Crop Yield Forecast Variance Log
This dataset provides detailed logs comparing projected and actual crop yields at the field level, including forecast methods, timing, and variance calculations. It enables precise analysis of forecast accuracy, supports agricultural planning, and helps identify patterns or anomalies in yield prediction. The data is ideal for farm management optimization, agronomic research, and improving predictive models.
Sample rows
preview · 8 of 30 rows · all 15 columns| variance_log_idinteger | yield_unitstring | forecasted_yieldfloat | crop_typestring | field_idstring | field_namestring | planting_datedate | harvest_datedate | season_yearinteger | actual_yieldfloat | forecast_datedate | variancefloat | variance_percentagefloat | forecast_methodstring | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | bushels/acre | 175.5 | corn | F1001 | North Pasture | 2023-04-15 | 2023-10-12 | 2023 | 181.2 | 2023-07-01 | 5.7 | 3.25 | historical average | Favorable weather during pollination. |
| 2 | tons/hectare | 3.2 | soybeans | F1002 | South Fields | 2022-05-10 | 2022-09-28 | 2022 | 2.9 | 2022-06-15 | -0.3 | -9.38 | ML model | Drought stress in August. |
| 3 | tons/hectare | 6 | wheat | F1003 | East Lot | 2021-03-20 | 2021-07-22 | 2021 | 5.8 | 2021-05-10 | -0.2 | -3.33 | expert estimate | Mild disease pressure observed. |
| 4 | kg/ha | 4600 | barley | F1004 | Riverbank | 2023-04-03 | 2023-08-18 | 2023 | 4720 | 2023-04-20 | 120 | 2.61 | historical average | blank |
| 5 | bushels/acre | 168 | corn | F1005 | Meadow South | 2022-04-18 | 2022-10-09 | 2022 | 162.5 | 2022-07-05 | -5.5 | -3.27 | ML model | Heavy rain delayed maturity. |
| 6 | tons/hectare | 2.8 | soybeans | F1006 | West Field | 2021-05-14 | 2021-09-30 | 2021 | 2.9 | 2021-08-01 | 0.1 | 3.57 | expert estimate | blank |
| 7 | tons/hectare | 5.4 | wheat | F1007 | Gravel Ridge | 2023-03-23 | 2023-07-18 | 2023 | 5.6 | 2023-04-22 | 0.2 | 3.7 | ML model | Yield bump from late season rain. |
| 8 | bushels/acre | 182 | corn | F1008 | Old Orchard | 2022-04-12 | 2022-10-14 | 2022 | 178.7 | 2022-06-30 | -3.3 | -1.81 | historical average | blank |
| 9 | kg/ha | 4200 | barley | F1009 | Hillside | 2021-04-08 | 2021-08-28 | 2021 | 4110 | 2021-05-01 | -90 | -2.14 | expert estimate | Low rainfall year. |
| 10 | tons/hectare | 3.4 | soybeans | F1010 | Sandy Bend | 2023-05-09 | 2023-10-01 | 2023 | 3.6 | 2023-06-20 | 0.2 | 5.88 | ML model | blank |
| 11 | tons/hectare | 5.7 | wheat | F1011 | Willow Plot | 2022-03-30 | 2022-07-20 | 2022 | 5.4 | 2022-04-15 | -0.3 | -5.26 | historical average | Mild lodging observed. |
| 12 | bushels/acre | 170 | corn | F1012 | Maple End | 2021-04-20 | 2021-10-05 | 2021 | 174.2 | 2021-07-10 | 4.2 | 2.47 | expert estimate | blank |
| 13 | kg/ha | 4400 | barley | F1001 | North Pasture | 2021-04-11 | 2021-08-19 | 2021 | 4380 | 2021-05-05 | -20 | -0.45 | historical average | blank |
| 14 | bushels/acre | 177 | corn | F1002 | South Fields | 2023-04-17 | 2023-10-15 | 2023 | 179.8 | 2023-07-04 | 2.8 | 1.58 | ML model | Above average temperatures. |
| 15 | tons/hectare | 5.3 | wheat | F1013 | Birch Slope | 2022-03-25 | 2022-07-18 | 2022 | 5.5 | 2022-05-01 | 0.2 | 3.77 | blank | blank |
| 16 | tons/hectare | 3.1 | soybeans | F1014 | Elm Corner | 2023-05-11 | 2023-10-03 | 2023 | 2.8 | 2023-06-25 | -0.3 | -9.68 | expert estimate | Late planting reduced stand. |
| 17 | kg/ha | 4500 | barley | F1015 | Pine Flats | 2022-04-06 | 2022-08-21 | 2022 | 4540 | 2022-04-28 | 40 | 0.89 | ML model | blank |
| 18 | bushels/acre | 164 | corn | F1016 | Lakeview | 2021-04-18 | 2021-10-11 | 2021 | 162.1 | 2021-06-30 | -1.9 | -1.16 | historical average | blank |
| 19 | tons/hectare | 5.1 | wheat | F1017 | Rocky Edge | 2023-03-27 | 2023-07-16 | 2023 | 4.9 | 2023-05-13 | -0.2 | -3.92 | expert estimate | Slight hail damage in June. |
| 20 | bushels/acre | 172.5 | corn | F1018 | Golden Plain | 2022-04-16 | 2022-10-10 | 2022 | 174.9 | 2022-07-07 | 2.4 | 1.39 | ML model | blank |
What the 30 rows show
from the 30-row sampleTons/hectare (yield unit) stands out: mean forecasted_
- 165.5median forecasted_
yield - 3forecast methods
- 162.3median actual_
yield - 0.05median variance
- 0.45median variance_
percentage
Median 165.5, from 2.8 to 4,600.
- string 6
- integer 2
- float 4
- date 3
Columns
15 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 6 columns | |||
field_id | string | Unique identifier for the agricultural field or plot | F1001 |
field_name | string | Name or label of the field or plotoptional | North Pasture |
crop_type | string | Type or species of crop grown in the field (e.g., corn, wheat, soybeans)4 types | corn |
yield_unit | string | Unit of measurement for yield (e.g., bushels/acre, tons/hectare)bushels/acre · tons/hectare · kg/ha · quintals/ha | bushels/acre |
forecast_method | string | Method or model used for yield forecasting (e.g., historical average, ML model, expert estimate)3 methods · optional | historical average |
notes | string | Additional notes or comments about the variance or field conditionsoptional | Low rainfall year. |
| Numbers 6 columns | |||
variance_log_id | integer | Unique identifier for each crop yield variance log entryunique | 1 |
season_year | integer | Year in which the crop season took place1,900 to 2,100 | 2023 |
forecasted_yield | float | Projected crop yield for the field, typically in metric tons or bushels per acre/hectare0 or more | 175.5 |
actual_yield | float | Actual measured crop yield for the field, in the same units as forecasted_yield0 or more | 181.2 |
variance | float | Difference between actual and forecasted yield (actual_yield - forecasted_yield) | 5.7 |
variance_percentage | float | Variance as a percentage of forecasted yield ((variance / forecasted_yield) * 100) | 3.25 |
| Dates and times 3 columns | |||
planting_date | date | Date when the crop was planted in the fieldoptional | 2023-04-15 |
harvest_date | date | Date when the crop was harvested from the fieldoptional | 2023-10-12 |
forecast_date | date | Date when the yield forecast was made | 2023-07-01 |
Use it for
An agriculture dashboard
Forecasted_
yield by yield_ unit and a breakdown of crop_ type. Excel, Power BI or Tableau. Why do the 14 tons/hectare rows have a mean forecasted_
yield of 4.3? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Rows301175.5bushels/…23.2tons/hec…36tons/hec…
A software demo
Believable rows with field_
id, field_ name and crop_ type to fill a screen in front of a buyer.
This dataset30 rows15 columns
Yours1,000 rows15 columns
blueprint · crop-yield-forecast-variance-log
Behind this dataset
Same schema. As many rows as you need.
These 30 rows came out of a blueprint — 15 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
- Fields must be uniquely identified
- Record both forecast and actual yield per harvest
- Variance >10% flagged for review
- Include weather notes for each variance
- Aggregate accuracy by crop type
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
- crop-yield-forecast-variance-log