LandCart Expansion for Global Range Attributes | W81EWF 23 SOI 0014

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.

• 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).

Apply for W81EWF 23 SOI 0014

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