Interpret connected farm data to guide better recommendations.
Compare measurements across time, crops and management zones alongside targets, observations and applications. Use the analytical context when reviewing prescriptions and advisor plans, and compare later measurements with completed activities during follow-up.
A measurement becomes more useful when you can compare it.
One report can tell you what was measured on one day. Better agronomic interpretation comes from seeing how results change over time, how they compare with targets and other zones, and what happened around those measurements.
See what is changing
Follow nutrient measurements over time instead of reading each result as an isolated snapshot.
Compare like with like
Compare fields, crops, dates and management zones without rebuilding the analysis each time.
Focus on what matters
Use targets, filters, tables, heatmaps and maps to narrow attention to the most relevant signals.
Follow measurements in context over time.
Bring repeated measurements into one timeline so you can see direction, change and timing — with field observations and activities available alongside the data that may help explain what changed.
Compare measurements with targets, previous samples and other zones.
Compare measurements against target values, previous samples, other zones or other crops so differences become easier to interpret. Use standard targets where appropriate or apply agronomist-defined targets when the advisory approach requires it.
Focus the analysis on the relationships you care about.
Use agronomic filters to narrow large datasets to the measurements, relationships and periods relevant to the question. Move between charts, tables and heatmaps to inspect the same evidence from different angles.
Compare nutrient measurements across management zones.
Compare nutrient values and other measurements across management zones so spatial variation becomes easier to spot. Use maps alongside tables and charts to move from the farm-wide picture to a specific zone.
Analysis is most useful when it leads somewhere.
Farm data and history
Keep the measurements, observations, activities and history behind the analysis connected.
AI for agronomy
Investigate patterns and ask follow-up questions across the connected farm data and history.
Recommendations and fertiliser plans
Turn analytical understanding into a practical, reviewable agronomic plan.
Field activities and follow-up
Keep field work and follow-up connected to the evidence you analyse next.
See the agronomic picture more clearly.
Bring your existing measurements into SoilBeat, compare them in context and turn the patterns you see into better agronomic decisions.
Interpret connected farm data to guide better recommendations.
Compare measurements across time, crops and management zones alongside targets, observations and applications. Use the analytical context when reviewing prescriptions and advisor plans, and compare later measurements with completed activities during follow-up.
A measurement becomes more useful when you can compare it.
One report can tell you what was measured on one day. Better agronomic interpretation comes from seeing how results change over time, how they compare with targets and other zones, and what happened around those measurements.
See what is changing
Follow nutrient measurements over time instead of reading each result as an isolated snapshot.
Compare like with like
Compare fields, crops, dates and management zones without rebuilding the analysis each time.
Focus on what matters
Use targets, filters, tables, heatmaps and maps to narrow attention to the most relevant signals.
Follow measurements in context over time.
Bring repeated measurements into one timeline so you can see direction, change and timing — with field observations and activities available alongside the data that may help explain what changed.
Compare measurements with targets, previous samples and other zones.
Compare measurements against target values, previous samples, other zones or other crops so differences become easier to interpret. Use standard targets where appropriate or apply agronomist-defined targets when the advisory approach requires it.
Focus the analysis on the relationships you care about.
Use agronomic filters to narrow large datasets to the measurements, relationships and periods relevant to the question. Move between charts, tables and heatmaps to inspect the same evidence from different angles.
Compare nutrient measurements across management zones.
Compare nutrient values and other measurements across management zones so spatial variation becomes easier to spot. Use maps alongside tables and charts to move from the farm-wide picture to a specific zone.
Analysis is most useful when it leads somewhere.
Farm data and history
Keep the measurements, observations, activities and history behind the analysis connected.
AI for agronomy
Investigate patterns and ask follow-up questions across the connected farm data and history.
Recommendations and fertiliser plans
Turn analytical understanding into a practical, reviewable agronomic plan.
Field activities and follow-up
Keep field work and follow-up connected to the evidence you analyse next.
See the agronomic picture more clearly.
Bring your existing measurements into SoilBeat, compare them in context and turn the patterns you see into better agronomic decisions.