Soil and Water Conservationists

Gather information from geographic information systems (GIS) databases or applications to formulate land use recommendations.

AI: Fully automatable - AI can fully automate gathering GIS data and synthesizing spatial analyses to formulate land-use recommendations using existing databases and models.

Compute design specifications for implementation of conservation practices, using survey or field information, technical guides or engineering manuals.

AI: Fully automatable - AI can compute design specifications from survey/field data using technical guides and engineering rules via deterministic algorithms and modeling tools.

Compute cost estimates of different conservation practices, based on needs of land users, maintenance requirements, or life expectancy of practices.

AI: Fully automatable - AI can compute cost estimates using cost databases, lifecycle models, and user‑specified parameters with high accuracy given sufficient input data.

Provide information, knowledge, expertise, or training to government agencies at all levels to solve water or soil management problems or to assure coordination of resource protection activities.

AI: Fully automatable - AI can produce technical guidance, training materials, and coordination frameworks at scale and provide expert knowledge to agencies to support water and soil management efforts.

Analyze results of investigations to determine measures needed to maintain or restore proper soil management.

AI: Fully automatable - AI can analyze investigation data, run soil and hydrology models, and recommend evidence‑based measures to maintain or restore proper soil management.

Develop water conservation or harvest plans, using weather information systems, irrigation information management systems, or other sources of daily evapotranspiration (ET) data.

AI: Fully automatable - AI can ingest weather, irrigation, and ET datasets to produce actionable water conservation and harvesting plans and optimize schedules with minimal human input.

Identify or recommend integrated weed and pest management (IPM) strategies, such as resistant plants, cultural or behavioral controls, soil amendments, insects, natural enemies, barriers, or pesticides.

AI: Fully automatable - AI can identify and recommend IPM strategies by integrating agronomic data, pest risk models, and evidence on control efficacy tailored to local conditions.

Review proposed wetland restoration easements or provide technical recommendations.

AI: Fully automatable - AI can review proposed wetland restoration easement documents and provide technically grounded recommendations by analyzing site data, regulatory context, and best‑practice standards.

Enter local soil, water, or other environmental data into adaptive or Web-based decision tools to identify appropriate analyses or techniques.

AI: Fully automatable - AI systems can extract, format, and enter local environmental data into web‑based decision tools and interpret outputs automatically.

Provide access to programs or training to assist in completion of government groundwater protection plans.

AI: Fully automatable - AI can generate training content, manage enrollments, and deliver program access and guidance online to support completion of groundwater protection plans.

Calculate or compare efficiencies associated with changing from low-precision irrigation technologies, such as furrow irrigation, to high-precision technologies, such as computer-controlled systems.

AI: Fully automatable - Calculating and comparing irrigation-efficiency metrics is a deterministic modeling and data-analysis task that modern AI and decision-support tools can fully automate given the necessary inputs.

Implement soil or water management techniques, such as nutrient management, erosion control, buffers, or filter strips, in accordance with conservation plans.

AI: Partial - AI can generate and optimize soil and water management prescriptions and coordinate mechanized implementation, but cannot autonomously perform all site-specific physical work and adaptive adjustments in the field.

Advise land users, such as farmers or ranchers, on plans, problems, or alternative conservation solutions.

AI: Partial - AI can produce tailored conservation advice and alternative plans for land users, but human advisors remain necessary for trust, local nuance, and legal/accountability aspects.

Visit areas affected by erosion problems to identify causes or determine solutions.

AI: Partial - AI can identify likely erosion causes and potential solutions from remote sensing and models, yet often lacks the full on-site context and tactile assessment needed for final determinations.

Monitor projects during or after construction to ensure projects conform to design specifications.

AI: Partial - AI can monitor construction via sensors, drones, and imagery to detect deviations from design, but some inspections and judgment calls still require in-person assessment and human oversight.

Manage field offices or involve staff in cooperative ventures.

AI: Partial - AI can assist with administrative, scheduling, and coordination tasks but cannot fully assume human leadership, personnel management, or legal responsibilities for field offices.

Apply principles of specialized fields of science, such as agronomy, soil science, forestry, or agriculture, to achieve conservation objectives.

AI: Partial - AI can apply principles from agronomy, soil science, forestry, and related fields to generate conservation solutions, but human specialists are needed for nuanced application and field adaptation.

Plan soil management or conservation practices, such as crop rotation, reforestation, permanent vegetation, contour plowing, or terracing, to maintain soil or conserve water.

AI: Partial - AI can generate and optimize soil management and conservation plan recommendations from data and models but cannot fully replace on‑site assessment, local judgment, and implementation oversight.

Participate on work teams to plan, develop, or implement programs or policies for improving environmental habitats, wetlands, or groundwater or soil resources.

AI: Partial - AI can generate planning documents, models, and policy drafts and contribute to team discussions, but lacks legal authority and real‑world stakeholder negotiation and on‑the‑ground implementation capacity.

Conduct fact-finding or mediation sessions among government units, landowners, or other agencies to resolve disputes.

AI: Partial - AI can synthesize facts, suggest compromise options, and support mediators with data, but cannot reliably lead high‑stakes face‑to‑face mediation or substitute for human judgment and authority.

Revisit land users to view implemented land use practices or plans.

AI: Partial - AI can assess remote sensing, drone, and photographic data to evaluate implemented practices, but cannot perform physical on‑site inspections or observe conditions that require presence.

Respond to complaints or questions on wetland jurisdiction, providing information or clarification.

AI: Partial - AI can answer many routine questions about wetland jurisdiction and provide clarifying information and references, but cannot make legally binding determinations or replace official agency decisions.

Coordinate or implement technical, financial, or administrative assistance programs for local government units to ensure efficient program implementation or timely responses to requests for assistance.

AI: Partial - AI can automate much of the technical, financial, and administrative workflow, triage requests, and support program coordination, but cannot fully assume legal responsibilities or complex human stakeholder coordination.

Initiate, schedule, or conduct annual audits or compliance checks of program implementation by local government.

AI: Partial - AI can automate scheduling and preliminary compliance analyses from datasets, but official audits and in‑person verification by authorized personnel remain required.

Survey property to mark locations or measurements, using surveying instruments.

AI: Partial - AI can process and interpret surveying instrument data and plan surveys but cannot physically operate instruments or mark locations without integrated robotic hardware and field operators.

Review or approve amendments to comprehensive local water plans or conservation district plans.

AI: Partial - AI can review, analyze, and draft amendments to water or conservation plans but cannot exercise official approval or resolve political and regulatory judgments on its own.

Develop, conduct, or participate in surveys, studies, or investigations of various land uses to inform corrective action plans.

AI: Partial - AI can design and analyze land‑use surveys and synthesize findings but cannot fully perform field reconnaissance and stakeholder interactions independently.

Develop or conduct environmental studies, such as plant material field trials or wildlife habitat impact studies.

AI: Partial - AI can design environmental studies and analyze trial data but cannot fully conduct field trials, collect samples, or manage in‑field logistics and permitting alone.

Compile or interpret biodata to determine extent or type of wetlands or to aid in program formulation.

AI: Partial - AI and remote-sensing models in 2025 can compile and interpret biodata and map wetland extent/types with high accuracy from available data, but regulatory determinations and program formulation still require local ground-truthing and expert judgment.

Evaluate or recommend geographic information systems (GIS) applications to address issues such as surface water quality, groundwater quality, ecological risk assessments, air quality, or environmental contamination.

AI: Partial - AI can evaluate GIS software/features and recommend workflows based on use-case and data, but final selection and integration decisions typically require site-specific expertise and stakeholder constraints.

Review annual reports of counties, conservation districts, or watershed management organizations, certifying compliance with mandated reporting requirements.

AI: Partial - Automated systems can check reports against mandated reporting checklists and flag noncompliance, but formal certification and legal accountability still require human sign-off.

Review grant applications or make funding recommendations.

AI: Partial - AI can screen, score, and rank grant applications using objective criteria and past outcomes, but nuanced funding decisions and value judgments remain human-led.

Develop or maintain working relationships with local government staff or board members.

AI: Not automatable - AI cannot genuinely develop or maintain interpersonal relationships with local government staff or board members, which require trust, negotiation, and political navigation.