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Jonathan Ojeda (Jony)

Crop Ecophysiologist - Cropping Systems Modeller - Data Scientist

Regrow Ag ([email protected])

Bio

I apply data analytics, statistics and programming to understand the crop ecophysiology behind crop models and remote sensing algorithms to improve crop production and reduce environmental impacts. My interests are in quantifying the effects of crop management, genetics and climate variability on soil-crop processes and integrating data into mechanistic models at different spatio-temporal scales. I have a passionate interest in disentangling sources of uncertainties/variability to better predict crop growth, both yield and quality and environmental indicators. This requires a broad understanding of how the environment and genetics influences crop growth/development; how rainfall, irrigation, and fertilizer influence soil conditions; 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 (5+ years using Python) and 10 years developing the Agricultural Production Systems sIMulator ( APSIM) and applying data pipelining and optimization towards solving large-scale real farm problems. I collected (field experimental trials) and analysed data including >18 crop species (annual, perennials). I have more than 40 published articles [ 15 in high-impact journals (12 as first author)]. I received 17 research grants (70% as chief investigator) with a cash value of ~AUD8.13M. I am currently mentoring 5 PhD students in collaboration with international researchers and has previously supervised 5 Honours students to completion.

My current role at Regrow Ag is focus on developing digital solutions by linking geospatial data (crop, management, environment and genetics), models, sensors, and scientific knowledge to make decision support tools more efficient and world-wide applicable. I work with data scientists, software developers and engineers to develop models/algorithms linking remote sensing data with crop/soil mechanistic models to predict crop phenology/yield, soil water, N and C dynamics in a broad range of agricultural systems (focus on maize, wheat). I work with multi-cultural and multi-disciplinary teams (Academia and Industry) and have experience working in 6 countries (Argentina, Australia, New Zealand, United States, The Netherlands and Germany). My experience in Australia and overseas in the public and private sectors testify to my readiness and capacity to work with teams across different locations whilst creating long standing and productive relationships.

Interests

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

Education

  • PhD in Agricultural Sciences, 2017

    National University of Mar del Plata, Argentina

  • Agricultural Engineer, 2011

    National University of Entre Ríos, Argentina

Skills

Python

Linux

Remote Sensing

Ubuntu

R

Markdown

GitHub

Statistics

Plotting

International Networking

Communication

Team work

Education

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)

Courses

  • 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

image

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)

Courses

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

Experience

 
 
 
 
 

Senior Cropping Systems Scientist

Regrow Ag

Jul 2021 – Present Brisbane, Queensland, Australia
Responsibilities:

Applied Research

  • 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.
 
 
 
 
 

Postdoctoral Research Fellow

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

Oct 2020 – Jun 2021 Brisbane, Queensland, Australia
Responsibilities:

Research

  • 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)
 
 
 
 
 

Junior Research Fellow

Tasmanian Institute of Agriculture, University of Tasmania

Nov 2017 – Nov 2020 Hobart, Tasmania, Australia
Responsibilities:

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
Responsibilities:

Research

  • 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.

Teaching

  • 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
Responsibilities:

Research

  • 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.

Teaching

  • 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
Responsibilities:

Consultancy

  • 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
Responsibilities:

Teaching

  • 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

2021

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

2020

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

2017

  • 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

2015

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

2012

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

2004

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

Grants

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

2021 ($21,538)

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

ZALF

2020 - Visiting Researcher

University of Entre Rios

2019 - Course Coordinator

University of Gottingen

2019 - Visiting Researcher

Purdue University

2015 - Visiting Researcher

Partners

International Visitors

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Jose Capuano

Agricultural Engineer

Horticultural industry, Biosecurity, Potatoes, Perennial crops, Systems analysis

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Bahareh Kamali

Postdoctoral Researcher

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

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Fernando Lattanzi

Research Director

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

International Collaborators

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Hamish Brown

Scientist

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

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Gonzalo Irisarri

Senior Researcher

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

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Ehsan Eyshi Rezaei

Senior Researcher

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

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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

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Keith Pembleton

Associate Professor

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

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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

Bestiapop

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

Developers

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

Overview

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
Pypi
GitHub
readthedocs

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

APSIM Next Generation, APSIM Classic, DSSAT, SIMPLACE, MONICA, CropWat, pSIMS, DNDC

Tools development for automated data processing

pSIMSv2

Developers

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

Collaborators

  • 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)

Overview

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.

MappingTools

Developer

  • Jonathan Ojeda (QAAFI, The University of Queensland)

Overview

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…

RemoteSensingApplications

Developer

  • Jonathan Ojeda (QAAFI, The University of Queensland)

Overview

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.

VarianceDecomposition

Developers

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

Overview

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.

WebsiteBuilder

Developer

  • Jonathan Ojeda (QAAFI, The University of Queensland)

Overview

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

Developers

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

Video - Tutorial

Overview

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

Developer

  • Jonathan Ojeda (QAAFI, The University of Queensland)

Overview

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

Developers

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

Overview

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

Developer

  • Jonathan Ojeda (as Fulbright scholar at Purdue University)

Overview

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

Publications

Assessing errors during simulation configuration in crop models – A global case study using APSIM-Potato

We assessed the errors during simulation configuration in APSIM-Potato using GxExM experiments worldwide.

Modelling phenology to probe for trade-offs between frost and heat risk in lentil and faba bean

We model phenological variation with sowing date and cultivar for lentil and faba bean against the climatic patterns of frost and heat.

Conference and poster presentations

Historical and current approaches to decompose uncertainty in crop model predictions (poster)

We explored the diverse statistical approaches used to decompose crop model uncertainty

Talks List

Invited talks and guest lectures around the world 🌎

2022

The importance of simulation configuration to crop model development

20th Australian Agronomy Conference, Toowoomba, Queensland, Australia. 6-10 February 2022

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

20th Australian Agronomy Conference, Toowoomba, Queensland, Australia. 6-10 February 2022

2021

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)

2020

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

2019

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

2018

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

2017

Precipitation use efficiency in annual forage crop sequences and perennial pastures

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

2016

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

Crop Sequences Workshop. INTA General Villegas, Argentina

2015

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

2013

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

2012

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

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