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Forestry professors

Official title Forestry and Conservation Science Teachers, Postsecondary (SOC 25-1043)

Also called Conservation science professor, Forestry and conservation science teacher, Forest science postsec instructor, Forestry faculty, Conservation biology professor

In brief

In May 2025, BLS counted 1,520 US forestry professors, with median pay of $101,420 a year. Of the BLS counts since 2003, the highest was 2,990, in May 2005. The earliest figure here is an estimate of 40 from 1910, not measured the same way as today's count. The last full technology era for forestry professors: LiDAR + high-resolution remote sensing, 2005 to 2018.

What the sources say

Official outlook

BLS projects employment to grow 2.8% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Forestry professors: jobs and pay since 1910 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/25-1043

Chicago

Future History. "Forestry professors: jobs and pay since 1910." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/25-1043.

BibTeX

@misc{futurehistory_25_1043, author = {{Future History}}, title = {Forestry professors: jobs and pay since 1910}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/25-1043}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
19001925195019752000now
2026
Known then as Forest Instructor / Lecturer in Forestry (practical-school era)
Known then as Professor of Forestry / Instructor in Silviculture (university expansion era)
Known then as Professor of Forestry / Wildlife Management / Range Science (land-grant era)
Known then as Professor of Forestry and Natural Resources / Conservation Biology Professor (diversification era)
Known then as Forestry and Conservation Science Teachers, Postsecondary (BLS SOC 25-1043)
Known today as Forestry and Conservation Science Teachers, Postsecondary (BLS SOC 25-1043)
Projected to still be called Forestry and Conservation Science Teachers, Postsecondary (BLS SOC 25-1043)
Data from the May 2025 survey
US Employment
2K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$101,420
Est.

≈ $98,820 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1898

Biltmore Forest School opens: first forestry school in the United States. Carl Schenck teaches using the Vanderbilt estate forests as a 100,000-acre classroom. Two years later the Yale Forest School, co-founded by Gifford Pinchot, becomes the first permanent graduate-level forestry faculty program. Both founding institutions established the model still followed today: classroom instruction is inseparable from field experience in real working forests.

Beat · 1900

Yale Forest School founded by Gifford Pinchot and Henry S. Graves with Pinchot family funding. During its first four decades the School graduated the first four chiefs of the US Forest Service, embedding the forestry professor at the center of the conservation policy establishment. Pinchot himself served on the faculty from 1903 to 1936.

Beat · 1928

The McSweeney-McNary Forest Research Act establishes the legal foundation for federal support of forestry research at USDA experiment stations, creating funded research positions that sometimes combined with university teaching appointments. This act legitimized USDA experiment stations, authorized broad-scale forest research, and provided appropriations that supported faculty at land-grant universities through cooperative agreements for decades afterward.

Beat · 1935

Society of American Foresters begins formal accreditation of forestry degree programs, establishing the first external quality standard for what forestry faculty must teach and how programs must be structured. SAF accreditation has governed curriculum requirements for forestry programs at land-grant universities continuously since this date, and the accreditation framework has shaped what skills postsecondary forestry teachers must carry.

Beat · 1970

The National Environmental Policy Act (NEPA) of 1970 and the broader environmental movement that produced it triggered a surge in forestry and natural-resources enrollment. Undergraduate enrollment in forestry programs at North American universities reached its peak in the late 1970s. Programs expanded faculty positions to meet demand, producing the largest cohort of forestry and conservation science faculty the profession had ever seen. That enrollment surge would reverse sharply in the 1980s as job market realities and shifting student preferences toward broader environmental science programs depleted the applicant pool.

Beat · 1994

Following the Pacific Northwest spotted owl controversy (1990-1993) and the Northwest Forest Plan (1994), many forestry programs accelerated the shift from timber-production curricula toward ecosystem management, conservation biology, and watershed science. Faculty who could teach silviculture in a multi-value, multi-stakeholder framework were increasingly valued over specialists in timber management alone. This curricular pivot began the long transformation of the forestry professor role toward the broader "conservation science teacher" framing reflected in the modern SOC title.

Beat · 2010

Google Earth Engine becomes available to academic researchers, beginning its transformation into the standard platform for landscape-scale remote sensing analysis in forestry and conservation science courses. The platform's combination of petabyte-scale satellite imagery archives, built-in machine learning tools, and free academic access compresses research tasks that previously required significant computing infrastructure down to a browser-based lab exercise. By 2015-2018 it is standard curriculum at most research-active programs.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace forestry professors? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+2.8%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 25-1043 (Forestry and conservation science teachers, postsecondary): 1.8 thousand jobs in 2025, projected 1.9 thousand in 2035, a change of +0.1 thousand (+2.8%). Matrix employment counts all jobs in the occupation, including self-employed workers. The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
42.4%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 25-1043.00 Forestry and Conservation Science Teachers, Postsecondary. Shown: human-rated β = 0.424, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.217 and γ (all LLM-assisted) 0.63; GPT-4-rated β 0.478. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for forestry professors

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereGrade and provide feedback on student coursework (written field reports, remote sensing lab assignments, silvicultural prescription exercises, and conservation plan drafts) using AI-assisted grading tools (Gradescope for answer grouping on written field-data analysis and lab reports, Turnitin for academic integrity review) while applying expert ecological judgment to assess whether a student's interpretation of stand conditions, habitat mapping output, or species distribution model is scientifically valid, not just correctly formatted.

Grade and provide feedback on student coursework (written field reports, remote sensing lab assignments, silvicultural prescription exercises, and conservation plan drafts) using AI-assisted grading tools (Gradescope for answer grouping on written field-data analysis and lab reports, Turnitin for academic integrity review) while applying expert ecological judgment to assess whether a student's interpretation of stand conditions, habitat mapping output, or species distribution model is scientifically valid, not just correctly formatted.[13],[1]

Tools picking this up
Where your edge is

Deploy Gradescope for all forestry lab reports and written assignments: AI-assisted answer grouping clusters similar responses (same flawed interpretation of a forest cover change map, same incorrect prescription for a shelterwood cut) so you apply a rubric once per reasoning pattern rather than once per submission. Documented 30–50% grading-time savings apply directly to a large-enrollment forest ecology section. Reserve manual evaluation for the ecologically substantive judgment: did the student correctly interpret the silvicultural implications of the stand data, or just describe the numbers? Did the species distribution model output make ecological sense given the habitat covariates selected? That distinction requires your domain expertise, and it is the evaluation that builds genuine ecological reasoning in your students.

AI is sitting alongside you hereKeep current with rapid advances in AI-augmented forestry and conservation science (monitoring new releases of AI wildfire detection systems [Pano AI deployments, new USFS-adopted detection tools], geospatial AI tools [Google Earth Engine updates, new satellite constellations with ML products], AI species identification advances [SpeciesNet model updates, BirdNET language expansions], and newly published literature in forest ecology, silviculture, and conservation biology) using AI research synthesis tools (Elicit, NotebookLM) to monitor the frontier efficiently across multiple subfields.

Keep current with rapid advances in AI-augmented forestry and conservation science (monitoring new releases of AI wildfire detection systems [Pano AI deployments, new USFS-adopted detection tools], geospatial AI tools [Google Earth Engine updates, new satellite constellations with ML products], AI species identification advances [SpeciesNet model updates, BirdNET language expansions], and newly published literature in forest ecology, silviculture, and conservation biology) using AI research synthesis tools (Elicit, NotebookLM) to monitor the frontier efficiently across multiple subfields.[10],[14]

Where your edge is

Use Elicit to set up standing literature monitoring queries across Forest Ecology and Management, Ecological Applications, and Nature Sustainability: the field is advancing faster than any individual can track by reading journal tables of contents, and Elicit's semantic search across 138M+ papers reduces a month of new publications to a synthesized landscape in minutes. Use NotebookLM to synthesize a semester's worth of new AI-in-conservation-science developments before each course revision cycle. The expert judgment is deciding which new AI tools represent genuine capability advances worth incorporating into research workflows or course curricula versus incremental benchmarking papers that do not change practice: a discrimination that requires your domain expertise.

AI is sitting alongside you hereConduct and publish original forestry and conservation science research (designing field experiments, running remote sensing analyses, analyzing forest inventory and wildlife survey datasets, and submitting manuscripts to peer-reviewed journals [Forest Ecology and Management, Ecological Applications, Landscape Ecology, Conservation Biology]) using AI tools (Elicit for literature synthesis, Google Earth Engine for satellite data analysis, ChatGPT Edu for manuscript draft scaffolding) to accelerate support tasks while maintaining that experimental design, ecological interpretation, and intellectual contribution are the human scholarly act, and complying with USDA AFRI and NSF DEB AI disclosure requirements.

Conduct and publish original forestry and conservation science research (designing field experiments, running remote sensing analyses, analyzing forest inventory and wildlife survey datasets, and submitting manuscripts to peer-reviewed journals [Forest Ecology and Management, Ecological Applications, Landscape Ecology, Conservation Biology]) using AI tools (Elicit for literature synthesis, Google Earth Engine for satellite data analysis, ChatGPT Edu for manuscript draft scaffolding) to accelerate support tasks while maintaining that experimental design, ecological interpretation, and intellectual contribution are the human scholarly act, and complying with USDA AFRI and NSF DEB AI disclosure requirements.[14],[8]

Where your edge is

Use Elicit to run standing literature queries across Forest Ecology and Management, Ecological Applications, and Landscape Ecology: semantic search across 138M+ papers synthesizes a landscape of related work in minutes vs. hours of Web of Science review, and up to 80% time reduction on literature synthesis is documented. Use Google Earth Engine scripting to build your forest-change or habitat-analysis pipelines at landscape scale with petabytes of Landsat, Sentinel, and MODIS imagery available free for research. Use ChatGPT Edu to draft the methods-description and discussion-framing sections of manuscripts, then revise with ecological precision. However, be explicit about AI disclosure: NSF DEB requires it, and USDA AFRI is implementing parallel policies. The ecological reasoning, experimental design, field data collection, and scientific interpretation that journal reviewers scrutinize are your expert contribution that AI cannot provide.

Where forestry professors go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Education Administrators, Postsecondary

Forestry and conservation science faculty who have led curriculum redesign efforts, chaired SAF accreditation visits, managed field station or forest reserve operations, or served on institutional AI governance task forces are strong candidates for department chair, director of a natural resources school, or associate dean of science roles. SAF-accredited forestry programs are under significant curricular pressure in 2025–2026 to integrate geospatial AI, camera-trap AI workflows, and AI-assisted forest management tools into degree programs that were designed around manual field methods, while also responding to changing employer expectations from USFS, TNC, and private timber companies. Administrators with deep forestry credibility and governance experience who understand both the scientific substance and the pedagogical implications of AI integration are disproportionately valuable navigating these transitions.

What you'd add
  • · Higher education budget management: faculty line planning, field station and forest reserve equipment capital requests, indirect-cost negotiation on USDA NIFA and NSF grants
  • · SAF accreditation processes: Society of American Foresters accreditation self-study coordination, curriculum standard compliance, accreditation site visit leadership
  • · Faculty performance review: promotion and tenure facilitation in field-intensive programs, hiring committee leadership for balance between silvicultural, ecological, and computational faculty expertise
  • · Industry and agency partnership development: USFS Research Station and USDA NRCS cooperative agreement structures; state DNR and land trust advisory board membership; private timber company curriculum advisory relationships
  • · Institutional AI governance for field sciences: developing department policy on AI use in field coursework and thesis research; faculty AI tool procurement and training design; vendor evaluation for geospatial AI platforms
What it takesSome new skills to pick up
Tools of the era

How has the work of forestry professors changed since 1898?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Field instruments + hand-drawn maps (Biltmore stick, compass, chain)

    The Biltmore Forest School curriculum was built around instruments that had barely changed since the 18th century: the Biltmore stick (a calibrated rod for estimating diameter and volume of standing trees), the surveyor's compass, Gunter's chain for distance measurement, and field notebooks. Schenck taught tree mensuration by having students measure actual trees in actual stands. Maps were drawn by hand. The forest instructor's primary tool was the forest itself: the lecture hall was the hillside, and the exam was a timber cruise. No equivalent professional tool distinguished the postsecondary forestry teacher from the field forester; the roles were essentially the same person at different career stages.

  • Aerial photography + dendrometry (early photo interpretation, Abney level, increment borer)

    The Second World War accelerated the use of aerial photography for landscape mapping, and forestry faculties integrated photo interpretation into curricula through the 1940s and 1950s. By the mid-1950s, timber cruising courses at land-grant universities routinely combined field measurement with aerial photo estimation of canopy cover and stand structure. The increment borer, used to extract a core from a living tree and count growth rings, became a standard teaching instrument for forest dynamics and dendrochronology. These tools changed how forestry was taught: the professor now needed to be competent in both field mensuration and remote-sensing interpretation.

  • Landsat + statistical computing (early satellite remote sensing, USFS Forest Inventory Analysis)

    NASA launched the Earth Resources Technology Satellite (later renamed Landsat 1) in July 1972, opening the era of multispectral satellite observation of forests. By the late 1970s, forestry research faculties at Oregon State, University of Washington, and the University of Michigan were teaching basic image classification and integrating USFS Forest Inventory and Analysis (FIA) plot data into curricula. Mainframe and minicomputer statistical analysis (SAS, early FORTRAN for biometrics) became expected competencies for research-active forestry faculty. The role split more sharply into a teaching track and a research track: the teaching-focused professor still led field courses, but the research-active faculty member now needed data processing skills not required of the Biltmore-era instructor.

  • GIS desktop software (ArcView, ArcGIS) + laptop-based field data collection

    Esri's ArcView GIS (1992) brought geographic information systems from mainframe research environments into undergraduate forestry classrooms. By the mid-1990s, SAF-accredited programs at Virginia Tech, University of Maine, University of Idaho, and Oregon State included ArcView lab sections in required coursework. GPS receivers became affordable and portable enough for field courses by the late 1990s. The forestry faculty member now needed GIS competency that had not existed in the curriculum a decade earlier. Programs that were slow to add GIS instruction began losing enrollment to geography and environmental science departments that had adopted it faster.

    Effect on the work

    GIS integration accelerated the shift away from purely timber-focused curricula toward spatial natural-resource management. It helped revive enrollment interest at programs that modernized; those that did not modernize continued their enrollment slide through the 1990s.

  • LiDAR + high-resolution remote sensing (airborne LiDAR for canopy structure, UAV field surveys)

    Airborne LiDAR (Light Detection and Ranging) became a commercially available research tool for forestry in the early 2000s and was increasingly standard in research-active programs by 2005-2010. LiDAR could measure canopy height, vertical structure, and individual tree crowns from aircraft at a resolution and accuracy impossible with satellite imagery or traditional mensuration. Faculty at Oregon State, University of Washington, and University of Idaho integrated LiDAR into graduate courses in forest biometrics and ecosystem science. Consumer-grade UAVs (drones) arrived in the 2013-2015 window and further changed field data collection instruction: students could fly a survey plot, process a point cloud, and extract tree heights in a single lab session.

  • Cloud geospatial AI + biodiversity AI (Google Earth Engine, SpeciesNet, MegaDetector, BirdNET)

    Google Earth Engine, available free to researchers since 2010 but adopted at scale by forestry programs from roughly 2016-2018, consolidated decades of satellite imagery (Landsat, Sentinel, MODIS) into a cloud platform with built-in machine learning classifiers. A faculty member teaching landscape-scale deforestation dynamics could run a supervised Random Forest land-cover classification over the entire Pacific Northwest in a two-hour lab session using petabytes of data that would have required a supercomputer a decade earlier. SpeciesNet (Google, 2026) and MegaDetector (Microsoft AI for Earth) compressed the camera-trap backlog that had been a multi-year bottleneck in wildlife ecology research. BirdNET (Cornell Lab of Ornithology) automated passive acoustic monitoring for bird species diversity. Together these tools freed faculty research time from data processing toward ecological interpretation, experimental design, and grant strategy, the components of the job that AI cannot yet substitute.

    Effect on the work

    AI tools in the 2018-2026 window are estimated to compress several hours per week of faculty preparation and data-processing tasks at research-active programs. The Federal Reserve (2026) placed Life, Physical, and Social Science Teachers in the above-average AI-exposure band for postsecondary education. However, field safety supervision, doctoral mentorship, and landscape-scale ecological interpretation remained strongly defended tasks.

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Forestry and Conservation Science Teachers, Postsecondary employment and pay by year

On record since1898
Latest US employment1,520 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$101,420 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+2.8% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
191040n/aEst., Appalachian History Researched contextReport
1930200$2,800Est., Society of American Foresters (via… (employment) Researched contextEst., AAUP (pay) Researched contextReport
1975900$17,500Est., Journal of Forestry (employment) Researched contextEst., AAUP (pay) Researched contextReport
1995650n/aEst., Bosque (Scielo Chile) Researched contextReport
2000n/a$58,110BLS OEWS 2000 Official statisticReport
20032,660$64,500BLS OEWS May 2003 Official statisticReport
20042,970$65,530BLS OEWS May 2004 Official statisticReport
20052,990$64,870BLS OEWS May 2005 Official statisticReport
20062,630$64,430BLS OEWS May 2006 Official statisticReport
20072,640$63,790BLS OEWS May 2007 Official statisticReport
20082,450$62,140BLS OEWS May 2008 Official statisticReport
20092,380$66,930BLS OEWS May 2009 Official statisticReport
20102,410$78,290BLS OEWS May 2010 Official statisticReport
20112,520$80,330BLS OEWS May 2011 Official statisticReport
20122,490$81,930BLS OEWS May 2012 Official statisticReport
20132,140$81,630BLS OEWS May 2013 Official statisticReport
20141,850$84,090BLS OEWS May 2014 Official statisticReport
20151,660$88,270BLS OEWS May 2015 Official statisticReport
20161,750$85,880BLS OEWS May 2016 Official statisticReport
20172,000$87,420BLS OEWS May 2017 Official statisticReport
20182,070$86,900BLS OEWS May 2018 Official statisticReport
20191,770$85,450BLS OEWS May 2019 Official statisticReport
20201,460$87,400BLS OEWS May 2020 Official statisticReport
20211,180$82,330BLS OEWS May 2021 Official statisticReport
20221,270$96,500BLS OEWS May 2022 Official statisticReport
20231,260$101,650BLS OEWS May 2023 Official statisticReport
20241,310$100,830BLS OEWS May 2024 Official statisticReport
20251,520$101,420BLS OEWS May 2025 Official statisticReport
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