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Key Responsibilities and Required Skills for Agricultural Analyst

💰 $45,000 - $95,000

AgricultureAnalyticsResearchAgronomy

🎯 Role Definition

The Agricultural Analyst is a subject-matter and data-driven role that combines agronomy, field science, and quantitative analytics to produce actionable recommendations for farmers, agribusinesses, researchers, and policy teams. This role typically uses field trial data, remote sensing and GIS, crop models, and economic analysis to optimize input use, forecast yields, evaluate climate and pest risks, and translate insights into clear technical reports and grower-facing guidance. Strong domain knowledge in crop production, experimental design, and statistical/data science tools (R, Python, SQL, GIS) is required.

Keywords: agricultural analyst, agronomy, precision agriculture, crop modelling, yield forecasting, remote sensing, GIS, R, Python, SQL, agribusiness, climate-smart agriculture, pest and disease monitoring.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Agricultural Technician or Farm Field Technician
  • Agronomy Intern or Research Assistant
  • Data Analyst with agricultural experience

Advancement To:

  • Senior Agricultural Analyst / Lead Agronomist
  • Agronomy Manager or Precision Agriculture Manager
  • Research Scientist or Product Manager for agtech
  • Policy Advisor or Program Manager in agricultural development

Lateral Moves:

  • Crop Consultant / Independent Agronomist
  • Precision Agriculture Specialist or GIS Analyst
  • Data Scientist in AgTech or Supply Chain Analyst (agri-focused)

Core Responsibilities

Primary Functions

  • Design, implement and manage on-farm and research field trials (randomized block trials, strip trials, replicated trials), including treatment layout, sampling protocols, QA/QC procedures, and full lifecycle documentation to evaluate varietal performance, fertilizer regimes, pest control strategies, and management practices.
  • Collect, clean and validate field and laboratory data (soil tests, plant tissue analysis, phenology observations, yield data), ensuring traceability from sample to dataset and applying data quality checks and corrections before analysis.
  • Build and maintain crop models and yield forecasting tools (e.g., DSSAT, APSIM, CropSyst or custom models) that integrate weather, soil, management and remote sensing inputs to produce actionable short- and long-term yield projections.
  • Analyze multi-season agronomic datasets using advanced statistical methods (mixed models, ANOVA, linear and nonlinear regression, time series analysis) and translate results into practical recommendations for nutrient management, hybrid/variety selection, irrigation scheduling and integrated pest management.
  • Use remote sensing and GIS tools (Sentinel, Landsat, Planet, NDVI/EVI indices, QGIS/ArcGIS) to monitor crop health, detect stress patterns, map variability across fields and deliver precision application zones for fertilizer, seed, or agrochemicals.
  • Develop and deploy precision agriculture prescriptions (variable rate application maps, seeding rate maps, nitrogen timing recommendations) in collaboration with growers and application providers, ensuring compatibility with common farm equipment formats (ISOXML, shapefiles, CSV).
  • Create interactive dashboards and visualizations (Power BI, Tableau, R Shiny, Python Dash) that synthesize agronomic, economic and weather data for growers, advisors and commercial teams to support decision-making and field operations.
  • Conduct economic and return-on-investment analyses for agronomic interventions, input packages and technology adoption, including sensitivity analysis, break-even calculations and scenario modeling for different farm sizes and enterprise mixes.
  • Coordinate and interpret satellite and drone imagery workflows (image pre-processing, orthomosaic generation, vegetation indices, classification) to support scouting, early stress detection and biomass estimation.
  • Perform soil fertility and nutrient balance assessments, generate fertilizer recommendations based on lab results, crop removal rates and best-practice guidelines, and advise on lime, micronutrient and organic amendment needs.
  • Lead pest, disease and weed surveillance programs, synthesize scouting reports with climatic and cropping-history data to produce risk maps and timely management advisories, including integrated pest management (IPM) recommendations.
  • Prepare detailed technical reports, grower-facing summary briefs, scientific posters and peer-reviewed manuscripts communicating trial results, methodology and recommendations in accessible and actionable language.
  • Collaborate with multidisciplinary teams—researchers, extension agents, commercial agronomists, product managers and engineers—to turn field evidence into scalable products, services, or recommendations for customer segments.
  • Design and implement experimental protocols for new product trials, including regulatory-compliant pesticide or seed demonstrations, data collection templates, and safety/compliance documentation.
  • Manage trial logistics and relationships with growers and field hosts: site selection, contracts, scheduling, onsite supervision, troubleshooting and ethical stewardship of trial sites and data.
  • Apply climate risk and resilience assessments to cropping systems by integrating historical and forecasted weather data to advise on varietal selection, planting windows, and adaptive management strategies for extreme events.
  • Implement and document sample handling and laboratory coordination for tissue, soil and grain testing; liaise with accredited labs, review lab methods, and validate returned data against field records.
  • Maintain and curate agronomic databases and metadata (trial protocols, cultivar pedigrees, management histories) to ensure reproducibility and support longitudinal analysis across seasons and geographies.
  • Provide field training and technical extension to growers, cooperatives and field staff on data collection methods, technology adoption (e.g., farm management software), and interpretation of agronomic recommendations.
  • Evaluate and pilot emerging technologies (sensors, IoT soil moisture probes, automated weather stations, edge-computing camera systems) and recommend scalable deployments grounded in cost-benefit analysis and field validation.
  • Ensure compliance with regulatory and funding requirements (ethics approvals, trial permits, pesticide labels), prepare deliverables for funders and manage grant- or contract-based project reporting.
  • Troubleshoot field- and data-related issues: sensor calibration, GPS accuracy, missing data imputation strategies, and develop SOPs to reduce future risk and improve data integrity.
  • Synthesize market and supply-chain intelligence related to commodity price trends, input availability, and export constraints to inform strategic recommendations for production planning and risk mitigation.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to answer grower or commercial team questions quickly and reliably.
  • Contribute to the organization's data strategy and roadmap by recommending data standards, metadata practices and ingestion pipelines aligned with agronomy needs.
  • Collaborate with business units to translate data needs into engineering requirements for data lakes, pipelines and farm-management-platform integrations.
  • Participate in sprint planning and agile ceremonies within the data engineering or product teams to prioritize agronomic features and data quality fixes.
  • Mentor junior agronomists and technicians on field methods, data collection best practices and basic statistical analysis.
  • Assist marketing and sales teams with technical content, trial results summaries and customer-facing collateral to support product launches and farmer adoption.

Required Skills & Competencies

Hard Skills (Technical)

  • Agronomy and cropping systems expertise: crop physiology, phenology, nutrient cycling, pest/disease cycles and best-practice management for key commodity crops.
  • Experimental design and statistical analysis: randomized block designs, split-plots, mixed effects models, ANOVA, and reproducible workflows in R or Python.
  • Data science tools: R (tidyverse, lme4), Python (pandas, scikit-learn), Jupyter/RStudio for analysis and reproducible reporting.
  • Geospatial analysis and remote sensing: ArcGIS/QGIS, Google Earth Engine, NDVI/EVI processing, orthomosaic/dronescale analytics.
  • Crop modelling and yield forecasting: experience with simulation models (DSSAT, APSIM or comparable) and empirical forecasting approaches.
  • Database and query skills: SQL for data extraction, data cleaning and ETL familiarity, experience with relational and time-series databases.
  • Data visualization and dashboarding: Tableau, Power BI, R Shiny or Plotly for clear presentation of agronomic insights.
  • Precision agriculture tech: experience producing prescription maps, VRT workflows, and familiarity with farm machinery data formats and transfer protocols.
  • Soil and plant laboratory coordination: interpretation of soil test reports, plant tissue analysis, sampling methods and QA/QC.
  • Programming for automation and reproducibility: scripting in Bash, Python/R for pipeline automation and unit-tested analyses.
  • Cloud and data platforms (preferred): experience with AWS/GCP, Google Earth Engine, Databricks or similar for scaling analytics.
  • Familiarity with agribusiness economics: cost-benefit, ROI, enterprise budgeting and sensitivity analyses tailored to farm contexts.
  • Drone operation and data processing (preferred): flight planning, regulatory compliance and imagery analytics.

Soft Skills

  • Clear, audience-tailored communication: ability to translate technical analyses into practical recommendations for growers, advisors and executives.
  • Stakeholder management: building trust with growers, extension networks, research partners and commercial teams.
  • Problem-solving and critical thinking: pragmatic troubleshooting of in-field and analytical challenges under time constraints.
  • Project management: planning, budgeting, prioritizing tasks and delivering trial and analytic outputs on schedule.
  • Team collaboration: cross-functional work with engineers, agronomists, product and commercialization teams.
  • Teaching and extension: delivering workshops, field days and training to non-technical audiences.
  • Attention to detail and data stewardship: meticulous documentation, version control and metadata practices.
  • Adaptability and continuous learning: staying current with new technologies, climatic trends and agronomic research.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Agronomy, Crop Science, Agricultural Science, Agricultural Economics, Soil Science, Plant Science, or a closely related field.

Preferred Education:

  • Master’s degree or PhD in Agronomy, Crop Science, Precision Agriculture, Agroecology, Agricultural Data Science, or related discipline with a focus on quantitative methods.

Relevant Fields of Study:

  • Agronomy / Crop Science
  • Soil Science / Soil Fertility
  • Agricultural Economics
  • Plant Pathology / Entomology (applied)
  • Remote Sensing / GIS
  • Data Science / Statistics (applied to agriculture)

Experience Requirements

Typical Experience Range: 2–7 years of relevant agronomy, field research, precision agriculture or data analytics experience.

Preferred:

  • 3–5+ years in a role combining field agronomy and data analysis (e.g., research agronomist, precision ag analyst, agronomy data scientist).
  • Demonstrated experience managing multi-site trials, working directly with growers, and delivering both technical reports and grower-facing materials.
  • Proven track record using R or Python for agronomic analysis, and use of GIS/remote sensing tools for crop monitoring.
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