Conduct research in breeding, physiology, production, yield, and management of crops and agricultural plants or trees, shrubs, and nursery stock, their growth in soils, and control of pests; or study the chemical, physical, biological, and mineralogical composition of soils as they relate to plant or crop growth. May classify and map soils and investigate effects of alternative practices on soil and crop productivity.
U.S. Workers
16,600
Median Salary
$71,410
10-Year Growth
+5.4%
Annual Openings
1,700
Typical entry: Bachelor's degree
27 of 27 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Communicate research or project results to other professionals or the public or teach related courses, seminars, or workshops.
AI: Fully automatable - AI can generate reports, slide decks, and deliver automated seminars or tutorials and answer many audience questions, enabling full automation of much research communication and teaching content delivery as of 2025.
Provide information or recommendations to farmers or other landowners regarding ways in which they can best use land, promote plant growth, or avoid or correct problems such as erosion.
AI: Fully automatable - AI can fully provide tailored information and actionable recommendations using agronomic models, remote sensing, and farmer-provided data without requiring physical interventions.
Study soil characteristics to classify soils on the basis of factors such as geographic location, landscape position, or soil properties.
AI: Fully automatable - Given appropriate sensor, laboratory, or remote-sensing data, AI systems can analyze soil properties and apply classification frameworks to reliably classify soils without human-only processes.
Conduct experiments to develop new or improved varieties of field crops, focusing on characteristics such as yield, quality, disease resistance, nutritional value, or adaptation to specific soils or climates.
AI: Partial - AI can design breeding programs, analyze genotypic/phenotypic data and accelerate selection with predictive models, but cannot fully automate multi-year field experiments and biological growth processes.
Develop new or improved methods or products for controlling or eliminating weeds, crop diseases, or insect pests.
AI: Partial - AI can propose new control methods, design candidate molecules or biocontrols in silico and optimize integrated pest management strategies, but cannot replace laboratory/field validation and regulatory clearance.
Develop environmentally safe methods or products for controlling or eliminating weeds, crop diseases, or insect pests.
AI: Partial - AI can design and propose environmentally safe control methods and candidate compounds but cannot physically develop, test, and validate products without laboratory and field trials.
Investigate soil problems or poor water quality to determine sources and effects.
AI: Partial - AI can analyze remote sensing data, historical records, and laboratory results to infer sources and effects but cannot perform the necessary field sampling and laboratory measurements itself.
Conduct experiments investigating how soil forms, changes, or interacts with land-based ecosystems or living organisms.
AI: Partial - AI can design experiments, simulate soil processes, and analyze data but cannot carry out physical experiments and soil manipulations in the field or laboratory.
Conduct research to determine best methods of planting, spraying, cultivating, harvesting, storing, processing, or transporting horticultural products.
AI: Partial - AI can synthesize literature, model outcomes, and recommend optimized practices but cannot perform hands-on trials and operational testing required to fully determine best methods.
Investigate responses of soils to specific management practices to determine the effects of alternative practices on the environment.
AI: Partial - AI can model and predict soil responses to management practices and analyze monitoring data but cannot implement and observe field responses on its own.
Study ways to improve agricultural sustainability, such as the use of new methods of composting.
AI: Partial - AI can propose and optimize sustainability improvements such as composting methods and management workflows, yet empirical testing and local adaptation are required.
Develop methods of conserving or managing soil that can be applied by farmers or forestry companies.
AI: Partial - AI can develop and optimize conservation strategies and management plans from data and models, but real-world testing and practitioner-led implementation remain necessary.
Identify or classify species of insects or allied forms, such as mites or spiders.
AI: Partial - Image- and sequence-based AI tools can identify many insect taxa, but accurate species-level classification for diverse insects, mites, and spiders often requires microscopy, expert taxonomic judgement, or molecular analysis that AI alone cannot fully replicate.
Investigate responses of soils to specific management practices to determine the use capabilities of soils and the effects of alternative practices on soil productivity.
AI: Partial - AI can assess potential soil responses using models and existing datasets but cannot replace field experiments and long-term monitoring needed to determine actual use capabilities and productivity effects.
Provide advice regarding the development of regulatory standards for land reclamation or soil conservation.
AI: Partial - AI can synthesize scientific and regulatory literature and draft standards or policy options, but final regulatory development requires human legal, political, and stakeholder judgment and formal decision-making.
Identify degraded or contaminated soils and develop plans to improve their chemical, biological, or physical characteristics.
AI: Partial - AI can detect likely degraded or contaminated soils from remote data and generate remediation plans, but it cannot perform in-situ sampling, validation, or the physical remediation work itself.
Study insect distribution or habitat and recommend methods to prevent importation or spread of injurious species.
AI: Partial - AI can analyze distribution data, model pathways of spread, and suggest prevention strategies, but comprehensive surveillance, field verification, and policy/operational implementation need human and institutional action.
Consult with engineers or other technical personnel working on construction projects about the effects of soil problems and possible solutions to these problems.
AI: Partial - AI can assist engineers by interpreting soil data, predicting problems, and proposing solutions, but geotechnical consulting still requires on-site investigation, physical testing, and professional engineering certification.
Develop ways of altering soils to suit different types of plants.
AI: Partial - AI can generate tailored soil amendment strategies from data and models but cannot by itself perform the site-specific testing, field trials, or physical implementation required to fully develop and validate them.
Perform chemical analyses of the microorganism content of soils to determine microbial reactions or chemical mineralogical relationships to plant growth.
AI: Partial - AI can plan analyses, interpret microbial and chemical data, and control some automated lab platforms, but performing wet-lab chemical and microbiological assays and ensuring quality control still depend on laboratory infrastructure and human oversight.
Conduct experiments regarding causes of bee diseases or factors affecting yields of nectar or pollen.
AI: Partial - AI can help design experiments, analyze results, and generate hypotheses about bee diseases and nectar/pollen yields, but conducting biological experiments, maintaining colonies, and validating results require hands-on laboratory and field work.
Develop improved measurement techniques, soil conservation methods, soil sampling devices, or related technology.
AI: Partial - AI can propose improved measurement techniques and design concepts and run simulations, but physical prototyping, field testing, and hardware manufacturing remain manual processes requiring human engineers and technicians.
Research technical requirements or environmental impacts of urban green spaces, such as green roof installations.
AI: Partial - AI can research literature, model environmental impacts, and draft technical requirements for urban green spaces, but site-specific assessments, stakeholder engagement, and regulatory approvals need human-led work and validation.
Survey undisturbed or disturbed lands for classification, inventory, mapping, environmental impact assessments, environmental protection planning, conservation planning, or reclamation planning.
AI: Partial - AI can automate mapping, classification, remote sensing analysis and preliminary impact assessments, but cannot fully replace on-site sampling, tacit judgement, and regulatory field verification.
Conduct research into the use of plant species as green fuels or in the production of green fuels.
AI: Partial - AI can design experiments, model biofuel yields, and analyze literature and data, but cannot yet perform the full suite of wet-lab, field, and scale-up experimental work autonomously.
Plan or supervise land conservation or reclamation programs for industrial development projects.
AI: Partial - AI can generate conservation and reclamation plans, model outcomes, and optimize designs, but cannot fully perform on-the-ground supervision, stakeholder negotiation, and legal accountability.
Plan or supervise waste management programs for composting or farming.
AI: Partial - AI can plan, monitor, and optimize composting and farm waste systems using sensors and control algorithms, but cannot wholly replace human oversight and operational supervision in complex, variable settings.