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Key statistics

The identity block — every fact is canon, none is generated.

4 of 8

Track

Executive

Family rungs priced

E1 · E2 · E3 · E4 · E5

19-0000

industrial_energy_operations

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Top executive setting global agronomic strategy, land-use policy, and crop science direction across all business units and regions. At the Senior Vice President (E4) level, the role owns multiple divisions, enterprise strategy and external stakeholders, and drives cross-division / enterprise. Leads division leaders.

What this level does

Oversees large portfolios spanning multiple strategic areas (agronomic R&D, commercial agronomy services, sustainability/stewardship, precision-ag platform) with high autonomy and corporate impact
Participates in enterprise strategic development, integrating agronomic science strategy with overall corporate direction and capital allocation
Engages the board, CEO and industry leaders regularly on agronomic-portfolio bets, sustainability commitments and market-facing science positions
Drives cross-business agronomic strategy — aligning breeding, precision-ag, and grower-advisory arms behind a common yield-and-sustainability agenda
Accountable for the corporate reputation and long-term value created by the company's agronomic capabilities across all markets served

Knowledge applied: Participates in enterprise strategic development, integrating agronomic science strategy into overall corporate direction and capital allocation across multiple strategic areas.
Complexity & problem solving: Solves corporate-impact problems spanning R&D, commercial agronomy, sustainability and precision-ag platform with high autonomy and broad organizational reach.
Collaboration & interaction: Engages board, CEO and industry leaders regularly; drives cross-business/functional agronomic strategy.
Typical experience: 20+ years of extensive corporate strategy and executive leadership experience.

Skills at this level

Strategic planning: — Sets agronomic strategy and trial/investment priorities across regions and product lines, aligning crop-science direction with organizational and market goals.
Agronomic modeling: — Directs advanced modeling of crop and soil systems to forecast yield and resource-use outcomes that inform portfolio and product-line investment decisions.
Precision agriculture: — Governs enterprise deployment of geospatial and variable-rate technologies — yield mapping, variable-rate seeding and irrigation — as a strategic capability across the business.
Remote-sensing analytics: — Sets strategy for satellite/drone NDVI imagery programs that compare topography with soil, fertility, pest and weather data to guide large-scale agronomic decisions.
Regulatory compliance: — Establishes the agronomic regulatory and stewardship posture across business units, ensuring pesticide, fertility and field-practice programs meet agricultural regulations.
Stakeholder negotiation: — Negotiates critical agronomic licensing, supply and partnership matters with trait providers, ag-tech vendors and major grower cooperatives at the executive level.
Program management: — Oversees enterprise agronomic programs and trial networks, allocating agronomist and technician resources and securing grant and research funding.
Nutrient and fertility management: — Sets enterprise standards for analyzing nutrient levels and building fertilizer recommendations from soil, plant and water data across the crop portfolio.
Integrated Pest Management (IPM): — Directs the strategy for identifying and controlling pests, diseases and agronomic risks using IPM approaches at the division and enterprise scale.
Irrigation and water management: — Governs enterprise policy on soil and water management, crop rotation and irrigation practice as part of resource-use-efficiency commitments.
Data analysis: — Translates multi-year, large-scale crop-yield and soil datasets into commercial recommendations and enterprise strategic direction.
Experimental design: — Approves trial and experimental methodologies across the agronomy function to ensure new crop varieties, fertility and pest-control tests yield decision-grade evidence.
Soil science / soil chemistry: — Applies expert understanding of soil chemical, physical, biological and mineralogical composition to set the scientific basis for enterprise crop-production strategy.
Plant biology / plant physiology: — Grounds portfolio and R&D decisions in deep knowledge of plant biology and physiology underpinning crop growth, breeding and production.

Model-authored from 7 retrieved sources, then adversarially reviewed by an independent judge panel and held to a minimum-evidence floor before publication. Distinct from corpus-extracted content, which is labelled as such.

Pay across the ladder

Agronomist — median with , by . Real canon data.

National-base pricing — this family's function differential is pending review, so dollar levels are not function-adjusted (level-over-level % is unaffected).

Pay figures are modeled estimates from our market equation — level-differentiated, with honest-wide bands. They refresh as new leveling evidence lands; treat per-role dollars as directional.

Demand — is the field growing, or just turning over?

SOC 19-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+17.3%Soil and Plant Scientists · United States

14,150 → 16,600 jobs

1,700 openings / year, split into:

replacement / exits 1,588

Growth — new jobs (7%)Exits — backfill (93%)

7% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +5.4%
Growth share of hiring: 7%
Exit / retention pressure high: 93%
Openings / year: 1,700

Two quantities, kept apart: observed = employment growth differenced from two BLS OEWS vintages (what already happened); projected = BLS Employment Projections 2024–34, openings decomposed into growth + exits (growth derived from projected employment change). Neither is a pay figure.

Where this role sits — growth vs. pay

level E4 held

Demand axis is projected growth (not raw openings — that just measures churn). A curated, de-duplicated set of recognizable occupations held at this level; this role is highlighted.

Where it pays — by metro

OEWS metro median

San Jose-Sunnyvale-Santa Clara, CA$648k

San Francisco-Oakland-Fremont, CA$582k

Washington-Arlington-Alexandria, DC-VA-MD-WV$538k

Boulder, CO$531k

Seattle-Tacoma-Bellevue, WA$531k

Boston-Cambridge-Newton, MA-NH$508k

Lexington Park, MD$495k

Trenton-Princeton, NJ$492k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $389k; the demand-cube initiative will add growth-by-metro.

What changes across the ladder

Level-distinction canon isn't available for the adjacent levels of this profile yet.

Level differentiators

What separates E4 (E4 — Senior Vice President) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Multiple divisions
Autonomy: Owns cross-division strategy
Complexity: Enterprise strategy and external stakeholders
Impact: Cross-division / enterprise
Decision rights: Owns strategy and investment across divisions
Leadership: Leads division leaders

Full level-by-level matrix (side by side across the ladder) lives on the Ladder drill-down — not here.

Promotion

The next rung from E4 is E5.

Performance criteria for this profile render in the assessment kit →

People also watch

Nearest coordinate neighbors — same , same level. Each row's (confident / review / no match) comes from its — computed, not curated.

Profile	Key	Median	Δ structural
Senior Agronomy Manager	jf_agronomist::M4	$167,689†	confident0.06
Senior Agronomist	jf_agronomist::P4	$109,466†	confident0.06
Advanced Agronomist	jf_agronomist::S4	$56,408†	confident0.06
Chief Agronomy Officer	jf_agronomist::E3	$352,063†	confident0.06

†National-base pricing — this family's function differential is pending review, so the dollar level is not function-adjusted.

Capability cluster

O*NET 19-1013

Core to this role. Click any element with an ⓘ for its O*NET definition. A job-specific capability model (SkillFrame) will sharpen this and add worked examples.

The Job Swatch

E4

jf_agronomist::E4

Global Chief Agronomy Officer

Agronomist

The swatch is JobFrame's core primitive: a hueset deterministically by the role's superfunction, the profileKey as its color code, the level ladder, and a content fingerprint. Two swatches that look alike are alike.

Because every role carries computable coordinates, similarity, leveling, and pricing become measurable instead of arguable— the whole point of "Pantone for jobs."

Structural

structural@1

Where it sits— position in the work taxonomy + level (weighted Gower, level > function > focus).

Track: E
Superfunction: industrial_energy_operations
Function: engineering_expansion
Family: agronomist
Universal level: 4 / 8

Content

content@2

What it's about— a fingerprint over the role's tasks & capabilities.

Two roles can sit far apart structurally yet be close in content — that gap is itself a signal.

3-state matching

How any two swatches compare — a real distance in coordinate space, resolved to a calibrated verdict (never a fake "94% match").

Aeronautical · E4review0.17

Agricultural · E4review0.17

Application Services · E4review0.17

Chemical · E4review0.17

Communication · E4review0.17

Electrical Engineering · E4review0.17

Editions — frozen & versioned

structural@1live

Released 2026-06-17

content@2live

Released 2026-07-28

pay@2live

Released 2026-07-02

semantic@1live

Released 2026-07-02

Coordinates belong to a frozen, versioned edition — a coordinate computed today still means the same thing next quarter. Re-fitting issues a new edition, so comparisons are never silently redefined.

How to read this profile

Every number here is traceable — to a named source, a stated method, and an as-of date — and each value is labeled by how we know it: observed from data, computed by a model, projected forward, or governed canon. Nothing is a black box, and nothing is invented to fill a gap.

Where every number comes from

What	Source & method	Edition	State
Median pay + range	JobFrame pay equation (function base × family multiplier × level × focus differential)	pay@2	computed
Pay across the ladder	Same equation applied at each universal level	pay@2	computed
Demand — growth & openings	BLS Employment Projections; openings decomposed into growth + exits	EP 2024–34	projected
Pay by metro	BLS OEWS metropolitan medians	OEWS 2024	observed
Level distinctions	JobFrame universal-level canon (scope · autonomy · complexity · impact · decision rights · leadership)	canon	canon
Capability cluster	O*NET Knowledge / Skills / Abilities	O*NET 19-1013	computed
Coordinates	JobFrame coordinate system — structural (weighted Gower) + content fingerprint	structural@1 · content@2 · pay@2 · semantic@1	computed
Related roles	Nearest neighbors in the structural coordinate space	structural@1	computed

The principles behind the numbers

Ranges, not false precision

Pay is P10 · median · P90, never a single misleading number.

Three honest states, never a fake %

Role matches resolve to confident / review / no-match — a calibrated verdict, not a made-up percentage.

Provenance on every value

Each number carries whether it's observed, computed, projected, or canon — so you always know how much to lean on it.

Gaps shown as gaps

When a figure isn't available at the grain you need, we say so — we don't fabricate a number to fill the cell.

Frozen, versioned editions

Pay and coordinate editions are pinned, so a comparison you make today still means the same thing next quarter.

Privacy by min-N

Any aggregate over people is suppressed below a minimum count, so no individual is identifiable.

Freshness

As of2026-07-30Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)