Our latest publication tilted "A comprehensive uncertainty quantification of large-scale process-based crop modeling frameworks" is now formally accpeted and can be viewed here.
Congrats to Marissa Kivi for wining the Kraft/Frerichs graduate fellowship award. Well deserved !
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.
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
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.
In this project, we are leverging our APSIM - SDA workflow to predict next windows of opportunities for field operations including planting and harvesting.View Project
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 assimilationView Project
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
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.
Hamze Dokoohaki, et al., . 2021/6/29. Environmental Research Letters
Fernando Miguez, Sotirios Archontoulis, Hamze Dokoohaki. 2017/8/3. Applied Statistics in Agricultural, Biological, and Environmental Sciences
Hamze Dokoohaki, Fernando E Miguez, David Laird, Jerome Dumortier. 2019/3/29.Environmental Research Letters
Hamze Dokoohaki, Mahdi Gheysari, Sayed-Farhad Mousavi, Shahrokh Zand-Parsa, Fernando E. Miguez, Sotirios V. Archontoulis, Gerrit Hoogenboom. 2016/1/1. Agricultural Water Management
Hamze Dokoohaki, Fernando E Miguez, Sotirios Archontoulis, David Laird. 2018/9/30. Agricultural Water Management
Hamze Dokoohaki, Fernando E Miguez, David Laird, Robert Horton, Andres S Basso.2017/7/4 . Communications in Soil Science and Plant Analysis
Hamze Dokoohaki, Mahdi Gheysari, Abdolmohammad Mehnatkesh, Shamsollah Ayoubi.2015 .Iranian Water Research Journal
Hamze Dokoohaki, Mahdi Gheysari, Sayed-Frahad Mousavi, Gerrit Hoogenboom. 2017/7/1 . Ecohydrology & Hydrobiology
Masoomeh Fakhri, Hamze Dokohaki, Saeid Eslamian, Iman Fazeli Farsani, Mohammad Reza Farzaneh. 2014/3.Handbook of Engineering Hydrology: Modeling, Climate Change, and Variability