Impact Manager View
The executive view I would use to manage this as a regenerative organic soil impact program.
Soil Health Impact Score
A management score, not a lab diagnosis. It rewards strengths such as organic matter and nutrient-holding capacity, while penalizing systemic constraints such as low pH, sulfur/phosphorus gaps, and multiple out-of-range indicators. Use this as a baseline to improve over time.
Priority risk map
1. Correct acidity constraints
Low pH is the most important system-level constraint because it affects nutrient availability, root development, and micronutrient behavior.
2. Validate sulfur and phosphorus
Sulfur is below range in 91.6% of samples; phosphorus is below range in 59.7% and above range in 31.2%, so field context matters.
3. Investigate micronutrient excess
Manganese and iron are broadly above range. Interpret together with pH, soil texture, and amendment history before making claims.
Impact framing: the opportunity is not “perfect soil today”; it is a measurable baseline for improving balance, resilience, and organic soil function over time.
Impact KPIs I would track every cycle
Recommended management questions
Track correction plans, timing, and repeat test results.
Connect soil indicators with yield, quality, water stress, and farm practice data.
Use the sample explorer and out-of-range counts to prioritize field visits.
Key soil health signals
Dominant patterns based on the share of samples outside the lab reference range.
Executive interpretation for the impact report
This dataset gives FIF a strong baseline for regenerative organic monitoring: it shows where soils already have biological/organic strengths, and where mineral balance or acidity may limit long-term resilience.
- Acidity is a system-level constraint: pH is below range in most samples, which can affect nutrient availability and root performance.
- Sulfur and phosphorus deserve priority review: both show broad below-range patterns and should be interpreted with crop stage and field history.
- High organic matter is an impact asset: many samples show organic matter above the lab reference range, a useful regenerative baseline signal.
- Micronutrient excess patterns need context: manganese, iron, and copper appear frequently above range; this should be evaluated with pH, soil texture, and amendment history.
How to use this tab
Use these charts as the initial soil health snapshot before future regenerative organic interventions.
Focus field validation on pH, sulfur, phosphorus, zinc, and samples with multiple out-of-range indicators.
Repeat the same lab panel in future cycles and compare movement toward balanced ranges, not just yield.
Where the soil baseline is out of balance
Share of samples below, within, or above AGQ reference ranges for each soil indicator.
Average profile by crop
Indicators with the clearest differences between passion fruit and dragon fruit samples.
Soil texture classes
Distribution of reported texture classes across the sample set.
Samples requiring closer review
Simple count of out-of-range indicators per sample. Useful for prioritizing technical follow-up, not as a stand-alone agronomic diagnosis.
Indicator statistics
Median, average, and range for all numeric soil indicators.
Pitahaya dashboard
Same dashboard language as the main report, filtered only to pitahaya / dragon fruit farmer codes.
Pitahaya soil balance
Below / within / above range for pitahaya samples only.
Pitahaya texture classes
Distribution of reported texture classes for pitahaya.
Pitahaya samples requiring closer review
Simple out-of-range count per farmer/sample code.
Pitahaya indicator statistics
Median/average and observed sample range, plus classification against the benchmark ranges from the PDF soil results.
Pitahaya farmer-code explorer
Search by producer, code, or source file.
Passion fruit dashboard
Same dashboard language as the main report, filtered only to passion fruit / maracuyá farmer codes.
Passion fruit soil balance
Below / within / above range for passion fruit samples only.
Passion fruit texture classes
Distribution of reported texture classes for passion fruit.
Passion fruit samples requiring closer review
Simple out-of-range count per farmer/sample code.
Passion fruit indicator statistics
Median/average and observed sample range, plus classification against the benchmark ranges from the PDF soil results.
Passion fruit farmer-code explorer
Search by producer, code, or source file.
Sample explorer
Search by producer, code, crop, or file; review priority indicators by sample.