QGIS for Agriculture Training Course

Participants can prepare reliable vector and tabular datasets for agricultural analysis.

• CSV crop records joined to farm polygons using stable identifiers
• Geometry validity checks identify overlaps, gaps, duplicates and null features
• Field calculator standardises crop names, planting dates and area values
• Attribute domains distinguish crop types, irrigation status and tenure classes
• Missing values flagged through filters, expressions and summary statistics
• Data quality log records corrections, assumptions and unresolved source issues

Participants can digitise and maintain agricultural features from imagery and records.

• Digitising field boundaries from imagery using snapping and tracing controls
• Multipart parcels separated into operational fields for accurate area calculations
• Irrigation canals, pumps and storage facilities captured as asset layers
• Topology rules prevent unintended gaps and overlaps between adjoining fields
• Area and perimeter measurements calculated in appropriate projected coordinates
• Edit tracking records dates, editors and reasons for boundary revisions

Participants can design agricultural field-data workflows linked to QGIS datasets.

• Survey schema defines crop, growth stage, pest and input variables
• QField project packages prepared with offline layers, forms and basemaps
• Conditional form logic controls crop-specific observations and required fields
• Photo attachments document pest symptoms, infrastructure damage and crop stands
• Global Positioning System accuracy settings checked before field deployment
• Synchronisation procedures reconcile returned observations with master datasets

Participants can source, prepare and interpret satellite imagery for crop monitoring.

• Sentinel-2 imagery selected by acquisition date, cloud cover and resolution
• Band combinations display crop vigour, moisture patterns and bare soil
• Raster clipping isolates agricultural areas from larger satellite scenes
• Cloud and shadow contamination identified before seasonal crop interpretation
• Image metadata checked for projection, pixel size and processing level
• Time-series image folders organised by season, sensor and monitoring date

Participants can calculate and interpret vegetation indices for agricultural field monitoring.

• Normalized Difference Vegetation Index calculated from near-infrared and red bands
• Index values classified into interpretable crop-condition categories
• Zonal statistics summarise vegetation values by field boundary
• Seasonal index comparisons identify delayed establishment and potential stress
• Outlier fields investigated using imagery, observations and crop calendars
• Field-level monitoring tables combine index values with management records

Participants can create and assess a basic agricultural land-cover classification.

• Training samples represent crops, woodland, water, settlements and bare ground
• Semi-Automatic Classification Plugin supports supervised image classification workflows
• Spectral signatures compared to distinguish agricultural cover classes
• Classification outputs filtered to remove isolated pixels and obvious noise
• Confusion matrix measures agreement between classified and reference samples
• Land-cover areas summarised by administrative unit, scheme or watershed

Participants can derive terrain indicators for agricultural land and water management.

• Digital elevation models prepared for slope, aspect and hillshade analysis
• Slope thresholds identify machinery constraints and erosion-prone fields
• Flow direction and accumulation reveal drainage pathways and concentration zones
• Terrain derivatives support irrigation layout and water-harvesting assessment
• Buffer analysis identifies fields near gullies, streams and flood-prone areas
• Raster reclassification converts terrain values into planning suitability scores

Participants can integrate soil information into agricultural land capability assessments.

• Soil survey polygons joined with texture, depth and drainage attributes
• pH, salinity and organic matter layers mapped for management decisions
• Soil constraints classified using documented threshold values and source guidance
• Field observations validate mapped soil units and notable local variation
• Land capability classes symbolised for clear extension and planning communication
• Soil limitations compared with crop requirements and management options

Participants can construct weighted suitability models for agricultural investment decisions.

• Multi-criteria evaluation defines factors, constraints and decision assumptions
• Boolean masks exclude protected areas, water bodies and unsuitable slopes
• Standardised raster scores align soil, terrain, rainfall and access criteria
• Weighted overlay combines criteria using transparent stakeholder-agreed weights
• Sensitivity testing shows effects of changed weights and thresholds
• Suitability classes converted into candidate areas for field verification

Participants can map irrigation systems and analyse agricultural water-service coverage.

• Irrigation network layers distinguish canals, laterals, pumps and control structures
• Service areas estimate fields reachable from irrigation distribution points
• Water-source buffers identify farms within defined abstraction distances
• Canal alignment options compared against slope, elevation and land constraints
• Command-area polygons calculate irrigable area and potential beneficiary farms
• Infrastructure condition attributes prioritise repair, rehabilitation and inspection routes

Participants can assess farm access to markets, storage and agricultural services.

• Road networks cleaned for surface type, condition and seasonal accessibility
• Network analysis estimates travel distances to markets and collection centres
• Service catchments identify underserved farming communities and production areas
• Cold storage, warehouses and aggregation points mapped against crop volumes
• Travel-time assumptions documented for vehicle type, season and road condition
• Accessibility results compared with extension coverage and input supplier locations

Participants can combine agricultural and vulnerability data for programme targeting.

• Administrative boundaries linked to production, population and vulnerability indicators
• Choropleth maps use normalised rates rather than misleading absolute totals
• Hotspot analysis identifies clusters requiring further programme investigation
• Seasonal calendars connect crop cycles, rainfall patterns and household risks
• Targeting criteria documented to distinguish evidence from operational judgement
• Privacy controls protect household locations and sensitive beneficiary information

Participants can automate repeatable agricultural GIS tasks using QGIS processing tools.

• Graphical Modeler chains clipping, calculations, zonal statistics and exports
• Batch processing applies identical workflows across farms, districts or seasons
• Processing parameters expose dates, crop types and threshold values
• Model documentation records inputs, outputs, assumptions and version changes
• GDAL tools convert, reproject and compress raster datasets efficiently
• Reusable styles and layer definitions standardise recurring map products

Participants can produce clear maps and reporting outputs for agricultural decisions.

• Map purpose determines scale, extent, symbology and essential supporting information
• Graduated symbols and classified colours communicate agricultural indicators accurately
• Labels prioritise field identifiers, infrastructure assets and decision-relevant locations
• Print Layout creates map series for farms, extension zones or districts
• Charts and summary tables link mapped patterns with production statistics
• Map notes state data dates, sources, methods and interpretation limitations

Participants can review agricultural GIS outputs for technical quality and decision fitness.

• Quality assurance checklist reviews projection, geometry, attributes and analytical logic
• Peer review tests map readability, assumptions and recommendation traceability
• Ground-truth evidence compared with classification and suitability outputs
• Uncertainty statements distinguish measured results from modelled estimates
• Versioned deliverables preserve approved datasets, maps and analysis records
• Decision brief translates spatial findings into prioritised operational actions

Participants can build, review and present a complete agricultural GIS project with documented recommendations.

• Project brief defines agricultural decision, users, data sources and constraints
• Cleaned field, crop and contextual datasets assembled within a QGIS project
• Spatial analysis combines imagery, terrain, soils and operational criteria
• Suitability, crop-condition or access outputs validated against available evidence
• Map package presents findings, limitations, priorities and recommended actions
• Technical review documents methods, assumptions, data quality and next steps