Opportunity Information: Apply for W81EWF 23 SOI 0014
The Department of Defense, through the U.S. Army Engineer Research and Development Center (ERDC), is soliciting applications for a cooperative agreement to expand an existing rangeland prediction tool called LandCart from the western United States to rangelands worldwide. LandCart is described as a highly functional and highly accurate rangeland attribute model built using a large, standardized field dataset in the western U.S. The new effort aims to create a comparable predictive algorithm that can operate at a global scale, where field data are generally more limited, less consistent, and unevenly distributed. The core idea is to combine field observations with remote sensing so managers and researchers can map and monitor key rangeland conditions over huge areas without relying on constant, labor-intensive ground sampling.
The opportunity is motivated by the fact that rangelands cover roughly half of Earths land surface and are central to ecosystem health, sustainable grazing, and land management. Satellite imagery has long been used to estimate broad patterns like biomass and vegetation cover, but globally consistent predictions of rangeland vegetation properties are still relatively scarce. ERDC is looking to fill that gap by scaling up the LandCart approach, which would support repeatable, large-area assessments and help track spatial and temporal changes in rangeland condition. The most emphasized target attributes for the global model are vegetation cover and vegetation height, with the broader context including related properties such as biomass.
The work is framed as a phased, multi-year pathway with clear technical milestones. In the first year, the main task is a feasibility and data-readiness assessment: ERDC notes it has already assembled a global database of field-based rangeland vegetation measurements, but it is not yet clear whether that database has enough breadth, depth, consistency, and metadata quality to support a robust predictive model. Applicants are expected to bring expertise to review the database, determine whether it is adequate for algorithm development, identify gaps and practical steps to make it adequate if it is not, or conclude that the data cannot support the effort in its current form. This first phase is essentially about answering whether a LandCart-like model is realistically buildable with available information and what additional data or restructuring would be needed.
If the data review shows the foundation is workable, the second year focuses on building the predictive global rangeland attribute model, drawing on the ERDC database and any other suitable supplemental data sources. The emphasis in this development stage is on producing operational predictions for vegetative cover and height at global scale, presumably leveraging remote sensing inputs and modeling approaches that can generalize across diverse rangeland types, climates, and management regimes. After initial model development, years three and four are aimed at validation and improvement: this includes collaborative acquisition and incorporation of additional field data, structured accuracy assessment, and iterative refinement of the model to improve performance and reliability across regions where training data may be sparse or conditions differ strongly from the western U.S. environments where LandCart was originally proven.
The final stage of the effort is delivery and reporting to ensure technology transfer, meaning the resulting model and its outputs are expected to be packaged in a way that ERDC and partners can use, reproduce, and maintain. This implies deliverables beyond just scientific findings, such as documented workflows, model products, and clearly communicated performance metrics and limitations. Overall, ERDC is seeking a partner that can combine rangeland ecology knowledge, field-data quality assessment skills, remote sensing familiarity, and predictive modeling experience to move from a data feasibility question to a validated global product that can support decision-making at scale.
Administratively, this is a discretionary funding opportunity using a cooperative agreement, indicating substantial involvement and collaboration with the agency rather than a hands-off grant. The opportunity is listed under Funding Opportunity Number W81EWF 23 SOI 0014 and CFDA 12.630, with ERDC as the issuing agency. The posting indicates one expected award with an award ceiling of $60,000, and the original closing date was July 26, 2023 (with a creation date of May 24, 2023). Eligibility is listed broadly as "Others" with clarification referenced in the opportunities additional eligibility information, suggesting nontraditional or varied institutional applicants may be considered depending on the full eligibility language in the official notice.Apply for W81EWF 23 SOI 0014
- The Department of Defense, Engineer Research and Development Center in the science and technology and other research and development sector is offering a public funding opportunity titled "LandCart Expansion for Global Range Attributes" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.630.
- This funding opportunity was created on May 24, 2023.
- Applicants must submit their applications by Jul 26, 2023. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $60,000.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
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Frequently Asked Questions (FAQs)
What agency is offering this funding opportunity?
The opportunity is offered by the Department of Defense through the U.S. Army Engineer Research and Development Center (ERDC).
What type of funding instrument is being used?
This is a cooperative agreement, which typically means substantial involvement and collaboration with ERDC during the period of performance (as opposed to a more hands-off grant).
What is the main purpose of the opportunity?
The purpose is to expand an existing rangeland prediction tool called LandCart from the western United States to rangelands worldwide by creating a comparable global-scale predictive algorithm.
What is LandCart?
LandCart is described as a highly functional and highly accurate rangeland attribute model that was built using a large, standardized field dataset from the western United States.
Why is ERDC trying to scale LandCart globally?
ERDC cites that rangelands cover roughly half of Earths land surface and are important for ecosystem health, sustainable grazing, and land management. While satellite imagery has been used for broad measures like biomass and vegetation cover, globally consistent predictions of rangeland vegetation properties remain relatively scarce. This effort aims to help fill that gap.
What is the core technical idea behind the proposed work?
The core idea is to combine field observations with remote sensing so managers and researchers can map and monitor key rangeland conditions over very large areas without relying on constant, labor-intensive ground sampling.
What rangeland attributes are most emphasized for the global model?
The most emphasized target attributes are vegetation cover and vegetation height. The broader context also references related properties such as biomass.
What is the main challenge in building a global version of LandCart?
Globally, field data are generally more limited, less consistent, and unevenly distributed compared with the large, standardized dataset that supported the western U.S. LandCart model. The global effort must work within those constraints.
How is the work structured over time?
The work is described as a phased, multi-year pathway with clear technical milestones: Year 1 focuses on feasibility and data readiness; Year 2 focuses on building the global predictive model (if feasible); Years 3 and 4 focus on validation and improvement; and the final stage focuses on delivery and reporting to support technology transfer.
What is expected in Year 1?
Year 1 is a feasibility and data-readiness assessment. ERDC indicates it has assembled a global database of field-based rangeland vegetation measurements, but it is not yet clear whether the database has enough breadth, depth, consistency, and metadata quality to support a robust predictive model. Applicants are expected to review the database, assess adequacy for algorithm development, identify gaps and practical steps to address them (if possible), or conclude the data cannot support the effort in its current form.
Does ERDC already have a global field database for this effort?
Yes. ERDC states it has already assembled a global database of field-based rangeland vegetation measurements. The Year 1 task is to evaluate whether it is sufficient for model development.
What happens if the Year 1 assessment shows the data are not adequate?
Applicants are expected to identify gaps and practical steps to make the database adequate if it is not, or determine that the data cannot support the effort in its current form. The Year 1 phase is positioned as the decision point on whether a LandCart-like global model is realistically buildable with available information and what additional data or restructuring would be needed.
What is expected in Year 2?
If the Year 1 review indicates a workable foundation, Year 2 focuses on building the predictive global rangeland attribute model using the ERDC database and any other suitable supplemental data sources. The goal is to produce operational predictions for vegetation cover and vegetation height at global scale, presumably using remote sensing inputs and modeling approaches that can generalize across diverse rangeland types, climates, and management regimes.
What is expected in Years 3 and 4?
Years 3 and 4 focus on validation and improvement, including collaborative acquisition and incorporation of additional field data, structured accuracy assessment, and iterative refinement to improve performance and reliability, especially in regions with sparse training data or conditions that differ from the western U.S. environments where LandCart was originally proven.
What does "delivery and reporting" mean in this opportunity?
The final stage emphasizes technology transfer. That implies deliverables beyond scientific findings, such as packaged model outputs, documented workflows, and clearly communicated performance metrics and limitations so ERDC and partners can use, reproduce, and maintain the resulting model and its outputs.
What kind of expertise is ERDC looking for in an applicant?
ERDC is seeking a partner that can combine rangeland ecology knowledge, field-data quality assessment skills, remote sensing familiarity, and predictive modeling experience to move from a feasibility assessment to a validated global product.
How many awards does ERDC expect to make?
The posting indicates one expected award.
What is the maximum award amount?
The award ceiling is listed as $60,000.
What is the Funding Opportunity Number (FON)?
The Funding Opportunity Number is W81EWF 23 SOI 0014.
What CFDA number is associated with this opportunity?
The opportunity lists CFDA 12.630.
Who is eligible to apply?
Eligibility is listed broadly as "Others," with clarification referenced in the opportunities additional eligibility information. This suggests nontraditional or varied institutional applicants may be considered depending on the full eligibility language in the official notice.
When was the opportunity posted and when did it close?
The creation date is listed as May 24, 2023, and the original closing date is listed as July 26, 2023.
Is this opportunity focused only on the western United States?
No. The goal is to expand from the western United States (where LandCart was developed and validated using standardized field data) to rangelands worldwide and build a global-scale predictive algorithm.
What is meant by "operational predictions" at global scale?
Based on the posting, this refers to producing predictions (especially for vegetation cover and vegetation height) in a form that can be used for repeatable, large-area assessments and tracking spatial and temporal changes, rather than being limited to a one-off research demonstration.
How does the opportunity describe the role of remote sensing?
Remote sensing is positioned as a key ingredient to scale rangeland assessment over huge areas, combined with field observations to predict and monitor rangeland conditions without constant ground sampling.
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