The new study, published in the journal Environmental Management, focuses on the Conservation Reserve Program, or CRP, a federally funded initiative administered by the United States Department of Agriculture’s Farm Service Agency. Under CRP, farmers agree to adopt conservation practices on their land, such as establishing wildlife habitat, riparian buffers, or tree plantings, and in exchange receive annual payments and cost-share assistance. Contracts run 10 to 15 years, and a field can remain enrolled for up to 30 years. Across the Southeast, many producers have used the program to plant longleaf pine, supported by regionwide initiatives from USDA agencies. The problem, the researchers argue, is that enrollment is often opportunistic, driven by which landowners happen to sign up rather than by where new forest would deliver the greatest environmental benefit at the landscape scale.

Benjamin C. Townsend, Nathan P. Nibbelink, and Puneet Dwivedi set out to change that by building a spatial prioritization approach that identifies future CRP opportunities capable of improving habitat connectivity for longleaf pine-associated wildlife. Their logic is elegant: rather than asking simply where longleaf pine could grow, they asked where planting it would most reduce the cost that animals pay to move through the landscape. Connectivity, in the language of landscape ecology, describes the ease with which wildlife can travel between habitat patches. When forests are fragmented, animals must cross hostile terrain, and the harder that journey becomes, the more isolated populations grow, with consequences that include reduced species richness, abundance, and genetic diversity.

The team’s workflow began with the raw geography of American agriculture. Using the USDA National Agricultural Statistics Service’s Crop Sequence Boundaries dataset, which algorithmically delineates individual crop fields across the contiguous United States, they screened every field in the South Atlantic Gulf region for CRP eligibility. Following the rules laid out in the 2018 Farm Bill and its extensions, a field qualified if it had been planted in commodity crops for a sufficient share of the preceding years. They then filtered those eligible fields for longleaf pine suitability using two independent datasets: LANDFIRE’s Environmental Site Potential, which maps the vegetation best fitted to each pixel’s biophysical profile, and the Florida Natural Areas Inventory’s Longleaf Sustainability Analysis, a Maxent-based model built from existing longleaf occurrences and variables covering climate, fire history, soils, and vegetation type. Only fields that passed both tests survived the cut.

Suitability alone was not enough. For restoration to genuinely improve connectivity, existing longleaf pine must already be present nearby. The researchers drew a 3-kilometer buffer around each candidate field, a distance chosen because it approximates the average juvenile dispersal range of the keystone, threatened, and culturally important species that inhabit longleaf ecosystems, from the gopher tortoise to the red-cockaded woodpecker. If at least 10 percent of that buffer, roughly 280 hectares, was already longleaf pine, the field was flagged as a prime opportunity. The result was a shortlist of 3,103 agricultural fields that are CRP eligible, suitable for longleaf pine, and positioned to stitch fragmented habitat back together.

The heart of the method lies in how the team measured the benefit of each field. They constructed resistance layers, raster maps in which every pixel carries a value representing how difficult it is for wildlife to cross. Longleaf pine received the lowest resistance value of 1, other pine species a modest 5, non-pine natural vegetation such as wetlands and hardwood forests a value of 10, agriculture 100, and developed land, roads, quarries, and open water the punishing maximum of 1,000. These values, adapted from prior connectivity studies, capture the relative permeability of each land cover type rather than absolute movement costs, which remain unknown for most species. For every candidate field, the researchers then built two cost surfaces: one representing the landscape as it stands today, and one simulating the field’s conversion to longleaf pine.

To evaluate connectivity from every direction rather than between specific patches, the team employed an omnidirectional approach. Fifty nodes were placed at even spacing around the ring of each field’s 3-kilometer buffer, and least-cost paths, calculated with Dijkstra’s algorithm through the gdistance package in R, were computed for all 1,225 possible node pairs. Averaging these cost surfaces yielded a single map of mean movement cost for each pixel from any direction. The difference between the pre-CRP and post-CRP scenarios produced a measure the authors call uplift: the absolute reduction in movement cost that planting longleaf pine on that field would deliver. The higher the uplift, the greater the potential connectivity gain.

The geographic results are striking. High-uplift opportunities cluster in the Central Florida Ridges and Uplands, the Florida Panhandle, and the Carolina Sandhills, with Florida alone hosting 1,283 of the identified fields, followed by South Carolina with 808 and North Carolina with 796. At the county level, Aiken and Lexington in South Carolina and Levy, Okaloosa, and Santa Rosa in Florida each offer more than 200 opportunities. Central Florida’s Marion and Levy counties posted the highest median uplifts, at 2,598.7 and 904.6 respectively, while the Ocklawaha, Withlacoochee South, and Waccasassa watersheds emerged as connectivity hotspots. Crucially, many of these opportunities overlap the ranges of multiple focal species, including the federally threatened frosted flatwoods salamander, meaning a single well-placed field can benefit a wide suite of wildlife.

The statistical analysis revealed a counterintuitive pattern with real strategic value. In a linear regression explaining uplift, all five landscape variables proved significant, together accounting for 46 percent of the variation. Larger fields delivered more uplift, with each percentage increase in field size raising uplift by 1.4 percent. But the strongest driver was the presence of high-resistance land: every one-unit increase in the share of roads and developed areas within the buffer boosted uplift by 7 percent. Fields surrounded by clustered longleaf pine and aggregated land cover also performed better, with patch cohesion and contagion both showing positive relationships. In other words, the best places to plant are not the wildest landscapes but the fragmented ones, where new forest can punch a low-resistance corridor through an otherwise hostile matrix and link existing stands together.

The authors are careful to note the limits of their framework. Uplift represents a best-case scenario, since least-cost modeling captures only the single easiest path of movement, and the models assume a mature longleaf landscape that will take decades to materialize, a caveat that matters especially for the red-cockaded woodpecker, which requires trees at least 80 years old for its cavity nests. The approach also does not address the human dimension of enrollment, since landowners must voluntarily participate, and data limitations mean some suitable fields were inevitably missed. Still, the method’s reach extends well beyond the Southeast. The researchers point out that the same prioritization logic could be adapted to China’s Grain for Green Program or the European Union’s pledge to plant 3 billion additional trees by 2030, where choosing the right parcels of abandoned farmland could mean the difference between tree planting that helps biodiversity and planting that does not. For a foundation species clinging to fragments of its former empire, that kind of precision may be the lifeline it needs.

Subject of Research: Spatial prioritization of Conservation Reserve Program enrollment to enhance longleaf pine ecosystem habitat connectivity in the southeastern United States

Article Title: A Spatial Prioritization Approach for Identifying Future Conservation Reserve Program Opportunities to Enhance Longleaf Pine (Pinus palustris) Ecosystem Connectivity

Article References: Townsend, B. C., Nibbelink, N. P., & Dwivedi, P. (2026). A Spatial Prioritization Approach for Identifying Future Conservation Reserve Program Opportunities to Enhance Longleaf Pine (Pinus palustris) Ecosystem Connectivity. Environmental Management, 76(9), Article 303. https://doi.org/10.1007/s00267-026-02593-z

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02593-z

Keywords: longleaf pine, habitat connectivity, Conservation Reserve Program, spatial prioritization, habitat fragmentation, landscape ecology, least-cost path, foundation species, ecosystem restoration, Southeastern United States, conservation planning, resistance surfaces

The study, led by Daniel Egerson of Mississippi State University with colleagues including Shannon Westlake, Amanda Sesser, Diego Thompson, Mark McConnell, and Kristine Evans, examined stakeholder reactions to the CRP Menu Tool, a co-produced online decision-support system designed to help landowners and conservation practitioners navigate the Conservation Reserve Program. Rather than waiting years to see whether the tool would be adopted in the wild, the research team focused on something often overlooked in technology development: the earliest moments of exposure, when a user forms a first impression that may determine whether a tool is embraced or abandoned. Using a mixed-methods design, the researchers combined structured survey data with qualitative insights gathered during stakeholder workshops in which landowners and conservation professionals interacted with the tool for the very first time.

The theoretical backbone of the study is the technology acceptance model, a framework that has shaped how researchers think about technology adoption since Fred Davis formalized it in 1989. At its core, the model holds that two perceptions largely govern whether someone intends to use a new technology: perceived usefulness, the belief that the tool will actually improve the user’s performance or outcomes, and perceived ease of use, the sense that operating it will not require excessive effort. Decades of research have extended and critiqued the model, adding constructs such as social influence and subjective norms, the expectations of peers and community that can push an individual toward or away from adoption. In agricultural contexts, these social dynamics are often decisive, because farming communities tend to be tightly networked and land management traditions run deep.

What the researchers found is striking in its clarity. Perceived usefulness emerged as a key factor shaping landowners’ intention to use the CRP Menu Tool, confirming the central prediction of the acceptance framework. But the study also elevated a second driver that is easy to underestimate: social expectations. Landowners reported that what they believed others expected of them, and what they saw peers and trusted figures doing, influenced their willingness to engage with the tool. This finding aligns with a growing body of work showing that conservation behavior on private lands is not purely an economic calculation. It is embedded in social relationships, community norms, and the informal advice networks through which farming knowledge travels. A decision-support tool, in other words, does not exist in a social vacuum; its fate is partly decided by the community around it.

The qualitative side of the study added texture that surveys alone cannot capture. During the workshops, participants consistently emphasized that the tool’s greatest strength was its ability to consolidate information. Instead of hunting through scattered sources, users could find program details gathered in one place. The tool simplified comparisons among conservation practices, allowing landowners to weigh options side by side rather than parsing them one at a time from dense administrative documents. Participants also noted that the tool supported their understanding of program requirements, translating bureaucratic complexity into something more navigable. These functions address precisely the barriers that prior research has identified as dampening participation in the Conservation Reserve Program: dispersed information, complicated administrative requirements, and the sheer cognitive load of comparing practices that differ in cost, effort, and ecological payoff.

The stakes of this work are considerable. The Conservation Reserve Program is one of the largest private-lands conservation efforts in the world, delivering benefits that range from reduced soil erosion and improved water quality to critical habitat for grassland birds and pollinators, including the honey bees of the Great Plains that researchers have shown depend in part on CRP lands. Yet enrollment has fluctuated with commodity prices, rental rates, and administrative changes, and studies have documented declines in participation even when program caps rose. When land expiring from the program reverts to intensive crop production, the ecological gains can evaporate. Anything that lowers the friction of enrollment, the researchers argue, has the potential to ripple outward across millions of acres.

The study’s methodological choices deserve attention because they reflect a broader shift in how decision-support tools are built and evaluated. The CRP Menu Tool was co-produced, meaning that intended users and stakeholders were involved in shaping it rather than having it handed down as a finished product. Co-production has become a watchword in conservation informatics, with prior efforts, from coastal landscape planning tools on the Gulf Coast to agricultural conservation planning frameworks in the Midwest, demonstrating that tools designed with users tend to fit real workflows better than tools designed for them. The present study extends that logic to evaluation itself: by assessing user reactions at the earliest stage of exposure, developers can identify problems while they are still cheap to fix, rather than discovering after launch that a tool confuses or alienates the very people it was built for.

The research also speaks to a persistent gap in the decision-support literature. Reviews of agricultural decision-support systems have repeatedly found that many tools are developed, published, and then quietly ignored, a phenomenon sometimes described as the graveyard of decision support. The reasons vary: tools may be too complex, too generic, poorly matched to user needs, or simply never surfaced at the moment of decision. By grounding their evaluation in established acceptance theory and pairing quantitative measures with open-ended workshop feedback, Egerson and colleagues offer a template for diagnosing adoption risk early. Their approach treats the first encounter between user and tool as a critical diagnostic window, one that reveals both the psychological drivers of intended use and the concrete design features, such as information consolidation and side-by-side comparison, that users value most.

For the agencies and organizations that fund conservation outreach, the practical implications are direct. Investing in decision-support tools is worthwhile, but only if those tools are demonstrably useful to their audiences and introduced within the social fabric of the communities they serve. The finding that social expectations shape intention suggests that rollout strategies should leverage trusted intermediaries, such as extension agents, conservation district staff, and respected local landowners, rather than relying on standalone web traffic. It also suggests that testimonials and peer demonstration may matter as much as interface design. A tool that is technically elegant but socially invisible may never reach its audience; a slightly rougher tool championed by a trusted neighbor may spread widely.

Ultimately, the study is a reminder that conservation is as much a human decision problem as an ecological one. The science of grassland restoration, wetland hydrology, and wildlife habitat is mature; the harder question is how to move that science onto the ground, parcel by parcel, decision by decision. Digital tools like the CRP Menu Tool sit exactly at that junction, translating program complexity into actionable choices for individual landowners. By showing that perceived usefulness and social expectation jointly govern whether such tools will be used, and by demonstrating that early, user-centered evaluation can surface these dynamics before launch, the researchers have added a practical piece to the puzzle of scaling private-lands conservation. The next enrollment cycle will test whether first impressions, carefully studied and thoughtfully designed for, can grow into lasting stewardship.

Subject of Research: Stakeholder evaluation of a co-produced decision-support tool for U.S. Conservation Reserve Program participation

Article Title: Evaluating Decision-Support Tools for Conservation Program Participation: A Stakeholder Assessment of the CRP Menu Tool

Article References: Egerson, D., Westlake, S. M., Sesser, A. L., Thompson, D., McConnell, M. D., & Evans, K. O. (2026). Evaluating Decision-Support Tools for Conservation Program Participation: A Stakeholder Assessment of the CRP Menu Tool. Environmental Management, 76(10), Article 317. https://doi.org/10.1007/s00267-026-02614-x

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02614-x

Keywords: Conservation Reserve Program, decision-support tools, technology acceptance model, agricultural conservation, landowners, perceived usefulness, social norms, co-production, Environmental Management, USDA, conservation planning, stakeholder workshops