GIS for Precision Agriculture Training Course

Participants can create a farm geodatabase that supports seasonal analysis and reporting.

• Feature classes for fields, blocks, irrigation units and exclusion areas
• Attribute schemas for crop history, varieties, treatments and ownership
• Domains and subtypes controlling consistent data entry across farm records
• Relational tables linking soil tests, scouting visits and laboratory results
• Unique identifiers maintained across seasons, contractors and equipment platforms
• Geodatabase topology rules for gaps, overlaps and invalid field boundaries

Participants can prepare and validate GNSS-derived field observations for GIS analysis.

• GNSS accuracy classes, correction services and expected positional uncertainty
• Recording soil samples with identifiers, depths, dates and laboratory references
• Scouting forms for weeds, pests, disease symptoms and crop damage
• Point, line and polygon capture matched to the agronomic observation
• Field data collection protocols reducing duplicates, omissions and location errors
• Positional accuracy checks against imagery, boundaries and known control points

Participants can prepare accurate field boundaries and base layers for farm analysis.

• Digitising field boundaries from orthophotos using consistent edge interpretation
• Cleaning self-intersections, slivers and multipart polygons before analysis
• Headlands, waterways and buffer strips mapped as operational constraints
• Calculating field area, perimeter and accessible treatment area accurately
• Joining cadastral, drainage and infrastructure layers to farm boundaries
• Map scales and reference layers selected for field operations

Participants can acquire and prepare satellite imagery for crop-condition monitoring.

• Sentinel-2 spectral bands, revisit intervals and spatial resolution limits
• Cloud masks, shadows and poor-quality pixels removed before analysis
• Surface reflectance products selected for multi-date crop comparisons
• Image dates matched to crop growth stages and management events
• Clipping scenes to fields while retaining buffers for edge analysis
• Band composites prepared for visual inspection of crop variability

Participants can calculate and interpret vegetation indices within an agronomic context.

• NDVI calculated from near-infrared and red reflectance bands
• NDRE used where red-edge sensitivity better suits dense canopies
• SAVI applied where exposed soil affects early-season crop signals
• Zonal statistics summarising index values by field and management block
• Multi-date index profiles distinguishing persistent and temporary variability
• Index interpretation checked against crop stage, weather and field notes

Participants can assess drone-derived products before using them in field decisions.

• Flight planning variables affecting ground sampling distance and image overlap
• Ground control points and RTK positioning for spatially accurate mosaics
• Orthomosaic seams, blur and radiometric inconsistency identified during quality review
• Digital surface models distinguished from digital terrain models
• Canopy cover and plant-count products matched to crop-stage requirements
• Drone outputs compared with satellite imagery for operational monitoring

Participants can map soil-property variation using appropriate sampling and interpolation methods.

• Grid, zone and directed sampling designs matched to management questions
• Sample density assessed against field size, heterogeneity and budget
• Soil laboratory results standardised for units, depths and analytical methods
• Inverse distance weighting compared with ordinary kriging assumptions
• Semivariograms used to assess spatial autocorrelation and range
• Cross-validation statistics used to judge interpolation performance and bias

Participants can identify terrain-driven production constraints from elevation data.

• Digital elevation models prepared for hydrological and terrain analysis
• Slope, aspect and curvature derived for field-scale interpretation
• Flow direction and accumulation revealing likely runoff pathways
• Topographic wetness index identifying potential waterlogging risk areas
• Depressions, drainage channels and erosion-prone slopes mapped from terrain
• Terrain findings compared with crop vigour, soil data and field observations

Participants can clean yield-monitor records and produce reliable yield-variability maps.

• Yield-monitor attributes reviewed for moisture, flow, speed and header status
• Removing records affected by start-up delays and turning movements
• Outlier filters applied using plausible yield ranges and local context
• Correcting time lags between grain flow and recorded position
• Yield data aggregated into grids using documented cell sizes
• Multi-year yield patterns separated from one-season weather effects

Participants can quantify spatial patterns and test whether mapped differences are meaningful.

• Descriptive statistics summarising field variability before spatial modelling
• Histograms and boxplots identifying skewed distributions and outliers
• Moran's I used to test spatial autocorrelation in field data
• Local indicators identifying clusters of high and low observations
• Correlation matrices screening relationships between yield, soil and imagery
• Spatial patterns interpreted without confusing association with causation

Participants can create management zones from multiple agronomic evidence layers.

• Zone objectives defined for sampling, seeding, nutrition or irrigation
• Standardising input layers before combining different measurement scales
• Fuzzy overlay methods representing gradual transitions between field conditions
• K-means clustering applied to yield, soil, terrain and imagery variables
• Zone boundaries simplified for machinery operation and practical implementation
• Agronomic review used to challenge statistically derived zone patterns

Participants can prepare operational prescription maps with appropriate rate and safety controls.

• Prescription objectives linked to soil tests, crop targets and risk constraints
• Rate tables constructed from zone classes and agronomic recommendations
• Minimum, maximum and step-rate limits checked against equipment capability
• Exclusion zones applied around waterways, infrastructure and sensitive habitats
• Prescription polygons converted into machine-readable management units
• As-applied records retained for audit, evaluation and future analysis

Participants can use GIS evidence to support irrigation monitoring and prioritisation.

• Irrigation blocks mapped with valves, laterals, pumps and control points
• Soil-moisture sensor locations evaluated against representative field conditions
• Evapotranspiration data combined with crop stage and irrigation records
• Water-stress patterns identified from imagery, terrain and field observations
• Application uniformity maps used to investigate irrigation performance issues
• Irrigation priorities documented under water allocation and operational constraints

Participants can map crop-health observations and target field scouting activities.

• Scouting routes prioritised using vigour anomalies and historical problem areas
• Pest and disease observations recorded with severity scoring protocols
• Weed patches digitised for spot-treatment and herbicide resistance monitoring
• Image classification outputs verified through ground-truth observation records
• Threshold maps distinguished from confirmed diagnoses requiring field inspection
• Treatment maps documented with crop stage, product and application date

Participants can establish quality controls and governance rules for farm spatial data.

• Metadata records covering source, date, accuracy, processing and responsible person
• Completeness checks identifying missing attributes, dates and field identifiers
• Logical consistency tests detecting impossible rates, areas and crop histories
• Version control procedures preserving original, processed and published datasets
• Data-sharing agreements defining ownership, access rights and permitted use
• Retention schedules aligned with agronomic records and contractual obligations

Participants can produce maps that communicate recommended actions clearly and accurately.

• Map purpose defined before choosing layers, classifications and annotations
• Classification methods selected for continuous measurements and management categories
• Colour ramps chosen to distinguish ordered values from nominal classes
• Legends, scale bars and north arrows matched to operational use
• Map layouts presenting uncertainty, constraints and recommended field actions
• Web maps configured for farm managers, advisers and machinery operators

Participants can design repeatable GIS processes for recurring seasonal monitoring tasks.

• ModelBuilder and QGIS processing models documented for recurring operations
• Batch clipping and zonal statistics applied across multiple fields
• Parameterised workflows separating farm-specific inputs from standard processing steps
• File paths, outputs and intermediate datasets managed through project conventions
• Quality-control checkpoints inserted before map publication and prescription export
• Workflow documentation enabling another analyst to reproduce results

Participants can assess whether precision interventions produced measurable field outcomes.

• Treatment and control areas defined for practical on-farm comparisons
• Before-and-after maps interpreted with weather and management context
• Yield response evaluated by zone, treatment rate and crop season
• Partial-budget calculations comparing input changes with production outcomes
• Confounding factors recorded before attributing performance to an intervention
• Next-season sampling priorities derived from unresolved spatial uncertainty

Participants can build, review and present a complete precision agriculture GIS analysis with mapped recommendations.

• Project brief defines production problem, users, decisions and success criteria
• Source datasets assessed for suitability, accuracy, completeness and temporal relevance
• Farm geodatabase assembled from boundaries, observations, imagery and operational records
• Analysis combines terrain, soil, crop vigour and yield evidence
• Management zones or priority areas justified through documented analytical methods
• Final maps present recommendations, limitations, quality checks and implementation actions