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Agriculture professors

Official title Agricultural Sciences Teachers, Postsecondary (SOC 25-1041)

Also called Agricultural sciences professor, Plant science professor, Agronomy professor, Crop science professor, Soil science professor

In brief

In May 2025, BLS counted 8,920 US agriculture professors, with median pay of $98,700 a year. Of the BLS counts since 2003, the highest was 11,460, in May 2005. The earliest figure here is an estimate of 250 from 1870, not measured the same way as today's count. The last full technology era for agriculture professors: Precision agriculture platforms and remote sensing, 2005 to 2020.

What the sources say

Official outlook

BLS projects employment to grow 2.9% 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). Agriculture professors: jobs and pay since 1870 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/25-1041

Chicago

Future History. "Agriculture professors: jobs and pay since 1870." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/25-1041.

BibTeX

@misc{futurehistory_25_1041, author = {{Future History}}, title = {Agriculture professors: jobs and pay since 1870}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/25-1041}}, 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
187519001925195019752000now
2026
Known then as Professor of Agriculture / Teacher of Agricultural and Mechanical Arts (land-grant founding era)
Known then as Agricultural Experiment Station Scientist-Teacher (Hatch Act era)
Known then as Professor of Agricultural Sciences / College of Agriculture Faculty (extension era)
Known then as Agricultural Sciences Professor / Land-Grant Faculty (GI Bill expansion era)
Known then as Agricultural Sciences Professor (post-farm-crisis consolidation era)
Known then as Agricultural Sciences Teachers, Postsecondary (BLS SOC 25-1041)
Known today as Agricultural Sciences Teachers, Postsecondary (BLS SOC 25-1041)
Projected to still be called Agricultural Sciences Teachers, Postsecondary (BLS SOC 25-1041)
Data from the May 2025 survey
US Employment
9K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$98,700
Est.

≈ $96,170 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1862

President Lincoln signs the Morrill Land-Grant Act on July 2, 1862, allocating 30,000 acres of federal land per congressional representative to fund colleges focused on agriculture and the mechanical arts. Kansas State University opens September 2, 1863, as the first institution under the Act. The role of the agricultural sciences professor is thus a direct creation of federal legislation, making it one of the few occupations in US history that was essentially mandated into existence by Congress.

Beat · 1887

The Hatch Act of 1887, introduced by Rep. William Hatch of Missouri and inspired by a proposal from Iowa State professor Seaman Knapp, provides each land-grant college $15,000 annually for an agricultural experiment station. This federal funding transforms the role from teaching-only to a research-teaching hybrid and creates the first systematic funding for faculty field research in the US. The experiment station model is widely credited with professionalizing agricultural science as an academic discipline.

Beat · 1890

The Second Morrill Act of 1890 requires states to either integrate their land-grant colleges or establish separate institutions for Black students, resulting in the founding of 19 Historically Black Colleges and Universities (1890 institutions) including Tuskegee, Prairie View A&M, and Alcorn State. These institutions create a parallel lineage of agricultural sciences teaching, with distinct emphases on community food systems, smallholder farming, and agribusiness education for Black students and communities. The 1890 institutions remain active land-grant institutions today, with USDA providing specific funding support.

Beat · 1944

The Servicemen's Readjustment Act of 1944 (GI Bill) sends nearly 6 million veterans to college between 1944 and 1949, increasing national postsecondary enrollment by roughly 70% over pre-war levels. Land-grant agricultural colleges absorb a significant share of this surge: many returning veterans had rural backgrounds and chose agricultural or mechanical programs. The GI Bill forces rapid faculty expansion across all agricultural science disciplines, hiring many faculty who had been agricultural researchers during the war, and physically expanding campus facilities to accommodate the enrollment surge.

Beat · 1972

AAUP data documents the beginning of an eight-year period (1972-73 to 1980-81) in which average faculty salary increases lag behind inflation, meaning real wages for agricultural sciences faculty fall for nearly a decade. This era coincides with the early stages of the "farm crisis" that would peak in the mid-1980s, and with a broader squeeze on public university budgets during the stagflation years. Agricultural sciences faculty, concentrated at public land-grant universities more dependent on state appropriations than private endowments, are particularly exposed to this real-wage erosion.

Beat · 1989

A longitudinal survey of tenure-track faculty at 52 major US land-grant universities, conducted in 1989 and repeated in 1995, 2005, and 2015, documents the structural pressures reshaping agricultural sciences faculty work over a quarter century: research time falls from approximately 58% of work hours (1989) to 43% (2015) as administrative and grant management duties expand; female representation rises from 8% to 27%; peer-reviewed article output increases 25% while the competitive-grant funding burden grows sharply. The survey reveals a "rich get richer" pattern: top-20 ranked land-grant universities see 148% funding increases from 2005 to 2015, while non-top-20 institutions see only 33% increases.

Beat · 2025

USDA NIFA's Artificial Intelligence program within the Agriculture and Food Research Initiative (AFRI) actively funds research and applications of AI across agricultural systems, including machine learning, remote sensing, satellite imagery, drones, and precision technologies. The program signals federal recognition that AI tools in agriculture require faculty and researchers who can bridge automated agronomic recommendations with local soil, weather, and cropping-system knowledge -- sustaining demand for agricultural sciences faculty at land-grant institutions.

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 agriculture 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
+2.9%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 25-1041 (Agricultural sciences teachers, postsecondary): 10.9 thousand jobs in 2025, projected 11.2 thousand in 2035, a change of +0.3 thousand (+2.9%). 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)
58.9%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 25-1041.00 Agricultural Sciences Teachers, Postsecondary. Shown: human-rated β = 0.589, 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) 0.444 and γ (all LLM-assisted) 0.733; GPT-4-rated β 0.5. 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 agriculture professors

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

AI is sitting alongside you hereEvaluate and grade student laboratory reports, field data analyses, and written assignments: using Gradescope AI-assisted answer grouping for structured problem sets and short-answer items in soil science, crop physiology, and pest management, then applying expert agronomic judgment to assess whether students' field-data interpretations are agronomically sound and locally contextualized in ways automated scoring cannot evaluate.

Evaluate and grade student laboratory reports, field data analyses, and written assignments: using Gradescope AI-assisted answer grouping for structured problem sets and short-answer items in soil science, crop physiology, and pest management, then applying expert agronomic judgment to assess whether students' field-data interpretations are agronomically sound and locally contextualized in ways automated scoring cannot evaluate.[10],[1]

Tools picking this up
Where your edge is

Deploy Gradescope for all agricultural sciences assessments with deterministic answers: nutrient calculation problems, pesticide label math, soil texture classification from particle-size data, yield-loss estimation. Reserve expert effort for the qualitatively hard judgment: is this student's interpretation of their yield-monitor data agronomically coherent given the soil variability map? Is the proposed fertility recommendation consistent with the soil test results and crop removal rates? Use Turnitin AI detection as a flag for deeper scrutiny, not as standalone evidence, and supplement with oral defenses of field data interpretations for high-stakes assignments.

AI is sitting alongside you herePrepare course materials (syllabi, lecture slides, problem sets, and field lab protocols) using ChatGPT Edu to generate first-draft lecture content on crop rotation principles, soil fertility management, or integrated pest management, then editing with domain expertise to ensure agronomic accuracy, local relevance, and alignment with current USDA and state extension recommendations.

Prepare course materials (syllabi, lecture slides, problem sets, and field lab protocols) using ChatGPT Edu to generate first-draft lecture content on crop rotation principles, soil fertility management, or integrated pest management, then editing with domain expertise to ensure agronomic accuracy, local relevance, and alignment with current USDA and state extension recommendations.[1],[11]

Where your edge is

Use ChatGPT Edu to generate a first-draft 40-slide lecture on cover crop management systems or a problem set on soil nutrient balance calculations: it will produce reasonable structure and standard agronomic content. Then invest expert effort in verifying local relevance (does this apply to the corn-soybean systems your students will work in, or to wheat systems in a different region?), checking currency against the latest USDA or state extension bulletins, and adding field-specific nuance from your own research. AI accelerates the scaffolding phase; agronomic accuracy and regional calibration require the expert.

AI is sitting alongside you hereStay current with developments in agricultural sciences by synthesizing research literature, attending professional conferences (ASA-CSSA-SSSA Annual Meeting, Agronomy Society), and engaging with extension colleagues: using Elicit, Consensus, and Scite to reduce literature synthesis time and identify emerging research trends in precision agriculture, crop genomics, and soil health that should be incorporated into curricula.

Stay current with developments in agricultural sciences by synthesizing research literature, attending professional conferences (ASA-CSSA-SSSA Annual Meeting, Agronomy Society), and engaging with extension colleagues: using Elicit, Consensus, and Scite to reduce literature synthesis time and identify emerging research trends in precision agriculture, crop genomics, and soil health that should be incorporated into curricula.[12],[13]

Where your edge is

Use Elicit to run structured literature searches ("what is the current evidence on cover crop effects on soil nitrate leaching in the Midwest corn belt, 2020–2025?") that surface relevant papers in minutes rather than hours; use Consensus to identify where there is strong scientific agreement versus active debate on topics like soil carbon trading or nitrogen use efficiency benchmarks; use Scite to identify which papers supporting a claim have been contradicted by subsequent research. Invest the recovered time in conference networking and extension engagement: the relationship capital with practitioners and county agents is the context that makes literature synthesis agronomically meaningful.

Where agriculture professors go next

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

What each move takes

Agricultural sciences faculty (particularly those with farm backgrounds, land ownership, or strong connections to the production agriculture community) increasingly move into AgTech management, precision agriculture consulting, and farm management advisory roles as AI transforms the production agriculture sector. This is not traditional "go back to farming" but rather leveraging academic expertise in AI-augmented agronomy for high-value management roles: managing large-scale data-driven farming operations for institutional landowners (REITs, university endowment farms), leading precision agriculture adoption programs for large commodity producers, or directing agribusiness AI strategy at crop input companies. The CRI delta is positive and larger than usual because production-scale agricultural management with AI tools is growing rapidly in compensation and scope, and faculty who bring both the academic agronomic depth and the AI platform literacy are disproportionately competitive for these roles.

What you'd add
  • · Enterprise precision agriculture platform management: integrating Climate FieldView, John Deere Operations Center, and CropX across a multi-farm operation
  • · Farm financial management and cash-flow analysis for large-scale commercial production operations
  • · Agricultural lease negotiation, cash rent benchmarking, and farmland valuation for institutional landowner advisory roles
  • · USDA Farm Service Agency program administration: commodity programs, conservation compliance (HEL/WOD), and crop insurance basics
  • · AgTech vendor evaluation: assessing AI precision-ag tool ROI for commercial-scale operations and building internal adoption roadmaps
What it takesSome new skills to pick up
Tools of the era

How has the work of agriculture professors changed since 1862?

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

  • Field demonstration and chalk lecture (founding land-grant era)

    The earliest agricultural sciences professors at land-grant colleges taught with no specialized equipment beyond a blackboard, field plots, and whatever livestock or crop specimens the college maintained. Teaching was demonstration-based: showing students how to apply lime to acid soil, how to select healthy seed corn, how to drain a wet field. Research was rudimentary: professors observed crop performance in small plots and published descriptive bulletins. The dominant technology of instruction was the professor's own embodied knowledge, transferred in person. The absence of laboratory instrumentation meant that most early agricultural science faculty were generalists who covered the entire domain from animal husbandry to soil fertility within a single course sequence.

  • Agricultural experiment station laboratory (Hatch Act era)

    The Hatch Act of 1887 provided each land-grant college $15,000 annually to establish a permanent agricultural experiment station. For faculty, this was the first time research infrastructure was guaranteed: soil chemistry labs, plot replications, seed testing facilities, and statistical record-keeping. Teaching and research became formally intertwined. Faculty who had previously relied on observation and demonstration now conducted controlled experiments with measurable outcomes, published in new journals (Journal of the American Society of Agronomy, founded 1907), and presented results at national meetings. The experiment station era professionalized the role: faculty were now scientists who taught, not teachers who dabbled in science.

    Effect on the work

    The experiment station infrastructure roughly doubled the productive research output of each agricultural sciences faculty member compared to the pre-Hatch era, and created the template of the "faculty scientist" that governs the role today.

  • Cooperative extension publications and farm demonstration (Smith-Lever Act era)

    The Smith-Lever Act of 1914 codified the third leg of the land-grant faculty mission: extension. Faculty now generated printed bulletins, hosted farm demonstrations, and collaborated with county extension agents to translate experiment station findings to farming communities. The mimeograph and later the photocopier allowed faculty to distribute research findings at scale for the first time. The extension mission also shaped teaching: agricultural sciences faculty were expected to connect classroom content to current problems facing real farmers in their state, a contextual grounding that distinguished them from pure-science faculty. Seaman Knapp, credited as the founder of the cooperative extension concept, had pioneered the farm-demonstration teaching model that the Smith-Lever Act nationalized.

  • Statistical computing and Green Revolution genetics (IBM mainframe era)

    The 1960s introduced statistical computing to agricultural research, allowing faculty to run analysis of variance on multi-year, multi-location field trials without months of hand calculation. IBM mainframes installed at land-grant computing centers in the mid-1960s transformed the scale of field-trial analysis faculty could perform. Simultaneously, the Green Revolution -- the development of high-yielding cereal varieties through systematic plant breeding -- gave agricultural sciences faculty access to powerful new methodological tools: hybridization, genetic line development, and controlled environmental testing. Faculty who mastered statistical computing and genetics-informed plant breeding protocols produced far more publishable research per year than their predecessors. The land-grant system became a global model, with US faculty collaborating with CGIAR international agricultural research centers established in the 1970s.

    Effect on the work

    Statistical computing roughly tripled the scale of field-trial analysis agricultural scientists could conduct per season. The Green Revolution era produced the publication productivity norms that shaped land-grant faculty tenure expectations for the next decades.

  • Personal computer and statistical software (SAS, SPSS, spreadsheet era)

    The PC era democratized statistical analysis: SAS (developed at NC State for agricultural research) and SPSS moved from mainframe-only to desktop access in the mid-1980s, giving every agricultural sciences faculty member on-demand access to mixed-model analysis, regression, and survival analysis without batch computing queues. Word processing replaced typewritten manuscripts, email replaced postal correspondence with extension agents and collaborators, and the early web gave faculty access to USDA NASS crop statistics and NOAA climate data without library requests. The PC era also introduced the first generation of precision agriculture tools: yield monitors on combines (early 1990s), GPS-guided soil sampling, and GIS mapping of field variability, which became both research tools and course content for faculty teaching crop management.

  • Precision agriculture platforms and remote sensing (GIS, yield mapping, drone imagery)

    The 2000s and 2010s brought commercial precision agriculture platforms into both the research lab and the classroom. Climate FieldView (launched 2012 as the Climate Corporation, acquired by Bayer 2018) aggregated yield-monitor data, soil sampling grids, and satellite imagery at the field level, giving faculty a commercial-scale data platform to analyze in agronomy courses. Drone-based remote sensing allowed affordable high-resolution field imagery for disease scouting and biomass estimation, replacing expensive manned aircraft surveys. Faculty who integrated these platforms into course sequences gave students exposure to tools they would use immediately in agronomist careers. The era also saw the first wave of machine learning applied to agricultural research: neural networks for plant disease identification, random forests for yield prediction from climate and soil variables, and sensor-fusion approaches to livestock health monitoring.

  • AI-native precision agriculture and research tools (CropX, Julius AI, Elicit, LLM research assistants)

    The 2020s brought an AI-native toolstack to agricultural sciences teaching and research. AI-driven soil sensing platforms such as CropX provide real-time moisture, temperature, and nutrient dashboards for irrigation and fertility management courses. Research tools such as Julius AI and Elicit allow faculty to analyze field-trial datasets and synthesize literature with natural-language queries, compressing hours of statistical scripting or literature searching into minutes. USDA NIFA's AI in Agriculture program within AFRI is funding research and applications of machine learning, remote sensing, satellite imagery, drones, and precision technologies across agricultural systems. Faculty who can critically evaluate AI-generated agronomic prescriptions in local soil and weather context are increasingly valuable to land-grant institutions navigating this transition.

    Effect on the work

    Early evidence from land-grant faculty surveys suggests AI research tools are cutting literature synthesis time and routine data analysis by comparable amounts, shifting faculty effort toward higher-order tasks: experimental design, field interpretation, and mentoring.

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Agricultural Sciences Teachers, Postsecondary employment and pay by year

On record since1862
Latest US employment8,920 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$98,700 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+2.9% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 30 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1870250n/aEst., National Center for Education Statistics Researched contextReport
19001,500n/aEst., Wikipedia / NCES Researched contextReport
19203,500n/aEst., USDA NIFA / NCES Researched contextReport
19507,000n/aEst., National WWII Museum / NCES Researched contextReport
197011,000n/aEst., Wikipedia / NCES Researched contextReport
1972n/a$13,200Est., American Historical Association / AAUP Researched contextReport
19909,500$42,000Est., PMC (employment) Researched contextEst., AHA Perspectives on History (pay) Researched contextReport
200311,260$65,470BLS OEWS May 2003 Official statisticReport
200410,230$65,190BLS OEWS May 2004 Official statisticReport
200511,460$71,330BLS OEWS May 2005 Official statisticReport
200610,120$75,140BLS OEWS May 2006 Official statisticReport
200710,700$78,460BLS OEWS May 2007 Official statisticReport
200810,000$74,390BLS OEWS May 2008 Official statisticReport
200910,230$77,210BLS OEWS May 2009 Official statisticReport
201010,600$78,370BLS OEWS May 2010 Official statisticReport
201110,170$79,900BLS OEWS May 2011 Official statisticReport
201210,500$80,490BLS OEWS May 2012 Official statisticReport
201310,120$83,060BLS OEWS May 2013 Official statisticReport
20149,890$86,260BLS OEWS May 2014 Official statisticReport
20159,680$90,780BLS OEWS May 2015 Official statisticReport
201610,340$91,580BLS OEWS May 2016 Official statisticReport
201710,800$86,140BLS OEWS May 2017 Official statisticReport
201810,810$84,640BLS OEWS May 2018 Official statisticReport
20199,470$83,260BLS OEWS May 2019 Official statisticReport
20208,520$90,340BLS OEWS May 2020 Official statisticReport
20218,570$95,910BLS OEWS May 2021 Official statisticReport
20228,240$85,860BLS OEWS May 2022 Official statisticReport
20237,550$85,260BLS OEWS May 2023 Official statisticReport
20248,700$86,350BLS OEWS May 2024 Official statisticReport
20258,920$98,700BLS OEWS May 2025 Official statisticReport
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