-derived soil organic carbon in these systems. The main objective of this thesis was to provide original knowledge about the main ecophysiological aspects determining forage supply in livestock systems based on the use of FCS and PP.

The study was conducted in three steps (i) above-ground dry matter yield (AGDM) and precipitation use efficiency (PUE) were analyzed in Rafaela, Pergamino, General Villegas and Trenque Lauquen, (ii) in Balcarce were evaluated AGDM, below-ground dry matter yield (BGDM), PUE (i.e. water capture [WC] * water use efficiency [WUE]), radiation productivity (RP, i.e. radiation capture [RC] * radiation use efficiency [RUE]) and soil carbon (C) variations in different organic matter fractions. Finally, (iii) Agricultural Production Systems Simulator (APSIM) was calibrated and validated to analyze the accumulated annual precipitation, AGDM and PUE variability using a long-term climate database (30 years).

In general, the AGDM was higher for the FCS than the PP treatments, although more variable in the long term. Below-ground dry matter yield was similar for both treatments. Likewise, there was a greater association between the contribution of C and BGDM in sub-surface horizons below than 0,15 m soil depth. The PP treatments shown higher RC and similar WC than the FCS treatments. However, FCS shown higher RUE and WUE, which led to higher RP and PUE. In turn, the PP treatments shown lower inter-annual variability of PUE than FCS in the long term. The multi-environmental analysis on the impacts of different forage cropping systems on PUE, as well as on the soil C variations, provide key knowledge and information to develop management strategies to increase the sustainable productivity of livestock systems in the Argentinean Pampas.

Logical flow of the PhD thesis


The image shows the logical flow of the PhD thesis including the stages, chapters, scales, spatio-temporal levels of analysis, and the measured or estimated variables. DMa, Aerial Dry Matter Yield; WP, Water Productivity ; DMr, Root Dry Matter Yield; RP, Radiation Productivity; OM, Organic Matter

Agricultural Engineer (2011)

National University of Entre Ríos, Argentina (duration = 7 years; 3479 credit hours)

GPA: 8.8 (out of 10) No. failed: 0 (none) Historical GPA: 6.8 (out of 10)

Dissertation: Response to plant population density in different sunflower hybrids (in Spanish)


Basic courses

  • Introduction to Agro-productive Systems
  • Biology
  • Agricultural Microbiology
  • Computer Sciences
  • Morphological Botany
  • Systematic Botany
  • General Chemistry
  • Analytical Chemistry
  • Biological and Organic Chemistry
  • Mathematics
  • Physics
  • Experimental Statistics and Design
  • Research Methodology
  • Agricultural Policy and Legislation
Agronomic courses
  • Agricultural Climatology
  • Agricultural Automation
  • Agricultural Zoology (entomology)
  • Animal Anatomy and Physiology
  • Soil Sciences and Pedological Studies
  • Ecology of Agricultural Systems
  • Phytopathology
  • Land Technology
  • Plant Physiology
  • Plant and Animal Genetics and Improvement
  • Plant Therapeutics
Professional courses
  • Agricultural Economy
  • Geographical Information Systems
  • Business Management and Planning
  • Rural Extension and Sociology
  • Irrigation and Drainage
  • Integrated Workshop of Phytosanitary Management
  • Animal Nutrition
  • Forage Crops Management
  • Agrosilvopastoral Production
  • Grains and Oilseeds
  • Postharvest Management
  • Beef Cattle
  • Dairy Cattle
  • Small Ruminants
  • Apiculture
  • Horticulture
  • Forestry
  • Fruticulture
  • Pigs



Science Lead


Jul 2023 – Present Stanford, California, United States
Overall targets:
  • Build a global science team and a global MRV platform from scratch using cutting-edge tech and strong scientific knowledge.
  • Lead and own the full science Terradot R&D stack to support a soil carbon (organic and inorganic) MRV platform.
Specific targets:
  • Hire and lead a passionate team of soil scientists, geologists, agronomist, mineralogists, hydrologists, reactive transport and crop modelers, and engineers.
  • Develop a coupled multi-scale model using MIN3P and APSIM to estimate inorganic/organic C sequestration and crop yield through Enhanced Rock Weathering (ERW) practices.
  • Set the roadmap and direction for improving the measurement and verification of net CO2 emissions reduction in agricultural systems at field level.
  • Collaborate with the engineering team to set up and guide the development of Python infrastructure to integrate model runs into a core product.
  • Direct and oversee the modeling efforts (at plot and field scale), leveraging understanding of fundamental agronomy/soil science/ERW along with deep expertise working with MIN3P-APSIM to improve the current state-of-the-art.
  • Conduct simulations and calibration of APSIM for soil carbon measurement, utilizing data from field trials and farms to optimize the model and enhance accuracy.
  • Scale-up calibrated coupled models to cover additional fields in ERW projects.
  • Assess uncertainties, devise strategies to minimize them, and ensure compliance with soil organic (VERRA 0042 and 0053) and inorganic carbon (PURO, Cascade, Isometric) methodologies.
  • Communicate scientific work to various audiences, including government leaders and local communities, and collaborate across disciplines.
  • Serve as the primary liaison with the Terradot Science Advisory Board from Stanford University (Profs David Lobell, Scott Fendorf, Peter Nico and Alison Hoyt) and The University of British Columbia (Dr Ulrich Mayer), ensuring that the scientific endeavors align with industry best practices and cutting-edge research.
Funded by:
  • Sheryl Sandberg; Sandberg Bernthal Venture Partners
  • Tom Bernthal; Sandberg Bernthal Venture Partners
  • John Doerr; Kleiner Perkins / Speed & Scale
  • George Roberts; KKR
  • Ann Miura-Ko; Floodgate
  • Evi Steyer; Ponderosa Ventures

Adjunct Senior Research Fellow

Centre for Sustainable Agricultural Systems, University of Southern Queensland

Oct 2022 – Present Toowoomba, Queensland, Australia

Senior Cropping Systems Scientist

Regrow Ag

Jul 2021 – Jun 2023 Brisbane, Queensland, Australia

Applied Research

  • Lead a global project (Niche) focused on the development and deploy of predictive analytics framework to improve on-farm field trials, refine seed product profiles and optimize seed varietal placement to further climate adaptation for small-scale producers. Funded by Bill & Melinda Gates Foundation this project includes >12 team members from 4 organisations located in 5 continents more info here.
  • Develop and deploy new solutions for FluroSense, a digital agronomy and conservation monitoring platform which integrates large scale automated analysis with farm management systems (e.g. Climate FieldView, Agrian, Proagrica).
  • Work with the Agricultural Production Systems sIMulator (APSIM) science and remote sensing science teams to develop and implement algorithms that help improve soil-crop management practices internationally (focus on US, Australia, and LATAM, expanding into EU).
  • Work with customers’ data to evaluate the APSIM model accuracy in simulations of crop productivity in key agricultural systems worldwide.
  • Ensure engineering best practices, including phased project advancements, code documentation and review, and intellectual property documentation.
  • Work closely and guide the engineering and product to develop model-scaling approaches that would allow the decision support tools based on APSIM to be used across multiple crop types and in multiple geographies as well as across scales (from field to regional scale).
  • Develop algorithms to quantify the effects of crop management, genetics and climate variability on soil-crop processes.
  • Couple large multi-scale remotely sensed, geospatial datasets and advanced biophysical/mechanistic models at different spatial scales to evaluate drivers of yield variability to generate soil sampling schemes for large and complex conservation and soil sampling projects.
  • Implement algorithms that predict crop growth, yield, and soil health/environmental impacts in agricultural systems; bring together crop and soil models for yield, soil organic carbon, and ecosystem outcomes assessment.
  • Develop automated workflows and techniques for quantification of crop model uncertainties.
  • Research the effects of integrating cover crops into farming systems on nutrient recovery and recycling soil carbon, as well as reduced yield risks demonstrating mitigation of the impacts of climate change on agriculture.
  • Work in collaboration with CSIRO (Drs J. Whish, D. Gaydon, K. Verburg) in a project focused on the improvement of the FluroSense user interface based on farm/advisors’ interviews.

Scientific Advisor

Argentinian Scientific Network in Australia

Apr 2021 – Jul 2022 Brisbane, Queensland, Australia

Adjunct Researcher

Tasmanian Institute of Agriculture, University of Tasmania

Oct 2020 – Jul 2023 Hobart, Tasmania, Australia

Postdoctoral Research Fellow

Queensland Alliance for Agriculture and Food Innovation, The University of Queensland

Oct 2020 – Jun 2021 Brisbane, Queensland, Australia


  • I led the sorghum biomass modelling in the UQ component of a joint project with Purdue University (TERRA) in collaboration with Prof Scott Chapman, Prof Hammer, and Peter deVoil from UQ, Dr Archontoulis from Iowa State University and Prof Tuinstra, from Purdue University. We developed a new version of pSIMS to predict biomass sorghum across US environments using gridded data.
  • Assist in supervision and engagement with PhD and Master’s students at Purdue and UQ, and utilise APSIM to undertake simulations for the phenotyping of biomass sorghum using experiments from the project. Parameterise APSIM and use weather, soil and other datasets to undertake scenario analysis of adaptation of biomass sorghum to diverse environments, primarily in the USA.
  • Collaborate with Pacific Seeds Australia (ADVANTA) in an experimental trial combining farm, drone data and satellite imagery relating photosynthesis and RUE (Radiation Use Efficiency) with forage sorghum biomass. Use of LICOR 6800 to measure leaf photosynthesis at field scale level in sorghum trials including forage and grain genotypes.

Teaching and Learning

  • Teach undergraduate subjects into the UQ plant/crop science program.
  • Supervise students at honours and postgraduate level.
Funded by:

U.S. Department of Energy

  • First phase USD$6.6 million (2015)
  • Second phase USD$9 million (2019)

Data Network Co-director

University of Tasmania

Mar 2019 – Nov 2019 Hobart, Tasmania, Australia

Junior Research Fellow

Tasmanian Institute of Agriculture, University of Tasmania

Nov 2017 – Nov 2020 Hobart, Tasmania, Australia

Basic research and methods

  • Uncertainty analysis in climate change impact studies for irrigated maize systems in Spain.
  • Impact assessment of livestock farm performances regionally under different climate change scenarios in Australia, Argentina, and Uruguay.
  • Development of a tool to download gridded climate data in an APSIM format from SILO datasets across Australia.
  • Model phenological variation with sowing date and cultivar for lentil and faba bean against the climatic patterns of frost and heat in Australia.
  • Coupling existing multiple-scaling and remote-sensing techniques with advanced biophysical models to evaluate drivers of yield variability in Australia, Argentina, and Uruguay.
  • Quantification of crop model uncertainties (input, structure and parameter uncertainty) using sensitivity analysis tools such as Sobol, Morris, ANOVA.
  • Examine alternative existing model-scaling techniques and assess drivers of yield variability at the regional scale (gridded based crop simulations).
  • Point-based crop model calibration/validation under the Water for Profit program in Tasmania, Australia.
  • Develop a new potato module in APSIM Next Generation. Evaluating how accurately APSIM NG simulates potato productivity across several agricultural systems worldwide.

Applied research and industry engagement

  • Develop a virtual tool to analyze future scenarios to decision support for dual-purpose crops (canola and wheat) in Tasmania (Australia) in collaboration with GRDC and CSIRO.
  • Collect and organize several potato agricultural datasets (climate, soil and crop management) from industry partners across Tasmania (Simplot and McCain) for modelling purposes and scenario analysis with farmers.
  • Conduct workshops with Australian farming groups under a CRC Soil project focused on model development to evaluate the effect of soil constraints on crop and soil productivity.
  • Lead workshops with livestock industry decision-makers who will perform foresight exercises to explore alternative futures under climate change scenarios in Argentina, Uruguay, and Australia.

Coaching, supervision and leadership activities

  • Supervise Ph.D. students in collaboration with external co-supervisors (CSIRO Brisbane and Toowoomba).
  • UTAS Data Network Co-director (Mar-2019 to Nov-2019). Lead discussion and meeting for a group of researchers interested in data management across the University.
Funded by:

Tasmanian Institute of Agriculture (AUD $336,423)


Postdoctoral Research Fellow - Graduate Teaching Assistant

Research Council of Argentina, CONICET - Ecophysiology and Forage Production, National University of Entre Ríos

Apr 2017 – Nov 2017 Parana, Entre Rios, Argentina


  • Participate actively in the preparation for national and international proposals and research grants.
  • Participate in dissemination activities related to my research line.
  • Collect, analyze and interpret results, prepare seminars, workshop presentations, and present oral and written scientific reports papers in high-quality journals.


  • Post-graduate (~15 students) and undergraduate (~50 students) courses on crop ecophysiology and agricultural systems.
  • Advised one Honours thesis (Bachelor’s degree in Agronomy) at the National University of Entre Ríos. Honors thesis description: Impact of cover crops with different defoliation levels on soil carbon.
Funded by:

Research Council of Argentina, CONICET (AUD $35,568)


Postgraduate Research Fellow - Graduate Teaching Assistant

Research Council of Argentina, CONICET - Ecophysiology and Forage Production, National University of Entre Ríos

Apr 2012 – Apr 2017 Balcarce, Buenos Aires, Argentina


  • Apply ecophysiology concepts to the management of annual/perennial cropping systems for forage and grain production through field experimentation and biophysical simulation models, with emphasis on improving resource use efficiency with minimal environmental impact.
  • Use simulation models to analyze and predict the long-term productivity and stability of different cropping systems, encouraging the sustainable intensification of farm crop/forage production systems and improving environmental resources management strategies (solar radiation, soil water, and nutrients [mainly N]) while reducing environmental risks and climatic uncertainty.
  • Analyze and organize data for climate, soil, crop production, and management from five locations across Argentinian Pampas.
  • Calibrate and validate APSIM to predict biomass production at the crop and sequence level.
  • Design and conduct a 3-year field experiment in Balcarce, Argentina (May 2012 to May 2015).
  • Conduct probe calibration for Argiudoll soil in Balcarce, Argentina.
  • Data analysis and dissertation manuscript writing.


  • Research and teaching “Crop Physiology and Ecology”.
  • Undergraduate course (~50 students).
  • Supervision of staff (international students).
  • Supervision of Honours theses.
Funded by:

Research Council of Argentina, CONICET (AUD $69,240)


Industry Assistant and Agricultural Consultant

DASER AGRO S.A, Dow AgroSciences

Apr 2011 – Apr 2012 Maria Grande, Entre Rios, Argentina


  • Responsible for field experiments.
  • Assistant for the seed marketing project.
  • Private consultant in agricultural systems.
  • Invited talks for growers.

Undergraduate Teaching Assistant in Mathematics

National University of Entre Ríos

Apr 2007 – Apr 2011 Oro Verde, Entre Rios, Argentina


  • Research and teaching “Mathematics”.
  • Undergraduate course (>100 students).
  • Design and conduct a field experiment in Paraná (Argentina) for one summer season.
  • Data analysis and dissertation manuscript writing.
Funded by:

Argentinian Ministry of Education (AUD $9,471)

Awards, Prizes & Fellowships


  • Postdoctoral Fellowship (1.5 yr), The University of Queensland, Australia


  • JM Roberts Seed Funding for Sustainable Agriculture (1 yr), Tasmanian Institute of Agriculture, Australia


  • Junior Research Fellowship (3.5 yr), University of Tasmania, Australia
  • Postdoctoral Scholarship (2 yr), University of Pennsylvania, United States (Declined to take another scholarship)
  • Postdoctoral Scholarship (2 yr), The Swedish University of Agricultural Sciences, Sweden (Declined to take another scholarship)
  • Postdoctoral Research Fellowship (2 yr), National Research Council, Argentina


  • Fulbright Fellowship (9 months), Fulbright Commission, United States


  • Graduate Research Scholarship (5 yr), National Research Council, Argentina


  • National Undergraduate Scholarship (6 yr), Argentinian Ministry of Education, Argentina


Ability to get own funding as chief investigator or partner to do research 💸


DairyUp – Digitising forage options for intensive dairy systems

Responsibility: Collaborator

Funding body: University of Sydney

Partners: NSW Department of Primary Industries (Dr Gargiulo), University of Sydney (Drs Garcia and Islam).

2021 ($6,979,774)

Niche – Analytical framework for crop variety placement and trial site selection in Sub-Saharan Africa

Responsibility: Chief investigator

Funding body: Bill & Melinda Gates Foundation (4 yr).

Partners: University of Lincoln (Dr Grassini), NASA Harvest (Dr Inbal Becker-Reshef), Gates Foundation (Dr Hausmann), One Acre Fund (Dr Aston).

The role of biological fixation on crop productivity, nitrogen use efficiency and N2O emissions in reconfigured crop sequences. Quantification at different spatio-temporal scales (under review)

Responsibility: Chief investigator

Funding body: Argentinian Agency for the Promotion of Research, Technological Development and Innovation PICT-2020-SERIEA-III-A RAICES

Partners: University of Entre Rios, Argentina ( Dr Caviglia); PIRSA-SARDI, Australia ( Dr Sadras)

Radiation use efficiency of grasslands and its use for a better characterization of ecosystem change syndromes

Responsibility: Partner

Funding body: Argentinian Agency for the Promotion of Research, Technological Development and Innovation PICT-2019-I-D

Partners: Regional Analysis and Remote Sensing Lab, University of Buenos Aires, Argentina ( Dr Texeira & Dr Oesterheld)

2020 ($10,000)

Mapping potato yield and irrigation variability under climate change scenarios in Tasmania

Responsibility: Chief investigator

Funding body: JM Roberts Charitable Trust and the University of Tasmania

Partners: Simplot, McCain, Tasmanian Department of Primary Industries, Parks, Water and Environment (DPIPWE) & University of Sydney ( Mat Webb)

2019 ($228,803)

Biomass estimation of forage resources through remote sensing and crop growth simulation models

Responsibility: Partner

Funding body: Argentinian Agency for the Promotion of Research, Technological Development and Innovation PICT-I-A-2018

Partners: Regional Analysis and Remote Sensing Lab, University of Buenos Aires, Argentina ( Dr Irisarri & Dr Oesterheld)

Visualising agricultural uncertainties under climate change scenarios

Responsibility: Chief investigator

Funding body: College of Sciences and Engineering, University of Tasmania

Partners: Simplot, McCain and The Tasmanian Department of Primary Industries, Parks, Water and Environment (DPIPWE) & University of Sydney (Mat Webb)

Crop-livestock adaptation to climate change based on modelling and remote-sensing

Responsibility: Chief investigator

Funding body: Council on Australia Latin America Relations (COALAR) Australia’s Department of Foreign Affairs & Trade, Australian Government

Partners: University of Southern Queensland ( Assoc Prof Pembleton president of the APSIM Initiative), Regional Analysis and Remote Sensing Lab (University of Buenos Aires), National Institute of Agricultural Research (INIA; Uruguay), CREA Farmer Groups (Argentina-Uruguay)

The benefits and limits of diversity in agricultural systems

Responsibility: Chief investigator

Funding body: CSIRO-Tasmanian Institute of Agriculture

Partners: CSIRO Global Food and Nutrition Security, Australia ( Dr Katharina Waha)

Estimating Uncertainty in Crop Models

Responsibility: Chief investigator

Funding body: CSIRO-Tasmanian Institute of Agriculture

Partners: CSIRO Agriculture, Australia ( Dr Neil Huth)

2018 ($743,091)

Towards high crop productivity in agriculture based on multi-scale modelling and climate change impact studies

Responsibility: Chief investigator

Funding body: Universities Australia, German Academic Exchange Service

Partners: The University of Göttingen Prof Siebert, The Leibniz Centre for Agricultural Landscape Research (ZALF) ( Dr Rezaei, Dr Ewert), The University of Bonn ( Dr Kamali), Germany

Improving the representation of soil productivity/constraints in existing decision support systems and modelling platforms

Responsibility: Partner

Funding body: Soil CRC High-Performance Soils

Partners: University of Southern Queensland (Assoc Prof Pembleton), Federation University ( Assoc Prof Peter Dahlhaus & Dr Robinson), NSW Department of Primary Industries

Research Travel Funding Ag Systems Centre & Research Travel Funding Water for Profit Program

2004 – 2017 ($166,279)

Postdoctoral Research Fellowship (2 years)

Funding body: National Research Council, Argentina

Fulbright Fellowship (9 months)

Funding body: Fulbright Commission, United States

Fund INNOVA-T Grant

Funding body: National Research Council, Argentina

Graduate Research Scholarship (5 years)

Funding body: National Research Council, Argentina

National Undergraduate Scholarship (6 years)

Funding body: Argentinian Ministry of Education, Argentina

Research Exchange and Courses

Rwanda & Kenya

2022 - Project activities and visitor


2020 - Visiting Researcher

University of Entre Rios

2019 - Course Coordinator

University of Gottingen

2019 - Visiting Researcher

Purdue University

2015 - Visiting Researcher


International Visitors


Bahareh Kamali

Postdoctoral Researcher

Large scale agro-hydrological modeling, Model calibration, Parameter estimation, MONICA model


Fernando Lattanzi

Research Director

Pasture ecophysilogy, C and N dynamics, Forage crops, Dairy, Farm systems modelling

International Collaborators


Hamish Brown


Crop model development, Genotype-environment interactions, Crop eco-physiology, Model development, Soil-plant-climate interactions


Gonzalo Irisarri

Senior Researcher

Remote sensing, Livestock production, Pastures, Forage crops, Climate change, Spatial variability


Ehsan Eyshi Rezaei

Senior Researcher

Development of crop models, Crop phenology, Model up-scaling, Climate change, Heat stress, Drought stress


Heidi Webber

Head of Integrated Crop System Analysis Group

Modelling crop stress responses, Soil and water conservation in cropping systems, Develop modelling approaches, Integrated biophysical, economic and policy assessment

Collaborators in Australia


Yunru (Chloe) Lai

Postdoctoral Research Fellow

Pedometrics, Geostatistics, Plant-soil-climate-management interactions, Soil constraints, Agricultural systems modelling


Keith Pembleton

Associate Professor

Farming systems, Decision support systems, Crop modelling, Whole-farm analysis, Systems modelling, Dairy systems


Katharina Waha

Team Leader Food Systems and Global Change

Regional and global climate change, Model uncertainty analysis, Cross-scale spatial analysis and data integration, Agricultural Systems modelling


A python package to automate the extraction, processing and visualisation of climate data for crop modelling


  • Jonathan Ojeda (QAAFI, The University of Queensland)
  • Diego Perez (Data Analytics Specialist & Cyber Security Expert)


Bestiapop (a spanish word that translates to pop beast), is a Python package which allows climate and agricultural data scientists to automatically download SILO’s (Scientific Information for Land Owners) or NASAPOWER gridded climate data and convert this data to files that can be ingested by Crop Modelling Software like APSIM or DSSAT. The package offers the possibility to select a range of grids (0.05° x 0.05° for SILO and 0.5° x 0.5° for NASAPOWER) and years producing various types of output files: CSV, MET (for APSIM), WTH (for DSSAT) and soon JSON (which will become part of Bestiapop’s API in the future). Users can also visualise data statistics (mean, standard deviation, CV, etc) spatially for any selected region in the world.

If you would like to use Bestiapop in Jupyter Notebook, you can see here! You can also try it live in Binder Project without the need to install any software in your computer (Yes! 😄 you do not need to know about Python, Anaconda, etc. to use this tool).

Read more about this project

Data Science

Making art with Python 🌾 💻 👨‍🌾

Operating systems

Windows, Linux (Ubuntu), Unix.

Programming languages

Python, .NET (medium, APSIM Classic, APSIM Next Generation), C#, Markdown (advanced), R Studio, Shell.

Data analysis and exploration

Python (pandas, statsmodels, sqlite3, json, glob, os, functools, lxml [handling of XML and HTML files], csv).

Machine learning, optimization, linear algebra and statistics

Python (numpy, scipy, scikit-learn [e.g. KMeans used for data clustering], pandas, matplotlib, math).

Data visualization and mapping

Python (seaborn, dask, xarray, cartopy, pyproj, shapefile, netCDF4, geopandas, rasterio, GDAL), remote sensing imagery in vegetation and soil moisture mapping (MODIS, Sentinel2, Sentinel1-SAR), ArcGIS, QGis, netCDF file format, and relational databases. pSIMS (gridded crop model simulations), nco operators (manipulates and analyzes data stored in netCDF in Linux), FluroSense (Regrow Ag cloud-based crop management and analytics platform that drives planting and growing decisions).

Cloud computing, parallel computing and storage

Google Cloud Platform, Docker, Singularity, Amazon Web Services, GitHub repositories, APIs, Swift, SQL tools (Database Client, SQL editor, Visual Query Builder, e.g. DBeaver).

Software development and collaboration tools

Bestiapop (Python package to automate the extraction and processing of climate data for crop modelling, >3000 downloads), Atlassian (Jira and Confluence), Buddy (The DevOps Automation Platform), GitHub operations, Jupyter Notebook/Lab, Spyder, Anaconda, PostMan, Visual Studio.

Mechanistic models and decision support tools


Tools development for automated data processing



  • Jonathan Ojeda (QAAFI, The University of Queensland)
  • Pete deVoil (QAAFI, The University of Queensland)


  • Isaiah Huber (Iowa State University)
  • Chris James (School of Agriculture and Food Sciences, The University of Queensland)
  • Diego Perez (Data Analytics Specialist & Cyber Security Expert)


The original Parallel System for Integrating Impact Models and Sectors (pSIMS) was developed by Elliot et al. (2014) in Python 2, we updated pSIMS to pSIMSV2 which is able to run the APSIM sorghum module at a regional scale (US-wide) using netCDF input data (climate, soil and crop management). pSIMSV2 has the ability to run APSIM using a singularity image which avoid the need to install the soft dependencies manually.



  • Jonathan Ojeda (QAAFI, The University of Queensland)


Visualisation tools to map crop features and environmental variables across regions. Main functionalities include: import shp and tif files, use Basemap, edit legend and work with iso_3 codes, plot categories by country, edit legends in the map, inset charts in the map, read netCDF using xarray, explore and plot multidimensional files using xarray, create maps using xarray and dataframes, create 2D dataframe from xarray, create multi-dimensional xarray from 2D pandas dataframe, work with NASS API for crop statistics, etc…



  • Jonathan Ojeda (QAAFI, The University of Queensland)


Tools to use remote sensing data (MODIS, Sentinel2, NASA-POWER, etc) to validate crop models. These include the data curation and data analysis of remote sensing products before being used to validate models. Examples for linking APSIM Classic and Next Generation outputs with RS products are included.



  • Jonathan Ojeda (QAAFI, The University of Queensland)
  • Bahareh Kamali (University of Bonn)


This tool allows calculating the variance contribution of several factors on different crop model outputs. The theory developed by Monod 2006 was converted to a single Jupyter Notebook through Python. This tool produces a series of plots that allow the user to see the weight of each factor on the variance of crop yield.



  • Jonathan Ojeda (QAAFI, The University of Queensland)


During my free time, I enjoy writing my own webpage (the page you are reading right now!) in Markdown using the Hugo platform. Hugo is a popular static site generator written in the Go programming language. Hugo is jam-packed with features, but one of its main selling points is speed — Hugo takes mere seconds to generate a site with thousands of pages. By default, Hugo uses the Goldmark Markdown processor which is fully CommonMark-compliant.

APSIM Applications

Series of tools to develop and test APSIM using Python and C# 🤓

Variance decomposition of model outputs using APSIM Next Generation


  • Jonathan Ojeda (Regrow Ag)
  • Bahareh Kamali (University of Bonn)

Video - Tutorial


This code is able to retrieve APSIM Next Generation outputs and carried out a variance decomposition analysis to identify the main contributors to the variance in selected model outputs (e.g. crop yield). This code calculates the main (ME) and total effect (TE) of a series of factors on the variability of a selected variable (in this example crop biomass).

ME explains the share of the components to model output variability without interactions, i.e. if ME=1, the assessed factors explain the entire proportion of model output variability, but if ME<1, residuals exist which means additional factors are required to explain this variability. TE represents the interaction of a given factor with other factors, i.e. high TE values for a given factor denote high interactions of that factor with other factors, therefore, TE does not include residuals.

APSIMClassicTools & APSIMNextGenTools


  • Jonathan Ojeda (QAAFI, The University of Queensland)


Series of tools that allow users to interact between two APSIM versions (Classic and Next Generation) and Python through Jupyter Notebook. Main functionalities include: read .out files and .db files, create new variables, clean model outputs, create time series plots and XY plots, etc.

Data visualization for input model configuration


  • Jonathan Ojeda (QAAFI, The University of Queensland)
  • Hamish Brown (Plant & Food Research, New Zealand)


Crop models are usually developed using a test set of data and simulations representing a range of environment, soil, management and genotype combinations. Previous studies demonstrated that errors in the configuration of test simulations and aggregation of observed data sets are common and can cause major problems for model development. However, the extent and effect of such errors are not usually considered as a source of model uncertainty. This code presents a systematic method for testing simulation configuration using extensive visualisation approaches. A crop model – potato (Solanum tuberosum L.) is described to demonstrate the main sources of uncertainty from simulation configuration and data collation. A test set of 426 experiments conducted from 1970 to 2019 in 19 countries were run using the APSIM Next Generation model. Plots were made comparing simulation configuration across the entire test set . This identified a surprising number of errors and inappropriate assumptions that had been made which were influencing model predictions. The approach presented here moved the bulk of the effort from fitting model processes to setting up broad simulation configuration testing and detailed interrogation to identify current gaps for further model development.

APSIM Classic Miscanthus &

APSIM Classic Switchgrass


  • Jonathan Ojeda (as Fulbright scholar at Purdue University)


APSIM Classic was modified so that it could accurately predict growth and yield of switchgrass and Miscanthus; two plant species that were not represented in this large, multi-species model. Two existing APSIM sub-models (lucerne, sugarcane) were altered using knowledge of species-specific differences in growth, development and agronomic practices. Large databases for soils and weather were assembled for subsequently association with site-specific yield data of both species and successfully calibration and validation. These NEW APSIM sub-models predict the yield of both species across broad geographies from the East Coast to the Great Plains of the US.

Read the paper in Global Change Biology Bionenergy here

Conference and poster presentations

Niche - A scalable modelling tool to assess G×E×M interactions at continental scales (oral presentation)

Modelling approaches to select best genotypes by environment

The importance of simulation configuration to crop model development (oral presentation)

Model uncertainty decomposition and importance of input uncertainty quantification.

Variance decomposition of model outputs using APSIM Next Generation (video)

Identify the main contributors to the variance in model outputs

Talks List

Invited talks and guest lectures around the world 🌎


Niche - A scalable modelling tool to assess G×E×M interactions at continental scales

TropAg International Conference, Brisbane, Queensland, Australia. 31 October-2 November 2022

The importance of simulation configuration to crop model development

20th Australian Agronomy Conference, Toowoomba, Queensland, Australia. 18-22 September 2022

A Python package to automatically generate and visualise gridded climate data for crop model applications

20th Australian Agronomy Conference, Toowoomba, Queensland, Australia. 18-22 September 2022


BestiaPop: A Python Package to Automatically Generate and Visualize Gridded Climate Data for Crop Model Applications

5th Annual Crops in silico Symposium & Hackathon, University of Illinois, USA (online)

Variance Decomposition applied to crop models

APSIM monthly training YouTube videos, Brisbane, Australia (scheduled for November)


Quantifying data aggregation effects of model inputs on simulate yield and irrigation water demand

at regional scales APSIM Symposium 2020, Brisbane, Australia (cancelled due to COVID19)

Multi-resolution analysis of aggregated spatial data to simulate yield and irrigation water demand

at regional scales iCROPM2020 International Symposium, Montpellier, France


Can we trust in model predictions to assess questions at farm/regional levels?

Workshop Lucerne, Lincoln, New Zealand.

Minimum data requirements for modelling purposes

Simplot/McCain workshop, Devonport, Australia

APSIM Course Workshop

University of Entre Rios, Oro Verde, Argentina

Collaborations and data sharing to improve research outcomes: modelling across scales as a case of study

Data Network Hobart Teas and Workshop, University of Tasmania, Australia

The model up-scaling workshop

University of Gottingen, Germany

Improving the representation of soil productivity/constraints in existing decision support systems

and modelling platforms Soil CRC Conference, Newcastle, Australia


Can we trust in field-scale model predictions to assess the complexity of the agricultural

system at regional levels? Guest Lecture, TIA seminars, University of Tasmania, Australia

A modeller’s life. Guest Lecture, KLA312/KLA535: Farming Systems and Business Management

Guest Lecture, University of Tasmania, Australia


Precipitation use efficiency in annual forage crop sequences and perennial pastures

PhD Dissertation defense. National University of Mar del Plata, Argentina


Water productivity of annual cropping sequences and perennial pastures in Balcarce, Argentina

Crop Sequences Workshop. INTA General Villegas, Argentina


Biomass production and environmental resources use in annual forage crops sequences and perennial pastures in the Argentinian Pampas

University Seminar 2015. National University of Entre Rios, Argentina

Biomass production and environmental resources use in annual forage crops sequences and perennial pastures in the Argentinian Pampas

Oral and public defense of PhD Dissertation Project 2015. National University of Mar del Plata, Argentina


Evaluation of the Agricultural Production Systems Simulator simulating dry matter yield of forage crops sequences in the Argentinean Pampas

Postgraduate Seminar. Faculty of Veterinary Science. The University of Sydney, Australia


Sustainable intensification of forage production

Research Seminar, Instituto Nacional de Tecnologia Agropecuaria, Paraná, Argentina

Sustainable intensification of forage production

Forage Workshop, National Northwest University of Buenos Aires, Argentina

Eco-physiological assessment and analysis of different crops and pasture sequences Animal Production

Research Conference for PhD students. Instituto Nacional de Tecnologia Agropecuaria, Balcarce, Argentina

Production, quality and sustainable management of temperate and mega-thermal grasslands. Forage

Research Workshop for PhD students. Instituto Nacional de Tecnologia Agropecuaria, Rafaela, Argentina

Honours supervision

Agricultural Engineers

National University of Entre Ríos

Agr. Eng. Rodrigo Girard (2018)

Impact of cover crops with different defoliation levels on soil carbon

National University of Mar Del Plata

Agr. Eng. Ariel De Sarro (2017)

Comparative analysis of root production and root distribution in oats (Avena sativa) and tall fescue (Festuca arundinacea Schreb.)

Agr. Eng. Cecilia Gutheim (2015)

Effects of previous crop, additives and pre-wilted in the nutritional quality of oat silage (Avena sativa)

Agr. Eng. Gabriel Eriksen (2014)

Comparative analysis of water productivity between oats (Avena sativa) and tall fescue (Festuca arundinacea Schreb.)

Agr. Eng. Agustin Galleano (2014)

Nutritional evaluation of silage maize-soybean intercropping