AgMIP/NASA Harvest Hold Joint Symposium at ASA-CSSA-SSSA 2021 Annual Meeting
A joint symposium titled “AgMIP/NASA Harvest Activities to Advance the Use of Satellite Remote Sensing for Crop Modeling and Monitoring for Food Security” was organized by Dr. Varaprasad Bandaru (Harvest Hub Partner) and Dr. Alex Ruane (AgMIP Science Coordinator) with help from Dr. Christopher Justice (Harvest Chief Scientist) and Dr. Cynthia Rosenzweig, a member of the AgMIP Executive Committee, at this year’s ASA-CSSA-SSSA International Annual Meeting. Hosted in Salt Lake City, UT from November 7-10, 2021 by the American Society of Agronomy (ASA), the Crop Science Society of America (CSSA), and the Soil Science Society of America (SSSA), the international meeting brings together professionals across the agricultural sector who are working to advance the state of the science and develop research applications. The symposium focused on ongoing NASA Harvest and AgMIP activities with an emphasis on Earth Observations-crop modeling connections.
As part of the symposium, Dr. Ruane spoke about the ongoing AgMIP/Harvest initiative which focuses on developing protocols for improving configuration of process-based crop models through the integration and assimilation of satellite remote sensing datasets, and Dr. Bandaru presented on the use of the Geo-CropSim modeling framework for regional scale crop yield, water use and carbon assessment. In support of these efforts and on the underpinning topic of improved agricultural resource management, NASA Harvest partner Dr. Kaiyu Guan (University of Illinois - Urbana Champaign) spoke in detail about field-level carbon intensity quantification using a combination of remote sensing and crop modeling. Additional expertise on soil condition analysis, yield prediction methodology, and seasonal forecasting was provided by Drs. Sotirios V. Archontoulis (Iowa State University), Bruno Basso (Michigan State University), and Narendra Das (Michigan State University).
Watch full recordings of the symposium presentations and continue reading below for a detailed description of the session topics.
Full Session Description:
Crop failure, food shortages and food-price spikes have occurred in recent years and can be expected to continue under a changing climate. Earth observations and crop models can be used to provide timely and accurate information on crop area distribution, crop condition, yield forecasts from local to global scales; and potential interventions to inform farm management, mitigate food supply crises and stabilize markets. Earth observations play a central role in crop monitoring activities, aided by recent advances in sensor technology; satellite-based crop models; big data analytics; high performance computing, crop monitoring, and yield forecasting. Crop models allow for the synthesis of observations and biophysical process understanding, allowing for the exploration of interventions given shifting risk profiles associated with farming systems in a changing world.
This session will be co-hosted by leaders of the NASA Harvest and Agricultural Model Intercomparison and Improvement Project (AgMIP) communities. NASA Harvest, a NASA program on Food Security, is a multidisciplinary consortium commissioned by NASA and led by the University of Maryland to advance the use of satellite observations in decision-making related to food security and agriculture, domestically and globally. The Agricultural Model Intercomparison and Improvement Project (AgMIP) is an international community advancing the development and use of agricultural models including soil, crop, climate, economics, and food security models.
The session will begin with an overview of NASA Harvest and AgMIP activities with an emphasis on Earth Observations-crop modeling connections. The second part of the session will focus on important and active areas of research, including mechanisms for connection of improved datasets for crop model configuration, initialization, driving climate, and evaluation (of both crop and remote sensing products), as well as within-season data assimilation using remote sensing observations. We will conclude with an open discussion on challenges and research needs.
