Precision Farming Climate Trends
This dataset provides granular, time-series synthetic climate data tailored for precision agriculture, including temperature, precipitation, humidity, wind, and soil moisture metrics across microclimate regions. It enables researchers and agronomists to model crop yield responses to environmental changes, optimize resource allocation, and simulate future climate scenarios for sustainable farming practices.
Sample rows
preview · 8 of 200 rows · all 17 columns| record_idstring | data_sourcestring | temperature_min_cfloat | crop_typestring | region_idstring | region_namestring | latitudefloat | longitudefloat | observation_datedate | temperature_avg_cfloat | temperature_max_cfloat | precipitation_mmfloat | humidity_avg_pctfloat | wind_speed_avg_mpsfloat | solar_radiation_avg_wm2float | soil_moisture_avg_pctfloat | yield_estimate_kg_hafloat |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| REC-0001 | field_station | 12.9 | Wheat | R001 | Great Plains | 39.8 | -98.3 | 2023-06-10 | 19.7 | 26.4 | 5.2 | 56.1 | 4.3 | 810.2 | 42.2 | 6700 |
| REC-0002 | drone_survey | 15.3 | Barley | R002 | Central Valley | 36.5 | -120.6 | 2023-07-06 | 22.4 | 31.7 | 1.1 | 48.5 | 3.1 | 865.4 | 35.7 | 5900 |
| REC-0003 | satellite_imagery | 14.9 | Soybean | R003 | Suburban Fields | 40.1 | -83.2 | 2023-06-18 | 21.1 | 27.5 | 4.7 | 61.3 | 2.7 | 750.9 | 44.7 | 8100 |
| REC-0004 | field_station | 9.4 | Strawberry | R004 | Mountain Foothills | 38.6 | -106.1 | 2023-07-14 | 16.3 | 23.2 | 12.2 | 70.2 | 5.9 | 612 | 51.1 | 9800 |
| REC-0005 | drone_survey | 12.4 | Grapes | R005 | Coastal Uplands | 37.7 | -122.3 | 2023-08-03 | 17.5 | 24.8 | 3.7 | 59.6 | 6.1 | 815.6 | 36.9 | 9200 |
| REC-0006 | field_station | 23.6 | Rice | R006 | Tropical Rainforest | -2.1 | 60.9 | 2023-03-22 | 28.1 | 34.4 | 97.3 | 97.8 | 3.6 | 610.4 | 87.7 | 22000 |
| REC-0007 | drone_survey | 24.2 | Banana | R007 | Delta Wetlands | 9.6 | 106.7 | 2023-04-15 | 29.7 | 35.3 | 82.6 | 92.2 | 2.1 | 720.5 | 96.4 | 41000 |
| REC-0008 | remote_sensor | 21.8 | Cassava | R008 | Lowland Floodplain | 16.8 | 102.9 | 2023-05-02 | 27.3 | 33.7 | 74.5 | 95.1 | 2.9 | 801.7 | 91.2 | 18300 |
| REC-0009 | field_station | 20.9 | Rice | R009 | East Coast Marsh | 24.2 | 112.3 | 2023-04-24 | 26.3 | 32.7 | 118.9 | 99.2 | 3.8 | 657.4 | 94.3 | 25600 |
| REC-0010 | satellite_imagery | 27.8 | Millet | R010 | Sahel Zone | 15.6 | 17.2 | 2023-02-16 | 40.1 | 51.7 | 0.5 | 13.4 | 8.2 | 930.8 | 11.7 | 2100 |
| REC-0011 | satellite_imagery | 29.5 | blank | R011 | Central Australian Desert | -23.7 | 133.9 | 2023-01-28 | 41.8 | 58.5 | 0 | 8.6 | 10.1 | 940.1 | 2.5 | blank |
| REC-0012 | field_station | 2.8 | Quinoa | R012 | High Andes | -12.9 | -70.3 | 2023-02-09 | 13.1 | 22.3 | 0.7 | 38.9 | 9.7 | 610.3 | 7.2 | 3300 |
| REC-0013 | weather_station | 8.7 | Olives | R013 | Mediterranean Basin | 41.3 | 13.9 | 2023-03-15 | 18.2 | 28.6 | 0.8 | 31.2 | 7.1 | 712.5 | 14.1 | 2900 |
| REC-0014 | polar_station | -59.9 | blank | R014 | Northern Tundra | 69.1 | 143.5 | 2023-12-21 | -42.7 | -30.1 | 2.7 | 99 | 7.8 | 19.7 | blank | blank |
| REC-0015 | polar_station | -59.7 | blank | R015 | Arctic Shelf | 83.2 | -150.6 | 2023-01-15 | -49.9 | -42 | 1.6 | 100 | 9.3 | 10.5 | blank | blank |
| REC-0016 | field_station | -12.3 | Potatoes | R016 | Subarctic Taiga | 61.4 | 135.7 | 2023-09-07 | -3.9 | 6.7 | 8.7 | 77.4 | 5.2 | 65.1 | 51.9 | 990 |
| REC-0017 | urban_sensor | 2.2 | blank | R017 | Urban Microclimate | 52.5 | 13.7 | 2023-11-05 | 5.7 | 9.1 | 0 | 66 | 4.1 | 320.5 | blank | blank |
| REC-0018 | drone_survey | 22.6 | Rice | R018 | East Coast Marsh | 27.4 | 115.2 | 2023-04-30 | 28.7 | 33.8 | 84.7 | 99.6 | 3.9 | 710.2 | 97.1 | 26200 |
| REC-0019 | satellite_imagery | 14.1 | Soybean | R019 | Central Valley | 37.3 | -121.6 | 2023-07-21 | 21.2 | 28.8 | 2.3 | 54.9 | 3.3 | 778.2 | 39.7 | 8700 |
| REC-0020 | weather_station | 26.9 | Millet | R020 | Sahel Zone | 13.2 | 14.8 | 2023-03-04 | 38.4 | 48.3 | 0.7 | 15.2 | 9.8 | 910.3 | 14.6 | 1800 |
What the 200 rows show
from the 200-row samplePolar_
- 12.3median temperature_
min_ c - 48region names
- 19.1median temperature_
avg_ c - 27.6median temperature_
max_ c - 5.9median precipitation_
mm - 66.0median humidity_
avg_ pct
Median 12.3, from -60.0 to 35.1.
- string 5
- float 11
- date 1
Columns
17 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 5 columns | |||
record_id | string | Unique identifier for each climate observation recordunique | REC-0001 |
region_id | string | Unique identifier for the region or microclimate zone | R001 |
region_name | string | Human-readable name of the region or microclimate zone | Great Plains |
crop_type | string | Type of crop grown in the region during the observation period12 types · optional | Wheat |
data_source | string | Source or method of synthetic data generation7 sources · optional | field_station |
| Numbers 11 columns | |||
latitude | float | Latitude coordinate of the observation location-90 to 90 | 39.8 |
longitude | float | Longitude coordinate of the observation location-180 to 180 | -98.3 |
temperature_avg_c | float | Average daily temperature in Celsius-50 to 60 | 19.7 |
temperature_min_c | float | Minimum daily temperature in Celsius-60 to 60 | 12.9 |
temperature_max_c | float | Maximum daily temperature in Celsius-60 to 65 | 26.4 |
precipitation_mm | float | Total daily precipitation in millimeters0 or more | 5.2 |
humidity_avg_pct | float | Average daily relative humidity as a percentage0 to 100 | 56.1 |
wind_speed_avg_mps | float | Average daily wind speed in meters per second0 or more | 4.3 |
solar_radiation_avg_wm2 | float | Average daily solar radiation in watts per square meter0 or more · optional | 810.2 |
soil_moisture_avg_pct | float | Average daily soil moisture as a percentage0 to 100 · optional | 42.2 |
yield_estimate_kg_ha | float | Estimated crop yield in kilograms per hectare for the observation period0 or more · optional | 6700 |
| Dates and times 1 column | |||
observation_date | date | Date of the climate observation | 2023-06-10 |
Use it for
An agriculture dashboard
Temperature_
min_ c by data_ source and a breakdown of crop_ type. Excel, Power BI or Tableau. Why do the 20 polar_
station rows have a mean temperature_ min_ c of -57.4? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records200REC-000112.9field_st…REC-000215.3drone_su…REC-000314.9satellit…
A software demo
Believable records with region_
id, region_ name and latitude to fill a screen in front of a buyer.
This dataset200 rows17 columns
Yours1,000 rows17 columnsregion_id: UK only
blueprint · precision-farming-climate-trends
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 17 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 daily climate for a plot
- Variables: temperature, humidity, rainfall
- Yield estimate calculated from climate factors
- Region code included for aggregation
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
- precision-farming-climate-trends