What We Do

NCAR Consortium for Agriculture and Food Security through Earth Observations (NCAFSEO)

NCAR Consortium for Agriculture and Food Security through Earth Observations (NCAFSEO)

Project Tabs - NCAFSEO

Food is a basic human need and right.

Significant advances were realized globally toward achieving the Sustainable Development Goal (SDG 2) to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture by “cutting by half the proportion of people who suffer from hunger by 2015”– in fact 216 million fewer people are undernourished now than in 1990-92. To meet demand as well as to feed increasing populations, global agricultural output has more than tripled in volume in the last 50 years and real prices have fallen. In the U.S., even starting from already high levels of productivity, farm production more than doubled between 1948 and 2011. Yet 795 million people globally still face food insecurity and concerns remain about the ability of food systems to meet future demands.

General team approach, with the teams working together to meet the target of enhanced food security. Supporting the specific teams is the management, including NASA and an expert advisory panel.
General team approach, with the teams working together to meet the target of enhanced food security. Supporting the specific teams is the management, including NASA and an expert advisory panel.

In spite of complex challenges, significant opportunities exist to further improve agricultural efficiency and further reduce food insecurity – to feed the world. In particular, applications developed in optimal ways using state-of-the-science Earth Observations (EOs) have the potential to bolster agriculture and improve decision making throughout the food system. To that end, we have formed the multi-sectoral transdisciplinary team, dubbed the NCAR Consortium for Agriculture and Food Security through Earth Observations (NCAFSEO), comprised of experts on all aspects of the agriculture and food security value chain. NCAFSEO will integrate EOs into core modeling and decision support, develop multiple domestic and international applications, undertake comprehensive user needs and capabilities assessment, implement innovative communications and dissemination, and provide socioeconomic analyses.

The Goal of NCAFSEO is to enhance food security and improve agricultural decision making by integrating earth observations to provide stakeholder-driven decision support frameworks to aid in meeting both domestic and international needs. 

Specific objectives of NCAFSEO include:

  • Enhance Agriculture and Food Security (AFS) using integrated EOs, models, and products;
  • Apply an information value process starting with end users and applications of information in mind to transform our understanding of, and approaches to AFS.
  • Establish a decision support framework to enhance AFS, improving domestic and foreign agriculture by integrating EOs, models, and big data algorithms.
  • Demonstrate the value of integrated EOs through applications that are measurable, transferable and rapidly deployed to real-world stakeholders.
  • Widely communicate and disseminate the applications and results to broadly share the benefits.
  • Provide a point of focus for applications related to agriculture and food security by creating a community of practice to advance cutting edge applications.

Methods

The figure depicts our approach. EOs underlie the entire collection of projects. The core framework will integrate existing models with a coherent data assimilation plan. Machine learning will optimize the models, correcting and blending model forecasts based on historical performance and skill as compared to observations. Working closely with end users and stakeholders, the team will assess their needs to determine how science can best inform decisions.

Framework for Agriculture and Food Security through Earth Observations (FAFSEO) system for enhancing agriculture and  food security through monitoring, vulnerability assessment, and forecasting with Earth observations forming the background for all.
Framework for Agriculture and Food Security through Earth Observations (FAFSEO) system for enhancing agriculture and  food security through monitoring, vulnerability assessment, and forecasting with Earth observations forming the background for all.

These assessments will drive planning, integrating, and testing the Framework for Agriculture and Food Security through Earth Observations (FAFSEO). Assessment and evaluation occurs at all levels, with sustained communication key to assuring that the integrated framework meets the needs of the stakeholders and enhances agriculture and food security through this unified process. FAFSEO will be modular, so that its elements can be utilized in applications as needed.

The core demonstration application will utilize the FAFSEO framework to enhance AFS in the U.S. and several core international locations (Caribbean, East Africa, and Southeast Asia with confirmed collaborators). A series of more specialized applications will leverage EOs and elements of the decision support framework to focus on subseasonal-to-seasonal (S2S) forecasting timescales; impacts of extreme events on crop risk; impacts of air pollution on crop production; and applications to climate scale variations.

For all aspects of the project, the team will apply an information value process (Figure) that enables socio-economic benefit (SEB) assessments of the use of EOs and how they enhance information creation, communication, and decision-making for AFS. Assessments will recognize that food security includes systems of energy, water, transportation, food processing, distribution, and trade, as well as institutions and cultures impacting access to and utilization of adequate nutritional resources across diverse populations.

Figure 3. Information value process example of working from EOs to produce enhanced food security.

NCAFSEO will engage its extensive international network and experience to actively invest in project and user community integration and capacity building through team-building workshops that integrate subject matter experts, NASA personnel (including from outside the Consortium), and stakeholders. Results of the integrated modeling and demonstration applications will be widely disseminated and all software developed in this project will be OpenSource.

Framework for Agriculture and Food Security through Earth Observations (FAFSEO)

FAFSEO will integrate EOs, operational forecasts and regionally customized hydro-meteorological forecasts, advanced machine learning, land data assimilation technologies, crop-model prediction and assessment developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013), and hierarchical modeling and data optimizing systems (see Fig. 3).  The process will consist of:

Earth observations impact all portions of the decision support framework.
Earth observations impact all portions of the decision support framework.

1) Assimilating EOs into a specialized version of NCAR’s Weather Research and Forecasting (WRF) model to provide very short-range forecasts of the timing of weather events;

2) Assimilating EOs into AgMIP process-based crop models to provide a more comprehensive view of current crop status.

3) Integrating existing operational day-to-season weather and water forecast products from different sources, and a cloud-permitting WRF-Crop 14-day enhanced weather forecast for selected regions;

4) Applying machine learning to ingest in-situ and NASA satellite data sets of weather, soil moisture, and crop phenology to generate forecasts of weather, hydrology, and ozone, with elements used to drive AgMIP crop models and as input to decision support systems;

5) Generating AgMIP crop forecasts and risk assessments;

6) Incorporating existing remote sensing products and agricultural indicators such as those seen on GIEWS or used in the GEOGLAM Crop monitor (normalized difference vegetation index (NDVI) anomalies, Vegetation Health Index, Solar-induced Fluorescence, soil moisture)); and

7) Applying machine learning to provide optimized day-to-season crop yield and production predictions.

The following table is a logic model that shows how the project is planned to enhance agriculture and food security. NCAFSEO is integrating EOs, models, machine learning technologies, and expert capabilities to provide the FAFSEO framework that can be used in a multitude of applications and is expected to achieve measureable outcomes and impacts. The goal is to develop a formal and comprehensive approach to NASA Earth science applications in the arena of agriculture and food security.Anticipated Results, Outcomes, and Impacts of Planned Activities and How they will be Measured.

  • NCAR
  • Agricultural Model Intercomparison and Improvement Project (AgMIP)
  • Harris Corporation
  • Computational Institute (CI) at the University of Chicago
  • Resources for the Future
  • Institute for Sustainable Futures at the University of Florida
  • Department of Computer Science at the University of Colorado Boulder
  • NASA Goddard Space Flight Center
  • Caribbean Institute of Meteoerology and Hydrology
  • University of the West Indies
  • Indian Institute for Tropical Meteorology
  • Indian Institute of Farming System Research
  • Bangldesh University of Engineering and Technology
  • Aryabhatta Research Intitute of Observational Sciences
  • Indian Council of Agriculture Research
  • GLOBE
  • GLOBE Kenya
  • Regional Center for Mapping of Resources for Development, Nairobe, Kenya
  • Panasonic
  • Climate Corporation
NCAR Consortium for Agriculture and Food Security through Earth Observations (NCAFSEO)

Projects