Cooperative Agreement for CESU-affiliated Partner with Californian Cooperative Ecosystem Studies Unit | G22AS00399

Funding Opportunity G22AS00399 (USGS) - Frequently Asked Questions

What is the funding opportunity number and who is offering it?

The funding opportunity number is G22AS00399. It is offered by the U.S. Geological Survey (USGS) as a discretionary cooperative agreement under the Cooperative Ecosystem Studies Unit (CESU) Program, specifically the Californian CESU.

What type of award is this?

This opportunity uses a cooperative agreement (not a standard grant). A cooperative agreement typically indicates substantial involvement by USGS during the project, such as collaboration on technical direction, alignment with agency priorities, and engagement on how methods and outputs are developed and used.

What is the main purpose of this opportunity?

The core purpose is to support applied research and development in science and technology that improves how crop production outcomes are anticipated, especially in data-constrained settings. The CESU framework is intended to leverage established partnerships that blend research capacity, technical expertise, and education-oriented collaboration between federal agencies and affiliated institutions.

What is the activity category for this award?

The activity category is listed as Science and Technology and other Research and Development.

What is the Assistance Listing (CFDA) number?

The Assistance Listing (CFDA) number is 15.808, which corresponds to USGS research and related assistance.

Who is eligible to apply?

Eligibility is limited to participating partners of the Californian Cooperative Ecosystem Studies Unit (CESU). While the notice references eligible applicants as "Others," the eligibility is constrained by the CESU requirement, meaning the opportunity is essentially reserved for institutions already formally affiliated with the Californian CESU.

What are the main research topics or needs USGS is trying to address?

The scope focuses on two closely related needs:

1. Advances in long-lead crop yield forecasting and crop condition outlooks to provide earlier and more actionable expectations of production outcomes.
2. Methods to generate yield forecasts and crop condition outlooks in FEWS NET countries where sub-national crop yield data are missing, incomplete, or too limited for conventional modeling.

What does "long-lead" crop yield forecasting mean in this context?

"Long-lead" forecasting generally refers to producing useful yield expectations earlier in the growing season (and potentially even before key growth stages). The goal is to help decision-makers plan earlier for food supply risks, market shocks, and humanitarian needs.

What are crop condition outlooks, and how do they relate to yield forecasts?

Crop condition outlooks provide an assessment of how crops are developing relative to normal conditions. They can complement yield forecasts by adding context about crop development, often informed by signals from climate, remote sensing indicators, and agro-environmental modeling. Together, yield forecasts and condition outlooks support earlier detection of potential shortfalls or surpluses.

What is the significance of focusing on FEWS NET countries?

FEWS NET (Famine Early Warning Systems Network) supports early warning and analysis related to food insecurity. Many FEWS NET countries have major gaps in agricultural statistics, especially at the sub-national level. This opportunity emphasizes building approaches that remain credible and decision-relevant even when local yield data are sparse or incomplete.

Why does the opportunity emphasize sub-national yield data gaps?

Sub-national crop yield data may be missing, incomplete, or too limited in many settings. Conventional modeling often relies on reliable historical yield labels at finer geographic scales. This opportunity highlights the need for methods that can still produce actionable forecasts and outlooks despite limited ground-truth data.

What kinds of methods or data sources does the notice suggest may be relevant in data-constrained settings?

The notice points to methodological innovation and notes that, in practice, this often implies using alternative data sources such as:

• Satellite-derived vegetation metrics
• Evapotranspiration metrics
• Weather reanalysis products
• Soil moisture estimates
• Crop calendars
• Limited survey or administrative data

It also notes that approaches may involve statistical or machine learning techniques that can work with sparse labels, such as transfer learning across regions or model calibration under uncertainty.

How might USGS be involved during the project given that this is a cooperative agreement?

Because it is a cooperative agreement, USGS is likely to be more directly involved than under a typical grant. The notice indicates this can include collaboration on technical direction, integration with agency priorities, and potentially review or engagement related to methods, data workflows, or how outputs are operationalized.

What is the award ceiling (maximum amount) listed in the notice?

The award ceiling listed is $618,180.

How many awards does USGS expect to make under this opportunity?

The provided notice references expected awards, but the number of expected awards is not specified in the information provided.

What are the key dates mentioned for this opportunity?

The notice lists a creation date of August 4, 2022 and an original closing date of September 6, 2022.

Is this opportunity designed to support basic research or applied research?

Based on the description, it is aimed at applied research and development in science and technology, with an emphasis on improving practical forecasting and early warning capabilities.

How does the CESU program relate to this funding opportunity?

This opportunity is issued through the CESU framework, specifically tied to the Californian CESU. CESUs are partnership networks designed to streamline collaboration and funding for research, technical assistance, and education, leveraging the strengths of federal agencies and affiliated institutions.

What kinds of decisions or uses are the expected outputs meant to support?

The described outputs (long-lead yield forecasts and crop condition outlooks) are intended to support earlier and more actionable assessments that can inform planning around food supply risks, potential market shocks, and humanitarian needs, particularly where data limitations would otherwise prevent timely analysis.