Agronomic Forecasting Lab Unversity of Illinois at Urbana - CHampaign

News

  • New publication

    Check our first attempt at providing a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the CONUS using sequential data assimilation. Click here.

    04/26/2022
  • Open Positions

    We are looking for motivated students with a strong background in statistics and computer coding to work on interesting data fusion crop modeling projects. Please contact me to learn more about the available positions

    1/11/2022
  • New publication

    Check out this exciting work by Marissa Kivi, which uses Ensemble Kalman Filter to fuse soil moisture measurements into APSIM. Marissa showed constraining upstream estimates of soil moisture in APSIM results in substantially higher model accuracy and lower uncertainty in estimates of nitrate leaching. Click here.

    01/26/2022
  • Our latest publication

    Our latest publication tilted "A comprehensive uncertainty quantification of large-scale process-based crop modeling frameworks" is now formally accpeted and can be viewed Click here.

    06/29/2021
  • Fellowship award

    Congrats to Marissa Kivi for wining the Kraft/Frerichs graduate fellowship award. Well deserved !

    05/20/2021
  • Precision Agriculture Talent Pipeline Initiative

    A grant proposal led by our group was awarded $200k by CHS foundation to develop a precision agriculture talent pipline. This program aims to ‘even playing field’ for young and brilliant under-represented minorities seeking for an opportunity in precision Agriculture.

    11/23/2020
  • Open Positions

    We are looking for motivated students with a background in statistics and computer coding to work on interesting data fusion crop modeling projects. Please contact me to learn more about the available positions

    11/10/2020

Vision

The long-term vision for our lab is to synthesize, analyze and create new value from the rich data streams generated by all phases of agricultural production. We will work towards developing novel data-driven approaches at the interface of computer science, statistics and crop science with the goal of improving and protecting our food system. In addition, we are always excited to welcome new graduate and undergraduate students with research interests aligned with the focus of the lab.

Get In Touch

Image

Projects

Regional cover crop modeling for the state of Illinois

In this project, we are leveraging high performance computing to explore the impacts of different managment decisions in cover crop implementation across the state of Illinois.

View Project

Multi-Site and multi-year remote sensing soil moisture assimilation

In this project, we are exploring the impact of RS soil moisture assimilation in APSIM model by expanding site-level soil moisture assimilation to more 20 site x year.

View Project

Linking remote sensing and machine learning with APSIM model through emulation and Bayesian optimization to improve yield prediction in the U.S Midwest

In this project, we are exploring the use of earth observations (LAI and NDVI) for calibrating crop model parameters through different Bayesian optimization schemes (site-level, hierarchical, global).

View Project

Improving Soil N cycle simulation through sequential data assimilation

We are interested in assimilating in-situ soil moisture and soil temperature data obtained from soil sensors into APSIM, a robust process-based crop model, to see if the model is better able to predict soil N and yield at Energy Farm when compared to a model run without data assimilation

View Project

Uncertainty assessment of large-scale crop simulation models

In this study we use a multi-model regional crop simulatin platform to study the contribution of different sources of uncertainty in total variance simulated around crop yield and nitrate leaching for corn and soybean.

View Project

Tools/Data Products

Not all tools and data products presented in this section is developed in our lab.


Yield response to biochar

This tool allows you to estimate the probability of crop yield increase following Biochar application for the state of Illinois.

View


Corn Productivity Index

This tool allows you to explore the new corn productivity index across the U.S Midwest.

View


NO3 leaching susceptibility Index

This tool allows you to explore the new Nitrate leaching susceptibility index across the U.S Midwest.

View

Teaching

Digital Agriculture

Digital agriculture is concerned with the use of digital technologies for understanding underlying processes and solving agricultural issues. The ultimate goal of the Digital Agriculture course is to help students develop an understanding around three main components in digital agriculture including:

1) Statistical modeling and machine learning
2) Process-based crop modeling and
3) Remote sensing, where they all revolve around crop physiology and production.

In this course students will start by learning the fundamentals of crop physiology and familiarizing themselves with the current major issues around crop production in large. Then, students will learn about three powerful tools including statistical modeling, process-based modeling and remote sensing to addresses those challenges. This section is restricted to online programs only.

Regression Analysis

Publications

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Hamze Dokoohaki, et al., . 2022/4/26. Geoscientific Model Development

View

Development of a data-assimilation system to forecast agricultural systems: A case study of constraining soil water and soil nitrogen dynamics in the APSIM model

Marissa Kivi, et al., . 2022/1/26. Science of The Total Environment

View

A comprehensive uncertainty quantification of large-scale process-based crop modeling frameworks

Hamze Dokoohaki, et al., . 2021/6/29. Environmental Research Letters

View

Nonlinear Regression Models and Applications

Fernando Miguez, Sotirios Archontoulis, Hamze Dokoohaki. 2017/8/3. Applied Statistics in Agricultural, Biological, and Environmental Sciences

View

Where should we apply biochar?

Hamze Dokoohaki, Fernando E Miguez, David Laird, Jerome Dumortier. 2019/3/29.Environmental Research Letters

View

Coupling and testing a new soil water module in DSSAT CERES-Maize model for maize production under semi-arid condition

Hamze Dokoohaki, Mahdi Gheysari, Sayed-Farhad Mousavi, Shahrokh Zand-Parsa, Fernando E. Miguez, Sotirios V. Archontoulis, Gerrit Hoogenboom. 2016/1/1. Agricultural Water Management

View

Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties

Hamze Dokoohaki, Fernando E Miguez, Sotirios Archontoulis, David Laird. 2018/9/30. Agricultural Water Management

View

Assessing the biochar effects on selected physical properties of a sandy soil: an analytical approach

Hamze Dokoohaki, Fernando E Miguez, David Laird, Robert Horton, Andres S Basso.2017/7/4 . Communications in Soil Science and Plant Analysis

View

Applying the CSM-CERES-Wheat model for rainfed wheat with specified soil characteristic in undulating area in Iran

Hamze Dokoohaki, Mahdi Gheysari, Abdolmohammad Mehnatkesh, Shamsollah Ayoubi.2015 .Iranian Water Research Journal

View

Effects of different irrigation regimes on soil moisture availability evaluated by CSM-CERES-Maize model under semi-arid condition

Hamze Dokoohaki, Mahdi Gheysari, Sayed-Frahad Mousavi, Gerrit Hoogenboom. 2017/7/1 . Ecohydrology & Hydrobiology

View

Flow and Sediment Transport Modeling in Rivers

Masoomeh Fakhri, Hamze Dokohaki, Saeid Eslamian, Iman Fazeli Farsani, Mohammad Reza Farzaneh. 2014/3.Handbook of Engineering Hydrology: Modeling, Climate Change, and Variability

View

Our Lab

We are always excited to welcome new graduate and under graduate students with research interests aligned with the focus of the lab.

Hamze Dokoohaki
Principle Investigator
Teerath Rai
Postdoctoral associate
Iris Chow
Graduate Student

Alumni

Marissa Kivi
Graduate Student
Alexandra Jakucewicz
Research Assistant

Contact Us

N315 Turner Hall 1102 S. Goodwin Ave. Urbana, IL 61801

+1 217 - 300 - 6792

hamzed@illinois.edu