2020 Convention session – Phenotyping & Remote Sensing to Facilitate Minimum Data Set Requirements for Crop Simulation Modeling
This session by the Crop Modeling Community of Practice aired live at the virtual 2020 CGIAR Convention on Big Data in Agriculture.
Crop models can require extensive and/or intensive data sets to drive simulations as well as for calibration purposes. While some of the information is straightforward, such as agronomic performance traits (e.g. yield, phenology) as well as weather data, other types of data require significant resources such as green area index, light interception and water and nitrogen availability in the soil. As a result, the vast majority of field data sets are not ‘model friendly’, lacking key inputs required for simulation purposes. This workshop will discuss ideas on how the digital technologies such as remote sensing for high throughput phenotyping can supplement or potentially serve as proxies for some of the harder to phenotype traits required, in different modelling contexts.
- Matthew Reynolds, Head of Wheat Physiology at CIMMYT and Crop Modeling CoP Leader | Download presentation
- Kai Sonder, Head of Geographic Information Systems Unit at CIMMYT and Crop Modeling CoP Co-Leader
- Davide Cammarano, Associate Professor at Purdue University | Download presentation
- Heidi Webber, Research Scientist at Leibniz Centre for Agricultural Landscape Research (ZALF) | Download presentation
October 28, 2020
Crop Modeling Community of Practice
CGIAR Platform for Big Data in Agriculture