AGRICULTURAL DATA ANALYTICS COURSE

Get to know your instructor and explore the course structure. Learn how each assignment leads to the final Row Crop Data Dashboard project and practice using the data download script to access pre-clipped field data.

• Meet your instructor
• Course structure
• Assignment pathway
• Introduction to the data download script

Explore how farming has evolved from tradition to technology. Understand the agri-data value chain and meet the key players shaping it. Through case studies like NASA Harvest and John Deere, see how data informs decisions on yield, profit, and sustainability – while unpacking the ongoing debate over data ownership in agriculture.

• Traditional to data-driven farming evolution
• The agri-data value chain
• Who’s shaping the field
• Case studies: NASA Harvest, John Deere
• Case studies: Regional agricultural diversity: Corn Belt, Great Plains, Southeast row crop production
• How data drives decisions 
• Discussion: The promise and tension of data ownership in farming

Set up your cloud-based VS Code environment and learn the essentials of Python, Git, and Jupyter notebooks. Follow the instructor in a live demo to install libraries, run the data download script, explore pre-processed field data, and push your first project files to GitHub—laying the groundwork for your Row Crop Dashboard.

• Cloud-hosted vs code setup
• Python 3.x & library management 
• Git fundamentals
• AI-assisted programming
• Jupyter notebook basics
• Demo: Introduction to the data download script

Discover how agricultural data is collected, structured, and shared across the US. Learn to identify major data sources like USDA, NASA, and NOAA, and understand key spatial data types and projections. In the workshop, you’ll run the data download script to generate and explore your own field dataset, previewing soil, weather, and satellite layers for your project.

• Collection methods
• Key US data sources
• Common data types
• File formats
• Spatial data fundamentals
• Data download script
• Workshop: Download and preview open-source datasets

Assignment #1: Field Data Acquisition and Documentation
Generate your own field dataset, document spatial characteristics, and upload initial observations to your Git repo.

Learn how to clean, merge, and manage agricultural datasets using Python and SQL. Explore key operations like filtering, aggregation, and joins while understanding when to use each tool. In the demo, the instructor combines soil and field boundary data and uses AI assistance to debug and refine the workflow.

• Data wrangling fundamentals
• SQL introduction
• Merging datasets from multiple sources
• Field surveys, USDA statistics, & weather data
• Using AI assistants 
• Demo: Cleaning and merging two datasets with Python and SQL

Assignment #2: Data Cleaning and Integration with Python & SQL
Clean, standardize, and merge two agricultural datasets using Python and/or SQL. Push cleaned data to Git.

Dashboard Element: Create one data summary table for your final dashboard.

Uncover patterns and relationships in your farm dataset through exploratory data analysis (EDA). Practice filtering, aggregating, and visualizing trends in soil, yield, and weather data using pandas, seaborn, and plotly. Follow a guided demo to interpret correlations and use AI suggestions to refine your metrics and visualizations.

• Dataframes & exploratory data analysis
• Pandas/polars operations & time-series basics
• Descriptive statistics for farm data
• Visualization fundamentals & correlation analysis
• AI for alternative metrics or visualizations
• Demo: Python EDA notebook walkthrough 
• Peer share

Assignment #3: Exploratory Data Analysis of Row Crop Fields
Conduct EDA on your row crop field datasets. Create at least 3 visualizations exploring relationships in your data. 

Dashboard Element: Design 2 plots for your final dashboard.

Learn to work with spatial data using GeoPandas, from loading shapefiles to creating maps of field boundaries and soil variability. Explore coordinate systems, reprojection, and spatial operations like joins and buffers. In the workshop, you’ll load your own field data, combine it with soil or yield statistics, and build your first choropleth map showing spatial differences across fields.

• Introduction to GeoDataFrames 
• Workshop: Loading shapefiles and spatial data 
• Working with geometries
• Checking CRS
• Reprojection & spatial operations
• Demo: Creating simple maps of US row crop fields
• Exporting maps & field boundaries

Assignment #4: Geospatial Mapping and Field Variability Visualization
Create a geospatial visualization combining at least two spatial datasets from your row crop package. Include brief interpretation. 

Dashboard Element: Design 1 map visualization for your final dashboard.

Discover how remote sensing transforms farm management through tools like NDVI and satellite imagery. Learn to access and analyze Sentinel-2 and Landsat data to assess crop health. In the demo, the instructor calculates NDVI and visualizes crop stress zones, while in the workshop, you’ll create your own NDVI map and interpret patterns across your farm fields.

• Remote sensing fundamentals 
• NDVI & crop health monitoring
• Raster data & image bands 
• Sentinel-2 and Landsat imagery 
• Demo: Calculate NDVI and visualize crop stress zones
• Workshop: Generate an NDVI map for your farm fields
• Case Study: Midwest Corn Health Monitoring

Assignment #5: Vegetation Index (NDVI) Calculation and Crop Health Analysis
Calculate and visualize NDVI or another vegetation index for your row crop fields. Interpret what the patterns show across different fields. 

Dashboard Element: Design 1 NDVI/crop health visualization for your dashboard.

Explore how weather and climate data shape agricultural decisions. Learn to access NOAA and NASA POWER datasets, analyze time-series trends like rainfall and temperature, and visualize seasonal and climate variations. In the demo, the instructor imports weather data to create plots highlighting key patterns and anomalies affecting major US crops.

• NOAA and NASA POWER weather data 
• Working with weather data
• Time-series analysis
• Seasonal changes & climate anomalies
• Climate variability impacts 
• AI assistants for temporal irregularities
• Case study: Midwest Drought Monitoring

Assignment #6: Weather and Climate Trend Analysis for Row Crops
Analyze weather data for your row crop field locations. Create time-series visualizations showing seasonal patterns and at least one climate trend or anomaly. 

Dashboard Element: Design 1-2 weather/climate plots for your dashboard.

Deepen your geospatial skills by combining multiple datasets—soil, NDVI, weather, and field boundaries—into unified analyses. Learn how to reproject data, select suitable coordinate systems, and extract zonal statistics for precision insights. In the workshop, you’ll overlay layers, calculate per-field NDVI and soil metrics, and replicate key steps of the download script’s clipping and merging process.

• Overlaying multiple spatial datasets 
• Coordinate transformations in practice
• Workshop: Zonal statistics: Extracting raster values by field polygons
• Spatial analysis workflows
• Replicating the download script 
• QGIS demo: Visualizing multiple layers 

Assignment #7: Integrated Spatial Analysis and Zonal Statistics
Create an integrated spatial analysis combining at least 3 layers from your row crop data package. Perform zonal statistics extracting average values per field. 

Dashboard Element: Design 1 integrated spatial visualization showing overlapping data layers.

Examine how precision agriculture technologies like variable rate systems, auto-guidance, and IoT sensors are transforming US row crop farming. Learn to interpret planter and combine datasets and explore their economic impact. In the demo, the instructor analyzes real equipment data to map field variability, followed by a discussion on data interoperability and access challenges for small farms.

• Variable rate technology 
• Auto-guidance & section control adoption 
• Planter and combine data
• Working with equipment data files
• IoT sensors & farm machinery data integration
• Precision ag economics
• Case Studies: John Deere Operations Center, Climate FieldView, Real Equipment Data Example

Learn to interpret soil data for farm planning and sustainability assessment using SSURGO and your own dataset. Explore key indicators like pH, organic matter, and moisture, and understand their role in regenerative agriculture. In the workshop, you’ll analyze field-level soil variability and build a soil health scorecard reflecting sustainability metrics for your fields.

• Case study: NRCS soil survey data (SSURGO) for farm planning
• Soil data & soil health indicators
• Interpreting soil sensor data
• Carbon sequestration & regenerative agriculture metrics 
• Soil conservation practices & USDA programs
• Workshop: Creating sustainability scorecards for farms

Assignment #8: Soil Health and Sustainability Metrics Assessment
Develop a soil health analysis for your row crop fields using the NRCS data in your package. Create visualizations showing soil variability across fields and at least 2 sustainability metrics. 

Dashboard Element: Design soil health section for your dashboard (1-2 visualizations).

Learn how to turn complex agricultural data into clear, visual stories through effective dashboards. Explore KPI design, layout principles, and lightweight BI tools like Plotly Dash and Streamlit. In the demo, the instructor builds a live farm dashboard, and in the workshop, you’ll begin assembling your own final dashboard using insights from earlier assignments.

• Data storytelling & KPI selection
• Lightweight BI in Python 
• Workshop: Integrating multiple data sources and visualizations, layout design and visual hierarchy
• Making dashboards actionable 
• Demo: Farm performance dashboard build-through
• AI for narrative captions and layout code

Final Project: Row Crop Intelligence Data Dashboard 
Build a multi-page or multi-section dashboard prototype integrating at least 5 visualizations from previous assignments. Include KPI summary tiles. 

Examine the ethical and political dimensions of agricultural data—who owns it, who profits, and who is protected. Explore legal frameworks, privacy challenges, and AI bias in agri-tech. Through real-world case studies and insights from a guest speaker, discuss how data transparency and farmer rights shape the future of ethical digital agriculture in the US.

• Farm data ownership
• Legal frameworks & farmer data rights
• Privacy concerns 
• AI bias 
• Data sharing ethics
• Agricultural policy & USDA programs
• Case Studies: Farm Data Ownership Debates & USDA Data Transparency

Wrap up the course by looking ahead to the next frontier of agricultural data, from blockchain traceability to AI-driven smart farming. Discuss how technology supports climate resilience, explore emerging career paths, and reflect on how your final project connects to the broader global agri-data landscape.

• Future of agri-data
• AI-driven farm management & smart ecosystems
• Climate resilience & data-informed policy directions
• Emerging career paths 
• Reflective discussion: How your project fits into the global agri-data landscape
• Instructor Q&A and course wrap-up