GIS Mapping for Agriculture Training Course

Participants can prepare spatial data using appropriate coordinate systems and accuracy controls.

• Coordinate reference systems, datums and projections for farm-scale spatial analysis
• Select UTM zones and local projections using area-of-use criteria
• Transform vector layers while preserving geometry, attributes and positional accuracy
• Assess horizontal accuracy, scale and minimum mapping unit requirements
• Document spatial metadata using ISO 19115 core elements
• Diagnose projection mismatches from layer offsets and coordinate values

Participants can create and validate farm boundary and infrastructure layers.

• Digitise field boundaries from imagery using snapping and topology rules
• Capture roads, canals, pumps and storage facilities as mapped assets
• Topology validation identifies gaps, overlaps, slivers and duplicate features
• Calculate field area, perimeter and compactness for management reporting
• Edit attribute tables using crop codes, seasons and ownership identifiers
• Reconcile surveyed boundaries with cadastral records and operational farm blocks

Participants can design field-data collection workflows and assess incoming data quality.

• Field survey forms capture crop stage, pests, soils and irrigation observations
• Define mandatory fields, value ranges and coded response lists
• GPS waypoint collection records accuracy, timestamp and observer identity
• Photo geotagging links visual evidence to farm inspection locations
• Quality-control checks identify missing values, duplicates and impossible measurements
• Sampling protocols distinguish random, systematic and stratified field observations

Participants can derive terrain variables for drainage, erosion and access planning.

• Digital elevation models support slope, aspect and drainage analysis
• Derive slope classes for mechanisation, erosion and irrigation planning
• Hillshade interpretation reveals terraces, channels, ridges and field microrelief
• Flow direction and accumulation identify drainage pathways and waterlogging risks
• Contour generation supports farm layout and soil-conservation planning
• Terrain derivatives require resolution checks and edge-effect inspection

Participants can map soil observations and produce defensible interpolated surfaces.

• Soil sampling points record depth, texture, pH and nutrient measurements
• Join laboratory results to sampling locations using unique identifiers
• Inverse distance weighting estimates continuous soil-property surfaces from samples
• Compare interpolation outputs using cross-validation error statistics
• Reclassify pH and nutrient surfaces into agronomic management categories
• Map uncertainty where sparse sampling weakens confidence in predictions

Participants can prepare raster datasets and interpret imagery for agricultural features.

• Raster resolution, extent, cell size and resampling affect agricultural interpretation
• Clip imagery to farm boundaries and analysis areas
• Band combinations distinguish vegetation, bare soil, water and built surfaces
• Radiometric values require inspection before index calculation or classification
• Nearest-neighbour, bilinear and cubic resampling serve different data types
• Raster alignment checks prevent misleading overlay and zonal statistics

Participants can obtain and prepare Sentinel-2 imagery for field-scale crop monitoring.

• Sentinel-2 bands provide visible, red-edge, near-infrared and shortwave information
• Scene selection considers cloud cover, acquisition date and phenological stage
• Level-2A surface reflectance supports comparable multi-date agricultural analysis
• Cloud and shadow masks remove contaminated pixels before analysis
• Build image composites where multiple scenes cover operational farm areas
• Inspect acquisition metadata, spatial resolution and processing baseline information

Participants can calculate and interpret vegetation indices for crop-condition analysis.

• NDVI calculation compares near-infrared and red reflectance values
• Interpret NDVI patterns against crop stage, soil exposure and moisture
• NDRE supports chlorophyll assessment using Sentinel-2 red-edge bands
• Generate field-level index summaries with zonal statistics
• Compare multi-date indices to identify emerging crop stress areas
• Separate genuine crop signals from clouds, shadows and mixed pixels

Participants can classify agricultural imagery and assess classification accuracy.

• Training samples represent crop classes, fallow land, water and settlements
• Supervised classification assigns pixels using spectral training signatures
• Random forest classification handles multiband imagery and predictor variables
• Separate training and validation samples before accuracy assessment
• Confusion matrices report producer accuracy, user accuracy and overall accuracy
• Post-classification filtering removes isolated pixels and implausible small patches

Participants can define management zones from spatial variability in agricultural data.

• Management zones combine yield, soil, terrain and vegetation information
• Standardise input layers before combining variables with different measurement scales
• Cluster analysis groups fields into statistically similar management zones
• Field boundaries constrain zones to operational equipment and ownership limits
• Zone summaries quantify area, mean values and recommended treatment classes
• Review zone stability across seasons before changing input-management plans

Participants can analyse irrigation assets, water demand indicators and drainage constraints.

• Map irrigation blocks, canals, pipes, pumps and water-control structures
• Buffer analysis identifies fields served by irrigation distribution infrastructure
• Overlay soil texture, slope and water sources for irrigation screening
• Waterlogging risk combines low slope, drainage accumulation and wetness indicators
• Evapotranspiration datasets support spatial comparison of crop water demand
• Trace network connectivity between water sources, pumps and delivery blocks

Participants can produce a transparent land-suitability model for a specified crop.

• FAO land evaluation principles define crop-specific suitability criteria
• Reclassify slope, soil depth, pH and water availability thresholds
• Weighted overlay combines standardised criteria using documented relative importance
• Constraint masks exclude protected areas, settlements and unsuitable land
• Sensitivity testing examines effects of changing criterion weights
• Suitability classes communicate highly suitable, suitable, marginal and unsuitable areas

Participants can compare seasonal spatial data to identify crop-production risks.

• Time-series imagery tracks planting, canopy development and harvest progression
• Change detection compares index values between defined agricultural dates
• Anomaly mapping compares current conditions with seasonal reference patterns
• Drought indicators combine rainfall, vegetation response and soil-moisture information
• Flood extent mapping uses water indices and pre-event reference imagery
• Risk maps prioritise scouting, extension visits and field verification

Participants can calculate spatial summaries that support agricultural management decisions.

• Zonal statistics summarise raster values for fields and management blocks
• Spatial joins connect observations with administrative areas and farm units
• Buffer and proximity analysis measure access to roads, water and markets
• Hot spot analysis identifies statistically significant clusters of crop incidents
• Outlier review distinguishes measurement errors from meaningful spatial variation
• Decision tables connect mapped thresholds to recommended operational actions

Participants can produce clear maps and technical outputs for agricultural stakeholders.

• Map purpose determines scale, symbols, labels and supporting reference layers
• Apply visual hierarchy to fields, crop conditions and management recommendations
• Classified colour schemes communicate ordered suitability and risk categories
• Legends state units, dates, sources and classification methods
• Layout templates include title, north arrow, scale bar and locator map
• Export PDF and image outputs with appropriate resolution and file naming

Participants can document and quality-check agricultural GIS outputs before release.

• Data lineage records sources, transformations, assumptions and responsible analysts
• ISO 19115 metadata captures identification, quality, extent and distribution details
• Review attribute completeness, geometry validity and coordinate-system consistency
• Version control conventions protect approved datasets and map production files
• Processing logs document raster calculations, classification settings and thresholds
• Peer review checks map readability, analytical logic and decision relevance

Participants can plan a scoped GIS project aligned with an agricultural decision.

• Problem statements define decisions, users, geographic scope and reporting deadlines
• Data inventories assess availability, quality, ownership and update frequency
• Workflow diagrams sequence collection, processing, analysis, validation and publication
• Acceptance criteria specify accuracy, map scale and required decision outputs
• Risk registers address cloud cover, incomplete boundaries and restricted data access
• Implementation plans assign roles, milestones, review points and maintenance tasks

Participants can build, review and present a complete agricultural GIS mapping project for a defined management decision.

• Define a farm-management question using available spatial and agricultural datasets
• Assemble validated boundaries, observations, terrain, soils and satellite imagery
• Apply documented analysis methods to produce decision-ready agricultural layers
• Calculate field summaries, suitability classes or crop-condition indicators
• Review outputs against accuracy checks, metadata requirements and stakeholder needs
• Present maps, assumptions, findings and recommended actions to technical reviewers