Regional Agri-Supply Resilience Dashboard
This dataset provides granular, region-level monitoring of agricultural supply chain resilience, capturing disruption signals across inputs, logistics, and distribution channels. It enables stakeholders to assess food security risks, identify critical causes, and track mitigation actions for proactive planning and operational optimization in dynamic environments.
Sample rows
preview · 8 of 75 rows · all 16 columns| region_idstring | food_security_statusstring | overall_resilience_scorefloat | critical_risk_flagboolean | primary_disruption_causestring | region_namestring | countrystring | report_datedate | input_disruption_signalfloat | logistics_disruption_signalfloat | distribution_disruption_signalfloat | affected_inputsstring | affected_logistics_modesstring | affected_distribution_channelsstring | mitigation_actionsstring | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| northville-001 | stable | 72.6 | false | labor shortage | Northville | USA | 2024-05-01 | 8.2 | 12.4 | 7.6 | labor, fertilizer | blank | blank | alternate suppliers, workforce incentives | Seasonal labor shortages noted; mitigation underway. |
| greenfield-002 | stable | 89.2 | false | blank | Greenfield | Canada | 2024-05-02 | 3.1 | 2.7 | 1.8 | blank | blank | blank | blank | No major disruptions; market conditions remain normal. |
| eastbay-003 | critical | 53.2 | true | weather | East Bay | USA | 2024-04-27 | 21.3 | 29.7 | 25.4 | water | road | blank | emergency transport, water rationing | Flooding has disrupted water supply and road transport. |
| midlands-004 | stable | 81.2 | false | blank | Midlands | UK | 2024-05-03 | 5.6 | 6.1 | 4.7 | blank | blank | blank | blank | Logistics delays minimal; stable supply chain. |
| southridge-005 | at risk | 68.9 | false | transportation | Southridge | Australia | 2024-04-29 | 13.9 | 19.1 | 10.2 | blank | road, air | blank | alternate routes, government support | Road maintenance and air freight costs increasing. |
| lakeside-006 | stable | 91.7 | false | blank | Lakeside | USA | 2024-05-04 | 2.8 | 3.5 | 2.1 | blank | blank | blank | blank | Strong supplier relationships maintain stability. |
| harveston-007 | critical | 39.7 | true | market closure | Harveston | USA | 2024-05-04 | 33.1 | 22.6 | 27.9 | blank | blank | retail, wholesale | local market incentives, alternate retail options | Local market closure disrupting distribution. |
| westgrove-008 | stable | 72.8 | false | labor shortage | Westgrove | Canada | 2024-05-02 | 13.8 | 9.4 | 11.2 | labor | blank | blank | training programs, shift incentives | Labor shortages in early season. |
| highlands-009 | stable | 78.8 | false | blank | Highlands | UK | 2024-05-03 | 7.4 | 8.2 | 7.9 | blank | blank | blank | blank | No significant supply chain issues. |
| riverbend-010 | at risk | 65.3 | false | weather | Riverbend | Australia | 2024-05-01 | 17.5 | 15.2 | 17.9 | water | blank | blank | water conservation, irrigation upgrades | Dry spell causing water shortages. |
| meadowpark-011 | stable | 86.7 | false | blank | Meadow Park | USA | 2024-05-04 | 4.1 | 3.4 | 6.2 | blank | blank | blank | blank | Distribution bottlenecks resolved quickly. |
| hillcrest-012 | stable | 76.8 | false | labor shortage | Hillcrest | Canada | 2024-05-02 | 11.7 | 8.6 | 9.1 | labor | blank | blank | hiring drive, increased wages | Labor issues impacting input supply. |
| oakwood-013 | at risk | 56.8 | true | transportation | Oakwood | UK | 2024-05-03 | 19.4 | 24.3 | 15.8 | blank | rail, road | blank | government support, alternate routes | Rail strikes and road closures affecting logistics. |
| springdale-014 | stable | 80.2 | false | blank | Springdale | Australia | 2024-05-01 | 6.6 | 7.1 | 5.8 | blank | blank | blank | blank | Logistics delays minimal. |
| valleyview-015 | at risk | 61.4 | false | weather | Valley View | USA | 2024-04-30 | 24.9 | 19.3 | 17.7 | water, fertilizer | blank | blank | water conservation, alternate suppliers | Spring storms affecting fertilizer and water supply. |
| granite-016 | stable | 93.6 | false | blank | Granite District | Canada | 2024-05-02 | 2.1 | 4.9 | 2.7 | blank | blank | blank | blank | Above average resilience; no disruptions. |
| blueridge-017 | critical | 41.9 | true | market closure | Blue Ridge | UK | 2024-05-03 | 31.7 | 23.1 | 29.3 | blank | blank | retail, wholesale | alternate retail channels, direct distribution | Retail and wholesale market closures. |
| sunnydale-018 | stable | 75 | false | transportation | Sunnydale | Australia | 2024-04-30 | 10.5 | 13.2 | 12.8 | blank | road | blank | alternate transport, government support | Road delays due to construction. |
| maplewood-019 | stable | 84.7 | false | blank | Maplewood | USA | 2024-05-04 | 6.2 | 5.1 | 4.6 | blank | blank | blank | blank | No major supply chain disruptions. |
| pinecrest-020 | stable | 79.6 | false | labor shortage | Pinecrest | Canada | 2024-05-02 | 8.9 | 7.5 | 9.8 | labor | blank | blank | seasonal hiring, worker incentives | Labor shortages during planting season. |
What the 75 rows show
from the 75-row sampleStable (food security status) stands out: mean overall_
- 21%critical_
risk_ flag = true - 75.0median overall_
resilience_ score - 3affected distribution channelses
- 5affected logistics modeses
- 12.6median input_
disruption_ signal - 13.2median logistics_
disruption_ signal
Median 75.0, from 39.7 to 95.9.
- string 10
- float 4
- date 1
- boolean 1
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 10 columns | |||
region_id | string | Unique identifier for the geographic region being monitoredunique | northville-001 |
region_name | string | Human-readable name of the region (e.g., city, district, county) | Northville |
country | string | Country where the region is located | USA |
primary_disruption_cause | string | Main cause of disruption in the supply chain (e.g., 'weather', 'labor shortage', 'transportation', 'market closure')4 causes · optional | labor shortage |
affected_inputs | string | Comma-separated list of specific agricultural inputs affected (e.g., 'fertilizer, water')9 inputses · optional | labor, fertilizer |
affected_logistics_modes | string | Comma-separated list of logistics modes affected (e.g., 'road, rail')5 modeses · optional | road |
affected_distribution_channels | string | Comma-separated list of distribution channels affected (e.g., 'retail, wholesale')3 channelses · optional | retail, wholesale |
mitigation_actions | string | Comma-separated list of recommended or implemented mitigation actions (e.g., 'alternate suppliers, emergency transport')optional | emergency transport |
food_security_status | string | Current food security status for the region (e.g., 'stable', 'at risk', 'critical')stable · at risk · critical | stable |
notes | string | Additional comments or context about the region's supply chain situationoptional | No disruptions reported. |
| Numbers 4 columns | |||
input_disruption_signal | float | Aggregated disruption score for agricultural inputs (e.g., seeds, fertilizers, water, labor); higher values indicate greater disruption0 or more | 8.2 |
logistics_disruption_signal | float | Aggregated disruption score for logistics (e.g., transportation delays, infrastructure issues)0 or more | 12.4 |
distribution_disruption_signal | float | Aggregated disruption score for distribution channels (e.g., market closures, retail bottlenecks)0 or more | 7.6 |
overall_resilience_score | float | Composite resilience score for the region, calculated from input, logistics, and distribution signals; higher values indicate greater resilience0 to 100 | 72.6 |
| Dates and times 1 column | |||
report_date | date | Date of the resilience report or data snapshot | 2024-05-01 |
| True or false 1 column | |||
critical_risk_flag | boolean | Indicates if the region is currently facing critical supply chain risks | false |
Use it for
An agriculture dashboard
The critical_
risk_ flag rate, overall_ resilience_ score by food_ security_ status and a breakdown of primary_ disruption_ cause. Excel, Power BI or Tableau. Why do the 47 stable rows have a mean overall_
resilience_ score of 81.8? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Regions75northville-00172.6stableeastbay-00353.2criticalharveston-00739.7critical
A software demo
Believable regions with region_
name, country and report_ date to fill a screen in front of a buyer.
This dataset75 rows16 columns
Yours1,000 rows16 columnsregion_id: UK only
blueprint · regional-agri-supply-resilience-dashboard
Behind this dataset
Same schema. As many rows as you need.
These 75 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row represents a unique regional supply chain segment.
- Disruption signals include weather, transport delays, and supplier issues.
- Input sources must be tagged by type (seed, fertilizer, equipment, etc.).
- Logistics status updated weekly, with timestamp.
- Distribution risk assessed using multi-factor scoring (1-5 scale).
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- regional-agri-supply-resilience-dashboard